Econstudentlog

Black Hole Magnetospheres

The lecturer says ‘ah’ and ‘ehm’ a lot, especially in the beginning (it gets much better later in the talk), but this is not a good reason for not watching the lecture. The last five minutes of the lecture after the wrap-up can safely be skipped without missing out on anything.

I’ve added some links related to the coverage below.

Astrophysical jet.
Magnetosphere.
The Optical Variability of the Quasar 3C 279: The Signature of a Decelerating Jet? (Böttcher & Principe, 2009).
The slope of the black-hole mass versus velocity dispersion correlation (Tremaine et al., 2002).
Radio-Loudness of Active Galactic Nuclei: Observational Facts and Theoretical Implications (Sikora, Stawarz & Lasota, 2007).
Jet Launching Structure Resolved Near the Supermassive Black Hole in M87 (Doeleman et al., 2012).
Event Horizon Telescope.
The effective acceleration of plasma outflow in the paraboloidal magnetic field (Beskin & Nokhrina, 2006).
Toroidal magnetic field.
Current sheet.
No-hair theorem.
Frame-dragging.
Alfvén velocity.
Lorentz factor.
Magnetic acceleration of ultrarelativistic jets in gamma-ray burst sources (Komissarov et al., 2009).
Asymptotic domination of cold relativistic MHD winds by kinetic energy flux (Begelman & Li, 1994).
Magnetic nozzle.
Mach cone.
Collimated beam.
Magnetohydrodynamic simulations of gamma-ray burst jets: Beyond the progenitor star (Tchekhovskoy, Narayan & McKinney, 2010).

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October 31, 2018 Posted by | Astronomy, Lectures, Physics, Studies | Leave a comment

Supermassive BHs Mergers

This is the first post I’ve posted in a while; as mentioned earlier the blogging hiatus was due to internet connectivity issues secondary to me moving. Those issues should now have been solved and I hope to soon get back to blogging regularly.

Some links related to the lecture’s coverage:

Supermassive black hole.
Binary black hole. Final parsec problem.
LIGO (Laser Interferometer Gravitational-Wave Observatory). Laser Interferometer Space Antenna (LISA).
Dynamical friction.
Science with the space-based interferometer eLISA: Supermassive black hole binaries (Klein et al., 2016).
Off the Beaten Path: A New Approach to Realistically Model The Orbital Decay of Supermassive Black Holes in Galaxy Formation Simulations (Tremmel et al., 2015).
Dancing to ChaNGa: A Self-Consistent Prediction For Close SMBH Pair Formation Timescales Following Galaxy Mergers (Tremmel et al., 2017).
Growth and activity of black holes in galaxy mergers with varying mass ratios (Capelo et al., 2015).
Tidal heating. Tidal stripping.
Nuclear coups: dynamics of black holes in galaxy mergers (Wassenhove et al., 2013).
The birth of a supermassive black hole binary (Pfister et al., 2017).
Massive black holes and gravitational waves (I assume this is the lecturer’s own notes for a similar talk held at another point in time – there’s a lot of overlap between these notes and stuff covered in the lecture, so if you’re curious you could go have a look. As far as I could see all figures in the second half of the link, as well as a few of the earlier ones, are figures which were also included in this lecture).

September 18, 2018 Posted by | Astronomy, Lectures, Physics, Studies | Leave a comment

A few diabetes papers of interest

i. Islet Long Noncoding RNAs: A Playbook for Discovery and Characterization.

“This review will 1) highlight what is known about lncRNAs in the context of diabetes, 2) summarize the strategies used in lncRNA discovery pipelines, and 3) discuss future directions and the potential impact of studying the role of lncRNAs in diabetes.”

“Decades of mouse research and advances in genome-wide association studies have identified several genetic drivers of monogenic syndromes of β-cell dysfunction, as well as 113 distinct type 2 diabetes (T2D) susceptibility loci (1) and ∼60 loci associated with an increased risk of developing type 1 diabetes (T1D) (2). Interestingly, these studies discovered that most T1D and T2D susceptibility loci fall outside of coding regions, which suggests a role for noncoding elements in the development of disease (3,4). Several studies have demonstrated that many causal variants of diabetes are significantly enriched in regions containing islet enhancers, promoters, and transcription factor binding sites (5,6); however, not all diabetes susceptibility loci can be explained by associations with these regulatory regions. […] Advances in RNA sequencing (RNA-seq) technologies have revealed that mammalian genomes encode tens of thousands of RNA transcripts that have similar features to mRNAs, yet are not translated into proteins (7). […] detailed characterization of many of these transcripts has challenged the idea that the central role for RNA in a cell is to give rise to proteins. Instead, these RNA transcripts make up a class of molecules called noncoding RNAs (ncRNAs) that function either as “housekeeping” ncRNAs, such as transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs), that are expressed ubiquitously and are required for protein synthesis or as “regulatory” ncRNAs that control gene expression. While the functional mechanisms of short regulatory ncRNAs, such as microRNAs (miRNAs), small interfering RNAs (siRNAs), and Piwi-interacting RNAs (piRNAs), have been described in detail (810), the most abundant and functionally enigmatic regulatory ncRNAs are called long noncoding RNAs (lncRNAs) that are loosely defined as RNAs larger than 200 nucleotides (nt) that do not encode for protein (1113). Although using a definition based strictly on size is somewhat arbitrary, this definition is useful both bioinformatically […] and technically […]. While the 200-nt size cutoff has simplified identification of lncRNAs, this rather broad classification means several features of lncRNAs, including abundance, cellular localization, stability, conservation, and function, are inherently heterogeneous (1517). Although this represents one of the major challenges of lncRNA biology, it also highlights the untapped potential of lncRNAs to provide a novel layer of gene regulation that influences islet physiology and pathophysiology.”

“Although the role of miRNAs in diabetes has been well established (9), analyses of lncRNAs in islets have lagged behind their short ncRNA counterparts. However, several recent studies provide evidence that lncRNAs are crucial components of the islet regulome and may have a role in diabetes (27). […] misexpression of several lncRNAs has been correlated with diabetes complications, such as diabetic nephropathy and retinopathy (2931). There are also preliminary studies suggesting that circulating lncRNAs, such as Gas5, MIAT1, and SENCR, may represent effective molecular biomarkers of diabetes and diabetes-related complications (32,33). Finally, several recent studies have explored the role of lncRNAs in the peripheral metabolic tissues that contribute to energy homeostasis […]. In addition to their potential as genetic drivers and/or biomarkers of diabetes and diabetes complications, lncRNAs can be exploited for the treatment of diabetes. For example, although tremendous efforts have been dedicated to generating replacement β-cells for individuals with diabetes (35,36), human pluripotent stem cell–based β-cell differentiation protocols remain inefficient, and the end product is still functionally and transcriptionally immature compared with primary human β-cells […]. This is largely due to our incomplete knowledge of in vivo differentiation regulatory pathways, which likely include a role for lncRNAs. […] Inherent characteristics of lncRNAs have also made them attractive candidates for drug targeting, which could be exploited for developing new diabetes therapies.”

“With the advancement of high-throughput sequencing techniques, the list of islet-specific lncRNAs is growing exponentially; however, functional characterization is missing for the majority of these lncRNAs. […] Tens of thousands of lncRNAs have been identified in different cell types and model organisms; however, their functions largely remain unknown. Although the tools for determining lncRNA function are technically restrictive, uncovering novel regulatory mechanisms will have the greatest impact on understanding islet function and identifying novel therapeutics for diabetes. To date, no biochemical assay has been used to directly determine the molecular mechanisms by which islet lncRNAs function, which highlights both the infancy of the field and the difficulty in implementing these techniques. […] Due to the infancy of the lncRNA field, most of the biochemical and genetic tools used to interrogate lncRNA function have only recently been developed or are adapted from techniques used to study protein-coding genes and we are only beginning to appreciate the limits and challenges of borrowing strategies from the protein-coding world.”

“The discovery of lncRNAs as a novel class of tissue-specific regulatory molecules has spawned an exciting new field of biology that will significantly impact our understanding of pancreas physiology and pathophysiology. As the field continues to grow, there is growing appreciation that lncRNAs will provide many of the missing components to existing molecular pathways that regulate islet biology and contribute to diabetes when they become dysfunctional. However, to date, most of the experimental emphasis on lncRNAs has focused on large-scale discovery using genome-wide approaches, and there remains a paucity of functional analysis.”

ii. Diabetes and Trajectories of Estimated Glomerular Filtration Rate: A Prospective Cohort Analysis of the Atherosclerosis Risk in Communities Study.

“Diabetes is among the strongest common risk factors for end-stage renal disease, and in industrialized countries, diabetes contributes to ∼50% of cases (3). Less is known about the pattern of kidney function decline associated with diabetes that precedes end-stage renal disease. Identifying patterns of estimated glomerular filtration rate (eGFR) decline could inform monitoring practices for people at high risk of chronic kidney disease (CKD) progression. A better understanding of when and in whom eGFR decline occurs would be useful for the design of clinical trials because eGFR decline >30% is now often used as a surrogate end point for CKD progression (4). Trajectories among persons with diabetes are of particular interest because of the possibility for early intervention and the prevention of CKD development. However, eGFR trajectories among persons with new diabetes may be complex due to the hypothesized period of hyperfiltration by which GFR increases, followed by progressive, rapid decline (5). Using data from the Atherosclerosis Risk in Communities (ARIC) study, an ongoing prospective community-based cohort of >15,000 participants initiated in 1987 with serial measurements of creatinine over 26 years, our aim was to characterize patterns of eGFR decline associated with diabetes, identify demographic, genetic, and modifiable risk factors within the population with diabetes that were associated with steeper eGFR decline, and assess for evidence of early hyperfiltration.”

“We categorized people into groups of no diabetes, undiagnosed diabetes, and diagnosed diabetes at baseline (visit 1) and compared baseline clinical characteristics using ANOVA for continuous variables and Pearson χ2 tests for categorical variables. […] To estimate individual eGFR slopes over time, we used linear mixed-effects models with random intercepts and random slopes. These models were fit on diabetes status at baseline as a nominal variable to adjust the baseline level of eGFR and included an interaction term between diabetes status at baseline and time to estimate annual decline in eGFR by diabetes categories. Linear mixed models were run unadjusted and adjusted, with the latter model including the following diabetes and kidney disease–related risk factors: age, sex, race–center, BMI, systolic blood pressure, hypertension medication use, HDL, prevalent coronary heart disease, annual family income, education status, and smoking status, as well as each variable interacted with time. Continuous covariates were centered at the analytic population mean. We tested model assumptions and considered different covariance structures, comparing nested models using Akaike information criteria. We identified the unstructured covariance model as the most optimal and conservative approach. From the mixed models, we described the overall mean annual decline by diabetes status at baseline and used the random effects to estimate best linear unbiased predictions to describe the distributions of yearly slopes in eGFR by diabetes status at baseline and displayed them using kernel density plots.”

“Because of substantial variation in annual eGFR slope among people with diagnosed diabetes, we sought to identify risk factors that were associated with faster decline. Among those with diagnosed diabetes, we compared unadjusted and adjusted mean annual decline in eGFR by race–APOL1 risk status (white, black– APOL1 low risk, and black–APOL1 high risk) [here’s a relevant link, US], systolic blood pressure […], smoking status […], prevalent coronary heart disease […], diabetes medication use […], HbA1c […], and 1,5-anhydroglucitol (≥10 and <10 μg/mL) [relevant link, US]. Because some of these variables were only available at visit 2, we required that participants included in this subgroup analysis attend both visits 1 and 2 and not be missing information on APOL1 or the variables assessed at visit 2 to ensure a consistent sample size. In addition to diabetes and kidney disease–related risk factors in the adjusted model, we also included diabetes medication use and HbA1c to account for diabetes severity in these analyses. […] to explore potential hyperfiltration, we used a linear spline model to allow the slope to change for each diabetes category between the first 3 years of follow-up (visit 1 to visit 2) and the subsequent time period (visit 2 to visit 5).”

“There were 15,517 participants included in the analysis: 13,698 (88%) without diabetes, 634 (4%) with undiagnosed diabetes, and 1,185 (8%) with diagnosed diabetes at baseline. […] At baseline, participants with undiagnosed and diagnosed diabetes were older, more likely to be black or have hypertension and coronary heart disease, and had higher mean BMI and lower mean HDL compared with those without diabetes […]. Income and education levels were also lower among those with undiagnosed and diagnosed diabetes compared with those without diabetes. […] Overall, there was a nearly linear association between eGFR and age over time, regardless of diabetes status […]. The crude mean annual decline in eGFR was slowest among those without diabetes at baseline (decline of −1.6 mL/min/1.73 m2/year [95% CI −1.6 to −1.5]), faster among those with undiagnosed diabetes compared with those without diabetes (decline of −2.1 mL/min/1.73 m2/year [95% CI −2.2 to −2.0][…]), and nearly twice as rapid among those with diagnosed diabetes compared with those without diabetes (decline of −2.9 mL/min/1.73 m2/year [95% CI −3.0 to −2.8][…]). Adjustment for diabetes and kidney disease–related risk factors attenuated the results slightly, but those with undiagnosed and diagnosed diabetes still had statistically significantly steeper declines than those without diabetes (decline among no diabetes −1.4 mL/min/1.73 m2/year [95% CI −1.5 to −1.4] and decline among undiagnosed diabetes −1.8 mL/min/1.73 m2/year [95% CI −2.0 to −1.7], difference vs. no diabetes of −0.4 mL/min/1.73 m2/year [95% CI −0.5 to −0.3; P < 0.001]; decline among diagnosed diabetes −2.5 mL/min/1.73 m2/year [95% CI −2.6 to −2.4], difference vs. no diabetes of −1.1 mL/min/1.73 m2/ year [95% CI −1.2 to −1.0; P < 0.001]). […] The decline in eGFR per year varied greatly across individuals, particularly among those with diabetes at baseline […] Among participants with diagnosed diabetes at baseline, those who were black, had systolic blood pressure ≥140 mmHg, used diabetes medications, had an HbA1c ≥7% [≥53 mmol/mol], or had 1,5-anhydroglucitol <10 μg/mL were at risk for steeper annual declines than their counterparts […]. Smoking status and prevalent coronary heart disease were not associated with significantly steeper eGFR decline in unadjusted analyses. Adjustment for risk factors, diabetes medication use, and HbA1c attenuated the differences in decline for all subgroups with the exception of smoking status, leaving black race along with APOL1-susceptible genotype, systolic blood pressure ≥140 mmHg, current smoking, insulin use, and HbA1c ≥9% [≥75 mmol/mol] as the risk factors indicative of steeper decline.”

CONCLUSIONS Diabetes is an important risk factor for kidney function decline. Those with diagnosed diabetes declined almost twice as rapidly as those without diabetes. Among people with diagnosed diabetes, steeper declines were seen in those with modifiable risk factors, including hypertension and glycemic control, suggesting areas for continued targeting in kidney disease prevention. […] Few other community-based studies have evaluated differences in kidney function decline by diabetes status over a long period through mid- and late life. One study of 10,184 Canadians aged ≥66 years with creatinine measured during outpatient visits showed results largely consistent with our findings but with much shorter follow-up (median of 2 years) (19). Other studies of eGFR change in a general population have found smaller declines than our results (20,21). A study conducted in Japanese participants aged 40–79 years found a decline of only −0.4 mL/min/1.73 m2/year over the course of two assessments 10 years apart (compared with our estimate among those without diabetes: −1.6 mL/min/1.73 m2/year). This is particularly interesting, as Japan is known to have a higher prevalence of CKD and end-stage renal disease than the U.S. (20). However, this study evaluated participants over a shorter time frame and required attendance at both assessments, which may have decreased the likelihood of capturing severe cases and resulted in underestimation of decline.”

“The Baltimore Longitudinal Study of Aging also assessed kidney function over time in a general population of 446 men, ranging in age from 22 to 97 years at baseline, each with up to 14 measurements of creatinine clearance assessed between 1958 and 1981 (21). They also found a smaller decline than we did (−0.8 mL/min/year), although this study also had notable differences. Their main analysis excluded participants with hypertension and history of renal disease or urinary tract infection and those treated with diuretics and/or antihypertensive medications. Without those exclusions, their overall estimate was −1.1 mL/min/year, which better reflects a community-based population and our results. […] In our evaluation of risk factors that might explain the variation in decline seen among those with diagnosed diabetes, we observed that black race, systolic blood pressure ≥140 mmHg, insulin use, and HbA1c ≥9% (≥75 mmol/mol) were particularly important. Although the APOL1 high-risk genotype is a known risk factor for eGFR decline, African Americans with low-risk APOL1 status continued to be at higher risk than whites even after adjustment for traditional risk factors, diabetes medication use, and HbA1c.”

“Our results are relevant to the design and conduct of clinical trials. Hard clinical outcomes like end-stage renal disease are relatively rare, and a 30–40% decline in eGFR is now accepted as a surrogate end point for CKD progression (4). We provide data on patient subgroups that may experience accelerated trajectories of kidney function decline, which has implications for estimating sample size and ensuring adequate power in future clinical trials. Our results also suggest that end points of eGFR decline might not be appropriate for patients with new-onset diabetes, in whom declines may actually be slower than among persons without diabetes. Slower eGFR decline among those with undiagnosed diabetes, who are likely early in the course of diabetes, is consistent with the hypothesis of hyperfiltration. Similar to other studies, we found that persons with undiagnosed diabetes had higher GFR at the outset, but this was a transient phenomenon, as they ultimately experienced larger declines in kidney function than those without diabetes over the course of follow-up (2325). Whether hyperfiltration is a universal aspect of early disease and, if not, whether it portends worse long-term outcomes is uncertain. Existing studies investigating hyperfiltration as a precursor to adverse kidney outcomes are inconsistent (24,26,27) and often confounded by diabetes severity factors like duration (27). We extended this literature by separating undiagnosed and diagnosed diabetes to help address that confounding.”

iii. Saturated Fat Is More Metabolically Harmful for the Human Liver Than Unsaturated Fat or Simple Sugars.

OBJECTIVE Nonalcoholic fatty liver disease (i.e., increased intrahepatic triglyceride [IHTG] content), predisposes to type 2 diabetes and cardiovascular disease. Adipose tissue lipolysis and hepatic de novo lipogenesis (DNL) are the main pathways contributing to IHTG. We hypothesized that dietary macronutrient composition influences the pathways, mediators, and magnitude of weight gain-induced changes in IHTG.

RESEARCH DESIGN AND METHODS We overfed 38 overweight subjects (age 48 ± 2 years, BMI 31 ± 1 kg/m2, liver fat 4.7 ± 0.9%) 1,000 extra kcal/day of saturated (SAT) or unsaturated (UNSAT) fat or simple sugars (CARB) for 3 weeks. We measured IHTG (1H-MRS), pathways contributing to IHTG (lipolysis ([2H5]glycerol) and DNL (2H2O) basally and during euglycemic hyperinsulinemia), insulin resistance, endotoxemia, plasma ceramides, and adipose tissue gene expression at 0 and 3 weeks.

RESULTS Overfeeding SAT increased IHTG more (+55%) than UNSAT (+15%, P < 0.05). CARB increased IHTG (+33%) by stimulating DNL (+98%). SAT significantly increased while UNSAT decreased lipolysis. SAT induced insulin resistance and endotoxemia and significantly increased multiple plasma ceramides. The diets had distinct effects on adipose tissue gene expression.”

CONCLUSIONS NAFLD has been shown to predict type 2 diabetes and cardiovascular disease in multiple studies, even independent of obesity (1), and also to increase the risk of progressive liver disease (17). It is therefore interesting to compare effects of different diets on liver fat content and understand the underlying mechanisms. We examined whether provision of excess calories as saturated (SAT) or unsaturated (UNSAT) fats or simple sugars (CARB) influences the metabolic response to overfeeding in overweight subjects. All overfeeding diets increased IHTGs. The SAT diet induced a greater increase in IHTGs than the UNSAT diet. The composition of the diet altered sources of excess IHTGs. The SAT diet increased lipolysis, whereas the CARB diet stimulated DNL. The SAT but not the other diets increased multiple plasma ceramides, which increase the risk of cardiovascular disease independent of LDL cholesterol (18). […] Consistent with current dietary recommendations (3638), the current study shows that saturated fat is the most harmful dietary constituent regarding IHTG accumulation.”

iv. Primum Non Nocere: Refocusing Our Attention on Severe Hypoglycemia Prevention.

“Severe hypoglycemia, defined as low blood glucose requiring assistance for recovery, is arguably the most dangerous complication of type 1 diabetes as it can result in permanent cognitive impairment, seizure, coma, accidents, and death (1,2). Since the Diabetes Control and Complications Trial (DCCT) demonstrated that intensive intervention to normalize glucose prevents long-term complications but at the price of a threefold increase in the rate of severe hypoglycemia (3), hypoglycemia has been recognized as the major limitation to achieving tight glycemic control. Severe hypoglycemia remains prevalent among adults with type 1 diabetes, ranging from ∼1.4% per year in the DCCT/EDIC (Epidemiology of Diabetes Interventions and Complications) follow-up cohort (4) to ∼8% in the T1D Exchange clinic registry (5).

One the greatest risk factors for severe hypoglycemia is impaired awareness of hypoglycemia (6), which increases risk up to sixfold (7,8). Hypoglycemia unawareness results from deficient counterregulation (9), where falling glucose fails to activate the autonomic nervous system to produce neuroglycopenic symptoms that normally help patients identify and respond to episodes (i.e., sweating, palpitations, hunger) (2). An estimated 20–25% of adults with type 1 diabetes have impaired hypoglycemia awareness (8), which increases to more than 50% after 25 years of disease duration (10).

Screening for hypoglycemia unawareness to identify patients at increased risk of severe hypoglycemic events should be part of routine diabetes care. Self-identified impairment in awareness tends to agree with clinical evaluation (11). Therefore, hypoglycemia unawareness can be easily and effectively screened […] Interventions for hypoglycemia unawareness include a range of behavioral and medical options. Avoiding hypoglycemia for at least several weeks may partially reverse hypoglycemia unawareness and reduce risk of future episodes (1). Therefore, patients with hypoglycemia and unawareness may be advised to raise their glycemic and HbA1c targets (1,2). Diabetes technology can play a role, including continuous subcutaneous insulin infusion (CSII) to optimize insulin delivery, continuous glucose monitoring (CGM) to give technological awareness in the absence of symptoms (14), or the combination of the two […] Aside from medical management, structured or hypoglycemia-specific education programs that aim to prevent hypoglycemia are recommended for all patients with severe hypoglycemia or hypoglycemia unawareness (14). In randomized trials, psychoeducational programs that incorporate increased education, identification of personal risk factors, and behavior change support have improved hypoglycemia unawareness and reduced the incidence of both nonsevere and severe hypoglycemia over short periods of follow-up (17,18) and extending up to 1 year (19).”

“Given that the presence of hypoglycemia unawareness increases the risk of severe hypoglycemia, which is the strongest predictor of a future episode (2,4), the implication that intervention can break the life-threatening and traumatizing cycle of hypoglycemia unawareness and severe hypoglycemia cannot be overstated. […] new evidence of durability of effect across treatment regimen without increasing the risk for long-term complications creates an imperative for action. In combination with existing screening tools and a body of literature investigating novel interventions for hypoglycemia unawareness, these results make the approach of screening, recognition, and intervention very compelling as not only a best practice but something that should be incorporated in universal guidelines on diabetes care, particularly for individuals with type 1 diabetes […] Hyperglycemia is […] only part of the puzzle in diabetes management. Long-term complications are decreasing across the population with improved interventions and their implementation (24). […] it is essential to shift our historical obsession with hyperglycemia and its long-term complications to equally emphasize the disabling, distressing, and potentially fatal near-term complication of our treatments, namely severe hypoglycemia. […] The health care providers’ first dictum is primum non nocere — above all, do no harm. ADA must refocus our attention on severe hypoglycemia as an iatrogenic and preventable complication of our interventions.”

v. Anti‐vascular endothelial growth factor combined with intravitreal steroids for diabetic macular oedema.

“Background

The combination of steroid and anti‐vascular endothelial growth factor (VEGF) intravitreal therapeutic agents could potentially have synergistic effects for treating diabetic macular oedema (DMO). On the one hand, if combined treatment is more effective than monotherapy, there would be significant implications for improving patient outcomes. Conversely, if there is no added benefit of combination therapy, then people could be potentially exposed to unnecessary local or systemic side effects.

Objectives

To assess the effects of intravitreal agents that block vascular endothelial growth factor activity (anti‐VEGF agents) plus intravitreal steroids versus monotherapy with macular laser, intravitreal steroids or intravitreal anti‐VEGF agents for managing DMO.”

“There were eight RCTs (703 participants, 817 eyes) that met our inclusion criteria with only three studies reporting outcomes at one year. The studies took place in Iran (3), USA (2), Brazil (1), Czech Republic (1) and South Korea (1). […] When comparing anti‐VEGF/steroid with anti‐VEGF monotherapy as primary therapy for DMO, we found no meaningful clinical difference in change in BCVA [best corrected visual acuity] […] or change in CMT [central macular thickness] […] at one year. […] There was very low‐certainty evidence on intraocular inflammation from 8 studies, with one event in the anti‐VEGF/steroid group (313 eyes) and two events in the anti‐VEGF group (322 eyes). There was a greater risk of raised IOP (Peto odds ratio (OR) 8.13, 95% CI 4.67 to 14.16; 635 eyes; 8 RCTs; moderate‐certainty evidence) and development of cataract (Peto OR 7.49, 95% CI 2.87 to 19.60; 635 eyes; 8 RCTs; moderate‐certainty evidence) in eyes receiving anti‐VEGF/steroid compared with anti‐VEGF monotherapy. There was low‐certainty evidence from one study of an increased risk of systemic adverse events in the anti‐VEGF/steroid group compared with the anti‐VEGF alone group (Peto OR 1.32, 95% CI 0.61 to 2.86; 103 eyes).”

“One study compared anti‐VEGF/steroid versus macular laser therapy. At one year investigators did not report a meaningful difference between the groups in change in BCVA […] or change in CMT […]. There was very low‐certainty evidence suggesting an increased risk of cataract in the anti‐VEGF/steroid group compared with the macular laser group (Peto OR 4.58, 95% 0.99 to 21.10, 100 eyes) and an increased risk of elevated IOP in the anti‐VEGF/steroid group compared with the macular laser group (Peto OR 9.49, 95% CI 2.86 to 31.51; 100 eyes).”

“Authors’ conclusions

Combination of intravitreal anti‐VEGF plus intravitreal steroids does not appear to offer additional visual benefit compared with monotherapy for DMO; at present the evidence for this is of low‐certainty. There was an increased rate of cataract development and raised intraocular pressure in eyes treated with anti‐VEGF plus steroid versus anti‐VEGF alone. Patients were exposed to potential side effects of both these agents without reported additional benefit.”

vi. Association between diabetic foot ulcer and diabetic retinopathy.

“More than 25 million people in the United States are estimated to have diabetes mellitus (DM), and 15–25% will develop a diabetic foot ulcer (DFU) during their lifetime [1]. DFU is one of the most serious and disabling complications of DM, resulting in significantly elevated morbidity and mortality. Vascular insufficiency and associated neuropathy are important predisposing factors for DFU, and DFU is the most common cause of non-traumatic foot amputation worldwide. Up to 70% of all lower leg amputations are performed on patients with DM, and up to 85% of all amputations are preceded by a DFU [2, 3]. Every year, approximately 2–3% of all diabetic patients develop a foot ulcer, and many require prolonged hospitalization for the treatment of ensuing complications such as infection and gangrene [4, 5].

Meanwhile, a number of studies have noted that diabetic retinopathy (DR) is associated with diabetic neuropathy and microvascular complications [610]. Despite the magnitude of the impact of DFUs and their consequences, little research has been performed to investigate the characteristics of patients with a DFU and DR. […] the aim of this study was to investigate the prevalence of DR in patients with a DFU and to elucidate the potential association between DR and DFUs.”

“A retrospective review was conducted on DFU patients who underwent ophthalmic and vascular examinations within 6 months; 100 type 2 diabetic patients with DFU were included. The medical records of 2496 type 2 diabetic patients without DFU served as control data. DR prevalence and severity were assessed in DFU patients. DFU patients were compared with the control group regarding each clinical variable. Additionally, DFU patients were divided into two groups according to DR severity and compared. […] Out of 100 DFU patients, 90 patients (90%) had DR and 55 (55%) had proliferative DR (PDR). There was no significant association between DR and DFU severities (R = 0.034, p = 0.734). A multivariable analysis comparing type 2 diabetic patients with and without DFUs showed that the presence of DR [OR, 226.12; 95% confidence interval (CI), 58.07–880.49; p < 0.001] and proliferative DR [OR, 306.27; 95% CI, 64.35–1457.80; p < 0.001), higher HbA1c (%, OR, 1.97, 95% CI, 1.46–2.67; p < 0.001), higher serum creatinine (mg/dL, OR, 1.62, 95% CI, 1.06–2.50; p = 0.027), older age (years, OR, 1.12; 95% CI, 1.06–1.17; p < 0.001), higher pulse pressure (mmHg, OR, 1.03; 95% CI, 1.00–1.06; p = 0.025), lower cholesterol (mg/dL, OR, 0.94; 95% CI, 0.92–0.97; p < 0.001), lower BMI (kg/m2, OR, 0.87, 95% CI, 0.75–1.00; p = 0.044) and lower hematocrit (%, OR, 0.80, 95% CI, 0.74–0.87; p < 0.001) were associated with DFUs. In a subgroup analysis of DFU patients, the PDR group had a longer duration of diabetes mellitus, higher serum BUN, and higher serum creatinine than the non-PDR group. In the multivariable analysis, only higher serum creatinine was associated with PDR in DFU patients (OR, 1.37; 95% CI, 1.05–1.78; p = 0.021).

Conclusions

Diabetic retinopathy is prevalent in patients with DFU and about half of DFU patients had PDR. No significant association was found in terms of the severity of these two diabetic complications. To prevent blindness, patients with DFU, and especially those with high serum creatinine, should undergo retinal examinations for timely PDR diagnosis and management.”

August 29, 2018 Posted by | Diabetes, Epidemiology, Genetics, Medicine, Molecular biology, Nephrology, Ophthalmology, Statistics, Studies | Leave a comment

Nephrology Board Review

Some links related to the lecture’s coverage:

Diabetic nephropathy.
Henoch–Schönlein purpura.
Leukocytoclastic Vasculitis.
Glomerulonephritis. Rapidly progressive glomerulonephritis.
Nephrosis.
Analgesic nephropathy.
Azotemia.
Allergic Interstitial Nephritis: Clinical Features and Pathogenesis.
Nonsteroidal anti-inflammatory drugs: effects on kidney function (Whelton & Hamilton, J Clin Pharmacol. 1991 Jul;31(7):588-98).
Goodpasture syndrome.
Creatinine. Limitations of serum creatinine as a marker of renal function.
Hyperkalemia.
U wave.
Nephrolithiasis. Calcium oxalate.
Calcium gluconate.
Bicarbonate.
Effect of various therapeutic approaches on plasma potassium and major regulating factors in terminal renal failure (Blumberg et al., 1988).
Effect of prolonged bicarbonate administration on plasma potassium in terminal renal failure (Blumberg et al., 1992).
Renal tubular acidosis.
Urine anion gap.
Metabolic acidosis.
Contrast-induced nephropathy.
Rhabdomyolysis.
Lipiduria. Urinary cast.
Membranous glomerulonephritis.
Postinfectious glomerulonephritis.

August 28, 2018 Posted by | Cardiology, Chemistry, Diabetes, Lectures, Medicine, Nephrology, Pharmacology, Studies | Leave a comment

A few diabetes papers of interest

i. Clinical Inertia in Type 2 Diabetes Management: Evidence From a Large, Real-World Data Set.

Despite clinical practice guidelines that recommend frequent monitoring of HbA1c (every 3 months) and aggressive escalation of antihyperglycemic therapies until glycemic targets are reached (1,2), the intensification of therapy in patients with uncontrolled type 2 diabetes (T2D) is often inappropriately delayed. The failure of clinicians to intensify therapy when clinically indicated has been termed “clinical inertia.” A recently published systematic review found that the median time to treatment intensification after an HbA1c measurement above target was longer than 1 year (range 0.3 to >7.2 years) (3). We have previously reported a rather high rate of clinical inertia in patients uncontrolled on metformin monotherapy (4). Treatment was not intensified early (within 6 months of metformin monotherapy failure) in 38%, 31%, and 28% of patients when poor glycemic control was defined as an HbA1c >7% (>53 mmol/mol), >7.5% (>58 mmol/mol), and >8% (>64 mmol/mol), respectively.

Using the electronic health record system at Cleveland Clinic (2005–2016), we identified a cohort of 7,389 patients with T2D who had an HbA1c value ≥7% (≥53 mmol/mol) (“index HbA1c”) despite having been on a stable regimen of two oral antihyperglycemic drugs (OADs) for at least 6 months prior to the index HbA1c. This HbA1c threshold would generally be expected to trigger treatment intensification based on current guidelines. Patient records were reviewed for the 6-month period following the index HbA1c, and changes in diabetes therapy were evaluated for evidence of “intensification” […] almost two-thirds of patients had no evidence of intensification in their antihyperglycemic therapy during the 6 months following the index HbA1c ≥7% (≥53 mmol/mol), suggestive of poor glycemic control. Most alarming was the finding that even among patients in the highest index HbA1c category (≥9% [≥75 mmol/mol]), therapy was not intensified in 44% of patients, and slightly more than half (53%) of those with an HbA1c between 8 and 8.9% (64 and 74 mmol/mol) did not have their therapy intensified.”

“Unfortunately, these real-world findings confirm a high prevalence of clinical inertia with regard to T2D management. The unavoidable conclusion from these data […] is that physicians are not responding quickly enough to evidence of poor glycemic control in a high percentage of patients, even in those with HbA1c levels far exceeding typical treatment targets.

ii. Gestational Diabetes Mellitus and Diet: A Systematic Review and Meta-analysis of Randomized Controlled Trials Examining the Impact of Modified Dietary Interventions on Maternal Glucose Control and Neonatal Birth Weight.

“Medical nutrition therapy is a mainstay of gestational diabetes mellitus (GDM) treatment. However, data are limited regarding the optimal diet for achieving euglycemia and improved perinatal outcomes. This study aims to investigate whether modified dietary interventions are associated with improved glycemia and/or improved birth weight outcomes in women with GDM when compared with control dietary interventions. […]

From 2,269 records screened, 18 randomized controlled trials involving 1,151 women were included. Pooled analysis demonstrated that for modified dietary interventions when compared with control subjects, there was a larger decrease in fasting and postprandial glucose (−4.07 mg/dL [95% CI −7.58, −0.57]; P = 0.02 and −7.78 mg/dL [95% CI −12.27, −3.29]; P = 0.0007, respectively) and a lower need for medication treatment (relative risk 0.65 [95% CI 0.47, 0.88]; P = 0.006). For neonatal outcomes, analysis of 16 randomized controlled trials including 841 participants showed that modified dietary interventions were associated with lower infant birth weight (−170.62 g [95% CI −333.64, −7.60]; P = 0.04) and less macrosomia (relative risk 0.49 [95% CI 0.27, 0.88]; P = 0.02). The quality of evidence for these outcomes was low to very low. Baseline differences between groups in postprandial glucose may have influenced glucose-related outcomes. […] we were unable to resolve queries regarding potential concerns for sources of bias because of lack of author response to our queries. We have addressed this by excluding these studies in the sensitivity analysis. […] after removal of the studies with the most substantial methodological concerns in the sensitivity analysis, differences in the change in fasting plasma glucose were no longer significant. Although differences in the change in postprandial glucose and birth weight persisted, they were attenuated.”

“This review highlights limitations of the current literature examining dietary interventions in GDM. Most studies are too small to demonstrate significant differences in our primary outcomes. Seven studies had fewer than 50 participants and only two had more than 100 participants (n = 125 and 150). The short duration of many dietary interventions and the late gestational age at which they were started (38) may also have limited their impact on glycemic and birth weight outcomes. Furthermore, we cannot conclude if the improvements in maternal glycemia and infant birth weight are due to reduced energy intake, improved nutrient quality, or specific changes in types of carbohydrate and/or protein. […] These data suggest that dietary interventions modified above and beyond usual dietary advice for GDM have the potential to offer better maternal glycemic control and infant birth weight outcomes. However, the quality of evidence was judged as low to very low due to the limitations in the design of included studies, the inconsistency between their results, and the imprecision in their effect estimates.”

iii. Lifetime Prevalence and Prognosis of Prediabetes Without Progression to Diabetes.

Impaired fasting glucose, also termed prediabetes, is increasingly prevalent and is associated with adverse cardiovascular risk (1). The cardiovascular risks attributed to prediabetes may be driven primarily by the conversion from prediabetes to overt diabetes (2). Given limited data on outcomes among nonconverters in the community, the extent to which some individuals with prediabetes never go on to develop diabetes and yet still experience adverse cardiovascular risk remains unclear. We therefore investigated the frequency of cardiovascular versus noncardiovascular deaths in people who developed early- and late-onset prediabetes without ever progressing to diabetes.”

“We used data from the Framingham Heart Study collected on the Offspring Cohort participants aged 18–77 years at the time of initial fasting plasma glucose (FPG) assessment (1983–1987) who had serial FPG testing over subsequent examinations with continuous surveillance for outcomes including cause-specific mortality (3). As applied in prior epidemiological investigations (4), we used a case-control design focusing on the cause-specific outcome of cardiovascular death to minimize the competing risk issues that would be encountered in time-to-event analyses. To focus on outcomes associated with a given chronic glycemic state maintained over the entire lifetime, we restricted our analyses to only those participants for whom data were available over the life course and until death. […] We excluded individuals with unknown age of onset of glycemic impairment (i.e., age ≥50 years with prediabetes or diabetes at enrollment). […] We analyzed cause-specific mortality, allowing for relating time-varying exposures with lifetime risk for an event (4). We related glycemic phenotypes to cardiovascular versus noncardiovascular cause of death using a case-control design, where cases were defined as individuals who died of cardiovascular disease (death from stroke, heart failure, or other vascular event) or coronary heart disease (CHD) and controls were those who died of other causes.”

“The mean age of participants at enrollment was 42 ± 7 years (43% women). The mean age at death was 73 ± 10 years. […] In our study, approximately half of the individuals presented with glycemic impairment in their lifetime, of whom two-thirds developed prediabetes but never diabetes. In our study, these individuals had lower cardiovascular-related mortality compared with those who later developed diabetes, even if the prediabetes onset was early in life. However, individuals with early-onset prediabetes, despite lifelong avoidance of overt diabetes, had greater propensity for death due to cardiovascular or coronary versus noncardiovascular disease compared with those who maintained lifelong normal glucose status. […] Prediabetes is a heterogeneous entity. Whereas some forms of prediabetes are precursors to diabetes, other types of prediabetes never progress to diabetes but still confer increased propensity for death from a cardiovascular cause.”

iv. Learning From Past Failures of Oral Insulin Trials.

Very recently one of the largest type 1 diabetes prevention trials using daily administration of oral insulin or placebo was completed. After 9 years of study enrollment and follow-up, the randomized controlled trial failed to delay the onset of clinical type 1 diabetes, which was the primary end point. The unfortunate outcome follows the previous large-scale trial, the Diabetes Prevention Trial–Type 1 (DPT-1), which again failed to delay diabetes onset with oral insulin or low-dose subcutaneous insulin injections in a randomized controlled trial with relatives at risk for type 1 diabetes. These sobering results raise the important question, “Where does the type 1 diabetes prevention field move next?” In this Perspective, we advocate for a paradigm shift in which smaller mechanistic trials are conducted to define immune mechanisms and potentially identify treatment responders. […] Mechanistic trials will allow for better trial design and patient selection based upon molecular markers prior to large randomized controlled trials, moving toward a personalized medicine approach for the prevention of type 1 diabetes.

“Before a disease can be prevented, it must be predicted. The ability to assess risk for developing type 1 diabetes (T1D) has been well documented over the last two decades (1). Using genetic markers, human leukocyte antigen (HLA) DQ and DR typing (2), islet autoantibodies (1), and assessments of glucose tolerance (intravenous or oral glucose tolerance tests) has led to accurate prediction models for T1D development (3). Prospective birth cohort studies Diabetes Autoimmunity Study in the Young (DAISY) in Colorado (4), Type 1 Diabetes Prediction and Prevention (DIPP) study in Finland (5), and BABYDIAB studies in Germany have followed genetically at-risk children for the development of islet autoimmunity and T1D disease onset (6). These studies have been instrumental in understanding the natural history of T1D and making T1D a predictable disease with the measurement of antibodies in the peripheral blood directed against insulin and proteins within β-cells […]. Having two or more islet autoantibodies confers an ∼85% risk of developing T1D within 15 years and nearly 100% over time (7). […] T1D can be predicted by measuring islet autoantibodies, and thousands of individuals including young children are being identified through screening efforts, necessitating the need for treatments to delay and prevent disease onset.”

“Antigen-specific immunotherapies hold the promise of potentially inducing tolerance by inhibiting effector T cells and inducing regulatory T cells, which can act locally at tissue-specific sites of inflammation (12). Additionally, side effects are minimal with these therapies. As such, insulin and GAD have both been used as antigen-based approaches in T1D (13). Oral insulin has been evaluated in two large randomized double-blinded placebo-controlled trials over the last two decades. First in the Diabetes Prevention Trial–Type 1 (DPT-1) and then in the TrialNet clinical trials network […] The DPT-1 enrolled relatives at increased risk for T1D having islet autoantibodies […] After 6 years of treatment, there was no delay in T1D onset. […] The TrialNet study screened, enrolled, and followed 560 at-risk relatives over 9 years from 2007 to 2016, and results have been recently published (16). Unfortunately, this trial failed to meet the primary end point of delaying or preventing diabetes onset.”

“Many factors influence the potency and efficacy of antigen-specific therapy such as dose, frequency of dosing, route of administration, and, importantly, timing in the disease process. […] Over the last two decades, most T1D clinical trial designs have randomized participants 1:1 or 2:1, drug to placebo, in a double-blind two-arm design, especially those intervention trials in new-onset T1D (18). Primary end points have been delay in T1D onset for prevention trials or stimulated C-peptide area under the curve at 12 months with new-onset trials. These designs have served the field well and provided reliable human data for efficacy. However, there are limitations including the speed at which these trials can be completed, the number of interventions evaluated, dose optimization, and evaluation of mechanistic hypotheses. Alternative clinical trial designs, such as adaptive trial designs using Bayesian statistics, can overcome some of these issues. Adaptive designs use accumulating data from the trial to modify certain aspects of the study, such as enrollment and treatment group assignments. This “learn as we go” approach relies on biomarkers to drive decisions on planned trial modifications. […] One of the significant limitations for adaptive trial designs in the T1D field, at the present time, is the lack of validated biomarkers for short-term readouts to inform trial adaptations. However, large-scale collaborative efforts are ongoing to define biomarkers of T1D-specific immune dysfunction and β-cell stress and death (9,22).”

T1D prevention has proven much more difficult than originally thought, challenging the paradigm that T1D is a single disease. T1D is indeed a heterogeneous disease in terms of age of diagnosis, islet autoantibody profiles, and the rate of loss of residual β-cell function after clinical onset. Children have a much more rapid loss of residual insulin production (measured as C-peptide area under the curve following a mixed-meal tolerance test) after diagnosis than older adolescents and adults (23,24), indicating that childhood and adult-onset T1D are not identical. Further evidence for subtypes of T1D come from studies of human pancreata of T1D organ donors in which children (0–14 years of age) within 1 year of diagnosis had many more inflamed islets compared with older adolescents and adults aged 15–39 years old (25). Additionally, a younger age of T1D onset (<7 years) has been associated with higher numbers of CD20+ B cells within islets and fewer insulin-containing islets compared with an age of onset ≥13 years associated with fewer CD20+ islet infiltrating cells and more insulin-containing islets (26,27). This suggests a much more aggressive autoimmune process in younger children and distinct endotypes (a subtype of a condition defined by a distinct pathophysiologic mechanism), which has recently been proposed for T1D (27).”

“Safe and specific therapies capable of being used in children are needed for T1D prevention. The vast majority of drug development involves small biotechnology companies, specialty pharmaceutical firms, and large pharmaceutical companies, more so than traditional academia. A large amount of preclinical and clinical research (phase 1, 2, and 3 studies) are needed to advance a drug candidate through the development pipeline to achieve U.S. Food and Drug Administration (FDA) approval for a given disease. A recent analysis of over 4,000 drugs from 835 companies in development during 2003–2011 revealed that only 10.4% of drugs that enter clinical development at phase 1 (safety studies) advance to FDA approval (32). However, the success rate increases 50% for the lead indication of a drug, i.e., a drug specifically developed for one given disease (32). Reasons for this include strong scientific rationale and early efficacy signals such as correlating pharmacokinetic (drug levels) to pharmacodynamic (drug target effects) tests for the lead indication. Lead indications also tend to have smaller, better-defined “homogenous” patient populations than nonlead indications for the same drug. This would imply that the T1D field needs more companies developing drugs specifically for T1D, not type 2 diabetes or other autoimmune diseases with later testing to broaden a drug’s indication. […] In a similar but separate analysis, selection biomarkers were found to substantially increase the success rate of drug approvals across all phases of drug development. Using a selection biomarker as part of study inclusion criteria increased drug approval threefold from 8.4% to 25.9% when used in phase 1 trials, 28% to 46% when transitioning from a phase 2 to phase 3 efficacy trial, and 55% to 76% for a phase 3 trial to likelihood of approval (33). These striking data support the concept that enrichment of patient enrollment at the molecular level is a more successful strategy than heterogeneous enrollment in clinical intervention trials. […] Taken together, new drugs designed specifically for children at risk for T1D and a biomarker selecting patients for a treatment response may increase the likelihood for a successful prevention trial; however, experimental confirmation in clinical trials is needed.”

v. Metabolic Karma — The Atherogenic Legacy of Diabetes: The 2017 Edwin Bierman Award Lecture.

“Cardiovascular (CV) disease remains the major cause of mortality and is associated with significant morbidity in both type 1 and type 2 diabetes (14). Despite major improvements in the management of traditional risk factors, including hypertension, dyslipidemia, and glycemic control prevention, retardation and reversal of atherosclerosis, as manifested clinically by myocardial infarction, stroke, and peripheral vascular disease, remain a major unmet need in the population with diabetes. For example, in the Steno-2 study and in its most recent report of the follow-up phase, at least a decade after cessation of the active treatment phase, there remained a high risk of death, primarily from CV disease despite aggressive control of the traditional risk factors, in this originally microalbuminuric population with type 2 diabetes (5,6). In a meta-analysis of major CV trials where aggressive glucose lowering was instituted […] the beneficial effect of intensive glycemic control on CV disease was modest, at best (7). […] recent trials with two sodium–glucose cotransporter 2 inhibitors, empagliflozin and canagliflozin (11,12), and two long-acting glucagon-like peptide 1 agonists, liraglutide and semaglutide (13,14), have reported CV benefits that have led in some of these trials to a decrease in CV and all-cause mortality. However, even with these recent positive CV outcomes, CV disease remains the major burden in the population with diabetes (15).”

“This unmet need of residual CV disease in the population with diabetes remains unexplained but may occur as a result of a range of nontraditional risk factors, including low-grade inflammation and enhanced thrombogenicity as a result of the diabetic milieu (16). Furthermore, a range of injurious pathways as a result of chronic hyperglycemia previously studied in vitro in endothelial cells (17) or in models of microvascular complications may also be relevant and are a focus of this review […] [One] major factor that is likely to promote atherosclerosis in the diabetes setting is increased oxidative stress. There is not only increased generation of ROS from diverse sources but also reduced antioxidant defense in diabetes (40). […] vascular ROS accumulation is closely linked to atherosclerosis and vascular inflammation provide the impetus to consider specific antioxidant strategies as a novel therapeutic approach to decrease CV disease, particularly in the setting of diabetes.”

“One of the most important findings from numerous trials performed in subjects with type 1 and type 2 diabetes has been the identification that previous episodes of hyperglycemia can have a long-standing impact on the subsequent development of CV disease. This phenomenon known as “metabolic memory” or the “legacy effect” has been reported in numerous trials […] The underlying explanation at a molecular and/or cellular level for this phenomenon remains to be determined. Our group, as well as others, has postulated that epigenetic mechanisms may participate in conferring metabolic memory (5153). In in vitro studies initially performed in aortic endothelial cells, transient incubation of these cells in high glucose followed by subsequent return of these cells to a normoglycemic environment was associated with increased gene expression of the p65 subunit of NF-κB, NF-κB activation, and expression of NF-κB–dependent proteins, including MCP-1 and VCAM-1 (54).

In further defining a potential epigenetic mechanism that could explain the glucose-induced upregulation of genes implicated in vascular inflammation, a specific histone methylation mark was identified in the promoter region of the p65 gene (54). This histone 3 lysine 4 monomethylation (H3K4m1) occurred as a result of mobilization of the histone methyl transferase, Set7. Furthermore, knockdown of Set7 attenuated glucose-induced p65 upregulation and prevented the persistent upregulation of this gene despite these endothelial cells returning to a normoglycemic milieu (55). These findings, confirmed in animal models exposed to transient hyperglycemia (54), provide the rationale to consider Set7 as an appropriate target for end-organ protective therapies in diabetes. Although specific Set7 inhibitors are currently unavailable for clinical development, the current interest in drugs that block various enzymes, such as Set7, that influence histone methylation in diseases, such as cancer (56), could lead to agents that warrant testing in diabetes. Studies addressing other sites of histone methylation as well as other epigenetic pathways including DNA methylation and acetylation have been reported or are currently in progress (55,57,58), particularly in the context of diabetes complications. […] As in vitro and preclinical studies increase our knowledge and understanding of the pathogenesis of diabetes complications, it is likely that we will identify new molecular targets leading to better treatments to reduce the burden of macrovascular disease. Nevertheless, these new treatments will need to be considered in the context of improved management of traditional risk factors.”

vi. Perceived risk of diabetes seriously underestimates actual diabetes risk: The KORA FF4 study.

“According to the International Diabetes Federation (IDF), almost half of the people with diabetes worldwide are unaware of having the disease, and even in high-income countries, about one in three diabetes cases is not diagnosed [1,2]. In the USA, 28% of diabetes cases are undiagnosed [3]. In DEGS1, a recent population-based German survey, 22% of persons with HbA1c ≥ 6.5% were unaware of their disease [4]. Persons with undiagnosed diabetes mellitus (UDM) have a more than twofold risk of mortality compared to persons with normal glucose tolerance (NGT) [5,6]; many of them also have undiagnosed diabetes complications like retinopathy and chronic kidney disease [7,8]. […] early detection of diabetes and prediabetes is beneficial for patients, but may be delayed by patients´ being overly optimistic about their own health. Therefore, it is important to address how persons with UDM or prediabetes perceive their diabetes risk.”

“The proportion of persons who perceived their risk of having UDM at the time of the interview as “negligible”, “very low” or “low” was 87.1% (95% CI: 85.0–89.0) in NGT [normal glucose tolerance individuals], 83.9% (81.2–86.4) in prediabetes, and 74.2% (64.5–82.0) in UDM […]. The proportion of persons who perceived themselves at risk of developing diabetes in the following years ranged from 14.6% (95% CI: 12.6–16.8) in NGT to 20.6% (17.9–23.6) in prediabetes to 28.7% (20.5–38.6) in UDM […] In univariate regression models, perceiving oneself at risk of developing diabetes was associated with younger age, female sex, higher school education, obesity, self-rated poor general health, and parental diabetes […] the proportion of better educated younger persons (age ≤ 60 years) with prediabetes, who perceived themselves at risk of developing diabetes was 35%, whereas this figure was only 13% in less well educated older persons (age > 60 years).”

The present study shows that three out of four persons with UDM [undiagnosed diabetes mellitus] believed that the probability of having undetected diabetes was low or very low. In persons with prediabetes, more than 70% believed that they were not at risk of developing diabetes in the next years. People with prediabetes were more inclined to perceive themselves at risk of diabetes if their self-rated general health was poor, their mother or father had diabetes, they were obese, they were female, their educational level was high, and if they were younger. […] People with undiagnosed diabetes or prediabetes considerably underestimate their probability of having or developing diabetes. […] perceived diabetes risk was lower in men, lower educated and older persons. […] Our results showed that people with low and intermediate education strongly underestimate their risk of diabetes and may qualify as target groups for detection of UDM and prediabetes.”

“The present results were in line with results from the Dutch Hoorn Study [18,19]. Adriaanse et al. reported that among persons with UDM, only 28.3% perceived their likeliness of having diabetes to be at least 10% [18], and among persons with high risk of diabetes (predicted from a symptom risk questionnaire), the median perceived likeliness of having diabetes was 10.8% [19]. Again, perceived risk did not fully reflect the actual risk profiles. For BMI, there was barely any association with perceived risk of diabetes in the Dutch study [19].”

July 2, 2018 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Immunology, Medicine, Molecular biology, Pharmacology, Studies | Leave a comment

Blood (II)

Below I have added some quotes from the chapters of the book I did not cover in my first post, as well as some supplementary links.

Haemoglobin is of crucial biological importance; it is also easy to obtain safely in large quantities from donated blood. These properties have resulted in its becoming the most studied protein in human history. Haemoglobin played a key role in the history of our understanding of all proteins, and indeed the science of biochemistry itself. […] Oxygen transport defines the primary biological function of blood. […] Oxygen gas consists of two atoms of oxygen bound together to form a symmetrical molecule. However, oxygen cannot be transported in the plasma alone. This is because water is very poor at dissolving oxygen. Haemoglobin’s primary function is to increase this solubility; it does this by binding the oxygen gas on to the iron in its haem group. Every haem can bind one oxygen molecule, increasing the amount of oxygen able to dissolve in the blood.”

“An iron atom can exist in a number of different forms depending on how many electrons it has in its atomic orbitals. In its ferrous (iron II) state iron can bind oxygen readily. The haemoglobin protein has therefore evolved to stabilize its haem iron cofactor in this ferrous state. The result is that over fifty times as much oxygen is stored inside the confines of the red blood cell compared to outside in the watery plasma. However, using iron to bind oxygen comes at a cost. Iron (II) can readily lose one of its electrons to the bound oxygen, a process called ‘oxidation’. So the same form of iron that can bind oxygen avidly (ferrous) also readily reacts with that same oxygen forming an unreactive iron III state, called ‘ferric’. […] The complex structure of the protein haemoglobin is required to protect the ferrous iron from oxidizing. The haem iron is held in a precise configuration within the protein. Specific amino acids are ideally positioned to stabilize the iron–oxygen bond and prevent it from oxidizing. […] the iron stays ferrous despite the presence of the nearby oxygen. Having evolved over many hundreds of millions of years, this stability is very difficult for chemists to mimic in the laboratory. This is one reason why, desirable as it might be in terms of cost and convenience, it is not currently possible to replace blood transfusions with a simple small chemical iron oxygen carrier.”

“Given the success of the haem iron and globin combination in haemoglobin, it is no surprise that organisms have used this basic biochemical architecture for a variety of purposes throughout evolution, not just oxygen transport in blood. One example is the protein myoglobin. This protein resides inside animal cells; in the human it is found in the heart and skeletal muscle. […] Myoglobin has multiple functions. Its primary role is as an aid to oxygen diffusion. Whereas haemoglobin transports oxygen from the lung to the cell, myoglobin transports it once it is inside the cell. As oxygen is so poorly soluble in water, having a chain of molecules inside the cell that can bind and release oxygen rapidly significantly decreases the time it takes the gas to get from the blood capillary to the part of the cell—the mitochondria—where it is needed. […] Myoglobin can also act as an emergency oxygen backup store. In humans this is trivial and of questionable importance. Not so in diving mammals such as whales and dolphins that have as much as thirty times the myoglobin content of the terrestrial equivalent; indeed those mammals that dive for the longest duration have the most myoglobin. […] The third known function of myoglobin is to protect the muscle cells from damage by nitric oxide gas.”

“The heart is the organ that pumps blood around the body. If the heart stops functioning, blood does not flow. The driving force for this flow is the pressure difference between the arterial blood leaving the heart and the returning venous blood. The decreasing pressure in the venous side explains the need for unidirectional valves within veins to prevent the blood flowing in the wrong direction. Without them the return of the blood through the veins to the heart would be too slow, especially when standing up, when the venous pressure struggles to overcome gravity. […] normal [blood pressure] ranges rise slowly with age. […] high resistance in the arterial circulation at higher blood pressures [places] additional strain on the left ventricle. If the heart is weak, it may fail to achieve the extra force required to pump against this resistance, resulting in heart failure. […] in everyday life, a low blood pressure is rarely of concern. Indeed, it can be a sign of fitness as elite athletes have a much lower resting blood pressure than the rest of the population. […] the effect of exercise training is to thicken the muscles in the walls of the heart and enlarge the chambers. This enables more blood to be pumped per beat during intense exercise. The consequence of this extra efficiency is that when an athlete is resting—and therefore needs no more oxygen than a more sedentary person—the heart rate and blood pressure are lower than average. Most people’s experience of hypotension will be reflected by dizzy spells and lack of balance, especially when moving quickly to an upright position. This is because more blood pools in the legs when you stand up, meaning there is less blood for the heart to pump. The immediate effect should be for the heart to beat faster to restore the pressure. If there is a delay, the decrease in pressure can decrease the blood flow to the brain and cause dizziness; in extreme cases this can lead to fainting.”

“If hypertension is persistent, patients are most likely to be treated with drugs that target specific pathways that the body uses to control blood pressure. For example angiotensin is a protein that can trigger secretion of the hormone aldosterone from the adrenal gland. In its active form angiotensin can directly constrict blood vessels, while aldosterone enhances salt and water retention, so raising blood volume. Both these effects increase blood pressure. Angiotensin is converted into its active form by an enzyme called ‘Angiotensin Converting Enzyme’ (ACE). An ACE inhibitor drug prevents this activity, keeping angiotensin in its inactive form; this will therefore drop the patient’s blood pressure. […] The metal calcium controls many processes in the body. Its entry into muscle cells triggers muscle contraction. Preventing this entry can therefore reduce the force of contraction of the heart and the ability of arteries to constrict. Both of these will have the effect of decreasing blood pressure. Calcium enters muscle cells via specific protein-based channels. Drugs that block these channels (calcium channel blockers) are therefore highly effective at treating hypertension.”

Autoregulation is a homeostatic process designed to ensure that blood flow remains constant [in settings where constancy is desirable]. However, there are many occasions when an organism actively requires a change in blood flow. It is relatively easy to imagine what these are. In the short term, blood supplies oxygen and nutrients. When these are used up rapidly, or their supply becomes limited, the response will be to increase blood flow. The most obvious example is the twenty-fold increase in oxygen and glucose consumption that occurs in skeletal muscle during exercise when compared to rest. If there were no accompanying increase in blood flow to the muscle the oxygen supply would soon run out. […] There are hundreds of molecules known that have the ability to increase or decrease blood flow […] The surface of all blood vessels is lined by a thin layer of cells, the ‘endothelium’. Endothelial cells form a barrier between the blood and the surrounding tissue, controlling access of materials into and out of the blood. For example white blood cells can enter or leave the circulation via interacting with the endothelium; this is the route by which neutrophils migrate from the blood to the site of tissue damage or bacterial/viral attack as part of the innate immune response. However, the endothelium is not just a selective barrier. It also plays an active role in blood physiology and biochemistry.”

“Two major issues [related to blood transfusions] remained at the end of the 19th century: the problem of clotting, which all were aware of; and the problem of blood group incompatbility, which no one had the slightest idea even existed. […] For blood transfusions to ever make a recovery the key issues of blood clotting and adverse side effects needed to be resolved. In 1875 the Swedish biochemist Olof Hammarsten showed that adding calcium accelerated the rate of blood clotting (we now know the mechanism for this is that key enzymes in blood platelets that catalyse fibrin formation require calcium for their function). It therefore made sense to use chemicals that bind calcium to try to prevent clotting. Calcium ions are positively charged; adding negatively charged ions such as oxalate and citrate neutralized the calcium, preventing its clot-promoting action. […] At the same time as anticoagulants were being discovered, the reason why some blood transfusions failed even when there were no clots was becoming clear. It had been shown that animal blood given to humans tended to clump together or agglutinate, eventually bursting and releasing free haemoglobin and causing kidney damage. In the early 1900s, working in Vienna, Karl Landsteiner showed the same effect could occur with human-to-human transfusion. The trick was the ability to separate blood cells from serum. This enabled mixing blood cells from a variety of donors with plasma from a variety of participants. Using his laboratory staff as subjects, Landsteiner showed that only some combinations caused the agglutination reaction. Some donor cells (now known as type O) never clumped. Others clumped depending on the nature of the plasma in a reproducible manner. A careful study of Landsteiner’s results revealed the ABO blood type distinctions […]. Versions of these agglutination tests still form the basis of checking transfused blood today.”

“No blood product can be made completely sterile, no matter how carefully it is processed. The best that can be done is to ensure that no new bacteria or viruses are added during the purification, storage, and transportation processes. Nothing can be done to inactivate any viruses that are already present in the donor’s blood, for the harsh treatments necessary to do this would inevitably damage the viability of the product or be prohibitively expensive to implement on the industrial scale that the blood market has become. […] In the 1980s over half the US haemophiliac population was HIV positive.”

“Three fundamentally different ways have been attempted to replace red blood cell transfusions. The first uses a completely chemical approach and makes use of perfluorocarbons, inert chemicals that, in liquid form, can dissolve gasses without reacting with them. […] Perfluorocarbons can dissolve oxygen much more effectively than water. […] The problem with their use as a blood substitute is that the amount of oxygen dissolved in these solutions is linear with increasing pressure. This means that the solution lacks the advantages of the sigmoidal binding curve of haemoglobin, which has evolved to maximize the amount of oxygen captured from the limited fraction found in air (20 per cent oxygen). However, to deliver the same amount of oxygen as haemoglobin, patients using the less efficient perfluorocarbons in their blood need to breathe gas that is almost 100 per cent pure oxygen […]; this restricts the use of these compounds. […] The second type of blood substitute makes use of haemoglobin biology. Initial attempts used purified haemoglobin itself. […] there is no haemoglobin-based blood substitute in general use today […] The problem for the lack of uptake is not that blood substitutes cannot replace red blood cell function. A variety of products have been shown to stay in the vasculature for several days, provide volume support, and deliver oxygen. However, they have suffered due to adverse side effects, most notably cardiac complications. […] In nature the plasma proteins haptoglobin and haemopexin bind and detoxify any free haemoglobin and haem released from red blood cells. The challenge for blood substitute research is to mimic these effects in a product that can still deliver oxygen. […] Despite ongoing research, these problems may prove to be insurmountable. There is therefore interest in a third approach. This is to grow artificial red blood cells using stem cell technology.”

Links:

Porphyrin. Globin.
Felix Hoppe-Seyler. Jacques Monod. Jeffries Wyman. Jean-Pierre Changeux.
Allosteric regulation. Monod-Wyman-Changeux model.
Structural Biochemistry/Hemoglobin (wikibooks). (Many of the topics covered in this link – e.g. comments on affinity, T/R-states, oxygen binding curves, the Bohr effect, etc. – are also covered in the book, so although I do link to some of the other topics also covered in this link below it should be noted that I did in fact leave out quite a few potentially relevant links on account of those topics being covered in the above link).
1,3-Bisphosphoglycerate.
Erythrocruorin.
Haemerythrin.
Hemocyanin.
Cytoglobin.
Neuroglobin.
Sickle cell anemia. Thalassaemia. Hemoglobinopathy. Porphyria.
Pulse oximetry.
Daniel Bernoulli. Hydrodynamica. Stephen Hales. Karl von Vierordt.
Arterial line.
Sphygmomanometer. Korotkoff sounds. Systole. Diastole. Blood pressure. Mean arterial pressure. Hypertension. Antihypertensive drugs. Atherosclerosis Pathology. Beta blocker. Diuretic.
Autoregulation.
Guanylate cyclase. Glyceryl trinitrate.
Blood transfusion. Richard Lower. Jean-Baptiste Denys. James Blundell.
Parabiosis.
Penrose Inquiry.
ABLE (Age of Transfused Blood in Critically Ill Adults) trial.
RECESS trial.

June 7, 2018 Posted by | Biology, Books, Cardiology, Chemistry, History, Medicine, Molecular biology, Pharmacology, Studies | Leave a comment

A few diabetes papers of interest

i. Reevaluating the Evidence for Blood Pressure Targets in Type 2 Diabetes.

“There is general consensus that treating adults with type 2 diabetes mellitus (T2DM) and hypertension to a target blood pressure (BP) of <140/90 mmHg helps prevent cardiovascular disease (CVD). Whether more intensive BP control should be routinely targeted remains a matter of debate. While the American Diabetes Association (ADA) BP guidelines recommend an individualized assessment to consider different treatment goals, the American College of Cardiology/American Heart Association BP guidelines recommend a BP target of <130/80 mmHg for most individuals with hypertension, including those with T2DM (13).

In large part, these discrepant recommendations reflect the divergent results of the Action to Control Cardiovascular Risk in Diabetes-BP trial (ACCORD-BP) among people with T2DM and the Systolic Blood Pressure Intervention Trial (SPRINT), which excluded people with diabetes (4,5). Both trials evaluated the effect of intensive compared with standard BP treatment targets (<120 vs. <140 mmHg systolic) on a composite CVD end point of nonfatal myocardial infarction or stroke or death from cardiovascular causes. SPRINT also included unstable angina and acute heart failure in its composite end point. While ACCORD-BP did not show a significant benefit from the intervention (hazard ratio [HR] 0.88; 95% CI 0.73–1.06), SPRINT found a significant 25% relative risk reduction on the primary end point favoring intensive therapy (0.75; 0.64–0.89).”

“To some extent, CVD mechanisms and causes of death differ in T2DM patients compared with the general population. Microvascular disease (particularly kidney disease), accelerated vascular calcification, and diabetic cardiomyopathy are common in T2DM (1315). Moreover, the rate of sudden cardiac arrest is markedly increased in T2DM and related, in part, to diabetes-specific factors other than ischemic heart disease (16). Hypoglycemia is a potential cause of CVD mortality that is specific to diabetes (17). In addition, polypharmacy is common and may increase CVD risk (18). Furthermore, nonvascular causes of death account for approximately 40% of the premature mortality burden experienced by T2DM patients (19). Whether these disease processes may render patients with T2DM less amenable to derive a mortality benefit from intensive BP control, however, is not known and should be the focus of future research.

In conclusion, the divergent results between ACCORD-BP and SPRINT are most readily explained by the apparent lack of benefit of intensive BP control on CVD and all-cause mortality in ACCORD-BP, rather than differences in the design, population characteristics, or interventions between the trials. This difference in effects on mortality may be attributable to differential mechanisms underlying CVD mortality in T2DM, to chance, or to both. These observations suggest that caution should be exercised extrapolating the results of SPRINT to patients with T2DM and support current ADA recommendations to individualize BP targets, targeting a BP of <140/90 mmHg in the majority of patients with T2DM and considering lower BP targets when it is anticipated that individual benefits outweigh risks.”

ii. Modelling incremental benefits on complications rates when targeting lower HbA1c levels in people with Type 2 diabetes and cardiovascular disease.

“Glucose‐lowering interventions in Type 2 diabetes mellitus have demonstrated reductions in microvascular complications and modest reductions in macrovascular complications. However, the degree to which targeting different HbA1c reductions might reduce risk is unclear. […] Participant‐level data for Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) participants with established cardiovascular disease were used in a Type 2 diabetes‐specific simulation model to quantify the likely impact of different HbA1c decrements on complication rates. […] The use of the TECOS data limits our findings to people with Type 2 diabetes and established cardiovascular disease. […] Ten‐year micro‐ and macrovascular rates were estimated with HbA1c levels fixed at 86, 75, 64, 53 and 42 mmol/mol (10%, 9%, 8%, 7% and 6%) while holding other risk factors constant at their baseline levels. Cumulative relative risk reductions for each outcome were derived for each HbA1c decrement. […] Of 5717 participants studied, 72.0% were men and 74.2% White European, with a mean (sd) age of 66.2 (7.9) years, systolic blood pressure 134 (16.9) mmHg, LDL‐cholesterol 2.3 (0.9) mmol/l, HDL‐cholesterol 1.13 (0.3) mmol/l and median Type 2 diabetes duration 9.6 (5.1–15.6) years. Ten‐year cumulative relative risk reductions for modelled HbA1c values of 75, 64, 53 and 42 mmol/mol, relative to 86 mmol/mol, were 4.6%, 9.3%, 15.1% and 20.2% for myocardial infarction; 6.0%, 12.8%, 19.6% and 25.8% for stroke; 14.4%, 26.6%, 37.1% and 46.4% for diabetes‐related ulcer; 21.5%, 39.0%, 52.3% and 63.1% for amputation; and 13.6%, 25.4%, 36.0% and 44.7 for single‐eye blindness. […] We did not investigate outcomes for renal failure or chronic heart failure as previous research conducted to create the model did not find HbA1c to be a statistically significant independent risk factor for either condition, therefore no clinically meaningful differences would be expected from modelling different HbA1c levels 11.”

“For microvascular complications, the absolute median estimates tended to be lower than for macrovascular complications at the same HbA1c level, but cumulative relative risk reductions were greater. For amputation the 10‐year absolute median estimate for a modelled constant HbA1c of 86 mmol/mol (10%) was 3.8% (3.7, 3.9), with successively lower values for each modelled 1% HbA1c decrement. Compared with the 86 mmol/mol (10%) HbA1c level, median relative risk reductions for amputation were 21.5% (21.1, 21.9) at 75 mmol/mol (9%) increasing to 52.3% (52.0, 52.6) at 53 mmol/mol (7%). […] Relative risk reductions in micro‐ and macrovascular complications for each 1% HbA1c reduction were similar for each decrement. The exception was all‐cause mortality, where the relative risk reductions for 1% HbA1c decrements were greater at higher baseline HbA1c levels. These simulated outcomes differ from the Diabetes Control and Complications Trial outcome in people with Type 1 diabetes, where lowering HbA1c from higher baseline levels had a greater impact on microvascular risk reduction 18.”

iii. Laser photocoagulation for proliferative diabetic retinopathy (Cochrane review).

“Diabetic retinopathy is a complication of diabetes in which high blood sugar levels damage the blood vessels in the retina. Sometimes new blood vessels grow in the retina, and these can have harmful effects; this is known as proliferative diabetic retinopathy. Laserphotocoagulation is an intervention that is commonly used to treat diabetic retinopathy, in which light energy is applied to the retinawith the aim of stopping the growth and development of new blood vessels, and thereby preserving vision. […] The aim of laser photocoagulation is to slow down the growth of new blood vessels in the retina and thereby prevent the progression of visual loss (Ockrim 2010). Focal laser photocoagulation uses the heat of light to seal or destroy abnormal blood vessels in the retina. Individual vessels are treated with a small number of laser burns.

PRP [panretinal photocoagulation, US] aims to slow down the growth of new blood vessels in a wider area of the retina. Many hundreds of laser burns are placed on the peripheral parts of the retina to stop blood vessels from growing (RCOphth 2012). It is thought that the anatomic and functional changes that result from photocoagulation may improve the oxygen supply to the retina, and so reduce the stimulus for neovascularisation (Stefansson 2001). Again the exact mechanisms are unclear, but it is possible that the decreased area of retinal tissue leads to improved oxygenation and a reduction in the levels of anti-vascular endothelial growth factor. A reduction in levels of anti-vascular endothelial growth factor may be important in reducing the risk of harmful new vessels forming. […] Laser photocoagulation is a well-established common treatment for DR and there are many different potential strategies for delivery of laser treatment that are likely to have different effects. A systematic review of the evidence for laser photocoagulation will provide important information on benefits and harms to guide treatment choices. […] This is the first in a series of planned reviews on laser photocoagulation. Future reviews will compare different photocoagulation techniques.”

“We identified a large number of trials of laser photocoagulation of diabetic retinopathy (n = 83) but only five of these studies were eligible for inclusion in the review, i.e. they compared laser photocoagulation with currently available lasers to no (or deferred) treatment. Three studies were conducted in the USA, one study in the UK and one study in Japan. A total of 4786 people (9503 eyes) were included in these studies. The majority of participants in four of these trials were people with proliferative diabetic retinopathy; one trial recruited mainly people with non-proliferative retinopathy.”

“At 12 months there was little difference between eyes that received laser photocoagulation and those allocated to no treatment (or deferred treatment), in terms of loss of 15 or more letters of visual acuity (risk ratio (RR) 0.99, 95% confidence interval (CI) 0.89 to1.11; 8926 eyes; 2 RCTs, low quality evidence). Longer term follow-up did not show a consistent pattern, but one study found a 20% reduction in risk of loss of 15 or more letters of visual acuity at five years with laser treatment. Treatment with laser reduced the risk of severe visual loss by over 50% at 12 months (RR 0.46, 95% CI 0.24to 0.86; 9276 eyes; 4 RCTs, moderate quality evidence). There was a beneficial effect on progression of diabetic retinopathy with treated eyes experiencing a 50% reduction in risk of progression of diabetic retinopathy (RR 0.49, 95% CI 0.37 to 0.64; 8331 eyes; 4 RCTs, low quality evidence) and a similar reduction in risk of vitreous haemorrhage (RR 0.56, 95% CI 0.37 to 0.85; 224 eyes; 2RCTs, low quality evidence).”

“Overall there is not a large amount of evidence from RCTs on the effects of laser photocoagulation compared to no treatment or deferred treatment. The evidence is dominated by two large studies conducted in the US population (DRS 1978; ETDRS 1991). These two studies were generally judged to be at low or unclear risk of bias, with the exception of inevitable unmasking of patients due to differences between intervention and control. […] In current clinical guidelines, e.g. RCOphth 2012, PRP is recommended in high-risk PDR. The recommendation is that “as retinopathy approaches the proliferative stage, laser scatter treatment (PRP) should be increasingly considered to prevent progression to high risk PDR” based on other factors such as patients’ compliance or planned cataract surgery.

These recommendations need to be interpreted while considering the risk of visual loss associated with different levels of severity of DR, as well as the risk of progression. Since PRP reduces the risk of severe visual loss, but not moderate visual loss that is more related to diabetic maculopathy, most ophthalmologists judge that there is little benefit in treating non-proliferative DR at low risk of severe visual damage, as patients would incur the known adverse effects of PRP, which, although mild, include pain and peripheral visual field loss and transient DMO [diabetic macular oedema, US]. […] This review provides evidence that laser photocoagulation is beneficial in treating diabetic retinopathy. […] based on the baseline risk of progression of the disease, and risk of visual loss, the current approach of caution in treating non-proliferative DR with laser would appear to be justified.

By current standards the quality of the evidence is not high, however, the effects on risk of progression and risk of severe visual loss are reasonably large (50% relative risk reduction).”

iv. Immune Recognition of β-Cells: Neoepitopes as Key Players in the Loss of Tolerance.

I should probably warn beforehand that this one is rather technical. It relates reasonably closely to topics covered in the molecular biology book I recently covered here on the blog, and if I had not read that book quite recently I almost certainly would not have been able to read the paper – so the coverage below is more ‘for me’ than ‘for you’. Anyway, some quotes:

“Prior to the onset of type 1 diabetes, there is progressive loss of immune self-tolerance, evidenced by the accumulation of islet autoantibodies and emergence of autoreactive T cells. Continued autoimmune activity leads to the destruction of pancreatic β-cells and loss of insulin secretion. Studies of samples from patients with type 1 diabetes and of murine disease models have generated important insights about genetic and environmental factors that contribute to susceptibility and immune pathways that are important for pathogenesis. However, important unanswered questions remain regarding the events that surround the initial loss of tolerance and subsequent failure of regulatory mechanisms to arrest autoimmunity and preserve functional β-cells. In this Perspective, we discuss various processes that lead to the generation of neoepitopes in pancreatic β-cells, their recognition by autoreactive T cells and antibodies, and potential roles for such responses in the pathology of disease. Emerging evidence supports the relevance of neoepitopes generated through processes that are mechanistically linked with β-cell stress. Together, these observations support a paradigm in which neoepitope generation leads to the activation of pathogenic immune cells that initiate a feed-forward loop that can amplify the antigenic repertoire toward pancreatic β-cell proteins.”

“Enzymatic posttranslational processes that have been implicated in neoepitope generation include acetylation (10), citrullination (11), glycosylation (12), hydroxylation (13), methylation (either protein or DNA methylation) (14), phosphorylation (15), and transglutamination (16). Among these, citrullination and transglutamination are most clearly implicated as processes that generate neoantigens in human disease, but evidence suggests that others also play a role in neoepitope formation […] Citrulline, which is among the most studied PTMs in the context of autoimmunity, is a diagnostic biomarker of rheumatoid arthritis (RA). […] Anticitrulline antibodies are among the earliest immune responses that are diagnostic of RA and often correlate with disease severity (18). We have recently documented the biological consequences of citrulline modifications and autoimmunity that arise from pancreatic β-cell proteins in the development of T1D (19). In particular, citrullinated GAD65 and glucose-regulated protein (GRP78) elicit antibody and T-cell responses in human T1D and in NOD diabetes, respectively (20,21).”

Carbonylation is an irreversible, iron-catalyzed oxidative modification of the side chains of lysine, arginine, threonine, or proline. Mitochondrial functions are particularly sensitive to carbonyl modification, which also has detrimental effects on other intracellular enzymatic pathways (30). A number of diseases have been linked with altered carbonylation of self-proteins, including Alzheimer and Parkinson diseases and cancer (27). There is some data to support that carbonyl PTM is a mechanism that directs unstable self-proteins into cellular degradation pathways. It is hypothesized that carbonyl PTM [post-translational modification] self-proteins that fail to be properly degraded in pancreatic β-cells are autoantigens that are targeted in T1D. Recently submitted studies have identified several carbonylated pancreatic β-cell neoantigens in human and murine models of T1D (27). Among these neoantigens are chaperone proteins that are required for the appropriate folding and secretion of insulin. These studies imply that although some PTM self-proteins may be direct targets of autoimmunity, others may alter, interrupt, or disturb downstream metabolic pathways in the β-cell. In particular, these studies indicated that upstream PTMs resulted in misfolding and/or metabolic disruption between proinsulin and insulin production, which provides one explanation for recent observations of increased proinsulin-to-insulin ratios in the progression of T1D (31).”

“Significant hypomethylation of DNA has been linked with several classic autoimmune diseases, such as SLE, multiple sclerosis, RA, Addison disease, Graves disease, and mixed connective tissue disease (36). Therefore, there is rationale to consider the possible influence of epigenetic changes on protein expression and immune recognition in T1D. Relevant to T1D, epigenetic modifications occur in pancreatic β-cells during progression of diabetes in NOD mice (37). […] Consequently, DNMTs [DNA methyltransferases] and protein arginine methyltransferases are likely to play a role in the regulation of β-cell differentiation and insulin gene expression, both of which are pathways that are altered in the presence of inflammatory cytokines. […] Eizirik et al. (38) reported that exposure of human islets to proinflammatory cytokines leads to modulation of transcript levels and increases in alternative splicing for a number of putative candidate genes for T1D. Their findings suggest a mechanism through which alternative splicing may lead to the generation of neoantigens and subsequent presentation of novel β-cell epitopes (39).”

“The phenomenon of neoepitope recognition by autoantibodies has been shown to be relevant in a variety of autoimmune diseases. For example, in RA, antibody responses directed against various citrullinated synovial proteins are remarkably disease-specific and routinely used as a diagnostic test in the clinic (18). Appearance of the first anticitrullinated protein antibodies occurs years prior to disease onset, and accumulation of additional autoantibody specificities correlates closely with the imminent onset of clinical arthritis (44). There is analogous evidence supporting a hierarchical emergence of autoantibody specificities and multiple waves of autoimmune damage in T1D (3,45). Substantial data from longitudinal studies indicate that insulin and GAD65 autoantibodies appear at the earliest time points during progression, followed by additional antibody specificities directed at IA-2 and ZnT8.”

“Multiple autoimmune diseases often cluster within families (or even within one person), implying shared etiology. Consequently, relevant insights can be gleaned from studies of more traditional autoantibody-mediated systemic autoimmune diseases, such as SLE and RA, where inter- and intramolecular epitope spreading are clearly paradigms for disease progression (47). In general, early autoimmunity is marked by restricted B- and T-cell epitopes, followed by an expanded repertoire coinciding with the onset of more significant tissue pathology […] Akin to T1D, other autoimmune syndromes tend to cluster to subcellular tissues or tissue components that share biological or biochemical properties. For example, SLE is marked by autoimmunity to nucleic acid–bearing macromolecules […] Unlike other systemic autoantibody-mediated diseases, such as RA and SLE, there is no clear evidence that T1D-related autoantibodies play a pathogenic role. Autoantibodies against citrulline-containing neoepitopes of proteoglycan are thought to trigger or intensify arthritis by forming immune complexes with this autoantigen in the joints of RA patients with anticitrullinated protein antibodies. In a similar manner, autoantibodies and immune complexes are hallmarks of tissue pathology in SLE. Therefore, it remains likely that autoantibodies or the B cells that produce them contribute to the pathogenesis of T1D.”

“In summation, the existing literature demonstrates that oxidation, citrullination, and deamidation can have a direct impact on T-cell recognition that contributes to loss of tolerance.”

“There is a general consensus that the pathogenesis of T1D is initiated when individuals who possess a high level of genetic risk (e.g., susceptible HLA, insulin VNTR, PTPN22 genotypes) are exposed to environmental factors (e.g., enteroviruses, diet, microbiome) that precipitate a loss of tolerance that manifests through the appearance of insulin and/or GAD autoantibodies. This early autoimmunity is followed by epitope spreading, increasing both the number of antigenic targets and the diversity of epitopes within these targets. These processes create a feed-forward loop antigen release that induces increasing inflammation and increasing numbers of distinct T-cell specificities (64). The formation and recognition of neoepitopes represents one mechanism through which epitope spreading can occur. […] mechanisms related to neoepitope formation and recognition can be envisioned at multiple stages of T1D pathogenesis. At the level of genetic risk, susceptible individuals may exhibit a genetically driven impairment of their stress response, increasing the likelihood of neoepitope formation. At the level of environmental exposure, many of the insults that are thought to initiate T1D are known to cause neoepitope formation. During the window of β-cell destruction that encompasses early autoimmunity through dysglycemia and diagnosis of T1D it remains unclear when neoepitope responses appear in relation to “classic” responses to insulin and GAD65. However, by the time of onset, neoepitope responses are clearly present and remain as part of the ongoing autoimmunity that is present during established T1D. […] The ultimate product of both direct and indirect generation of neoepitopes is an accumulation of robust and diverse autoimmune B- and T-cell responses, accelerating the pathological destruction of pancreatic islets. Clearly, the emergence of sophisticated methods of tissue and single-cell proteomics will identify novel neoepitopes, including some that occur at near the earliest stages of disease. A detailed mechanistic understanding of the pathways that lead to specific classes of neoepitopes will certainly suggest targets of therapeutic manipulation and intervention that would be hoped to impede the progression of disease.”

v. Diabetes technology: improving care, improving patient‐reported outcomes and preventing complications in young people with Type 1 diabetes.

“With the evolution of diabetes technology, those living with Type 1 diabetes are given a wider arsenal of tools with which to achieve glycaemic control and improve patient‐reported outcomes. Furthermore, the use of these technologies may help reduce the risk of acute complications, such as severe hypoglycaemia and diabetic ketoacidosis, as well as long‐term macro‐ and microvascular complications. […] Unfortunately, diabetes goals are often unmet and people with Type 1 diabetes too frequently experience acute and long‐term complications of this condition, in addition to often having less than ideal psychosocial outcomes. Increasing realization of the importance of patient‐reported outcomes is leading to diabetes care delivery becoming more patient‐centred. […] Optimal diabetes management requires both the medical and psychosocial needs of people with Type 1 diabetes and their caregivers to be addressed. […] The aim of this paper was to demonstrate how, by incorporating technology into diabetes care, we can increase patient‐centered care, reduce acute and chronic diabetes complications, and improve clinical outcomes and quality of life.”

[The paper’s Table 2 on page 422 of the pdf-version is awesome, it includes a lot of different Hba1c estimates from various patient populations all across the world. The numbers included in the table are slightly less awesome, as most populations only achieve suboptimal metabolic control.]

“The risks of all forms of complications increase with higher HbA1c concentration, increasing diabetes duration, hypertension, presence of other microvascular complications, obesity, insulin resistance, hyperlipidaemia and smoking 6. Furthermore, the Diabetes Research in Children (DirecNet) study has shown that individuals with Type 1 diabetes have white matter differences in the brain and cognitive differences compared with individuals without Type 1 diabetes. These studies showed that the degree of structural differences in the brain were related to the degree of chronic hyperglycaemia, hypoglycaemia and glucose variability 7. […] In addition to long‐term complications, people with Type 1 diabetes are also at risk of acute complications. Severe hypoglycaemia, a hypoglycaemic event resulting in altered/loss of consciousness or seizures, is a serious complication of insulin therapy. If unnoticed and untreated, severe hypoglycaemia can result in death. […] The incidence of diabetic ketoacidosis, a life‐threatening consequence of diabetes, remains unacceptably high in children with established diabetes (Table 5). The annual incidence of ketoacidosis was 5% in the Prospective Diabetes Follow‐Up Registry (DPV) in Germany and Austria, 6.4% in the National Paediatric Diabetes Audit (NPDA), and 7.1% in the Type 1 Diabetes Exchange (T1DX) registry 10. Psychosocial factors including female gender, non‐white race, lower socio‐economic status, and elevated HbA1c all contribute to increased risk of diabetic ketoacidosis 11.”

“Depression is more common in young people with Type 1 diabetes than in young people without a chronic disease […] Depression can make it more difficult to engage in diabetes self‐management behaviours, and as a result, contributes to suboptimal glycaemic control and lower rates of self‐monitoring of blood glucose (SMBG) in young people with Type 1 diabetes 15. […] Unlike depression, diabetes distress is not a clinical diagnosis but rather emotional distress that comes from the burden of living with and managing diabetes 16. A recent systematic review found that roughly one‐third of young people with Type 1 diabetes (age 10–20 years) have some level of diabetes distress and that diabetes distress was consistently associated with higher HbA1c and worse self‐management 17. […] Eating and weight‐related comorbidities also exist for individuals with Type 1 diabetes. There is a higher incidence of obesity in individuals with Type 1 diabetes on intensive insulin therapy. […] Adolescent girls and young adult women with Type 1 diabetes are more likely to omit insulin for weight loss and have disordered eating habits 20.”

“In addition to screening for and treating depression and diabetes distress to improve overall diabetes management, it is equally important to assess quality of life as well as positive coping factors that may also influence self‐management and well‐being. For example, lower scores on the PROMIS® measure of global health, which assesses social relationships as well as physical and mental well‐being, have been linked to higher depression scores and less frequent blood glucose checks 13. Furthermore, coping strategies such as problem‐solving, emotional expression, and acceptance have been linked to lower HbA1c and enhanced quality of life 21.”

“Self‐monitoring of blood glucose via multiple finger sticks for capillary blood samples per day has been the ‘gold standard’ for glucose monitoring, but SMBG only provides glucose measurements as snapshots in time. Still, the majority of young people with Type 1 diabetes use SMBG as their main method to assess glycaemia. Data from the T1DX registry suggest that an increased frequency of SMBG is associated with lower HbA1c levels 23. The development of continuous glucose monitoring (CGM) provides more values, along with the rate and direction of glucose changes. […] With continued use, CGM has been shown to decrease the incidence of hypoglycaemia and HbA1c levels 26. […] Insulin can be administered via multiple daily injections or continuous subcutaneous insulin infusion (insulin pumps). Over the last 30 years, insulin pumps have become smaller with more features, making them a valuable alternative to multiple daily injections. Insulin pump use in various registries ranges from as low as 5.9% among paediatric patients in the New Zealand national register 28 to as high as 74% in the German/Austrian DPV in children aged <6 years (Table 2) 29. Recent data suggest that consistent use of insulin pumps can result in improved HbA1c values and decreased incidence of severe hypoglycaemia 30, 31. Insulin pumps have been associated with improved quality of life 32. The data on insulin pumps and diabetic ketoacidosis are less clear.”

“The majority of Type 1 diabetes management is carried out outside the clinical setting and in individuals’ daily lives. People with Type 1 diabetes must make complex treatment decisions multiple times daily; thus, diabetes self‐management skills are central to optimal diabetes management. Unfortunately, many people with Type 1 diabetes and their caregivers are not sufficiently familiar with the necessary diabetes self‐management skills. […] Parents are often the first who learn these skills. As children become older, they start receiving more independence over their diabetes care; however, the transition of responsibilities from caregiver to child is often unstructured and haphazard. It is important to ensure that both individuals with diabetes and their caregivers have adequate self‐management skills throughout the diabetes journey.”

“In the developed world (nations with the highest gross domestic product), 87% of the population has access to the internet and 68% report using a smartphone 39. Even in developing countries, 54% of people use the internet and 37% own smartphones 39. In many areas, smartphones are the primary source of internet access and are readily available. […] There are >1000 apps for diabetes on the Apple App Store and the Google Play store. Many of these apps have focused on nutrition, blood glucose logging, and insulin dosing. Given the prevalence of smartphones and the interest in having diabetes apps handy, there is the potential for using a smartphone to deliver education and decision support tools. […] The new psychosocial position statement from the ADA recommends routine psychosocial screening in clinic. These recommendations include screening for: 1) depressive symptoms annually, at diagnosis, or with changes in medical status; 2) anxiety and worry about hypoglycaemia, complications and other diabetes‐specific worries; 3) disordered eating and insulin omission for purposes of weight control; 4) and diabetes distress in children as young as 7 or 8 years old 16. Implementation of in‐clinic screening for depression in young people with Type 1 diabetes has already been shown to be feasible, acceptable and able to identify individuals in need of treatment who may otherwise have gone unnoticed for a longer period of time which would have been having a detrimental impact on physical health and quality of life 13, 40. These programmes typically use tablets […] to administer surveys to streamline the screening process and automatically score measures 13, 40. This automation allows psychologists and social workers to focus on care delivery rather than screening. In addition to depression screening, automated tablet‐based screening for parental depression, distress and anxiety; problem‐solving skills; and resilience/positive coping factors can help the care team understand other psychosocial barriers to care. This approach allows the development of patient‐ and caregiver‐centred interventions to improve these barriers, thereby improving clinical outcomes and complication rates.”

“With the advent of electronic health records, registries and downloadable medical devices, people with Type 1 diabetes have troves of data that can be analysed to provide insights on an individual and population level. Big data analytics for diabetes are still in the early stages, but present great potential for improving diabetes care. IBM Watson Health has partnered with Medtronic to deliver personalized insights to individuals with diabetes based on device data 48. Numerous other systems […] allow people with Type 1 diabetes to access their data, share their data with the healthcare team, and share de‐identified data with the research community. Data analysis and insights such as this can form the basis for the delivery of personalized digital health coaching. For example, historical patterns can be analysed to predict activity and lead to pro‐active insulin adjustment to prevent hypoglycaemia. […] Improvements to diabetes care delivery can occur at both the population level and at the individual level using insights from big data analytics.”

vi. Route to improving Type 1 diabetes mellitus glycaemic outcomes: real‐world evidence taken from the National Diabetes Audit.

“While control of blood glucose levels reduces the risk of diabetes complications, it can be very difficult for people to achieve. There has been no significant improvement in average glycaemic control among people with Type 1 diabetes for at least the last 10 years in many European countries 6.

The National Diabetes Audit (NDA) in England and Wales has shown relatively little change in the levels of HbA1c being achieved in people with Type 1 diabetes over the last 10 years, with >70% of HbA1c results each year being >58 mmol/mol (7.5%) 7.

Data for general practices in England are published by the NDA. NHS Digital publishes annual prescribing data, including British National Formulary (BNF) codes 7, 8. Together, these data provide an opportunity to investigate whether there are systematic associations between HbA1c levels in people with Type 1 diabetes and practice‐level population characteristics, diabetes service levels and use of medication.”

“The Quality and Outcomes Framework (a payment system for general practice performance) provided a baseline list of all general practices in England for each year, the practice list size and number of people (both with Type 1 and Type 2 diabetes) on their diabetes register. General practice‐level data of participating practices were taken from the NDA 2013–2014, 2014–2015 and 2015–2016 (5455 practices in the last year). They include Type 1 diabetes population characteristics, routine review checks and the proportions of people achieving target glycaemic control and/or being at higher glycaemic risk.

Diabetes medication data for all people with diabetes were taken from the general practice prescribing in primary care data for 2013–2014, 2014–2015 and 2015–2016, including insulin and blood glucose monitoring (BGM) […] A total of 20 indicators were created that covered the epidemiological, service, medication, technological, costs and outcomes performance for each practice and year. The variance in these indicators over the 4‐year period and among general practices was also considered. […] The values of the indicators found to be in the 90th percentile were used to quantify the potential of highest performing general practices. […] In total 13 085 practice‐years of data were analysed, covering 437 000 patient‐years of management.”

“There was significant variation among the participating general practices (Fig. 3) in the proportion of people achieving target glycaemic control target [percentage of people with HbA1c ≤58 mmol/mol (7.5%)] and in the proportion at high glycaemic risk [percentage of people with HbA1c >86 mmol/mol (10%)]. […] Our analysis showed that, at general practice level, the median target glycaemic control attainment was 30%, while the 10th percentile was 16%, and the 90th percentile was 45%. The corresponding median for the high glycaemic risk percentage was 16%, while the 10th percentile (corresponding to the best performing practices) was 6% and the 90th percentile (greatest proportion of Type 1 diabetes at high glycaemic risk) was 28%. Practices in the deciles for both lowest target glycaemic control and highest high glycaemic risk had 49% of the results in the 58–86 mmol/mol range. […] A very wide variation was found in the percentage of insulin for presumed pump use (deduced from prescriptions of fast‐acting vial insulin), with a median of 3.8% at general practice level. The 10th percentile was 0% and the 90th percentile was 255% of the median inferred pump usage.”

“[O]ur findings suggest that if all practices optimized service and therapies to the levels achieved by the top decile then 16 100 (7%) more people with Type 1 diabetes would achieve the glycaemic control target of 58 mmol/mol (7.5%) and 11 500 (5%) fewer people would have HbA1c >86 mmol/mol (10%). Put another way, if the results for all practices were at the top decile level, 36% vs 29% of people with Type 1 diabetes would achieve the glycaemic control target of HbA1c ≤ 58 mmol/mol (7.5%), and as few as 10% could have HbA1c levels > 86 mmol/mol (10%) compared with 15% currently (Fig. 6). This has significant implications for the potential to improve the longer‐term outcomes of people with Type 1 diabetes, given the close link between glycaemia and complications in such individuals 5, 10, 11.”

“We found that the significant variation among the participating general practices (Fig. 2) in terms of the proportion of people with HbA1c ≤58 mmol/mol (7.5%) was only partially related to a lower proportion of people with HbA1c >86 mmol/mol (10%). There was only a weak relationship between level of target glycaemia achieved and avoidance of very suboptimal glycaemia. The overall r2 value was 0.6. This suggests that there is a degree of independence between these outcomes, so that success factors at a general practice level differ for people achieving optimal glycaemia vs those factors affecting avoiding a level of at risk glycaemia.”

May 30, 2018 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Immunology, Medicine, Molecular biology, Ophthalmology, Studies | Leave a comment

100 cases in emergency medicine and critical care (I)

“This book has been written for medical students, doctors and nurse practitioners. One of the best methods of learning is case-based learning. This book presents a hundred such ‘cases’ or ‘patients’ which have been arranged by system. Each case has been written to stand alone […] the focus of each case is to recognise the initial presentation, the underlying pathophysiology, and to understand broad treatment principles.”

I really liked the book; as was also the case for the surgery book I recently read the cases included in these publications are slightly longer than they were in some of the previous publications in the series I’ve read, and I think this makes a big difference in terms of how much you actually get out of each case.

Below I have added some links and quotes related to the first half of the book’s coverage.

Tracheostomy.
Malnutrition (“it is estimated that around a quarter of hospital inpatients are inadequately nourished. This may be due to increased nutritional requirements […], nutritional losses (e.g. malabsorption, vomiting, diarrhoea) or reduced intake […] A patient’s basal energy expenditure is doubled in head injuries and burns.”)
Acute Adult Supraglottitis. (“It is important to appreciate that halving the radius of the airway will increase its resistance by 16 times (Poiseuille’s equation), and hearing stridor means there is around 75% airway obstruction.”)
Out-of-hospital cardiac arrest. (“After successful resuscitation from an OHCA, only 10% of patients will survive to discharge, and many of these individuals will have significant neurologic disability.”)
Bacterial meningitis. (“Meningococcal meningitis has a high mortality, with 10%-15% of patients dying of the disease despite appropriate therapy.”)
Diabetic ketoacidosis.
Anaphylaxis (“Always think of anaphylaxis when seeing patients with skin/mucosal symptoms, respiratory difficulty and/or hypotension, especially after exposure to a potential allergen.”)
Early goal-directed therapy. (“While randomised evidence on the benefit of [this approach] is conflicting, it is standard practice in most centres.” I’m not sure I’d agree with the authors that the evidence is ‘conflicting’, it looks to me like it’s reasonably clear at this point: “In this meta-analysis of individual patient data, EGDT did not result in better outcomes than usual care and was associated with higher hospitalization costs across a broad range of patient and hospital characteristics.”)
Cardiac tamponade. Hypovolaemic shock. Permissive hypotensionFocused Assessment with Sonography in Trauma (FAST). (“Shock refers to inadequate tissue perfusion and tissue oxygenation. The commonest cause in an injured patient is hypovolaemic shock due to blood loss, but other causes include cardiogenic shock due to myocardial dysfunction, neurogenic shock due to sympathetic dysfunction or obstructive shock due to obstruction of the great vessels or heart. […] tachycardia, cool skin and reduced pulse pressure are early signs of shock until proven otherwise.”)
Intravenous therapy. A Comparison of Albumin and Saline for Fluid Resuscitation in the Intensive Care Unit.
Thermal burns. Curling’s ulcer. Escharotomy. Wallace rule of nines. Fluid management in major burn injuries. (“Alkali burns are more harmful than acidic. […] Electrical burns cause more destruction than the external burn may suggest. They are associated with internal destruction, as the path of least resistance is nerves and blood vessels. They can also cause arrhythmias and an electrocardiogram should be performed.”)
Steven Johnson syndrome. Nikolsky’s sign. SCORTEN scale.
Cardiac arrest. (“The mantra in the ED is that ‘you are not dead until you are warm and dead'”).
Myocardial infarction. (“The most important goal of the acute management of STEMI is coronary reperfusion, which may be achieved either by percutaneous coronary intervention (PCI) or use of fibrinolytic agents (thrombolysis). PCI is the preferred strategy if it can be delivered within 120 minutes of first medical contact (and ideally within 90 minutes) […] several randomised trials have shown that PCI provides improved short- and long-term survival outcomes compared to fibrinolysis, providing it can be performed within the appropriate time frame.”)
Asthma exacerbation. (“the prognosis for asthmatics admitted to the Intensive Care Unit is guarded, with an in-hospital mortality of 7% in those who are mechanically ventilated.”)
Acute exacerbation of COPD. Respiratory Failure.
Pulmonary embolism. CT pulmonary angiography. (“Obstructive cardiopulmonary disease is the main diagnosis to exclude in patients presenting with shortness of breath and syncope.”)
Sepsis. Sepsis Six. qSOFA. (“The main clinical features of sepsis include hypotension […], tachycardia […], a high (>38.3°C) or low (<36°C) temperature, altered mental status and signs of peripheral shutdown (cool skin, prolonged capillary refill, cyanosis) in severe cases. […] Sepsis is associated with substantial in-hospital morbidity and mortality, and an increased risk of death and re-admission to hospital even if the patient survives until discharge. Prognostic factors in sepsis include patient factors (increasing age, higher comorbidity), site of infection (urosepsis is associated with better outcomes compared to other sources), type of pathogen (nosocomial infections have higher mortality), early administration of antibiotics (which may reduce mortality by 50%) and restoration of perfusion.”)
Acute kidney injury. (“Classically there are three major causative categories of AKI: (i) pre-renal (i.e. hypoperfusion), (ii) renal (i.e. an intrinsic process with the kidneys) and (iii) post-renal (i.e. urinary tract obstruction). The initial evaluation should attempt to determine which of these are leading to AKI in the patient. […] two main complications that arise with AKI [are] volume and electrolyte issues.”)
Acute chest syndrome.
Thrombotic thrombocytopenic purpura. Schistocyte. Plasmapheresis.
Lower gastrointestinal bleeding. WarfarinProthrombin complex concentrate. (“Warfarin is associated with a 1%-3% risk of bleeding each year in patients with atrial fibrillation, and the main risk factors for this include presence of comorbities, interacting medications, poor patient compliance, acute illness and dietary variation in vitamin K intake.”)
Acute back pain. Malignant spinal cord compression (-MSCC). (“Acute back pain is not an uncommon reason for presentation to the Emergency Department […] Although the majority of such presentations represent benign pathology, it is important to exclude more serious pathology such as cord or cauda equina compression, infection or abscess. Features in the history warranting greater concern include a prior history of cancer, recent infection or steroid use, fever, pain in the thoracic region, pain that improves with rest and the presence of urinary symptoms. Similarly, ‘red flag’ examination findings include gait ataxia, generalized weakness, upper motor neurone signs (clonus, hyper-reflexia, extensor plantars), a palpable bladder, saddle anaesthesia and reduced anal tone. […] MSCC affects up to 5% of all cancer patients and is the first manifestation of cancer in a fifth of patients.”)
Neutropenic sepsis. (“Neutropaenic sepsis […] arises as a result of cytotoxic chemotherapy suppressing the bone marrow, leading to depletion of white blood cells and leaving the individual vulnerable to infection. It is one of the most common complications of cancer therapy, carrying a significant mortality rate of ~5%-10%, and should be regarded as a medical emergency. Any patient receiving chemotherapy and presenting with a fever should be assumed to have neutropaenic sepsis until proven otherwise.”)
Bacterial Pneumonia. CURB-65 Pneumonia Severity Score.
Peptic ulcer diseaseUpper gastrointestinal bleeding. Glasgow-Blatchford score. Rockall score.
Generalised tonic-clonic seizure. Status Epilepticus.
“Chest pain is an extremely common presentation in the ED […] Key features that may help point towards particular diagnoses include • Location and radiation – Central chest pain that radiates to the face, neck or arms is classic for MI, whereas the pain may be more posterior (between should blades) in aortic dissection and unilateral in lung disease. • Onset – Sudden or acute onset pain usually indicates a vascular cause (e.g. PE or aortic dissection), whereas cardiac chest pain is typically more subacute in onset and increases over time. • Character – Cardiac pain is usually described as crushing but may often be a gnawing discomfort, whereas pain associated with aortic dissection and gastrointestinal disorders is usually tearing/ripping and burning, respectively. • Exacerbation/alleviation […] myocardial ischaemia will manifest as pain brought on by exercise and relieved by rest, which is a good discriminator between cardiac and non-cardiac pain.”
Syncope. Mobitz type II AV block. (The differential diagnosis for syncope is seizure, and the two may be distinguished by the absence of a quick or spontaneous recovery with a seizure, where a post-ictal state (sleepiness, confusion, lethargy) is present.”)
Atrial Fibrillation. CHADSVASC and HASBLED risk scores. (“AF with rapid ventricular rates is generally managed with control of heart rates through use of beta-blockers or calcium-channel blockers. • Unstable patients with AF may require electrical cardioversion to restore sinus rhythm.”)
Typhoid fever. Dysentery.
Alcohol toxicity. (“Differentials which may mimic acute alcohol intoxication include • Hypoglycemia • Electrolyte disturbance • Vitamin depletion (B12/folate) • Head trauma • Sepsis • Other toxins or drug overdose • Other causes for CNS depression”)
Tricyclic Antidepressant Toxicity. (“Over 50% of suicidal overdoses involve more than one medication and are often taken with alcohol.”)
Suicide. SADPERSONS scale. (“Intentional self-harm results in around 150,000 attendances to the ED [presumably ‘every year’ – US]. These patients are 100 times more likely to commit suicide within the next year compared to the general population. Self-harm and suicide are often used interchangeably, but are in fact two separate entities. Suicide is a self-inflicted intentional act to cause death, whereas self-harm is a complex behaviour to inflict harm but not associated with the thought of dying – a method to relieve mental stress by inflicting physical pain.”)
Cauda equina syndrome (-CES). (“signs and symptoms of lower extremity weakness and pain developing acutely after heavy lifting should raise suspicion for a herniated intervertebral disc, which is the commonest cause of CES. […] CES is a neurosurgical emergency. The goal is to prevent irreversible loss of bowel and bladder function and motor function of the lower extremities. […] A multitude of alternative diagnoses may masquerade as CES – stroke, vascular claudication, deep venous thrombosis, muscle cramps and peripheral neuropathy.”)
Concussion.
Subarachnoid hemorrhage. Arteriovenous malformation.
Ischemic Stroke. AlteplaseMechanical thrombectomy for acute ischemic stroke. (“evaluation and treatment should be based on the understanding that the damage that is done (infarcted brain) is likely to be permanent, and the goal is to prevent further damage (ischaemic brain) and treat reversible causes (secondary prevention). Along those lines, time is critical to the outcome of the patient.”)
Mechanical back pain. Sciatica.
Dislocated shoulder. Bankart lesion. Hill-Sachs lesion. Kocher’s method.
Supracondylar Humerus Fractures. (“Supracondular fractures in the adult are relatively uncommon but are seen in major trauma or in elderly patients where bone quality may be compromised. Elbow fractures need careful neurovascular evaluation […] There are three major nerves that pass through the region: 1. The median nerve […] 2. The radial nerve […] 3. The ulnar nerve […] It is important to assess these three nerves and to document their function individually. The brachial artery passes through the cubital fossa and may be directly injured by bone fragments or suffer intimal damage. […] This is a true orthopaedic and vascular emergency as the upper limb can only tolerate an ischaemia time of around 90 minutes before irreparable damage is sustained.”)
Boxer’s fracture.

May 2, 2018 Posted by | Books, Cancer/oncology, Cardiology, Infectious disease, Medicine, Nephrology, Neurology, Psychiatry, Studies | Leave a comment

A few diabetes papers of interest

i. Economic Costs of Diabetes in the U.S. in 2017.

“This study updates previous estimates of the economic burden of diagnosed diabetes and quantifies the increased health resource use and lost productivity associated with diabetes in 2017. […] The total estimated cost of diagnosed diabetes in 2017 is $327 billion, including $237 billion in direct medical costs and $90 billion in reduced productivity. For the cost categories analyzed, care for people with diagnosed diabetes accounts for 1 in 4 health care dollars in the U.S., and more than half of that expenditure is directly attributable to diabetes. People with diagnosed diabetes incur average medical expenditures of ∼$16,750 per year, of which ∼$9,600 is attributed to diabetes. People with diagnosed diabetes, on average, have medical expenditures ∼2.3 times higher than what expenditures would be in the absence of diabetes. Indirect costs include increased absenteeism ($3.3 billion) and reduced productivity while at work ($26.9 billion) for the employed population, reduced productivity for those not in the labor force ($2.3 billion), inability to work because of disease-related disability ($37.5 billion), and lost productivity due to 277,000 premature deaths attributed to diabetes ($19.9 billion). […] After adjusting for inflation, economic costs of diabetes increased by 26% from 2012 to 2017 due to the increased prevalence of diabetes and the increased cost per person with diabetes. The growth in diabetes prevalence and medical costs is primarily among the population aged 65 years and older, contributing to a growing economic cost to the Medicare program.”

The paper includes a lot of details about how they went about estimating these things, but I decided against including these details here – read the full paper if you’re interested. I did however want to add some additional details, so here goes:

Absenteeism is defined as the number of work days missed due to poor health among employed individuals, and prior research finds that people with diabetes have higher rates of absenteeism than the population without diabetes. Estimates from the literature range from no statistically significant diabetes effect on absenteeism to studies reporting 1–6 extra missed work days (and odds ratios of more absences ranging from 1.5 to 3.3) (1214). Analyzing 2014–2016 NHIS data and using a negative binomial regression to control for overdispersion in self-reported missed work days, we estimate that people with diabetes have statistically higher missed work days—ranging from 1.0 to 4.2 additional days missed per year by demographic group, or 1.7 days on average — after controlling for age-group, sex, race/ethnicity, diagnosed hypertension status (yes/no), and body weight status (normal, overweight, obese, unknown). […] Presenteeism is defined as reduced productivity while at work among employed individuals and is generally measured through worker responses to surveys. Multiple recent studies report that individuals with diabetes display higher rates of presenteeism than their peers without diabetes (12,1517). […] We model productivity loss associated with diabetes-attributed presenteeism using the estimate (6.6%) from the 2012 study—which is toward the lower end of the 1.8–38% range reported in the literature. […] Reduced performance at work […] accounted for 30% of the indirect cost of diabetes.”

It is of note that even with a somewhat conservative estimate of presenteeism, this cost component is an order of magnitude larger than the absenteeism variable. It is worth keeping in mind that this ratio is likely to be different elsewhere; due to the way the American health care system is structured/financed – health insurance is to a significant degree linked to employment – you’d expect the estimated ratio to be different from what you might observe in countries like the UK or Denmark. Some more related numbers from the paper:

Inability to work associated with diabetes is estimated using a conservative approach that focuses on unemployment related to long-term disability. Logistic regression with 2014–2016 NHIS data suggests that people aged 18–65 years with diabetes are significantly less likely to be in the workforce than people without diabetes. […] we use a conservative approach (which likely underestimates the cost associated with inability to work) to estimate the economic burden associated with reduced labor force participation. […] Study results suggest that people with diabetes have a 3.1 percentage point higher rate of being out of the workforce and receiving disability payments compared with their peers without diabetes. The diabetes effect increases with age and varies by demographic — ranging from 2.1 percentage points for non-Hispanic white males aged 60–64 years to 10.6 percentage points for non-Hispanic black females aged 55–59 years.”

“In 2017, an estimated 24.7 million people in the U.S. are diagnosed with diabetes, representing ∼7.6% of the total population (and 9.7% of the adult population). The estimated national cost of diabetes in 2017 is $327 billion, of which $237 billion (73%) represents direct health care expenditures attributed to diabetes and $90 billion (27%) represents lost productivity from work-related absenteeism, reduced productivity at work and at home, unemployment from chronic disability, and premature mortality. Particularly noteworthy is that excess costs associated with medications constitute 43% of the total direct medical burden. This includes nearly $15 billion for insulin, $15.9 billion for other antidiabetes agents, and $71.2 billion in excess use of other prescription medications attributed to higher disease prevalence associated with diabetes. […] A large portion of medical costs associated with diabetes costs is for comorbidities.”

Insulin is ~$15 billion/year, out of a total estimated cost of $327 billion. This is less than 5% of the total cost. Take note of the 70 billion. I know I’ve said this before, but it bears repeating: Most of diabetes-related costs are not related to insulin.

“…of the projected 162 million hospital inpatient days in the U.S. in 2017, an estimated 40.3 million days (24.8%) are incurred by people with diabetes [who make up ~7.6% of the population – see above], of which 22.6 million days are attributed to diabetes. About one-fourth of all nursing/residential facility days are incurred by people with diabetes. About half of all physician office visits, emergency department visits, hospital outpatient visits, and medication prescriptions (excluding insulin and other antidiabetes agents) incurred by people with diabetes are attributed to their diabetes. […] The largest contributors to the cost of diabetes are higher use of prescription medications beyond antihyperglycemic medications ($71.2 billion), higher use of hospital inpatient services ($69.7 billion), medications and supplies to directly treat diabetes ($34.6 billion), and more office visits to physicians and other health providers ($30.0 billion). Approximately 61% of all health care expenditures attributed to diabetes are for health resources used by the population aged ≥65 years […] we estimate the average annual excess expenditures for the population aged <65 years and ≥65 years, respectively, at $6,675 and $13,239. Health care expenditures attributed to diabetes generally increase with age […] The population with diabetes is older and sicker than the population without diabetes, and consequently annual medical expenditures are much higher (on average) than for people without diabetes“.

“Of the estimated 24.7 million people with diagnosed diabetes, analysis of NHIS data suggests that ∼8.1 million are in the workforce. If people with diabetes participated in the labor force at rates similar to their peers without diabetes, there would be ∼2 million additional people aged 18–64 years in the workforce.”

Comparing the 2017 estimates with those produced for 2012, the overall cost of diabetes appears to have increased by ∼25% after adjusting for inflation, reflecting an 11% increase in national prevalence of diagnosed diabetes and a 13% increase in the average annual diabetes-attributed cost per person with diabetes.”

ii. Current Challenges and Opportunities in the Prevention and Management of Diabetic Foot Ulcers.

“Diabetic foot ulcers remain a major health care problem. They are common, result in considerable suffering, frequently recur, and are associated with high mortality, as well as considerable health care costs. While national and international guidance exists, the evidence base for much of routine clinical care is thin. It follows that many aspects of the structure and delivery of care are susceptible to the beliefs and opinion of individuals. It is probable that this contributes to the geographic variation in outcome that has been documented in a number of countries. This article considers these issues in depth and emphasizes the urgent need to improve the design and conduct of clinical trials in this field, as well as to undertake systematic comparison of the results of routine care in different health economies. There is strong suggestive evidence to indicate that appropriate changes in the relevant care pathways can result in a prompt improvement in clinical outcomes.”

“Despite considerable advances made over the last 25 years, diabetic foot ulcers (DFUs) continue to present a very considerable health care burden — one that is widely unappreciated. DFUs are common, the median time to healing without surgery is of the order of 12 weeks, and they are associated with a high risk of limb loss through amputation (14). The 5-year survival following presentation with a new DFU is of the order of only 50–60% and hence worse than that of many common cancers (4,5). While there is evidence that mortality is improving with more widespread use of cardiovascular risk reduction (6), the most recent data — derived from a Veterans Health Adminstration population—reported that 1-, 2-, and 5-year survival was only 81, 69, and 29%, respectively, and the association between mortality and DFU was stronger than that of any macrovascular disease (7). […] There is […] wide variation in clinical outcome within the same country (1315), suggesting that some people are being managed considerably less well than others.”

“Data on community-wide ulcer incidence are very limited. Overall incidences of 5.8 and 6.0% have been reported in selected populations of people with diabetes in the U.S. (2,12,20) while incidences of 2.1 and 2.2% have been reported from less selected populations in Europe—either in all people with diabetes (21) or in those with type 2 disease alone (22). It is not known whether the incidence is changing […] Although a number of risk factors associated with the development of ulceration are well recognized (23), there is no consensus on which dominate, and there are currently no reports of any studies that might justify the adoption of any specific strategy for population selection in primary prevention.”

“The incidence of major amputation is used as a surrogate measure of the failure of DFUs to heal. Its main value lies in the relative ease of data capture, but its value is limited because it is essentially a treatment and not a true measure of disease outcome. In no other major disease (including malignancies, cardiovascular disease, or cerebrovascular disease) is the number of treatments used as a measure of outcome. But despite this and other limitations of major amputation as an outcome measure (36), there is evidence that the overall incidence of major amputation is falling in some countries with nationwide databases (37,38). Perhaps the most convincing data come from the U.K., where the unadjusted incidence has fallen dramatically from about 3.0–3.5 per 1,000 people with diabetes per year in the mid-1990s to 1.0 or less per 1,000 per year in both England and Scotland (14,39).”

New ulceration after healing is high, with ∼40% of people having a new ulcer (whether at the same site or another) within 12 months (10). This is a critical aspect of diabetic foot disease—emphasizing that when an ulcer heals, foot disease must be regarded not as cured, but in remission (10). In this respect, diabetic foot disease is directly analogous to malignancy. It follows that the person whose foot disease is in remission should receive the same structured follow-up as a person who is in remission following treatment for cancer. Of all areas concerned with the management of DFUs, this long-term need for specialist surveillance is arguably the one that should command the greatest attention.

“There is currently little evidence to justify the adoption of very many of the products and procedures currently promoted for use in clinical practice. Guidelines are required to encourage clinicians to adopt only those treatments that have been shown to be effective in robust studies and principally in RCTs. The design and conduct of such RCTs needs improved governance because many are of low standard and do not always provide the evidence that is claimed.”

Incidence numbers like the ones included above will not always give you the full picture when there are a lot of overlapping data points in the sample (due to recurrence), but sometimes that’s all you have. However in the type 1 context we also do have some additional numbers that make it easier to appreciate the scale of the problem in that context. Here are a few additional data from a related publication I blogged some time ago (do keep in mind that estimates are likely to be lower in community samples of type 2 diabetics, even if perhaps nobody actually know precisely how much lower):

“The rate of nontraumatic amputation in T1DM is high, occurring at 0.4–7.2% per year (28). By 65 years of age, the cumulative probability of lower-extremity amputation in a Swedish administrative database was 11% for women with T1DM and 20.7% for men (10). In this Swedish population, the rate of lower-extremity amputation among those with T1DM was nearly 86-fold that of the general population.” (link)

Do keep in mind that people don’t stop getting ulcers once they reach retirement age (the 11%/20.7% is not lifetime risk, it’s a biased lower bound).

iii. Excess Mortality in Patients With Type 1 Diabetes Without Albuminuria — Separating the Contribution of Early and Late Risks.

“The current study investigated whether the risk of mortality in patients with type 1 diabetes without any signs of albuminuria is different than in the general population and matched control subjects without diabetes.”

“Despite significant improvements in management, type 1 diabetes remains associated with an increase in mortality relative to the age- and sex-matched general population (1,2). Acute complications of diabetes may initially account for this increased risk (3,4). However, with increasing duration of disease, the leading contributor to excess mortality is its vascular complications including diabetic kidney disease (DKD) and cardiovascular disease (CVD). Consequently, patients who subsequently remain free of complications may have little or no increased risk of mortality (1,2,5).”

“Mortality was evaluated in a population-based cohort of 10,737 children (aged 0–14 years) with newly diagnosed type 1 diabetes in Finland who were listed on the National Public Health Institute diabetes register, Central Drug Register, and Hospital Discharge Register in 1980–2005 […] We excluded patients with type 2 diabetes and diabetes occurring secondary to other conditions, such as steroid use, Down syndrome, and congenital malformations of the pancreas. […] FinnDiane participants who died were more likely to be male, older, have a longer duration of diabetes, and later age of diabetes onset […]. Notably, none of the conventional variables associated with complications (e.g., HbA1c, hypertension, smoking, lipid levels, or AER) were associated with all-cause mortality in this cohort of patients without albuminuria. […] The most frequent cause of death in the FinnDiane cohort was IHD [ischaemic heart disease, US] […], largely driven by events in patients with long-standing diabetes and/or previously established CVD […]. The mortality rate ratio for IHD was 4.34 (95% CI 2.49–7.57, P < 0.0001). There remained a number of deaths due to acute complications of diabetes, including ketoacidosis and hypoglycemia. This was most significant in patients with a shorter duration of diabetes but still apparent in those with long-standing diabetes[…]. Notably, deaths due to “risk-taking behavior” were lower in adults with type 1 diabetes compared with matched individuals without diabetes: mortality rate ratio was 0.42 (95% CI 0.22–0.79, P = 0.006) […] This was largely driven by the 80% reduction (95% CI 0.06–0.66) in deaths due to alcohol and drugs in males with type 1 diabetes (Table 3). No reduction was observed in female patients (rate ratio 0.90 [95% CI 0.18–4.44]), although the absolute event rate was already more than seven times lower in Finnish women than in men.”

The chief determinant of excess mortality in patients with type 1 diabetes is its complications. In the first 10 years of type 1 diabetes, the acute complications of diabetes dominate and result in excess mortality — more than twice that observed in the age- and sex-matched general population. This early excess explains why registry studies following patients with type 1 diabetes from diagnosis have consistently reported reduced life expectancy, even in patients free of chronic complications of diabetes (68). By contrast, studies of chronic complications, like FinnDiane and the Pittsburgh Epidemiology of Diabetes Complications Study (1,2), have followed participants with, usually, >10 years of type 1 diabetes at baseline. In these patients, the presence or absence of chronic complications of diabetes is critical for survival. In particular, the presence and severity of albuminuria (as a marker of vascular burden) is strongly associated with mortality outcomes in type 1 diabetes (1). […] the FinnDiane normoalbuminuric patients showed increased all-cause mortality compared with the control subjects without diabetes in contrast to when the comparison was made with the Finnish general population, as in our previous publication (1). Two crucial causes behind the excess mortality were acute diabetes complications and IHD. […] Comparisons with the general population, rather than matched control subjects, may overestimate expected mortality, diluting the SMR estimate”.

Despite major improvements in the delivery of diabetes care and other technological advances, acute complications remain a major cause of death both in children and in adults with type 1 diabetes. Indeed, the proportion of deaths due to acute events has not changed significantly over the last 30 years. […] Even in patients with long-standing diabetes (>20 years), the risk of death due to hypoglycemia or ketoacidosis remains a constant companion. […] If it were possible to eliminate all deaths from acute events, the observed mortality rate would have been no different from the general population in the early cohort. […] In long-term diabetes, avoiding chronic complications may be associated with mortality rates comparable with those of the general population; although death from IHD remains increased, this is offset by reduced risk-taking behavior, especially in men.”

“It is well-known that CVD is strongly associated with DKD (15). However, in the current study, mortality from IHD remained higher in adults with type 1 diabetes without albuminuria compared with matched control subjects in both men and women. This is concordant with other recent studies also reporting increased mortality from CVD in patients with type 1 diabetes in the absence of DKD (7,8) and reinforces the need for aggressive cardiovascular risk reduction even in patients without signs of microvascular disease. However, it is important to note that the risk of death from CVD, though significant, is still at least 10-fold lower than observed in patients with albuminuria (1). Alcohol- and drug-related deaths were substantially lower in patients with type 1 diabetes compared with the age-, sex-, and region-matched control subjects. […] This may reflect a selection bias […] Nonparticipation in health studies is associated with poorer health, stress, and lower socioeconomic status (17,18), which are in turn associated with increased risk of premature mortality. It can be speculated that with inclusion of patients with risk-taking behavior, the mortality rate in patients with diabetes would be even higher and, consequently, the SMR would also be significantly higher compared with the general population. Selection of patients who despite long-standing diabetes remained free of albuminuria may also have included individuals more accepting of general health messages and less prone to depression and nihilism arising from treatment failure.”

I think the selection bias problem is likely to be quite significant, as these results don’t really match what I’ve seen in the past. For example a recent Norwegian study on young type 1 diabetics found high mortality in their sample in significant degree due to alcohol-related causes and suicide: “A relatively high proportion of deaths were related to alcohol. […] Death was related to alcohol in 15% of cases. SMR for alcohol-related death was 6.8 (95% CI 4.5–10.3), for cardiovascular death was 7.3 (5.4–10.0), and for violent death was 3.6 (2.3–5.3).” That doesn’t sound very similar to the study above, and that study’s also from Scandinavia. In this study, in which they used data from diabetic organ donors, they found that a large proportion of the diabetics included in the study used illegal drugs: “we observed a high rate of illicit substance abuse: 32% of donors reported or tested positive for illegal substances (excluding marijuana), and multidrug use was common.”

Do keep in mind that one of the main reasons why ‘alcohol-related’ deaths are higher in diabetes is likely to be that ‘drinking while diabetic’ is a lot more risky than is ‘drinking while not diabetic’. On a related note, diabetics may not appreciate the level of risk they’re actually exposed to while drinking, due to community norms etc., so there might be a disconnect between risk preferences and observed behaviour (i.e., a diabetic might be risk averse but still engage in risky behaviours because he doesn’t know how risky those behaviours in which he’s engaging actually are).

Although the illicit drugs study indicates that diabetics at least in some samples are not averse to engaging in risky behaviours, a note of caution is probably warranted in the alcohol context: High mortality from alcohol-mediated acute complications needn’t be an indication that diabetics drink more than non-diabetics; that’s a separate question, you might see numbers like these even if they in general drink less. And a young type 1 diabetic who suffers a cardiac arrhythmia secondary to long-standing nocturnal hypoglycemia and subsequently is found ‘dead in bed’ after a bout of drinking is conceptually very different from a 50-year old alcoholic dying from a variceal bleed or acute pancreatitis. Parenthetically, if it is true that illicit drugs use is common in type 1 diabetics one reason might be that they are aware of the risks associated with alcohol (which is particularly nasty in terms of the metabolic/glycemic consequences in diabetes, compared to some other drugs) and thus they deliberately make a decision to substitute this drug with other drugs less likely to cause acute complications like severe hypoglycemic episodes or DKA (depending on the setting and the specifics, alcohol might be a contributor to both of these complications). If so, classical ‘risk behaviours’ may not always be ‘risk behaviours’ in diabetes. You need to be careful, this stuff’s complicated.

iv. Are All Patients With Type 1 Diabetes Destined for Dialysis if They Live Long Enough? Probably Not.

“Over the past three decades there have been numerous innovations, supported by large outcome trials that have resulted in improved blood glucose and blood pressure control, ultimately reducing cardiovascular (CV) risk and progression to nephropathy in type 1 diabetes (T1D) (1,2). The epidemiological data also support the concept that 25–30% of people with T1D will progress to end-stage renal disease (ESRD). Thus, not everyone develops progressive nephropathy that ultimately requires dialysis or transplantation. This is a result of numerous factors […] Data from two recent studies reported in this issue of Diabetes Care examine the long-term incidence of chronic kidney disease (CKD) in T1D. Costacou and Orchard (7) examined a cohort of 932 people evaluated for 50-year cumulative kidney complication risk in the Pittsburgh Epidemiology of Diabetes Complications study. They used both albuminuria levels and ESRD/transplant data for assessment. By 30 years’ duration of diabetes, ESRD affected 14.5% and by 40 years it affected 26.5% of the group with onset of T1D between 1965 and 1980. For those who developed diabetes between 1950 and 1964, the proportions developing ESRD were substantially higher at 34.6% at 30 years, 48.5% at 40 years, and 61.3% at 50 years. The authors called attention to the fact that ESRD decreased by 45% after 40 years’ duration between these two cohorts, emphasizing the beneficial roles of improved glycemic control and blood pressure control. It should also be noted that at 40 years even in the later cohort (those diagnosed between 1965 and 1980), 57.3% developed >300 mg/day albuminuria (7).”

Numbers like these may seem like ancient history (data from the 60s and 70s), but it’s important to keep in mind that many type 1 diabetics are diagnosed in early childhood, and that they don’t ‘get better’ later on – if they’re still alive, they’re still diabetic. …And very likely macroalbuminuric, at least if they’re from Pittsburgh. I was diagnosed in ’87.

“Gagnum et al. (8), using data from a Norwegian registry, also examined the incidence of CKD development over a 42-year follow-up period in people with childhood-onset (<15 years of age) T1D (8). The data from the Norwegian registry noted that the cumulative incidence of ESRD was 0.7% after 20 years and 5.3% after 40 years of T1D. Moreover, the authors noted the risk of developing ESRD was lower in women than in men and did not identify any difference in risk of ESRD between those diagnosed with diabetes in 1973–1982 and those diagnosed in 1989–2012. They concluded that there is a very low incidence of ESRD among patients with childhood-onset T1D diabetes in Norway, with a lower risk in women than men and among those diagnosed at a younger age. […] Analyses of population-based studies, similar to the Pittsburgh and Norway studies, showed that after 30 years of T1D the cumulative incidences of ESRD were only 10% for those diagnosed with T1D in 1961–1984 and 3% for those diagnosed in 1985–1999 in Japan (11), 3.3% for those diagnosed with T1D in 1977–2007 in Sweden (12), and 7.8% for those diagnosed with T1D in 1965–1999 in Finland (13) (Table 1).”

Do note that ESRD (end stage renal disease) is not the same thing as DKD (diabetic kidney disease), and that e.g. many of the Norwegians who did not develop ESRD nevertheless likely have kidney complications from their diabetes. That 5.3% is not the number of diabetics in that cohort who developed diabetes-related kidney complications, it’s the proportion of them who did and as a result of this needed a new kidney or dialysis in order not to die very soon. Do also keep in mind that both microalbuminuria and macroalbuminuria will substantially increase the risk of cardiovascular disease and -cardiac death. I recall a study where they looked at the various endpoints and found that more diabetics with microalbuminuria eventually died of cardiovascular disease than did ever develop kidney failure – cardiac risk goes up a lot long before end-stage renal disease. ESRD estimates don’t account for the full risk profile, and even if you look at mortality risk the number accounts for perhaps less than half of the total risk attributable to DKD. One thing the ESRD diagnosis does have going for it is that it’s a much more reliable variable indicative of significant pathology than is e.g. microalbuminuria (see e.g. this paper). The paper is short and not at all detailed, but they do briefly discuss/mention these issues:

“…there is a substantive difference between the numbers of people with stage 3 CKD (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m2) versus those with stages 4 and 5 CKD (eGFR <30 mL/min/1.73 m2): 6.7% of the National Health and Nutrition Examination Survey (NHANES) population compared with 0.1–0.3%, respectively (14). This is primarily because of competing risks, such as death from CV disease that occurs in stage 3 CKD; hence, only the survivors are progressing into stages 4 and 5 CKD. Overall, these studies are very encouraging. Since the 1980s, risk of ESRD has been greatly reduced, while risk of CKD progression persists but at a slower rate. This reduced ESRD rate and slowed CKD progression is largely due to improvements in glycemic and blood pressure control and probably also to the institution of RAAS blockers in more advanced CKD. These data portend even better future outcomes if treatment guidance is followed. […] many medications are effective in blood pressure control, but RAAS blockade should always be a part of any regimen when very high albuminuria is present.”

v. New Understanding of β-Cell Heterogeneity and In Situ Islet Function.

“Insulin-secreting β-cells are heterogeneous in their regulation of hormone release. While long known, recent technological advances and new markers have allowed the identification of novel subpopulations, improving our understanding of the molecular basis for heterogeneity. This includes specific subpopulations with distinct functional characteristics, developmental programs, abilities to proliferate in response to metabolic or developmental cues, and resistance to immune-mediated damage. Importantly, these subpopulations change in disease or aging, including in human disease. […] We will discuss recent findings revealing functional β-cell subpopulations in the intact islet, the underlying basis for these identified subpopulations, and how these subpopulations may influence in situ islet function.”

I won’t cover this one in much detail, but this part was interesting:

“Gap junction (GJ) channels electrically couple β-cells within mouse and human islets (25), serving two main functions. First, GJ channels coordinate oscillatory dynamics in electrical activity and Ca2+ under elevated glucose or GLP-1, allowing pulsatile insulin secretion (26,27). Second, GJ channels lower spontaneous elevations in Ca2+ under low glucose levels (28). GJ coupling is also heterogeneous within the islet (29), leading to some β-cells being highly coupled and others showing negligible coupling. Several studies have examined how electrically heterogeneous cells interact via GJ channels […] This series of experiments indicate a “bistability” in islet function, where a threshold number of poorly responsive β-cells is sufficient to totally suppress islet function. Notably, when islets lacking GJ channels are treated with low levels of the KATP activator diazoxide or the GCK inhibitor mannoheptulose, a subpopulation of cells are silenced, presumably corresponding to the less functional population (30). Only diazoxide/mannoheptulose concentrations capable of silencing >40% of these cells will fully suppress Ca2+ elevations in normal islets. […] this indicates that a threshold number of poorly responsive cells can inhibit the whole islet. Thus, if there exists a threshold number of functionally competent β-cells (∼60–85%), then the islet will show coordinated elevations in Ca2+ and insulin secretion.

Below this threshold number, the islet will lack Ca2+ elevation and insulin secretion (Fig. 2). The precise threshold depends on the characteristics of the excitable and inexcitable populations: small numbers of inexcitable cells will increase the number of functionally competent cells required for islet activity, whereas small numbers of highly excitable cells will do the opposite. However, if GJ coupling is lowered, then inexcitable cells will exert a reduced suppression, also decreasing the threshold required. […] Paracrine communication between β-cells and other endocrine cells is also important for regulating insulin secretion. […] Little is known how these paracrine and juxtacrine mechanisms impact heterogeneous cells.”

vi. Closing in on the Mechanisms of Pulsatile Insulin Secretion.

“Insulin secretion from pancreatic islet β-cells occurs in a pulsatile fashion, with a typical period of ∼5 min. The basis of this pulsatility in mouse islets has been investigated for more than four decades, and the various theories have been described as either qualitative or mathematical models. In many cases the models differ in their mechanisms for rhythmogenesis, as well as other less important details. In this Perspective, we describe two main classes of models: those in which oscillations in the intracellular Ca2+ concentration drive oscillations in metabolism, and those in which intrinsic metabolic oscillations drive oscillations in Ca2+ concentration and electrical activity. We then discuss nine canonical experimental findings that provide key insights into the mechanism of islet oscillations and list the models that can account for each finding. Finally, we describe a new model that integrates features from multiple earlier models and is thus called the Integrated Oscillator Model. In this model, intracellular Ca2+ acts on the glycolytic pathway in the generation of oscillations, and it is thus a hybrid of the two main classes of models. It alone among models proposed to date can explain all nine key experimental findings, and it serves as a good starting point for future studies of pulsatile insulin secretion from human islets.”

This one covers material closely related to the study above, so if you find one of these papers interesting you might want to check out the other one as well. The paper is quite technical but if you were wondering why people are interested in this kind of stuff, one reason is that there’s good evidence at this point that insulin pulsativity is disturbed in type 2 diabetics and so it’d be nice to know why that is so that new drugs can be developed to correct this.

April 25, 2018 Posted by | Biology, Cardiology, Diabetes, Epidemiology, Health Economics, Medicine, Nephrology, Pharmacology, Studies | Leave a comment

A few (more) diabetes papers of interest

Earlier this week I covered a couple of papers, but the second paper turned out to include a lot of interesting stuff so I decided to cut the post short and postpone my coverage of the other papers I’d intended to cover in that post until a later point in time; this post includes some of those other papers I’d intended to cover in that post.

i. TCF7L2 Genetic Variants Contribute to Phenotypic Heterogeneity of Type 1 Diabetes.

“Although the autoimmune destruction of β-cells has a major role in the development of type 1 diabetes, there is growing evidence that the differences in clinical, metabolic, immunologic, and genetic characteristics among patients (1) likely reflect diverse etiology and pathogenesis (2). Factors that govern this heterogeneity are poorly understood, yet these may have important implications for prognosis, therapy, and prevention.

The transcription factor 7 like 2 (TCF7L2) locus contains the single nucleotide polymorphism (SNP) most strongly associated with type 2 diabetes risk, with an ∼30% increase per risk allele (3). In a U.S. cohort, heterozygous and homozygous carriers of the at-risk alleles comprised 40.6% and 7.9%, respectively, of the control subjects and 44.3% and 18.3%, respectively, of the individuals with type 2 diabetes (3). The locus has no known association with type 1 diabetes overall (48), with conflicting reports in latent autoimmune diabetes in adults (816). […] Our studies in two separate cohorts have shown that the type 2 diabetes–associated TCF7L2 genetic variant is more frequent among specific subsets of individuals with autoimmune type 1 diabetes, specifically those with fewer markers of islet autoimmunity (22,23). These observations support a role of this genetic variant in the pathogenesis of diabetes at least in a subset of individuals with autoimmune diabetes. However, whether individuals with type 1 diabetes and this genetic variant have distinct metabolic abnormalities has not been investigated. We aimed to study the immunologic and metabolic characteristics of individuals with type 1 diabetes who carry a type 2 diabetes–associated allele of the TCF7L2 locus.”

“We studied 810 TrialNet participants with newly diagnosed type 1 diabetes and found that among individuals 12 years and older, the type 2 diabetes–associated TCF7L2 genetic variant is more frequent in those presenting with a single autoantibody than in participants who had multiple autoantibodies. These TCF7L2 variants were also associated with higher mean C-peptide AUC and lower mean glucose AUC levels at the onset of type 1 diabetes. […] These findings suggest that, besides the well-known link with type 2 diabetes, the TCF7L2 locus may play a role in the development of type 1 diabetes. The type 2 diabetes–associated TCF7L2 genetic variant identifies a subset of individuals with autoimmune type 1 diabetes and fewer markers of islet autoimmunity, lower glucose, and higher C-peptide at diagnosis. […] A possible interpretation of these data is that TCF7L2-encoded diabetogenic mechanisms may contribute to diabetes development in individuals with limited autoimmunity […]. Because the risk of progression to type 1 diabetes is lower in individuals with single compared with multiple autoantibodies, it is possible that in the absence of this type 2 diabetes–associated TCF7L2 variant, these individuals may have not manifested diabetes. If that is the case, we would postulate that disease development in these patients may have a type 2 diabetes–like pathogenesis in which islet autoimmunity is a significant component but not necessarily the primary driver.”

“The association between this genetic variant and single autoantibody positivity was present in individuals 12 years or older but not in children younger than 12 years. […] The results in the current study suggest that the type 2 diabetes–associated TCF7L2 genetic variant plays a larger role in older individuals. There is mounting evidence that the pathogenesis of type 1 diabetes varies by age (31). Younger individuals appear to have a more aggressive form of disease, with faster decline of β-cell function before and after onset of disease, higher frequency and severity of diabetic ketoacidosis, which is a clinical correlate of severe insulin deficiency, and lower C-peptide at presentation (3135). Furthermore, older patients are less likely to have type 1 diabetes–associated HLA alleles and islet autoantibodies (28). […] Taken together, we have demonstrated that individuals with autoimmune type 1 diabetes who carry the type 2 diabetes–associated TCF7L2 genetic variant have a distinct phenotype characterized by milder immunologic and metabolic characteristics than noncarriers, closer to those of type 2 diabetes, with an important effect of age.”

ii. Heart Failure: The Most Important, Preventable, and Treatable Cardiovascular Complication of Type 2 Diabetes.

“Concerns about cardiovascular disease in type 2 diabetes have traditionally focused on atherosclerotic vasculo-occlusive events, such as myocardial infarction, stroke, and limb ischemia. However, one of the earliest, most common, and most serious cardiovascular disorders in patients with diabetes is heart failure (1). Following its onset, patients experience a striking deterioration in their clinical course, which is marked by frequent hospitalizations and eventually death. Many sudden deaths in diabetes are related to underlying ventricular dysfunction rather than a new ischemic event. […] Heart failure and diabetes are linked pathophysiologically. Type 2 diabetes and heart failure are each characterized by insulin resistance and are accompanied by the activation of neurohormonal systems (norepinephrine, angiotensin II, aldosterone, and neprilysin) (3). The two disorders overlap; diabetes is present in 35–45% of patients with chronic heart failure, whether they have a reduced or preserved ejection fraction.”

“Treatments that lower blood glucose do not exert any consistently favorable effect on the risk of heart failure in patients with diabetes (6). In contrast, treatments that increase insulin signaling are accompanied by an increased risk of heart failure. Insulin use is independently associated with an enhanced likelihood of heart failure (7). Thiazolidinediones promote insulin signaling and have increased the risk of heart failure in controlled clinical trials (6). With respect to incretin-based secretagogues, liraglutide increases the clinical instability of patients with existing heart failure (8,9), and the dipeptidyl peptidase 4 inhibitors saxagliptin and alogliptin are associated with an increased risk of heart failure in diabetes (10). The likelihood of heart failure with the use of sulfonylureas may be comparable to that with thiazolidinediones (11). Interestingly, the only two classes of drugs that ameliorate hyperinsulinemia (metformin and sodium–glucose cotransporter 2 inhibitors) are also the only two classes of antidiabetes drugs that appear to reduce the risk of heart failure and its adverse consequences (12,13). These findings are consistent with experimental evidence that insulin exerts adverse effects on the heart and kidneys that can contribute to heart failure (14). Therefore, physicians can prevent many cases of heart failure in type 2 diabetes by careful consideration of the choice of agents used to achieve glycemic control. Importantly, these decisions have an immediate effect; changes in risk are seen within the first few months of changes in treatment. This immediacy stands in contrast to the years of therapy required to see a benefit of antidiabetes drugs on microvascular risk.”

“As reported by van den Berge et al. (4), the prognosis of patients with heart failure has improved over the past two decades; heart failure with a reduced ejection fraction is a treatable disease. Inhibitors of the renin-angiotensin system are a cornerstone of the management of both disorders; they prevent the onset of heart failure and the progression of nephropathy in patients with diabetes, and they reduce the risk of cardiovascular death and hospitalization in those with established heart failure (3,15). Diabetes does not influence the magnitude of the relative benefit of ACE inhibitors in patients with heart failure, but patients with diabetes experience a greater absolute benefit from treatment (16).”

“The totality of evidence from randomized trials […] demonstrates that in patients with diabetes, heart failure is not only common and clinically important, but it can also be prevented and treated. This conclusion is particularly significant because physicians have long ignored heart failure in their focus on glycemic control and their concerns about the ischemic macrovascular complications of diabetes (1).”

iii. Closely related to the above study: Mortality Reduction Associated With β-Adrenoceptor Inhibition in Chronic Heart Failure Is Greater in Patients With Diabetes.

“Diabetes increases mortality in patients with chronic heart failure (CHF) and reduced left ventricular ejection fraction. Studies have questioned the safety of β-adrenoceptor blockers (β-blockers) in some patients with diabetes and reduced left ventricular ejection fraction. We examined whether β-blockers and ACE inhibitors (ACEIs) are associated with differential effects on mortality in CHF patients with and without diabetes. […] We conducted a prospective cohort study of 1,797 patients with CHF recruited between 2006 and 2014, with mean follow-up of 4 years.”

RESULTS Patients with diabetes were prescribed larger doses of β-blockers and ACEIs than were patients without diabetes. Increasing β-blocker dose was associated with lower mortality in patients with diabetes (8.9% per mg/day; 95% CI 5–12.6) and without diabetes (3.5% per mg/day; 95% CI 0.7–6.3), although the effect was larger in people with diabetes (interaction P = 0.027). Increasing ACEI dose was associated with lower mortality in patients with diabetes (5.9% per mg/day; 95% CI 2.5–9.2) and without diabetes (5.1% per mg/day; 95% CI 2.6–7.6), with similar effect size in these groups (interaction P = 0.76).”

“Our most important findings are:

  • Higher-dose β-blockers are associated with lower mortality in patients with CHF and LVSD, but patients with diabetes may derive more benefit from higher-dose β-blockers.

  • Higher-dose ACEIs were associated with comparable mortality reduction in people with and without diabetes.

  • The association between higher β-blocker dose and reduced mortality is most pronounced in patients with diabetes who have more severely impaired left ventricular function.

  • Among patients with diabetes, the relationship between β-blocker dose and mortality was not associated with glycemic control or insulin therapy.”

“We make the important observation that patients with diabetes may derive more prognostic benefit from higher β-blocker doses than patients without diabetes. These data should provide reassurance to patients and health care providers and encourage careful but determined uptitration of β-blockers in this high-risk group of patients.”

iv. Diabetes, Prediabetes, and Brain Volumes and Subclinical Cerebrovascular Disease on MRI: The Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS).

“Diabetes and prediabetes are associated with accelerated cognitive decline (1), and diabetes is associated with an approximately twofold increased risk of dementia (2). Subclinical brain pathology, as defined by small vessel disease (lacunar infarcts, white matter hyperintensities [WMH], and microhemorrhages), large vessel disease (cortical infarcts), and smaller brain volumes also are associated with an increased risk of cognitive decline and dementia (37). The mechanisms by which diabetes contributes to accelerated cognitive decline and dementia are not fully understood, but contributions of hyperglycemia to both cerebrovascular disease and primary neurodegenerative disease have been suggested in the literature, although results are inconsistent (2,8). Given that diabetes is a vascular risk factor, brain atrophy among individuals with diabetes may be driven by increased cerebrovascular disease. Brain magnetic resonance imaging (MRI) provides a noninvasive opportunity to study associations of hyperglycemia with small vessel disease (lacunar infarcts, WMH, microhemorrhages), large vessel disease (cortical infarcts), and brain volumes (9).”

“Overall, the mean age of participants [(n = 1,713)] was 75 years, 60% were women, 27% were black, 30% had prediabetes (HbA1c 5.7 to <6.5%), and 35% had diabetes. Compared with participants without diabetes and HbA1c <5.7%, those with prediabetes (HbA1c 5.7 to <6.5%) were of similar age (75.2 vs. 75.0 years; P = 0.551), were more likely to be black (24% vs. 11%; P < 0.001), have less than a high school education (11% vs. 7%; P = 0.017), and have hypertension (71% vs. 63%; P = 0.012) (Table 1). Among participants with diabetes, those with HbA1c <7.0% versus ≥7.0% were of similar age (75.4 vs. 75.1 years; P = 0.481), but those with diabetes and HbA1c ≥7.0% were more likely to be black (39% vs. 28%; P = 0.020) and to have less than a high school education (23% vs. 16%; P = 0.031) and were more likely to have a longer duration of diabetes (12 vs. 8 years; P < 0.001).”

“Compared with participants without diabetes and HbA1c <5.7%, those with diabetes and HbA1c ≥7.0% had smaller total brain volume (β −0.20 SDs; 95% CI −0.31, −0.09) and smaller regional brain volumes, including frontal, temporal, occipital, and parietal lobes; deep gray matter; Alzheimer disease signature region; and hippocampus (all P < 0.05) […]. Compared with participants with diabetes and HbA1c <7.0%, those with diabetes and HbA1c ≥7.0% had smaller total brain volume (P < 0.001), frontal lobe volume (P = 0.012), temporal lobe volume (P = 0.012), occipital lobe volume (P = 0.008), parietal lobe volume (P = 0.015), deep gray matter volume (P < 0.001), Alzheimer disease signature region volume (0.031), and hippocampal volume (P = 0.016). Both participants with diabetes and HbA1c <7.0% and those with prediabetes (HbA1c 5.7 to <6.5%) had similar total and regional brain volumes compared with participants without diabetes and HbA1c <5.7% (all P > 0.05). […] No differences in the presence of lobar microhemorrhages, subcortical microhemorrhages, cortical infarcts, and lacunar infarcts were observed among the diabetes-HbA1c categories (all P > 0.05) […]. Compared with participants without diabetes and HbA1c <5.7%, those with diabetes and HbA1c ≥7.0% had increased WMH volume (P = 0.016). The WMH volume among participants with diabetes and HbA1c ≥7.0% was also significantly greater than among those with diabetes and HbA1c <7.0% (P = 0.017).”

“Those with diabetes duration ≥10 years were older than those with diabetes duration <10 years (75.9 vs. 75.0 years; P = 0.041) but were similar in terms of race and sex […]. Compared with participants with diabetes duration <10 years, those with diabetes duration ≥10 years has smaller adjusted total brain volume (β −0.13 SDs; 95% CI −0.20, −0.05) and smaller temporal lobe (β −0.14 SDs; 95% CI −0.24, −0.03), parietal lobe (β − 0.11 SDs; 95% CI −0.21, −0.01), and hippocampal (β −0.16 SDs; 95% CI −0.30, −0.02) volumes […]. Participants with diabetes duration ≥10 years also had a 2.44 times increased odds (95% CI 1.46, 4.05) of lacunar infarcts compared with those with diabetes duration <10 years”.

Conclusions
In this community-based population, we found that ARIC-NCS participants with diabetes with HbA1c ≥7.0% have smaller total and regional brain volumes and an increased burden of WMH, but those with prediabetes (HbA1c 5.7 to <6.5%) and diabetes with HbA1c <7.0% have brain volumes and markers of subclinical cerebrovascular disease similar to those without diabetes. Furthermore, among participants with diabetes, those with more-severe disease (as measured by higher HbA1c and longer disease duration) had smaller total and regional brain volumes and an increased burden of cerebrovascular disease compared with those with lower HbA1c and shorter disease duration. However, we found no evidence that associations of diabetes with smaller brain volumes are mediated by cerebrovascular disease.

The findings of this study extend the current literature that suggests that diabetes is strongly associated with brain volume loss (11,2527). Global brain volume loss (11,2527) has been consistently reported, but associations of diabetes with smaller specific brain regions have been less robust (27,28). Similar to prior studies, the current results show that compared with individuals without diabetes, those with diabetes have smaller total brain volume (11,2527) and regional brain volumes, including frontal and occipital lobes, deep gray matter, and the hippocampus (25,27). Furthermore, the current study suggests that greater severity of disease (as measured by HbA1c and diabetes duration) is associated with smaller total and regional brain volumes. […] Mechanisms whereby diabetes may contribute to brain volume loss include accelerated amyloid-β and hyperphosphorylated tau deposition as a result of hyperglycemia (29). Another possible mechanism involves pancreatic amyloid (amylin) infiltration of the brain, which then promotes amyloid-β deposition (29). […] Taken together, […] the current results suggest that diabetes is associated with both lower brain volumes and increased cerebrovascular pathology (WMH and lacunes).”

v. Interventions to increase attendance for diabetic retinopathy screening (Cochrane review).

“The primary objective of the review was to assess the effectiveness of quality improvement (QI) interventions that seek to increase attendance for DRS in people with type 1 and type 2 diabetes.

Secondary objectives were:
To use validated taxonomies of QI intervention strategies and behaviour change techniques (BCTs) to code the description of interventions in the included studies and determine whether interventions that include particular QI strategies or component BCTs are more effective in increasing screening attendance;
To explore heterogeneity in effect size within and between studies to identify potential explanatory factors for variability in effect size;
To explore differential effects in subgroups to provide information on how equity of screening attendance could be improved;
To critically appraise and summarise current evidence on the resource use, costs and cost effectiveness.”

“We included 66 RCTs conducted predominantly (62%) in the USA. Overall we judged the trials to be at low or unclear risk of bias. QI strategies were multifaceted and targeted patients, healthcare professionals or healthcare systems. Fifty-six studies (329,164 participants) compared intervention versus usual care (median duration of follow-up 12 months). Overall, DRS [diabetic retinopathy screening] attendance increased by 12% (risk difference (RD) 0.12, 95% confidence interval (CI) 0.10 to 0.14; low-certainty evidence) compared with usual care, with substantial heterogeneity in effect size. Both DRS-targeted (RD 0.17, 95% CI 0.11 to 0.22) and general QI interventions (RD 0.12, 95% CI 0.09 to 0.15) were effective, particularly where baseline DRS attendance was low. All BCT combinations were associated with significant improvements, particularly in those with poor attendance. We found higher effect estimates in subgroup analyses for the BCTs ‘goal setting (outcome)’ (RD 0.26, 95% CI 0.16 to 0.36) and ‘feedback on outcomes of behaviour’ (RD 0.22, 95% CI 0.15 to 0.29) in interventions targeting patients, and ‘restructuring the social environment’ (RD 0.19, 95% CI 0.12 to 0.26) and ‘credible source’ (RD 0.16, 95% CI 0.08 to 0.24) in interventions targeting healthcare professionals.”

“Ten studies (23,715 participants) compared a more intensive (stepped) intervention versus a less intensive intervention. In these studies DRS attendance increased by 5% (RD 0.05, 95% CI 0.02 to 0.09; moderate-certainty evidence).”

“Overall, we found that there is insufficient evidence to draw robust conclusions about the relative cost effectiveness of the interventions compared to each other or against usual care.”

“The results of this review provide evidence that QI interventions targeting patients, healthcare professionals or the healthcare system are associated with meaningful improvements in DRS attendance compared to usual care. There was no statistically significant difference between interventions specifically aimed at DRS and those which were part of a general QI strategy for improving diabetes care.”

vi. Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication.

“The incidence of type 2 diabetes (T2D) has rapidly increased over recent decades, and T2D has become a leading public health challenge in China. Compared with European descents, Chinese patients with T2D are diagnosed at a relatively young age and low BMI. A better understanding of the factors contributing to the diabetes epidemic is crucial for determining future prevention and intervention programs. In addition to environmental factors, genetic factors contribute substantially to the development of T2D. To date, more than 100 susceptibility loci for T2D have been identified. Individually, most T2D genetic variants have a small effect size (10–20% increased risk for T2D per risk allele); however, a genetic risk score that combines multiple T2D loci could be used to predict the risk of T2D and to identify individuals who are at a high risk. […] In this article, we review the epidemiological trends and recent progress in the understanding of T2D genetic etiology and further discuss personalized medicine involved in the treatment of T2D.”

“Over the past three decades, the prevalence of diabetes in China has sharply increased. The prevalence of diabetes was reported to be less than 1% in 1980 (2), 5.5% in 2001 (3), 9.7% in 2008 (4), and 10.9% in 2013, according to the latest published nationwide survey (5) […]. The prevalence of diabetes was higher in the senior population, men, urban residents, individuals living in economically developed areas, and overweight and obese individuals. The estimated prevalence of prediabetes in 2013 was 35.7%, which was much higher than the estimate of 15.5% in the 2008 survey. Similarly, the prevalence of prediabetes was higher in the senior population, men, and overweight and obese individuals. However, prediabetes was more prevalent in rural residents than in urban residents. […] the 2013 survey also compared the prevalence of diabetes among different races. The crude prevalence of diabetes was 14.7% in the majority group, i.e., Chinese Han, which was higher than that in most minority ethnic groups, including Tibetan, Zhuang, Uyghur, and Muslim. The crude prevalence of prediabetes was also higher in the Chinese Han ethnic group. The Tibetan participants had the lowest prevalence of diabetes and prediabetes (4.3% and 31.3%).”

“[T]he prevalence of diabetes in young people is relatively high and increasing. The prevalence of diabetes in the 20- to 39-year age-group was 3.2%, according to the 2008 national survey (4), and was 5.9%, according to the 2013 national survey (5). The prevalence of prediabetes also increased from 9.0% in 2008 to 28.8% in 2013 […]. Young people suffering from diabetes have a higher risk of chronic complications, which are the major cause of mortality and morbidity in diabetes. According to a study conducted in Asia (6), patients with young-onset diabetes had higher mean concentrations of HbA1c and LDL cholesterol and a higher prevalence of retinopathy (20% vs. 18%, P = 0.011) than those with late-onset diabetes. In the Chinese, patients with early-onset diabetes had a higher risk of nonfatal cardiovascular disease (7) than did patients with late-onset diabetes (odds ratio [OR] 1.91, 95% CI 1.81–2.02).”

“As approximately 95% of patients with diabetes in China have T2D, the rapid increase in the prevalence of diabetes in China may be attributed to the increasing rates of overweight and obesity and the reduction in physical activity, which is driven by economic development, lifestyle changes, and diet (3,11). According to a series of nationwide surveys conducted by the China Physical Fitness Surveillance Center (12), the prevalence of overweight (BMI ≥23.0 to <27.5 kg/m2) in Chinese adults aged 20–59 years increased from 37.4% in 2000 to 39.2% in 2005, 40.7% in 2010, and 41.2% in 2014, with an estimated increase of 0.27% per year. The prevalence of obesity (BMI ≥27.5 kg/m2) increased from 8.6% in 2000 to 10.3% in 2005, 12.2% in 2010, and 12.9% in 2014, with an estimated increase of 0.32% per year […]. The prevalence of central obesity increased from 13.9% in 2000 to 18.3% in 2005, 22.1% in 2010, and 24.9% in 2014, with an estimated increase of 0.78% per year. Notably, T2D develops at a considerably lower BMI in the Chinese population than that in European populations. […] The relatively high risk of diabetes at a lower BMI could be partially attributed to the tendency toward visceral adiposity in East Asian populations, including the Chinese population (13). Moreover, East Asian populations have been found to have a higher insulin sensitivity with a much lower insulin response than European descent and African populations, implying a lower compensatory β-cell function, which increases the risk of progressing to overt diabetes (14).”

“Over the past two decades, linkage analyses, candidate gene approaches, and large-scale GWAS have successfully identified more than 100 genes that confer susceptibility to T2D among the world’s major ethnic populations […], most of which were discovered in European populations. However, less than 50% of these European-derived loci have been successfully confirmed in East Asian populations. […] there is a need to identify specific genes that are associated with T2D in other ethnic populations. […] Although many genetic loci have been shown to confer susceptibility to T2D, the mechanism by which these loci participate in the pathogenesis of T2D remains unknown. Most T2D loci are located near genes that are related to β-cell function […] most single nucleotide polymorphisms (SNPs) contributing to the T2D risk are located in introns, but whether these SNPs directly modify gene expression or are involved in linkage disequilibrium with unknown causal variants remains to be investigated. Furthermore, the loci discovered thus far collectively account for less than 15% of the overall estimated genetic heritability.”

“The areas under the receiver operating characteristic curves (AUCs) are usually used to assess the discriminative accuracy of an approach. The AUC values range from 0.5 to 1.0, where an AUC of 0.5 represents a lack of discrimination and an AUC of 1 represents perfect discrimination. An AUC ≥0.75 is considered clinically useful. The dominant conventional risk factors, including age, sex, BMI, waist circumference, blood pressure, family history of diabetes, physical activity level, smoking status, and alcohol consumption, can be combined to construct conventional risk factor–based models (CRM). Several studies have compared the predictive capacities of models with and without genetic information. The addition of genetic markers to a CRM could slightly improve the predictive performance. For example, one European study showed that the addition of an 11-SNP GRS to a CRM marginally improved the risk prediction (AUC was 0.74 without and 0.75 with the genetic markers, P < 0.001) in a prospective cohort of 16,000 individuals (37). A meta-analysis (38) consisting of 23 studies investigating the predictive performance of T2D risk models also reported that the AUCs only slightly increased with the addition of genetic information to the CRM (median AUC was increased from 0.78 to 0.79). […] Despite great advances in genetic studies, the clinical utility of genetic information in the prediction, early identification, and prevention of T2D remains in its preliminary stage.”

“An increasing number of studies have highlighted that early nutrition has a persistent effect on the risk of diabetes in later life (40,41). China’s Great Famine of 1959–1962 is considered to be the largest and most severe famine of the 20th century […] Li et al. (43) found that offspring of mothers exposed to the Chinese famine have a 3.9-fold increased risk of diabetes or hyperglycemia as adults. A more recent study (the Survey on Prevalence in East China for Metabolic Diseases and Risk Factors [SPECT-China]) conducted in 2014, among 6,897 adults from Shanghai, Jiangxi, and Zhejiang provinces, had the same conclusion that famine exposure during the fetal period (OR 1.53, 95% CI 1.09–2.14) and childhood (OR 1.82, 95% CI 1.21–2.73) was associated with diabetes (44). These findings indicate that undernutrition during early life increases the risk of hyperglycemia in adulthood and this association is markedly exaggerated when facing overnutrition in later life.”

February 23, 2018 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Health Economics, Immunology, Medicine, Neurology, Ophthalmology, Pharmacology, Studies | Leave a comment

A few diabetes papers of interest

(I hadn’t expected to only cover two papers in this post, but the second paper turned out to include a lot of stuff I figured might be worth adding here. I might add another post later this week including some of the other studies I had intended to cover in this post.)

i. Burden of Mortality Attributable to Diagnosed Diabetes: A Nationwide Analysis Based on Claims Data From 65 Million People in Germany.

“Diabetes is among the 10 most common causes of death worldwide (2). Between 1990 and 2010, the number of deaths attributable to diabetes has doubled (2). People with diabetes have a reduced life expectancy of ∼5 to 6 years (3). The most common cause of death in people with diabetes is cardiovascular disease (3,4). Over the past few decades, a reduction of diabetes mortality has been observed in several countries (59). However, the excess risk of death is still higher than in the population without diabetes, particularly in younger age-groups (4,9,10). Unfortunately, in most countries worldwide, reliable data on diabetes mortality are lacking (1). In a few European countries, such as Denmark (5) and Sweden (4), mortality analyses are based on national diabetes registries that include all age-groups. However, Germany and many other European countries do not have such national registries. Until now, age-standardized hazard ratios for diabetes mortality between 1.4 and 2.6 have been published for Germany on the basis of regional studies and surveys with small respondent numbers (1114). To the best of our knowledge, no nationwide estimates of the number of excess deaths due to diabetes have been published for Germany, and no information on older age-groups >79 years is currently available.

In 2012, changes in the regulation of data transparency enabled the use of nationwide routine health care data from the German statutory health insurance system, which insures ∼90% of the German population (15). These changes have allowed for new possibilities for estimating the burden of diabetes in Germany. Hence, this study estimates the number of excess deaths due to diabetes (ICD-10 codes E10–E14) and type 2 diabetes (ICD-10 code E11) in Germany, which is the number of deaths that could have been prevented if the diabetes mortality rate was as high as that of the population without diabetes.”

“Nationwide data on mortality ratios for diabetes and no diabetes are not available for Germany. […] the age- and sex-specific mortality rate ratios between people with diabetes and without diabetes were used from a Danish study wherein the Danish National Diabetes Register was linked to the individual mortality data from the Civil Registration System that includes all people residing in Denmark (5). Because the Danish National Diabetes Register is one of the most accurate diabetes registries in Europe, with a sensitivity of 86% and positive predictive value of 90% (5), we are convinced that the Danish estimates are highly valid and reliable. Denmark and Germany have a comparable standard of living and health care system. The diabetes prevalence in these countries is similar (Denmark 7.2%, Germany 7.4% [20]) and mortality of people with and without diabetes comparable, as shown in the European mortality database”

“In total, 174,627 excess deaths (137,950 from type 2 diabetes) could have been prevented in 2010 if mortality was the same in people with and without diabetes. Overall, 21% of all deaths in Germany were attributable to diabetes, and 16% were attributable to type 2 diabetes […] Most of the excess deaths occurred in the 70- to 79- and 80- to 89-year-old age-groups (∼34% each) […]. Substantial sex differences were found in diabetes-related excess deaths. From the age of ∼40 years, the number of male excess deaths due to diabetes started to grow, but the number of female excess deaths increased with a delay. Thus, the highest number of male excess deaths due to diabetes occurred at the age of ∼75 years, whereas the peak of female excess deaths was ∼10 years later. […] The diabetes mortality rates increased with age and were always higher than in the population without diabetes. The largest differences in mortality rates between people with and without diabetes were observed in the younger age-groups. […] These results are in accordance with previous studies worldwide (3,4,7,9) and regional studies in Germany (1113).”

“According to official numbers from the Federal Statistical Office, 858,768 people died in Germany in 2010, with 23,131 deaths due to diabetes, representing 2.7% of the all-cause mortality (26). Hence, in Germany, diabetes is not ranked among the top 10 most common causes of death […]. We found that 21% of all deaths were attributable to diabetes and 16% were attributable to type 2 diabetes; hence, we suggest that the number of excess deaths attributable to diabetes is strongly underestimated if we rely on reported causes of death from death certificates, as official statistics do. Estimating diabetes-related mortality is challenging because most people die as a result of diabetes complications and comorbidities, such as cardiovascular disease and renal failure, which often are reported as the underlying cause of death (1,23). For this reason, another approach is to focus not only on the underlying cause of death but also on the multiple causes of death to assess any mention of a disease on the death certificate (27). In a study from Italy, the method of assessing multiple causes of death revealed that in 12.3% of all studied death certificates, diabetes was mentioned, whereas only 2.9% reported diabetes as the underlying cause of death (27), corresponding to a four times higher proportion of death related to diabetes. Another nationwide analysis from Canada found that diabetes was more than twice as likely to be a contributing factor to death than the underlying cause of death from the years 2004–2008 (28). A recently published study from the U.S. that was based on two representative surveys from 1997 to 2010 found that 11.5% of all deaths were attributable to diabetes, which reflects a three to four times higher proportion of diabetes-related deaths (29). Overall, these results, together with the current calculations, demonstrate that deaths due to diabetes contribute to a much higher burden than previously assumed.”

ii. Standardizing Clinically Meaningful Outcome Measures Beyond HbA1c for Type 1 Diabetes: A Consensus Report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange.

“Type 1 diabetes is a life-threatening, autoimmune disease that strikes children and adults and can be fatal. People with type 1 diabetes have to test their blood glucose multiple times each day and dose insulin via injections or an infusion pump 24 h a day every day. Too much insulin can result in hypoglycemia, seizures, coma, or death. Hyperglycemia over time leads to kidney, heart, nerve, and eye damage. Even with diligent monitoring, the majority of people with type 1 diabetes do not achieve recommended target glucose levels. In the U.S., approximately one in five children and one in three adults meet hemoglobin A1c (HbA1c) targets and the average patient spends 7 h a day hyperglycemic and over 90 min hypoglycemic (13). […] HbA1c is a well-accepted surrogate outcome measure for evaluating the efficacy of diabetes therapies and technologies in clinical practice as well as in research (46). […] While HbA1c is used as a primary outcome to assess glycemic control and as a surrogate for risk of developing complications, it has limitations. As a measure of mean blood glucose over 2 or 3 months, HbA1c does not capture short-term variations in blood glucose or exposure to hypoglycemia and hyperglycemia in individuals with type 1 diabetes; HbA1c also does not capture the impact of blood glucose variations on individuals’ quality of life. Recent advances in type 1 diabetes technologies have made it feasible to assess the efficacy of therapies and technologies using a set of outcomes beyond HbA1c and to expand definitions of outcomes such as hypoglycemia. While definitions for hypoglycemia in clinical care exist, they have not been standardized […]. The lack of standard definitions impedes and can confuse their use in clinical practice, impedes development processes for new therapies, makes comparison of studies in the literature challenging, and may lead to regulatory and reimbursement decisions that fail to meet the needs of people with diabetes. To address this vital issue, the type 1 diabetes–stakeholder community launched the Type 1 Diabetes Outcomes Program to develop consensus definitions for a set of priority outcomes for type 1 diabetes. […] The outcomes prioritized under the program include hypoglycemia, hyperglycemia, time in range, diabetic ketoacidosis (DKA), and patient-reported outcomes (PROs).”

“Hypoglycemia is a significant — and potentially fatal — complication of type 1 diabetes management and has been found to be a barrier to achieving glycemic goals (9). Repeated exposure to severe hypoglycemic events has been associated with an increased risk of cardiovascular events and all-cause mortality in people with type 1 or type 2 diabetes (10,11). Hypoglycemia can also be fatal, and severe hypoglycemic events have been associated with increased mortality (1214). In addition to the physical aspects of hypoglycemia, it can also have negative consequences on emotional status and quality of life.

While there is some variability in how and when individuals manifest symptoms of hypoglycemia, beginning at blood glucose levels <70 mg/dL (3.9 mmol/L) (which is at the low end of the typical post-absorptive plasma glucose range), the body begins to increase its secretion of counterregulatory hormones including glucagon, epinephrine, cortisol, and growth hormone. The release of these hormones can cause moderate autonomic effects, including but not limited to shaking, palpitations, sweating, and hunger (15). Individuals without diabetes do not typically experience dangerously low blood glucose levels because of counterregulatory hormonal regulation of glycemia (16). However, in individuals with type 1 diabetes, there is often a deficiency of the counterregulatory response […]. Moreover, as people with diabetes experience an increased number of episodes of hypoglycemia, the risk of hypoglycemia unawareness, impaired glucose counterregulation (for example, in hypoglycemia-associated autonomic failure [17]), and level 2 and level 3 hypoglycemia […] all increase (18). Therefore, it is important to recognize and treat all hypoglycemic events in people with type 1 diabetes, particularly in populations (children, the elderly) that may not have the ability to recognize and self-treat hypoglycemia. […] More notable clinical symptoms begin at blood glucose levels <54 mg/dL (3.0 mmol/L) (19,20). As the body’s primary utilizer of glucose, the brain is particularly sensitive to decreases in blood glucose concentrations. Both experimental and clinical evidence has shown that, at these levels, neurogenic and neuroglycopenic symptoms including impairments in reaction times, information processing, psychomotor function, and executive function begin to emerge. These neurological symptoms correlate to altered brain activity in multiple brain areas including the prefrontal cortex and medial temporal lobe (2124). At these levels, individuals may experience confusion, dizziness, blurred or double vision, tremors, and tingling sensations (25). Hypoglycemia at this glycemic level may also increase proinflammatory and prothrombotic markers (26). Left untreated, these symptoms can become severe to the point that an individual will require assistance from others to move or function. Prolonged untreated hypoglycemia that continues to drop below 50 mg/dL (2.8 mmol/L) increases the risk of seizures, coma, and death (27,28). Hypoglycemia that affects cognition and stamina may also increase the risk of accidents and falls, which is a particular concern for older adults with diabetes (29,30).

The glycemic thresholds at which these symptoms occur, as well as the severity with which they manifest themselves, may vary in individuals with type 1 diabetes depending on the number of hypoglycemic episodes they have experienced (3133). Counterregulatory physiological responses may evolve in patients with type 1 diabetes who endure repeated hypoglycemia over time (34,35).”

“The Steering Committee defined three levels of hypoglycemia […] Level 1 hypoglycemia is defined as a measurable glucose concentration <70 mg/dL (3.9 mmol/L) but ≥54 mg/dL (3.0 mmol/L) that can alert a person to take action. A blood glucose concentration of 70 mg/dL (3.9 mmol/L) has been recognized as a marker of physiological hypoglycemia in humans, as it approximates the glycemic threshold for neuroendocrine responses to falling glucose levels in individuals without diabetes. As such, blood glucose in individuals without diabetes is generally 70–100 mg/dL (3.9–5.6 mmol/L) upon waking and 70–140 mg/dL (3.9–7.8 mmol/L) after meals, and any excursions beyond those levels are typically countered with physiological controls (16,37). However, individuals with diabetes who have impaired or altered counterregulatory hormonal and neurological responses do not have the same internal regulation as individuals without diabetes to avoid dropping below 70 mg/dL (3.9 mmol/L) and becoming hypoglycemic. Recurrent episodes of hypoglycemia lead to increased hypoglycemia unawareness, which can become dangerous as individuals cease to experience symptoms of hypoglycemia, allowing their blood glucose levels to continue falling. Therefore, glucose levels <70 mg/dL (3.9 mmol/L) are clinically important, independent of the severity of acute symptoms.

Level 2 hypoglycemia is defined as a measurable glucose concentration <54 mg/dL (3.0 mmol/L) that needs immediate action. At ∼54 mg/dL (3.0 mmol/L), neurogenic and neuroglycopenic hypoglycemic symptoms begin to occur, ultimately leading to brain dysfunction at levels <50 mg/dL (2.8 mmol/L) (19,20). […] Level 3 hypoglycemia is defined as a severe event characterized by altered mental and/or physical status requiring assistance. Severe hypoglycemia captures events during which the symptoms associated with hypoglycemia impact a patient to such a degree that the patient requires assistance from others (27,28). […] Hypoglycemia that sets in relatively rapidly, such as in the case of a significant insulin overdose, may induce level 2 or level 3 hypoglycemia with little warning (38).”

“The data regarding the effects of chronic hyperglycemia on long-term outcomes is conclusive, indicating that chronic hyperglycemia is a major contributor to morbidity and mortality in type 1 diabetes (41,4345). […] Although the correlation between long-term poor glucose control and type 1 diabetes complications is well established, the impact of short-term hyperglycemia is not as well understood. However, hyperglycemia has been shown to have physiological effects and in an acute-care setting is linked to morbidity and mortality in people with and without diabetes. Short-term hyperglycemia, regardless of diabetes diagnosis, has been shown to reduce survival rates among patients admitted to the hospital with stroke or myocardial infarction (47,48). In addition to increasing mortality, short-term hyperglycemia is correlated with stroke severity and poststroke disability (49,50).

The effects of short-term hyperglycemia have also been observed in nonacute settings. Evidence indicates that hyperglycemia alters retinal cell firing through sensitization in patients with type 1 diabetes (51). This finding is consistent with similar findings showing increased oxygen consumption and blood flow in the retina during hyperglycemia. Because retinal cells absorb glucose through an insulin-independent process, they respond more strongly to increases in glucose in the blood than other cells in patients with type 1 diabetes. The effects of acute hyperglycemia on retinal response may underlie part of the development of retinopathy known to be a long-term complication of type 1 diabetes.”

“The Steering Committee defines hyperglycemia for individuals with type 1 diabetes as the following:

  • Level 1—elevated glucose: glucose >180 mg/dL (10 mmol/L) and glucose ≤250 mg/dL (13.9 mmol/L)

  • Level 2—very elevated glucose: glucose >250 mg/dL (13.9 mmol/L) […]

Elevated glucose is defined as a glucose concentration >180 mg/dL (10.0 mmol/L) but ≤250 mg/dL (13.9 mmol/L). In clinical practice, measures of hyperglycemia differ based on time of day (e.g., pre- vs. postmeal). This program, however, focused on defining outcomes for use in product development that are universally applicable. Glucose profiles and postprandial blood glucose data for individuals without diabetes suggest that 140 mg/dL (7.8 mmol/L) is the appropriate threshold for defining hyperglycemia. However, data demonstrate that the majority of individuals without diabetes exceed this threshold every day. Moreover, people with diabetes spend >60% of their day above this threshold, which suggests that 140 mg/dL (7.8 mmol/L) is too low of a threshold for measuring hyperglycemia in individuals with diabetes. Current clinical guidelines for people with diabetes indicate that peak prandial glucose should not exceed 180 mg/dL (10.0 mmol/L). As such, the Steering Committee identified 180 mg/dL (10.0 mmol/L) as the initial threshold defining elevated glucose. […]

Very elevated glucose is defined as a glucose concentration >250 mg/dL (13.9 mmol/L). Evidence examining the impact of hyperglycemia does not examine the incremental effects of increasing blood glucose. However, blood glucose values exceeding 250 mg/dL (13.9 mmol/L) increase the risk for DKA (58), and HbA1c readings at that level have been associated with a high likelihood of complications.”

“An individual whose blood glucose levels rarely extend beyond the thresholds defined for hypo- and hyperglycemia is less likely to be subject to the short-term or long-term effects experienced by those with frequent excursions beyond one or both thresholds. It is also evident that if the intent of a given intervention is to safely manage blood glucose but the intervention does not reliably maintain blood glucose within safe levels, then the intervention should not be considered effective.

The time in range outcome is distinguished from traditional HbA1c testing in several ways (4,59). Time in range captures fluctuations in glucose levels continuously, whereas HbA1c testing is done at static points in time, usually months apart (60). Furthermore, time in range is more specific and sensitive than traditional HbA1c testing; for example, a treatment that addresses acute instances of hypo- or hyperglycemia may be detected in a time in range assessment but not necessarily in an HbA1c assessment. As a percentage, time in range is also more likely to be comparable across patients than HbA1c values, which are more likely to have patient-specific variations in significance (61). Finally, time in range may be more likely than HbA1c levels to correlate with PROs, such as quality of life, because the outcome is more representative of the whole patient experience (62). Table 3 illustrates how the concept of time in range differs from current HbA1c testing. […] [V]ariation in what is considered “normal” glucose fluctuations across populations, as well as what is realistically achievable for people with type 1 diabetes, must be taken into account so as not to make the target range definition too restrictive.”

“The Steering Committee defines time in range for individuals with type 1 diabetes as the following:

  • Percentage of readings in the range of 70–180 mg/dL (3.9–10.0 mmol/L) per unit of time

The Steering Committee considered it important to keep the time in range definition wide in order to accommodate variations across the population with type 1 diabetes — including different age-groups — but limited enough to preclude the possibility of negative outcomes. The upper and lower bounds of the time in range definition are consistent with the definitions for hypo- and hyperglycemia defined above. For individuals without type 1 diabetes, 70–140 mg/dL (3.9–7.8 mmol/L) represents a normal glycemic range (66). However, spending most of the day in this range is not generally achievable for people with type 1 diabetes […] To date, there is limited research correlating time in range with positive short-term and long-term type 1 diabetes outcomes, as opposed to the extensive research demonstrating the negative consequences of excursions into hyper- or hypoglycemia. More substantial evidence demonstrating a correlation or a direct causative relationship between time in range for patients with type 1 diabetes and positive health outcomes is needed.”

“DKA is often associated with hyperglycemia. In most cases, in an individual with diabetes, the cause of hyperglycemia is also the cause of DKA, although the two conditions are distinct. DKA develops when a lack of glucose in cells prompts the body to begin breaking down fatty acid reserves. This increases the levels of ketones in the body (ketosis) and causes a drop in blood pH (acidosis). At its most severe, DKA can cause cerebral edema, acute respiratory distress, thromboembolism, coma, and death (69,70). […] Although the current definition for DKA includes a list of multiple criteria that must be met, not all information currently included in the accepted definition is consistently gathered or required to diagnose DKA. The Steering Committee defines DKA in individuals with type 1 diabetes in a clinical setting as the following:

  • Elevated serum or urine ketones (greater than the upper limit of the normal range), and

  • Serum bicarbonate <15 mmol/L or blood pH <7.3

Given the seriousness of DKA, it is unnecessary to stratify DKA into different levels or categories, as the presence of DKA—regardless of the differences observed in the separate biochemical tests—should always be considered serious. In individuals with known diabetes, plasma glucose values are not necessary to diagnose DKA. Further, new therapeutic agents, specifically sodium–glucose cotransporter 2 inhibitors, have been linked to euglycemic DKA, or DKA with blood glucose values <250 mg/dL (13.9 mmol/L).”

“In guidance released in 2009 (72), the U.S. Food and Drug Administration (FDA) defined PROs as “any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else.” In the same document, the FDA clearly acknowledged the importance of PROs, advising that they be used to gather information that is “best known by the patient or best measured from the patient perspective.”

Measuring and using PROs is increasingly seen as essential to evaluating care from a patient-centered perspective […] Given that type 1 diabetes is a chronic condition primarily treated on an outpatient basis, much of what people with type 1 diabetes experience is not captured through standard clinical measurement. Measures that capture PROs can fill these important information gaps. […] The use of validated PROs in type 1 diabetes clinical research is not currently widespread, and challenges to effectively measuring some PROs, such as quality of life, continue to confront researchers and developers.”

February 20, 2018 Posted by | Cardiology, Diabetes, Medicine, Neurology, Ophthalmology, Studies | Leave a comment

A few diabetes papers of interest

i. Type 2 Diabetes in the Real World: The Elusive Nature of Glycemic Control.

“Despite U.S. Food and Drug Administration (FDA) approval of over 40 new treatment options for type 2 diabetes since 2005, the latest data from the National Health and Nutrition Examination Survey show that the proportion of patients achieving glycated hemoglobin (HbA1c) <7.0% (<53 mmol/mol) remains around 50%, with a negligible decline between the periods 2003–2006 and 2011–2014. The Healthcare Effectiveness Data and Information Set reports even more alarming rates, with only about 40% and 30% of patients achieving HbA1c <7.0% (<53 mmol/mol) in the commercially insured (HMO) and Medicaid populations, respectively, again with virtually no change over the past decade. A recent retrospective cohort study using a large U.S. claims database explored why clinical outcomes are not keeping pace with the availability of new treatment options. The study found that HbA1c reductions fell far short of those reported in randomized clinical trials (RCTs), with poor medication adherence emerging as the key driver behind the disconnect. In this Perspective, we examine the implications of these findings in conjunction with other data to highlight the discrepancy between RCT findings and the real world, all pointing toward the underrealized promise of FDA-approved therapies and the critical importance of medication adherence. While poor medication adherence is not a new issue, it has yet to be effectively addressed in clinical practice — often, we suspect, because it goes unrecognized. To support the busy health care professional, innovative approaches are sorely needed.”

“To better understand the differences between usual care and clinical trial HbA1c results, multivariate regression analysis assessed the relative contributions of key biobehavioral factors, including baseline patient characteristics, drug therapy, and medication adherence (21). Significantly, the key driver was poor medication adherence, accounting for 75% of the gap […]. Adherence was defined […] as the filling of one’s diabetes prescription often enough to cover ≥80% of the time one was recommended to be taking the medication (34). By this metric, proportion of days covered (PDC) ≥80%, only 29% of patients were adherent to GLP-1 RA treatment and 37% to DPP-4 inhibitor treatment. […] These data are consistent with previous real-world studies, which have demonstrated that poor medication adherence to both oral and injectable antidiabetes agents is very common (3537). For example, a retrospective analysis [of] adults initiating oral agents in the DPP-4 inhibitor (n = 61,399), sulfonylurea (n = 134,961), and thiazolidinedione (n = 42,012) classes found that adherence rates, as measured by PDC ≥80% at the 1-year mark after the initial prescription, were below 50% for all three classes, at 47.3%, 41.2%, and 36.7%, respectively (36). Rates dropped even lower at the 2-year follow-up (36)”

“Our current ability to assess adherence and persistence is based primarily on review of pharmacy records, which may underestimate the extent of the problem. For example, using the definition of adherence of the Centers for Medicare & Medicaid Services — PDC ≥80% — a patient could miss up to 20% of days covered and still be considered adherent. In retrospective studies of persistence, the permissible gap after the last expected refill date often extends up to 90 days (39,40). Thus, a patient may have a gap of up to 90 days and still be considered persistent.

Additionally, one must also consider the issue of primary nonadherence; adherence and persistence studies typically only include patients who have completed a first refill. A recent study of e-prescription data among 75,589 insured patients found that nearly one-third of new e-prescriptions for diabetes medications were never filled (41). Finally, none of these measures take into account if the patient is actually ingesting or injecting the medication after acquiring his or her refills.”

“Acknowledging and addressing the problem of poor medication adherence is pivotal because of the well-documented dire consequences: a greater likelihood of long-term complications, more frequent hospitalizations, higher health care costs, and elevated mortality rates (4245). In patients younger than 65, hospitalization risk in one study (n = 137,277) was found to be 30% at the lowest level of adherence to antidiabetes medications (1–19%) versus 13% at the highest adherence quintile (80–100%) […]. In patients over 65, a separate study (n = 123,235) found that all-cause hospitalization risk was 37.4% in adherent cohorts (PDC ≥80%) versus 56.2% in poorly adherent cohorts (PDC <20%) (45). […] Furthermore, for every 1,000 patients who increased adherence to their antidiabetes medications by just 1%, the total medical cost savings was estimated to be $65,464 over 3 years (45). […] “for reasons that are still unclear, the N.A. [North American] patient groups tend to have lower compliance and adherence compared to global rates during large cardiovascular studies” (46,47).”

“There are many potential contributors to poor medication adherence, including depressive affect, negative treatment perceptions, lack of patient-physician trust, complexity of the medication regimen, tolerability, and cost (48). […] A recent review of interventions addressing problematic medication adherence in type 2 diabetes found that few strategies have been shown consistently to have a marked positive impact, particularly with respect to HbA1c lowering, and no single intervention was identified that could be applied successfully to all patients with type 2 diabetes (53). Additional evidence indicates that improvements resulting from the few effective interventions, such as pharmacy-based counseling or nurse-managed home telemonitoring, often wane once the programs end (54,55). We suspect that the efficacy of behavioral interventions to address medication adherence will continue to be limited until there are more focused efforts to address three common and often unappreciated patient obstacles. First, taking diabetes medications is a burdensome and often difficult activity for many of our patients. Rather than just encouraging patients to do a better job of tolerating this burden, more work is needed to make the process easier and more convenient. […] Second, poor medication adherence often represents underlying attitudinal problems that may not be a strictly behavioral issue. Specifically, negative beliefs about prescribed medications are pervasive among patients, and behavioral interventions cannot be effective unless these beliefs are addressed directly (35). […] Third, the issue of access to medications remains a primary concern. A study by Kurlander et al. (51) found that patients selectively forgo medications because of cost; however, noncost factors, such as beliefs, satisfaction with medication-related information, and depression, are also influential.”

ii. Diabetes Research and Care Through the Ages. An overview article which might be of interest especially to people who’re not much familiar with the history of diabetes research and -treatment (a topic which is also very nicely covered in Tattersall’s book). Despite including a historical review of various topics, it also includes many observations about e.g. current (and future?) practice. Some random quotes:

“Arnoldo Cantani established a new strict level of treatment (9). He isolated his patients “under lock and key, and allowed them absolutely no food but lean meat and various fats. In the less severe cases, eggs, liver, and shell-fish were permitted. For drink the patients received water, plain or carbonated, and dilute alcohol for those accustomed to liquors, the total fluid intake being limited to one and one-half to two and one-half liters per day” (6).

Bernhard Naunyn encouraged a strict carbohydrate-free diet (6,10). He locked patients in their rooms for 5 months when necessary for “sugar-freedom” (6).” […let’s just say that treatment options have changed slightly over time – US]

“The characteristics of insulin preparations include the purity of the preparation, the concentration of insulin, the species of origin, and the time course of action (onset, peak, duration) (25). From the 1930s to the early 1950s, one of the major efforts made was to develop an insulin with extended action […]. Most preparations contained 40 (U-40) or 80 (U-80) units of insulin per mL, with U-10 and U-20 eliminated in the early 1940s. U-100 was introduced in 1973 and was meant to be a standard concentration, although U-500 had been available since the early 1950s for special circumstances. Preparations were either of mixed beef and pork origin, pure beef, or pure pork. There were progressive improvements in the purity of preparations as chemical techniques improved. Prior to 1972, conventional preparations contained 8% noninsulin proteins. […] In the early 1980s, “human” insulins were introduced (26). These were made either by recombinant DNA technology in bacteria (Escherichia coli) or yeast (Saccharomyces cerevisiae) or by enzymatic conversion of pork insulin to human insulin, since pork differed by only one amino acid from human insulin. The powerful nature of recombinant DNA technology also led to the development of insulin analogs designed for specific effects. These include rapid-acting insulin analogs and basal insulin analogs.”

“Until 1996, the only oral medications available were biguanides and sulfonylureas. Since that time, there has been an explosion of new classes of oral and parenteral preparations. […] The management of type 2 diabetes (T2D) has undergone rapid change with the introduction of several new classes of glucose-lowering therapies. […] the treatment guidelines are generally clear in the context of using metformin as the first oral medication for T2D and present a menu approach with respect to the second and third glucose-lowering medication (3032). In order to facilitate this decision, the guidelines list the characteristics of each medication including side effects and cost, and the health care provider is expected to make a choice that would be most suited for patient comorbidities and health care circumstances. This can be confusing and contributes to the clinical inertia characteristic of the usual management of T2D (33).”

“Perhaps the most frustrating barrier to optimizing diabetes management is the frequent occurrence of clinical inertia (whenever the health care provider does not initiate or intensify therapy appropriately and in a timely fashion when therapeutic goals are not reached). More broadly, the failure to advance therapy in an appropriate manner can be traced to physician behaviors, patient factors, or elements of the health care system. […] Despite clear evidence from multiple studies, health care providers fail to fully appreciate that T2D is a progressive disease. T2D is associated with ongoing β-cell failure and, as a consequence, we can safely predict that for the majority of patients, glycemic control will deteriorate with time despite metformin therapy (35). Continued observation and reinforcement of the current therapeutic regimen is not likely to be effective. As an example of real-life clinical inertia for patients with T2D on monotherapy metformin and an HbA1c of 7 to <8%, it took on the average 19 months before additional glucose-lowering therapy was introduced (36). The fear of hypoglycemia and weight gain are appropriate concerns for both patient and physician, but with newer therapies these undesirable effects are significantly diminished. In addition, health care providers must appreciate that achieving early and sustained glycemic control has been demonstrated to have long-term benefits […]. Clinicians have been schooled in the notion of a stepwise approach to therapy and are reluctant to initiate combination therapy early in the course of T2D, even if the combination intervention is formulated as a fixed-dose combination. […] monotherapy metformin failure rates with a starting HbA1c >7% are ∼20% per year (35). […] To summarize the current status of T2D at this time, it should be clearly emphasized that, first and foremost, T2D is characterized by a progressive deterioration of glycemic control. A stepwise medication introduction approach results in clinical inertia and frequently fails to meet long-term treatment goals. Early/initial combination therapies that are not associated with hypoglycemia and/or weight gain have been shown to be safe and effective. The added value of reducing CV outcomes with some of these newer medications should elevate them to a more prominent place in the treatment paradigm.”

iii. Use of Adjuvant Pharmacotherapy in Type 1 Diabetes: International Comparison of 49,996 Individuals in the Prospective Diabetes Follow-up and T1D Exchange Registries.

“The majority of those with type 1 diabetes (T1D) have suboptimal glycemic control (14); therefore, use of adjunctive pharmacotherapy to improve control has been of clinical interest. While noninsulin medications approved for type 2 diabetes have been reported in T1D research and clinical practice (5), little is known about their frequency of use. The T1D Exchange (T1DX) registry in the U.S. and the Prospective Diabetes Follow-up (DPV) registry in Germany and Austria are two large consortia of diabetes centers; thus, they provide a rich data set to address this question.

For the analysis, 49,996 pediatric and adult patients with diabetes duration ≥1 year and a registry update from 1 April 2015 to 1 July 2016 were included (19,298 individuals from 73 T1DX sites and 30,698 individuals from 354 DPV sites). Adjuvant medication use (metformin, glucagon-like peptide 1 [GLP-1] receptor agonists, dipeptidyl peptidase 4 [DPP-4] inhibitors, sodium–glucose cotransporter 2 [SGLT2] inhibitors, and other noninsulin diabetes medications including pramlintide) was extracted from participant medical records. […] Adjunctive agents, whose proposed benefits may include the ability to improve glycemic control, reduce insulin doses, promote weight loss, and suppress dysregulated postprandial glucagon secretion, have had little penetrance as part of the daily medical regimen of those in the registries studied. […] The use of any adjuvant medication was 5.4% in T1DX and 1.6% in DPV (P < 0.001). Metformin was the most commonly reported medication in both registries, with 3.5% in the T1DX and 1.3% in the DPV (P < 0.001). […] Use of adjuvant medication was associated with older age, higher BMI, and longer diabetes duration in both registries […] it is important to note that registry data did not capture the intent of adjuvant medications, which may have been to treat polycystic ovarian syndrome in women […here’s a relevant link, US].”

iv. Prevalence of and Risk Factors for Diabetic Peripheral Neuropathy in Youth With Type 1 and Type 2 Diabetes: SEARCH for Diabetes in Youth Study. I recently covered a closely related paper here (paper # 2) but the two papers cover different data sets so I decided it would be worth including this one in this post anyway. Some quotes:

“We previously reported results from a small pilot study comparing the prevalence of DPN in a subset of youth enrolled in the SEARCH for Diabetes in Youth (SEARCH) study and found that 8.5% of 329 youth with T1D (mean ± SD age 15.7 ± 4.3 years and diabetes duration 6.2 ± 0.9 years) and 25.7% of 70 youth with T2D (age 21.6 ± 4.1 years and diabetes duration 7.6 ± 1.8 years) had evidence of DPN (9). […this is the paper I previously covered here, US] Recently, we also reported the prevalence of microvascular and macrovascular complications in youth with T1D and T2D in the entire SEARCH cohort (10).

In the current study, we examined the cross-sectional and longitudinal risk factors for DPN. The aims were 1) to estimate prevalence of DPN in youth with T1D and T2D, overall and by age and diabetes duration, and 2) to identify risk factors (cross-sectional and longitudinal) associated with the presence of DPN in a multiethnic cohort of youth with diabetes enrolled in the SEARCH study.”

“The SEARCH Cohort Study enrolled 2,777 individuals. For this analysis, we excluded participants aged <10 years (n = 134), those with no antibody measures for etiological definition of diabetes (n = 440), and those with incomplete neuropathy assessment […] (n = 213), which reduced the analysis sample size to 1,992 […] There were 1,734 youth with T1D and 258 youth with T2D who participated in the SEARCH study and had complete data for the variables of interest. […] Seven percent of the participants with T1D and 22% of those with T2D had evidence of DPN.”

“Among youth with T1D, those with DPN were older (21 vs. 18 years, P < 0.0001), had a longer duration of diabetes (8.7 vs. 7.8 years, P < 0.0001), and had higher DBP (71 vs. 69 mmHg, P = 0.02), BMI (26 vs. 24 kg/m2, P < 0.001), and LDL-c levels (101 vs. 96 mg/dL, P = 0.01); higher triglycerides (85 vs. 74 mg/dL, P = 0.005); and lower HDL-c levels (51 vs. 55 mg/dL, P = 0.01) compared to those without DPN. The prevalence of DPN was 5% among nonsmokers vs. 10% among the current and former smokers (P = 0.001). […] Among youth with T2D, those with DPN were older (23 vs. 22 years, P = 0.01), had longer duration of diabetes (8.6 vs. 7.6 years; P = 0.002), and had lower HDL-c (40 vs. 43 mg/dL, P = 0.04) compared with those without DPN. The prevalence of DPN was higher among males than among females: 30% of males had DPN compared with 18% of females (P = 0.02). The prevalence of DPN was twofold higher in current smokers (33%) compared with nonsmokers (15%) and former smokers (17%) (P = 0.01). […] [T]he prevalence of DPN was further assessed by 5-year increment of diabetes duration in individuals with T1D or T2D […]. There was an approximately twofold increase in the prevalence of DPN with an increase in duration of diabetes from 5–10 years to >10 years for both the T1D group (5–13%) (P < 0.0001) and the T2D group (19–36%) (P = 0.02). […] in an unadjusted logistic regression model, youth with T2D were four times more likely to develop DPN compared with those with T1D, and though this association was attenuated, it remained significant independent of age, sex, height, and glycemic control (OR 2.99 [1.91; 4.67], P < 0.001)”.

“The prevalence estimates for DPN found in our study for youth with T2D are similar to those in the Australian cohort (8) but lower for youth with T1D than those reported in the Danish (7) and Australian (8) cohorts. The nationwide Danish Study Group for Diabetes in Childhood reported a prevalence of 62% among 339 adolescents and youth with T1D (age 12–27 years, duration 9–25 years, and HbA1c 9.7 ± 1.7%) using the vibration perception threshold to assess DPN (7). The higher prevalence in this cohort compared with ours (62 vs. 7%) could be due to the longer duration of diabetes (9–25 vs. 5–13 years) and reliance on a single measure of neuropathy (vibration perception threshold) as opposed to our use of the MNSI, which includes vibration as well as other indicators of neuropathy. In the Australian study, Eppens et al. (8) reported abnormalities in peripheral nerve function in 27% of the 1,433 adolescents with T1D (median age 15.7 years, median diabetes duration 6.8 years, and mean HbA1c 8.5%) and 21% of the 68 adolescents with T2D (median age 15.3 years, median diabetes duration 1.3 years, and mean HbA1c 7.3%) based on thermal and vibration perception threshold. These data are thus reminiscent of the persistent inconsistencies in the definition of DPN, which are reflected in the wide range of prevalence estimates being reported.”

“The alarming rise in rates of DPN for every 5-year increase in duration, coupled with poor glycemic control and dyslipidemia, in this cohort reinforces the need for clinicians rendering care to youth with diabetes to be vigilant in screening for DPN and identifying any risk factors that could potentially be modified to alter the course of the disease (2830). The modifiable risk factors that could be targeted in this young population include better glycemic control, treatment of dyslipidemia, and smoking cessation (29,30) […]. The sharp increase in rates of DPN over time is a reminder that DPN is one of the complications of diabetes that must be a part of the routine annual screening for youth with diabetes.”

v. Diabetes and Hypertension: A Position Statement by the American Diabetes Association.

“Hypertension is common among patients with diabetes, with the prevalence depending on type and duration of diabetes, age, sex, race/ethnicity, BMI, history of glycemic control, and the presence of kidney disease, among other factors (13). Furthermore, hypertension is a strong risk factor for atherosclerotic cardiovascular disease (ASCVD), heart failure, and microvascular complications. ASCVD — defined as acute coronary syndrome, myocardial infarction (MI), angina, coronary or other arterial revascularization, stroke, transient ischemic attack, or peripheral arterial disease presumed to be of atherosclerotic origin — is the leading cause of morbidity and mortality for individuals with diabetes and is the largest contributor to the direct and indirect costs of diabetes. Numerous studies have shown that antihypertensive therapy reduces ASCVD events, heart failure, and microvascular complications in people with diabetes (48). Large benefits are seen when multiple risk factors are addressed simultaneously (9). There is evidence that ASCVD morbidity and mortality have decreased for people with diabetes since 1990 (10,11) likely due in large part to improvements in blood pressure control (1214). This Position Statement is intended to update the assessment and treatment of hypertension among people with diabetes, including advances in care since the American Diabetes Association (ADA) last published a Position Statement on this topic in 2003 (3).”

“Hypertension is defined as a sustained blood pressure ≥140/90 mmHg. This definition is based on unambiguous data that levels above this threshold are strongly associated with ASCVD, death, disability, and microvascular complications (1,2,2427) and that antihypertensive treatment in populations with baseline blood pressure above this range reduces the risk of ASCVD events (46,28,29). The “sustained” aspect of the hypertension definition is important, as blood pressure has considerable normal variation. The criteria for diagnosing hypertension should be differentiated from blood pressure treatment targets.

Hypertension diagnosis and management can be complicated by two common conditions: masked hypertension and white-coat hypertension. Masked hypertension is defined as a normal blood pressure in the clinic or office (<140/90 mmHg) but an elevated home blood pressure of ≥135/85 mmHg (30); the lower home blood pressure threshold is based on outcome studies (31) demonstrating that lower home blood pressures correspond to higher office-based measurements. White-coat hypertension is elevated office blood pressure (≥140/90 mmHg) and normal (untreated) home blood pressure (<135/85 mmHg) (32). Identifying these conditions with home blood pressure monitoring can help prevent overtreatment of people with white-coat hypertension who are not at elevated risk of ASCVD and, in the case of masked hypertension, allow proper use of medications to reduce side effects during periods of normal pressure (33,34).”

“Diabetic autonomic neuropathy or volume depletion can cause orthostatic hypotension (35), which may be further exacerbated by antihypertensive medications. The definition of orthostatic hypotension is a decrease in systolic blood pressure of 20 mmHg or a decrease in diastolic blood pressure of 10 mmHg within 3 min of standing when compared with blood pressure from the sitting or supine position (36). Orthostatic hypotension is common in people with type 2 diabetes and hypertension and is associated with an increased risk of mortality and heart failure (37).

It is important to assess for symptoms of orthostatic hypotension to individualize blood pressure goals, select the most appropriate antihypertensive agents, and minimize adverse effects of antihypertensive therapy.”

“Taken together, […] meta-analyses consistently show that treating patients with baseline blood pressure ≥140 mmHg to targets <140 mmHg is beneficial, while more intensive targets may offer additional though probably less robust benefits. […] Overall, compared with people without diabetes, the relative benefits of antihypertensive treatment are similar, and absolute benefits may be greater (5,8,40). […] Multiple-drug therapy is often required to achieve blood pressure targets, particularly in the setting of diabetic kidney disease. However, the use of both ACE inhibitors and ARBs in combination is not recommended given the lack of added ASCVD benefit and increased rate of adverse events — namely, hyperkalemia, syncope, and acute kidney injury (7173). Titration of and/or addition of further blood pressure medications should be made in a timely fashion to overcome clinical inertia in achieving blood pressure targets. […] there is an absence of high-quality data available to guide blood pressure targets in type 1 diabetes. […] Of note, diastolic blood pressure, as opposed to systolic blood pressure, is a key variable predicting cardiovascular outcomes in people under age 50 years without diabetes and may be prioritized in younger adults (46,47). Though convincing data are lacking, younger adults with type 1 diabetes might more easily achieve intensive blood pressure levels and may derive substantial long-term benefit from tight blood pressure control.”

“Lifestyle management is an important component of hypertension treatment because it lowers blood pressure, enhances the effectiveness of some antihypertensive medications, promotes other aspects of metabolic and vascular health, and generally leads to few adverse effects. […] Lifestyle therapy consists of reducing excess body weight through caloric restriction, restricting sodium intake (<2,300 mg/day), increasing consumption of fruits and vegetables […] and low-fat dairy products […], avoiding excessive alcohol consumption […] (53), smoking cessation, reducing sedentary time (54), and increasing physical activity levels (55). These lifestyle strategies may also positively affect glycemic and lipid control and should be encouraged in those with even mildly elevated blood pressure.”

“Initial treatment for hypertension should include drug classes demonstrated to reduce cardiovascular events in patients with diabetes: ACE inhibitors (65,66), angiotensin receptor blockers (ARBs) (65,66), thiazide-like diuretics (67), or dihydropyridine CCBs (68). For patients with albuminuria (urine albumin-to-creatinine ratio [UACR] ≥30 mg/g creatinine), initial treatment should include an ACE inhibitor or ARB in order to reduce the risk of progressive kidney disease […]. In the absence of albuminuria, risk of progressive kidney disease is low, and ACE inhibitors and ARBs have not been found to afford superior cardioprotection when compared with other antihypertensive agents (69). β-Blockers may be used for the treatment of coronary disease or heart failure but have not been shown to reduce mortality as blood pressure–lowering agents in the absence of these conditions (5,70).”

vi. High Illicit Drug Abuse and Suicide in Organ Donors With Type 1 Diabetes.

“Organ donors with type 1 diabetes represent a unique population for research. Through a combination of immunological, metabolic, and physiological analyses, researchers utilizing such tissues seek to understand the etiopathogenic events that result in this disorder. The Network for Pancreatic Organ Donors with Diabetes (nPOD) program collects, processes, and distributes pancreata and disease-relevant tissues to investigators throughout the world for this purpose (1). Information is also available, through medical records of organ donors, related to causes of death and psychological factors, including drug use and suicide, that impact life with type 1 diabetes.

We reviewed the terminal hospitalization records for the first 100 organ donors with type 1 diabetes in the nPOD database, noting cause, circumstance, and mechanism of death; laboratory results; and history of illicit drug use. Donors were 45% female and 79% Caucasian. Mean age at time of death was 28 years (range 4–61) with mean disease duration of 16 years (range 0.25–52).”

“Documented suicide was found in 8% of the donors, with an average age at death of 21 years and average diabetes duration of 9 years. […] Similarly, a type 1 diabetes registry from the U.K. found that 6% of subjects’ deaths were attributed to suicide (2). […] Additionally, we observed a high rate of illicit substance abuse: 32% of donors reported or tested positive for illegal substances (excluding marijuana), and multidrug use was common. Cocaine was the most frequently abused substance. Alcohol use was reported in 35% of subjects, with marijuana use in 27%. By comparison, 16% of deaths in the U.K. study were deemed related to drug misuse (2).”

“We fully recognize the implicit biases of an organ donor–based population, which may not be […’may not be’ – well, I guess that’s one way to put it! – US] directly comparable to the general population. Nevertheless, the high rate of suicide and drug use should continue to spur our energy and resources toward caring for the emotional and psychological needs of those living with type 1 diabetes. The burden of type 1 diabetes extends far beyond checking blood glucose and administering insulin.”

January 10, 2018 Posted by | Cardiology, Diabetes, Epidemiology, Medicine, Nephrology, Neurology, Pharmacology, Psychiatry, Studies | Leave a comment

Random stuff

I have almost stopped posting posts like these, which has resulted in the accumulation of a very large number of links and studies which I figured I might like to blog at some point. This post is mainly an attempt to deal with the backlog – I won’t cover the material in too much detail.

i. Do Bullies Have More Sex? The answer seems to be a qualified yes. A few quotes:

“Sexual behavior during adolescence is fairly widespread in Western cultures (Zimmer-Gembeck and Helfland 2008) with nearly two thirds of youth having had sexual intercourse by the age of 19 (Finer and Philbin 2013). […] Bullying behavior may aid in intrasexual competition and intersexual selection as a strategy when competing for mates. In line with this contention, bullying has been linked to having a higher number of dating and sexual partners (Dane et al. 2017; Volk et al. 2015). This may be one reason why adolescence coincides with a peak in antisocial or aggressive behaviors, such as bullying (Volk et al. 2006). However, not all adolescents benefit from bullying. Instead, bullying may only benefit adolescents with certain personality traits who are willing and able to leverage bullying as a strategy for engaging in sexual behavior with opposite-sex peers. Therefore, we used two independent cross-sectional samples of older and younger adolescents to determine which personality traits, if any, are associated with leveraging bullying into opportunities for sexual behavior.”

“…bullying by males signal the ability to provide good genes, material resources, and protect offspring (Buss and Shackelford 1997; Volk et al. 2012) because bullying others is a way of displaying attractive qualities such as strength and dominance (Gallup et al. 2007; Reijntjes et al. 2013). As a result, this makes bullies attractive sexual partners to opposite-sex peers while simultaneously suppressing the sexual success of same-sex rivals (Gallup et al. 2011; Koh and Wong 2015; Zimmer-Gembeck et al. 2001). Females may denigrate other females, targeting their appearance and sexual promiscuity (Leenaars et al. 2008; Vaillancourt 2013), which are two qualities relating to male mate preferences. Consequently, derogating these qualities lowers a rivals’ appeal as a mate and also intimidates or coerces rivals into withdrawing from intrasexual competition (Campbell 2013; Dane et al. 2017; Fisher and Cox 2009; Vaillancourt 2013). Thus, males may use direct forms of bullying (e.g., physical, verbal) to facilitate intersexual selection (i.e., appear attractive to females), while females may use relational bullying to facilitate intrasexual competition, by making rivals appear less attractive to males.”

The study relies on the use of self-report data, which I find very problematic – so I won’t go into the results here. I’m not quite clear on how those studies mentioned in the discussion ‘have found self-report data [to be] valid under conditions of confidentiality’ – and I remain skeptical. You’ll usually want data from independent observers (e.g. teacher or peer observations) when analyzing these kinds of things. Note in the context of the self-report data problem that if there’s a strong stigma associated with being bullied (there often is, or bullying wouldn’t work as well), asking people if they have been bullied is not much better than asking people if they’re bullying others.

ii. Some topical advice that some people might soon regret not having followed, from the wonderful Things I Learn From My Patients thread:

“If you are a teenage boy experimenting with fireworks, do not empty the gunpowder from a dozen fireworks and try to mix it in your mother’s blender. But if you do decide to do that, don’t hold the lid down with your other hand and stand right over it. This will result in the traumatic amputation of several fingers, burned and skinned forearms, glass shrapnel in your face, and a couple of badly scratched corneas as a start. You will spend months in rehab and never be able to use your left hand again.”

iii. I haven’t talked about the AlphaZero-Stockfish match, but I was of course aware of it and did read a bit about that stuff. Here’s a reddit thread where one of the Stockfish programmers answers questions about the match. A few quotes:

“Which of the two is stronger under ideal conditions is, to me, neither particularly interesting (they are so different that it’s kind of like comparing the maximum speeds of a fish and a bird) nor particularly important (since there is only one of them that you and I can download and run anyway). What is super interesting is that we have two such radically different ways to create a computer chess playing entity with superhuman abilities. […] I don’t think there is anything to learn from AlphaZero that is applicable to Stockfish. They are just too different, you can’t transfer ideas from one to the other.”

“Based on the 100 games played, AlphaZero seems to be about 100 Elo points stronger under the conditions they used. The current development version of Stockfish is something like 40 Elo points stronger than the version used in Google’s experiment. There is a version of Stockfish translated to hand-written x86-64 assembly language that’s about 15 Elo points stronger still. This adds up to roughly half the Elo difference between AlphaZero and Stockfish shown in Google’s experiment.”

“It seems that Stockfish was playing with only 1 GB for transposition tables (the area of memory used to store data about the positions previously encountered in the search), which is way too little when running with 64 threads.” [I seem to recall a comp sci guy observing elsewhere that this was less than what was available to his smartphone version of Stockfish, but I didn’t bookmark that comment].

“The time control was a very artificial fixed 1 minute/move. That’s not how chess is traditionally played. Quite a lot of effort has gone into Stockfish’s time management. It’s pretty good at deciding when to move quickly, and when to spend a lot of time on a critical decision. In a fixed time per move game, it will often happen that the engine discovers that there is a problem with the move it wants to play just before the time is out. In a regular time control, it would then spend extra time analysing all alternative moves and trying to find a better one. When you force it to move after exactly one minute, it will play the move it already know is bad. There is no doubt that this will cause it to lose many games it would otherwise have drawn.”

iv. Thrombolytics for Acute Ischemic Stroke – no benefit found.

“Thrombolysis has been rigorously studied in >60,000 patients for acute thrombotic myocardial infarction, and is proven to reduce mortality. It is theorized that thrombolysis may similarly benefit ischemic stroke patients, though a much smaller number (8120) has been studied in relevant, large scale, high quality trials thus far. […] There are 12 such trials 1-12. Despite the temptation to pool these data the studies are clinically heterogeneous. […] Data from multiple trials must be clinically and statistically homogenous to be validly pooled.14 Large thrombolytic studies demonstrate wide variations in anatomic stroke regions, small- versus large-vessel occlusion, clinical severity, age, vital sign parameters, stroke scale scores, and times of administration. […] Examining each study individually is therefore, in our opinion, both more valid and more instructive. […] Two of twelve studies suggest a benefit […] In comparison, twice as many studies showed harm and these were stopped early. This early stoppage means that the number of subjects in studies demonstrating harm would have included over 2400 subjects based on originally intended enrollments. Pooled analyses are therefore missing these phantom data, which would have further eroded any aggregate benefits. In their absence, any pooled analysis is biased toward benefit. Despite this, there remain five times as many trials showing harm or no benefit (n=10) as those concluding benefit (n=2), and 6675 subjects in trials demonstrating no benefit compared to 1445 subjects in trials concluding benefit.”

“Thrombolytics for ischemic stroke may be harmful or beneficial. The answer remains elusive. We struggled therefore, debating between a ‘yellow’ or ‘red’ light for our recommendation. However, over 60,000 subjects in trials of thrombolytics for coronary thrombosis suggest a consistent beneficial effect across groups and subgroups, with no studies suggesting harm. This consistency was found despite a very small mortality benefit (2.5%), and a very narrow therapeutic window (1% major bleeding). In comparison, the variation in trial results of thrombolytics for stroke and the daunting but consistent adverse effect rate caused by ICH suggested to us that thrombolytics are dangerous unless further study exonerates their use.”

“There is a Cochrane review that pooled estimates of effect. 17 We do not endorse this choice because of clinical heterogeneity. However, we present the NNT’s from the pooled analysis for the reader’s benefit. The Cochrane review suggested a 6% reduction in disability […] with thrombolytics. This would mean that 17 were treated for every 1 avoiding an unfavorable outcome. The review also noted a 1% increase in mortality (1 in 100 patients die because of thrombolytics) and a 5% increase in nonfatal intracranial hemorrhage (1 in 20), for a total of 6% harmed (1 in 17 suffers death or brain hemorrhage).”

v. Suicide attempts in Asperger Syndrome. An interesting finding: “Over 35% of individuals with AS reported that they had attempted suicide in the past.”

Related: Suicidal ideation and suicide plans or attempts in adults with Asperger’s syndrome attending a specialist diagnostic clinic: a clinical cohort study.

“374 adults (256 men and 118 women) were diagnosed with Asperger’s syndrome in the study period. 243 (66%) of 367 respondents self-reported suicidal ideation, 127 (35%) of 365 respondents self-reported plans or attempts at suicide, and 116 (31%) of 368 respondents self-reported depression. Adults with Asperger’s syndrome were significantly more likely to report lifetime experience of suicidal ideation than were individuals from a general UK population sample (odds ratio 9·6 [95% CI 7·6–11·9], p<0·0001), people with one, two, or more medical illnesses (p<0·0001), or people with psychotic illness (p=0·019). […] Lifetime experience of depression (p=0·787), suicidal ideation (p=0·164), and suicide plans or attempts (p=0·06) did not differ significantly between men and women […] Individuals who reported suicide plans or attempts had significantly higher Autism Spectrum Quotient scores than those who did not […] Empathy Quotient scores and ages did not differ between individuals who did or did not report suicide plans or attempts (table 4). Patients with self-reported depression or suicidal ideation did not have significantly higher Autism Spectrum Quotient scores, Empathy Quotient scores, or age than did those without depression or suicidal ideation”.

The fact that people with Asperger’s are more likely to be depressed and contemplate suicide is consistent with previous observations that they’re also more likely to die from suicide – for example a paper I blogged a while back found that in that particular (large Swedish population-based cohort-) study, people with ASD were more than 7 times as likely to die from suicide than were the comparable controls.

Also related: Suicidal tendencies hard to spot in some people with autism.

This link has some great graphs and tables of suicide data from the US.

Also autism-related: Increased perception of loudness in autism. This is one of the ‘important ones’ for me personally – I am much more sound-sensitive than are most people.

vi. Early versus Delayed Invasive Intervention in Acute Coronary Syndromes.

“Earlier trials have shown that a routine invasive strategy improves outcomes in patients with acute coronary syndromes without ST-segment elevation. However, the optimal timing of such intervention remains uncertain. […] We randomly assigned 3031 patients with acute coronary syndromes to undergo either routine early intervention (coronary angiography ≤24 hours after randomization) or delayed intervention (coronary angiography ≥36 hours after randomization). The primary outcome was a composite of death, myocardial infarction, or stroke at 6 months. A prespecified secondary outcome was death, myocardial infarction, or refractory ischemia at 6 months. […] Early intervention did not differ greatly from delayed intervention in preventing the primary outcome, but it did reduce the rate of the composite secondary outcome of death, myocardial infarction, or refractory ischemia and was superior to delayed intervention in high-risk patients.”

vii. Some wikipedia links:

Behrens–Fisher problem.
Sailing ship tactics (I figured I had to read up on this if I were to get anything out of the Aubrey-Maturin books).
Anatomical terms of muscle.
Phatic expression (“a phatic expression […] is communication which serves a social function such as small talk and social pleasantries that don’t seek or offer any information of value.”)
Three-domain system.
Beringian wolf (featured).
Subdural hygroma.
Cayley graph.
Schur polynomial.
Solar neutrino problem.
Hadamard product (matrices).
True polar wander.
Newton’s cradle.

viii. Determinant versus permanent (mathematics – technical).

ix. Some years ago I wrote a few English-language posts about some of the various statistical/demographic properties of immigrants living in Denmark, based on numbers included in a publication by Statistics Denmark. I did it by translating the observations included in that publication, which was only published in Danish. I was briefly considering doing the same thing again when the 2017 data arrived, but I decided not to do it as I recalled that it took a lot of time to write those posts back then, and it didn’t seem to me to be worth the effort – but Danish readers might be interested to have a look at the data, if they haven’t already – here’s a link to the publication Indvandrere i Danmark 2017.

x. A banter blitz session with grandmaster Peter Svidler, who recently became the first Russian ever to win the Russian Chess Championship 8 times. He’s currently shared-second in the World Rapid Championship after 10 rounds and is now in the top 10 on the live rating list in both classical and rapid – seems like he’s had a very decent year.

xi. I recently discovered Dr. Whitecoat’s blog. The patient encounters are often interesting.

December 28, 2017 Posted by | Astronomy, autism, Biology, Cardiology, Chess, Computer science, History, Mathematics, Medicine, Neurology, Physics, Psychiatry, Psychology, Random stuff, Statistics, Studies, Wikipedia, Zoology | Leave a comment

A few diabetes papers of interest

i. Mechanisms and Management of Diabetic Painful Distal Symmetrical Polyneuropathy.

“Although a number of the diabetic neuropathies may result in painful symptomatology, this review focuses on the most common: chronic sensorimotor distal symmetrical polyneuropathy (DSPN). It is estimated that 15–20% of diabetic patients may have painful DSPN, but not all of these will require therapy. […] Although the exact pathophysiological processes that result in diabetic neuropathic pain remain enigmatic, both peripheral and central mechanisms have been implicated, and extend from altered channel function in peripheral nerve through enhanced spinal processing and changes in many higher centers. A number of pharmacological agents have proven efficacy in painful DSPN, but all are prone to side effects, and none impact the underlying pathophysiological abnormalities because they are only symptomatic therapy. The two first-line therapies approved by regulatory authorities for painful neuropathy are duloxetine and pregabalin. […] All patients with DSPN are at increased risk of foot ulceration and require foot care, education, and if possible, regular podiatry assessment.”

“The neuropathies are the most common long-term microvascular complications of diabetes and affect those with both type 1 and type 2 diabetes, with up to 50% of older type 2 diabetic patients having evidence of a distal neuropathy (1). These neuropathies are characterized by a progressive loss of nerve fibers affecting both the autonomic and somatic divisions of the nervous system. The clinical features of the diabetic neuropathies vary immensely, and only a minority are associated with pain. The major portion of this review will be dedicated to the most common painful neuropathy, chronic sensorimotor distal symmetrical polyneuropathy (DSPN). This neuropathy has major detrimental effects on its sufferers, confirming an increased risk of foot ulceration and Charcot neuroarthropathy as well as being associated with increased mortality (1).

In addition to DSPN, other rarer neuropathies may also be associated with painful symptoms including acute painful neuropathy that often follows periods of unstable glycemic control, mononeuropathies (e.g., cranial nerve palsies), radiculopathies, and entrapment neuropathies (e.g., carpal tunnel syndrome). By far the most common presentation of diabetic polyneuropathy (over 90%) is typical DSPN or chronic DSPN. […] DSPN results in insensitivity of the feet that predisposes to foot ulceration (1) and/or neuropathic pain (painful DSPN), which can be disabling. […] The onset of DSPN is usually gradual or insidious and is heralded by sensory symptoms that start in the toes and then progress proximally to involve the feet and legs in a stocking distribution. When the disease is well established in the lower limbs in more severe cases, there is upper limb involvement, with a similar progression proximally starting in the fingers. As the disease advances further, motor manifestations, such as wasting of the small muscles of the hands and limb weakness, become apparent. In some cases, there may be sensory loss that the patient may not be aware of, and the first presentation may be a foot ulcer. Approximately 50% of patients with DSPN experience neuropathic symptoms in the lower limbs including uncomfortable tingling (dysesthesia), pain (burning; shooting or “electric-shock like”; lancinating or “knife-like”; “crawling”, or aching etc., in character), evoked pain (allodynia, hyperesthesia), or unusual sensations (such as a feeling of swelling of the feet or severe coldness of the legs when clearly the lower limbs look and feel fine, odd sensations on walking likened to “walking on pebbles” or “walking on hot sand,” etc.). There may be marked pain on walking that may limit exercise and lead to weight gain. Painful DSPN is characteristically more severe at night and often interferes with normal sleep (3). It also has a major impact on the ability to function normally (both mental and physical functioning, e.g., ability to maintain work, mood, and quality of life [QoL]) (3,4). […] The unremitting nature of the pain can be distressing, resulting in mood disorders including depression and anxiety (4). The natural history of painful DSPN has not been well studied […]. However, it is generally believed that painful symptoms may persist over the years (5), occasionally becoming less prominent as the sensory loss worsens (6).”

“There have been relatively few epidemiological studies that have specifically examined the prevalence of painful DSPN, which range from 10–26% (79). In a recent study of a large cohort of diabetic patients receiving community-based health care in northwest England (n = 15,692), painful DSPN assessed using neuropathy symptom and disability scores was found in 21% (7). In one population-based study from Liverpool, U.K., the prevalence of painful DSPN assessed by a structured questionnaire and examination was estimated at 16% (8). Notably, it was found that 12.5% of these patients had never reported their symptoms to their doctor and 39% had never received treatment for their pain (8), indicating that there may be considerable underdiagnosis and undertreatment of painful neuropathic symptoms compared with other aspects of diabetes management such as statin therapy and management of hypertension. Risk factors for DSPN per se have been extensively studied, and it is clear that apart from poor glycemic control, cardiovascular risk factors play a prominent role (10): risk factors for painful DSPN are less well known.”

“A broad spectrum of presentations may occur in patients with DSPN, ranging from one extreme of the patient with very severe painful symptoms but few signs, to the other when patients may present with a foot ulcer having lost all sensation without ever having any painful or uncomfortable symptoms […] it is well recognized that the severity of symptoms may not relate to the severity of the deficit on clinical examination (1). […] Because DSPN is a diagnosis of exclusion, a careful clinical history and a peripheral neurological and vascular examination of the lower limbs are essential to exclude other causes of neuropathic pain and leg/foot pain such as peripheral vascular disease, arthritis, malignancy, alcohol abuse, spinal canal stenosis, etc. […] Patients with asymmetrical symptoms and/or signs (such as loss of an ankle jerk in one leg only), rapid progression of symptoms, or predominance of motor symptoms and signs should be carefully assessed for other causes of the findings.”

“The fact that diabetes induces neuropathy and that in a proportion of patients this is accompanied by pain despite the loss of input and numbness, suggests that marked changes occur in the processes of pain signaling in the peripheral and central nervous system. Neuropathic pain is characterized by ongoing pain together with exaggerated responses to painful and nonpainful stimuli, hyperalgesia, and allodynia. […] the changes seen suggest altered peripheral signaling and central compensatory changes perhaps driven by the loss of input. […] Very clear evidence points to the key role of changes in ion channels as a consequence of nerve damage and their roles in the disordered activity and transduction in damaged and intact fibers (50). Sodium channels depolarize neurons and generate an action potential. Following damage to peripheral nerves, the normal distribution of these channels along a nerve is disrupted by the neuroma and “ectopic” activity results from the accumulation of sodium channels at or around the site of injury. Other changes in the distribution and levels of these channels are seen and impact upon the pattern of neuronal excitability in the nerve. Inherited pain disorders arise from mutated sodium channels […] and polymorphisms in this channel impact on the level of pain in patients, indicating that inherited differences in channel function might explain some of the variability in pain between patients with DSPN (53). […] Where sodium channels act to generate action potentials, potassium channels serve as the molecular brakes of excitable cells, playing an important role in modulating neuronal hyperexcitability. The drug retigabine, a potassium channel opener acting on the channel (KV7, M-current) opener, blunts behavioral hypersensitivity in neuropathic rats (56) and also inhibits C and Aδ-mediated responses in dorsal horn neurons in both naïve and neuropathic rats (57), but has yet to reach the clinic as an analgesic”.

and C fibers terminate primarily in the superficial laminae of the dorsal horn where the large majority of neurons are nociceptive specific […]. Some of these neurons gain low threshold inputs after neuropathy and these cells project predominantly to limbic brain areas […] spinal cord neurons provide parallel outputs to the affective and sensory areas of the brain. Changes induced in these neurons by repeated noxious inputs underpin central sensitization where the resultant hyperexcitability of neurons leads to greater responses to all subsequent inputs — innocuous and noxious — expanded receptive fields and enhanced outputs to higher levels of the brain […] As a consequence of these changes in the sending of nociceptive information within the peripheral nerve and then the spinal cord, the information sent to the brain becomes amplified so that pain ratings become higher. Alongside this, the persistent input into the limbic brain areas such as the amygdala are likely to be causal in the comorbidities that patients often report due to ongoing painful inputs disrupting normal function and generating fear, depression, and sleep problems […]. Of course, many patients report that their pains are worse at night, which may be due to nocturnal changes in these central pain processing areas. […] overall, the mechanisms of pain in diabetic neuropathy extend from altered channel function in peripheral nerves through enhanced spinal processing and finally to changes in many higher centers”.

Pharmacological treatment of painful DSPN is not entirely satisfactory because currently available drugs are often ineffective and complicated by adverse events. Tricyclic compounds (TCAs) have been used as first-line agents for many years, but their use is limited by frequent side effects that may be central or anticholinergic, including dry mouth, constipation, sweating, blurred vision, sedation, and orthostatic hypotension (with the risk of falls particularly in elderly patients). […] Higher doses have been associated with an increased risk of sudden cardiac death, and caution should be taken in any patient with a history of cardiovascular disease (65). […] The selective serotonin noradrenalin reuptake inhibitors (SNRI) duloxetine and venlafaxine have been used for the management of painful DSPN (65). […] there have been several clinical trials involving pregabalin in painful DSPN, and these showed clear efficacy in management of painful DSPN (69). […] The side effects include dizziness, somnolence, peripheral edema, headache, and weight gain.”

A major deficiency in the area of the treatment of neuropathic pain in diabetes is the relative lack of comparative or combination studies. Virtually all previous trials have been of active agents against placebo, whereas there is a need for more studies that compare a given drug with an active comparator and indeed lower-dose combination treatments (64). […] The European Federation of Neurological Societies proposed that first-line treatments might comprise of TCAs, SNRIs, gabapentin, or pregabalin (71). The U.K. National Institute for Health and Care Excellence guidelines on the management of neuropathic pain in nonspecialist settings proposed that duloxetine should be the first-line treatment with amitriptyline as an alternative, and pregabalin as a second-line treatment for painful DSPN (72). […] this recommendation of duloxetine as the first-line therapy was not based on efficacy but rather cost-effectiveness. More recently, the American Academy of Neurology recommended that pregabalin is “established as effective and should be offered for relief of [painful DSPN] (Level A evidence)” (73), whereas venlafaxine, duloxetine, amitriptyline, gabapentin, valproate, opioids, and capsaicin were considered to be “probably effective and should be considered for treatment of painful DSPN (Level B evidence)” (63). […] this recommendation was primarily based on achievement of greater than 80% completion rate of clinical trials, which in turn may be influenced by the length of the trials. […] the International Consensus Panel on Diabetic Neuropathy recommended TCAs, duloxetine, pregabalin, and gabapentin as first-line agents having carefully reviewed all the available literature regarding the pharmacological treatment of painful DSPN (65), the final drug choice tailored to the particular patient based on demographic profile and comorbidities. […] The initial selection of a particular first-line treatment will be influenced by the assessment of contraindications, evaluation of comorbidities […], and cost (65). […] caution is advised to start at lower than recommended doses and titrate gradually.”

ii. Sex Differences in All-Cause and Cardiovascular Mortality, Hospitalization for Individuals With and Without Diabetes, and Patients With Diabetes Diagnosed Early and Late.

“A challenge with type 2 diabetes is the late diagnosis of the disease because many individuals who meet the criteria are often asymptomatic. Approximately 183 million people, or half of those who have diabetes, are unaware they have the disease (1). Furthermore, type 2 diabetes can be present for 9 to 12 years before being diagnosed and, as a result, complications are often present at the time of diagnosis (3). […] Cardiovascular disease (CVD) is the most common comorbidity associated with diabetes, and with 50% of those with diabetes dying of CVD it is the most common cause of death (1). […] Newfoundland and Labrador has the highest age-standardized prevalence of diabetes in Canada (2), and the age-standardized mortality and hospitalization rates for CVD, AMI, and stroke are some of the highest in the country (21,22). A better understanding of mortality and hospitalizations associated with diabetes for males and females is important to support diabetes prevention and management. Therefore, the objectives of this study were to compare the risk of all-cause, CVD, AMI, and stroke mortality and hospitalizations for males and females with and without diabetes and those with early and late diagnoses of diabetes. […] We conducted a population-based retrospective cohort study including 73,783 individuals aged 25 years or older in Newfoundland and Labrador, Canada (15,152 with diabetes; 9,517 with late diagnoses). […] mean age at baseline was 60.1 years (SD, 14.3 years). […] Diabetes was classified as being diagnosed “early” and “late” depending on when diabetes-related comorbidities developed. Individuals early in the disease course would not have any diabetes-related comorbidities at the time of their case dates. On the contrary, a late-diagnosed diabetes patient would have comorbidities related to diabetes at the time of diagnosis.”

“For males, 20.5% (n = 7,751) had diabetes, whereas 20.6% (n = 7,401) of females had diabetes. […] Males and females with diabetes were more likely to die, to be younger at death, to have a shorter survival time, and to be admitted to the hospital than males and females without diabetes (P < 0.01). When admitted to the hospital, individuals with diabetes stayed longer than individuals without diabetes […] Both males and females with late diagnoses were significantly older at the time of diagnosis than those with early diagnoses […]. Males and females with late diagnoses of diabetes were more likely to be deceased at the end of the study period compared with those with early diagnoses […]. Those with early diagnoses were younger at death compared with those with late diagnoses (P < 0.01); however, median survival time for both males and females with early diagnoses was significantly longer than that of those with late diagnoses (P < 0.01). During the study period, males and females with late diabetes diagnoses were more likely to be hospitalized (P < 0.01) and have a longer length of hospital stay compared with those with early diagnoses (P < 0.01).”

“[T]he hospitalization results show that an early diagnosis […] increase the risk of all-cause, CVD, and AMI hospitalizations compared with individuals without diabetes. After adjusting for covariates, males with late diabetes diagnoses had an increased risk of all-cause and CVD mortality and hospitalizations compared with males without diabetes. Similar findings were found for females. A late diabetes diagnosis was positively associated with CVD mortality (HR 6.54 [95% CI 4.80–8.91]) and CVD hospitalizations (5.22 [4.31–6.33]) for females, and the risk was significantly higher compared with their male counterparts (3.44 [2.47–4.79] and 3.33 [2.80–3.95]).”

iii. Effect of Type 1 Diabetes on Carotid Structure and Function in Adolescents and Young Adults.

I may have discussed some of the results of this study before, but a search of the blog told me that I have not covered the study itself. I thought it couldn’t hurt to add a link and a few highlights here.

“Type 1 diabetes mellitus causes increased carotid intima-media thickness (IMT) in adults. We evaluated IMT in young subjects with type 1 diabetes. […] Participants with type 1 diabetes (N = 402) were matched to controls (N = 206) by age, sex, and race or ethnicity. Anthropometric and laboratory values, blood pressure, and IMT were measured.”

“Youth with type 1 diabetes had thicker bulb IMT, which remained significantly different after adjustment for demographics and cardiovascular risk factors. […] Because the rate of progression of IMT in healthy subjects (mean age, 40 years) in the Bogalusa Heart study was 0.017–0.020 mm/year (4), our difference of 0.016 mm suggests that our type 1 diabetic subjects had a vascular age 1 year advanced from their chronological age. […] adjustment for HbA1c ablated the case-control difference in IMT, suggesting that the thicker carotid IMT in the subjects with diabetes could be attributed to diabetes-related hyperglycemia.”

“In the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study, progression of IMT over the course of 6 years was faster in subjects with type 1 diabetes, yielding a thicker final IMT in cases (5). There was no difference in IMT at baseline. However, DCCT/EDIC did not image the bulb, which is likely the earliest site of thickening according to the Bogalusa Heart Study […] Our analyses reinforce the importance of imaging the carotid bulb, often the site of earliest detectible subclinical atherosclerosis in youth. The DCCT/EDIC study demonstrated that the intensive treatment group had a slower progression of IMT (5) and that mean HbA1c levels explained most of the differences in IMT progression between treatment groups (12). One longitudinal study of youth found children with type 1 diabetes who had progression of IMT over the course of 2 years had higher HbA1c (13). Our data emphasize the role of diabetes-related hyperglycemia in increasing IMT in youth with type 1 diabetes. […] In summary, our study provides novel evidence that carotid thickness is increased in youth with type 1 diabetes compared with healthy controls and that this difference is not accounted for by traditional cardiovascular risk factors. Better control of diabetes-related hyperglycemia may be needed to reduce future cardiovascular disease.”

iv. Factors Associated With Microalbuminuria in 7,549 Children and Adolescents With Type 1 Diabetes in the T1D Exchange Clinic Registry.

“Elevated urinary albumin excretion is an early sign of diabetic kidney disease (DKD). The American Diabetes Association (ADA) recommends screening for microalbuminuria (MA) annually in people with type 1 diabetes after 10 years of age and 5 years of diabetes duration, with a diagnosis of MA requiring two of three tests to be abnormal (1). Early diagnosis of MA is important because effective treatments exist to limit the progression of DKD (1). However, although reduced rates of MA have been reported over the past few decades in some (24) but not all (5,6) studies, it has been suggested that the development of proteinuria has not been prevented but, rather, has been delayed by ∼10 years and that further improvements in care are needed (7).

Limited data exist on the frequency of a clinical diagnosis of MA in the pediatric population with type 1 diabetes in the U.S. Our aim was to use the data from the T1D Exchange clinic registry to assess factors associated with MA in 7,549 children and adolescents with type 1 diabetes.”

“The analysis cohort included 7,549 participants, with mean age of 13.8 ± 3.5 years (range 2 to 19), mean age at type 1 diabetes onset of 6.9 ± 3.9 years, and mean diabetes duration of 6.5 ± 3.7 years; 49% were female. The racial/ethnic distribution was 78% non-Hispanic white, 6% non-Hispanic black, 10% Hispanic, and 5% other. The average of all HbA1c levels (for up to the past 13 years) was 8.4 ± 1.3% (69 ± 13.7 mmol/mol) […]. MA was present in 329 of 7,549 (4.4%) participants, with a higher frequency associated with longer diabetes duration, higher mean glycosylated hemoglobin (HbA1c) level, older age, female sex, higher diastolic blood pressure (BP), and lower BMI […] increasing age [was] mainly associated with an increase in the frequency of MA when HbA1c was ≥9.5% (≥80 mmol/mol). […] MA was uncommon (<2%) among participants with HbA1c <7.5% (<58 mmol/mol). Of those with MA, only 36% were receiving ACEI/ARB treatment. […] Our results provide strong support for prior literature in emphasizing the importance of good glycemic and BP control, particularly as diabetes duration increases, in order to reduce the risk of DKD.

v. Secular Changes in the Age-Specific Prevalence of Diabetes Among U.S. Adults: 1988–2010.

“This study included 22,586 adults sampled in three periods of the National Health and Nutrition Examination Survey (1988–1994, 1999–2004, and 2005–2010). Diabetes was defined as having self-reported diagnosed diabetes or having a fasting plasma glucose level ≥126 mg/dL or HbA1c ≥6.5% (48 mmol/mol). […] The number of adults with diabetes increased by 75% from 1988–1994 to 2005–2010. After adjusting for sex, race/ethnicity, and education level, the prevalence of diabetes increased over the two decades across all age-groups. Younger adults (20–34 years of age) had the lowest absolute increase in diabetes prevalence of 1.0%, followed by middle-aged adults (35–64) at 2.7% and older adults (≥65) at 10.0% (all P < 0.001). Comparing 2005–2010 with 1988–1994, the adjusted prevalence ratios (PRs) by age-group were 2.3, 1.3, and 1.5 for younger, middle-aged, and older adults, respectively (all P < 0.05). After additional adjustment for body mass index (BMI), waist-to-height ratio (WHtR), or waist circumference (WC), the adjusted PR remained statistically significant only for adults ≥65 years of age.

CONCLUSIONS During the past two decades, the prevalence of diabetes increased across all age-groups, but adults ≥65 years of age experienced the largest increase in absolute change. Obesity, as measured by BMI, WHtR, or WC, was strongly associated with the increase in diabetes prevalence, especially in adults <65.”

The crude prevalence of diabetes changed from 8.4% (95% CI 7.7–9.1%) in 1988–1994 to 12.1% (11.3–13.1%) in 2005–2010, with a relative increase of 44.8% (28.3–61.3%) between the two survey periods. There was less change of prevalence of undiagnosed diabetes (P = 0.053). […] The estimated number (in millions) of adults with diabetes grew from 14.9 (95% CI 13.3–16.4) in 1988–1994 to 26.1 (23.8–28.3) in 2005–2010, resulting in an increase of 11.2 prevalent cases (a 75.5% [52.1–98.9%] increase). Younger adults contributed 5.5% (2.5–8.4%), middle-aged adults contributed 52.9% (43.4–62.3%), and older adults contributed 41.7% (31.9–51.4%) of the increased number of cases. In each survey time period, the number of adults with diabetes increased with age until ∼60–69 years; thereafter, it decreased […] the largest increase of cases occurred in middle-aged and older adults.”

vi. The Expression of Inflammatory Genes Is Upregulated in Peripheral Blood of Patients With Type 1 Diabetes.

“Although much effort has been devoted toward discoveries with respect to gene expression profiling in human T1D in the last decade (15), previous studies had serious limitations. Microarray-based gene expression profiling is a powerful discovery platform, but the results must be validated by an alternative technique such as real-time RT-PCR. Unfortunately, few of the previous microarray studies on T1D have been followed by a validation study. Furthermore, most previous gene expression studies had small sample sizes (<100 subjects in each group) that are not adequate for the human population given the expectation of large expression variations among individual subjects. Finally, the selection of appropriate reference genes for normalization of quantitative real-time PCR has a major impact on data quality. Most of the previous studies have used only a single reference gene for normalization. Ideally, gene transcription studies using real-time PCR should begin with the selection of an appropriate set of reference genes to obtain more reliable results (68).

We have previously carried out extensive microarray analysis and identified >100 genes with significantly differential expression between T1D patients and control subjects. Most of these genes have important immunological functions and were found to be upregulated in autoantibody-positive subjects, suggesting their potential use as predictive markers and involvement in T1D development (2). In this study, real-time RT-PCR was performed to validate a subset of the differentially expressed genes in a large sample set of 928 T1D patients and 922 control subjects. In addition to the verification of the gene expression associated with T1D, we also identified genes with significant expression changes in T1D patients with diabetes complications.

“Of the 18 genes analyzed here, eight genes […] had higher expression and three genes […] had lower expression in T1D patients compared with control subjects, indicating that genes involved in inflammation, immune regulation, and antigen processing and presentation are significantly altered in PBMCs from T1D patients. Furthermore, one adhesion molecule […] and three inflammatory genes mainly expressed by myeloid cells […] were significantly higher in T1D patients with complications (odds ratio [OR] 1.3–2.6, adjusted P value = 0.005–10−8), especially those patients with neuropathy (OR 4.8–7.9, adjusted P value <0.005). […] These findings suggest that inflammatory mediators secreted mainly by myeloid cells are implicated in T1D and its complications.

vii. Overexpression of Hemopexin in the Diabetic Eye – A new pathogenic candidate for diabetic macular edema.

“Diabetic retinopathy remains the leading cause of preventable blindness among working-age individuals in developed countries (1). Whereas proliferative diabetic retinopathy (PDR) is the commonest sight-threatening lesion in type 1 diabetes, diabetic macular edema (DME) is the primary cause of poor visual acuity in type 2 diabetes. Because of the high prevalence of type 2 diabetes, DME is the main cause of visual impairment in diabetic patients (2). When clinically significant DME appears, laser photocoagulation is currently indicated. However, the optimal period of laser treatment is frequently passed and, moreover, is not uniformly successful in halting visual decline. In addition, photocoagulation is not without side effects, with visual field loss and impairment of either adaptation or color vision being the most frequent. Intravitreal corticosteroids have been successfully used in eyes with persistent DME and loss of vision after the failure of conventional treatment. However, reinjections are commonly needed, and there are substantial adverse effects such as infection, glaucoma, and cataract formation. Intravitreal anti–vascular endothelial growth factor (VEGF) agents have also found an improvement of visual acuity and decrease of retinal thickness in DME, even in nonresponders to conventional treatment (3). However, apart from local side effects such as endophthalmitis and retinal detachment, the response to treatment of DME by VEGF blockade is not prolonged and is subject to significant variability. For all these reasons, new pharmacological treatments based on the understanding of the pathophysiological mechanisms of DME are needed.”

“Vascular leakage due to the breakdown of the blood-retinal barrier (BRB) is the main event involved in the pathogenesis of DME (4). However, little is known regarding the molecules primarily involved in this event. By means of a proteomic analysis, we have found that hemopexin was significantly increased in the vitreous fluid of patients with DME in comparison with PDR and nondiabetic control subjects (5). Hemopexin is the best characterized permeability factor in steroid-sensitive nephrotic syndrome (6,7). […] T cell–associated cytokines like tumor necrosis factor-α are able to enhance hemopexin production in mesangial cells in vitro, and this effect is prevented by corticosteroids (8). However, whether hemopexin also acts as a permeability factor in the BRB and its potential response to corticosteroids remains to be elucidated. […] the aims of the current study were 1) to compare hemopexin and hemopexin receptor (LDL receptor–related protein [LRP1]) levels in retina and in vitreous fluid from diabetic and nondiabetic patients, 2) to evaluate the effect of hemopexin on the permeability of outer and inner BRB in cell cultures, and 3) to determine whether anti-hemopexin antibodies and dexamethasone were able to prevent an eventual hemopexin-induced hyperpermeability.”

“In the current study, we […] confirmed our previous results obtained by a proteomic approach showing that hemopexin is higher in the vitreous fluid of diabetic patients with DME in comparison with diabetic patients with PDR and nondiabetic subjects. In addition, we provide the first evidence that hemopexin is overexpressed in diabetic eye. Furthermore, we have shown that hemopexin leads to the disruption of RPE [retinal pigment epithelium] cells, thus increasing permeability, and that this effect is prevented by dexamethasone. […] Our findings suggest that hemopexin can be considered a new candidate in the pathogenesis of DME and a new therapeutic target.”

viii. Relationship Between Overweight and Obesity With Hospitalization for Heart Failure in 20,985 Patients With Type 1 Diabetes.

“We studied patients with type 1 diabetes included in the Swedish National Diabetes Registry during 1998–2003, and they were followed up until hospitalization for HF, death, or 31 December 2009. Cox regression was used to estimate relative risks. […] Type 1 diabetes is defined in the NDR as receiving treatment with insulin only and onset at age 30 years or younger. These characteristics previously have been validated as accurate in 97% of cases (11). […] In a sample of 20,985 type 1 diabetic patients (mean age, 38.6 years; mean BMI, 25.0 kg/m2), 635 patients […] (3%) were admitted for a primary or secondary diagnosis of HF during a median follow-up of 9 years, with an incidence of 3.38 events per 1,000 patient-years (95% CI, 3.12–3.65). […] Cox regression adjusting for age, sex, diabetes duration, smoking, HbA1c, systolic and diastolic blood pressures, and baseline and intercurrent comorbidities (including myocardial infarction) showed a significant relationship between BMI and hospitalization for HF (P < 0.0001). In reference to patients in the BMI 20–25 kg/m2 category, hazard ratios (HRs) were as follows: HR 1.22 (95% CI, 0.83–1.78) for BMI <20 kg/m2; HR 0.94 (95% CI, 0.78–1.12) for BMI 25–30 kg/m2; HR 1.55 (95% CI, 1.20–1.99) for BMI 30–35 kg/m2; and HR 2.90 (95% CI, 1.92–4.37) for BMI ≥35 kg/m2.

CONCLUSIONS Obesity, particularly severe obesity, is strongly associated with hospitalization for HF in patients with type 1 diabetes, whereas no similar relation was present in overweight and low body weight.”

“In contrast to type 2 diabetes, obesity is not implicated as a causal factor in type 1 diabetes and maintaining normal weight is accordingly less of a focus in clinical practice of patients with type 1 diabetes. Because most patients with type 2 diabetes are overweight or obese and glucose levels can normalize in some patients after weight reduction, this is usually an important part of integrated diabetes care. Our findings indicate that given the substantial risk of cardiovascular disease in type 1 diabetic patients, it is crucial for clinicians to also address weight issues in type 1 diabetes. Because many patients are normal weight when diabetes is diagnosed, careful monitoring of weight with a view to maintaining normal weight is probably more essential than previously thought. Although overweight was not associated with an increased risk of HF, higher BMI levels probably increase the risk of future obesity. Our finding that 71% of patients with BMI >35 kg/m2 were women is potentially important, although this should be tested in other populations given that it could be a random finding. If not random, especially because the proportion was much higher than in the entire cohort (45%), then it may indicate that severe obesity is a greater problem in women than in men with type 1 diabetes.”

November 30, 2017 Posted by | Cardiology, Diabetes, Genetics, Molecular biology, Nephrology, Neurology, Ophthalmology, Pharmacology, Studies | Leave a comment

A few diabetes papers of interest

i. Thirty Years of Research on the Dawn Phenomenon: Lessons to Optimize Blood Glucose Control in Diabetes.

“More than 30 years ago in Diabetes Care, Schmidt et al. (1) defined “dawn phenomenon,” the night-to-morning elevation of blood glucose (BG) before and, to a larger extent, after breakfast in subjects with type 1 diabetes (T1D). Shortly after, a similar observation was made in type 2 diabetes (T2D) (2), and the physiology of glucose homeostasis at night was studied in normal, nondiabetic subjects (35). Ever since the first description, the dawn phenomenon has been studied extensively with at least 187 articles published as of today (6). […] what have we learned from the last 30 years of research on the dawn phenomenon? What is the appropriate definition, the identified mechanism(s), the importance (if any), and the treatment of the dawn phenomenon in T1D and T2D?”

“Physiology of glucose homeostasis in normal, nondiabetic subjects indicates that BG and plasma insulin concentrations remain remarkably flat and constant overnight, with a modest, transient increase in insulin secretion just before dawn (3,4) to restrain hepatic glucose production (4) and prevent hyperglycemia. Thus, normal subjects do not exhibit the dawn phenomenon sensu strictiori because they secrete insulin to prevent it.

In T1D, the magnitude of BG elevation at dawn first reported was impressive and largely secondary to the decrease of plasma insulin concentration overnight (1), commonly observed with evening administration of NPH or lente insulins (8) (Fig. 1). Even in early studies with intravenous insulin by the “artificial pancreas” (Biostator) (2), plasma insulin decreased overnight because of progressive inactivation of insulin in the pump (9). This artifact exaggerated the dawn phenomenon, now defined as need for insulin to limit fasting hyperglycemia (2). When the overnight waning of insulin was prevented by continuous subcutaneous insulin infusion (CSII) […] or by the long-acting insulin analogs (LA-IAs) (8), it was possible to quantify the real magnitude of the dawn phenomenon — 15–25 mg/dL BG elevation from nocturnal nadir to before breakfast […]. Nocturnal spikes of growth hormone secretion are the most likely mechanism of the dawn phenomenon in T1D (13,14). The observation from early pioneering studies in T1D (1012) that insulin sensitivity is higher after midnight until 3 a.m. as compared to the period 4–8 a.m., soon translated into use of more physiological replacement of basal insulin […] to reduce risk of nocturnal hypoglycemia while targeting fasting near-normoglycemia”.

“In T2D, identification of diurnal changes in BG goes back decades, but only quite recently fasting hyperglycemia has been attributed to a transient increase in hepatic glucose production (both glycogenolysis and gluconeogenesis) at dawn in the absence of compensatory insulin secretion (1517). Monnier et al. (7) report on the overnight (interstitial) glucose concentration (IG), as measured by continuous ambulatory IG monitoring, in three groups of 248 subjects with T2D […] Importantly, the dawn phenomenon had an impact on mean daily IG and A1C (mean increase of 0.39% [4.3 mmol/mol]), which was independent of treatment. […] Two messages from the data of Monnier et al. (7) are important. First, the dawn phenomenon is confirmed as a frequent event across the heterogeneous population of T2D independent of (oral) treatment and studied in everyday life conditions, not only in the setting of specialized clinical research units. Second, the article reaffirms that the primary target of treatment in T2D is to reestablish near-normoglycemia before and after breakfast (i.e., to treat the dawn phenomenon) to lower mean daily BG and A1C (8). […] the dawn phenomenon induces hyperglycemia not only before, but, to a larger extent, after breakfast as well (7,18). Over the years, fasting (and postbreakfast) hyperglycemia in T2D worsens as result of progressively impaired pancreatic B-cell function on the background of continued insulin resistance primarily at dawn (8,1518) and independently of age (19). Because it is an early metabolic abnormality leading over time to the vicious circle of “hyperglycemia begets hyperglycemia” by glucotoxicity and lipotoxicity, the dawn phenomenon in T2D should be treated early and appropriately before A1C continues to increase (20).”

“Oral medications do not adequately control the dawn phenomenon, even when given in combination (7,18). […] The evening replacement of basal insulin, which abolishes the dawn phenomenon by restraining hepatic glucose production and lipolysis (21), is an effective treatment as it mimics the physiology of glucose homeostasis in normal, nondiabetic subjects (4). Early use of basal insulin in T2D is an add-on option treatment after failure of metformin to control A1C <7.0% (20). However, […] it would be wise to consider initiation of basal insulin […] before — not after — A1C has increased well beyond 7.0%, as usually it is done in practice currently.”

ii. Peripheral Neuropathy in Adolescents and Young Adults With Type 1 and Type 2 Diabetes From the SEARCH for Diabetes in Youth Follow-up Cohort.

“Diabetic peripheral neuropathy (DPN) is among the most distressing of all the chronic complications of diabetes and is a cause of significant disability and poor quality of life (4). Depending on the patient population and diagnostic criteria, the prevalence of DPN among adults with diabetes ranges from 30 to 70% (57). However, there are insufficient data on the prevalence and predictors of DPN among the pediatric population. Furthermore, early detection and good glycemic control have been proven to prevent or delay adverse outcomes associated with DPN (5,8,9). Near-normal control of blood glucose beginning as soon as possible after the onset of diabetes may delay the development of clinically significant nerve impairment (8,9). […] The American Diabetes Association (ADA) recommends screening for DPN in children and adolescents with type 2 diabetes at diagnosis and 5 years after diagnosis for those with type 1 diabetes, followed by annual evaluations thereafter, using simple clinical tests (10). Since subclinical signs of DPN may precede development of frank neuropathic symptoms, systematic, preemptive screening is required in order to identify DPN in its earliest stages.

There are various measures that can be used for the assessment of DPN. The Michigan Neuropathy Screening Instrument (MNSI) is a simple, sensitive, and specific tool for the screening of DPN (11). It was validated in large independent cohorts (12,13) and has been widely used in clinical trials and longitudinal cohort studies […] The aim of this pilot study was to provide preliminary estimates of the prevalence of and factors associated with DPN among children and adolescents with type 1 and type 2 diabetes.”

“A total of 399 youth (329 with type 1 and 70 with type 2 diabetes) participated in the pilot study. Youth with type 1 diabetes were younger (mean age 15.7 ± 4.3 years) and had a shorter duration of diabetes (mean duration 6.2 ± 0.9 years) compared with youth with type 2 diabetes (mean age 21.6 ± 4.1 years and mean duration 7.6 ± 1.8 years). Participants with type 2 diabetes had a higher BMI z score and waist circumference, were more likely to be smokers, and had higher blood pressure and lipid levels than youth with type 1 diabetes (all P < 0.001). A1C, however, did not significantly differ between the two groups (mean A1C 8.8 ± 1.8% [73 ± 2 mmol/mol] for type 1 diabetes and 8.5 ± 2.9% [72 ± 3 mmol/mol] for type 2 diabetes; P = 0.5) but was higher than that recommended by the ADA for this age-group (A1C ≤7.5%) (10). The prevalence of DPN (defined as the MNSIE score >2) was 8.2% among youth with type 1 diabetes and 25.7% among those with type 2 diabetes. […] Youth with DPN were older and had a longer duration of diabetes, greater central obesity (increased waist circumference), higher blood pressure, an atherogenic lipid profile (low HDL cholesterol and marginally high triglycerides), and microalbuminuria. A1C […] was not significantly different between those with and without DPN (9.0% ± 2.0 […] vs. 8.8% ± 2.1 […], P = 0.58). Although nearly 37% of youth with type 2 diabetes came from lower-income families with annual income <25,000 USD per annum (as opposed to 11% for type 1 diabetes), socioeconomic status was not significantly associated with DPN (P = 0.77).”

“In the unadjusted logistic regression model, the odds of having DPN was nearly four times higher among those with type 2 diabetes compared with youth with type 1 diabetes (odds ratio [OR] 3.8 [95% CI 1.9–7.5, P < 0.0001). This association was attenuated, but remained significant, after adjustment for age and sex (OR 2.3 [95% CI 1.1–5.0], P = 0.03). However, this association was no longer significant (OR 2.1 [95% CI 0.3–15.9], P = 0.47) when additional covariates […] were added to the model […] The loss of the association between diabetes type and DPN with addition of covariates in the fully adjusted model could be due to power loss, given the small number of youth with DPN in the sample, or indicative of stronger associations between these covariates and DPN such that conditioning on them eliminates the observed association between DPN and diabetes type.”

“The prevalence of DPN among type 1 diabetes youth in our pilot study is lower than that reported by Eppens et al. (15) among 1,433 Australian adolescents with type 1 diabetes assessed by thermal threshold testing and VPT (prevalence of DPN 27%; median age and duration 15.7 and 6.8 years, respectively). A much higher prevalence was also reported among Danish (62.5%) and Brazilian (46%) cohorts of type 1 diabetes youth (16,17) despite a younger age (mean age among Danish children 13.7 years and Brazilian cohort 12.9 years). The prevalence of DPN among youth with type 2 diabetes (26%) found in our study is comparable to that reported among the Australian cohort (21%) (15). The wide ranges in the prevalence estimates of DPN among the young cannot solely be attributed to the inherent racial/ethnic differences in this population but could potentially be due to the differing criteria and diagnostic tests used to define and characterize DPN.”

“In our study, the duration of diabetes was significantly longer among those with DPN, but A1C values did not differ significantly between the two groups, suggesting that a longer duration with its sustained impact on peripheral nerves is an important determinant of DPN. […] Cho et al. (22) reported an increase in the prevalence of DPN from 14 to 28% over 17 years among 819 Australian adolescents with type 1 diabetes aged 11–17 years at baseline, despite improvements in care and minor improvements in A1C (8.2–8.7%). The prospective Danish Study Group of Diabetes in Childhood also found no association between DPN (assessed by VPT) and glycemic control (23).”

“In conclusion, our pilot study found evidence that the prevalence of DPN in adolescents with type 2 diabetes approaches rates reported in adults with diabetes. Several CVD risk factors such as central obesity, elevated blood pressure, dyslipidemia, and microalbuminuria, previously identified as predictors of DPN among adults with diabetes, emerged as independent predictors of DPN in this young cohort and likely accounted for the increased prevalence of DPN in youth with type 2 diabetes.

iii. Disturbed Eating Behavior and Omission of Insulin in Adolescents Receiving Intensified Insulin Treatment.

“Type 1 diabetes appears to be a risk factor for the development of disturbed eating behavior (DEB) (1,2). Estimates of the prevalence of DEB among individuals with type 1 diabetes range from 10 to 49% (3,4), depending on methodological issues such as the definition and measurement of DEB. Some studies only report the prevalence of full-threshold diagnoses of anorexia nervosa, bulimia nervosa, and eating disorders not otherwise specified, whereas others also include subclinical eating disorders (1). […] Although different terminology complicates the interpretation of prevalence rates across studies, the findings are sufficiently robust to indicate that there is a higher prevalence of DEB in type 1 diabetes compared with healthy controls. A meta-analysis reported a three-fold increase of bulimia nervosa, a two-fold increase of eating disorders not otherwise specified, and a two-fold increase of subclinical eating disorders in patients with type 1 diabetes compared with controls (2). No elevated rates of anorexia nervosa were found.”

“When DEB and type 1 diabetes co-occur, rates of morbidity and mortality are dramatically increased. A Danish study of comorbid type 1 diabetes and anorexia nervosa showed that the crude mortality rate at 10-year follow-up was 2.5% for type 1 diabetes and 6.5% for anorexia nervosa, but the rate increased to 34.8% when occurring together (the standardized mortality rates were 4.06, 8.86, and 14.5, respectively) (9). The presence of DEB in general also can severely impair metabolic control and advance the onset of long-term diabetes complications (4). Insulin reduction or omission is an efficient weight loss strategy uniquely available to patients with type 1 diabetes and has been reported in up to 37% of patients (1012). Insulin restriction is associated with poorer metabolic control, and previous research has found that self-reported insulin restriction at baseline leads to a three-fold increased risk of mortality at 11-year follow-up (10).

Few population-based studies have specifically investigated the prevalence of and relationship between DEBs and insulin restriction. The generalizability of existing research remains limited by relatively small samples and a lack of males. Further, many studies have relied on generic measures of DEBs, which may not be appropriate for use in individuals with type 1 diabetes. The Diabetes Eating Problem Survey–Revised (DEPS-R) is a newly developed and diabetes-specific screening tool for DEBs. A recent study demonstrated satisfactory psychometric properties of the Norwegian version of the DEPS-R among children and adolescents with type 1 diabetes 11–19 years of age (13). […] This study aimed to assess young patients with type 1 diabetes to assess the prevalence of DEBs and frequency of insulin omission or restriction, to compare the prevalence of DEB between males and females across different categories of weight and age, and to compare the clinical features of participants with and without DEBs and participants who restrict and do not restrict insulin. […] The final sample consisted of 770 […] children and adolescents with type 1 diabetes 11–19 years of age. There were 380 (49.4%) males and 390 (50.6%) females.”

27.7% of female and 9% of male children and adolescents with type 1 diabetes receiving intensified insulin treatment scored above the predetermined cutoff on the DEPS-R, suggesting a level of disturbed eating that warrants further attention by treatment providers. […] Significant differences emerged across age and weight categories, and notable sex-specific trends were observed. […] For the youngest (11–13 years) and underweight (BMI <18.5) categories, the proportion of DEB was <10% for both sexes […]. Among females, the prevalence of DEB increased dramatically with age to ∼33% among 14 to 16 year olds and to nearly 50% among 17 to 19 year olds. Among males, the rate remained low at 7% for 14 to 16 year olds and doubled to ∼15% for 17 to 19 year olds.

A similar sex-specific pattern was detected across weight categories. Among females, the prevalence of DEB increased steadily and significantly from 9% among the underweight category to 23% for normal weight, 42% for overweight, and 53% for the obese categories, respectively. Among males, ∼6–7% of both the underweight and normal weight groups reported DEB, with rates increasing to ∼15% for both the overweight and obese groups. […] When separated by sex, females scoring above the cutoff on the DEPS-R had significantly higher HbA1c (9.2% [SD, 1.9]) than females scoring below the cutoff (8.4% [SD, 1.3]; P < 0.001). The same trend was observed among males (9.2% [SD, 1.6] vs. 8.4% [SD, 1.3]; P < 0.01). […] A total of 31.6% of the participants reported using less insulin and 6.9% reported skipping their insulin dose entirely at least occasionally after overeating. When assessing the sexes separately, we found that 36.8% of females reported restricting and 26.2% reported skipping insulin because of overeating. The rates for males were 9.4 and 4.5%, respectively.”

“The finding that DEBs are common in young patients with type 1 diabetes is in line with previous literature (2). However, because of different assessment methods and different definitions of DEB, direct comparison with other studies is complicated, especially because this is the first study to have used the DEPS-R in a prevalence study. However, two studies using the original DEPS have reported similar results, with 37.9% (23) and 53.8% (24) of the participants reporting engaging in unhealthy weight control practices. In our study, females scored significantly higher than males, which is not surprising given previous studies demonstrating an increased risk of development of DEB in nondiabetic females compared with males. In addition, the prevalence rates increased considerably by increasing age and weight. A relationship between eating pathology and older age and higher BMI also has been demonstrated in previous research conducted in both diabetic and nondiabetic adolescent populations.”

“Consistent with existent literature (1012,27), we found a high frequency of insulin restriction. For example, Bryden et al. (11) assessed 113 males and females (aged 17–25 years) with type 1 diabetes and found that a total of 37% of the females (no males) reported a history of insulin omission or reduction for weight control purposes. Peveler et al. (12) investigated 87 females with type 1 diabetes aged 11–25 years, and 36% reported intentionally reducing or omitting their insulin doses to control their weight. Finally, Goebel-Fabbri et al. (10) examined 234 females 13–60 years of age and found that 30% reported insulin restriction. Similarly, 36.8% of the participants in our study reported reducing their insulin doses occasionally or more often after overeating.”

iv. Clinical Inertia in People With Type 2 Diabetes. A retrospective cohort study of more than 80,000 people.

“Despite good-quality evidence of tight glycemic control, particularly early in the disease trajectory (3), people with type 2 diabetes often do not reach recommended glycemic targets. Baseline characteristics in observational studies indicate that both insulin-experienced and insulin-naïve people may have mean HbA1c above the recommended target levels, reflecting the existence of patients with poor glycemic control in routine clinical care (810). […] U.K. data, based on an analysis reflecting previous NICE guidelines, show that it takes a mean of 7.7 years to initiate insulin after the start of the last OAD [oral antidiabetes drugs] (in people taking two or more OADs) and that mean HbA1c is ~10% (86 mmol/mol) at the time of insulin initiation (12). […] This failure to intensify treatment in a timely manner has been termed clinical inertia; however, data are lacking on clinical inertia in the diabetes-management pathway in a real-world primary care setting, and studies that have been carried out are, relatively speaking, small in scale (13,14). This retrospective cohort analysis investigates time to intensification of treatment in people with type 2 diabetes treated with OADs and the associated levels of glycemic control, and compares these findings with recommended treatment guidelines for diabetes.”

“We used the Clinical Practice Research Datalink (CPRD) database. This is the world’s largest computerized database, representing the primary care longitudinal records of >13 million patients from across the U.K. The CPRD is representative of the U.K. general population, with age and sex distributions comparable with those reported by the U.K. National Population Census (15). All information collected in the CPRD has been subjected to validation studies and been proven to contain consistent and high-quality data (16).”

“50,476 people taking one OAD, 25,600 people taking two OADs, and 5,677 people taking three OADs were analyzed. Mean baseline HbA1c (the most recent measurement within 6 months before starting OADs) was 8.4% (68 mmol/mol), 8.8% (73 mmol/mol), and 9.0% (75 mmol/mol) in people taking one, two, or three OADs, respectively. […] In people with HbA1c ≥7.0% (≥53 mmol/mol) taking one OAD, median time to intensification with an additional OAD was 2.9 years, whereas median time to intensification with insulin was >7.2 years. Median time to insulin intensification in people with HbA1c ≥7.0% (≥53 mmol/mol) taking two or three OADs was >7.2 and >7.1 years, respectively. In people with HbA1c ≥7.5% or ≥8.0% (≥58 or ≥64 mmol/mol) taking one OAD, median time to intensification with an additional OAD was 1.9 or 1.6 years, respectively; median time to intensification with insulin was >7.1 or >6.9 years, respectively. In those people with HbA1c ≥7.5% or ≥8.0% (≥58 or ≥64 mmol/mol) and taking two OADs, median time to insulin was >7.2 and >6.9 years, respectively; and in those people taking three OADs, median time to insulin intensification was >6.1 and >6.0 years, respectively.”

“By end of follow-up, treatment of 17.5% of people with HbA1c ≥7.0% (≥53 mmol/mol) taking three OADs was intensified with insulin, treatment of 20.6% of people with HbA1c ≥7.5% (≥58 mmol/mol) taking three OADs was intensified with insulin, and treatment of 22.0% of people with HbA1c ≥8.0% (≥64 mmol/mol) taking three OADs was intensified with insulin. There were minimal differences in the proportion of patients intensified between the groups. […] In people taking one OAD, the probability of an additional OAD or initiation of insulin was 23.9% after 1 year, increasing to 48.7% by end of follow-up; in people taking two OADs, the probability of an additional OAD or initiation of insulin was 11.4% after 1 year, increasing to 30.1% after 2 years; and in people taking three OADs, the probability of an additional OAD or initiation of insulin was 5.7% after 1 year, increasing to 12.0% by the end of follow-up […] Mean ± SD HbA1c in patients taking one OAD was 8.7 ± 1.6% in those intensified with an additional OAD (n = 14,605), 9.4 ± 2.3% (n = 1,228) in those intensified with insulin, and 8.7 ± 1.7% (n = 15,833) in those intensified with additional OAD or insulin. Mean HbA1c in patients taking two OADs was 8.8 ± 1.5% (n = 3,744), 9.8 ± 1.9% (n = 1,631), and 9.1 ± 1.7% (n = 5,405), respectively. In patients taking three OADs, mean HbA1c at intensification with insulin was 9.7 ± 1.6% (n = 514).”

This analysis shows that there is a delay in intensifying treatment in people with type 2 diabetes with suboptimal glycemic control, with patients remaining in poor glycemic control for >7 years before intensification of treatment with insulin. In patients taking one, two, or three OADs, median time from initiation of treatment to intensification with an additional OAD for any patient exceeded the maximum follow-up time of 7.2–7.3 years, dependent on subcohort. […] Despite having HbA1c levels for which diabetes guidelines recommend treatment intensification, few people appeared to undergo intensification (4,6,7). The highest proportion of people with clinical inertia was for insulin initiation in people taking three OADs. Consequently, these people experienced prolonged periods in poor glycemic control, which is detrimental to long-term outcomes.”

“Previous studies in U.K. general practice have shown similar findings. A retrospective study involving 14,824 people with type 2 diabetes from 154 general practice centers contributing to the Doctors Independent Network Database (DIN-LINK) between 1995 and 2005 observed that median time to insulin initiation for people prescribed multiple OADs was 7.7 years (95% CI 7.4–8.5 years); mean HbA1c before insulin was 9.85% (84 mmol/mol), which decreased by 1.34% (95% CI 1.24–1.44%) after therapy (12). A longitudinal observational study from health maintenance organization data in 3,891 patients with type 2 diabetes in the U.S. observed that, despite continued HbA1c levels >7% (>53 mmol/mol), people treated with sulfonylurea and metformin did not start insulin for almost 3 years (21). Another retrospective cohort study, using data from the Health Improvement Network database of 2,501 people with type 2 diabetes, estimated that only 25% of people started insulin within 1.8 years of multiple OAD failure, if followed for 5 years, and that 50% of people delayed starting insulin for almost 5 years after failure of glycemic control with multiple OADs (22). The U.K. cohort of a recent, 26-week observational study examining insulin initiation in clinical practice reported a large proportion of insulin-naïve people with HbA1c >9% (>75 mmol/mol) at baseline (64%); the mean HbA1c in the global cohort was 8.9% (74 mmol/mol) (10). Consequently, our analysis supports previous findings concerning clinical inertia in both U.K. and U.S. general practice and reflects little improvement in recent years, despite updated treatment guidelines recommending tight glycemic control.

v. Small- and Large-Fiber Neuropathy After 40 Years of Type 1 Diabetes. Associations with glycemic control and advanced protein glycation: the Oslo Study.

“How hyperglycemia may cause damage to the nervous system is not fully understood. One consequence of hyperglycemia is the generation of advanced glycation end products (AGEs) that can form nonenzymatically between glucose, lipids, and amino groups. It is believed that AGEs are involved in the pathophysiology of neuropathy. AGEs tend to affect cellular function by altering protein function (11). One of the AGEs, N-ε-(carboxymethyl)lysine (CML), has been found in excessive amounts in the human diabetic peripheral nerve (12). High levels of methylglyoxal in serum have been found to be associated with painful peripheral neuropathy (13). In recent years, differentiation of affected nerves is possible by virtue of specific function tests to distinguish which fibers are damaged in diabetic polyneuropathy: large myelinated (Aα, Aβ), small thinly myelinated (Aδ), or small nonmyelinated (C) fibers. […] Our aims were to evaluate large- and small-nerve fiber function in long-term type 1 diabetes and to search for longitudinal associations with HbA1c and the AGEs CML and methylglyoxal-derived hydroimidazolone.”

“27 persons with type 1 diabetes of 40 ± 3 years duration underwent large-nerve fiber examinations, with nerve conduction studies at baseline and years 8, 17, and 27. Small-fiber functions were assessed by quantitative sensory thresholds (QST) and intraepidermal nerve fiber density (IENFD) at year 27. HbA1c was measured prospectively through 27 years. […] Fourteen patients (52%) reported sensory symptoms. Nine patients reported symptoms of a sensory neuropathy (reduced sensibility in feet or impaired balance), while three of these patients described pain. Five patients had symptoms compatible with carpal tunnel syndrome (pain or paresthesias within the innervation territory of the median nerve […]. An additional two had no symptoms but abnormal neurological tests with absent tendon reflexes and reduced sensibility. A total of 16 (59%) of the patients had symptoms or signs of neuropathy. […] No patient with symptoms of neuropathy had normal neurophysiological findings. […] Abnormal autonomic testing was observed in 7 (26%) of the patients and occurred together with neurophysiological signs of peripheral neuropathy. […] Twenty-two (81%) had small-fiber dysfunction by QST. Heat pain thresholds in the foot were associated with hydroimidazolone and HbA1c. IENFD was abnormal in 19 (70%) and significantly lower in diabetic patients than in age-matched control subjects (4.3 ± 2.3 vs. 11.2 ± 3.5 mm, P < 0.001). IENFD correlated negatively with HbA1c over 27 years (r = −0.4, P = 0.04) and CML (r = −0.5, P = 0.01). After adjustment for age, height, and BMI in a multiple linear regression model, CML was still independently associated with IENFD.”

Our study shows that small-fiber dysfunction is more prevalent than large-fiber dysfunction in diabetic neuropathy after long duration of type 1 diabetes. Although large-fiber abnormalities were less common than small-fiber abnormalities, almost 60% of the participants had their large nerves affected after 40 years with diabetes. Long-term blood glucose estimated by HbA1c measured prospectively through 27 years and AGEs predict large- and small-nerve fiber function.”

vi. Subarachnoid Hemorrhage in Type 1 Diabetes. A prospective cohort study of 4,083 patients with diabetes.

“Subarachnoid hemorrhage (SAH) is a life-threatening cerebrovascular event, which is usually caused by a rupture of a cerebrovascular aneurysm. These aneurysms are mostly found in relatively large-caliber (≥1 mm) vessels and can often be considered as macrovascular lesions. The overall incidence of SAH has been reported to be 10.3 per 100,000 person-years (1), even though the variation in incidence between countries is substantial (1). Notably, the population-based incidence of SAH is 35 per 100,000 person-years in the adult (≥25 years of age) Finnish population (2). The incidence of nonaneurysmal SAH is globally unknown, but it is commonly believed that 5–15% of all SAHs are of nonaneurysmal origin. Prospective, long-term, population-based SAH risk factor studies suggest that smoking (24), high blood pressure (24), age (2,3), and female sex (2,4) are the most important risk factors for SAH, whereas diabetes (both types 1 and 2) does not appear to be associated with an increased risk of SAH (2,3).

An increased risk of cardiovascular disease is well recognized in people with diabetes. There are, however, very few studies on the risk of cerebrovascular disease in type 1 diabetes since most studies have focused on type 2 diabetes alone or together with type 1 diabetes. Cerebrovascular mortality in the 20–39-year age-group of people with type 1 diabetes is increased five- to sevenfold in comparison with the general population but accounts only for 15% of all cardiovascular deaths (5). Of the cerebrovascular deaths in patients with type 1 diabetes, 23% are due to hemorrhagic strokes (5). However, the incidence of SAH in type 1 diabetes is unknown. […] In this prospective cohort study of 4,083 patients with type 1 diabetes, we aimed to determine the incidence and characteristics of SAH.”

“52% [of participants] were men, the mean age was 37.4 ± 11.8 years, and the duration of diabetes was 21.6 ± 12.1 years at enrollment. The FinnDiane Study is a nationwide multicenter cohort study of genetic, clinical, and environmental risk factors for microvascular and macrovascular complications in type 1 diabetes. […] all type 1 diabetic patients in the FinnDiane database with follow-up data and without a history of stroke at baseline were included. […] Fifteen patients were confirmed to have an SAH, and thus the crude incidence of SAH was 40.9 (95% CI 22.9–67.4) per 100,000 person-years. Ten out of these 15 SAHs were nonaneurysmal SAHs […] The crude incidence of nonaneurysmal SAH was 27.3 (13.1–50.1) per 100,000 person-years. None of the 10 nonaneurysmal SAHs were fatal. […] Only 3 out of 10 patients did not have verified diabetic microvascular or macrovascular complications prior to the nonaneurysmal SAH event. […] Four patients with type 1 diabetes had a fatal SAH, and all these patients died within 24 h after SAH.”

The presented study results suggest that the incidence of nonaneurysmal SAH is high among patients with type 1 diabetes. […] It is of note that smoking type 1 diabetic patients had a significantly increased risk of nonaneurysmal and all-cause SAHs. Smoking also increases the risk of microvascular complications in insulin-treated diabetic patients, and these patients more often have retinal and renal microangiopathy than never-smokers (8). […] Given the high incidence of nonaneurysmal SAH in patients with type 1 diabetes and microvascular changes (i.e., diabetic retinopathy and nephropathy), the results support the hypothesis that nonaneurysmal SAH is a microvascular rather than macrovascular subtype of stroke.”

“Only one patient with type 1 diabetes had a confirmed aneurysmal SAH. Four other patients died suddenly due to an SAH. If these four patients with type 1 diabetes and a fatal SAH had an aneurysmal SAH, which, taking into account the autopsy reports and imaging findings, is very likely, aneurysmal SAH may be an exceptionally deadly event in type 1 diabetes. Population-based evidence suggests that up to 45% of people die during the first 30 days after SAH, and 18% die at emergency rooms or outside hospitals (9). […] Contrary to aneurysmal SAH, nonaneurysmal SAH is virtually always a nonfatal event (1014). This also supports the view that nonaneurysmal SAH is a disease of small intracranial vessels, i.e., a microvascular disease. Diabetic retinopathy, a chronic microvascular complication, has been associated with an increased risk of stroke in patients with diabetes (15,16). Embryonically, the retina is an outgrowth of the brain and is similar in its microvascular properties to the brain (17). Thus, it has been suggested that assessments of the retinal vasculature could be used to determine the risk of cerebrovascular diseases, such as stroke […] Most interestingly, the incidence of nonaneurysmal SAH was at least two times higher than the incidence of aneurysmal SAH in type 1 diabetic patients. In comparison, the incidence of nonaneurysmal SAH is >10 times lower than the incidence of aneurysmal SAH in the general adult population (21).”

vii. HbA1c and the Risks for All-Cause and Cardiovascular Mortality in the General Japanese Population.

Keep in mind when looking at these data that this is type 2 data. Type 1 diabetes is very rare in Japan and the rest of East Asia.

“The risk for cardiovascular death was evaluated in a large cohort of participants selected randomly from the overall Japanese population. A total of 7,120 participants (2,962 men and 4,158 women; mean age 52.3 years) free of previous CVD were followed for 15 years. Adjusted hazard ratios (HRs) and 95% CIs among categories of HbA1c (<5.0%, 5.0–5.4%, 5.5–5.9%, 6.0–6.4%, and ≥6.5%) for participants without treatment for diabetes and HRs for participants with diabetes were calculated using a Cox proportional hazards model.

RESULTS During the study, there were 1,104 deaths, including 304 from CVD, 61 from coronary heart disease, and 127 from stroke (78 from cerebral infarction, 25 from cerebral hemorrhage, and 24 from unclassified stroke). Relations to HbA1c with all-cause mortality and CVD death were graded and continuous, and multivariate-adjusted HRs for CVD death in participants with HbA1c 6.0–6.4% and ≥6.5% were 2.18 (95% CI 1.22–3.87) and 2.75 (1.43–5.28), respectively, compared with participants with HbA1c <5.0%. Similar associations were observed between HbA1c and death from coronary heart disease and death from cerebral infarction.

CONCLUSIONS High HbA1c levels were associated with increased risk for all-cause mortality and death from CVD, coronary heart disease, and cerebral infarction in general East Asian populations, as in Western populations.”

November 15, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Medicine, Neurology, Pharmacology, Studies | Leave a comment

A few diabetes papers of interest

i. Impact of Sex and Age at Onset of Diabetes on Mortality From Ischemic Heart Disease in Patients With Type 1 Diabetes.

“The study examined long-term IHD-specific mortality in a Finnish population-based cohort of patients with early-onset (0–14 years) and late-onset (15–29 years) T1D (n = 17,306). […] Follow-up started from the time of diagnosis of T1D and ended either at the time of death or at the end of 2011. […] ICD codes used to define patients as having T1D were 2500B–2508B, E10.0–E10.9, or O24.0. […] The median duration of diabetes was 24.4 (interquartile range 17.6–32.2) years. Over a 41-year study period totaling 433,782 person-years of follow-up, IHD accounted for 27.6% of the total 1,729 deaths. Specifically, IHD was identified as the cause of death in 478 patients, in whom IHD was the primary cause of death in 303 and a contributory cause in 175. […] Within the early-onset cohort, the average crude mortality rate in women was 33.3% lower than in men (86.3 [95% CI 65.2–112.1] vs. 128.2 [104.2–156.1] per 100,000 person-years, respectively, P = 0.02). When adjusted for duration of diabetes and the year of diabetes diagnosis, the mortality RR between women and men of 0.64 was only of borderline significance (P = 0.05) […]. In the late-onset cohort, crude mortality in women was, on average, only one-half that of men (117.2 [92.0–147.1] vs. 239.7 [210.9–271.4] per 100,000 person-years, respectively, P < 0.0001) […]. An RR of 0.43 remained highly significant after adjustment for duration of diabetes and year of diabetes diagnosis. Every year of duration of diabetes increased the risk 10–13%”

“The number of deaths from IHD in the patients with T1D were compared with the number of deaths from IHD in the background population, and the SMRs were calculated. For the total cohort (early and late onset pooled), the SMR was 7.2 (95% CI 6.4–8.0) […]. In contrast to the crude mortality rates, the SMRs were higher in women (21.6 [17.2–27.0]) than in men (5.8 [5.1–6.6]). When stratified by the age at onset of diabetes, the SMR was considerably higher in patients with early onset (16.9 [13.5–20.9]) than in those with late onset (5.9 [5.2–6.8]). In both the late- and the early-onset cohorts, there was a striking difference in the SMRs between women and men, and this was especially evident in the early-onset cohort where the SMR for women was 52.8 (36.3–74.5) compared with 12.1 (9.2–15.8) for men. This higher risk of death from IHD compared with the background population was evident in all women, regardless of age. However, the most pronounced effect was seen in women in the early-onset cohort <40 years of age, who were 83 times more likely to die of IHD than the age-matched women in the background population. This compares with a 37 times higher risk of death from IHD in women aged >40 years. The corresponding SMRs for men aged <40 and ≥40 years were 19.4 and 8.5, respectively.”

“Overall, the 40-year cumulative mortality for IHD was 8.8% (95% CI 7.9–9.7%) in all patients […] The 40-year cumulative IHD mortality in the early-onset cohort was 6.3% (4.8–7.8%) for men and 4.5% (3.1–5.9%) for women (P = 0.009 by log-rank test) […]. In the late-onset cohort, the corresponding cumulative mortality rates were 16.6% (14.3–18.7%) in men and 8.5% (6.5–10.4%) in women (P < 0.0001 by log-rank test)”

“The major findings of the current study are that women with early-onset T1D are exceptionally vulnerable to dying from IHD, which is especially evident in those receiving a T1D diagnosis during the prepubertal and pubertal years. Crude mortality rates were similar for women compared with men, highlighting the loss of cardioprotection in women. […] Although men of all ages have greater crude mortality rates than women regardless of the age at onset of T1D, the current study shows that mortality from IHD attributable to diabetes is much more pronounced in women than in men. […] it is conceivable that one of the underlying reasons for the loss of female sex as a protective factor against the development of CVD in the setting of diabetes may be the loss of ovarian hormones. Indeed, women with T1D have been shown to have reduced levels of plasma estradiol compared with age-matched nondiabetic women (23) possibly because of idiopathic ovarian failure or dysregulation of the hypothalamic-pituitary-ovarian axis.”

“One of the novelties of the present study is that the risk of death from IHD highly depends on the age at onset of T1D. The data show that the SMR was considerably higher in early-onset (0–14 years) than in late-onset (15–29 years) T1D in both sexes. […] the risk of dying from IHD is high in both women and men receiving a diagnosis of T1D at a young age.

ii. Microalbuminuria as a Risk Predictor in Diabetes: The Continuing Saga.

“The term “microalbuminuria” (MA) originated in 1964 when Professor Harry Keen first used it to signify a small amount of albumin in the urine of patients with type 1 diabetes (1). […] Whereas early research focused on the relevance of MA as a risk factor for diabetic kidney disease, research over the past 2 decades has shifted to examine whether MA is a true risk factor. To appreciate fully the contribution of MA to overall cardiorenal risk, it is important to distinguish between a risk factor and risk marker. A risk marker is a variable that identifies a pathophysiological state, such as inflammation or infection, and is not necessarily involved, directly or causally, in the genesis of a specified outcome (e.g., association of a cardiovascular [CV] event with fever, high-sensitivity C-reactive protein [hs-CRP], or MA). Conversely, a risk factor is involved clearly and consistently with the cause of a specified event (e.g., a CV event associated with persistently elevated blood pressure or elevated levels of LDL). Both a risk marker and a risk factor can predict an adverse outcome, but only one lies within the causal pathway of a disease. Moreover, a reduction (or alteration in a beneficial direction) of a risk factor (i.e., achievement of blood pressure goal) generally translates into a reduction of adverse outcomes, such as CV events; this is not necessarily true for a risk marker.”

“The data sources included in this article were all PubMed-referenced articles in English-language peer-reviewed journals since 1964. Studies selected had to have a minimum follow-up of 1 year; include at least 100 participants; be either a randomized trial, a systematic review, a meta-analysis, or a large observational cohort study in patients with any type of diabetes; or be trials of high CV risk that included at least 50% of patients with diabetes. All studies had to assess changes in MA tied to CV or CKD outcomes and not purely reflect changes in MA related to blood pressure, unless they were mechanistic studies. On the basis of these inclusion criteria, 31 studies qualified and provide the data used for this review.”

“Early studies in patients with diabetes supported the concept that as MA increases to higher levels, the risk of CKD progression and CV risk also increases […]. Moreover, evidence from epidemiological studies in patients with diabetes suggested that the magnitude of urine albumin excretion should be viewed as a continuum of CV risk, with the lower the albumin excretion, the lower the CV risk (15,16). However, MA values can vary daily up to 100% (11). These large biological variations are a result of a variety of conditions, with a central core tied to inflammation associated with factors ranging from increased blood pressure variability, high blood glucose levels, high LDL cholesterol, and high uric acid levels to high sodium ingestion, smoking, and exercise (17) […]. Additionally, any febrile illness, regardless of etiology, will increase urine albumin excretion (18). Taken together, these data support the concept that MA is highly variable and that values over a short time period (i.e., 3–6 months) are meaningless in predicting any CV or kidney disease outcome.”

“Initial studies to understand the mechanisms of MA examined changes in glomerular membrane permeability as a key determinant in patients with diabetes […]. Many factors affect the genesis and level of MA, most of which are linked to inflammatory conditions […]. A good evidence base, however, supports the concept that MA directly reflects the amount of inflammation and vascular “leakiness” present in patients with diabetes (16,18,19).

More recent studies have found a number of other factors that affect glomerular permeability by modifying cytokines that affect permeability. Increased amounts of glycated albumin reduce glomerular nephrin and increase vascular endothelial growth factor (20). Additionally, increases in sodium intake (21) as well as intraglomerular pressure secondary to high protein intake or poorly controlled blood pressure (22,23) increase glomerular permeability in diabetes and, hence, MA levels.

In individuals with diabetes, albumin is glycated and associated with the generation of reactive oxygen species. In addition, many other factors such as advanced glycation end products, reactive oxygen species, and other cellular toxins contribute to vascular injury. Once such injury occurs, the effect of pressor hormones, such as angiotensin II, is magnified, resulting in a faster progression of vascular injury. The end result is direct injury to the vascular smooth muscle cells, endothelial cells, and visceral epithelial cells (podocytes) of the glomerular capillary wall membrane as well as to the proximal tubular cells and podocyte basement membrane of the nephron (20,24,25). All these contribute to the development of MA. […] better glycemic control is associated with far lower levels of inflammatory markers (31).”

“MA is accepted as a CV risk marker for myocardial infarction and stroke, regardless of diabetes status. […] there is good evidence in those with type 2 diabetes that the presence of MA >100 mg/day is associated with higher CV events and greater likelihood of kidney disease development (6). Evidence for this association comes from many studies and meta-analyses […] a meta-analysis by Perkovic et al. (37) demonstrated a dose-response relationship between the level of albuminuria and CV risk. In this meta-analysis, individuals with MA were at 50% greater risk of coronary heart disease (risk ratio 1.47 [95% CI 1.30–1.66]) than those without. Those with macroalbuminuria (i.e., >300 mg/day) had more than a twofold risk for coronary heart disease (risk ratio 2.17 [95% CI 1.87–2.52]) (37). Despite these data indicating a higher CV risk in patients with MA regardless of diabetes status and other CV risk factors, there is no consensus that the addition of MA to conventional CV risk stratification for the general population (e.g., Framingham or Reynolds scoring systems) is of any clinical value, and that includes patients with diabetes (38).”

“Given that MA was evaluated in a post hoc manner in almost all interventional studies, it is likely that the reduction in MA simply reflects the effects of either renin-angiotensin system (RAS) blockade on endothelial function or significant blood pressure reduction rather than the MA itself being implicated as a CV disease risk factor (18). […] associations of lowering MA with angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) does not prove a direct benefit on CV event lowering associated with MA reduction in diabetes. […] Four long-term, appropriately powered trials demonstrated an inverse relationship between reductions in MA and primary event rates for CV events […]. Taken together, these studies support the concept that MA is a risk marker in diabetes and is consistent with data of other inflammatory markers, such as hs-CRP [here’s a relevant link – US], such that the higher the level, the higher the risk (15,39,42). The importance of MA as a CV risk marker is exemplified further by another meta-analysis that showed that MA has a similar magnitude of CV risk as hs-CRP and is a better predictor of CV events (43). Thus, the data supporting MA as a risk marker for CV events are relatively consistent, clearly indicate that an association exists, and help to identify the presence of underlying inflammatory states, regardless of etiology.”

“In people with early stage nephropathy (i.e., stage 2 or 3a [GFR 45–89 mL/min/1.73 m2]) and MA, there is no clear benefit on slowing GFR decline by reducing MA with drugs that block the RAS independent of lowering blood pressure (16). This is exemplified by many trials […]. Thus, blood pressure lowering is the key goal for all patients with early stage nephropathy associated with normoalbuminuria or MA. […] When albuminuria levels are in the very high or macroalbuminuria range (i.e., >300 mg/day), it is accepted that the patient has CKD and is likely to progress ultimately to ESRD, unless they die of a CV event (39,52). However, only one prospective randomized trial evaluated the role of early intervention to reduce blood pressure with an ACE inhibitor versus a calcium channel blocker in CKD progression by assessing change in MA and creatinine clearance in people with type 2 diabetes (Appropriate Blood Pressure Control in Diabetes [ABCD] trial) (23). After >7 years of follow-up, there was no relationship between changes in MA and CKD progression. Moreover, there was regression to the mean of MA.”

“Many observational studies used development of MA as indicating the presence of early stage CKD. Early studies by the individual groups of Mogensen and Parving demonstrated a relationship between increases in MA and progression to nephropathy in type 1 diabetes. These groups also showed that use of ACE inhibitors, blood pressure reduction, and glucose control reduced MA (9,58,59). However, more recent studies in both type 1 and type 2 diabetes demonstrated that only a subgroup of patients progress from MA to >300 mg/day albuminuria, and this subgroup accounts for those destined to progress to ESRD (29,32,6063). Thus, the presence of MA alone is not predictive of CKD progression. […] some patients with type 2 diabetes progress to ESRD without ever having developed albuminuria levels of ≥300 mg/day (67). […] Taken together, data from outcome trials, meta-analyses, and observations demonstrate that MA [Micro-Albuminuria] alone is not synonymous with the presence of clearly defined CKD [Chronic Kidney Disease] in diabetes, although it is used as part of the criteria for the diagnosis of CKD in the most recent CKD classification and staging (71). Note that only a subgroup of ∼25–30% of people with diabetes who also have MA will likely progress to more advanced stages of CKD. Predictors of progression to ESRD, apart from family history, and many years of poor glycemic and blood pressure control are still not well defined. Although there are some genetic markers, such as CUBN and APOL1, their use in practice is not well established.”

“In the context of the data presented in this article, MA should be viewed as a risk marker associated with an increase in CV risk and for kidney disease, but its presence alone does not indicate established kidney disease, especially if the eGFR is well above 60 mL/min/1.73 m2. Increases in MA, with blood pressure and other CV risk factors controlled, are likely but not proven to portend a poor prognosis for CKD progression over time. Achieving target blood pressure (<140/80 mmHg) and target HbA1c (<7%) should be priorities in treating patients with MA. Recent guidelines from both the American Diabetes Association and the National Kidney Foundation provide a strong recommendation for using agents that block the RAS, such as ACE inhibitors and ARBs, as part of the regimen for those with albuminuria levels >300 mg/day but not MA (73). […] maximal antialbuminuric effects will [however] not be achieved with these agents unless a low-sodium diet is strictly followed.”

iii. The SEARCH for Diabetes in Youth Study: Rationale, Findings, and Future Directions.

“The SEARCH for Diabetes in Youth (SEARCH) study was initiated in 2000, with funding from the Centers for Disease Control and Prevention and support from the National Institute of Diabetes and Digestive and Kidney Diseases, to address major knowledge gaps in the understanding of childhood diabetes. SEARCH is being conducted at five sites across the U.S. and represents the largest, most diverse study of diabetes among U.S. youth. An active registry of youth diagnosed with diabetes at age <20 years allows the assessment of prevalence (in 2001 and 2009), annual incidence (since 2002), and trends by age, race/ethnicity, sex, and diabetes type. Prevalence increased significantly from 2001 to 2009 for both type 1 and type 2 diabetes in most age, sex, and race/ethnic groups. SEARCH has also established a longitudinal cohort to assess the natural history and risk factors for acute and chronic diabetes-related complications as well as the quality of care and quality of life of persons with diabetes from diagnosis into young adulthood. […] This review summarizes the study methods, describes key registry and cohort findings and their clinical and public health implications, and discusses future directions.”

“SEARCH includes a registry and a cohort study […]. The registry study identifies incident cases each year since 2002 through the present with ∼5.5 million children <20 years of age (∼6% of the U.S. population <20 years) under surveillance annually. Approximately 3.5 million children <20 years of age were under surveillance in 2001 at the six SEARCH recruitment centers, with approximately the same number at the five centers under surveillance in 2009.”

“The prevalence of all types of diabetes was 1.8/1,000 youth in 2001 and was 2.2/1,000 youth in 2009, which translated to at least 154,000 children/youth in the U.S. with diabetes in 2001 (5) and at least 192,000 in 2009 (6). Overall, between 2001 and 2009, prevalence of type 1 diabetes in youth increased by 21.1% (95% CI 15.6–27.0), with similar increases for boys and girls and in most racial/ethnic and age groups (2) […]. The prevalence of type 2 diabetes also increased significantly over the same time period by 30.5% (95% CI 17.3–45.1), with increases observed in both sexes, 10–14- and 15–19-year-olds, and among Hispanic and non-Hispanic white and African American youth (2). These data on changes in type 2 are consistent with smaller U.S. studies (711).”

“The incidence of diabetes […] in 2002 to 2003 was 24.6/100,000/year (12), representing ∼15,000 new patients every year with type 1 diabetes and 3,700 with type 2 diabetes, increasing to 18,436 newly diagnosed type 1 and 5,089 with type 2 diabetes in 2008 to 2009 (13). Among non-Hispanic white youth, the incidence of type 1 diabetes increased by 2.7% (95% CI 1.2–4.3) annually between 2002 and 2009. Significant increases were observed among all age groups except the youngest age group (0–4 years) (14). […] The underlying factors responsible for this increase have not yet been identified.”

Over 50% of youth are hospitalized at diabetes onset, and ∼30% of children newly diagnosed with diabetes present with diabetic ketoacidosis (DKA) (19). Prevalence of DKA at diagnosis was three times higher among youth with type 1 diabetes (29.4%) compared with youth with type 2 diabetes (9.7%) and was lowest in Asian/Pacific Islanders (16.2%) and highest among Hispanics (27.0%).”

“A significant proportion of youth with diabetes, particularly those with type 2 diabetes, have very poor glycemic control […]: 17% of youth with type 1 diabetes and 27% of youth with type 2 diabetes had A1C levels ≥9.5% (≥80 mmol/mol). Minority youth were significantly more likely to have higher A1C levels compared with non-Hispanic white youth, regardless of diabetes type. […] Optimal care is an important component of successful long-term management for youth with diabetes. While there are high levels of adherence for some diabetes care indicators such as blood pressure checks (95%), urinary protein tests (83%), and lipid assessments (88%), approximately one-third of youth had no documentation of eye or A1C values at appropriate intervals and therefore were not meeting the American Diabetes Association (ADA)-recommended screening for diabetic control and complications (40). Participants ≥18 years old, particularly those with type 2 diabetes, and minority youth with type 1 diabetes had fewer tests of all kinds performed. […] Despite current treatment options, the prevalence of poor glycemic control is high, particularly among minority youth. Our initial findings suggest that a substantial number of youth with diabetes will develop serious, debilitating complications early in life, which is likely to have significant implications for their quality of life, as well as economic and health care implications.”

“Because recognition of the broader spectrum of diabetes in children and adolescents is recent, there are no gold-standard definitions for differentiating the types of diabetes in this population, either for research or clinical purposes or for public health surveillance. The ADA classification of diabetes as type 1 and type 2 does not include operational definitions for the specific etiologic markers of diabetes type, such as types and numbers of diabetes autoantibodies or measures of insulin resistance, hallmarks of type 1 and 2 diabetes, respectively (43). Moreover, obese adolescents with a clinical phenotype suggestive of type 2 diabetes can present with ketoacidosis (44) or have evidence of autoimmunity (45).”

“Using the ADA framework (43), we operationalized definitions of two main etiologic markers, autoimmunity and insulin sensitivity, to identify four etiologic subgroups based on the presence or absence of markers. Autoimmunity was based on presence of one or more diabetes autoantibodies (GAD65 and IA2). Insulin sensitivity was estimated using clinical variables (A1C, triglyceride level, and waist circumference) from a formula that was highly associated with estimated insulin sensitivity measured using a euglycemic-hyperinsulinemic clamp among youth with type 1 and 2 and normal control subjects (46). Participants were categorized as insulin resistant […] and insulin sensitive (47). Using this approach, 54.5% of SEARCH cases were classified as typical type 1 (autoimmune, insulin-sensitive) diabetes, while 15.9% were classified as typical type 2 (nonautoimmune, insulin-resistant) diabetes. Cases that were classified as autoimmune and insulin-resistant likely represent individuals with type 1 autoimmune diabetes and concomitant obesity, a phenotype becoming more prevalent as a result of the recent increase in the frequency of obesity, but is unlikely to be a distinct etiologic entity.”

“Ten percent of SEARCH participants had no evidence of either autoimmunity or insulin resistance and thus require additional testing, including additional measurements of diabetes-related autoantibodies (only two antibodies were measured in SEARCH) as well as testing for monogenic forms of diabetes to clarify etiology. Among antibody-negative youth, 8% of those tested had a mutation in one or more of the hepatocyte nuclear factor-1α (HNF-1α), glucokinase, and HNF-4α genes, an estimated monogenic diabetes population prevalence of at least 1.2% (48).”

iv. Does the Prevailing Hypothesis That Small-Fiber Dysfunction Precedes Large-Fiber Dysfunction Apply to Type 1 Diabetic Patients?

The short answer is ‘yes, it does’. Some observations from the paper:

“Diabetic sensorimotor polyneuropathy (DSP) is a common complication of diabetes, affecting 28–55% of patients (1). A prospective Finnish study found evidence of probable or definite neuropathy in 8.3% of diabetic patients at the time of diagnosis, 16.7% after 5 years, and 41.9% after 10 years (2). Diabetes-related peripheral neuropathy results in serious morbidity, including chronic neuropathic pain, leg weakness and falls, sensory loss and foot ulceration, and amputation (3). Health care costs associated with diabetic neuropathy were estimated at $10.9 billion in the U.S. in 2003 (4). However, despite the high prevalence of diabetes and DSP, and the important public health implications, there is a lack of serum- or tissue-based biomarkers to diagnose and follow patients with DSP longitudinally. Moreover, numerous attempts at treatment have yielded negative results.”

“DSP is known to cause injury to both large-diameter, myelinated (Aα and Aβ) fibers and small-diameter, unmyelinated nerve (Aδ and C) fibers; however, the sequence of nerve fiber damage remains uncertain. While earlier reports seemed to indicate simultaneous loss of small- and large-diameter nerve fibers, with preserved small/large ratios (5), more recent studies have suggested the presence of early involvement of small-diameter Aδ and C fibers (611). Some suggest a temporal relationship of small-fiber impairment preceding that of large fibers. For example, impairment in the density of the small intraepidermal nerve fibers in symptomatic patients with impaired glucose tolerance (prediabetes) have been observed in the face of normal large-fiber function, as assessed by nerve conduction studies (NCSs) (9,10). In addition, surveys of patients with DSP have demonstrated an overwhelming predominance of sensory and autonomic symptoms, as compared with motor weakness. Again, this has been interpreted as indicative of preferential small-fiber dysfunction (12). Though longitudinal studies are limited, such studies have lead to the current prevailing hypothesis for the natural history of DSP that measures of small-fiber morphology and function decline prior to those of large fibers. One implication of this hypothesis is that small-fiber testing could serve as an earlier, subclinical primary end point in clinical trials investigating interventions for DSP (13).

The hypothesis described above has been investigated exclusively in type 2 diabetic or prediabetic patients. Through the study of a cohort of healthy volunteers and type 1 diabetic subjects […], we had the opportunity to evaluate in cross-sectional analysis the relationship between measures of large-fiber function and small-fiber structure and function. Under the hypothesis that small-fiber abnormalities precede large-fiber dysfunction in the natural history of DSP, we sought to determine if: 1) the majority of subjects who meet criteria for large-fiber dysfunction have concurrent evidence of small-fiber dysfunction and 2) the subset of patients without DSP includes a spectrum with normal small-fiber tests (indicating lack of initiation of nerve injury) as well as abnormal small-fiber tests (indicating incipient DSP).”

“Overall, 57 of 131 (43.5%) type 1 diabetic patients met DSP criteria, and 74 of 131 (56.5%) did not meet DSP criteria. Abnormality of CCM [link] was present in 30 of 57 (52.6%) DSP patients and 6 of 74 (8.1%) type 1 diabetic patients without DSP. Abnormality of CDT [Cooling Detection Thresholds, relevant link] was present in 47 of 56 (83.9%) DSP patients and 17 of 73 (23.3%) without DSP. Abnormality of LDIflare [laser Doppler imaging of heat-evoked flare] was present in 30 of 57 (52.6%) DSP patients and 20 of 72 (27.8%) without DSP. Abnormality of HRV [Heart Rate Variability] was present in 18 of 45 (40.0%) DSP patients and 6 of 70 (8.6%) without DSP. […] sensitivity analysis […] revealed that abnormality of any one of the four small-fiber measures was present in 55 of 57 (96.5%) DSP patients […] and 39 of 74 (52.7%) type 1 diabetic patients without DSP. Similarly, abnormality of any two of the four small-fiber measures was present in 43 of 57 (75.4%) DSP patients […] and 9 of 74 (12.2%) without DSP. Finally, abnormality of either CDT or CCM (with these two tests selected based on their high reliability) was noted in 53 of 57 (93.0%) DSP patients and 21 of 74 (28.4%) patients without DSP […] When DSP was defined based on symptoms and signs plus abnormal sural SNAP [sensory nerve action potential] amplitude or conduction velocity, there were 68 of 131 patients who met DSP criteria and 63 of 131 who did not. Abnormality of any one of the four small-fiber measures was present in 63 of 68 (92.6%) DSP patients and 31 of 63 (49.2%) type 1 diabetic patients without DSP. […] Finally, if DSP was defined based on clinical symptoms and signs alone, with TCNS ≥5, there were 68 of 131 patients who met DSP criteria and 63 of 131 who did not. Abnormality of any one of the four small-fiber measures was present in 62 of 68 (91.2%) DSP patients and 32 of 63 (50.8%) type 1 diabetic patients without DSP.”

“Qualitative analysis of contingency tables shows that the majority of patients with DSP have concurrent evidence of small-fiber dysfunction, and patients without DSP include a spectrum with normal small-fiber tests (indicating lack of initiation of nerve injury) as well as abnormal small-fiber tests. Evidence of isolated large-fiber injury was much less frequent […]. These findings suggest that small-fiber damage may herald the onset of DSP in type 1 diabetes. In addition, the above findings remained true when alternative definitions of DSP were explored in a sensitivity analysis. […] The second important finding was the linear relationships noted between small-fiber structure and function tests (CDT, CNFL, LDIflare, and HRV) […] and the number of NCS abnormalities (a marker of large-fiber function). This might indicate that once the process of large-fiber nerve injury in DSP has begun, damage to large and small nerve fibers occurs simultaneously.”

v. Long-Term Complications and Mortality in Young-Onset Diabetes.

“Records from the Royal Prince Alfred Hospital Diabetes Clinical Database, established in 1986, were matched with the Australian National Death Index to establish mortality outcomes for all subjects until June 2011. Clinical and mortality outcomes in 354 patients with T2DM, age of onset between 15 and 30 years (T2DM15–30), were compared with T1DM in several ways but primarily with 470 patients with T1DM with a similar age of onset (T1DM15–30) to minimize the confounding effect of age on outcome.

RESULTS For a median observation period of 21.4 (interquartile range 14–30.7) and 23.4 (15.7–32.4) years for the T2DM and T1DM cohorts, respectively, 71 of 824 patients (8.6%) died. A significant mortality excess was noted in T2DM15–30 (11 vs. 6.8%, P = 0.03), with an increased hazard for death (hazard ratio 2.0 [95% CI 1.2–3.2], P = 0.003). Death for T2DM15–30 occurred after a significantly shorter disease duration (26.9 [18.1–36.0] vs. 36.5 [24.4–45.4] years, P = 0.01) and at a relatively young age. There were more cardiovascular deaths in T2DM15–30 (50 vs. 30%, P < 0.05). Despite equivalent glycemic control and shorter disease duration, the prevalence of albuminuria and less favorable cardiovascular risk factors were greater in the T2DM15–30 cohort, even soon after diabetes onset. Neuropathy scores and macrovascular complications were also increased in T2DM15–30 (P < 0.0001).

CONCLUSIONS Young-onset T2DM is the more lethal phenotype of diabetes and is associated with a greater mortality, more diabetes complications, and unfavorable cardiovascular disease risk factors when compared with T1DM.

“Only a few previous studies have looked at comparative mortality in T1DM and T2DM onset in patients <30 years of age. In a Swedish study of patients with diabetes aged 15–34 years compared with a general population, the standardized mortality ratio was higher for the T2DM than for the T1DM cohort (2.9 vs. 1.8) (17). […] Recently, Dart et al. (19) examined survival in youth aged 1–18 years with T2DM versus T1DM. Kaplan-Meier analysis revealed a statistically significant lower survival probability for the youth with T2DM, although the number at risk was low after 10 year’s duration. Taken together, these findings are in keeping with the present observations and are supportive evidence for a higher mortality in young-onset T2DM than in T1DM. The majority of deaths appear to be from cardiovascular causes and significantly more so for young T2DM.”

“Although the age of onset of T1DM diabetes is usually in little doubt because of a more abrupt presentation, it is possible that the age of onset of T2DM was in fact earlier than recognized. With a previously published method for estimating time delay until diagnosis of T2DM (26) by plotting the prevalence of retinopathy against duration and extrapolating to a point of zero retinopathy, we found that there is no difference in the slope and intercept of this relationship between the T2DM and the T1DM cohorts […] delay in diagnosis is unlikely to be an explanation for the differences in observed outcome.”

vi. Cardiovascular Risk Factors Are Associated With Increased Arterial Stiffness in Youth With Type 1 Diabetes.

“Increased arterial stiffness independently predicts all-cause and CVD mortality (3), and higher pulse pressure predicts CVD mortality, incidence, and end-stage renal disease development among adults with type 1 diabetes (1,4,5). Several reports have shown that youth and adults with type 1 diabetes have elevated arterial stiffness, though the mechanisms are largely unknown (6). The etiology of advanced atherosclerosis in type 1 diabetes is likely multifactorial, involving metabolic, behavioral, and diabetes-specific cardiovascular (CV) risk factors. Aging, high blood pressure (BP), obesity, the metabolic syndrome (MetS), and type 2 diabetes are the main contributors of sustained increased arterial stiffness in adults (7,8). However, the natural history, the age-related progression, and the possible determinants of increased arterial stiffness in youth with type 1 diabetes have not been studied systematically. […] There are currently no data examining the impact of CV risk factors and their clustering in youth with type 1 diabetes on subsequent CVD morbidity and mortality […]. Thus, the aims of this report were: 1) to describe the progression of arterial stiffness, as measured by pulse wave velocity (PWV), over time, among youth with type 1 diabetes, and 2) to explore the association of CV risk factors and their clustering as MetS with PWV in this cohort.”

“Youth were age 14.5 years (SD 2.8) and had an average disease duration of 4.8 (3.8) years at baseline, 46.3% were female, and 87.6% were of NHW race/ethnicity. At baseline, 10.0% had high BP, 10.9% had a large waist circumference, 11.6% had HDL-c ≤40 mg/dL, 10.9% had a TG level ≥110 mg/dL, and 7.0% had at least two of the above CV risk factors (MetS). In addition, 10.3% had LDL-c ≥130 mg/dL, 72.0% had an HbA1c ≥7.5% (58 mmol/mol), and 9.2% had ACR ≥30 μg/mL. Follow-up measures were obtained on average at age 19.2 years, when the average duration of diabetes was 10.1 (3.9) years.”

“Over an average follow-up period of ∼5 years, there was a statistically significant increase of 0.7 m/s in PWV (from 5.2 to 5.9 m/s), representing an annual increase of 2.8% or 0.145 m/s. […] Based on our data, if this rate of change is stable over time, the estimated average PWV by the time these youth enter their third decade of life will be 11.3 m/s, which was shown to be associated with a threefold increased hazard for major CV events (26). There are no similar studies in youth to compare these findings. In adults, the rate of change in PWV was 0.081 m/s/year in nondiabetic normotensive patients, although it was higher in hypertensive adults (0.147 m/s/year) (7). We also showed that the presence of central adiposity and elevated BP at baseline, as well as clustering of at least two CV risk factors, was associated with significantly worse PWV over time, although these baseline factors did not significantly influence the rate of change in PWV over this period of time. Changes in CV risk factors, specifically increases in central adiposity, LDL-c levels, and worsening glucose control, were independently associated with worse PWV over time. […] Our inability to detect a difference in the rate of change in PWV in our youth with MetS (vs. those without MetS) may be due to several factors, including a combination of a relatively small sample size, short period of follow-up, and young age of the cohort (thus with lower baseline PWV levels).”

 

November 8, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Medicine, Nephrology, Neurology, Studies | Leave a comment

Acute Coronary Syndromes

A few quotes from the lecture, as well as some links to related stuff:

“You might say: Why doesn’t coronary stenting prevent heart attacks? You got an 80 % blockage causing some angina and you stent it, why doesn’t that prevent a heart attack? And the answer is very curious. The plaques that are most likely to rupture are mild. They’re typically less than 50 %. They have a thin fibrous cap, a lot of lipid, and they rupture during stress. This has been the real confusion for my specialty over the last 30 years, starting to realize that, you know, when you get angina we find the blockage and we fix it and your angina’s better, but the lesions that were gonna cause next week’s heart attack often are not the lesion we fixed, but there’s 25 other moderate plaques in the coronary tree and one of them is heating up and it’s vulnerable. […] ACS, the whole thing here is the idea of a vulnerable plaque rupture. And it’s often not a severe narrowing.” (3-5 minutes in)

[One of the plaque rupture triggers of relevance is inflammatory cytokines…] “What’s a good example of that? Influenza. Right, influenza releases things like, IL-6 and other cytokines. What do they do? Well, they make you shake and shiver and feel like your muscles are dying. They also dissolve plaques. […] If you take a town like Ann Arbor and vaccinate everybody for influenza, we reduce heart attacks by a lot … 20-30 % during flu season.” (~11-12 minutes in)

“What happens to your systolic function as you get older? Any ideas? I’m happy to tell you it stays strong. […] What happens to diastole? […] As your myocardial cells die, a few die every day, […] those cells get replaced by fibrous tissue. So an aging heart becomes gradually stiffer [this is apparently termed ‘presbycardia’]. It beats well because the cells that are alive can overcome the fibrosis and squeeze, but it doesn’t relax as well. So left ventricular and diastolic pressure goes up. Older patients are much more likely to develop heart failure [in the ACS setting] because they already have impaired diastole from […] presbycardia.” (~1.14-1.15)

Some links to coverage of topics covered during the lecture:

Acute Coronary Syndrome.
Unstable angina.
Pathology of Acute Myocardial Infarction.
Acute Coronary Syndrome Workup.
Acute Coronary Syndrome Treatment & Management.
The GRACE risk score.
Complications of Myocardial Infarction.
Early versus Delayed Invasive Intervention in Acute Coronary Syndromes (Mehta et al. 2009).

November 3, 2017 Posted by | Cardiology, Lectures, Medicine, Studies | Leave a comment

A few diabetes papers of interest

i. Chronic Fatigue in Type 1 Diabetes: Highly Prevalent but Not Explained by Hyperglycemia or Glucose Variability.

“Fatigue is a classical symptom of hyperglycemia, but the relationship between chronic fatigue and diabetes has not been systematically studied. […] glucose control [in diabetics] is often suboptimal with persistent episodes of hyperglycemia that may result in sustained fatigue. Fatigue may also sustain in diabetic patients because it is associated with the presence of a chronic disease, as has been demonstrated in patients with rheumatoid arthritis and various neuromuscular disorders (2,3).

It is important to distinguish between acute and chronic fatigue, because chronic fatigue, defined as severe fatigue that persists for at least 6 months, leads to substantial impairments in patients’ daily functioning (4,5). In contrast, acute fatigue can largely vary during the day and generally does not cause functional impairments.

Literature provides limited evidence for higher levels of fatigue in diabetic patients (6,7), but its chronicity, impact, and determinants are unknown. In various chronic diseases, it has been proven useful to distinguish between precipitating and perpetuating factors of chronic fatigue (3,8). Illness-related factors trigger acute fatigue, while other factors, often cognitions and behaviors, cause fatigue to persist. Sleep disturbances, low self-efficacy concerning fatigue, reduced physical activity, and a strong focus on fatigue are examples of these fatigue-perpetuating factors (810). An episode of hyperglycemia or hypoglycemia could trigger acute fatigue for diabetic patients (11,12). However, variations in blood glucose levels might also contribute to chronic fatigue, because these variations continuously occur.

The current study had two aims. First, we investigated the prevalence and impact of chronic fatigue in a large sample of type 1 diabetic (T1DM) patients and compared the results to a group of age- and sex-matched population-based controls. Secondly, we searched for potential determinants of chronic fatigue in T1DM.”

“A significantly higher percentage of T1DM patients were chronically fatigued (40%; 95% CI 34–47%) than matched controls (7%; 95% CI 3–10%). Mean fatigue severity was also significantly higher in T1DM patients (31 ± 14) compared with matched controls (17 ± 9; P < 0.001). T1DM patients with a comorbidity_mr [a comorbidity affecting patients’ daily functioning, based on medical records – US] or clinically relevant depressive symptoms [based on scores on the Beck Depression Inventory for Primary Care – US] were significantly more often chronically fatigued than patients without a comorbidity_mr (55 vs. 36%; P = 0.014) or without clinically relevant depressive symptoms (88 vs. 31%; P < 0.001). Patients who reported neuropathy, nephropathy, or cardiovascular disease as complications of diabetes were more often chronically fatigued […] Chronically fatigued T1DM patients were significantly more impaired compared with nonchronically fatigued T1DM patients on all aspects of daily functioning […]. Fatigue was the most troublesome symptom of the 34 assessed diabetes-related symptoms. The five most troublesome symptoms were overall sense of fatigue, lack of energy, increasing fatigue in the course of the day, fatigue in the morning when getting up, and sleepiness or drowsiness”.

“This study establishes that chronic fatigue is highly prevalent and clinically relevant in T1DM patients. While current blood glucose level was only weakly associated with chronic fatigue, cognitive behavioral factors were by far the strongest potential determinants.”

“Another study found that type 2 diabetic, but not T1DM, patients had higher levels of fatigue compared with healthy controls (7). This apparent discrepancy may be explained by the relatively small sample size of this latter study, potential selection bias (patients were not randomly selected), and the use of a different fatigue questionnaire.”

“Not only was chronic fatigue highly prevalent, fatigue also had a large impact on T1DM patients. Chronically fatigued T1DM patients had more functional impairments than nonchronically fatigued patients, and T1DM patients considered fatigue as the most burdensome diabetes-related symptom.

Contrary to what was expected, there was at best a weak relationship between blood glucose level and chronic fatigue. Chronically fatigued T1DM patients spent slightly less time in hypoglycemia, but average glucose levels, glucose variability, hyperglycemia, or HbA1c were not related to chronic fatigue. In type 2 diabetes mellitus also, no relationship was found between fatigue and HbA1c (7).”

“Regarding demographic characteristics, current health status, diabetes-related factors, and fatigue-related cognitions and behaviors as potential determinants of chronic fatigue, we found that sleeping problems, physical activity, self-efficacy concerning fatigue, age, depression, and pain were significantly associated with chronic fatigue in T1DM. Although depression was strongly related, it could not completely explain the presence of chronic fatigue (38), as 31% was chronically fatigued without having clinically relevant depressive symptoms.”

Some comments may be worth adding here. It’s important to note to people who may not be aware of this that although chronic fatigue is a weird entity that’s hard to get a handle on (and, to be frank, is somewhat controversial), specific organic causes have been identified that greatly increases the risk. Many survivors of cancer experience chronic fatigue (see e.g. this paper, or wikipedia), and chronic fatigue is also not uncommon in a kidney failure setting (“The silence of renal disease creeps up on us (doctors and patients). Do not dismiss odd chronic symptoms such as fatigue or ‘not being quite with it’ without considering checking renal function” (Oxford Handbook of Clinical Medicine, 9th edition. My italics – US)). As observed above, linkage with RA and some neuromuscular disorders has also been observed. The brief discussion of related topics in Houghton & Grey made it clear to me that some people with chronic fatigue are almost certainly suffering from an organic illness which has not been diagnosed or treated. Here’s a relevant quote from that book’s coverage: “it is unusual to find a definite organic cause for fatigue. However, consider anaemia, thyroid dysfunction, Addison’s disease and hypopituitarism.” It’s sort of neat, if you think about the potential diabetes-fatigue link investigated by the guys above, that some of these diseases are likely to be relevant, as type 1 diabetics are more likely to develop them (anemia is not linked to diabetes, as far as I know, and I believe the relationship between autoimmune hypophysitis – which is a cause of hypopituitarism – and type 1 diabetes is at best unclear, but the others are definitely involved) due to their development being caused by some of the same genetic mutations which cause type 1 diabetes; the combinations of some of these diseases even have fancy names of their own, like ‘Type I Polyglandular Autoimmune Syndrome’ and ‘Schmidt Syndrome’ (if you’re interested here are a couple of medscape links). It’s noteworthy that although most of these diseases are uncommon in the general population, their incidence/prevalence is likely to be greatly increased in type 1 diabetics due to the common genetic pathways at play (variants regulating T-cell function seem to be important, but there’s no need to go into these details here). Sperling et al. note in their book that: “Hypothyroid or hyperthyroid AITD [autoimmune thyroid disease] has been observed in 10–24% of patients with type 1 diabetes”. In one series including 151 patients with APS [/PAS]-2, when they looked at disease combinations they found that: “Of combinations of the component diseases, [type 1] diabetes with thyroid disease was the most common, occurring in 33%. The second, diabetes with adrenal insufficiency, made up 15%” (same source).

It seems from estimates like these likely that a not unsubstantial proportion of type 1 diabetics over time go on to develop other health problems that might if unaddressed/undiagnosed cause fatigue, and this may in my opinion be a potentially much more important cause than direct metabolic effects such as hyperglycemia, or chronic inflammation. If this is the case you’d however expect to see a substantial sex difference, as the autoimmune syndromes are in general much more likely to hit females than males. I’m not completely sure how to interpret a few of the results reported, but to me it doesn’t look like the sex differences in this study are anywhere near ‘large enough’ to support such an explanatory model, though. Another big problem is also that fatigue seems to be more common in young patients, which is weird; most long-term complications display significant (positive) duration dependence, and when diabetes is a component of an autoimmune syndrome diabetes tend to develop first, with other diseases hitting later, usually in middle age. Duration and age are strongly correlated, and a negative duration dependence in a diabetes complication setting is a surprising and unusual finding that needs to be explained, badly; it’s unexpected and may in my opinion be the sign of a poor disease model. It’d make more sense for disease-related fatigue to present late, rather than early, I don’t really know what to make of that negative age gradient. ‘More studies needed’ (preferably by people familiar with those autoimmune syndromes..), etc…

ii. Risk for End-Stage Renal Disease Over 25 Years in the Population-Based WESDR Cohort.

“It is well known that diabetic nephropathy is the leading cause of end-stage renal disease (ESRD) in many regions, including the U.S. (1). Type 1 diabetes accounts for >45,000 cases of ESRD per year (2), and the incidence may be higher than in people with type 2 diabetes (3). Despite this, there are few population-based data available regarding the prevalence and incidence of ESRD in people with type 1 diabetes in the U.S. (4). A declining incidence of ESRD has been suggested by findings of lower incidence with increasing calendar year of diagnosis and in comparison with older reports in some studies in Europe and the U.S. (58). This is consistent with better diabetes management tools becoming available and increased renoprotective efforts, including the greater use of ACE inhibitors and angiotensin type II receptor blockers, over the past two to three decades (9). Conversely, no reduction in the incidence of ESRD across enrollment cohorts was found in a recent clinic-based study (9). Further, an increase in ESRD has been suggested for older but not younger people (9). Recent improvements in diabetes care have been suggested to delay rather than prevent the development of renal disease in people with type 1 diabetes (4).

A decrease in the prevalence of proliferative retinopathy by increasing calendar year of type 1 diabetes diagnosis was previously reported in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) cohort (10); therefore, we sought to determine if a similar pattern of decline in ESRD would be evident over 25 years of follow-up. Further, we investigated factors that may mediate a possible decline in ESRD as well as other factors associated with incident ESRD over time.”

“At baseline, 99% of WESDR cohort members were white and 51% were male. Individuals were 3–79 years of age (mean 29) with diabetes duration of 0–59 years (mean 15), diagnosed between 1922 and 1980. Four percent of individuals used three or more daily insulin injections and none used an insulin pump. Mean HbA1c was 10.1% (87 mmol/mol). Only 16% were using an antihypertensive medication, none was using an ACE inhibitor, and 3% reported a history of renal transplant or dialysis (ESRD). At 25 years, 514 individuals participated (52% of original cohort at baseline, n = 996) and 367 were deceased (37% of baseline). Mean HbA1c was much lower than at baseline (7.5%, 58 mmol/mol), the decline likely due to the improvements in diabetes care, with 80% of participants using intensive insulin management (three or more daily insulin injections or insulin pump). The decline in HbA1c was steady, becoming slightly steeper following the results of the DCCT (25). Overall, at the 25-year follow-up, 47% had proliferative retinopathy, 53% used aspirin daily, and 54% reported taking antihypertensive medications, with the majority (87%) using an ACE inhibitor. Thirteen percent reported a history of ESRD.”

“Prevalence of ESRD was negligible until 15 years of diabetes duration and then steadily increased with 5, 8, 10, 13, and 14% reporting ESRD by 15–19, 20–24, 25–29, 30–34, and 35+ years of diabetes duration, respectively. […] After 15 years of diagnosis, prevalence of ESRD increased with duration in people diagnosed from 1960 to 1980, with the lowest increase in people with the most recent diagnosis. People diagnosed from 1922 to 1959 had consistent rather than increasing levels of ESRD with duration of 20+ years. If not for their greater mortality (at the 25-year follow-up, 48% of the deceased had been diagnosed prior to 1960), an increase with duration may have also been observed.

From baseline, the unadjusted cumulative 25-year incidence of ESRD was 17.9% (95% CI 14.3–21.5) in males, 10.3% (7.4–13.2) in females, and 14.2% (11.9–16.5) overall. For those diagnosed in 1970–1980, the cumulative incidence at 14, 20, and 25 years of follow-up (or ∼15–25, 20–30, and 25–35 years diabetes duration) was 5.2, 7.9, and 9.3%, respectively. At 14, 20, and 25 years of follow-up (or 35, 40, and 45 up to 65+ years diabetes duration), the cumulative incidence in those diagnosed during 1922–1969 was 13.6, 16.3, and 18.8%, respectively, consistent with the greater prevalence observed for these diagnosis periods at longer duration of diabetes.”

“The unadjusted hazard of ESRD was reduced by 70% among those diagnosed in 1970–1980 as compared with those in 1922–1969 (HR 0.29 [95% CI 0.19–0.44]). Duration (by 10%) and HbA1c (by an additional 10%) partially mediated this association […] Blood pressure and antihypertensive medication use each further attenuated the association. When fully adjusted for these and [other risk factors included in the model], period of diagnosis was no longer significant (HR 0.89 [0.55–1.45]). Sensitivity analyses for the hazard of incident ESRD or death due to renal disease showed similar findings […] The most parsimonious model included diabetes duration, HbA1c, age, sex, systolic and diastolic blood pressure, and history of antihypertensive medication […]. A 32% increased risk for incident ESRD was found per increasing year of diabetes duration at 0–15 years (HR 1.32 per year [95% CI 1.16–1.51]). The hazard plateaued (1.01 per year [0.98–1.05]) after 15 years of duration of diabetes. Hazard of ESRD increased with increasing HbA1c (1.28 per 1% or 10.9 mmol/mol increase [1.14–1.45]) and blood pressure (1.51 per 10 mmHg increase in systolic pressure [1.35–1.68]; 1.12 per 5 mmHg increase in diastolic pressure [1.01–1.23]). Use of antihypertensive medications increased the hazard of incident ESRD nearly fivefold [this finding is almost certainly due to confounding by indication, as also noted by the authors later on in the paper – US], and males had approximately two times the risk as compared with females. […] Having proliferative retinopathy was strongly associated with increased risk (HR 5.91 [3.00–11.6]) and attenuated the association between sex and ESRD.”

“The current investigation […] sought to provide much-needed information on the prevalence and incidence of ESRD and associated risk specific to people with type 1 diabetes. Consistent with a few previous studies (5,7,8), we observed decreased prevalence and incidence of ESRD among individuals with type 1 diabetes diagnosed in the 1970s compared with prior to 1970. The Epidemiology of Diabetes Complications (EDC) Study, another large cohort of people with type 1 diabetes followed over a long period of time, reported cumulative incidence rates of 2–6% for those diagnosed after 1970 and with similar duration (7), comparable to our findings. Slightly higher cumulative incidence (7–13%) reported from older studies at slightly lower duration also supports a decrease in incidence of ESRD (2830). Cumulative incidences through 30 years in European cohorts were even lower (3.3% in Sweden [6] and 7.8% in Finland [5]), compared with the 9.3% noted for those diagnosed during 1970–1980 in the WESDR cohort. The lower incidence could be associated with nationally organized care, especially in Sweden where a nationwide intensive diabetes management treatment program was implemented at least a decade earlier than recommendations for intensive care followed from the results of the DCCT in the U.S.”

“We noted an increased risk of incident ESRD in the first 15 years of diabetes not evident at longer durations. This pattern also demonstrated by others could be due to a greater earlier risk among people most genetically susceptible, as only a subset of individuals with type 1 diabetes will develop renal disease (27,28). The risk plateau associated with greater durations of diabetes and lower risk associated with increasing age may also reflect more death at longer durations and older ages. […] Because age and duration are highly correlated, we observed a positive association between age and ESRD only in univariate analyses, without adjustment for duration. The lack of adjustment for diabetes duration may have, in part, explained the increasing incidence of ESRD shown with age for some people in a recent investigation (9). Adjustment for both age and duration was found appropriate after testing for collinearity in the current analysis.”

In conclusion, this U.S. population-based report showed a lower prevalence and incidence of ESRD among those more recently diagnosed, explained by improvements in glycemic and blood pressure control over the last several decades. Even lower rates may be expected for those diagnosed during the current era of diabetes care. Intensive diabetes management, especially for glycemic control, remains important even in long-standing diabetes as potentially delaying the development of ESRD.

iii. Earlier Onset of Complications in Youth With Type 2 Diabetes.

The prevalence of type 2 diabetes in youth is increasing worldwide, coinciding with the rising obesity epidemic (1,2). […] Diabetes is associated with both microvascular and macrovascular complications. The evolution of these complications has been well described in type 1 diabetes (6) and in adult type 2 diabetes (7), wherein significant complications typically manifest 15–20 years after diagnosis (8). Because type 2 diabetes is a relatively new disease in children (first described in the 1980s), long-term outcome data on complications are scant, and risk factors for the development of complications are incompletely understood. The available literature suggests that development of complications in youth with type 2 diabetes may be more rapid than in adults, thus afflicting individuals at the height of their individual and social productivity (9). […] A small but notable proportion of type 2 diabetes is associated with a polymorphism of hepatic nuclear factor (HNF)-1α, a transcription factor expressed in many tissues […] It is not yet known what effect the HNF-1α polymorphism has on the risk of complications associated with diabetes.”

“The main objective of the current study was to describe the time course and risk factors for microvascular complications (nephropathy, retinopathy, and neuropathy) and macrovascular complications (cardiac, cerebrovascular, and peripheral vascular diseases) in a large cohort of youth [diagnosed with type 2 diabetes] who have been carefully followed for >20 years and to compare this evolution with that of youth with type 1 diabetes. We also compared vascular complications in the youth with type 2 diabetes with nondiabetic control youth. Finally, we addressed the impact of HNF-1α G319S on the evolution of complications in young patients with type 2 diabetes.”

“All prevalent cases of type 2 diabetes and type 1 diabetes (control group 1) seen between January 1986 and March 2007 in the DER-CA for youth aged 1–18 years were included. […] The final type 2 diabetes cohort included 342 youth, and the type 1 diabetes control group included 1,011. The no diabetes control cohort comprised 1,710 youth matched to the type 2 diabetes cohort from the repository […] Compared with the youth with type 1 diabetes, the youth with type 2 diabetes were, on average, older at the time of diagnosis and more likely to be female. They were more likely to have a higher BMIz, live in a rural area, have a low SES, and have albuminuria at diagnosis. […] one-half of the type 2 diabetes group was either a heterozygote (GS) or a homozygote (SS) for the HNF-1α polymorphism […] At the time of the last available follow-up in the DER-CA, the youth with diabetes were, on average, between 15 and 16 years of age. […] The median follow-up times in the repository were 4.4 (range 0–27.4) years for youth with type 2 diabetes, 6.7 ( 0–28.2) years for youth with type 1 diabetes, and 6.0 (0–29.9) years for nondiabetic control youth.”

“After controlling for low SES, sex, and BMIz, the risk associated with type 2 versus type 1 diabetes of any complication was an HR of 1.47 (1.02–2.12, P = 0.04). […] In the univariate analysis, youth with type 2 diabetes were at significantly higher risk of developing any vascular (HR 6.15 [4.26–8.87], P < 0.0001), microvascular (6.26 [4.32–9.10], P < 0.0001), or macrovascular (4.44 [1.71–11.52], P < 0.0001) disease compared with control youth without diabetes. In addition, the youth with type 2 diabetes had an increased risk of opthalmologic (19.49 [9.75–39.00], P < 0.0001), renal (16.13 [7.66–33.99], P < 0.0001), and neurologic (2.93 [1.79–4.80], P ≤ 0.001) disease. There were few cardiovascular, cerebrovascular, and peripheral vascular disease events in all groups (five or fewer events per group). Despite this, there was still a statistically significant higher risk of peripheral vascular disease in the type 2 diabetes group (6.25 [1.68–23.28], P = 0.006).”

“Differences in renal and neurologic complications between the two diabetes groups began to occur before 5 years postdiagnosis, whereas differences in ophthalmologic complications began 10 years postdiagnosis. […] Both cardiovascular and cerebrovascular complications were rare in both groups, but peripheral vascular complications began to occur 15 years after diagnosis in the type 2 diabetes group […] The presence of HNF-1α G319S polymorphism in youth with type 2 diabetes was found to be protective of complications. […] Overall, major complications were rare in the type 1 diabetes group, but they occurred in 1.1% of the type 2 diabetes cohort at 10 years, in 26.0% at 15 years, and in 47.9% at 20 years after diagnosis (P < 0.001) […] youth with type 2 diabetes have a higher risk of any complication than youth with type 1 diabetes and nondiabetic control youth. […] The time to both renal and neurologic complications was significantly shorter in youth with type 2 diabetes than in control youth, whereas differences were not significant with respect to opthalmologic and cardiovascular complications between cohorts. […] The current study is consistent with the literature, which has shown high rates of cardiovascular risk factors in youth with type 2 diabetes. However, despite the high prevalence of risk, this study reports low rates of clinical events. Because the median follow-up time was between 5 and 8 years, it is possible that a longer follow-up period would be required to correctly evaluate macrovascular outcomes in young adults. Also possible is that diagnoses of mild disease are not being made because of a low index of suspicion in 20- and 30-year-old patients.”

In conclusion, youth with type 2 diabetes have an increased risk of complications early in the course of their disease. Microvascular complications and cardiovascular risk factors are highly prevalent, whereas macrovascular complications are rare in young adulthood. HbA1c is an important modifiable risk factor; thus, optimizing glycemic control should remain an important goal of therapy.”

iv. HbA1c and Coronary Heart Disease Risk Among Diabetic Patients.

“We prospectively investigated the association of HbA1c at baseline and during follow-up with CHD risk among 17,510 African American and 12,592 white patients with type 2 diabetes. […] During a mean follow-up of 6.0 years, 7,258 incident CHD cases were identified. The multivariable-adjusted hazard ratios of CHD associated with different levels of HbA1c at baseline (<6.0 [reference group], 6.0–6.9, 7.0–7.9, 8.0–8.9, 9.0–9.9, 10.0–10.9, and ≥11.0%) were 1.00, 1.07 (95% CI 0.97–1.18), 1.16 (1.04–1.31), 1.15 (1.01–1.32), 1.26 (1.09–1.45), 1.27 (1.09–1.48), and 1.24 (1.10–1.40) (P trend = 0.002) for African Americans and 1.00, 1.04 (0.94–1.14), 1.15 (1.03–1.28), 1.29 (1.13–1.46), 1.41 (1.22–1.62), 1.34 (1.14–1.57), and 1.44 (1.26–1.65) (P trend <0.001) for white patients, respectively. The graded association of HbA1c during follow-up with CHD risk was observed among both African American and white diabetic patients (all P trend <0.001). Each one percentage increase of HbA1c was associated with a greater increase in CHD risk in white versus African American diabetic patients. When stratified by sex, age, smoking status, use of glucose-lowering agents, and income, this graded association of HbA1c with CHD was still present. […] The current study in a low-income population suggests a graded positive association between HbA1c at baseline and during follow-up with the risk of CHD among both African American and white diabetic patients with low socioeconomic status.”

A few more observations from the conclusions:

“Diabetic patients experience high mortality from cardiovascular causes (2). Observational studies have confirmed the continuous and positive association between glycemic control and the risk of cardiovascular disease among diabetic patients (4,5). But the findings from RCTs are sometimes uncertain. Three large RCTs (79) designed primarily to determine whether targeting different glucose levels can reduce the risk of cardiovascular events in patients with type 2 diabetes failed to confirm the benefit. Several reasons for the inconsistency of these studies can be considered. First, small sample sizes, short follow-up duration, and few CHD cases in some RCTs may limit the statistical power. Second, most epidemiological studies only assess a single baseline measurement of HbA1c with CHD risk, which may produce potential bias. The recent analysis of 10 years of posttrial follow-up of the UKPDS showed continued reductions for myocardial infarction and death from all causes despite an early loss of glycemic differences (10). The scientific evidence from RCTs was not sufficient to generate strong recommendations for clinical practice. Thus, consensus groups (AHA, ACC, and ADA) have provided a conservative endorsement (class IIb recommendation, level of evidence A) for the cardiovascular benefits of glycemic control (11). In the absence of conclusive evidence from RCTs, observational epidemiological studies might provide useful information to clarify the relationship between glycemia and CHD risk. In the current study with 30,102 participants with diabetes and 7,258 incident CHD cases during a mean follow-up of 6.0 years, we found a graded positive association by various HbA1c intervals of clinical relevance or by using HbA1c as a continuous variable at baseline and during follow-up with CHD risk among both African American and white diabetic patients. Each one percentage increase in baseline and follow-up HbA1c was associated with a 2 and 5% increased risk of CHD in African American and 6 and 11% in white diabetic patients. Each one percentage increase of HbA1c was associated with a greater increase in CHD risk in white versus African American diabetic patients.”

v. Blood Viscosity in Subjects With Normoglycemia and Prediabetes.

“Blood viscosity (BV) is the force that counteracts the free sliding of the blood layers within the circulation and depends on the internal cohesion between the molecules and the cells. Abnormally high BV can have several negative effects: the heart is overloaded to pump blood in the vascular bed, and the blood itself, more viscous, can damage the vessel wall. Furthermore, according to Poiseuille’s law (1), BV is inversely related to flow and might therefore reduce the delivery of insulin and glucose to peripheral tissues, leading to insulin resistance or diabetes (25).

It is generally accepted that BV is increased in diabetic patients (68). Although the reasons for this alteration are still under investigation, it is believed that the increase in osmolarity causes increased capillary permeability and, consequently, increased hematocrit and viscosity (9). It has also been suggested that the osmotic diuresis, consequence of hyperglycemia, could contribute to reduce plasma volume and increase hematocrit (10).

Cross-sectional studies have also supported a link between BV, hematocrit, and insulin resistance (1117). Recently, a large prospective study has demonstrated that BV and hematocrit are risk factors for type 2 diabetes. Subjects in the highest quartile of BV were >60% more likely to develop diabetes than their counterparts in the lowest quartile (18). This finding confirms previous observations obtained in smaller or selected populations, in which the association between hemoglobin or hematocrit and occurrence of type 2 diabetes was investigated (1922).

These observations suggest that the elevation in BV may be very early, well before the onset of diabetes, but definite data in subjects with normal glucose or prediabetes are missing. In the current study, we evaluated the relationship between BV and blood glucose in subjects with normal glucose or prediabetes in order to verify whether alterations in viscosity are appreciable in these subjects and at which blood glucose concentration they appear.”

“According to blood glucose levels, participants were divided into three groups: group A, blood glucose <90 mg/dL; group B, blood glucose between 90 and 99 mg/dL; and group C, blood glucose between 100 and 125 mg/dL. […] Hematocrit (P < 0.05) and BV (P between 0.01 and 0.001) were significantly higher in subjects with prediabetes and in those with blood glucose ranging from 90 to 99 mg/dL compared with subjects with blood glucose <90 mg/dL. […] The current study shows, for the first time, a direct relationship between BV and blood glucose in nondiabetic subjects. It also suggests that, even within glucose values ​​considered completely normal, individuals with higher blood glucose levels have increases in BV comparable with those observed in subjects with prediabetes. […] Overall, changes in viscosity in diabetic patients are accepted as common and as a result of the disease. However, the relationship between blood glucose, diabetes, and viscosity may be much more complex. […] the main finding of the study is that BV significantly increases already at high-normal blood glucose levels, independently of other common determinants of hemorheology. Intervention studies are needed to verify whether changes in BV can influence the development of type 2 diabetes.”

vi. Higher Relative Risk for Multiple Sclerosis in a Pediatric and Adolescent Diabetic Population: Analysis From DPV Database.

“Type 1 diabetes and multiple sclerosis (MS) are organ-specific inflammatory diseases, which result from an autoimmune attack against either pancreatic β-cells or the central nervous system; a combined appearance has been described repeatedly (13). For children and adolescents below the age of 21 years, the prevalence of type 1 diabetes in Germany and Austria is ∼19.4 cases per 100,000 population, and for MS it is 7–10 per 100,000 population (46). A Danish cohort study revealed a three times higher risk for the development of MS in patients with type 1 diabetes (7). Further, an Italian study conducted in Sardinia showed a five times higher risk for the development of type 1 diabetes in MS patients (8,9). An American study on female adults in whom diabetes developed before the age of 21 years yielded an up to 20 times higher risk for the development of MS (10).

These findings support the hypothesis of clustering between type 1 diabetes and MS. The pathogenesis behind this association is still unclear, but T-cell cross-reactivity was discussed as well as shared disease associations due to the HLA-DRB1-DQB1 gene loci […] The aim of this study was to evaluate the prevalence of MS in a diabetic population and to look for possible factors related to the co-occurrence of MS in children and adolescents with type 1 diabetes using a large multicenter survey from the Diabetes Patienten Verlaufsdokumentation (DPV) database.”

“We used a large database of pediatric and adolescent type 1 diabetic patients to analyze the RR of MS co-occurrence. The DPV database includes ∼98% of the pediatric diabetic population in Germany and Austria below the age of 21 years. In children and adolescents, the RR for MS in type 1 diabetes was estimated to be three to almost five times higher in comparison with the healthy population.”

November 2, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Immunology, Medicine, Nephrology, Statistics, Studies | Leave a comment

A few diabetes papers of interest

i. The Pharmacogenetics of Type 2 Diabetes: A Systematic Review.

“We performed a systematic review to identify which genetic variants predict response to diabetes medications.

RESEARCH DESIGN AND METHODS We performed a search of electronic databases (PubMed, EMBASE, and Cochrane Database) and a manual search to identify original, longitudinal studies of the effect of diabetes medications on incident diabetes, HbA1c, fasting glucose, and postprandial glucose in prediabetes or type 2 diabetes by genetic variation.

RESULTS Of 7,279 citations, we included 34 articles (N = 10,407) evaluating metformin (n = 14), sulfonylureas (n = 4), repaglinide (n = 8), pioglitazone (n = 3), rosiglitazone (n = 4), and acarbose (n = 4). […] Significant medication–gene interactions for glycemic outcomes included 1) metformin and the SLC22A1, SLC22A2, SLC47A1, PRKAB2, PRKAA2, PRKAA1, and STK11 loci; 2) sulfonylureas and the CYP2C9 and TCF7L2 loci; 3) repaglinide and the KCNJ11, SLC30A8, NEUROD1/BETA2, UCP2, and PAX4 loci; 4) pioglitazone and the PPARG2 and PTPRD loci; 5) rosiglitazone and the KCNQ1 and RBP4 loci; and 5) acarbose and the PPARA, HNF4A, LIPC, and PPARGC1A loci. Data were insufficient for meta-analysis.

CONCLUSIONS We found evidence of pharmacogenetic interactions for metformin, sulfonylureas, repaglinide, thiazolidinediones, and acarbose consistent with their pharmacokinetics and pharmacodynamics.”

“In this systematic review, we identified 34 articles on the pharmacogenetics of diabetes medications, with several reporting statistically significant interactions between genetic variants and medications for glycemic outcomes. Most pharmacogenetic interactions were only evaluated in a single study, did not use a control group, and/or did not report enough information to judge internal validity. However, our results do suggest specific, biologically plausible, gene–medication interactions, and we recommend confirmation of the biologically plausible interactions as a priority, including those for drug transporters, metabolizers, and targets of action. […] Given the number of comparisons reported in the included studies and the lack of accounting for multiple comparisons in approximately 53% of studies, many of the reported findings may [however] be false positives.”

ii. Insights Offered by Economic Analyses.

“This issue of Diabetes Care includes three economic analyses. The first describes the incremental costs of diabetes over a lifetime and highlights how interventions to prevent diabetes may reduce lifetime costs (1). The second demonstrates that although an expensive, intensive lifestyle intervention for type 2 diabetes does not reduce adverse cardiovascular outcomes over 10 years, it significantly reduces the costs of non-intervention−related medical care (2). The third demonstrates that although the use of the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria for the screening and diagnosis of gestational diabetes mellitus (GDM) results in a threefold increase in the number of people labeled as having GDM, it reduces the risk of maternal and neonatal adverse health outcomes and reduces costs (3). The first report highlights the enormous potential value of intervening in adults at high risk for type 2 diabetes to prevent its development. The second illustrates the importance of measuring economic outcomes in addition to standard clinical outcomes to fully assess the value of new treatments. The third demonstrates the importance of rigorously weighing the costs of screening and treatment against the costs of health outcomes when evaluating new approaches to care.”

“The costs of diabetes monitoring and treatment accrue as of function of the duration of diabetes, so adults who are younger at diagnosis are more likely to survive to develop the late, expensive complications of diabetes, thus they incur higher lifetime costs attributable to diabetes. Zhuo et al. report that people with diabetes diagnosed at age 40 spend approximately $125,000 more for medical care over their lifetimes than people without diabetes. For people diagnosed with diabetes at age 50, the discounted lifetime excess medical spending is approximately $91,000; for those diagnosed at age 60, it is approximately $54,000; and for those diagnosed at age 65, it is approximately $36,000 (1).

These results are very consistent with results reported by the Diabetes Prevention Program (DPP) Research Group, which assessed the cost-effectiveness of diabetes prevention. […] In the simulated lifetime economic analysis [included in that study] the lifestyle intervention was more cost-effective in younger participants than in older participants (5). By delaying the onset of type 2 diabetes, the lifestyle intervention delayed or prevented the need for diabetes monitoring and treatment, surveillance of diabetic microvascular and neuropathic complications, and treatment of the late, expensive complications and comorbidities of diabetes, including end-stage renal disease and cardiovascular disease (5). Although this finding was controversial at the end of the randomized, controlled clinical trial, all but 1 of 12 economic analyses published by 10 research groups in nine countries have demonstrated that lifestyle intervention for the prevention of type 2 diabetes is very cost-effective, if not cost-saving, compared with a placebo intervention (6).

Empiric, within-trial economic analyses of the DPP have now demonstrated that the incremental costs of the lifestyle intervention are almost entirely offset by reductions in the costs of medical care outside the study, especially the cost of self-monitoring supplies, prescription medications, and outpatient and inpatient care (7). Over 10 years, the DPP intensive lifestyle intervention cost only ∼$13,000 per quality-adjusted life-year gained when the analysis used an intent-to-treat approach (7) and was even more cost-effective when the analysis assessed outcomes and costs among adherent participants (8).”

“The American Diabetes Association has reported that although institutional care (hospital, nursing home, and hospice care) still account for 52% of annual per capita health care expenditures for people with diabetes, outpatient medications and supplies now account for 30% of expenditures (9). Between 2007 and 2012, annual per capita expenditures for inpatient care increased by 2%, while expenditures for medications and supplies increased by 51% (9). As the costs of diabetes medications and supplies continue to increase, it will be even more important to consider cost savings arising from the less frequent use of medications when evaluating the benefits of nonpharmacologic interventions.”

iii. The Lifetime Cost of Diabetes and Its Implications for Diabetes Prevention. (This is the Zhuo et al. paper mentioned above.)

“We aggregated annual medical expenditures from the age of diabetes diagnosis to death to determine lifetime medical expenditure. Annual medical expenditures were estimated by sex, age at diagnosis, and diabetes duration using data from 2006–2009 Medical Expenditure Panel Surveys, which were linked to data from 2005–2008 National Health Interview Surveys. We combined survival data from published studies with the estimated annual expenditures to calculate lifetime spending. We then compared lifetime spending for people with diabetes with that for those without diabetes. Future spending was discounted at 3% annually. […] The discounted excess lifetime medical spending for people with diabetes was $124,600 ($211,400 if not discounted), $91,200 ($135,600), $53,800 ($70,200), and $35,900 ($43,900) when diagnosed with diabetes at ages 40, 50, 60, and 65 years, respectively. Younger age at diagnosis and female sex were associated with higher levels of lifetime excess medical spending attributed to diabetes.

CONCLUSIONS Having diabetes is associated with substantially higher lifetime medical expenditures despite being associated with reduced life expectancy. If prevention costs can be kept sufficiently low, diabetes prevention may lead to a reduction in long-term medical costs.”

The selection criteria employed in this paper are not perfect; they excluded all individuals below the age of 30 “because they likely had type 1 diabetes”, which although true is only ‘mostly true’. Some of those individuals had(/have) type 2, but if you’re evaluating prevention schemes it probably makes sense to error on the side of caution (better to miss some type 2 patients than to include some type 1s), assuming the timing of the intervention is not too important. This gets more complicated if prevention schemes are more likely to have large and persistent effects in young people – however I don’t think that’s the case, as a counterpoint drug adherence studies often seem to find that young people aren’t particularly motivated to adhere to their treatment schedules compared to their older counterparts (who might have more advanced disease and so are more likely to achieve symptomatic relief by adhering to treatments).

A few more observations from the paper:

“The prevalence of participants with diabetes in the study population was 7.4%, of whom 54% were diagnosed between the ages of 45 and 64 years. The mean age at diagnosis was 55 years, and the mean length of time since diagnosis was 9.4 years (39% of participants with diabetes had been diagnosed for ≤5 years, 32% for 6–15 years, and 27% for ≥16 years). […] The observed annual medical spending for people with diabetes was $13,966—more than twice that for people without diabetes.”

“Regardless of diabetes status, the survival-adjusted annual medical spending decreased after age 60 years, primarily because of a decreasing probability of survival. Because the probability of survival decreased more rapidly in people with diabetes than in those without, corresponding spending declined as people died and no longer accrued medical costs. For example, among men diagnosed with diabetes at age 40 years, 34% were expected to survive to age 80 years; among men of the same age who never developed diabetes, 55% were expected to survive to age 80 years. The expected annual expenditure for a person diagnosed with diabetes at age 40 years declined from $8,500 per year at age 40 years to $3,400 at age 80 years, whereas the expenses for a comparable person without diabetes declined from $3,900 to $3,200 over that same interval. […] People diagnosed with diabetes at age 40 years lived with the disease for an average of 34 years after diagnosis. Those diagnosed when older lived fewer years and, therefore, lost fewer years of life. […] The annual excess medical spending attributed to diabetes […] was smaller among people who were diagnosed at older ages. For men diagnosed at age 40 years, annual medical spending was $3,700 higher than that of similar men without diabetes; spending was $2,900 higher for those diagnosed at age 50 years; $2,200 higher for those diagnosed at age 60 years; and $2,000 higher for those diagnosed at age 65 years. Among women diagnosed with diabetes, the excess annual medical spending was consistently higher than for men of the same age at diagnosis.”

“Regardless of age at diagnosis, people with diabetes spent considerably more on health care after age 65 years than their nondiabetic counterparts. Health care spending attributed to diabetes after age 65 years ranged from $23,900 to $40,900, depending on sex and age at diagnosis. […] Of the total excess lifetime medical spending among an average diabetic patient diagnosed at age 50 years, prescription medications and inpatient care accounted for 44% and 35% of costs, respectively. Outpatient care and other medical care accounted for 17% and 4% of costs, respectively.”

“Our findings differed from those of studies of the lifetime costs of other chronic conditions. For instance, smokers have a lower average lifetime medical cost than nonsmokers (29) because of their shorter life spans. Smokers have a life expectancy about 10 years less than those who do not smoke (30); life expectancy is 16 years less for those who develop smoking-induced cancers (31). As a result, smoking cessation leads to increased lifetime spending (32). Studies of the lifetime costs for an obese person relative to a person with normal body weight show mixed results: estimated excess lifetime medical costs for people with obesity range from $3,790 less to $39,000 more than costs for those who are nonobese (33,34). […] obesity, when considered alone, results in much lower annual excess medical costs than diabetes (–$940 to $1,150 for obesity vs. $2,000 to $4,700 for diabetes) when compared with costs for people who are nonobese (33,34).”

iv. Severe Hypoglycemia and Mortality After Cardiovascular Events for Type 1 Diabetic Patients in Sweden.

“This study examines factors associated with all-cause mortality after cardiovascular complications (myocardial infarction [MI] and stroke) in patients with type 1 diabetes. In particular, we aim to determine whether a previous history of severe hypoglycemia is associated with increased mortality after a cardiovascular event in type 1 diabetic patients.

Hypoglycemia is the most common and dangerous acute complication of type 1 diabetes and can be life threatening if not promptly treated (1). The average individual with type 1 diabetes experiences about two episodes of symptomatic hypoglycemia per week, with an annual prevalence of 30–40% for hypoglycemic episodes requiring assistance for recovery (2). We define severe hypoglycemia to be an episode of hypoglycemia that requires hospitalization in this study. […] Patients with type 1 diabetes are more susceptible to hypoglycemia than those with type 2 diabetes, and therefore it is potentially of greater relevance if severe hypoglycemia is associated with mortality (6).”

“This study uses a large linked data set comprising health records from the Swedish National Diabetes Register (NDR), which were linked to administrative records on hospitalization, prescriptions, and national death records. […] [The] study is based on data from four sources: 1) risk factor data from the Swedish NDR […], 2) hospital records of inpatient episodes from the National Inpatients Register (IPR) […], 3) death records […], and 4) prescription data records […]. A study comparing registered diagnoses in the IPR with information in medical records found positive predictive values of IPR diagnoses were 85–95% for most diagnoses (8). In terms of NDR coverage, a recent study found that 91% of those aged 18–34 years and with type 1 diabetes in the Prescribed Drug Register could be matched with those in the NDR for 2007–2009 (9).”

“The outcome of the study was all-cause mortality after a major cardiovascular complication (MI or stroke). Our sample for analysis included patients with type 1 diabetes who visited a clinic after 2002 and experienced a major cardiovascular complication after this clinic visit. […] We define type 1 diabetes as diabetes diagnosed under the age of 30 years, being reported as being treated with insulin only at some clinic visit, and when alive, having had at least one prescription for insulin filled per year between 2006 and 2010 […], and not having filled a prescription for metformin at any point between July 2005 and December 2010 (under the assumption that metformin users were more likely to be type 2 diabetes patients).”

“Explanatory variables included in both models were type of complication (MI or stroke), age at complication, duration of diabetes, sex, smoking status, HbA1c, BMI, systolic blood pressure, diastolic blood pressure, chronic kidney disease status based on estimated glomerular filtration rate, microalbuminuria and macroalbuminuria status, HDL, LDL, total–to–HDL cholesterol ratio, triglycerides, lipid medication status, clinic visits within the year prior to the CVD event, and prior hospitalization events: hypoglycemia, hyperglycemia, MI, stroke, heart failure, AF, amputation, PVD, ESRD, IHD/unstable angina, PCI, and CABG. The last known value for each clinical risk factor, prior to the cardiovascular complication, was used for analysis. […] Initially, all explanatory variables were included and excluded if the variable was not statistically significant at a 5% level (P < 0.05) via stepwise backward elimination.” [Aaaaaaargh! – US. These guys are doing a lot of things right, but this is not one of them. Just to mention this one more time: “Generally, hypothesis testing is a very poor basis for model selection […] There is no statistical theory that supports the notion that hypothesis testing with a fixed α level is a basis for model selection.” (Burnham & Anderson)]

“Patients who had prior hypoglycemic events had an estimated HR for mortality of 1.79 (95% CI 1.37–2.35) in the first 28 days after a CVD event and an estimated HR of 1.25 (95% CI 1.02–1.53) of mortality after 28 days post CVD event in the backward regression model. The univariate analysis showed a similar result compared with the backward regression model, with prior hypoglycemic events having an estimated HR for mortality of 1.79 (95% CI 1.38–2.32) and 1.35 (95% CI 1.11–1.65) in the logistic and Cox regressions, respectively. Even when all explanatory factors were included in the models […], the mortality increase associated with a prior severe hypoglycemic event was still significant, and the P values and SE are similar when compared with the backward stepwise regression. Similarly, when explanatory factors were included individually, the mortality increase associated with a prior severe hypoglycemic event was also still significant.” [Again, this sort of testing scheme is probably not a good approach to getting at a good explanatory model, but it’s what they did – US]

“The 5-year cumulative estimated mortality risk for those without complications after MI and stroke were 40.1% (95% CI 35.2–45.1) and 30.4% (95% CI 26.3–34.6), respectively. Patients with prior heart failure were at the highest estimated 5-year cumulative mortality risk, with those who suffered an MI and stroke having a 56.0% (95% CI 47.5–64.5) and 44.0% (95% CI 35.8–52.2) 5-year cumulative mortality risk, respectively. Patients who had a prior severe hypoglycemic event and suffered an MI had an estimated 5-year cumulative mortality risk at age 60 years of 52.4% (95% CI 45.3–59.5), and those who suffered a stroke had a 5-year cumulative mortality risk of 39.8% (95% CI 33.4–46.3). Patients at age 60 years who suffer a major CVD complication have over twofold risk of 5-year mortality compared with the general type 1 diabetic Swedish population, who had an estimated 5-year mortality risk of 13.8% (95% CI 12.0–16.1).”

“We found evidence that prior severe hypoglycemia is associated with reduced survival after a major CVD event but no evidence that prior severe hypoglycemia is associated with an increased risk of a subsequent CVD event.

Compared with the general type 1 diabetic Swedish population, a major CVD complication increased 5-year mortality risk at age 60 years by >25% and 15% in patients with an MI and stroke, respectively. Patients with a history of a hypoglycemic event had an even higher mortality after a major CVD event, with approximately an additional 10% being dead at the 5-year mark. This risk was comparable with that in those with late-stage kidney disease. This information is useful in determining the prognosis of patients after a major cardiovascular event and highlights the need to include this as a risk factor in simulation models (18) that are used to improve decision making (19).”

“This is the first study that has found some evidence of a dose-response relationship, where patients who experienced two or more severe hypoglycemic events had higher mortality after a cardiovascular event compared with those who experienced one severe hypoglycemic event. A lack of statistical power prevented us from investigating this further when we tried to stratify by number of prior severe hypoglycemic events in our regression models. There was no evidence of a dose-response relationship between repeated episodes of severe hypoglycemia and vascular outcomes or death in previous type 2 diabetes studies (5).”

v. Alterations in White Matter Structure in Young Children With Type 1 Diabetes.

“Careful regulation of insulin dosing, dietary intake, and activity levels are essential for optimal glycemic control in individuals with type 1 diabetes. However, even with optimal treatment many children with type 1 diabetes have blood glucose levels in the hyperglycemic range for more than half the day and in the hypoglycemic range for an hour or more each day (1). Brain cells may be especially sensitive to aberrant blood glucose levels, as glucose is the brain’s principal substrate for its energy needs.

Research in animal models has shown that white matter (WM) may be especially sensitive to dysglycemia-associated insult in diabetes (24). […] Early childhood is a period of rapid myelination and brain development (6) and of increased sensitivity to insults affecting the brain (6,7). Hence, study of the developing brain is particularly important in type 1 diabetes.”

“WM structure can be measured with diffusion tensor imaging (DTI), a method based on magnetic resonance imaging (MRI) that uses the movement of water molecules to characterize WM brain structure (8,9). Results are commonly reported in terms of mathematical scalars (representing vectors in vector space) such as fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). FA reflects the degree of diffusion anisotropy of water (how diffusion varies along the three axes) within a voxel (three-dimensional pixel) and is determined by fiber diameter and density, myelination, and intravoxel fiber-tract coherence (increases in which would increase FA), as well as extracellular diffusion and interaxonal spacing (increases in which would decrease FA) (10). AD, a measure of water diffusivity along the main axis of diffusion within a voxel, is thought to reflect fiber coherence and structure of axonal membranes (increases in which would increase AD), as well as microtubules, neurofilaments, and axonal branching (increases in which would decrease AD) (11,12). RD, the mean of the diffusivities perpendicular to the vector with the largest eigenvalue, is thought to represent degree of myelination (13,14) (more myelin would decrease RD values) and axonal “leakiness” (which would increase RD). Often, however, a combination of these WM characteristics results in opposing contributions to the final observed FA/AD/RD value, and thus DTI scalars should not be interpreted globally as “good” or “bad” (15). Rather, these scalars can show between-group differences and relationships between WM structure and clinical variables and are suggestive of underlying histology. Definitive conclusions about histology of WM can only be derived from direct microscopic examination of biological tissue.”

“Children (ages 4 to <10 years) with type 1 diabetes (n = 127) and age-matched nondiabetic control subjects (n = 67) had diffusion weighted magnetic resonance imaging scans in this multisite neuroimaging study. Participants with type 1 diabetes were assessed for HbA1c history and lifetime adverse events, and glucose levels were monitored using a continuous glucose monitor (CGM) device and standardized measures of cognition.

RESULTS Between-group analysis showed that children with type 1 diabetes had significantly reduced axial diffusivity (AD) in widespread brain regions compared with control subjects. Within the type 1 diabetes group, earlier onset of diabetes was associated with increased radial diffusivity (RD) and longer duration was associated with reduced AD, reduced RD, and increased fractional anisotropy (FA). In addition, HbA1c values were significantly negatively associated with FA values and were positively associated with RD values in widespread brain regions. Significant associations of AD, RD, and FA were found for CGM measures of hyperglycemia and glucose variability but not for hypoglycemia. Finally, we observed a significant association between WM structure and cognitive ability in children with type 1 diabetes but not in control subjects. […] These results suggest vulnerability of the developing brain in young children to effects of type 1 diabetes associated with chronic hyperglycemia and glucose variability.”

“The profile of reduced overall AD in type 1 diabetes observed here suggests possible axonal damage associated with diabetes (30). Reduced AD was associated with duration of type 1 diabetes suggesting that longer exposure to diabetes worsens the insult to WM structure. However, measures of hyperglycemia and glucose variability were either not associated or were positively associated with AD values, suggesting that these measures did not contribute to the observed decreased AD in the type 1 diabetes group. A possible explanation for these observations is that several biological processes influence WM structure in type 1 diabetes. Some processes may be related to insulin insufficiency or C-peptide levels independent of glucose levels (31,32) and may affect WM coherence (and reduce AD values as observed in the between-group results). Other processes related to hyperglycemia and glucose variability may target myelin (resulting in reduced FA and increased RD) as well as reduced axonal branching (both would result in increased AD values). Alternatively, these seemingly conflicting AD observations may be due to a dominant effect of age, which could overshadow effects from dysglycemia.

Early age of onset is one of the most replicable risk factors for cognitive impairments in type 1 diabetes (33,34). It has been hypothesized that young children are especially vulnerable to brain insults resulting from episodes of chronic hyperglycemia, hypoglycemia, and acute hypoglycemic complications of type 1 diabetes (seizures and severe hypoglycemic episodes). In addition, fear of hypoglycemia often results in caregivers maintaining relatively higher blood glucose to avoid lows altogether (1), especially in very young children. However, our study suggests that this approach of aggressive hypoglycemia avoidance resulting in hyperglycemia may not be optimal and may be detrimental to WM structure in young children.

Neuronal damage (reflected in altered WM structure) may affect neuronal signal transfer and, thus, cognition (35). Cognitive domains commonly reported to be affected in children with type 1 diabetes include general intellectual ability, visuospatial abilities, attention, memory, processing speed, and executive function (3638). In our sample, even though the duration of illness was relatively short (2.9 years on average), there were modest but significant cognitive differences between children with type 1 diabetes and control subjects (24).”

“In summary, we present results from the largest study to date investigating WM structure in very young children with type 1 diabetes. We observed significant and widespread brain differences in the WM microstructure of children with type 1 diabetes compared with nondiabetic control subjects and significant associations between WM structure and measures of hyperglycemia, glucose variability, and cognitive ability in the type 1 diabetic population.”

vi. Ultrasound Findings After Surgical Decompression of the Tarsal Tunnel in Patients With Painful Diabetic Polyneuropathy: A Prospective Randomized Study.

“Polyneuropathy is a common complication in diabetes. The prevalence of neuropathy in patients with diabetes is ∼30%. During the course of the disease, up to 50% of the patients will eventually develop neuropathy (1). Its clinical features are characterized by numbness, tingling, or burning sensations and typically extend in a distinct stocking and glove pattern. Prevention plays a key role since poor glucose control is a major risk factor in the development of diabetic polyneuropathy (DPN) (1,2).

There is no clear definition for the onset of painful diabetic neuropathy. Different hypotheses have been formulated.

Hyperglycemia in diabetes can lead to osmotic swelling of the nerves, related to increased glucose conversion into sorbitol by the enzyme aldose reductase (2,3). High sorbitol concentrations might also directly cause axonal degeneration and demyelination (2). Furthermore, stiffening and thickening of ligamental structures and the plantar fascia make underlying structures more prone to biomechanical compression (46). A thicker and stiffer retinaculum might restrict movements and lead to alterations of the nerve in the tarsal tunnel.

Both swelling of the nerve and changes in the tarsal tunnel might lead to nerve damage through compression.

Furthermore, vascular changes may diminish endoneural blood flow and oxygen distribution. Decreased blood supply in the (compressed) nerve might lead to ischemic damage as well as impaired nerve regeneration.

Several studies suggest that surgical decompression of nerves at narrow anatomic sites, e.g., the tarsal tunnel, is beneficial and has a positive effect on pain, sensitivity, balance, long-term risk of ulcers and amputations, and quality of life (3,710). Since the effect of decompression of the tibial nerve in patients with DPN has not been proven with a randomized clinical trial, its contribution as treatment for patients with painful DPN is still controversial. […] In this study, we compare the mean CSA and any changes in shape of the tibial nerve before and after decompression of the tarsal tunnel using ultrasound in order to test the hypothesis that the tarsal tunnel leads to compression of the tibial nerve in patients with DPN.”

“This study, with a large sample size and standardized sonographic imaging procedure with a good reliability, is the first randomized controlled trial that evaluates the effect of decompression of the tibial nerve on the CSA. Although no effect on CSA after surgery was found, this study using ultrasound demonstrates a larger and swollen tibial nerve and thicker flexor retinaculum at the ankle in patients with DPN compared with healthy control subjects.”

I would have been interested to know if there were any observable changes in symptom relief measures post-surgery, even if such variables are less ‘objective’ than measures like CSA (less objective, but perhaps more relevant to the patient…), but the authors did not look at those kinds of variables.

vii. Nonalcoholic Fatty Liver Disease Is Independently Associated With an Increased Incidence of Chronic Kidney Disease in Patients With Type 1 Diabetes.

“Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions worldwide (1). Up to 30% of adults in the U.S. and Europe have NAFLD, and the prevalence of this disease is much higher in people with diabetes (1,2). Indeed, the prevalence of NAFLD on ultrasonography ranges from ∼50 to 70% in patients with type 2 diabetes (35) and ∼40 to 50% in patients with type 1 diabetes (6,7). Notably, patients with diabetes and NAFLD are also more likely to develop more advanced forms of NAFLD that may result in end-stage liver disease (8). However, accumulating evidence indicates that NAFLD is associated not only with liver-related morbidity and mortality but also with an increased risk of developing cardiovascular disease (CVD) and other serious extrahepatic complications (810).”

“Increasing evidence indicates that NAFLD is strongly associated with an increased risk of CKD [chronic kidney disease, US] in people with and without diabetes (11). Indeed, we have previously shown that NAFLD is associated with an increased prevalence of CKD in patients with both type 1 and type 2 diabetes (1517), and that NAFLD independently predicts the development of incident CKD in patients with type 2 diabetes (18). However, many of the risk factors for CKD are different in patients with type 1 and type 2 diabetes, and to date, it is uncertain whether NAFLD is an independent risk factor for incident CKD in type 1 diabetes or whether measurement of NAFLD improves risk prediction for CKD, taking account of traditional risk factors for CKD.

Therefore, the aim of the current study was to investigate 1) whether NAFLD is associated with an increased incidence of CKD and 2) whether measurement of NAFLD improves risk prediction for CKD, adjusting for traditional risk factors, in type 1 diabetic patients.”

“Using a retrospective, longitudinal cohort study design, we have initially identified from our electronic database all Caucasian type 1 diabetic outpatients with preserved kidney function (i.e., estimated glomerular filtration rate [eGFR] ≥60 mL/min/1.73 m2) and with no macroalbuminuria (n = 563), who regularly attended our adult diabetes clinic between 1999 and 2001. Type 1 diabetes was diagnosed by the typical presentation of disease, the absolute dependence on insulin treatment for survival, the presence of undetectable fasting C-peptide concentrations, and the presence of anti–islet cell autoantibodies. […] Overall, 261 type 1 diabetic outpatients were included in the final analysis and were tested for the development of incident CKD during the follow-up period […] All participants were periodically seen (every 3–6 months) for routine medical examinations of glycemic control and chronic complications of diabetes. No participants were lost to follow-up. […] For this study, the development of incident CKD was defined as occurrence of eGFR <60 mL/min/1.73 m2 and/or macroalbuminuria (21). Both of these outcome measures were confirmed in all participants in a least two consecutive occasions (within 3–6 months after the first examination).”

“At baseline, the mean eGFRMDRD was 92 ± 23 mL/min/1.73 m2 (median 87.9 [IQR 74–104]), or eGFREPI was 98.6 ± 19 mL/min/1.73 m2 (median 99.7 [84–112]). Most patients (n = 234; 89.7%) had normal albuminuria, whereas 27 patients (10.3%) had microalbuminuria. NAFLD was present in 131 patients (50.2%). […] At baseline, patients who developed CKD at follow-up were older, more likely to be female and obese, and had a longer duration of diabetes than those who did not. These patients also had higher values of systolic blood pressure, A1C, triglycerides, serum GGT, and urinary ACR and lower values of eGFRMDRD and eGFREPI. Moreover, there was a higher percentage of patients with hypertension, metabolic syndrome, microalbuminuria, and some degree of diabetic retinopathy in patients who developed CKD at follow-up compared with those remaining free from CKD. The proportion using antihypertensive drugs (that always included the use of ACE inhibitors or angiotensin receptor blockers) was higher in those who progressed to CKD. Notably, […] this patient group also had a substantially higher frequency of NAFLD on ultrasonography.”

“During follow-up (mean duration 5.2 ± 1.7 years, range 2–10), 61 patients developed CKD using the MDRD study equation to estimate eGFR (i.e., ∼4.5% of participants progressed every year to eGFR <60 mL/min/1.73 m2 or macroalbuminuria). Of these, 28 developed an eGFRMDRD <60 mL/min/1.73 m2 with abnormal albuminuria (micro- or macroalbuminuria), 21 developed a reduced eGFRMDRD with normal albuminuria (but 9 of them had some degree of diabetic retinopathy at baseline), and 12 developed macroalbuminuria alone. None of them developed kidney failure requiring chronic dialysis. […] The annual eGFRMDRD decline for the whole cohort was 2.68 ± 3.5 mL/min/1.73 m2 per year. […] NAFLD patients had a greater annual decline in eGFRMDRD than those without NAFLD at baseline (3.28 ± 3.8 vs. 2.10 ± 3.0 mL/min/1.73 m2 per year, P < 0.005). Similarly, the frequency of a renal functional decline (arbitrarily defined as ≥25% loss of baseline eGFRMDRD) was greater among those with NAFLD than among those without the disease (26 vs. 11%, P = 0.005). […] Interestingly, BMI was not significantly associated with CKD.”

“Our novel findings indicate that NAFLD is strongly associated with an increased incidence of CKD during a mean follow-up of 5 years and that measurement of NAFLD improves risk prediction for CKD, independently of traditional risk factors (age, sex, diabetes duration, A1C, hypertension, baseline eGFR, and microalbuminuria [i.e., the last two factors being the strongest known risk factors for CKD]), in type 1 diabetic adults. Additionally, although NAFLD was strongly associated with obesity, obesity (or increased BMI) did not explain the association between NAFLD and CKD. […] The annual cumulative incidence rate of CKD in our cohort of patients (i.e., ∼4.5% per year) was essentially comparable to that previously described in other European populations with type 1 diabetes and similar baseline characteristics (∼2.5–9% of patients who progressed every year to CKD) (25,26). In line with previously published information (2528), we also found that hypertension, microalbuminuria, and lower eGFR at baseline were strong predictors of incident CKD in type 1 diabetic patients.”

“There is a pressing and unmet need to determine whether NAFLD is associated with a higher risk of CKD in people with type 1 diabetes. It has only recently been recognized that NAFLD represents an important burden of disease for type 2 diabetic patients (11,17,18), but the magnitude of the problem of NAFLD and its association with risk of CKD in type 1 diabetes is presently poorly recognized. Although there is clear evidence that NAFLD is closely associated with a higher prevalence of CKD both in those without diabetes (11) and in those with type 1 and type 2 diabetes (1517), only four prospective studies have examined the association between NAFLD and risk of incident CKD (18,2931), and only one of these studies was published in patients with type 2 diabetes (18). […] The underlying mechanisms responsible for the observed association between NAFLD and CKD are not well understood. […] The possible clinical implication for these findings is that type 1 diabetic patients with NAFLD may benefit from more intensive surveillance or early treatment interventions to decrease the risk for CKD. Currently, there is no approved treatment for NAFLD. However, NAFLD and CKD share numerous cardiometabolic risk factors, and treatment strategies for NAFLD and CKD should be similar and aimed primarily at modifying the associated cardiometabolic risk factors.”

 

October 25, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Health Economics, Medicine, Nephrology, Neurology, Pharmacology, Statistics, Studies | Leave a comment

A few diabetes papers of interest

i. Burden of Diabetic Foot Ulcers for Medicare and Private Insurers.

Some observations from the paper (my bold):

According to the American Diabetes Association, the annual cost of diabetes, which affects 22.3 million people in the U.S., was $245 billion in 2012: $176 billion in excess health care expenditures and $69 billion in reduced workforce productivity (1). While much of the excess health care cost is attributable to treatment of diabetes itself, a substantial amount of the cost differential arises via treatment of chronic complications such as those related to the heart, kidneys, and nervous system (1).

One common complication of diabetes is the development of foot ulcers. Historically, foot ulcers have been estimated to affect 1–4% of patients with diabetes annually (2,3) and as many as 25% of the patients with diabetes over their lifetimes (2). More recently, Margolis et al. (3) have estimated that the annual incidence of foot ulcers among patients with diabetes may be as high as 6%. Treatment of diabetic foot ulcers (DFUs) includes conventional wound management (e.g., debridement, moist dressings, and offloading areas of high pressure or friction) as well as more sophisticated treatments such as bioengineered cellular technologies and hyperbaric oxygen therapy (HBO) (4).

DFUs often require extensive healing time and are associated with increased risk for infections and other sequelae that can result in severe and costly outcomes (4). […] DFU patients have a low survival prognosis, with a 3-year cumulative mortality rate of 28% (6) and rates among amputated patients approaching 50% (7).”

“While DFU patients can require substantial amounts of resource use, little is known about the burden of DFUs imposed on the U.S. health care system and payers. In fact, we are aware of only two studies to date that have estimated the incremental medical resource use and costs of DFU beyond that of diabetes alone (6,8). Neither of these analyses, however, accounted for the many underlying differences between DFU and non-DFU patient populations, such as disproportionate presence of costly underlying comorbid conditions among DFU patients […] Other existing literature on the burden of DFUs in the U.S. calculated the overall health care costs (as opposed to incremental) without reference to a non-DFU control population (911). As a result of the variety of data and methodologies used, it is not surprising that the burden of DFUs reported in the literature is wide-ranging, with the average per-patient costs, for example, ranging from $4,595 per episode (9) to over $35,000 annually for all services (6).

The objective of this study was to expand and improve on previous research to provide a more robust, current estimate of incremental clinical and economic burden of DFUs. To do so, this analysis examined the differences in medical resource use and costs between patients with DFUs during a recent time period (January 2007–September 2011) and a matched control population with diabetes but without DFUs, using administrative claims records from nationally representative databases for Medicare and privately insured populations. […] [Our] criteria resulted in a final analytic sample of 231,438 Medicare patients, with 29,681 (12.8%) identified as DFU patients and the remaining 201,757 comprising the potential control population of non-DFU diabetic patients. For private insurance, 119,018 patients met the sample selection criteria, with 5,681 (4.8%) DFU patients and 113,337 potential controls (Fig. 1).”

Prior to matching, DFU patients were statistically different from the non-DFU control population on nearly every dimension examined during the 12-month preindex period. […] The matching process resulted in the identification of 27,878 pairs of DFU and control patients for Medicare and 4,536 pairs for private insurance that were very similar with regards to preindex patient characteristics […] [I]mportantly, the matched DFU and control groups had comparable health care costs during the 12 months prior to the index date (Medicare, $17,744 DFU and controls; private insurance, $14,761 DFU vs. $14,766 controls). […] Despite having matched the groups to ensure similar patient characteristics, DFU patients used significantly (P < 0.0001) more medical resources during the 12-month follow-up period than did the matched controls […]. Among matched Medicare patients, DFU patients had 138.2% more days hospitalized, 85.4% more days of home health care, 40.6% more ED visits, and 35.1% more outpatient/physician office visits. The results were similar for the privately insured DFU patients, who had 173.5% more days hospitalized, 230.0% more days of home health care, 109.0% more ED visits, and 42.5% more outpatient/physician office visits than matched controls. […] The rate of lower limb amputations was 3.8% among matched Medicare DFU patients and 5.0% among matched privately insured DFU patients. In contrast, observed lower limb amputation rates among diabetic patients without foot ulcer were only 0.04% in Medicare and 0.02% in private insurance.”

Increased medical resource utilization resulted in DFU patients having approximately twice the costs as the matched non-DFU controls […], with annual incremental per-patient medical costs ranging from $11,710 for Medicare ($28,031 vs. $16,320; P < 0.0001) to $15,890 for private insurance ($26,881 vs. $10,991; P < 0.0001). All places of service (i.e., inpatient, ED, outpatient/physician office, home health care, and other) contributed approximately equally to the cost differential among Medicare patients. For the privately insured, however, increased inpatient costs ($17,061 vs. $6,501; P < 0.0001) were responsible for nearly two-thirds of the overall cost differential, […] resulting in total incremental direct health care (i.e., medical + prescription drug) costs of $16,883 ($31,419 vs. $14,536; P < 0.0001). Substantial proportions of the incremental medical costs were attributable to claims with DFU-related diagnoses or procedures for both Medicare (45.1%) and privately insured samples (60.3%).”

“Of the 4,536 matched pairs of privately insured patients, work-loss information was available for 575 DFU patients and 857 controls. DFU patients had $3,259 in excess work-loss costs ($6,311 vs. $3,052; P < 0.0001) compared with matched controls, with disability and absenteeism comprising $1,670 and $1,589 of the overall differential, respectively […] The results indicate that compared with diabetic patients without foot ulcers, DFU patients miss more days of work due to medical-related absenteeism and to disability, imposing additional burden on employers.”

“These estimates indicate that DFU imposes substantial burden on payers beyond that required to treat diabetes itself. For example, prior research has estimated annual per-patient incremental health care expenditures for patients with diabetes (versus those without diabetes) of approximately $7,900 (1). The estimates of this analysis suggest that the presence of DFU further compounds these incremental treatment costs by adding $11,710 to $16,883 per patient. Stated differently, the results indicate that the excess health care costs of DFU are approximately twice that attributable to treatment of diabetes itself, and that the presence of DFU approximately triples the excess cost differential versus a population of patients without diabetes.

“Using estimates of the total U.S. diabetes population (22.3 million) (1) and the midpoint (3.5%) of annual DFU incidence estimates (1–6%) (2,3), the results of this analysis suggest an annual incremental payer burden of DFU ranging from $9.1 billion (22.3 million patients with diabetes × 3.5% DFU incidence × $11,710 Medicare cost differential) to $13.2 billion (22.3 million patients with diabetes × 3.5% DFU incidence × $16,883 private insurance cost differential). These estimates, moreover, likely understate the actual burden of DFU because the incremental costs referenced in this calculation do not include excess work-loss costs described above, prescription drug costs for Medicare patients, out-of-pocket costs paid by the patient, costs borne by supplemental insurers, and other (non-work loss) indirect costs such as those associated with premature mortality, reduced quality of life, and informal caregiving.”

ii. Contributors to Mortality in High-Risk Diabetic Patients in the Diabetes Heart Study.

“Rates of cardiovascular disease (CVD) are two- to fourfold greater in individuals with type 2 diabetes compared with nondiabetic individuals, and up to 65% of all-cause mortality among individuals with type 2 diabetes is attributed to CVD (1,2). However, the risk profile is not uniform for all individuals affected by diabetes (35). Coronary artery calcified plaque (CAC), determined using computed tomography, is a measure of CVD burden (6,7). CAC scores have been shown to be an independent predictor of CVD outcomes and mortality in population-based studies (810) and a powerful predictor of all-cause and CVD mortality in individuals affected by type 2 diabetes (4,1115).

In the Diabetes Heart Study (DHS), individuals with CAC >1,000 were found to have greater than 6-fold (16) and 11-fold (17) increased risk for all-cause mortality and CVD mortality, respectively, after 7 years of follow-up. With this high risk for adverse outcomes, it is noteworthy that >50% of the DHS sample with CAC >1,000 have lived with this CVD burden for (now) an average of over 12 years. This suggests that outcomes vary in the type 2 diabetic patient population, even among individuals with the highest risk. This study examined the subset of DHS participants with CAC >1,000 and evaluated whether differences in a range of clinical factors and measurements, including modifiable CVD risk factors, provided further insights into risk for mortality.”

“This investigation focused on 371 high-risk participants (from 260 families) […] The goal of this analysis was to identify clinical and other characteristics that influence risk for all-cause mortality in high-risk (baseline CAC >1,000) DHS participants. […] a predominance of traditional CVD risk factors, including older age, male sex, elevated BMI, and high rates of dyslipidemia and hypertension, was evident in this high-risk subgroup (Table 1). These participants were followed for 8.2 ± 3.0 years (mean ± SD), over which time 41% died. […] a number of indices continued to significantly predict outcome following adjustment for other CVD risk factors (including age, sex, and medication use) […]. Higher cholesterol and LDL concentrations were associated with an increased risk (∼1.3-fold) for mortality […] Slightly larger increases in risk for mortality were observed with changes in kidney function (1.3- to 1.4-fold) and elevated CRP (∼1.4-fold) […] use of cholesterol-lowering medication was less common among the deceased participants; those reporting no use of cholesterol-lowering medication at baseline were at a 1.4-fold increased risk of mortality […] these results confirm that, even among this high-risk group, heterogeneity in known CVD risk factors and associations with adverse outcomes are still observed and support their ongoing consideration as useful tools for individual risk assessment. Finally, the data presented here suggest that use of cholesterol-lowering medication was strongly associated with protection, supporting the known beneficial effects of cholesterol management on CVD risk (28,29). […] data suggest that cholesterol-lowering medications may be used less than recommended and need to be more aggressively targeted as a critical modifiable risk factor.”

iii. Neurological Consequences of Diabetic Ketoacidosis at Initial Presentation of Type 1 Diabetes in a Prospective Cohort Study of Children.

“Patients aged 6–18 years with and without DKA at diagnosis were studied at four time points: <48 h, 5 days, 28 days, and 6 months postdiagnosis. Patients underwent magnetic resonance imaging (MRI) and spectroscopy with cognitive assessment at each time point. Relationships between clinical characteristics at presentation and MRI and neurologic outcomes were examined using multiple linear regression, repeated-measures, and ANCOVA analyses.”

“With DKA, cerebral white matter showed the greatest alterations with increased total white matter volume and higher mean diffusivity in the frontal, temporal, and parietal white matter. Total white matter volume decreased over the first 6 months. For gray matter in DKA patients, total volume was lower at baseline and increased over 6 months. […] Of note, although changes in total and regional brain volumes over the first 5 days resolved, they were associated with poorer delayed memory recall and poorer sustained and divided attention at 6 months. Age at time of presentation and pH level were predictors of neuroimaging and functional outcomes.

CONCLUSIONS DKA at type 1 diabetes diagnosis results in morphologic and functional brain changes. These changes are associated with adverse neurocognitive outcomes in the medium term.”

“This study highlights the common nature of transient focal cerebral edema and associated impaired mental state at presentation with new-onset type 1 diabetes in children. We demonstrate that alterations occur most markedly in cerebral white matter, particularly in the frontal lobes, and are most prominent in the youngest children with the most dramatic acidemia. […] early brain changes were associated with persisting alterations in attention and memory 6 months later. Children with DKA did not differ in age, sex, SES, premorbid need for school assistance/remediation, or postdiagnosis clinical trajectory. Earlier diagnosis of type 1 diabetes in children may avoid the complication of DKA and the neurological consequences documented in this study and is worthy of a major public health initiative.”

“In relation to clinical risk factors, the degree of acidosis and younger age appeared to be the greatest risk factors for alterations in cerebral structure. […] cerebral volume changes in the frontal, temporal, and parietal regions in the first week after diagnosis were associated with lower attention and memory scores 6 months later, suggesting that functional information processing difficulties persist after resolution of tissue water increases in cerebral white matter. These findings have not been reported to date but are consistent with the growing concern over academic performance in children with diabetes (2). […] Brain injury should no longer be considered a rare complication of DKA. This study has shown that it is both frequent and persistent.” (my bold)

iv. Antihypertensive Treatment and Resistant Hypertension in Patients With Type 1 Diabetes by Stages of Diabetic Nephropathy.

“High blood pressure (BP) is a risk factor for coronary artery disease, heart failure, and stroke, as well as for chronic kidney disease. Furthermore, hypertension has been estimated to affect ∼30% of patients with type 1 diabetes (1,2) and both parallels and precedes the worsening of kidney disease in these patients (35). […] Despite strong evidence that intensive treatment of elevated BP reduces the risk of cardiovascular disease and microvascular complications, as well as improves the prognosis of patients with diabetic nephropathy (especially with the use of ACE inhibitors [ACEIs] and angiotensin II antagonists [angiotensin receptor blockers, ARBs]) (1,911), treatment targets and recommendations seem difficult to meet in clinical practice (1215). This suggests that the patients might either show poor adherence to the treatment and lifestyle changes or have a suboptimal drug regimen. It is evident that most patients with hypertension might require multiple-drug therapy to reach treatment goals (16). However, certain subgroups of the patients have been considered to have resistant hypertension (RH). RH is defined as office BP that remains above target even after using a minimum of three antihypertensive drugs at maximal tolerated doses, from different classes, one of which is a diuretic. Also, patients with controlled BP using four or more antihypertensive drugs are considered resistant to treatment (17).”

“The true prevalence of RH is unknown, but clinical trials suggest a share between 10 and 30% of the hypertensive patients in the general population (18). […] Only a few studies have considered BP control and treatment in patients with type 1 diabetes (2,15,22). Typically these studies have been limited to a small number of participants, which has not allowed stratifying of the patients according to the nephropathy status. The rate of RH is therefore unknown in patients with type 1 diabetes in general and with respect to different stages of diabetic nephropathy. Therefore, we estimated to what extent patients with type 1 diabetes meet the BP targets proposed by the ADA guidelines. We also evaluated the use of antihypertensive medication and the prevalence of RH in the patients stratified by stage of diabetic nephropathy.”

“[A]ll adult patients with type 1 diabetes from >80 hospitals and primary healthcare centers across Finland were asked to participate. Type 1 diabetes was defined by age at onset of diabetes <40 years, C-peptide ≤0.3 nmol/L, and insulin treatment initiated within 1 year of diagnosis, if C-peptide was not measured. […] we used two different ADA BP targets: <130/85 mmHg, which was the target until 2000 (6), and <130/80 mmHg, which was the target between 2001 and 2012 (7). Patients were divided into groups based on whether their BP had reached the target or not and whether the antihypertensive drug was in use or not. […] uncontrolled hypertension was defined as failure to achieve target BP, based on these two different ADA guidelines, despite use of antihypertensive medication. RH was defined as failure to achieve the goal BP (<130/85 mmHg) even after using a minimum of three antihypertensive drugs, from different classes, one of which was a diuretic. […] On the basis of eGFR (mL/min/1.73 m2) level, patients were classified into five groups according to the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines: stage 1 eGFR ≥90, stage 2 eGFR 60–89, stage 3 eGFR 30–59, stage 4 eGFR 15–29, and stage 5 eGFR <15. Patients who were on dialysis were classified into stage 5. […] A total of 3,678 patients with complete data on systolic and diastolic BP and nephropathy status were identified from the FinnDiane database. […] The mean age was 38.0 ± 12.0 and mean duration of diabetes 22.1 ± 12.3 years.  […] The patients with advanced diabetic nephropathy had higher BP, worse dyslipidemia, poorer glycemic control, and more insulin resistance and macrovascular complications. BMI values were lower in the dialysis patients, probably due to renal cachexia.”

“Of all patients, 60.9% did not reach the BP target <130/85 mmHg, and the proportion was 70.3% with the target of <130/80 mmHg. […] The patients who were not on target had higher age and longer duration of diabetes and were more likely to be men. They also had poorer glycemic and lipid control as well as more micro- and macrovascular complications. […] Based on the BP target <130/85 mmHg, more than half of the patients in the normoalbuminuria group did not reach the BP target, and the share increased along with the worsening of nephropathy; two-thirds of the patients in the microalbuminuria group and fourfifths in the macroalbuminuria group were not on target, while even 90% of the dialysis and kidney transplant patients did not reach the target (Fig. 1A). Based on the stricter BP target of <130/80 mmHg, the numbers were obviously worse, but the trend was the same (Fig. 1B).”

“About 37% of the FinnDiane patients had antihypertensive treatment […] Whereas 14.1% of the patients with normal AER [Albumin Excretion Rate] had antihypertensive treatment, the proportions were 60.5% in the microalbuminuric, 90.3% in the macroalbuminuric, 88.6% in the dialysis, and 91.2% in the kidney transplant patients. However, in all groups, only a minority of the patients had BP values on target with the antihypertensive drug treatment they were prescribed […] The mean numbers of antihypertensive drugs varied within the nephropathy groups between those who had BP on target and those who did not […]. However, only in the micro- (P = 0.02) and macroalbuminuria (P = 0.003) groups were the mean numbers of the drugs higher if the BP was not on target, compared with those who had reached the targets. Notably, among the patients with normoalbuminuria who had not reached the BP target, 58% and, of the patients with microalbuminuria, 61% were taking only one antihypertensive drug. In contrast, more than half of the dialysis and 40% of the macroalbuminuric and transplanted patients, who had not reached the targets, had at least three drugs in their regimen. Moreover, one-fifth of the dialysis, 15% of the macroalbuminuric, and 10% of the transplanted patients had at least four antihypertensive drugs in use without reaching the target (Table 2). Almost all patients treated with antihypertensive drugs in the normo-, micro-, and macroalbuminuria groups (76% of normo-, 93% of micro-, and 89% of macrolbuminuric patients) had ACEIs or ARBs in the regimen. The proportions were lower in the ESRD groups: 42% of the dialysis and 29% of the transplanted patients were taking these drugs.”

“In general, the prevalence of RH was 7.9% for all patients with type 1 diabetes (n = 3,678) and 21.2% for the antihypertensive drug–treated patients (n = 1,370). The proportion was higher in men than in women (10.0 vs. 5.7%, P < 0.0001) […] When the patients were stratified by nephropathy status, the figures changed; in the normoalbuminuria group, the prevalence of RH was 1.2% of all and 8.7% of the drug treated patients. The corresponding numbers were 4.7 and 7.8% for the microalbuminuric patients, 28.1 and 31.2% for the macroalbuminuric patients, 36.6 and 41.3% for the patients on dialysis, and 26.3 and 28.8% for the kidney-transplanted patients, respectively […] The prevalence of RH also increased along with the worsening of renal function. The share was 1.4% for all and 7.4% for drug-treated patients at KDOQI stage 1. The corresponding numbers were 3.8 and 10.0% for the patients at stage 2, 26.6 and 30.0% for the patients at stage 3, 54.8 and 56.0% for the patients at stage 4, and 48.0 and 52.1% for those at stage 5, when kidney transplantation patients were excluded. […] In a multivariate logistic regression analysis, higher age, lower eGFR, higher waist-to-hip ratio, higher triglycerides, as well as microalbuminuria and macroalbuminuria, when normoalbuminuria was the reference category, were independently associated with RH […] A separate analysis also showed that dietary sodium intake, based on urinary sodium excretion rate, was independently associated with RH.”

“The current study shows that the prevalence of RH in patients with type 1 diabetes increases alongside the worsening of diabetic nephropathy. Whereas less than one-tenth of the antihypertensive drug–treated patients with normo- or microalbuminuria met the criteria for RH, the proportions were substantially higher among the patients with overt nephropathy: one-third of the patients with macroalbuminuria or a transplanted kidney and even 40% of the patients on dialysis. […] the prevalence of RH for the drug-treated patients was even higher (56%) in patients at the predialysis stage (eGFR 15–29). The findings are consistent with other studies that have demonstrated that chronic kidney disease is a strong predictor of failure to achieve BP targets despite the use of three or more different types of antihypertensive drugs in the general hypertensive population (26).”

“The prevalence of RH was 21.2% of the patients treated with antihypertensive drugs. Previous studies have indicated a prevalence of RH of 13% among patients being treated for hypertension (1921,27). […] the prevalence [of RH] seems to be […] higher among the drug-treated type 1 diabetic patients. These figures can only partly be explained by the use of a lower treatment target for BP, as recommended for patients with diabetes (6), since even when we used the BP target recommended for hypertensive patients (<140/90 mmHg), our data still showed a higher prevalence of RH (17%).”

“The study also confirmed previous findings that a large number of patients with type 1 diabetes do not achieve the recommended BP targets. Although the prevalence of RH increased with the severity of diabetic nephropathy, our data also suggest that patients with normo- and microalbuminuria might have a suboptimal drug regimen, since the majority of those who had not reached the BP target were taking only one antihypertensive drug. […] There is therefore an urgent need to improve antihypertensive treatment, not only in patients with overt nephropathy but also in those who have elevated BP without complications or early signs of renal disease. Moreover, further emphasis should be placed on the transplanted patients, since it is well known that hypertension affects both graft and patient survival negatively (30).” (my bold)

v. Association of Autoimmunity to Autonomic Nervous Structures With Nerve Function in Patients With Type 1 Diabetes: A 16-Year Prospective Study.

“Neuropathy is a chronic complication that includes a number of distinct syndromes and autonomic dysfunctions and contributes to increase morbidity and mortality in the diabetic population. In particular, cardiovascular autonomic neuropathy (CAN) is an independent risk factor for mortality in type 1 diabetes and is associated with poor prognosis and poor quality of life (13). Cardiovascular (CV) autonomic regulation rests upon a balance between sympathetic and parasympathetic innervation of the heart and blood vessels controlling heart rate and vascular dynamics. CAN encompasses several clinical manifestations, from resting tachycardia to fatal arrhythmia and silent myocardial infarction (4).

The mechanisms responsible for altered neural function in diabetes are not fully understood, and it is assumed that multiple mutually perpetuating pathogenic mechanisms may concur. These include dysmetabolic injury, neurovascular insufficiency, deficiency of neurotrophic growth factors and essential fatty acids, advanced glycosylation products (5,6), and autoimmune damage. Independent cross-sectional and prospective (713) studies identified circulating autoantibodies to autonomic nervous structures and hypothesized that immune determinants may be involved in autonomic nerve damage in type 1 diabetes. […] However, demonstration of a cause–effect relationship between antibodies (Ab) and diabetic autonomic neuropathy awaits confirmation.”

“We report on a 16-year follow-up study specifically designed to prospectively examine a cohort of patients with type 1 diabetes and aimed at assessing whether the presence of circulating Ab to autonomic nervous structures is associated with increased risk and predictive value of developing CAN. This, in turn, would be highly suggestive of the involvement of autoimmune mechanisms in the pathogenesis of this complication.”

“The present prospective study, conducted in young patients without established autonomic neuropathy at recruitment and followed for over 16 years until adulthood, strongly indicates that a cause–effect relationship may exist between auto-Ab to autonomic nervous tissues and development of diabetic autonomic neuropathy. Incipient or established CAN (22) reached a prevalence of 68% among the Ab-positive patients, significantly higher compared with the Ab-negative patients. […] Logistic regression analysis indicates that auto-Ab carry an almost 15-fold increased RR of developing an abnormal DB [deep breathing] test over 16 years and an almost sixfold increase of developing at least one abnormal CV [cardiovascular] test, independent of other variables. […] Circulating Ab to autonomic structures are associated with the development of autonomic dysfunction in young diabetic patients independent of glycemic control. […] autoimmune mechanisms targeting sympathetic and parasympathetic structures may play a primary etiologic role in the development and progression of autonomic dysfunction in type 1 diabetes in the long term. […] positivity for auto-Ab had a high positive predictive value for the later development of autonomic neuropathy.”

“Diabetic autonomic neuropathy, possibly the least recognized and most overlooked of diabetes complications, has increasingly gained attention as an independent predictor of silent myocardial ischemia and mortality, as consistently indicated by several cross-sectional studies (2,3,33). The pooled prevalence rate risk for silent ischemia is estimated at 1.96 by meta-analysis studies (5). In this report, established CAN (22) was detected in nearly 20% of young adult patients with acceptable metabolic control, after over approximately 23 years of diabetes duration, against 12% of patients of the same cohort with subtle asymptomatic autonomic dysfunction (one abnormal CV test) a decade earlier, in line with other studies in type 1 diabetes (2,24). Approximately 30% of the patients developed signs of peripheral somatic neuropathy not associated with autonomic dysfunction. This discrepancy suggests the participation of pathogenic mechanisms different from metabolic control and a distinct clinical course, as indicated by the DCCT study, where hyperglycemia had a less robust relationship with autonomic than somatic neuropathy (6).”

“Furthermore, this study shows that autonomic neuropathy, together with female sex and the occurrence of severe hypoglycemia, is a major determinant for poor quality of life in patients with type 1 diabetes. This is in agreement with previous reports (35) and linked to such invalidating symptoms as orthostatic hypotension and chronic diarrhea. […] In conclusion, the current study provides persuasive evidence for a primary pathogenic role of autoimmunity in the development of autonomic diabetic neuropathy. However, the mechanisms through which auto-Ab impair their target organ function, whether through classical complement action, proapoptotic effects of complement, enhanced antigen presentation, or channelopathy (26,39,40), remain to be elucidated.” (my bold)

vi. Body Composition Is the Main Determinant for the Difference in Type 2 Diabetes Pathophysiology Between Japanese and Caucasians.

“According to current understanding, the pathophysiology of type 2 diabetes is different in Japanese compared with Caucasians in the sense that Japanese are unable to compensate insulin resistance with increased insulin secretion to the same extent as Caucasians. Prediabetes and early stage diabetes in Japanese are characterized by reduced β-cell function combined with lower degree of insulin resistance compared with Caucasians (810). In a prospective, cross-sectional study of individuals with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT), it was demonstrated that Japanese in Japan were more insulin sensitive than Mexican Americans in the U.S. and Arabs in Israel (11). The three populations also differed with regards to β-cell response, whereas the disposition index — a measure of insulin secretion relative to insulin resistance — was similar across ethnicities for NGT and IGT participants. These studies suggest that profound differences in type 2 diabetes pathophysiology exist between different populations. However, few attempts have been made to establish the underlying demographic or lifestyle-related factors such as body composition, physical fitness, and physical activity leading to these differences.”

“The current study aimed at comparing Japanese and Caucasians at various glucose tolerance states, with respect to 1) insulin sensitivity and β-cell response and 2) the role of demographic, genetic, and lifestyle-related factors as underlying predictors for possible ethnic differences in insulin sensitivity and β-cell response. […] In our study, glucose profiles from OGTTs [oral glucose tolerance tests] were similar in Japanese and Caucasians, whereas insulin and C-peptide responses were lower in Japanese participants compared with Caucasians. In line with these observations, measures of β-cell response were generally lower in Japanese, who simultaneously had higher insulin sensitivity. Moreover, β-cell response relative to the degree of insulin resistance as measured by disposition indices was virtually identical in the two populations. […] We […] confirmed the existence of differences in insulin sensitivity and β-cell response between Japanese and Caucasians and showed for the first time that a major part of these differences can be explained by differences in body composition […]. On the basis of these results, we propose a similar pathophysiology of type 2 diabetes in Caucasians and Japanese with respect to insulin sensitivity and β-cell function.”

October 12, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Health Economics, Medicine, Nephrology, Neurology, Pharmacology, Studies | Leave a comment