A few diabetes papers of interest
Some observations from the paper:
“Type 1 diabetes is preceded by the presence of preclinical, persistent islet autoantibodies (1). Autoantibodies against insulin (IAA) (2), GAD (GADA), insulinoma-associated antigen 2 (IA-2A) (3), and/or zinc transporter 8 (ZnT8A) (4) are typically present prior to development of symptomatic hyperglycemia and progression to clinical disease. These autoantibodies may develop many years before onset of type 1 diabetes, and increasing autoantibody number and titers have been associated with increased risk of progression to disease (5–7).
Identical twins have an increased risk of progression of islet autoimmunity and type 1 diabetes after one twin is diagnosed, although reported rates have been highly variable (30–70%) (8–11). This risk is increased if the proband twin develops diabetes at a young age (12). Concordance rates for type 1 diabetes in monozygotic twins with long-term follow-up is >50% (13). Risk for development of islet autoimmunity and type 1 diabetes for nonidentical twins is thought to be similar to non-twin siblings (risk of 6–10% for diabetes) (14). Full siblings who inherit both high-risk HLA (HLA DQA1*05:01 DR3/4*0302) haplotypes identical to their proband sibling with type 1 diabetes have a much higher risk for development of diabetes than those who share only one or zero haplotypes (55% vs. 5% by 12 years of age, respectively; P = 0.03) (15). Despite sharing both HLA haplotypes with their proband, siblings without the HLA DQA1*05:01 DR3/4*0302 genotype had only a 25% risk for type 1 diabetes by 12 years of age (15).”
“The TrialNet Pathway to Prevention Study (previously the TrialNet Natural History Study; 16) has been screening relatives of patients with type 1 diabetes since 2004 and follows these subjects with serial autoantibody testing for the development of islet autoantibodies and type 1 diabetes. The study offers longitudinal monitoring for autoantibody-positive subjects through HbA1c testing and oral glucose tolerance tests (OGTTs).”
“The purpose of this study was to evaluate the prevalence of islet autoantibodies and analyze a logistic regression model to test the effects of genetic factors and common twin environment on the presence or absence of islet autoantibodies in identical twins, nonidentical twins, and full siblings screened in the TrialNet Pathway to Prevention Study. In addition, this study analyzed the presence of islet autoantibodies (GADA, IA-2A, and IAA) and risk of type 1 diabetes over time in identical twins, nonidentical twins, and full siblings followed in the TrialNet Pathway to Prevention Study. […] A total of 48,051 sibling subjects were initially screened (288 identical twins, 630 nonidentical twins, and 47,133 full siblings). Of these, 48,026 had an initial screening visit with GADA, IA2A, and IAA results (287 identical twins, 630 nonidentical twins, and 47,109 full siblings). A total of 17,226 participants (157 identical twins, 283 nonidentical twins and 16,786 full siblings) were followed for a median of 2.1 years (25th percentile 1.1 year and 75th percentile 4.0 years), with follow-up defined as at least ≥12 months follow-up after initial screening visit.”
“At the initial screening visit, GADA was present in 20.2% of identical twins (58 out of 287), 5.6% of nonidentical twins (35 out of 630), and 4.7% of full siblings (2,205 out of 47,109) (P < 0.0001). Additionally, IA-2A was present primarily in identical twins (9.4%; 27 out of 287) and less so in nonidentical twins (3.3%; 21 out of 630) and full siblings (2.2%; 1,042 out of 47,109) (P = 0.0001). Nearly 12% of identical twins (34 out of 287) were positive for IAA at initial screen, whereas 4.6% of nonidentical twins (29 out of 630) and 2.5% of full siblings (1,152 out of 47,109) were initially IAA positive (P < 0.0001).”
“At 3 years of follow-up, the risk for development of GADA was 16% for identical twins, 5% for nonidentical twins, and 4% for full siblings (P < 0.0001) (Fig. 1A). The risk for development of IA-2A by 3 years of follow-up was 7% for identical twins, 4% for nonidentical twins, and 2% for full siblings (P = 0.0005) (Fig. 1B). At 3 years of follow-up, the risk of development of IAA was 10% for identical twins, 5% for nonidentical twins, and 4% for full siblings (P = 0.006) […] In initially autoantibody-negative subjects, 1.5% of identical twins, 0% of nonidentical twins, and 0.5% of full siblings progressed to diabetes at 3 years of follow-up (P = 0.18) […] For initially single autoantibody–positive subjects, at 3 years of follow-up, 69% of identical twins, 13% of nonidentical twins, and 12% of full siblings developed type 1 diabetes (P < 0.0001) […] Subjects who were positive for multiple autoantibodies at screening had a higher risk of developing type 1 diabetes at 3 years of follow-up with 69% of identical twins, 72% of nonidentical twins, and 47% of full siblings developing type 1 diabetes (P = 0.079)”
“Because TrialNet is not a birth cohort and the median age at screening visit was 11 years overall, this study would not capture subjects who had initial seroconversion at a young age and then progressed through the intermediate stage of multiple antibody positivity before developing diabetes.”
“This study of >48,000 siblings of patients with type 1 diabetes shows that at initial screening, identical twins were more likely to have at least one positive autoantibody and be positive for GADA, IA-2A, and IAA than either nonidentical twins or full siblings. […] risk for development of type 1 diabetes at 3 years of follow-up was high for both single and multiple autoantibody–positive identical twins (62–69%) and multiple autoantibody–positive nonidentical twins (72%) compared with 47% for initially multiple autoantibody–positive full siblings and 12–13% for initially single autoantibody–positive nonidentical twins and full siblings. To our knowledge, this is the largest prediagnosis study to evaluate the effects of genetic factors and common twin environment on the presence or absence of islet autoantibodies.
In this study, younger age, male sex, and genetic factors were significantly associated with expression of IA-2A, IAA, more than one autoantibody, and more than two autoantibodies, whereas only genetic factors were significant for GADA. An influence of common twin environment (E) was not seen. […] Previous studies have shown that identical twin siblings of patients with type 1 diabetes have a higher concordance rate for development of type 1 diabetes compared with nonidentical twins, although reported rates for identical twins have been highly variable (30–70%) […]. Studies from various countries (Australia, Denmark, Finland, Great Britain, and U.S.) have reported concordance rates for nonidentical twins ∼5–15% […]. Concordance rates have been higher when the proband was diagnosed at a younger age (8), which may explain the variability in these reported rates. In this study, autoantibody-negative nonidentical and identical twins had a low risk of type 1 diabetes by 3 years of follow-up. In contrast, once twins developed autoantibodies, risk for type 1 diabetes was high for multiple autoantibody nonidentical twins and both single and multiple autoantibody identical twins.”
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This is another paper in the ‘‘ segment from the February edition of Diabetes Care – multiple other papers on related topics were also included in that edition, so if you’re interested in the genetics of diabetes it may be worth checking out.
Some observations from the paper:
“Type 2 diabetes is a progressive disease due to a gradual reduction in the capacity of the pancreatic islet cells (β-cells) to produce insulin (1). The clinical course of this progression is highly variable, with some patients progressing very rapidly to requiring insulin treatment, whereas others can be successfully treated with lifestyle changes or oral agents for many years (1,2). Being able to identify patients likely to rapidly progress may have clinical utility in prioritization monitoring and treatment escalation and in choice of therapy.
It has previously been shown that many patients with clinical features of type 2 diabetes have positive GAD65 autoantibodies (GADA) and that the presence of this autoantibody is associated with faster progression to insulin (3,4). This is often termed latent autoimmune diabetes in adults (LADA) (5,6). However, the predictive value of GADA testing is limited in a population with clinical type 2 diabetes, with many GADA-positive patients not requiring insulin treatment for many years (4,7). Previous research has suggested that genetic variants in the HLA region associated with type 1 diabetes are associated with more rapid progression to insulin in patients with clinically defined type 2 diabetes and positive GADA (8).
We have recently developed a type 1 diabetes genetic risk score (T1D GRS), which provides an inexpensive ($70 in our local clinical laboratory and <$20 where DNA has been previously extracted), integrated assessment of a person’s genetic susceptibility to type 1 diabetes (9). The score is composed of 30 type 1 diabetes risk variants weighted for effect size and aids discrimination of type 1 diabetes from type 2 diabetes. […] We aimed to determine if the T1D GRS could predict rapid progression to insulin (within 5 years of diagnosis) over and above GADA testing in patients with a clinical diagnosis of type 2 diabetes treated without insulin at diagnosis.”
“We examined the relationship between GADA, T1D GRS, and progression to insulin therapy using survival analysis in 8,608 participants with clinical type 2 diabetes initially treated without insulin therapy. […] In this large study of participants with a clinical diagnosis of type 2 diabetes, we have found that type 1 genetic susceptibility alters the clinical implications of a positive GADA when predicting rapid time to insulin. GADA-positive participants with high T1D GRS were more likely to require insulin within 5 years of diagnosis, with 48% progressing to insulin in this time in contrast to only 18% in participants with low T1D GRS. The T1D GRS was independent of and additive to participant’s age of diagnosis and BMI. However, T1D GRS was not associated with rapid insulin requirement in participants who were GADA negative.”
“Our findings have clear implications for clinical practice. The T1D GRS represents a novel clinical test that can be used to enhance the prognostic value of GADA testing. For predicting future insulin requirement in patients with apparent type 2 diabetes who are GADA positive, T1D GRS may be clinically useful and can be used as an additional test in the screening process. However, in patients with type 2 diabetes who are GADA negative, there is no benefit gained from genetic testing. This is unsurprising, as the prevalence of underlying autoimmunity in patients with a clinical phenotype of type 2 diabetes who are GADA negative is likely to be extremely low; therefore, most GADA-negative participants with high T1D GRS will have nonautoimmune diabetes. The use of this two-step testing approach may facilitate a precision medicine approach to patients with apparent type 2 diabetes; patients who are likely to progress rapidly are identified for targeted management, which may include increased monitoring, early therapy intensification, and/or interventions aimed at slowing progression (36,37).
The costs of analyzing the T1D GRS are relatively modest and may fall further, as genetic testing is rapidly becoming less expensive (38). […] In conclusion, a T1D GRS alters the clinical implications of a positive GADA test in patients with clinical type 2 diabetes and is independent of and additive to clinical features. This therefore represents a novel test for identifying patients with rapid progression in this population.”
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iii. Retinopathy and RAAS Activation: Results From the Canadian Study of Longevity in Type 1 Diabetes.
“Diabetic retinopathy is the most common cause of preventable blindness in individuals ages 20–74 years and is the most common vascular complication in type 1 and type 2 diabetes (1–3). On the basis of increasing severity, diabetic retinopathy is classified into nonproliferative diabetic retinopathy (NPDR), defined in early stages by the presence of microaneurysms, retinal vascular closure, and alteration, or proliferative diabetic retinopathy (PDR), defined by the growth of new aberrant blood vessels (neovascularization) susceptible to hemorrhage, leakage, and fibrosis (4). Diabetic macular edema (DME) can be present at any stage of retinopathy and is characterized by increased vascular permeability leading to retinal thickening.
Important risk factors for the development of retinopathy continue to be chronic hyperglycemia, hyperlipidemia, hypertension, and diabetes duration (5,6). Given the systemic nature of these risk factors, cooccurrence of retinopathy with other vascular complications is common in patients with diabetes.”
“A key pathway implicated in diabetes-related small-vessel disease is overactivation of neurohormones. Activation of the neurohormonal renin-angiotensin-aldosterone system (RAAS) pathway predominates in diabetes in response to hyperglycemia and sodium retention. The RAAS plays a pivotal role in regulating systemic BP through vasoconstriction and fluid-electrolyte homeostasis. At the tissue level, angiotensin II (ANGII), the principal mediator of the RAAS, is implicated in fibrosis, oxidative stress, endothelial damage, thrombosis, inflammation, and vascular remodeling. Of note, systemic RAAS blockers reduce the risk of progression of eye disease but not DKD [Diabetic Kidney Disease, US] in adults with type 1 diabetes with normoalbuminuria (12).
Several longitudinal epidemiologic studies of diabetic retinopathy have been completed in type 1 diabetes; however, few have studied the relationships between eye, nerve, and renal complications and the influence of RAAS activation after prolonged duration (≥50 years) in adults with type 1 diabetes. As a result, less is known about mechanisms that persist in diabetes-related microvascular complications after long-standing diabetes. Accordingly, in this cross-sectional analysis from the Canadian Study of Longevity in Type 1 Diabetes involving adults with type 1 diabetes for ≥50 years, our aims were to phenotype retinopathy stage and determine associations between the presence of retinopathy and other vascular complications. In addition, we examined the relationship between retinopathy stage and renal and systemic hemodynamic function, including arterial stiffness, at baseline and dynamically after RAAS activation with an infusion of exogenous ANGII.”
“Of the 75 participants, 12 (16%) had NDR [no diabetic retinopathy], 24 (32%) had NPDR, and 39 (52%) had PDR […]. At baseline, those with NDR had lower mean HbA1c compared with those with NPDR and PDR (7.4 ± 0.7% and 7.5 ± 0.9%, respectively; P for trend = 0.019). Of note, those with more severe eye disease (PDR) had lower systolic and diastolic BP values but a significantly higher urine albumin-to-creatine ratio (UACR) […] compared with those with less severe eye disease (NPDR) or with NDR despite higher use of RAAS inhibitors among those with PDR compared with NPDR or NDR. History of cardiovascular and peripheral vascular disease history was significantly higher in participants with PDR (33.3%) than in those with NPDR (8.3%) or NDR (0%). Diabetic sensory polyneuropathy was prevalent across all groups irrespective of retinopathy status but was numerically higher in the PDR group (95%) than in the NPDR (86%) or NDR (75%) groups. No significant differences were observed in retinal thickness across the three groups.”
One quick note: This was mainly an eye study, but some of the other figures here are well worth taking note of. 3 out of 4 people in the supposedly low-risk group without eye complications had sensory polyneuropathy after 50 years of diabetes.
“Conclusions
Hyperglycemia contributes to the pathogenesis of diabetic retinopathy through multiple interactive pathways, including increased production of advanced glycation end products, IGF-I, vascular endothelial growth factor, endothelin, nitric oxide, oxidative damage, and proinflammatory cytokines (29–33). Overactivation of the RAAS in response to hyperglycemia also is implicated in the pathogenesis of diabetes-related complications in the retina, nerves, and kidney and is an important therapeutic target in type 1 diabetes. Despite what is known about these underlying pathogenic mechanisms in the early development of diabetes-related complications, whether the same mechanisms are active in the setting of long-standing type 1 diabetes is not known. […] In this study, we observed that participants with PDR were more likely to be taking RAAS inhibitors, to have a higher frequency of cardiovascular or peripheral vascular disease, and to have higher UACR levels, likely reflecting the higher overall risk profile of this group. Although it is not possible to determine why some patients in this cohort developed PDR while others did not after similar durations of type 1 diabetes, it seems unlikely that glycemic control alone is sufficient to fully explain the observed between-group differences and differing vascular risk profiles. Whereas the NDR group had significantly lower mean HbA1c levels than the NPDR and PDR groups, differences between participants with NPDR and those with PDR were modest. Accordingly, other factors, such as differences in vascular function, neurohormones, growth factors, genetics, and lifestyle, may play a role in determining retinopathy severity at the individual level.
The association between retinopathy and risk for DKD is well established in diabetes (34). In the setting of type 2 diabetes, patients with high levels of UACR have twice the risk of developing diabetic retinopathy than those with normal UACR levels. For example, Rodríguez-Poncelas et al. (35) demonstrated that impaired renal function is linked with increased diabetic retinopathy risk. Consistent with these studies and others, the PDR group in this Canadian Study of Longevity in Type 1 Diabetes demonstrated significantly higher UACR, which is associated with an increased risk of DKD progression, illustrating that the interaction between eye and kidney disease progression also may exist in patients with long-standing type 1 diabetes. […] In conclusion, retinopathy was prevalent after prolonged type 1 diabetes duration, and retinopathy severity associated with several measures of neuropathy and with higher UACR. Differential exaggerated responses to RAAS activation in the peripheral vasculature of the PDR group highlights that even in the absence of DKD, neurohormonal abnormalities are likely still operant, and perhaps accentuated, in patients with PDR even after long-standing type 1 diabetes duration.”
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iv. Clinical and MRI Features of Cerebral Small-Vessel Disease in Type 1 Diabetes.
“Type 1 diabetes is associated with a fivefold increased risk of stroke (1), with cerebral small-vessel disease (SVD) as the most common etiology (2). Cerebral SVD in type 1 diabetes, however, remains scarcely investigated and is challenging to study in vivo per se owing to the size of affected vasculature (3); instead, MRI signs of SVD are studied. In this study, we aimed to assess the prevalence of cerebral SVD in subjects with type 1 diabetes compared with healthy control subjects and to characterize diabetes-related variables associated with SVD in stroke-free people with type 1 diabetes.”
“RESEARCH DESIGN AND METHODS This substudy was cross-sectional in design and included 191 participants with type 1 diabetes and median age 40.0 years (interquartile range 33.0–45.1) and 30 healthy age- and sex-matched control subjects. All participants underwent clinical investigation and brain MRIs, assessed for cerebral SVD.
RESULTS Cerebral SVD was more common in participants with type 1 diabetes than in healthy control subjects: any marker 35% vs. 10% (P = 0.005), cerebral microbleeds (CMBs) 24% vs. 3.3% (P = 0.008), white matter hyperintensities 17% vs. 6.7% (P = 0.182), and lacunes 2.1% vs. 0% (P = 1.000). Presence of CMBs was independently associated with systolic blood pressure (odds ratio 1.03 [95% CI 1.00–1.05], P = 0.035).”
“Conclusions
Cerebral SVD is more common in participants with type 1 diabetes than in healthy control subjects. CMBs especially are more prevalent and are independently associated with hypertension. Our results indicate that cerebral SVD starts early in type 1 diabetes but is not explained solely by diabetes-related vascular risk factors or the generalized microvascular disease that takes place in diabetes (7).
There are only small-scale studies on cerebral SVD, especially CMBs, in type 1 diabetes. Compared with the current study, one study with similar diabetes characteristics (i.e., diabetes duration, glycemic control, and blood pressure levels) as in the current study, but lacking a control population, showed a higher prevalence of WMHs, with more than half of the participants affected, but similar prevalence of lacunes and lower prevalence of CMBs (8). In another study, including 67 participants with type 1 diabetes and 33 control subjects, there was no difference in WMH prevalence but a higher prevalence of CMBs in participants with type 1 diabetes and retinopathy compared with control subjects (9). […] In type 1 diabetes, albuminuria and systolic blood pressure independently increase the risk for both ischemic and hemorrhagic stroke (12). […] We conclude that cerebral SVD is more common in subjects with type 1 diabetes than in healthy control subjects. Future studies will focus on longitudinal development of SVD in type 1 diabetes and the associations with brain health and cognition.”
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“In the U.S., an estimated 1.4 million adults are newly diagnosed with diabetes every year and present an important intervention opportunity for health care systems. In patients newly diagnosed with type 2 diabetes, the benefits of maintaining an HbA1c <7.0% (<53 mmol/mol) are well established. The UK Prospective Diabetes Study (UKPDS) found that a mean HbA1c of 7.0% (53 mmol/mol) lowers the risk of diabetes-related end points by 12–32% compared with a mean HbA1c of 7.9% (63 mmol/mol) (1,2). Long-term observational follow-up of this trial revealed that this early glycemic control has durable effects: Reductions in microvascular events persisted, reductions in cardiovascular events and mortality were observed 10 years after the trial ended, and HbA1c values converged (1). Similar findings were observed in the Diabetes Control and Complications Trial (DCCT) in patients with type 1 diabetes (2–4). These posttrial observations have been called legacy effects (also metabolic memory) (5), and they suggest the importance of early glycemic control for the prevention of future complications of diabetes. Although these clinical trial long-term follow-up studies demonstrated legacy effects, whether legacy effects exist in real-world populations, how soon after diabetes diagnosis legacy effects may begin, or for what level of glycemic control legacy effects may exist are not known.
In a previous retrospective cohort study, we found that patients with newly diagnosed diabetes and an initial 10-year HbA1c trajectory that was unstable (i.e., changed substantially over time) had an increased risk for future microvascular events, even after adjusting for HbA1c exposure (6). In the same cohort population, this study evaluates associations between the duration and intensity of glycemic control immediately after diagnosis and the long-term incidence of future diabetic complications and mortality. We hypothesized that a glycemic legacy effect exists in real-world populations, begins as early as the 1st year after diabetes diagnosis, and depends on the level of glycemic exposure.”
“RESEARCH DESIGN AND METHODS This cohort study of managed care patients with newly diagnosed type 2 diabetes and 10 years of survival (1997–2013, average follow-up 13.0 years, N = 34,737) examined associations between HbA1c <6.5% (<48 mmol/mol), 6.5% to <7.0% (48 to <53 mmol/mol), 7.0% to <8.0% (53 to <64 mmol/mol), 8.0% to <9.0% (64 to <75 mmol/mol), or ≥9.0% (≥75 mmol/mol) for various periods of early exposure (0–1, 0–2, 0–3, 0–4, 0–5, 0–6, and 0–7 years) and incident future microvascular (end-stage renal disease, advanced eye disease, amputation) and macrovascular (stroke, heart disease/failure, vascular disease) events and death, adjusting for demographics, risk factors, comorbidities, and later HbA1c.
RESULTS Compared with HbA1c <6.5% (<48 mmol/mol) for the 0-to-1-year early exposure period, HbA1c levels ≥6.5% (≥48 mmol/mol) were associated with increased microvascular and macrovascular events (e.g., HbA1c 6.5% to <7.0% [48 to <53 mmol/mol] microvascular: hazard ratio 1.204 [95% CI 1.063–1.365]), and HbA1c levels ≥7.0% (≥53 mmol/mol) were associated with increased mortality (e.g., HbA1c 7.0% to <8.0% [53 to <64 mmol/mol]: 1.290 [1.104–1.507]). Longer periods of exposure to HbA1c levels ≥8.0% (≥64 mmol/mol) were associated with increasing microvascular event and mortality risk.
CONCLUSIONS Among patients with newly diagnosed diabetes and 10 years of survival, HbA1c levels ≥6.5% (≥48 mmol/mol) for the 1st year after diagnosis were associated with worse outcomes. Immediate, intensive treatment for newly diagnosed patients may be necessary to avoid irremediable long-term risk for diabetic complications and mortality.”
Do note that the effect sizes here are very large and this stuff seems really quite important. Judging from the results of this study, if you’re newly diagnosed and you only obtain a HbA1c of say, 7.3% in the first year, that may translate into a close to 30% increased risk of death more than 10 years into the future, compared to a scenario of an HbA1c of 6.3%. People who did not get their HbA1c measured within the first 3 months after diagnosis had a more than 20% increased risk of mortality during the study period. This seems like critical stuff to get right.
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“Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 diabetes mellitus (T1DM) that results from absolute insulin deficiency and is marked by acidosis, ketosis, and hyperglycemia (1). Therefore, prevention of DKA is one goal in T1DM care, but recent data indicate increased incidence (2).
For adult patients, only limited data are available on rates and risk factors for development of DKA, and this complication remains epidemiologically poorly characterized. The Diabetes Prospective Follow-up Registry (DPV) has followed patients with diabetes from 1995. Data for this study were collected from 2000 to 2016. Inclusion criteria were diagnosis of T1DM, age at diabetes onset ≥6 months, patient age at follow-up ≥18 years, and diabetes duration ≥1 year to exclude DKA at manifestation. […] In total, 46,966 patients were included in this study (average age 38.5 years [median 21.2], 47.6% female). The median HbA1c was 7.7% (61 mmol/mol), median diabetes duration was 13.6 years, and 58.3% of the patients were treated in large diabetes centers.
On average, 2.5 DKA-related hospital admissions per 100 patient-years (PY) were observed (95% CI 2.1–3.0). The rate was highest in patients aged 18–30 years (4.03/100 PY) and gradually declined with increasing age […] No significant differences between males (2.46/100 PY) and females (2.59/100 PY) were found […] Patients with HbA1c levels <7% (53 mmol/mol) had significantly fewer DKA admissions than patients with HbA1c ≥9% (75 mmol/mol) (0.88/100 PY vs. 6.04/100 PY; P < 0.001)”
“Regarding therapy, use of an insulin pump (continuous subcutaneous insulin infusion [CSII]) was not associated with higher DKA rates […], while patients aged 31–50 years on CSII showed lower rates than patients using multiple daily injections (2.21 vs. 3.12/100 PY; adjusted P < 0.05) […]. Treatment in a large center was associated with lower DKA-related hospital admissions […] In both adults and children, poor metabolic control was the strongest predictor of hospital admission due to DKA. […] In conclusion, the results of this study identify patients with T1DM at risk for DKA (high HbA1c, diabetes duration 5–10 years, migrants, age 30 years and younger) in real-life diabetes care. These at-risk individuals may need specific attention since structured diabetes education has been demonstrated to specifically reduce and prevent this acute complication.”
A few diabetes papers of interest
i. The dynamic origins of type 1 diabetes.
“Over a century ago, there was diabetes and only diabetes. Subsequently, diabetes came to be much more discretely defined (1) by age at onset (childhood or adult onset), clinical phenotype (lean or obese), treatment (insulin dependent or not insulin dependent), and, more recently, immune genotype (type 1 or type 2 diabetes). Although these categories broadly describe groups, they are often insufficient to categorize specific individuals, such as children having non–insulin-dependent diabetes and adults having type 1 diabetes (T1D) even when not requiring insulin. Indeed, ketoacidosis at presentation can be a feature of either T1D or type 2 diabetes. That heterogeneity extends to the origins and character of both major types of diabetes. In this issue of Diabetes Care, Redondo et al. (2) leverage the TrialNet study of subjects with a single diabetes-associated autoantibody at screening in order to explore factors determining progression to multiple autoantibodies and, subsequently, the pathogenesis of T1D.
T1D is initiated by presumed nongenetic event(s) operating in children with potent genetic susceptibility. But there is substantial heterogeneity even within the origins of this disease. Those nongenetic events evoke different autoantibodies such that T1D patients with insulin autoantibodies (IAA) have different features from those with GAD autoantibodies (GADA) (3,4). The former, in contrast with the latter, are younger both at seroconversion and at development of clinical diabetes, the two groups having different genetic risk and those with IAA having greater insulin secretory loss […]. These observations hint at distinct disease-associated networks leading to T1D, perhaps induced by distinct nongenetic events. Such disease-associated pathways could operate in unison, especially in children with T1D, who often have multiple autoantibodies. […]
Genetic analyses of autoimmune diseases suggest that only a small number of pathways contribute to disease risk. These pathways include NF-κB signaling, T-cell costimulation, interleukin-2, and interleukin-21 pathways and type 1 interferon antiviral responses (5,6). T1D shares most risk loci with celiac disease and rheumatoid arthritis (5), while paradoxically most risk loci shared with inflammatory bowel disease are protective or involve different haplotypes at the same locus. […] Events leading to islet autoimmunity may be encountered very early in life and invoke disease risk or disease protection (4,7) […]. Islet autoantibodies rarely appear before age 6 months, and among children with a family history of T1D there are two peaks for autoantibody seroconversion (3,4), the first for IAA at approximately age 1–2 years, while GADA-restricted autoimmunity develops after age 3 years up to adolescence, with a peak at about age 11 years”
“The precise nature of […] disease-associated nongenetic events remains unclear, but knowledge of the disease heterogeneity (1,9) has cast light on their character. Nongenetic events are implicated in increasing disease incidence, disease discordance even between identical twins, and geographical variation; e.g., Finland has 100-fold greater childhood T1D incidence than China (9,10). That effect likely increases with older age at onset […] disease incidence in Finland is sixfold greater than in an adjacent, relatively impoverished Russian province, despite similar racial origins and frequencies of high-risk HLA DQ genotypes […] Viruses, especially enteroviruses, and dietary factors have been invoked (12–15). The former have been implicated because of the genetic association with antiviral interferon networks, seasonal pattern of autoantibody conversion, seroconversion being associated with enterovirus infections, and protection from seroconversion by maternal gestational respiratory infection, while respiratory infections even in the first year of life predispose to seroconversion (14) […]. Dietary factors also predispose to seroconversion and include the time of introduction of solid foods and the use of vitamin C and vitamin D (13,15). The Diabetes Autoimmunity Study in the Young (DAISY) found that early exposure to solid food (1–3 months of age) and vitamin C and late exposure to vitamin D and gluten (after 6 and 9 months of age, respectively) are T1D risk factors, leading the researchers to suggest that genetically at-risk children should have solid foods introduced at about 4 months of age with a diet high in dairy and fruit (13).” [my bold, US]
“This TCF7L2 locus is of particular interest in the context of T1D (9) as it is usually seen as the major type 2 diabetes signal worldwide. The rs7903146 SNP optimally captures that TCF7L2 disease association and is likely the causal variant. Intriguingly, this locus is associated, in some populations, with those adult-onset autoimmune diabetes patients with GADA alone who masquerade as having type 2 diabetes, since they initially do not require insulin therapy, and also markedly increases the diabetes risk in cystic fibrosis patients. One obvious explanation for these associations is that adult-onset autoimmune diabetes is simply a heterogeneous disease, an admixture of both T1D and type 2 diabetes (9), in which shared genes alter the threshold for diabetes. […] A high proportion of T1D cases present in adulthood (17,18), likely more than 50%, and many do not require insulin initially. The natural history, phenotype, and metabolic changes in adult-onset diabetes with GADA resemble a separate cluster of cases with type 2 diabetes but without GADA, which together constitute up to 24% of adult-onset diabetes (19). […] Knowledge of heterogeneity enables understanding of disease processes. In particular, identification of distinct pathways to clinical diabetes offers the possibility of defining distinct nongenetic events leading to T1D and, by implication, modulating those events could limit or eliminate disease progression. There is a growing appreciation that the two major types of diabetes may share common etiopathological factors. Just as there are a limited number of genes and pathways contributing to autoimmunity risk, there may also be a restricted number of pathways contributing to β-cell fragility.”
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“OBJECTIVE It is well established that diabetic nephropathy increases the risk of cardiovascular disease (CVD), but how severe diabetic retinopathy (SDR) impacts this risk has yet to be determined.
RESEARCH DESIGN AND METHODS The cumulative incidence of various CVD events, including coronary heart disease (CHD), peripheral artery disease (PAD), and stroke, retrieved from registries, was evaluated in 1,683 individuals with at least a 30-year duration of type 1 diabetes drawn from the Finnish Diabetic Nephropathy Study (FinnDiane).”
“RESULTS During 12,872 person-years of follow-up, 416 incident CVD events occurred. Even in the absence of DKD [Diabetic Kidney Disease], SDR increased the risk of any CVD (hazard ratio 1.46 [95% CI 1.11–1.92]; P < 0.01), after adjustment for diabetes duration, age at diabetes onset, sex, smoking, blood pressure, waist-to-hip ratio, history of hypoglycemia, and serum lipids. In particular, SDR alone was associated with the risk of PAD (1.90 [1.13–3.17]; P < 0.05) and CHD (1.50 [1.09–2.07; P < 0.05) but not with any stroke. Moreover, DKD increased the CVD risk further (2.85 [2.13–3.81]; P < 0.001). […]
CONCLUSIONS SDR alone, even without DKD, increases cardiovascular risk, particularly for PAD, independently of common cardiovascular risk factors in long-standing type 1 diabetes. More remains to be done to fully understand the link between SDR and CVD. This knowledge could help combat the enhanced cardiovascular risk beyond currently available regimens.”
“The 15-year cumulative incidence of any CVD in patients with and without SDR was 36.8% (95% CI 33.4–40.1) and 27.3% (23.3–31.0), respectively (P = 0.0004 for log-rank test) […] Patients without DKD and SDR at baseline had 4.0-fold (95% CI 3.3–4.7) increased risk of CVD compared with control subjects without diabetes up to 70 years of age […]. Intriguingly, after this age, the CVD incidence was similar to that in the matched control subjects (SIR 0.9 [95% CI 0.3–1.9]) in this subgroup of patients with diabetes. However, in patients without DKD but with SDR, the CVD risk was still increased after the patients had reached 70 years of age (SIR 3.4 [95% CI 1.8–6.2]) […]. Of note, in patients with both DKD and SDR, the CVD burden was high already at young ages.”
“This study highlights the role of SDR on a complete range of CVD outcomes in a large sample of patients with long-standing T1D and longitudinal follow-up. We showed that SDR alone, without concomitant DKD, increases the risk of macrovascular disease, independently of the traditional risk factors. The risk is further increased in case of accompanying DKD, especially if SDR is present together with DKD. Findings from this large and well-characterized cohort of patients have a direct impact on clinical practice, emphasizing the importance of regular screening for SDR in individuals with T1D and intensive multifactorial interventions for CVD prevention throughout their life span.
This study also confirms and complements previous data on the continuum of diabetic vascular disease, by which microvascular and macrovascular disease do not seem to be separate diseases, but rather interconnected (10,12,18). The link is most obvious for DKD, which clearly emerges as a major predictor of cardiovascular morbidity and mortality (2,24,25). The association of SDR with CVD is less clear. However, our recent cross-sectional study with the Joslin Medalist Study showed that the CVD risk was in fact increased in patients with SDR on top of DKD compared with DKD alone (19). In the present longitudinal study, we were able to extend those results also to show that SDR alone, without DKD and after the adjustment for other traditional risk factors, increases CVD risk substantially. SDR further increases CVD risk in case DKD is present as well. In addition, the role of SDR as an independent CVD risk predictor is also supported by our data using albuminuria as a marker of DKD. This is important because albuminuria is a known predictor of diabetic retinopathy progression (26) as well as a recognized biomarker for CVD.”
“A novel finding is that, independently of any signs of DKD, the risk of PAD is increased twofold in the presence of SDR. Although this association has recently been highlighted in individuals with type 2 diabetes (10,29), the data in T1D are scarce (16,30). Notably, the previous studies mostly lack adjustments for DKD, the major predictor of mortality in patients with shorter diabetes duration. Both complications, besides sharing some conventional cardiovascular risk factors, may be linked by additional pathological processes involving changes in the microvasculature in both the retina and the vasa vasorum of the conductance vessels (31). […] Patients with T1D duration of >30 years face a continuously increased CVD risk that is further increased by the occurrence of advanced PDR. Therefore, by examining the retina, additional insight into individual CVD risk is gained and can guide the clinician to a more tailored approach to CVD prevention. Moreover, our findings suggest that the link between SDR and CVD is at least partially independent of traditional risk factors, and the mechanism behind the phenomenon warrants further research, aiming to find new therapies to alleviate the CVD burden more efficiently.”
The model selection method employed in the paper is far from optimal [“Variables for the model were chosen based on significant univariable associations.” – This is not the way to do things!], but regardless these are interesting results.
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“Individuals with type 2 diabetes (T2D) have 50% greater risk for the development of neurocognitive dysfunction relative to those without T2D (1–3). The American Diabetes Association recommends screening for the early detection of cognitive impairment for adults ≥65 years of age with diabetes (4). Coupled with the increasing prevalence of prediabetes and diabetes, this calls for better understanding of the impact of diabetes on cerebral structure and function (5,6). Among older individuals with diabetes, higher intraindividual variability in glucose levels around the mean is associated with worse cognition and the development of Alzheimer disease (AD) (7,8). […] Our objectives were to characterize fasting glucose (FG) variability during young adulthood before the onset of diabetes and to assess whether such variability in FG is associated with cognitive function in middle adulthood. We hypothesized that a higher variability of FG during young adulthood would be associated with a lower level of cognitive function in midlife compared with lower FG variability.”
“We studied 3,307 CARDIA (Coronary Artery Risk Development in Young Adults) Study participants (age range 18–30 years and enrolled in 1985–1986) at baseline and calculated two measures of long-term glucose variability: the coefficient of variation about the mean FG (CV-FG) and the absolute difference between successive FG measurements (average real variability [ARV-FG]) before the onset of diabetes over 25 and 30 years of follow-up. Cognitive function was assessed at years 25 (2010–2011) and 30 (2015–2016) with the Digit Symbol Substitution Test (DSST), Rey-Auditory Verbal Learning Test (RAVLT), Stroop Test, Montreal Cognitive Assessment, and category and letter fluency tests. We estimated the association between glucose variability and cognitive function test score with adjustment for clinical and behavioral risk factors, mean FG level, change in FG level, and diabetes development, medication use, and duration.
RESULTS After multivariable adjustment, 1-SD increment of CV-FG was associated with worse cognitive scores at year 25: DSST, standardized regression coefficient −0.95 (95% CI −1.54, −0.36); RAVLT, −0.14 (95% CI −0.27, −0.02); and Stroop Test, 0.49 (95% CI 0.04, 0.94). […] We did not find evidence for effect modification by race or sex for any variability-cognitive function association”
CONCLUSIONS Higher intraindividual FG variability during young adulthood below the threshold of diabetes was associated with worse processing speed, memory, and language fluency in midlife independent of FG levels. […] In this cohort of black and white adults followed from young adulthood into middle age, we observed that greater intraindividual variability in FG below a diabetes threshold was associated with poorer cognitive function independent of behavioral and clinical risk factors. This association was observed above and beyond adjustment for concurrent glucose level; change in FG level during young adulthood; and diabetes status, duration, and medication use. Intraindividual glucose variability as determined by CV was more strongly associated with cognitive function than was absolute average glucose variability.”
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iv. Maternal Antibiotic Use During Pregnancy and Type 1 Diabetes in Children — A National Prospective Cohort Study. It is important that papers like these get published and read, even if the results may not sound particularly exciting:
“Prenatal prescription of antibiotics is common but may perturb the composition of the intestinal microbiota in the offspring. In childhood the latter may alter the developing immune system to affect the pathogenesis of type 1 diabetes (1). Previous epidemiological studies reported conflicting results regarding the association between early exposure to antibiotics and childhood type 1 diabetes (2,3). Here we investigated the association in a Danish register setting.
The Danish National Birth Cohort (DNBC) provided data from 100,418 pregnant women recruited between 1996 and 2002 and their children born between 1997 and 2003 (n = 96,840). The women provided information on exposures during and after pregnancy. Antibiotic prescription during pregnancy was obtained from the Danish National Prescription Registry (anatomical therapeutic chemical code J01) [it is important to note that: “In Denmark, purchasing antibiotics requires a prescription, and all purchases are registered at the Danish National Prescription Registry”], and type 1 diabetes diagnoses (diagnostic codes DE10 and DE14) during childhood and adolescence were obtained from the Danish National Patient Register. The children were followed until 2014 (mean follow-up time 14.3 years [range 11.5–18.4 years, SD 1.4]).”
“A total of 336 children developed type 1 diabetes during follow-up. Neither overall exposure (hazard ratio [HR] 0.90; 95% CI 0.68–1.18), number of courses (HR 0.36–0.97[…]), nor trimester-specific exposure (HR 0.81–0.89 […]) of antibiotics in utero was associated with childhood diabetes. Moreover, exposure to specific types of antibiotics in utero did not change the risk of childhood type 1 diabetes […] This large prospective Danish cohort study demonstrated that maternal use of antibiotics during pregnancy was not associated with childhood type 1 diabetes. Thus, the results from this study do not support a revision of the clinical recommendations on treatment with antibiotics during pregnancy.”
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“Diabetic nephropathy is a devastating complication to diabetes. It can lead to end-stage renal disease (ESRD), which demands renal replacement therapy (RRT) with dialysis or kidney transplantation. In addition, diabetic nephropathy is associated with increased risk of cardiovascular morbidity and mortality (1,2). As a nation, Sweden, next to Finland, has the highest incidence of type 1 diabetes in the world (3), and the incidence of childhood-onset diabetes is increasing globally (4,5). The incidence of ESRD caused by diabetic nephropathy in these Nordic countries is fairly low as shown in recent studies, 3–8% at maximum 30 years’ of diabetes duration (6,7). This is to be compared with studies from Denmark in the 1980s that showed a cumulative incidence of diabetic nephropathy of 41% at 40 years of diabetes duration. Older, hospital-based cohort studies found that the incidence of persistent proteinuria seemed to peak at 25 years of diabetes duration; after that, the incidence levels off (8,9). This implies the importance of genetic susceptibility as a risk factor for diabetic nephropathy, which has also been indicated in recent genome-wide scan studies (10,11). Still, modifiable factors such as metabolic control are clearly of major importance in the development of diabetic nephropathy (12–15). Already in 1994, a decreasing incidence of diabetic nephropathy was seen in a hospital-based study in Sweden, and the authors concluded that this was mainly driven by better metabolic control (16). Young age at onset of diabetes has previously been found to protect, or postpone, the development of ESRD caused by diabetic nephropathy, while diabetes onset at older ages is associated with increased risk (7,9,17). In a previous study, we found that age at onset of diabetes affects men and women differently (7). Earlier studies have indicated a male predominance (8,18), while our previous study showed that the incidence of ESRD was similar in men and women with diabetes onset before 20 years of age, but with diabetes onset after 20 years of age, men had increased risk of developing ESRD compared with women. The current study analyzes the incidence of ESRD due to type 1 diabetes, and changes over time, in a large Swedish population-based cohort with a maximum follow-up of 38 years.”
“Earlier studies have shown that it takes ∼15 years to develop persistent proteinuria and another 10 to proceed to ESRD (9,25). In the current study population, no patients developed ESRD because of type 1 diabetes at a duration <14 years; thus only patients with diabetes duration of ≥14 years were included in the study. […] A total of 18,760 unique patients were included in the study: 10,560 (56%) men and 8,200 (44%) women. The mean age at the end of the study was somewhat lower for women, 38.9 years, compared with 40.2 years for men. Women tend to develop type 1 diabetes about a year earlier than men: mean age 15.0 years for women compared with 16.5 years for men. There was no difference regarding mean diabetes duration between men and women in the study (23.8 years for women and 23.7 years for men). A total of 317 patients had developed ESRD due to diabetes. The maximum diabetes duration was 38.1 years for patients in the SCDR and 32.6 years for the NDR and the DISS. The median time from onset of diabetes to ESRD was 22.9 years (minimum 14.1 and maximum 36.6). […] At follow-up, 77 patients with ESRD and 379 without ESRD had died […]. The risk of dying during the course of the study was almost 12 times higher among the ESRD patients (HR 11.9 [95% CI 9.3–15.2]) when adjusted for sex and age. Males had almost twice as high a risk of dying as female patients (HR 1.7 [95% CI 1.4–2.1]), adjusted for ESRD and age.”
“The overall incidence rate of ESRD during 445,483 person-years of follow-up was 0.71 per 1,000 person-years. […] The incidence rate increases with diabetes duration. For patients with diabetes onset at 0–9 and 10–19 years of age, there was an increase in incidence up to 36 years of duration; at longer durations, the number of cases is too small and results must be interpreted with caution. With diabetes onset at 20–34 years of age the incidence rate increases until 25 years of diabetes duration, and then a decrease can be observed […] In comparison of different time periods, the risk of developing ESRD was lower in patients with diabetes onset in 1991–2001 compared with onset in 1977–1984 (HR 3.5 [95% CI 2.3–5.3]) and 1985–1990 (HR 2.6 [95% CI 1.7–3.8]), adjusted for age at follow-up and sex. […] The lowest risk of developing ESRD was found in the group with onset of diabetes before the age of 10 years — both for males and females […]. With this group as reference, males diagnosed with diabetes at 10–19 or 20–34 years of age had increased risk of ESRD (HR 2.4 [95% CI 1.6–3.5] and HR 2.2 [95% CI 1.4–3.3]), respectively. For females, the risk of developing ESRD was also increased with diabetes onset at 10–19 years of age (HR 2.4 [95% CI 1.5–3.6]); however, when diabetes was diagnosed after the age of 20 years, the risk of developing ESRD was not increased compared with an early onset of diabetes (HR 1.4 [95% CI 0.8–3.4]).”
“By combining data from the SCDR, DISS, and NDR registers and identifying ESRD cases via the SRR, we have included close to all patients with type 1 diabetes in Sweden with diabetes duration >14 years who developed ESRD since 1991. The cumulative incidence of ESRD in this study is low: 5.6% (5.9% and 5.3% for males and females, respectively) at maximum 38 years of diabetes duration. For the first time, we could see a clear decrease in ESRD incidence in Sweden by calendar year of diabetes onset. The results are in line with a recent study from Norway that reported a modest incidence of 5.3% after 40 years of diabetes duration (27). In the current study, we found a decrease in the incidence rate after 25 years of diabetes duration in the group with diabetes onset at 20–34 years. With age at onset of diabetes 0–9 or 10–19 years, the ESRD incidence rate increases until 35 years of diabetes duration, but owing to the limited number of patients with longer duration we cannot determine whether the peak incidence has been reached or not. We can, however, conclude that the onset of ESRD has been postponed at least 10 years compared with that in older prospective cohort studies (8,9). […] In conclusion, this large population-based study shows a low incidence of ESRD in Swedish patients with onset of type 1 diabetes after 1977 and an encouraging decrease in risk of ESRD, which is probably an effect of improved diabetes care. We confirm that young age at onset of diabetes protects against, or prolongs, the time until development of severe complications.”
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vi. Hypoglycemia and Incident Cognitive Dysfunction: A Post Hoc Analysis From the ORIGIN Trial. Another potentially important negative result, this one related to the link between hypoglycemia and cognitive impairment:
“Epidemiological studies have reported a relationship between severe hypoglycemia, cognitive dysfunction, and dementia in middle-aged and older people with type 2 diabetes. However, whether severe or nonsevere hypoglycemia precedes cognitive dysfunction is unclear. Thus, the aim of this study was to analyze the relationship between hypoglycemia and incident cognitive dysfunction in a group of carefully followed patients using prospectively collected data in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial.”
“This prospective cohort analysis of data from a randomized controlled trial included individuals with dysglycemia who had additional cardiovascular risk factors and a Mini-Mental State Examination (MMSE) score ≥24 (N = 11,495). Severe and nonsevere hypoglycemic events were collected prospectively during a median follow-up time of 6.2 years. Incident cognitive dysfunction was defined as either reported dementia or an MMSE score of <24. The hazard of at least one episode of severe or nonsevere hypoglycemia for incident cognitive dysfunction (i.e., the dependent variable) from the time of randomization was estimated using a Cox proportional hazards model after adjusting for baseline cardiovascular disease, diabetes status, treatment allocation, and a propensity score for either form of hypoglycemia.
RESULTS This analysis did not demonstrate an association between severe hypoglycemia and incident cognitive impairment either before (hazard ratio [HR] 1.16; 95% CI 0.89, 1.52) or after (HR 1.00; 95% CI 0.76, 1.31) adjusting for the severe hypoglycemia propensities. Conversely, nonsevere hypoglycemia was inversely related to incident cognitive impairment both before (HR 0.59; 95% CI 0.52, 0.68) and after (HR 0.58; 95% CI 0.51, 0.67) adjustment.
CONCLUSIONS Hypoglycemia did not increase the risk of incident cognitive dysfunction in 11,495 middle-aged individuals with dysglycemia. […] These findings provide no support for the hypothesis that hypoglycemia causes long-term cognitive decline and are therefore reassuring for patients and their health care providers.”
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“The VADT was a large randomized controlled trial aimed at determining the effects of intensive treatment of T2DM in U.S. veterans (9). In the current study, we examine predictors and consequences of severe hypoglycemia within the VADT and report several key findings. First, we identified risk factors for severe hypoglycemia that included intensive therapy, insulin use, proteinuria, and autonomic neuropathy. Consistent with prior reports in glucose-lowering studies, severe hypoglycemia occurred at a threefold significantly greater rate in those assigned to intensive glucose lowering. Second, severe hypoglycemia was associated with an increased risk of cardiovascular events, cardiovascular mortality, and all-cause mortality in both the standard and the intensive treatment groups. Of importance, however, severe hypoglycemia was associated with an even greater risk of all-cause mortality in the standard compared with the intensive treatment group. Third, the association between severe hypoglycemia and serious cardiovascular events was greater in individuals with an elevated risk for CVD at baseline.”
“Mean participant characteristics were as follows: age, 60.4 years; duration of diabetes, 11.5 years; BMI, 31.3 kg/m2; and HbA1c, 9.4%. Seventy-two percent had hypertension, 40% had a previous cardiovascular event, 62% had a microvascular complication, and 52% had baseline insulin use. The standard and intensive treatment groups included 899 and 892 participants, respectively. […] During the study, the standard treatment group averaged 3.7 severe hypoglycemic events per 100 patient-years versus 10.3 events per 100 patient-years in the intensive treatment group (P < 0.001). Overall, the combined rate of severe hypoglycemia during follow-up in the VADT from both study arms was 7.0 per 100 patient-years. […] Severe hypoglycemia within the prior 3 months was associated with an increased risk for composite cardiovascular outcome (HR 1.9 [95% CI 1.1, 3.5]; P = 0.03), cardiovascular mortality (3.7 [1.3, 10.4]; P = 0.01), and all-cause mortality (2.4 [1.1, 5.1]; P = 0.02) […]. More distant hypoglycemia (4–6 months prior) had no independently associated increased risk with adverse events or death. The association of severe hypoglycemia with cardiovascular events or cardiovascular mortality were not significantly different between the intensive and standard treatment groups […]. In contrast, the association of severe hypoglycemia with all-cause mortality was significantly greater in the standard versus the intensive treatment group (6.7 [2.7, 16.6] vs. 0.92 [0.2, 3.8], respectively; P = 0.019 for interaction). Because of the relative paucity of repeated severe hypoglycemic events in either study group, there was insufficient power to determine whether more than one episode of severe hypoglycemia increased the risk of subsequent outcomes.”
“Although recent severe hypoglycemia increased the risk of major cardiovascular events for those with a 10-year cardiovascular risk score of 35% (HR 2.88 [95% CI 1.57, 5.29]; absolute risk increase per 10 episodes = 0.252; number needed to harm = 4), hypoglycemia was not significantly associated with increased major cardiovascular events for those with a risk score of ≤7.5%. The absolute associated risk of major adverse cardiovascular events, cardiovascular mortality, and all-cause mortality increased with higher CVD risk for all three outcomes […]. We were not able to identify, however, any group of patients in either treatment arm in which severe hypoglycemia did not increase the risk of CVD events and mortality at least to some degree.”
“Although the explanation for the relatively greater risk of serious adverse events after severe hypoglycemia in the standard treatment group is unknown, we agree with previous reports that milder episodes of hypoglycemia, which are more frequent in the intensive treatment group, may quantitatively blunt the release of neuroendocrine and autonomic nervous system responses and their resultant metabolic and cardiovascular responses to hypoglycemia, thereby lessening the impact of subsequent severe hypoglycemic episodes (18,19). Episodes of prior hypoglycemia have rapid and significant effects on reducing (i.e., blunting) subsequent counterregulatory responses to a falling plasma glucose level (20,21). Thus, if one of the homeostatic counterregulatory responses (e.g., epinephrine) also can initiate unwanted intravascular atherothrombotic consequences, it may follow that severe hypoglycemia in a more intensively treated and metabolically well-controlled individual would provoke a reduced counterregulatory response. Although hypoglycemia frequency may be increased in these individuals, this may also lower unwanted and deleterious effects on the vasculature from counterregulatory responses. On the other hand, an isolated severe hypoglycemic event in a less well-controlled individual could provoke a relatively greater counterregulatory response with a proportionally attendant elevated risk for adverse vascular effects (22). In support of this, we previously reported in a subset of VADT participants that despite more frequent serious hypoglycemia in the intensive therapy group, progression of coronary artery calcium scores after severe hypoglycemia only occurred in the standard treatment group (23).”
“In the current study, we demonstrate that the association of severe hypoglycemia with subsequent serious adverse cardiovascular events and death occurred within the preceding 3 months but not beyond. The temporal relationship and proximity of severe hypoglycemia to a subsequent serious cardiovascular event and/or death has been investigated in a number of recent clinical trials in T2DM (2–5,13,14). All these trials consistently reported an association between severe hypoglycemic and subsequent serious adverse events. However, the proximity of severe hypoglycemic events to subsequent adverse events and death varies. In ADVANCE, a severe hypoglycemic episode increased the risk of major cardiovascular events for both the next 3 months and the following 6 months. In A Trial Comparing Cardiovascular Safety of Insulin Degludec Versus Insulin Glargine in Subjects With Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE) and the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial, there was an increased risk of either serious cardiovascular events or all-cause mortality starting 15 days and extending (albeit with decreasing risk) up to 1 year after severe hypoglycemia (13,14).”
Cardiology: Diabetes Mellitus
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Despite the title this is mainly a pharmacology lecture. It’s a bit dated, but on the other hand the action mechanism of a major drug class usually doesn’t change dramatically in a semi-decade, so the fact that the lecture is a few years old I don’t think is that much of a problem. This is not in my opinion a great lecture, but it was worth watching.
A few random links related to topics covered in the talk:
Thiazolidinedione.
PPAR agonist.
Pioglitazone.
Dipeptidyl peptidase-4 inhibitor.
Glucagon-like peptide-1 receptor agonist.
Pregnancy categories.
Alpha-glucosidase inhibitor.
Sulfonylurea.
SGLT2 inhibitor.
Pramlintide.
A few diabetes papers of interest
i. Glycemic Control and Risk of Infections Among People With Type 1 or Type 2 Diabetes in a Large Primary Care Cohort Study. From the paper:
“Infections are widely considered to be a source of significant health care costs and to reduce quality of life among people with diabetes mellitus (DM) (1). Nevertheless, relatively few, large, well-designed, epidemiological studies have explored relationships between poorer control of DM and infections; previous studies have important limitations (1). Most randomized controlled trials (RCTs) of DM control have not investigated the effect of improved glycemic control on infections and are unlikely to do so at present because of the high cost and lack of good-quality supporting observational evidence. […] A recent review of higher-quality population-based epidemiological studies found clinically important (∼1.5–3.5 times higher) infection risks associated with poorer DM control in some studies (usually defined as a glycated hemoglobin [HbA1c] level >7–8% [53–64 mmol/mol]) (1). However, the studies were inconsistent, generating uncertainty about the evidence.
A key concern with previous work is that the measurement of HbA1c usually was made at or near to the time of the infection, so any association could be explained by reverse causality. Any infectious disease episode can itself have an adverse effect on glycemic control, a process known as stress hyperglycemia (4); hence, blood glucose or HbA1c measurements near the time of an infection may be elevated, rendering determination of the chronology and relationship between the two difficult. Several studies with serial HbA1c measurements have shown that the stress hyperglycemia response can be substantial (4–6). Another important issue is that studies of incident DM often use measurements of HbA1c obtained during initial presentation, and these typically do not represent subsequent levels after initiation of treatment; use of such measurements may obscure associations between usual HbA1c level and infection risk. Other limitations of previous work include a lack of consideration of type of DM (especially T1DM) and fewer older people with DM. The current study uses a large English primary care database with repeated HbA1c measurements wherein we can classify individuals more precisely in terms of their baseline glycemic control as well as ensure that these HbA1c measurements were made before the infection episode.”
“With the use of English primary care data, average glycated hemoglobin (HbA1c) during 2008–2009 was estimated for 85,312 patients with DM ages 40–89 years. Infection rates during 2010–2015 compiled from primary care, linked hospital, and mortality records were estimated across 18 infection categories and further summarized as any requiring a prescription or hospitalization or as cause of death. Poisson regression was used to estimate adjusted incidence rate ratios (IRRs) by HbA1c categories across all DM, and type 1 and type 2 DM separately. IRRs also were compared with 153,341 age-sex-practice–matched controls without DM. Attributable fractions (AF%) among patients with DM were estimated for an optimal control scenario (HbA1c 6–7% [42–53 mmol/mol]).”
“Crude infection rates during 2010–2015 estimated across 18 different categories confirmed consistently higher rates among patients with DM […]. Long-term infection risk rose with increasing HbA1c for most outcomes. Compared with patients without DM, those with DM and optimal control (HbA1c 6–7% [42–53 mmol/mol], IRR 1.41 [95% CI 1.36–1.47]) and poor control (≥11% [97 mmol/mol], 4.70 [4.24–5.21]) had elevated hospitalization risks for infection. In patients with type 1 DM and poor control, this risk was even greater (IRR 8.47 [5.86–12.24]). Comparisons within patients with DM confirmed the risk of hospitalization with poor control (2.70 [2.43–3.00]) after adjustment for duration and other confounders. AF% of poor control were high for serious infections, particularly bone and joint (46%), endocarditis (26%), tuberculosis (24%), sepsis (21%), infection-related hospitalization (17%), and mortality (16%). […] even patients with DM with good control were at an increased risk compared with matched controls without DM. Thus, compared with patients without DM, patients with DM and good control (mean HbA1c 6–7%, IRR 1.41 [95% CI 1.36–1.47]) and those with poor control (≥11%, 4.70 [4.24–5.21]) had elevated hospitalization risks for infection. These risks were higher among patients with T1DM. For example, patients with T1DM with a mean HbA1c ≥11%, had more than eight times the risk of hospitalization than their matched controls without DM (IRR 8.47 [5.86–12.24]), whereas for T2DM, this was four times higher (4.31 [3.88–4.80]). […] Patients with T1DM […] had higher rates of hospitalization (1.12 [1.01–1.24]) and death as a result of infection (1.42 [1.03–1.96]) than patients with T2DM, even after accounting for duration of DM.”
“In terms of the overall population effect, almost one-half of bone and joint infections among patients with DM were attributed to poor control. […] The most novel and concerning finding is the substantial proportion of other serious infections statistically attributable to poor glycemic control, particularly endocarditis, tuberculosis, and sepsis. Between 20 and 30% of these infections in the English DM population could be attributed to poor control. […] [W]e estimated AF% for the three summary groupings […] plus individual infection types […] across HbA1c categories for patients with DM compared with the optimal control scenario of 6–7%. The largest AF% estimate was for bone and joint infections, with 46.0% of hospitalizations being attributed to HbA1c values outside of the range 6–7%. Other large AF% estimates were observed for endocarditis (26.2%) and tuberculosis (23.7%), but CIs were wide. Sepsis (20.8%), pneumonia (15.3%), skin infections (cellulitis 14.0%, other 12.1%), and candidiasis (16.5%) all produced AF% estimates of ≥10%. Overall, 15.7% of infection-related deaths, 16.5% of infection-related hospitalizations, and 6.8% of infections requiring a prescription were attributed to values of HbA1c outside the 6–7% range.”
“Prevalence of diagnosed T2DM has tripled in the U.K. over the past 20 years (17). Although some improvements in glycemic control also have been observed over this period, our analyses show that substantial numbers of patients still have very poor glycemic control (e.g., 16% of patients with T2DM and 41% of patients with T1DM had a mean HbA1c >9%). […] 14% of patients with DM in the current study were hospitalized for infection during follow-up […] The U.K. has a relatively low prevalence of DM and good control on the basis of international comparisons (18); therefore, in many low- and middle-income countries, the burden of infections attributable to poor glycemic control could be substantially higher (19).”
“A variety of mechanisms may link DM and hyperglycemia with infection response (1,20–22). Diabetes progression itself is associated with immune dysfunction; autoimmunity in T1DM and low-grade chronic inflammation in T2DM (1). Hyperglycemia may also have adverse effects on several types of immune cells (19,23); alter cytokine and chemokine gene expression (24), and inhibit effects of complement (25). Other important mechanisms may include peripheral diabetic neuropathy because this results in a loss of sensation and reduced awareness of minor injuries (13). Alongside ischemia, often as a result of related peripheral arterial disease, neuropathy can result in impaired barrier defenses, skin ulcers, and lesions with poor wound healing and an increased risk of secondary infections (19). Although numerous mechanisms exist, nearly all involve poor glycemic control. Thus, that improved control would reduce infections seems likely […] Overall, the current analyses demonstrate a strong and likely causal association between hyperglycemia and infection risk for both T1DM and T2DM. DM duration and other markers of severity cannot explain the increased risk, nor can longer duration explain the increased risk for T1DM compared with T2DM. This remains the case in older people in whom infections are common and often severe and more uncertainty exists about the vascular benefits of improving DM control. Substantial proportions of serious infections can be attributed to poor control, even though DM is managed well in the U.K. by international standards. Interventions to reduce infection risk largely have been ignored by the DM community and should be a high priority for future research.”
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ii. Poor Metabolic Control in Children and Adolescents With Type 1 Diabetes and Psychiatric Comorbidity. Some observations from the paper:
“Type 1 diabetes in childhood has been found to be associated with an increased risk of psychiatric comorbidities (1–3), which might intensify the burden of disease and accelerate metabolic deterioration (4–6), subsequently increasing the risk of mortality and long-term complications such as retinopathy, nephropathy, and neuropathy (7–9).
Metabolic dysregulation is closely linked to age and diabetes duration, showing a peak in adolescence and early adulthood (10,11). Early adolescence is also characterized as a time of psychological vulnerability (12), in which the incidence of major psychiatric disorders increases (13). A diagnosis of type 1 diabetes in early adolescence seems to increase psychological distress (1,2), and three large population-based studies have shown higher rates of psychiatric disorders in children and adolescents with type 1 diabetes compared with the general population (1–3). In particular, increased risk was seen for depression, anxiety, and eating disorders, where the pathogenesis is considered to involve reactive mechanisms and imbalances in the diathesis-stress system (1–3,14).”
“Despite clinical and research evidence that a child with type 1 diabetes often receives more than one psychiatric diagnosis (1,3), most studies evaluate one disorder at a time (4–6,16–20). Motivated by findings that Danish children and adolescents with type 1 diabetes have a higher risk of developing a psychiatric disorder compared with the background population (2), we performed two studies based on the NPR and the Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids). […] The NPR contains psychiatric and somatic diagnoses from all inpatient admissions to Danish public hospitals since 1977. […] The register has used the ICD-10 since 1994 (22,23). Data on registration of psychiatric and type 1 diabetes diagnoses were collected from the NPR, covering 1996 to April 2015. DanDiabKids collects information on all children and adolescents diagnosed with type 1 diabetes before the age of 15 years and monitors them until they are transferred to adult clinics at ∼18 years of age. All public hospital pediatric units must supply annual data on all patients with diabetes to DanDiabKids. […] DanDiabKids contains annual data on all registered patients since 1996, including information on quality indicators, demographic variables, associated conditions, diabetes classification, diabetes family history, growth, self-management, and treatment variables. DanDiabKids now covers 99% of all Danish children and adolescents diagnosed with type 1 diabetes before the age of 15 years. […] Our study population was generated by merging data from DanDiabKids and the NPR. The inclusion criteria were registration with type 1 diabetes in DanDiabKids, age at onset <15 years, year of onset 1995–2014, and year of birth after 1980.”
“After merging DanDiabKids with the NPR, 4,725 children and adolescents with type 1 diabetes were identified […]. Characteristics for the included subjects were as follows: mean age at onset of diabetes was 8.98 years (SD 3.81), birth year ranged from 1980 to 2013, mean age at last visit was 14.6 years (3.7), 2,462 (52.1%) were boys, mean duration of diabetes at last visit was 5.65 years (3.7), 4,434 (93.8%) were of Danish origin, 254 (5.4%) were immigrants or offspring of immigrants, and 36 (0.8%) had unknown ethnicity. […] The observed number of SH [severe hypoglycemia, US] and DKA events per 100 person-years was respectively 10.7 (SH) and 3.2 (DKA) in patients with neurodevelopmental/constitutional psychiatric disorder, 12.1 (SH) and 3.7 (DKA) in patients with potentially reactive psychiatric disorder, 12.3 (SH) and 6.4 (DKA) in patients with both types of psychiatric disorders, and 8.1 (SH) and 1.8 (DKA) in patients without psychiatric disorder. […] Among the 4,725 children and adolescents included in the study, 1,035 were diagnosed with at least one psychiatric disorder at some point. Of these, a total of 175 received their first psychiatric diagnosis before the onset of type 1 diabetes, 575 during pediatric care, and 285 were diagnosed after referral to adult care. […] Anxiety disorders were the most common (n = 492), followed by “behavioral and emotional disorders” (n = 310), mood disorders (n = 205), psychoactive substance misuse disorders (n = 190), and disorders of inattention and hyperactivity (ADHD/attention-deficit disorder [ADD]) (n = 172). Of the 1,035 patients, 46% were diagnosed with two or more psychiatric disorders and 22.8% were diagnosed with three or more psychiatric disorders.”
“Shortly after type 1 diabetes diagnosis, a higher estimated risk of psychiatric disorders was evident among patients who were 10–15 years old at onset of type 1 diabetes. However, after 15–20 years with diabetes, the differences among the groups leveled out at a risk of ∼30% […] Children with high mean HbA1c levels (>8.5% [>70 mmol/mol]) during the first 2 years showed the highest estimated risk of developing a psychiatric disorder, although these differences also appear to level out after 15–20 years with type 1 diabetes. […] The mean HbA1c level was higher in children with a psychiatric disorder (0.22% [95% CI 0.15; 0.29]; 2.45 mmol/mol [1.67; 3.22]) compared with children with no psychiatric disorder (P < 0.001) […] High HbA1c levels in the early period after type 1 diabetes onset seem to be a possible indicator for subsequent psychiatric disorders, and having a psychiatric disorder was associated with higher HbA1c levels, especially in patients with disorders of putative reactive pathogenesis. Given that the Kaplan-Meier plots showed that the estimated risk of being diagnosed with a psychiatric disorder within a period of 15–20 years of type 1 diabetes onset was close to 30% in most groups, our finding highlights an important clinical problem.”
“The estimated risk of developing a psychiatric disorder during the 15–20 years after type 1 diabetes diagnosis is high. The most vulnerable period appeared to be adolescence. Patients with poorly regulated diabetes shortly after onset had a higher estimated risk of developing psychiatric comorbidities. Young patients diagnosed with a psychiatric disorder had more episodes of DKA, and those diagnosed within the reactive spectrum had higher HbA1c levels. Children and adolescents with type 1 diabetes, and in particular those who fail to reach treatment goals, should be systematically evaluated regarding psychological vulnerabilities.”
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“The prevalence of type 1 diabetes has increased over the past decades (1,2). Increased life expectancy means that people live longer with diabetes (3–5); thus, potentially more years are lived with both macrovascular and microvascular complications (6,7). Type 1 diabetes is a complex disease, which develops in various complication states, and co-occurrence of multiple microvascular complications frequently is seen (8). So far, most studies are of a single complication, and the association between the worsening of one complication and the incidence of another is well described, although independently of other complications (9,10). At the same time, a sizeable group of individuals seems to be protected from microvascular complications (11–14), and some live several decades with type 1 diabetes without developing complications. Advanced statistical models, such as multistate models, offer an opportunity to explore the transition through various disease states and to quantify progression rates while considering the concurrent complication burden (15,16), that is, the complication burden at a given time point in the observation window.
Strong evidence indicates that some risk factors play a role in all types of microvascular complications. For example, the effects of the duration of diabetes and poor glycemic control are well documented (17–20). For other risk factors, such as hypertension, an association has been established mainly for retinopathy and diabetic kidney disease (21,22). Adverse cholesterol levels and previous cardiovascular disease (CVD) are indisputably associated with a higher risk of macrovascular complications (23) and may play a role in the development of microvascular complications (24). […] The complex interplay between microvascular complications and risk factors has been explored only to a limited extent. In this study, we developed a multistate model of microvascular complications to describe in detail complication development in type 1 diabetes. We describe the development of sequences of diabetes-related microvascular complications at various states and examine the associations between selected risk factors, both alone and combined with existing complication burden, and incidence of (further) microvascular complications.”
“In total, 5,031 individuals with type 1 diabetes were registered at the SDCC during the study period. We excluded 1,203 because of missing data for diabetic kidney disease, retinopathy, and/or neuropathy, which left 3,828 eligible individuals to be included in the study. Of these, 242 were first seen in the final state with three complications, which left 3,586 available for analysis, corresponding to 22,946 person-years (PY) […] The median follow-up time was 7.8 years (25th–75th percentile 3.3–10.7 years). HbA1c level at the end of follow-up was lower than at entry, whereas the levels of blood pressure, lipids, and BMI were unchanged. An increase in the use of all cardioprotective medications was observed.”
“We identified 523 individuals who developed diabetic kidney disease during the study. Of these, 84 events occurred in individuals with no complications (IR 12.9 per 1,000 PY), 221 in individuals with retinopathy (25.7 per 1,000 PY), 27 in individuals with neuropathy (36.6 per 1,000 PY), and 191 in individuals with both neuropathy and retinopathy (61.8 per 1,000 PY). […] In the adjusted model, individuals with both retinopathy and neuropathy had a threefold higher risk of diabetic kidney disease than individuals without complications. […] A total of 482 individuals developed neuropathy during follow-up. Of these, 75 incidents occurred in individuals with no complications (IR 11.5 per 1,000 PY), 14 in individuals with diabetic kidney disease (20.6 per 1,000 PY), 234 in individuals with retinopathy (27.2 per 1,000 PY), and 159 in individuals with both retinopathy and diabetic kidney disease (50.2 per 1,000 PY). Individuals with both retinopathy and diabetic kidney disease had a 70% higher risk of developing neuropathy than individuals without complications […] In total, we recorded 649 individuals with incident retinopathy from any previous complication state. Of these, 459 incidents occurred in individuals with no complications (IR 70.7 per 1,000 PY), 74 in individuals with diabetic kidney disease (109.1 per 1,000 PY), 71 in individuals with neuropathy (96.6 per 1,000 PY), and 45 in individuals with both neuropathy and diabetic kidney disease (224.7 per 1,000 PY). Individuals with both diabetic kidney disease and neuropathy had a twofold higher IRR of developing retinopathy than individuals without complications”.
“Baseline and concurrent values of HbA1c, systolic blood pressure, eGFR, and baseline CVD status were all strongly associated with a higher risk of developing diabetic kidney disease. […] The analysis that included complication state revealed that individuals without any other complications than CVD had an almost three times higher risk of diabetic kidney disease than individuals without either CVD or microvascular complications. […] Duration of diabetes, baseline and concurrent value of HbA1c, systolic blood pressure, and baseline LDL cholesterol values were all factors associated with a higher risk of developing retinopathy. None of the effects of the modifiable risk factors on retinopathy were modified by complication burden. […] men with diabetic kidney disease had a higher risk of developing retinopathy than women with diabetic kidney disease. […] All investigated risk factors, except LDL cholesterol, were associated with incidence of neuropathy at both baseline and concurrent levels.”
“[W]e conducted a sensitivity analysis with retinopathy defined as severe nonproliferative or proliferative retinopathy. The prevalence and incidence of retinopathy were much lower, but all associations were similar to the main analysis […]. We found no effect modification by lipid-lowering or antihypertensive treatment. […] We found a stepwise higher risk of any microvascular complication in individuals with higher concurrent complication burden. Baseline and concurrent HbA1c levels, systolic blood pressure, and duration of diabetes were associated with the development of all three microvascular complications. For most risk factors, we did not find evidence that concurrent complication burden modified the association with complication development. […] Concurrent HbA1c level was a strong risk factor for all microvascular complications, even when we adjusted for age, duration, and other traditional risk factors. The overall effects were of similar magnitude to the effect of baseline levels of HbA1c and to other reports (11,29).”
“The presented results are interpreted in the frame of a multistate model design, and the use of clinical data makes the results highly relevant in similar health care settings. However, because of the observational study design, we cannot draw conclusions about causality. The positive associations among complications might reflect that diabetic kidney disease takes the longest time to develop, whereas retinopathy and neuropathy develop faster. Associations of two disease complications to a third might not be causal. However, that the risk of a third complication, even after adjustment for multiple confounders, is higher regardless of the previous combination of complications indicates that an association cannot be explained by these risk factors alone. In addition, concurrent risk factor levels may be subject to reverse causality. The current results should be seen as a benchmark for others who aim to explore the occurrence of microvascular complications as a function of the concurrent total complication burden in individuals with type 1 diabetes. […] The findings demonstrate that high concurrent complication burden elevates the risk of all three investigated microvascular complications: diabetic kidney disease, retinopathy, and neuropathy. This means that if an individual develops a complication, the clinician should be aware of the increased risk of developing more complications. […] For most risk factors, including HbA1c, we found no evidence that the effect on the development of microvascular complications was modified by the burden of concurrent complications.”
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iv. Long-term Glycemic Control and Dementia Risk in Type 1 Diabetes.
“[P]rior work has established type 1 diabetes as a risk factor for dementia (15). However, the relationship between glycemic control and subsequent risk of dementia in those with type 1 diabetes remains unclear. Hemoglobin A1c (HbA1c) is an established measure that integrates glucose control over the prior 2–3 months and is widely used to guide clinical management of type 1 diabetes (16,17). Cumulative glycemic exposure, as measured by multiple HbA1c measures over time, has previously been used to evaluate glycemic trajectories and their association with a number of diabetes complications (18,19). Electronic health records capture HbA1c values collected over time allowing for a more thorough long-term characterization of glycemic exposure than is reflected by a single HbA1c measure. In this study, we leverage data [from northern California, US] collected over a span of 19 years to examine the association of cumulative glycemic exposure, as measured by repeated HbA1c values, with incident dementia among older adults with type 1 diabetes. We also examine the potential for a threshold of glycemic exposure above or below which risk of dementia increases.”
“The final analytic cohort consisted of 3,433 individuals (mean age at cohort entry = 56.1 years old; 47.1% female) […]. On average, individuals who developed dementia during follow-up were older at cohort entry (64.4 vs. 55.7 years) and were more likely to have a history of stroke (7.7% compared with 3.5%) at baseline. The mean follow-up time was 6.3 years (median 4.8 years [interquartile range (IQR) 1.7, 9.9]), and the mean number of HbA1c measurements was 13.5 (median 9.0 [IQR 3.0, 20.00]). By the end of follow-up on 30 September 2015, 155 members (4.5%) were diagnosed with dementia, 860 (25.1%) had a lapse of at least 90 days in membership coverage, 519 (15.1%) died without a dementia diagnosis, and 1,899 (55.3%) were still alive without dementia diagnosis. Among the 155 members who developed dementia over follow-up, the mean age at dementia diagnosis was 64.6 years (median 63.6 years [IQR 56.1, 72.3]).”
“In Cox proportional hazards models, dementia risk was higher in those with increased exposure to HbA1c 8–8.9% (64–74 mmol/mol) and ≥9% (≥75 mmol/mol) and lower in those with HbA1c 6–6.9% (42–52 mmol/mol) and 7–7.9% (53–63 mmol/mol). In fully adjusted models, compared with those with minimal exposure (<10% of HbA1c measurements) to HbA1c 8–8.9% and ≥9%, those with prolonged exposure (≥75% of measurements) were 2.51 and 2.13 times more likely to develop dementia, respectively (HbA1c 8–8.9% fully adjusted hazard ratio [aHR] 2.51 [95% CI 1.23, 5.11] and HbA1c ≥9% aHR 2.13 [95% CI 1.13, 4.01]) […]. In contrast, prolonged exposure to HbA1c 6–6.9 and 7–7.9% was associated with a 58% lower and 61% lower risk of dementia, respectively (HbA1c 6–6.9% aHR 0.42 [95% CI 0.21, 0.83] and HbA1c 7–7.9% aHR 0.39 [95% CI 0.18, 0.83]). […] Results were similar in Cox models examining cumulative glycemic exposure based on whether a majority (>50%) of an individual’s available HbA1c measurements fell into the following categories of HbA1c: <6, 6–6.9, 7–7.9, 8–8.9, and ≥9% […]. Majority exposure to HbA1c 8–8.9 and ≥9% was associated with an increased risk of dementia (HbA1c 8–8.9% aHR 1.65 [95% CI 1.06, 2.57] and HbA1c ≥9% aHR 1.79 [95% CI 1.11, 2.90]), while majority exposure to HbA1c 6–6.9 and 7–7.9% was associated with a reduced risk of dementia (HbA1c 6–6.9% aHR 0.55 [95% CI 0.34, 0.88] and HbA1c 7–7.9% aHR 0.55 [95% CI 0.37, 0.82]). Majority exposure to HbA1c <6% (<42 mmol/mol) was associated with increased dementia risk in age-adjusted models (HR 2.06 [95% CI 1.11, 3.82]), though findings did not remain significant in fully adjusted models (aHR 1.45 [95% CI 0.71, 2.92]). Findings were similar in sensitivity analyses among the subset of members who were ≥65 years of age at baseline (n = 1,082 [32% of the sample]), though the increased risk associated with majority time at HbA1c ≥9% was no longer statistically significant”.
“In this large sample of older adults with type 1 diabetes, we found that cumulative exposure to higher levels of HbA1c (8–8.9 and ≥9%) was associated with an increased risk of dementia, while cumulative exposure to well-controlled HbA1c (6–6.9 and 7–7.9%) was associated with a decreased risk of dementia. In fully adjusted models, compared with those with minimal exposure to HbA1c 8–8.9% and HbA1c ≥9%, those with prolonged exposure were more than twice as likely to develop dementia over the course of follow-up […]. By contrast, dementia risk was ∼60% lower among those with prolonged exposure to well-controlled HbA1c (6–6.9 and 7–7.9%) compared with those with minimal time at well-controlled levels of HbA1c.”
“Our results complement and extend previous studies that have reported an association between chronic hyperglycemia and decreased cognitive function in children and adolescents with type 1 diabetes (25,26), as well as studies reporting an association between poor glycemic control and decreased cognitive functioning in middle-aged adults with type 1 diabetes and older adults with type 2 diabetes (7–11). Our findings are also consistent with previous studies that found an increased dementia risk associated with poorer glycemic control among adults with type 2 diabetes and adults without diabetes (11–13). Whether these findings applied to dementia risk among older adults with type 1 diabetes was previously unknown.”
“In our study of 3,433 older adults with type 1 diabetes, 155 (4.5%) individuals developed dementia over an average of 6.3 years of follow-up. Among those who developed dementia, the average age at dementia diagnosis was 64.6 years. A large-scale study using administrative health data from 1998 to 2011 in England reported a similar incidence of dementia among a subset of adults aged ≥50 years with type 1 diabetes (3.99% developed dementia), though the average length of follow-up was not reported for this specific age-group (15). Prior studies have also found type 1 diabetes to be a risk factor for dementia (15) and have reported the average age at onset of dementia to be 2–5 years earlier in those with diabetes compared with those without diabetes (27,28). Taken together, these results provide further evidence that older adults with type 1 diabetes are at increased risk of developing dementia and may have increased risk at younger ages than the general population. Our results, however, suggest that effective glycemic control could be an important tool for reducing risk of dementia among older adults with type 1 diabetes.”
“Pathophysiological mechanisms by which glycemic control may affect dementia risk are still poorly understood but are hypothesized to result from structural brain abnormalities stemming from chronic exposure to hyperglycemia and/or recurrent severe hypoglycemia. Studies in adults and youth with type 1 diabetes have reported an association between chronic hyperglycemia (defined using lifetime HbA1c history and using retinopathy as an indicator of chronic exposure) and gray matter density loss (35–37). Studies examining the association between severe hypoglycemic events and changes in brain structure have been less consistent, with some reporting increased gray matter density loss and a higher prevalence of cortical atrophy in those with a history of frequent exposure to severe hypoglycemia (36,38), while another study reported no association (37). In the ACCORD MIND (Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes) trial, compared with standard glycemic control, intensive glycemic control was associated with greater total brain volume, suggesting that intensive glycemic control may reduce brain atrophy related to diabetes (39). […] Understanding why glycemic patterns are associated with dementia is a much-needed area for future study, particularly with regard of the potential role of intercurrent micro- and macrovascular complications.”
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“Hypertension is one of the most common comorbidities among adults with diabetes. Prior studies have estimated the prevalence of hypertension to be twice as high among adults with diabetes compared with age-matched control subjects without diabetes (1,2). Among adults with diabetes, the presence of hypertension has been associated with a two times higher risk for cardiovascular disease (CVD) events and mortality (3,4).
The 2017 American College of Cardiology (ACC)/American Heart Association (AHA) Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults provides a comprehensive set of recommendations for the diagnosis and treatment of hypertension among adults, including those with diabetes (5). This guideline defines hypertension in adults, including those with diabetes, as an average systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥80 mmHg […]. According to this guideline, pharmacological antihypertensive treatment should be initiated in adults with diabetes if they have an average SBP ≥130 mmHg or DBP ≥80 mmHg, and the treatment goal is SBP <130 mmHg and DBP <80 mmHg (5).
The American Diabetes Association (ADA) published a position statement on diabetes and hypertension in 2017 that recommends blood pressure (BP) levels different from the ACC/AHA guideline for defining hypertension and for initiating pharmacological antihypertensive treatment (for both, SBP ≥140 mmHg or DBP ≥90 mmHg) (6). The ADA position statement recommends that BP goals should be individualized based on patient priorities and clinician judgment. Treatment goals for those taking antihypertensive medication are SBP <140 mmHg and DBP <90 mmHg, with SBP <130 mmHg and DBP <80 mmHg to be considered for those with high CVD risk as long as these levels can be achieved without undo treatment burden.
The purpose of the current study was to estimate the impact of differences in the definition of hypertension and recommendations for pharmacological antihypertensive treatment initiation and intensification of therapy in U.S. adults with diabetes according to the ACC/AHA guideline and the ADA diabetes and hypertension position statement (5,6). To accomplish these goals, we analyzed data from the U.S. National Health and Nutrition Examination Survey (NHANES).”
“According to data from NHANES 2011–2016, 56.6% (95% CI 53.3, 59.9) of U.S. adults with diabetes were taking antihypertensive medication. Of U.S. adults with diabetes, 57.4% (53.1, 61.6) of those not taking and 80.2% (76.6, 83.4) of those taking antihypertensive medication had high CVD risk. Among U.S. adults with diabetes, those with high CVD risk (history of CVD or 10-year ASCVD risk ≥10%) were on average 15–20 years older and the prevalence of smoking and chronic kidney disease was 10–20% higher when compared with their counterparts without high CVD risk […]. Among U.S. adults with diabetes without high CVD risk, the mean 10-year and 30-year predicted CVD risks were 3.8% (3.5, 4.2) and 25.0% (23.4, 26.6), respectively, for those not taking antihypertensive medication and 5.8% (5.3, 6.4) and 37.4% (34.5, 40.3), respectively, for those taking antihypertensive medication.
The prevalence of hypertension was 77.1% (95% CI 73.9, 80.0) according to the ACC/AHA guideline and 66.3% (63.4, 69.1) according to the ADA position statement […]. Overall, 10.8% (9.0, 12.8) of U.S. adults with diabetes had hypertension according to the ACC/AHA guideline but not the ADA position statement. Among U.S. adults with diabetes not taking antihypertensive medication, 52.8% (47.7, 57.8), 24.8% (20.6, 29.6) and 22.4% (19.2, 25.9) were recommended antihypertensive medication initiation by neither document, by the 2017 ACC/AHA guideline only, and by both documents, respectively […]. Among U.S. adults with diabetes taking antihypertensive medication, 45.3% (41.3, 49.4), 4.3% (2.8, 6.6), and 50.4% (46.5, 54.2) had an average BP that met the goal in both documents, was above the ACC/AHA goal but not the ADA goal, and was above the goals in both documents, respectively […] The overall agreement between the ACC/AHA guideline and the ADA position statement was 89.2% (87.2, 91.0) for the presence of hypertension, 75.2% (70.4, 79.4) for the recommendation to initiate antihypertensive medication, and 95.7% (93.4, 97.2) for having a BP above the recommended treatment goal. “
“Based on both the ACC/AHA guideline and ADA position statement, 17.8 (95% CI 16.2, 19.3) million U.S. adults with diabetes had hypertension […]. An additional 2.9 (2.3, 3.5) million U.S. adults had hypertension based on the ACC/AHA guideline only. Among U.S. adults with diabetes not taking antihypertensive medication, 2.6 (2.1, 3.1) million were recommended to initiate antihypertensive medication by both the ACC/AHA guideline and the ADA position statement with an additional 2.9 (2.3, 3.5) million recommended to initiate antihypertensive medication by the ACC/AHA guideline only […]. Among U.S. adults with diabetes taking antihypertensive medication, 7.6 (6.8, 8.5) million had a BP above the goal in both documents, with an additional 700,000 (400,000, 900,000) having a BP above the goal recommended in the ACC/AHA guideline only […]. Among U.S. adults with diabetes not taking antihypertensive medication, the mean 10-year CVD risk was 10.7% (95% CI 9.4, 12.0) for those not recommended treatment initiation by either the ACC/AHA guideline or the ADA position statement, 14.6% (11.5, 17.6) for those recommended treatment initiation by the ACC/AHA guideline but not the ADA position statement, and 23.2% (19.5, 27.0) among those recommended treatment initiation by the ACC/AHA guideline and the ADA position statement […]. The mean 30-year CVD risk exceeded 25% in each of these groups. Among U.S. adults with diabetes taking antihypertensive medication, the mean 10-year CVD risk was 10.6% (9.4, 12.0), 6.5% (CI 5.6, 7.3), and 33.8% (32.1, 35.5) among those with above-goal BP according to neither document, the ACC/AHA guideline only, and both documents, respectively […]. The 30-year CVD risk exceeded 40% in each group.”
“In conclusion, the current study demonstrates a high degree of concordance between the 2017 ACC/AHA BP guideline and the 2017 ADA position statement on diabetes and hypertension. Using either document, the majority of U.S. adults with diabetes have hypertension. A substantial proportion of U.S. adults with diabetes not taking antihypertensive medication are recommended to initiate treatment by both documents […] Among U.S. adults with diabetes not taking antihypertensive medication, 75.2% had an identical recommendation for initiation of antihypertensive drug therapy according to the ACC/AHA guideline and the ADA position statement. The majority of those who were recommended to initiate pharmacological antihypertensive therapy according to the ACC/AHA guideline but not the ADA position statement had high CVD risk. […] At the population level, the ACC/AHA guideline and ADA position statement have more similarities than differences. However, at the individual level, some patients with diabetes will have fundamental changes in their care depending on which advice is followed. The decision to initiate and intensify antihypertensive medication should always be individualized, based on discussions between patients and their clinicians. Both the ACC/AHA BP guideline and ADA position statement acknowledge the need to individualize treatment decisions to align with patients’ interests.”
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vi. Treatment-induced neuropathy of diabetes: an acute, iatrogenic complication of diabetes.
“Treatment-induced neuropathy in diabetes (also referred to as insulin neuritis) is considered a rare iatrogenic small fibre neuropathy caused by an abrupt improvement in glycaemic control in the setting of chronic hyperglycaemia. The prevalence and risk factors of this disorder are not known. In a retrospective review of all individuals referred to a tertiary care diabetic neuropathy clinic over 5 years, we define the proportion of individuals that present with and the risk factors for development of treatment-induced neuropathy in diabetes. Nine hundred and fifty-four individuals were evaluated for a possible diabetic neuropathy. Treatment-induced neuropathy in diabetes was defined as the acute onset of neuropathic pain and/or autonomic dysfunction within 8 weeks of a large improvement in glycaemic control—specified as a decrease in glycosylated haemoglobin A1C (HbA1c) of ≥2% points over 3 months. Detailed structured neurologic examinations, glucose control logs, pain scores, autonomic symptoms and other microvascular complications were measured every 3–6 months for the duration of follow-up. Of 954 patients evaluated for diabetic neuropathy, 104/954 subjects (10.9%) met criteria for treatment-induced neuropathy in diabetes with an acute increase in neuropathic or autonomic symptoms or signs coinciding with a substantial decrease in HbA1c. Individuals with a decrease in HbA1c had a much greater risk of developing a painful or autonomic neuropathy than those individuals with no change in HbA1c (P < 0.001), but also had a higher risk of developing retinopathy (P < 0.001) and microalbuminuria (P < 0.001). There was a strong correlation between the magnitude of decrease in HbA1c, the severity of neuropathic pain (R = 0.84, P < 0.001), the degree of parasympathetic dysfunction (R = −0.52, P < 0.01) and impairment of sympathetic adrenergic function as measured by fall in blood pressure on tilt-table testing (R = −0.63, P < 0.001). With a decrease in HbA1c of 2–3% points over 3 months there was a 20% absolute risk of developing treatment-induced neuropathy in diabetes, with a decrease in HbA1c of >4% points over 3 months the absolute risk of developing treatment-induced neuropathy in diabetes exceeded 80%. Treatment-induced neuropathy of diabetes is an underestimated iatrogenic disorder associated with diffuse microvascular complications. Rapid glycaemic change in patients with uncontrolled diabetes increases the risk of this complication.”
“Typically, individuals with TIND reported the onset of severe burning pain (pain scores 4–10/10) within 2–6 weeks of the improvement in glucose control. Burning pain was present in all subjects with TIND. Paraesthesias were present in 93/104 subjects and shooting pain in 88/104 subjects. Hyperalgesia and allodynia were common in the distribution of the pain. […] Individuals with TIND all reported ongoing sleep disturbances typically described as difficulty with sleep initiation and sleep duration secondary to neuropathic pain. These individuals reported no record of sleep problems prior to the development of TIND. […] Erectile dysfunction was noted in 28/31 males with TIND, compared to 135/417 males without TIND (P < 0.001, X2). […] Seventy-three individuals completed autonomic testing within 2–5 months of the onset of neuropathic pain. […] The results for both groups, in all tests, were abnormal compared to age-related normative values. There were strong correlations between the magnitude of decrease in HbA1c over 3 months and worsening autonomic function. A greater change in HbA1c resulted in worsening parasympathetic function as determined by the expiratory to inspiratory ratio (R = −0.52, P < 0.01) and the Valsalva ratio (R = −0.55, P < 0.01). Greater sympathetic adrenergic dysfunction also correlated with a greater change in HbA1c over 3 months as determined by the fall in systolic blood pressure during tilt-table test (R = −0.63, P < 0.001), the fall in blood pressure during phase 2 of the Valsalva manoeuvre (R = 0.49, P < 0.001), and the diminished phase 4 blood pressure overshoot during the Valsalva manoeuvre (R = −0.59, P < 0.001). […] individuals with type 1 diabetes had greater autonomic dysfunction than those with type 2 diabetes across all tests. The slopes of the regression lines describing the correlation between the change in HbA1c and a particular autonomic test did not differ by the type of diabetes, or by the type of treatment used to control glucose.”
“Most patients with TIND had rapid progression of retinopathy that developed in conjunction with the onset of neuropathic pain […] Prior to development of TIND, 65/104 individuals had no retinopathy, 35/104 had non-proliferative retinopathy, whereas 4/104 had proliferative retinopathy. Twelve months after the development of TIND, 10/104 individuals had no retinopathy, 54/104 had non-proliferative retinopathy and 40/104 had proliferative retinopathy (P < 0.001, Fisher’s exact test). Prior to development of TIND, 18/104 had evidence of microalbuminuria, while 12 months after the development of TIND, 87/104 had evidence of microalbuminuria (P < 0.001, X2).”
“TIND is a small fibre and autonomic neuropathy that appears after rapid improvements in glucose control. In this manuscript, we demonstrate that: (i) there is an unexpectedly high proportion of individuals with TIND in a tertiary referral diabetic clinic; (ii) the risk of developing TIND is associated with the magnitude and rate of change in HbA1c; (iii) neuropathic pain and autonomic dysfunction severity correlate with the magnitude of change in HbA1c; (iv) patients with Type 1 diabetes and a history of eating disorders are at high risk for developing TIND; and (v) TIND can occur with use of insulin or oral hypoglycaemic agents. […] TIND differs from the most prevalent generalized neuropathy of diabetes, the distal sensory-motor polyneuropathy, in several respects. The neuropathic pain has an acute onset, appearing within 8 weeks of glycaemic change, in contrast with the more insidious onset in the distal sensory-motor polyneuropathy […]. The pain in TIND is more severe, and poorly responsive to interventions including opioids, whereas most patients with distal sensory-motor polyneuropathy respond to non-opioid interventions […]. Although the distribution of the pain is length-dependent in individuals with TIND, it is frequently far more extensive than in distal sensory-motor polyneuropathy and the associated allodynia and hyperalgesia are much more prevalent […]. Autonomic symptoms and signs are common, prominent and appear acutely, in contrast to the relatively lower prevalence, gradual onset and slow progression in distal sensory-motor polyneuropathy […]. Finally, both the pain and autonomic features may be reversible in some patients […].
Our data indicate that the severity of TIND is associated with the magnitude of the change of HbA1c, however, it is also clear that the rate of change is important (e.g. a 4% point fall in the HbA1c will have a greater impact if occurring over 3 months than over 6 months). The pathogenic mechanisms whereby this change in glucose results in nerve damage and/or dysfunction are not known. Proposed mechanisms include endoneurial ischaemia due to epineurial arterio-venous shunts […], apoptosis due to glucose deprivation […], microvascular neuronal damage due to recurrent hypoglycaemia […], and ectopic firing of regenerating axon sprouts, but these possibilities are unproven. […] Additional mechanistic studies are necessary to determine the underlying pathophysiology.”
Nephrology Board Review
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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.
Circadian Rhythms (I)
“Circadian rhythms are found in nearly every living thing on earth. They help organisms time their daily and seasonal activities so that they are synchronized to the external world and the predictable changes in the environment. These biological clocks provide a cross-cutting theme in biology and they are incredibly important. They influence everything, from the way growing sunflowers track the sun from east to west, to the migration timing of monarch butterflies, to the morning peaks in cardiac arrest in humans. […] Years of work underlie most scientific discoveries. Explaining these discoveries in a way that can be understood is not always easy. We have tried to keep the general reader in mind but in places perseverance on the part of the reader may be required. In the end we were guided by one of our reviewers, who said: ‘If you want to understand calculus you have to show the equations.’”
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The above quote is from the book‘s foreword. I really liked this book and I was close to giving it five stars on goodreads. Below I have added some observations and links related to the first few chapters of the book’s coverage (as noted in my review on goodreads the second half of the book is somewhat technical, and I’ve not yet decided if I’ll be blogging that part of the book in much detail, if at all).
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“There have been over a trillion dawns and dusks since life began some 3.8 billion years ago. […] This predictable daily solar cycle results in regular and profound changes in environmental light, temperature, and food availability as day follows night. Almost all life on earth, including humans, employs an internal biological timer to anticipate these daily changes. The possession of some form of clock permits organisms to optimize physiology and behaviour in advance of the varied demands of the day/night cycle. Organisms effectively ‘know’ the time of day. Such internally generated daily rhythms are called ‘circadian rhythms’ […] Circadian rhythms are embedded within the genomes of just about every plant, animal, fungus, algae, and even cyanobacteria […] Organisms that use circadian rhythms to anticipate the rotation of the earth are thought to have a major advantage over both their competitors and predators. For example, it takes about 20–30 minutes for the eyes of fish living among coral reefs to switch vision from the night to daytime state. A fish whose eyes are prepared in advance for the coming dawn can exploit the new environment immediately. The alternative would be to wait for the visual system to adapt and miss out on valuable activity time, or emerge into a world where it would be more difficult to avoid predators or catch prey until the eyes have adapted. Efficient use of time to maximize survival almost certainly provides a large selective advantage, and consequently all organisms seem to be led by such anticipation. A circadian clock also stops everything happening within an organism at the same time, ensuring that biological processes occur in the appropriate sequence or ‘temporal framework’. For cells to function properly they need the right materials in the right place at the right time. Thousands of genes have to be switched on and off in order and in harmony. […] All of these processes, and many others, take energy and all have to be timed to best effect by the millisecond, second, minute, day, and time of year. Without this internal temporal compartmentalization and its synchronization to the external environment our biology would be in chaos. […] However, to be biologically useful, these rhythms must be synchronized or entrained to the external environment, predominantly by the patterns of light produced by the earth’s rotation, but also by other rhythmic changes within the environment such as temperature, food availability, rainfall, and even predation. These entraining signals, or time-givers, are known as zeitgebers. The key point is that circadian rhythms are not driven by an external cycle but are generated internally, and then entrained so that they are synchronized to the external cycle.”
“It is worth emphasizing that the concept of an internal clock, as developed by Richter and Bünning, has been enormously powerful in furthering our understanding of biological processes in general, providing a link between our physiological understanding of homeostatic mechanisms, which try to maintain a constant internal environment despite unpredictable fluctuations in the external environment […], versus the circadian system which enables organisms to anticipate periodic changes in the external environment. The circadian system provides a predictive 24-hour baseline in physiological parameters, which is then either defended or temporarily overridden by homeostatic mechanisms that accommodate an acute environmental challenge. […] Zeitgebers and the entrainment pathway synchronize the internal day to the astronomical day, usually via the light/dark cycle, and multiple output rhythms in physiology and behaviour allow appropriately timed activity. The multitude of clocks within a multicellular organism can all potentially tick with a different phase angle […], but usually they are synchronized to each other and by a central pacemaker which is in turn entrained to the external world via appropriate zeitgebers. […] Most biological reactions vary greatly with temperature and show a Q10 temperature coefficient of about 2 […]. This means that the biological process or reaction rate doubles as a consequence of increasing the temperature by 10°C up to a maximum temperature at which the biological reaction stops. […] a 10°C temperature increase doubles muscle performance. By contrast, circadian rhythms exhibit a Q10 close to 1 […] Clocks without temperature compensation are useless. […] Although we know that circadian clocks show temperature compensation, and that this phenomenon is a conserved feature across all circadian rhythms, we have little idea how this is achieved.”
“The systematic study of circadian rhythms only really started in the 1950s, and the pioneering studies of Colin Pittendrigh brought coherence to this emerging new discipline. […] From [a] mass of emerging data, Pittendrigh had key insights and defined the essential properties of circadian rhythms across all life. Namely that: all circadian rhythms are endogenous and show near 24-hour rhythms in a biological process (biochemistry, physiology, or behaviour); they persist under constant conditions for several cycles; they are entrained to the astronomical day via synchronizing zeitgebers; and they show temperature compensation such that the period of the oscillation does not alter appreciably with changes in environmental temperature. Much of the research since the 1950s has been the translation of these formalisms into biological structures and processes, addressing such questions as: What is the clock and where is it located within the intracellular processes of the cell? How can a set of biochemical reactions produce a regular self-sustaining rhythm that persists under constant conditions and has a period of about 24 hours? How is this internal oscillation synchronized by zeitgebers such as light to the astronomical day? Why is the clock not altered by temperature, speeding up when the environment gets hotter and slowing down in the cold? How is the information of the near 24-hour rhythm communicated to the rest of the organism?”
“There have been hundreds of studies showing that a broad range of activities, both physical and cognitive, vary across the 24-hour day: tooth pain is lowest in the morning; proofreading is best performed in the evening; labour pains usually begin at night and most natural births occur in the early morning hours. The accuracy of short and long badminton serves is higher in the afternoon than in the morning and evening. Accuracy of first serves in tennis is better in the morning and afternoon than in the evening, although speed is higher in the evening than in the morning. Swimming velocity over 50 metres is higher in the evening than in the morning and afternoon. […] The majority of studies report that performance increases from morning to afternoon or evening. […] Typical ‘optimal’ times of day for physical or cognitive activity are gathered routinely from population studies […]. However, there is considerable individual variation. Peak performance will depend upon age, chronotype, time zone, and for behavioural tasks how many hours the participant has been awake when conducting the task, and even the nature of the task itself. As a general rule, the circadian modulation of cognitive functioning results in an improved performance over the day for younger adults, while in older subjects it deteriorates. […] On average the circadian rhythms of an individual in their late teens will be delayed by around two hours compared with an individual in their fifties. As a result the average teenager experiences considerable social jet lag, and asking a teenager to get up at 07.00 in the morning is the equivalent of asking a 50-year-old to get up at 05.00 in the morning.”
“Day versus night variations in blood pressure and heart rate are among the best-known circadian rhythms of physiology. In humans, there is a 24-hour variation in blood pressure with a sharp rise before awakening […]. Many cardiovascular events, such as sudden cardiac death, myocardial infarction, and stroke, display diurnal variations with an increased incidence between 06.00 and 12.00 in the morning. Both atrial and ventricular arrhythmias appear to exhibit circadian patterning as well, with a higher frequency during the day than at night. […] Myocardial infarction (MI) is two to three times more frequent in the morning than at night. In the early morning, the increased systolic blood pressure and heart rate results in an increased energy and oxygen demand by the heart, while the vascular tone of the coronary artery rises in the morning, resulting in a decreased coronary blood flow and oxygen supply. This mismatch between supply and demand underpins the high frequency of onset of MI. Plaque blockages are more likely to occur in the morning as platelet surface activation markers have a circadian pattern producing a peak of thrombus formation and platelet aggregation. The resulting hypercoagulability partially underlies the morning onset of MI.”
“A critical area where time of day matters to the individual is the optimum time to take medication, a branch of medicine that has been termed ‘chronotherapy’. Statins are a family of cholesterol-lowering drugs which inhibit HMGCR-reductase […] HMGCR is under circadian control and is highest at night. Hence those statins with a short half-life, such as simvastatin and lovastatin, are most effective when taken before bedtime. In another clinical domain entirely, recent studies have shown that anti-flu vaccinations given in the morning provoke a stronger immune response than those given in the afternoon. The idea of using chronotherapy to improve the efficacy of anti-cancer drugs has been around for the best part of 30 years. […] In experimental models more than thirty anti-cancer drugs have been found to vary in toxicity and efficacy by as much as 50 per cent as a function of time of administration. Although Lévi and others have shown the advantages to treating individual patients by different timing regimes, few hospitals have taken it up. One reason is that the best time to apply many of these treatments is late in the day or during the night, precisely when most hospitals lack the infrastructure and personnel to deliver such treatments.”
“Flying across multiple time zones and shift work has significant economic benefits, but the costs in terms of ill health are only now becoming clear. Sleep and circadian rhythm disruption (SCRD) is almost always associated with poor health. […] The impact of jet lag has long been known by elite athletes […] even when superbly fit individuals fly across time zones there is a very prolonged disturbance of circadian-driven rhythmic physiology. […] Horses also suffer from jet lag. […] Even bees can get jet lag. […] The misalignments that occur as a result of the occasional transmeridian flight are transient. Shift working represents a chronic misalignment. […] Nurses are one of the best-studied groups of night shift workers. Years of shift work in these individuals has been associated with a broad range of health problems including type II diabetes, gastrointestinal disorders, and even breast and colorectal cancers. Cancer risk increases with the number of years of shift work, the frequency of rotating work schedules, and the number of hours per week working at night [For people who are interested to know more about this, I previously covered a text devoted exclusively to these topics here and here.]. The correlations are so strong that shift work is now officially classified as ‘probably carcinogenic [Group 2A]’ by the World Health Organization. […] the partners and families of night shift workers need to be aware that mood swings, loss of empathy, and irritability are common features of working at night.”
“There are some seventy sleep disorders recognized by the medical community, of which four have been labelled as ‘circadian rhythm sleep disorders’ […] (1) Advanced sleep phase disorder (ASPD) […] is characterized by difficulty staying awake in the evening and difficulty staying asleep in the morning. Typically individuals go to bed and rise about three or more hours earlier than the societal norm. […] (2) Delayed sleep phase disorder (DSPD) is a far more frequent condition and is characterized by a 3-hour delay or more in sleep onset and offset and is a sleep pattern often found in some adolescents and young adults. […] ASPD and DSPD can be considered as pathological extremes of morning or evening preferences […] (3) Freerunning or non-24-hour sleep/wake rhythms occur in blind individuals who have either had their eyes completely removed or who have no neural connection from the retina to the brain. These people are not only visually blind but are also circadian blind. Because they have no means of detecting the synchronizing light signals they cannot reset their circadian rhythms, which freerun with a period of about 24 hours and 10 minutes. So, after six days, internal time is on average 1 hour behind environmental time. (4) Irregular sleep timing has been observed in individuals who lack a circadian clock as a result of a tumour in their anterior hypothalamus […]. Irregular sleep timing is [also] commonly found in older people suffering from dementia. It is an extremely important condition because one of the major factors in caring for those with dementia is the exhaustion of the carers which is often a consequence of the poor sleep patterns of those for whom they are caring. Various protocols have been attempted in nursing homes using increased light in the day areas and darkness in the bedrooms to try and consolidate sleep. Such approaches have been very successful in some individuals […] Although insomnia is the commonly used term to describe sleep disruption, technically insomnia is not a ‘circadian rhythm sleep disorder’ but rather a general term used to describe irregular or disrupted sleep. […] Insomnia is described as a ‘psychophysiological’ condition, in which mental and behavioural factors play predisposing, precipitating, and perpetuating roles. The factors include anxiety about sleep, maladaptive sleep habits, and the possibility of an underlying vulnerability in the sleep-regulating mechanism. […] Even normal ‘healthy ageing’ is associated with both circadian rhythm sleep disorders and insomnia. Both the generation and regulation of circadian rhythms have been shown to become less robust with age, with blunted amplitudes and abnormal phasing of key physiological processes such as core body temperature, metabolic processes, and hormone release. Part of the explanation may relate to a reduced light signal to the clock […]. In the elderly, the photoreceptors of the eye are often exposed to less light because of the development of cataracts and other age-related eye disease. Both these factors have been correlated with increased SCRD.”
“Circadian rhythm research has mushroomed in the past twenty years, and has provided a much greater understanding of the impact of both imposed and illness-related SCRD. We now appreciate that our increasingly 24/7 society and social disregard for biological time is having a major impact upon our health. Understanding has also been gained about the relationship between SCRD and a spectrum of different illnesses. SCRD in illness is not simply the inconvenience of being unable to sleep at an appropriate time but is an agent that exacerbates or causes serious health problems.”
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Links:
Circadian rhythm.
Acrophase.
Phase (waves). Phase angle.
Jean-Jacques d’Ortous de Mairan.
Heliotropism.
Kymograph.
John Harrison.
Munich Chronotype Questionnaire.
Chronotype.
Seasonal affective disorder. Light therapy.
Parkinson’s disease. Multiple sclerosis.
Melatonin.
100 Cases in Orthopaedics and Rheumatology (II)
Below I have added some links related to the last half of the book’s coverage, as well as some more observations from the book.
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Scaphoid fracture. Watson’s test. Dorsal intercalated segment instability. (“Non-union is not uncommon as a complication after scaphoid fractures because the blood supply to this bone is poor. Smokers have a higher incidence of non-union. Occasionally, the blood supply is poor enough to lead to avascular necrosis. If non-union is not detected, subsequent arthritis in the wrist can develop.”)
Septic arthritis. (“Septic arthritis is an orthopaedic emergency. […] People with septic arthritis are typically unwell with fevers and malaise and the joint pain is severe. […] Any acutely hot or painful joint is septic arthritis until proven otherwise.”)
Rheumatoid arthritis. (“[RA is] the most common of the inflammatory arthropathies. […] early-morning stiffness and pain, combined with soft-tissue rather than bony swelling, are classic patterns for inflammatory disease. Although […] RA affects principally the small joints of the hands (and feet), it may progress to involve any synovial joint and may be complicated by extra-articular features […] family history [of the disease] is not unusual due to the presence of susceptibility genes such as HLA-DR. […] Not all patients with RA have rheumatoid factor (RF), and not all patients with RF have RA; ACPA has greater specificity for RA than rheumatoid factor. […] Medical therapy focuses on disease-modifying anti-rheumatic drugs (DMARDs) such as methotrexate, sulphasalazine, leflunomide and hydroxychloroquine which may be used individually or in combination. […] Disease activity in RA is measured by the disease activity score (DAS), which is a composite score of the clinical evidence of synovitis, the current inflammatory response and the patient’s own assessment of their health. […] Patients who have high disease activity as determined by the DAS and have either failed or failed to tolerate standard disease modifying therapy qualify for biologic therapy – monoclonal antibodies that are directed against key components of the inflammatory response. […] TNF-α blockade is highly effective in up to 70 per cent of patients, reducing both inflammation and the progressive structural damage associated with severe active disease.”)
Ankylosing spondylitis. Ankylosis. Schober’s index. Costochondritis.
Mononeuritis multiplex. (“Mononeuritis multiplex arises due to interruption of the vasa nervorum, the blood supply to peripheral nerves […] Mononeuritis multiplex is commonly caused by diabetes or vasculitis. […] Vasculitis – inflammation of blood vessels and subsequent obstruction to blood flow – can be primary (idiopathic) or secondary, in which case it is associated with an underlying condition such as rheumatoid arthritis. The vasculitides are classified according to the size of the vessel involved. […] Management of mononeuritis multiplex is based on potent immunosuppression […] and the treatment of underlying infections such as hepatitis.”)
Multiple myeloma. Bence-Jones protein. (“The combination of bone pain and elevated ESR and calcium is suggestive of multiple myeloma.”)
Osteoporosis. DEXA scan. T-score. (“Postmenopausal bone loss is the most common cause of osteoporosis, but secondary osteoporosis may occur in the context of a number of medical conditions […] Steroid-induced osteoporosis is a significant problem in medical practice. […] All patients receiving corticosteroids should have bone protection […] Pharmacological treatment in the form of calcium supplementation and biphosphonates to reduce osteoclast activity is effective but compliance is typically poor.”)
Osteomalacia. Rickets. Craniotabes.
Paget’s disease (see also this post). (“In practical terms, the main indication to treat Paget’s disease is pain […] although bone deformity or compression syndromes (or risk thereof) would also prompt therapy. The treatment of choice is a biphosphonate to diminish osteoclast activity”).
Stress fracture. Female athlete triad. (“Stress fractures are overuse injuries and occur when periosteal resorption exceeds bone formation. They are commonly seen in two main patient groups: soldiers may suffer so-called march fractures in the metatarsals, while athletes may develop them in different sites according to their sporting activity. Although the knee is a common site in runners due to excess mechanical loading, stress fractures may also result in non-weight-bearing sites due to repetitive and excessive traction […]. The classic symptom […] is of pain that occurs throughout running and crucially persists with rest; this is in contrast to shin splints, a traction injury to the tibial periosteum in which the pain diminishes somewhat with continued activity […] The crucial feature of rehabilitation is a graded return to sport to prevent progression or recurrence.”)
Psoriatic arthritis. (“Arthropathy and rash is a common combination in rheumatology […] Psoriatic arthritis is a common inflammatory arthropathy that affects up to 15 per cent of those with psoriasis. […] Nail disease is very helpful in differentiating psoriatic arthritis from other forms of inflammatory arthropathy.”)
Ehlers–Danlos syndromes. Marfan syndrome. Beighton (hypermobility) score.
Carpal tunnel syndrome. (“Carpal tunnel syndrome is the most common entrapment neuropathy […] The classic symptoms are of tingling in the sensory distribution of the median nerve (i.e. the lateral three and a half digits); loss of thumb abduction is a late feature. Symptoms are often worse at night (when the hand might be quite painful) and in certain postures […] The majority of cases are idiopathic, but pregnancy and rheumatoid arthritis are very common precipitating causes […] The majority of patients will respond well to conservative management […] If these measures fail, corticosteroid injection into the carpal tunnel can be very effective in up to 80 per cent of patients. Surgical decompression should be reserved for those with persistent disabling symptoms or motor loss.”)
Mixed connective tissue disease.
Crystal arthropathy. Tophus. Uric acid nephropathy. Chondrocalcinosis. (“In any patient presenting with an acutely painful and swollen joint, the most important diagnoses to consider are septic arthritis and crystal arthropathy. Crystal arthropathy such as gout is more common than septic arthritis […] Gout may be precipitated by diuretics, renal impairment and aspirin use”).
Familial Mediterranean fever. Amyloidosis.
Systemic lupus erythematosus (see also this). Jaccoud arthropathy. Lupus nephritis. (“Renal disease is the most feared complication of SLE.”)
Scleroderma. Raynaud’s phenomenon. (“Scleroderma is an uncommon disorder characterized by thickening of the skin and, to a greater or lesser degree, fibrosis of internal organs.”)
Henoch-Schönlein purpura. Cryoglobulinemia. (“Purpura are the result of a spontaneous extravasation of blood from the capillaries into the skin. If small they are known as petechiae, when they are large they are termed ecchymoses. There is an extensive differential diagnosis for purpura […] The combination of palpable purpura (distributed particularly over the buttocks and extensor surfaces of legs), abdominal pain, arthritis and renal disease is a classic presentation of Henoch–Schönlein purpura (HSP). HSP is a distinct and frequently self-limiting small-vessel vasculitis that can affect any age; but the majority of cases present in children aged 2–10 years, in whom the prognosis is more benign than the adult form, often remitting entirely within 3–4 months. The abdominal pain may mimic a surgical abdomen and can presage intussusception, haemorrhage or perforation. The arthritis, in contrast, is relatively mild and tends to affect the knees and ankles.”)
Rheumatic fever.
Erythema nodosum. (“Mild idiopathic erythema nodosum […] needs no specific treatment”).
Rheumatoid lung disease. Bronchiolitis obliterans. Methotrexate-induced pneumonitis. Hamman–Rich syndrome.
Antiphospholipid syndrome. Sapporo criteria. (“Antiphospholipid syndrome is a hypercoagulable state characterized by recurrent arteriovenous thrombosis and/or pregnancy morbidity in the presence of either a lupus anticoagulant or anticardiolipin antibody (both phospholipid-related proteins). […] The most common arteriovenous thrombotic events in antiphospholipid syndrome are deep venous thrombosis and pulmonary embolus […], but any part of the circulation may be involved, with arterial events such as myocardial infarction and stroke carrying a high mortality rate. Poor placental circulation is thought to be responsible for the high pregnancy morbidity, with recurrent first- and second-trimester loss and a higher rate of pre-eclampsia being typical clinical features.”)
Still’s disease. (“Consider inflammatory disease in cases of pyrexia of unknown origin.”)
Polymyalgia rheumatica. Giant cell arteritis. (“[P]olymyalgia rheumatica (PMR) [is] a systemic inflammatory syndrome affecting the elderly that is characterized by bilateral pain and stiffness in the shoulders and hip girdles. The stiffness can be profound and limits mobility although true muscle weakness is not a feature. […] The affected areas are diffusely tender, with movements limited by pain. […] care must be taken not to attribute joint inflammation to PMR until other diagnoses have been excluded; for example, a significant minority of RA patients may present with a polymyalgic onset. […] The treatment for PMR is low-dose corticosteroids. […] Many physicians would consider a dramatic response to low-dose prednisolone as almost diagnostic for PMR, so if a patients symptoms do not improve rapidly it is wise to re-evaluate the original diagnosis.”)
Relapsing polychondritis. (“Relapsing polychondritis is characterized histologically by inflammatory infiltration and later fibrosis of cartilage. Any cartilage, in any location, is at risk. […] Treatment of relapsing polychondritis is with corticosteroids […] Surgical reconstruction of collapsed structures is not an option as the deformity tends to continue postoperatively.”)
Dermatomyositis. Gottron’s Papules.
Enteropathic arthritis. (“A seronegative arthritis may develop in up to 15 per cent of patients with any form of inflammatory bowel disease, including ulcerative colitis (UC), Crohn’s disease or microscopic and collagenous colitis. The most common clinical presentations are a peripheral arthritis […] and spondyloarthritis.”)
Reflex sympathetic dystrophy.
Whipple’s disease. (“Although rare, consider Whipple’s disease in any patient presenting with malabsorption, weight loss and arthritis.”)
Wegener’s granulomatosis. (“Small-vessel vasculitis may cause a pulmonary-renal syndrome. […] The classic triad of Weneger’s granulomatosis is the presence of upper and lower respiratory tract disease and renal impairment.”)
Reactive arthritis. Reiter’s syndrome. (“Consider reactive arthritis in any patient presenting with a monoarthropathy. […] Reactive arthritis is generally benign, with up to 80 per cent making a full recovery.”)
Sarcoidosis. Löfgren syndrome.
Polyarteritis nodosa. (“Consider mesenteric ischaemia in any patient presenting with a systemic illness and postprandial abdominal pain.”)
Sjögren syndrome. Schirmer’s test.
Behçet syndrome.
Lyme disease. Erythema chronicum migrans. (“The combination of rash leading to arthralgia and cranial neuropathy is a classic presentation of Lyme disease.”)
Takayasu arteritis. (“Takayasu’s arteritis is an occlusive vasculitis leading to stenoses of the aorta and its principal branches. The symptoms and signs of the disease depend on the distribution of the affected vessel but upper limbs are generally affected more commonly than the iliac tributaries. […] the disease is a chronic relapsing and remitting condition […] The mainstay of treatment is high-dose corticosteroids plus a steroid-sparing agent such as methotrexate. […] Cyclophosphamide is reserved for those patients who do not achieve remission with standard therapy. Surgical intervention such as bypass or angioplasty may improve ischaemic symptoms once the inflammation is under control.”)
Lymphoma.
Haemarthrosis. (“Consider synovial tumours in a patient with unexplained haemarthrosis.”)
Juvenile idiopathic arthritis.
Drug-induced lupus erythematosus. (“Drug-induced lupus (DIL) generates a different spectrum of clinical manifestations from idiopathic disease. DIL is less severe than idiopathic SLE, and nephritis or central nervous system involvement is very rare. […] The most common drugs responsible for a lupus-like syndrome are procainamide, hydralazine, quinidine, isoniazid, methyldopa, chlorpromazine and minocycline. […] Treatment involves stopping the offending medication and the symptoms will gradually resolve.”)
Churg–Strauss syndrome.
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.”
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“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.”
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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.”
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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.”
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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 (1–4). 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 (51–53). 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.”
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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].”
Gastrointestinal complications of diabetes (I)
I really liked this book. It covered a lot of stuff also covered in Horowitz & Samsom’s excellent book on these topics, but it’s shorter and so probably easier for the relevant target group to justify reading. I recommend the book if you want to know more about these topics but don’t quite feel like reading a long textbook on these topics.
Below I’ve added some observations from the first half of the book. In the quotes below I’ve added some links and highlighted some key observations by the use of bold text.
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“Gastrointestinal (GI) symptoms occur more commonly in patients with diabetes than in the general population [2]. […] GI symptoms such as nausea, abdominal pain, bloating, diarrhea, constipation, and delayed gastric emptying occur in almost 75 % of patients with diabetes [3]. A majority of patients with GI symptoms stay undiagnosed or undertreated due to a lack of awareness of these complications among clinicians. […] Diabetes can affect the entire GI tract from the oral cavity and esophagus to the large bowel and anorectal region, either in isolation or in a combination. The extent and the severity of the presenting symptoms may vary widely depending upon which part of the GI tract is involved. In patients with long-term type 1 DM, upper GI symptoms seem to be particularly common [4]. Of the different types […] gastroparesis seems to be the most well known and most serious complication, occurring in about 50 % of patients with diabetes-related GI complications [5].”
“The enteric nervous system (ENS) is an independent network of neurons and glial cells that spread from the esophagus up to the internal anal sphincter. […] the ENS regulates GI tract functions including motility, secretion, and participation in immune regulation [12, 13]. GI complications and their symptoms in patients with diabetes arise secondary to both abnormalities of gastric function (sensory and motor modality), as well as impairment of GI hormonal secretion [14], but these abnormalities are complex and incompletely understood. […] It has been known for a long time that diabetic autonomic neuropathy […] leads to abnormalities in the GI motility, sensation, secretion, and absorption, serving as the main pathogenic mechanism underlying GI complications. Recently, evidence has emerged to suggest that other processes might also play a role. Loss of the pacemaker interstitial cells of Cajal, impairment of the inhibitory nitric oxide-containing nerves, abnormal myenteric neurotransmission, smooth muscle dysfunction, and imbalances in the number of excitatory and inhibitory enteric neurons can drastically alter complex motor functions causing dysfunction of the enteric system [7, 11, 15, 16]. This dysfunction can further lead to the development of dysphagia and reflux esophagitis in the esophagus, gastroparesis, and dyspepsia in the stomach, pseudo-obstruction of the small intestine, and constipation, diarrhea, and incontinence in the colon. […] Compromised intestinal vascular flow arising due to ischemia and hypoxia from microvascular disease of the GI tract can also cause abdominal pain, bleeding, and mucosal dysfunction. Mitochondrial dysfunction has been implicated in the pathogenesis of gastric neuropathy. […] Another possible association between DM and the gastrointestinal tract can be infrequent autoimmune diseases associated with type I DM like autoimmune chronic pancreatitis, celiac disease (2–11 %), and autoimmune gastropathy (2 % prevalence in general population and three- to fivefold increase in patients with type 1 DM) [28, 29]. GI symptoms are often associated with the presence of other diabetic complications, especially autonomic and peripheral neuropathy [2, 30, 31]. In fact, patients with microvascular complications such as retinopathy, nephropathy, or neuropathy should be presumed to have GI abnormalities until proven otherwise. In a large cross-sectional questionnaire study of 1,101 subjects with DM, 57 % of patients reported at least one GI complication [31]. Poor glycemic control has also been found to be associated with increased severity of the upper GI symptoms. […] management of DM-induced GI complications is challenging, is generally suboptimal, and needs improvement.“
“Diabetes mellitus (DM) has multiple clinically important effects on the esophagus. Diabetes results in several esophageal motility disturbances, increases the risk of esophageal candidiasis, and increases the risk of Barrett’s esophagus and esophageal carcinoma. Finally, “black esophagus,” or acute esophageal necrosis, is also associated with DM. […] Esophageal dysmotility has been shown to be associated with diabetic neuropathy; however, symptomatic esophageal dysmotility is not often considered an important complication of diabetes. […] In general, the manometric effects of diabetes on the esophagus are not specific and mostly related to speed and strength of peristalsis. […] The pathological findings which amount to loss of cholinergic stimulation are consistent with the manometric findings in the esophagus, which are primarily related to slowed or weakened peristalsis. […] The association between DM and GERD is complex and conflicting. […] A recent meta-analysis suggests an overall positive association in Western countries [12]. […] The underlying pathogenesis of DM contributing to GERD is not fully elucidated, but is likely related to reduced acid clearance due to slow, weakened esophageal peristalsis. The association between DM and gastroesophageal reflux (GER) is well established, but the link between DM and GERD, which requires symptoms or esophagitis, is more complex because sensation may be blunted in diabetics with neuropathy. Asymptomatic gastroesophageal reflux (GER) confirmed by pH studies is significantly more frequent in diabetic patients than in healthy controls [13]. […] long-standing diabetics with neuropathy are at higher risk for GERD even if they have no symptoms. […] Abnormal pH and motility studies do not correlate very well with the GI symptoms of diabetics, possibly due to DM-related sensory dysfunction.”
“Gastroparesis is defined as a chronic disorder characterized by delayed emptying of the stomach occurring in the absence of mechanical obstruction. It is a well-known and potentially serious complication of diabetes. […] Diabetic gastroparesis affects up to 40 % of patients with type 1 diabetes and up to 30 % of patients with type 2 diabetes [1, 2]. Diabetic gastroparesis generally affects patients with longstanding diabetes mellitus, and patients often have other diabetic complications […] For reasons that remain unclear, approximately 80 % of patients with gastroparesis are women [3]. […] In diabetes, delayed gastric emptying can often be asymptomatic. Therefore, the term gastroparesis should only be reserved for patients that have both delayed gastric emptying and upper gastrointestinal symptoms. Additionally, discordance between the pattern and type of symptoms and the magnitude of delayed gastric emptying is a well-established phenomenon. Accelerating gastric emptying may not improve symptoms, and patients can have symptomatic improvement while gastric emptying time remains unchanged. Furthermore, patients with severe symptoms can have mild delays in gastric emptying. Clinical features of gastroparesis include nausea, vomiting, bloating, abdominal pain, and malnutrition. […] Gastroparesis affects oral drug absorption and can cause hyperglycemia that is challenging to manage, in addition to unexplained hypoglycemia. […] Nutritional and caloric deficits are common in patients with gastroparesis […] Possible complications of gastroparesis include volume depletion with renal failure, malnutrition, electrolyte abnormalities, esophagitis, Mallory–Weiss tear (from vomiting), or bezoar formation. […] Unfortunately, there is a dearth of medications available to treat gastroparesis. Additionally, many of the medications used are based on older trials with small sample sizes […and some of them have really unpleasant side effects – US]. […] Gastroparesis can be associated with abdominal pain in as many as 50 % of patients with gastroparesis at tertiary care centers. There are no trials to guide the choice of agents. […] Abdominal pain […] is often difficult to treat [3]. […] In a subset of patients with diabetes [less than 10%, according to Horowitz & Samsom – US], gastric emptying can be abnormally accelerated […]. Symptoms are often difficult to distinguish from those with delayed gastric emptying. […] Worsening symptoms with a prokinetic agent can be a sign of possible accelerated emptying.”
“Diabetic enteropathy encompasses small intestinal and colorectal dysfunctions such as diarrhea, constipation, and/or fecal incontinence. It is more commonly seen in patients with long-standing diabetes, especially in those with gastroparesis. Development of diabetic enteropathy is complex and multifactorial. […] gastrointestinal symptoms and complications do not always correlate with the duration of diabetes, glycemic control, or with the presence of autonomic neuropathy, which is often assumed to be the major cause of many gastrointestinal symptoms. Other pathophysiologic processes operative in diabetic enteropathy include enteric myopathy and neuropathy; however, causes of these abnormalities are unknown [1]. […] Collectively, the effects of diabetes on several targets cause aberrations in gastrointestinal function and regulation. Loss of ICC, autonomic neuropathy, and imbalances in the number of excitatory and inhibitory enteric neurons can drastically alter complex motor functions such as peristalsis, reflexive relaxation, sphincter tone, vascular flow, and intestinal segmentation [5]. […] Diarrhea is a common complaint in DM. […] Etiologies of diarrhea in diabetes are multifactorial and include rapid intestinal transit, drug-induced diarrhea, small-intestine bacterial overgrowth, celiac disease, pancreatic exocrine insufficiency, dietary factors, anorectal dysfunction, fecal incontinence, and microscopic colitis [1]. […] It is important to differentiate whether diarrhea is caused by rapid intestinal transit vs. SIBO. […] This differentiation has key clinical implications with regard to the use of antimotility agents or antibiotics in a particular case. […] Constipation is a common problem seen with long-standing DM. It is more common than in general population, where the incidence varies from 2 % to 30 % [30]. It affects 60 % of the patients with DM and is more common than diarrhea [14]. […] There are no specific treatments for diabetes-associated constipation […] In most cases, patients are treated in the same way as those with idiopathic chronic constipation. […] Colorectal cancer is the third most common cancer in men and the second in women [33]. Individuals with type 2 DM have an increased risk of colorectal cancer when compared with their nondiabetic counterparts […] According to a recent large observational population-based cohort study, type 2 DM was associated with a 1.3-fold increased risk of colorectal cancer compared to the general population.”
“Nonalcoholic fatty liver disease (NAFLD) is the main hepatic complication of obesity, insulin resistance, and diabetes and soon to become the leading cause for end-stage liver disease in the United States [1]. […] NAFLD is a spectrum of disease that ranges from steatosis (hepatic fat without significant hepatocellular injury) to nonalcoholic steatohepatitis (NASH; hepatic fat with hepatocellular injury) to advanced fibrosis and cirrhosis. As a direct consequence of the obesity epidemic, NAFLD is the most common cause of chronic liver disease, while NASH is the second leading indication for liver transplantation [1]. NAFLD prevalence is estimated at 25 % globally [2] and up to 30 % in the United States [3–5]. Roughly 30 % of individuals with NAFLD also have NASH, the progressive subtype of NAFLD. […] NASH is estimated at 22 % among patients with diabetes, compared to 5 % of the general population [4, 14]. […] Insulin resistance is strongly associated with NASH. […] Simple steatosis (also known as nonalcoholic fatty liver) is characterized by the presence of steatosis without ballooned hepatocytes (which represents hepatocyte injury) or fibrosis. Mild inflammation may be present. Simple steatosis is associated with a very low risk of progressive liver disease and liver-related mortality. […] Patients with NASH are at risk for progressive liver fibrosis and liver-related mortality, cardiovascular complications, and hepatocellular carcinoma (HCC) even in the absence of cirrhosis [26]. Liver fibrosis stage progresses at an estimated rate of one stage every 7 years [27]. Twenty percent of patients with NASH will eventually develop liver cirrhosis [9]. […] The risk of cardiovascular disease is increased across the entire NAFLD spectrum. […] Cardiovascular risk reduction should be aggressively managed in all patients.“
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.
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“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.”
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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.
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 (1–3).
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 (13–15). 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.”
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“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.”
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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).”
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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.”
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“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.”
…
“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.”
Alcohol and Aging
I’m currently reading this book. Below I have added some observations from the first five chapters. The book has 17 chapters in total, covering a wide variety of topics. I like the coverage so far. All the highlighted observations below were highlighted by me; they were not written in bold in the book.
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“Alcohol consumption and alcohol-related deaths or problems have recently increased among older age groups in many developed countries […]. This increase in consumption, in combination with the ageing of populations worldwide, means that the absolute number of older people with alcohol problems is on the increase and a real danger exists that a “silent epidemic” may be evolving [2]. Although there is growing recognition of this public health problem, clinicians consistently under-detect alcohol problems and under-deliver behaviour change interventions to older people [8, 9] […] While older adults historically demonstrate much lower rates of alcohol use compared with younger adults [4, 5] and present to substance abuse treatment programs less frequently than their younger counterparts [6], substantial evidence suggests that at-risk alcohol use and alcohol use disorder (AUD) among older adults has been under-identified for decades [7, 8]. […] Individuals who have had alcohol-related problems over several decades and have survived into old age tend to be referred to as early onset drinkers. It is estimated that two-thirds of older drinkers fall into this category [2]. […] Late-onset drinking accounts for the remaining one-third of older people who use alcohol excessively [2]. Late-onset drinkers usually begin drinking in their 50s or 60s and tend to be of a higher socio-economic status than early onset drinkers with higher levels of education and income [2]. Stressful life events, such as bereavement or retirement, may trigger late-onset drinking […]. One study demonstrated that 70 % of late-onset drinkers had experienced stressful life events, compared with 25 % of early onset drinkers [17]. Those whose alcohol problems are of late onset tend to have fewer health problems and are more receptive to treatment than those with early onset problems […] Our data highlighted that losing a parent or partner was often pinpointed as an event that had prompted an escalation in alcohol use […] A recent systematic review which examined the relationship between late-life spousal bereavement and changes in routine health behaviour over 32 different studies [however] found only moderate evidence for increased alcohol consumption [41].”
“Understanding alcohol use among older adults requires a life course perspective [2] […]. Broadly speaking, to understand alcohol consumption patterns and associated risks among older adults, one must consider both biopsychosocial processes that emerge earlier in life and aging-specific processes, such as multimorbidity and retirement. […] In the population overall, older adulthood is a life stage in which overall alcohol consumption decreases, binge drinking becomes less common, and individuals give up drinking. […] data collected internationally supports the assertion that older adulthood is a period of declining drinking. […] Two forces specific to later life may be at work in decreasing levels of alcohol consumption in late life. First, the “sick-quitter” hypothesis [12, 13] suggests that changes in health during the aging process limit alcohol consumption. With declines in health, older adults decrease the quantity and frequency of their drinking leading to lower average consumption in the overall older adult population [11, 14]. Similarly, differential mortality of heavy drinkers may lead to decreases in alcohol use among cohorts of older adults; these changes in average drinking may be a function of early mortality of heavy drinkers [15]. Although alcohol use generally declines throughout the course of older adulthood, the population of older adults exhibits a great deal of variability in drinking patterns. […] longitudinal research studies have found that older men tend to consume alcohol at higher levels than women, and their consumption levels decline more slowly than women’s [6]. […] National survey data [from the UK] estimate that approximately 40–45% of older adults (65+) drank alcohol in the past year […] Numerous studies suggest that lifetime nondrinkers are more likely to be female, display greater religiosity (e.g., attend religious services), and have lower levels of education than their moderate drinking peers [20, 21]. […] Older adult nondrinkers are a heterogeneous population, and as such, lifetime nondrinkers and former drinkers should be studied separately. This is especially important when considering the issue of health and drinking because the context for abstinence may be different in these two groups [23, 24].”
“[V]ersion 5 of the DSM manual abandoned separate alcohol abuse and alcohol dependence diagnoses, and combined them into a single diagnosis: alcohol use disorder (AUD). […] The NSDUH survey estimated a past-year prevalence rate of alcohol abuse or dependence of 6.1 % among those aged 50–54 and 2.2 % among those ages 65 and older. […] AUD is the most severe manifestation of alcohol-related pathology among older adults, but most alcohol-related harm is not a function of disordered drinking [55]. […] older adults commonly take medications that interact with alcohol. A recent study of community-dwelling older adults (aged 57+) found that 41% consumed alcohol regularly and among regular alcohol consumers, 51 % used at least one alcohol interacting medication [57]. An analysis of the Irish Longitudinal Study on Ageing identified a high prevalence of alcohol use (60 %) among individuals taking alcohol interacting medications [58]. Falls are also a common health concern for older adults, and there is evidence of increased risk of falls among older adults who drink more than 14 drinks per week [59] […] a study by Holahan and colleagues [44] explored longitudinal outcomes for individuals who were moderate drinkers (below the weekly at-risk threshold) but who engaged in heavy episodic drinking (exceeded day threshold). Individuals were first surveyed between the ages of 55 and 65 and followed for 20 years. Episodic heavy drinkers were twice as likely to have died in the 20-year follow-up period compared with those who were not episodic heavy drinkers […to clarify, none of the episodic heavy drinkers in that study would qualify for a diagnosis of AUD, US] […] Alcohol use in the aging population has been defined through various thresholds of risk. Each approach brings certain advantages and problems. Using alcohol related disorders as a benchmark misses many older adults who may experience alcohol-related consequences to their health and well-being even though they do not meet criteria for disordered drinking. More conservative measures of alcohol risk may identify at-risk drinking in those for whom alcohol use may never compromise their health. […] among light to moderate drinkers, the level of risk is uncertain.”
“Among adults 65 years old and older in 2000–2001, just under 49.6% reported lifetime use [of tobacco] and 14% reported use in the last 12 months [30]. […] Data collected by the Centers for Disease Control in 2008 revealed that only 9% of individuals aged 65 and older reported being current smokers [42]. […] data from the 2001–2002 NESARC reveal a strong relationship between AUDs and tobacco use […] in 2012, 19.3% of adults 65 and older reported having ever used illicit drugs in their lifetime, whereas 47.6% of adults between the ages 60 and 64 reported lifetime drug use. […] In the 2005–2006 NSDUH […] 3.9% of adults aged 50–64, the bulk of the Baby Boomers at that time, reported past year marijuana use, compared to only 0.7% of those 65 years old and older [53]. Among those aged 50 and older reporting marijuana use, 49% reported using marijuana more than 30 days in the past year, with a mean of 81 days. […] The increasingly widespread, legal availability and acceptance of cannabis, for both medicinal and recreational use, may pose unique risks in an aging population. Across age groups, cannabis is known to impair short-term memory, increase one’s heart and respiratory rate, and elevate blood pressure [56]. […] For older adults, these risks may be particularly pronounced, especially for those whose cognitive or cardiovascular systems may already be compromised. […] Most researchers generally consider existing estimations of mental health and substance use disorders to be underestimations among older adults. […] Assumptions that older adults do not drink or use illicit substances should not be made.“
“Although several studies in the United States and elsewhere have shown that moderate alcohol consumption is associated with reduced risk for heart disease [16–20] and that heavy intake is associated with increased risk of CVD incidence [6, 21] and all-cause mortality in various populations […], data specific to effects of alcohol in elderly populations remain scant. The few studies available, e.g., the Cardiovascular Health Study, suggest that moderate alcohol use is beneficial and may be associated with reduced Medicare costs among individuals with CVD [25]. The benefits and risks of alcohol consumption are dose dependent with a consistent cut-point for cardiovascular benefits being 1 drink per day for women and about 2 drinks per day for men [21]. These cut-points have also been observed for associations between alcohol consumption and all-cause mortality [21, 26]. Although there are many similarities in the effects of alcohol on CVD across many populations, the magnitude and significance of the association between amount of alcohol consumed and CVD risk remain inconsistent, especially within countries, regions, age, sex, race, and other population strata […] As shown in a recent review [33], a drinking pattern characterized by moderate drinking without episodes of heavy drinking may be more beneficial for CVD protection when compared to patterns that include heavy drinking episodes. […] In additional to amount of alcohol consumed per se, the pattern of alcohol consumption, commonly defined as the number of drinking days per week is also associated with CVD outcomes independent of the amount of alcohol consumed [18, 24, 34–37]. In general, a drinking pattern characterized by alcohol consumption on 4 or more days of the week is inversely associated with MI, stroke, and CVD risk factors“.
“The relation between moderate alcohol consumption and intermediate CVD markers was summarized in two recent reviews [6, 42]. Overall, moderate alcohol consumption is associated with improved concentrations of CVD risk markers, particularly HDL-C concentrations [18, 31, 43, 44]. Whether HDL-C resulting from moderate alcohol intake is functional and beneficial for cardioprotection remains unknown […] While moderate alcohol consumption shows no appreciable benefit on LDL-C, it is associated with significant improvement in insulin sensitivity […] Alcohol intake may also influence CVD markers through its effects on absorption and metabolism of nutrients in the body. This is critical especially in the elderly who may have deficiencies or insufficiencies of nutrients such as folate, vitamin B12, vitamin D, magnesium, and iron. Indeed, moderate alcohol consumption has been shown to improve status of nutrients associated with cardiovascular effects. For example, it improves iron absorption in humans [52, 53] and is associated with higher vitamin D levels in men [54]. […] heavy alcohol consumption [on the other hand] leads to deficiencies of magnesium [55], zinc, folate [56], and other nutrients and damages the intestinal lining and the liver impairing nutrient absorption and metabolism [57]. These effects of alcohol are likely to be worse in the elderly. […] chronic heavy drinking lowers magnesium [55], a nutrient needed for proper metabolism of vitamin D [58], implying that supplementation with vitamin D in heavy drinkers may not be as effective as intended. These effects of alcohol could also extend to prescription medications that are in common use among the elderly. […] Taken together, moderate alcohol seems to protect against cardiovascular disease across the whole life span but the data on older age groups are scanty. Theoretical considerations as well as emerging data on intermediate outcomes such as lipids, suggest that moderate alcohol could beneficially interact with medications such as statins to improve cardiovascular health but heavy alcohol could worsen CVD risk, especially in the elderly.”
“Alcohol is one of the main risk factors for cancer, with alcohol use attributed to up to 44% of some cancers [2, 3] and between 3.2 and 3.7 % of all cancer deaths [4, 5]. Since 1988, alcohol has been classified as a carcinogen [6]. Types of cancers linked to alcohol use include cancers of the liver, pancreas, esophagus, breast, pharynx, and larynx with most convincing evidence for alcohol-related cancers of the upper aerodigestive tract, stomach, colorectum, liver, and the lungs [2, 7]. All of these cancers have a much higher incidence and mortality rate in older adults […] For alcohol-associated cancers, 66–95% of new cases appear in those 55 years of age or older [8, 9]. For alcohol-associated cancers, other than breast cancer, 75–95 % of new cases occur in those 55 years of age or older [8, 10, 11]. […] Four countries with a decline in alcohol use (France, the UK, Sweden, and US) have […] demonstrated a stabilization or decline in the incidence and mortality rates for types of cancers closely associated with alcohol use [12]. […] The increased risk for cancer related to alcohol use is based on a combination of both quantity/frequency and duration of use, with those consuming alcohol for 20 or more years at increased risk [14]. […] consumption of alcohol at lower levels may also increase the risk for alcohol-related cancers. Nelson et al. reported that daily consumption of 1.5 drinks or greater accounted for 26–35% of alcohol-attributable deaths [5]. Thus, the evidence is growing that daily drinking, even at lower levels, increases the risk for developing cancer in later life with the conclusion that there may be no safe threshold level for alcohol consumption below which there is no risk for cancer [6, 16, 17].”
“The risk for developing alcohol-related cancer is increased among those who have a history of concurrent tobacco use and at-risk alcohol use […] Among individuals who have a history of smoking two or more packs of cigarettes and consuming more than four alcoholic drinks per day, the risk of head and neck cancer is increased greater than 35-fold [22]. […] At least 75 % of head and neck cancer is associated with alcohol and tobacco use[9]. […] There are gender differences in alcohol attributable cancer deaths with over half (56–66 %) of all alcohol-attributable cancer deaths in females resulting from breast cancer [5]. […] For women, even low-risk alcohol use (5–14.9 g/day or one standard drink of alcohol or less) increases the risk of cancer, mainly breast cancer [18]. […] Alcohol use during cancer treatment can complicate the treatment regimen and lead to poor long-term outcomes. […] Alcohol use is correlated with poor survival outcomes in oncology patients. […] Another issue for patients during cancer treatment is quality of life. Alcohol consumption at higher levels […] or patients who screened positive for a possible AUD during cancer treatment experienced worse quality of life outcomes, including problems with pain, sleep, dyspnea, total distress, anxiety, coping, shortness of breath, diarrhea, poor emotional functioning, fatigue, and poor appetite [58, 59]. Current alcohol use has also been associated with higher pain scores and long-term use of opioids [48, 49].”
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.”
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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.
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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 hypotension. Focused 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. Warfarin. Prothrombin 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 disease. Upper 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. Alteplase. Mechanical 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.
Endocrinology (part 6 – neuroendocrine disorders and Paget’s disease)
I’m always uncertain as to how much content to cover when covering books like this one, and I usually cover handbooks in less detail (relatively) than I cover other books because of the amount of work it takes to cover all topics of interest – however I didn’t feel after writing my last post in the series that I had really finished with this book, in terms of blogging it; in fact I remember distinctly feeling a bit annoyed towards the end of writing my fifth post by the fact that I didn’t find that I could justify covering the detailed account of Paget’s disease included in the last part of the chapter, even though all of that stuff was new knowledge to me, and quite interesting – but these posts take some effort, and sometimes I cut them short just to at least blog something, rather than just have an unpublished draft lying around.
In this post I’ll first include some belated coverage of Paget’s disease, which is from the book’s chapter 6, and then I’ll cover some of the stuff included in chapter 8 of the book, about neuroendocrine disorders. Chapter 8 deals exclusively with various types of (usually quite rare) tumours. I decided to not cover chapter 7, which is devoted to paediatric endocrinology.
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“Paget’s disease is the result of greatly ↑ local bone turnover, which occurs particularly in the elderly […] The 1° abnormality in Paget’s disease is gross overactivity of the osteoclasts, resulting in greatly increased ↑ bone resorption. This secondarily results in ↑ osteoblastic activity. The new bone is laid down in a highly disorganized manner […] Paget’s disease can affect any bone in the skeleton […] In most patients, it affects several sites, but, in about 20% of cases, a single bone is affected (monostotic disease). Typically, the disease will start in one end of a long bone and spread along the bone at a rate of about 1cm per year. […] Paget’s disease alters the mechanical properties of the bone. Thus, pagetic bones are more likely to bend under normal physiological loads and are thus liable to fracture. […] Pagetic bones are also larger than their normal counterparts. This can lead to ↑ arthritis at adjacent joints and to pressure on nerves, leading to neurological compression syndromes and, when it occurs in the skull base, sensorineural deafness.”
“Paget’s disease is present in about 2% of the UK population over the age of 55. It’s prevalence increases with age, and it is more common in ♂ than ♀. Only about 10% of affected patients will have symptomatic disease. […] Most notable feature is pain. […] The diagnosis of Paget’s disease is primarily radiological. […] An isotope bone scan is frequently helpful in assessing the extent of skeletal involvement […] Deafness is present in up to half of cases of skull base Paget’s. • Other neurological complications are rare. […] Osteogenic sarcoma [is a] very rare complication of Paget’s disease. […] Any increase of pain in a patient with Paget’s disease should arouse suspicion of sarcomatous degeneration. A more common cause, however, is resumption of activity of disease. […] Treatment with agents that decrease bone turnover reduces disease activity […] Although such treatment has been shown to help pain, there is little evidence that it benefits other consequences of Paget’s disease. In particular, the deafness of Paget’s disease does not regress after treatment […] Bisphosphonates have become the mainstay of treatment. […] Goals of treatment [are to:] • Minimize symptoms. • Prevent long-term complications. • Normalize bone turnover. • Alkaline phosphatase in normal range. • No actual evidence that treatment achieves this.”
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The rest of this post will be devoted to covering topics from chapter 8:
“Neuroendocrine cells are found in many sites throughout the body. They are particularly prominent in the GI tract and pancreas and […] have the ability to synthesize, store, and release peptide hormones. […] the majority of neuroendocrine tumours occur within the gastroenteropancreatic axis. […] >50% are traditionally termed carcinoid tumours […] with the remainder largely comprising pancreatic islet cell tumours. • Carcinoid and islet cell tumours are generally slow-growing. […] There is a move towards standardizing the terminology of these tumours […] The term NEN [neuroendocrine neoplasia] included low- and intermediate-grade neoplasia (previously referred to as carcinoid or atypical carcinoid) which are now referred to as neuroendocrine tumours (NETs) and high-grade neoplasia (neuroendocrine carcinoma, NEC). There is a confusing array of classifications of NENs, based on anatomical origin, histology, and secretory activity. • Many of these classifications are well established and widely used.”
“It is important to understand the differences between ‘differentiation’, which is the extent to which the neoplastic cells resemble their non-tumourous counterparts, and ‘grade’, which is the inherent agressiveness of the tumour. […] Neuroendocrine carcinomas are the most aggressive NENs and can be either small or large cell type. […] NENs are diagnosed based on histological features of biopsy specimens. The presenting features of the tumours vary like any other tumour, based on their anatomical location, such as abdominal pain, intestinal obstruction. Many are incidentally discovered during endoscopy or imaging for unrelated conditions. In a database study, 49% of NENs were localized, 24% had regional metastases, and 27% had distant metastases. […] These tumours rarely manifest themselves due to their secretory effect. [This is quite different from some of the other tumours they covered elsewhere in the book – US] [….] Only a third of patients with neuroendocrine tumours develop symptoms due to hormone secretion.”
“Surgery is the treatment of choice for NENs grades 1 and 2, except in the presence of widespread distant metastases and extensive local invasion. […] Somatostatin analogues (SSA) have relatively minor side effects and provide long-term symptom control. •Octreotide and lanreotide […] reduce the level of biochemical tumour markers in the majority of patients and control symptoms in around 70% of cases. […] A combination of interferon with octreotide has been shown to produce biochemical and symptomatic improvement in patients who have previously had no significant benefit from either drug alone. […] Cytotoxic chemotherapy may be considered in patients with progressive, advanced, or uncontrolled symptomatic disease.”
“Despite the changes in nomenclature of NENs […] the ‘carcinoid crisis’ [apparently also termed ‘malignant carcinoid syndrome‘, US] is still an important descriptive term. It is a potentially life-threatening condition that should be prevented, where possible, and treated as an emergency. • Clinical features include hypotension, tachycardia, arrhythmias, flushing, diarrhoea, broncospasm, and altered sensoriom. […] carcinoid crisis can be triggered by manipulation of the tumours, such as during biopsy, surgery, or palpation. • These result in the release of biologically active compounds from the tumours. […] Carcinoid heart disease […] result in valvular stenosis or regurgitation and eventually heart failure. This condition is seen in 40-50% of patients with carcinoid syndrome and 3-4% of patients with neuroendocrine tumours”.
“An insulinoma is a functioning neuroendocrine tumour of the pancreas that causes hypoglycemia through inappropriate secretion of insulin. • Unlike other neuroendocrine tumours of the pancreas, more than 90% of insulinomas are benign. […] annual incidence of insulinomas is of the order of 1-2 per million population. […] The treatment of choice in all, but poor, surgical candidates is operative removal. […] In experienced surgical hands, the mortality is less than 1%. […] Following the removal of solitary insulinoma [>80% of cases], life expectancy is restored to normal. Malignant insulinomas, with metastases usually to the liver, have a natural history of years, rather than months, and may be controlled with medical therapy or specific antitumour therapy […] • Average 5-year survival estimated to be approximately 35% for malignant insulinomas. […] Gastrinomas are the most common functional malignant pancreatic endocrine tumours. […] The incidence of gastrinomas is 0.5-2/million population/year. […] Gastrin […] is the principal gut hormone stimulating gastric acid secretion. • The Zollinger-Ellison (ZE) syndrome is characterized by gastric acid oversecretion and manifests itself as severe peptic ulcer disease (PUD), gastro-oesophageal reflux, and diarrhoea. […] 10-year survival [in patients with gastrinomas] without liver metastases is 95%. […] Where there are diffuse metastases, […] a 10-year survival of approximately 15% [is observed].”
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One of the things I was thinking about before deciding whether or not to blog this chapter was whether the (fortunately!) rare conditions encountered in the chapter really ‘deserved’ to be covered. Unlike what is the case for, say, breast cancer or colon cancer, most people won’t know someone who’ll die from malignant insulinoma. However although these conditions are very rare, I also can’t stop myself from thinking they’re also quite interesting, and I don’t care much about whether I know someone with a disease I’ve read about. And if you think these conditions are rare, well, for glucagonomas “The annual incidence is estimated at 1 per 20 million population”. These very rare conditions really serve as a reminder of how great our bodies are at dealing with all kinds of problems we’ve never even thought about. We don’t think about them precisely because a problem so rarely arises – but just now and then, well…
Let’s talk a little bit more about those glucagonomas:
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“Glucagonomas are neuroendocrine tumours that usually arise from the α cells of the pancreas and produce the glucagonoma syndrome through the secretion of glucagon and other peptides derived from the preproglucagon gene. • The large majority of glucagonomas are malignant, but they are also very indolent tumours, and the diagnosis may be overlooked for many years. • Up to 90% of patients will have lymph node or liver metastases at the time of presentation. • They are classically associated with the rash of necrolytic migratory erythema. […] The characteristic rash [….] occurs in >70% of cases […] glucose intolerance is a frequent association (>90%). • Sustained gluconeogenesis also causes amino acid deficiencies and results in protein catabolism which can be associated with unrelenting weight loss in >60% of patients. • Glucagon has a direct suppressive effect on the bone marrow, resulting in a normochromic normocytic anaemia in almost all patients. […] Surgery is the only curative option, but the potential for a complete cure may be as low as 5%.”
“In 1958, Verner and Morrison1 first described a syndrome consisting of refractory watery diarrhoea and hypokalaemia, associated with a neuroendocrine tumour of the pancreas. • The syndrome of watery diarrhea, hypokalaemia and acidosis (WDHA) is due to secretion of vasoactive intestinal polypeptide (VIP). • Tumours that secrete VIP are known as VIPomas. VIPomas account for <10% of islet cell tumours and mainly occur as solitary tumours. >60% are malignant […] The most prominent symptom in most patients is profuse watery diarrhoea […] Surgery to remove the tumour is the treatment of first choice […] and may be curative in around 40% of patients. […] Somatostatin analogues produce effective symptomatic relief from the diarrhoea in most patients. Long-term use does not result in tumour regression. […] Chemotherapy […] has resulted in response rates of >30%.”
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So by now we know that somatostatin analogues can provide symptom relief in a variety of contexts when you’re dealing with these conditions. But wait, what happens if you get a functional tumour of the cells that produce somatostatins? Will this mean that you just feel great all the time, or that you at least don’t have any symptoms of disease? Well, not exactly…
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“Somatostatinomas are very rare neuroendocrine tumours, occurring both in the pancreas and in the duodenum. • >60% are large tumours located in the head or body of the pancreas. • The clinical syndrome may be diagnosed late in the course of disease when metastatic spread to local lymph nodes and the liver has already occurred. […] • Glucose intolerance or frank diabetes mellitus may have been observed for many years prior to the diagnosis and retrospectively often represents the first clinical sign. It is probably due to the inhibitory effect of somatostatin on insulin secretion. • A high incidence of gallstones has been described similar to that seen as a side effect with long-term somatostatin analogue therapy. • Diarrhoea, steatorrhoea, and weight loss appear to be consistent clinical features […this despite the fact that you use the hormone produced by these tumours to manage diarrhea in other endocrine tumours – it’s stuff like this which makes these rare disorders far from boring to read about! US] and may be associated with inhibition of the exocrine pancreas by somatostatin.”
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) (12–14). 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 (12,15–17). […] 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.”
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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.”
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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.
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“…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.”
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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 (1–4). 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 (13–15), 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.”
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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).
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“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 (6–8). 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.”
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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.
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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).”
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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).”
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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.”
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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.”
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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.”
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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.
100 cases in surgery (II)
Below I have added some links and quotes related to the last half of the book’s coverage.
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Ischemic rest pain. (“Rest pain indicates inadequate tissue perfusion. *Urgent investigation and treatment is required to salvage the limb. […] The material of choice for bypass grafting is autogenous vein. […] The long-term patency of prosthetic grafts is inferior compared with autogenous vein.”)
Deep vein thrombosis.
Lymphedema. (“In lymphoedema, the vast majority of patients (>90 per cent) are treated conservatively. […] Debulking operations […] are only considered for a selected few patients where the function of the limb is impaired or those with recurrent attacks of severe cellulitis.”)
Varicose veins. Trendelenburg Test. (“Surgery on the superficial venous system should be avoided in patients with an incompetent deep venous system.”)
Testicular Torsion.
Benign Prostatic Hyperplasia.
Acute pyelonephritis. (“In patients with recurrent infection in the urinary system, significant pathology needs excluding such as malignancy, urinary tract stone disease and abnormal urinary tract anatomy.”)
Renal cell carcinoma. von Hippel-Lindau syndrome. (“Approximately one-quarter to one-third of patients with renal cell carcinomas have metastases at presentation. […] The classic presenting triad of loin pain, a mass and haematuria only occurs in about 10 per cent of patients. More commonly, one of these features appears in isolation.”)
Haematuria. (“When taking the history, it is important to elicit the following: •Visible or non-visible: duration of haematuria • Age: cancers are more common with increasing age •Sex: females more likely to have urinary tract infections• Location: during micturition, was the haematuria always present (indicative of renal, ureteric or bladder pathology) or was it only present initially (suggestive of anterior urethral pathology) or present at the end of the stream (posterior urethra, bladder neck)? •Pain: more often associated with infection/inflammation/calculi, whereas malignancy tends to be painless •Associated lower urinary tract symptoms that will be helpful in determining aetiology •History of trauma •Travel abroad […] •Previous urological surgery/history/recent instrumentation/prostatic biopsy •Medication, e.g. anticoagulants •Family history •Occupational history, e.g. rubber/dye occupational hazards are risk factors for developing transitional carcinoma of the bladder […] •Smoking status: increased risk, particularly of bladder cancer •General status, e.g. weight loss, reduced appetite […] Anticoagulation can often unmask other pathology in the urinary tract. […] Patients on oral anticoagulation who develop haematuria still require investigation.”)
Urinary retention. (“Acute and chronic retention are usually differentiated by the presence or absence of pain. Acute retention is painful, unlike chronic retention, when the bladder accommodates the increase in volume over time.”)
Colles’ fracture/Distal Radius Fractures. (“In all fractures the distal neurological and vascular status should be assessed.”)
Osteoarthritis. (“Radiological evidence of osteoarthritis is common, with 80 per cent of individuals over 80 years demonstrating some evidence of the condition. […] The commonest symptoms are pain, a reduction in mobility, and deformity of the affected joint.”)
Simmonds’ test.
Patella fracture.
Dislocated shoulder.
Femur fracture. (“Fractured neck of the femur is a relatively common injury following a fall in the elderly population. The rate of hip fracture doubles every decade from the age of 50 years. There is a female preponderance of three to one. […] it is important to take a comprehensive history, concentrating on the mechanism of injury. It is incorrect to assume that all falls are mechanical; it is not uncommon to find that the cause of the fall is actually due to a urinary or chest infection or even a silent myocardial infarction.”)
The Ottawa Ankle Rules.
Septic arthritis.
Carpal tunnel syndrome. Tinel’s test. Phalen’s Test. (“It is important, when examining a patient with suspected carpal tunnel syndrome, to carefully examine their neck, shoulder, and axilla. […] the source of the neurological compression may be proximal to the carpal tunnel”)
Acute Compartment Syndrome. (“Within the limbs there are a number of myofascial compartments. These consist of muscles contained within a relatively fixed-volume structure, bounded by fascial layers and bone. After trauma the pressure in the myofascial compartment increases. This pressure may exceed the venous capillary pressure, resulting in a loss of venous outflow from the compartment. The failure to clear metabolites also leads to the accumulation of fluid as a result of osmosis. If left untreated, the pressure will eventually exceed arterial pressure, leading to significant tissue ischaemia. The damage is irreversible after 4–6 h. Tibial fractures are the commonest cause of an acute compartment syndrome, which is thought to complicate up to 20 per cent of these injuries. […] The classical description of ‘pain out of proportion to the injury’ may [unfortunately] be difficult to determine if the clinician is inexperienced.”)
Hemarthrosis. (“Most knee injuries result in swelling which develops over hours rather than minutes. [A] history of immediate knee swelling suggests that there is a haemarthrosis.”)
Sickle cell crisis.
Cervical Spine Fracture. Neurogenic shock. NEXUS Criteria for C-Spine Imaging.
Slipped Capital Femoral Epiphysis. Trethowan sign. (“At any age, a limp in a child should always be taken seriously.”)
ATLS guidelines. (“The ATLS protocol should be followed even in the presence of obvious limb deformity, to ensure a potentially life-threatening injury is not missed.”)
Peritonsillar Abscess.
Epistaxis. Little’s area.
Croup. Acute epiglottitis. (“Acute epiglottitis is an absolute emergency and is usually caused by Haemophilus influenzae. There is significant swelling, and any attempt to examine the throat may result in airway obstruction. […] In adults it tends to cause a supraglottitis. It has a rapid progression and can lead to total airway obstruction. […] Stridor is an ominous sign and needs to be taken seriously.”)
Bell’s palsy.
Subarachnoid hemorrhage. International subarachnoid aneurysm trial.
Chronic subdural hematoma. (“This condition is twice as common in men as women. Risk factors include chronic alcoholism, epilepsy, anticoagulant therapy (including aspirin) and thrombocytopenia. CSDH is more common in elderly patients due to cerebral atrophy. […] Initial misdiagnosis is, unfortunately, quite common. […] a chronic subdural haematoma should be suspected in confused patients with a history of a fall.”)
Extradural Haematoma. Cushing response. (“A direct blow to the temporo-parietal area is the commonest cause of an extradural haematoma. The bleed is normally arterial in origin. In 85 per cent of cases there is an associated skull fracture that causes damage to the middle meningeal artery. […] This situation represents a neurosurgical emergency. Without urgent decompression the patient will die. Unlike the chronic subdural, which can be treated with Burr hole drainage, the more dense acute arterial haematoma requires a craniotomy in order to evacuate it.”)
Cauda equina syndrome. Neurosurgery for Cauda Equina Syndrome.
ASA classification. (“Patients having an operation within 3 months of a myocardial infarction carry a 30 per cent risk of reinfarction or cardiac death. This drops to 5 per cent after 6 months. […] Patients with COPD have difficulty clearing secretions from the lungs during the postoperative period. They also have a higher risk of basal atelectasis and are more prone to chest infections. These factors in combination with postoperative pain (especially in thoracic or abdominal major surgery) make them prone to respiratory complications. […] Patients with diabetes have an increased risk of postoperative complications because of the presence of microvascular and macrovascular disease: •Atherosclerosis: ischaemic heart disease/peripheral vascular disease/cerebrovascular disease •Nephropathy: renal insufficiency […] •Autonomic neuropathy: gastroparesis, decreased bladder tone •Peripheral neuropathy: lower-extremity ulceration, infection, gangrene •Poor wound healing •Increased risk of infection Tight glycaemic control (6–10 mmol/L) and the prevention of hypoglycaemia are critical in preventing perioperative and postoperative complications. The patient with diabetes should be placed first on the operating list to avoid prolonged fasting.“)
Malnutrition. Hartmann’s procedure. (“Malnutrition leads to delayed wound healing, reduced ventilatory capacity, reduced immunity and an increased risk of infection. […] The two main methods of feeding are either by the enteral route or the parenteral route. Enteral feeding is via the gastrointestinal tract. It is less expensive and is associated with fewer complications than feeding by the parenteral route. […] The parenteral route should only be used if there is an inability to ingest, digest, absorb or propulse nutrients through the gastrointestinal tract. It can be administered by either a peripheral or central line. Peripheral parenteral nutrition can cause thrombophlebitis […] Sepsis is the most frequent and serious complication of centrally administered parenteral nutrition.”)
Acute Kidney Injury. (“The aetiology of acute renal failure can be thought of in three main categories: •Pre-renal: the glomerular filtration is reduced because of poor renal perfusion. This is usually caused by hypovolaemia as a result of acute blood loss, fluid depletion or hypotension. […] • Renal: this is the result of damage directly to the glomerulus or tubule. The use of drugs such as NSAIDs, contrast agents or aminoglycosides all have direct nephrotoxic effects. Acute tubular necrosis can occur as a result of prolonged hypoperfusion […]. Pre-existing renal disease such as diabetic nephropathy or glomerulonephritis makes patients more susceptible to further renal injury. •Post-renal: this can be simply the result of a blocked catheter. […] Calculi, blood clots, ureteric ligation and prostatic hypertrophy can also all lead to obstruction of urinary flow.”)
Post-operative ileus.
Pulmonary embolism.
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.
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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 (4–8), with conflicting reports in latent autoimmune diabetes in adults (8–16). […] 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 (31–35). 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.”
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“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).”
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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:
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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.
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Higher-dose ACEIs were associated with comparable mortality reduction in people with and without diabetes.
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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.
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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.”
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“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 (3–7). 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 (11,25–27). Global brain volume loss (11,25–27) 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 (11,25–27) 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).”
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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.”
…
“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.”
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.)
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“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 (5–9). 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 (11–14). 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 (11–13).”
“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.”
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“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 (1–3). […] 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 (4–6). […] 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 (12–14). 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 (21–24). 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 (31–33). 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 (41,43–45). […] 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:
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Level 1—elevated glucose: glucose >180 mg/dL (10 mmol/L) and glucose ≤250 mg/dL (13.9 mmol/L)
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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:
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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:
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Elevated serum or urine ketones (greater than the upper limit of the normal range), and
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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.”
Prevention of Late-Life Depression (II)
Some more observations from the book:
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“In contrast to depression in childhood and youth when genetic and developmental vulnerabilities play a significant role in the development of depression, the development of late-life depression is largely attributed to its interactions with acquired factors, especially medical illness [17, 18]. An analysis of the WHO World Health Survey indicated that the prevalence of depression among medical patients ranged from 9.3 to 23.0 %, significantly higher than that in individuals without medical conditions [19]. Wells et al. [20] found in the Epidemiologic Catchment Area Study that the risk of developing lifetime psychiatric disorders among individuals with at least one medical condition was 27.9 % higher than among those without medical conditions. […] Depression and disability mutually reinforce the risk of each other, and adversely affect disease progression and prognosis [21, 25]. […] disability caused by medical conditions serves as a risk factor for depression [26]. When people lose their normal sensory, motor, cognitive, social, or executive functions, especially in a short period of time, they can become very frustrated or depressed. Inability to perform daily tasks as before decreases self-esteem, reduces independence, increases the level of psychological stress, and creates a sense of hopelessness. On the other hand, depression increases the risk for disability. Negative interpretation, attention bias, and learned hopelessness of depressed persons may increase risky health behaviors that exacerbate physical disorders or disability. Meanwhile, depression-related cognitive impairment also affects role performance and leads to functional disability [25]. For example, Egede [27] found in the 1999 National Health Interview Survey that the risk of having functional disability among patients with the comorbidity of diabetes and depression were approximately 2.5–5 times higher than those with either depression or diabetes alone. […] A leading cause of disability among medical patients is pain and pain-related fears […] Although a large proportion of pain complaints can be attributed to physiological changes from physical disorders, psychological factors (e.g., attention, interpretation, and coping skills) play an important role in perception of pain […] Bair et al. [31] indicated in a literature review that the prevalence of pain was higher among depressed patients than non-depressed patients, and the prevalence of major depression was also higher among pain patients comparing to those without pain complaints.”
“Alcohol use has more serious adverse health effects on older adults than other age groups, since aging-related physiological changes (e.g. reduced liver detoxification and renal clearance) affect alcohol metabolism, increase the blood concentration of alcohol, and magnify negative consequences. More importantly, alcohol interacts with a variety of frequently prescribed medications potentially influencing both treatment and adverse effects. […] Due to age-related changes in pharmacokinetics and pharmacodynamics, older adults are a vulnerable population to […] adverse drug effects. […] Adverse drug events are frequently due to failure to adjust dosage or to account for drug–drug interactions in older adults [64]. […] Loneliness […] is considered as an independent risk factor for depression [46, 47], and has been demonstrated to be associated with low physical activity, increased cardiovascular risks, hyperactivity of the hypothalamic-pituitary-adrenal axis, and activation of immune response [for details, see Cacioppo & Patrick’s book on these topics – US] […] Hopelessness is a key concept of major depression [54], and also an independent risk factor of suicidal ideation […] Hopelessness reduces expectations for the future, and negatively affects judgment for making medical and behavioral decisions, including non-adherence to medical regimens or engaging in unhealthy behaviors.”
“Co-occurring depression and medical conditions are associated with more functional impairment and mortality than expected from the severity of the medical condition alone. For example, depression accompanying diabetes confers increased functional impairment [27], complications of diabetes [65, 66], and mortality [67–71]. Frasure-Smith and colleagues highlighted the prognostic importance of depression among persons who had sustained a myocardial infarction (MI), finding that depression was a significant predictor of mortality at both 6 and 18 months post MI [72, 73]. Subsequent follow-up studies have borne out the increased risk conferred by depression on the mortality of patients with cardiovascular disease [10, 74, 75]. Over the course of a 2-year follow-up interval, depression contributed as much to mortality as did myocardial infarction or diabetes, with the population attributable fraction of mortality due to depression approximately 13 % (similar to the attributable risk associated with heart attack at 11 % and diabetes at 9 %) [76]. […] Although the bidirectional relationship between physical disorders and depression has been well known, there are still relatively few randomized controlled trials on preventing depression among medically ill patients. […] Rates of attrition [in post-stroke depression prevention trials has been observed to be] high […] Stroke, acute coronary syndrome, cancer, and other conditions impose a variety of treatment burdens on patients so that additional interventions without direct or immediate clinical effects may not be acceptable [95]. So even with good participation rates, lack of adherence to the intervention might limit effects.”
“Late-life depression (LLD) is a heterogeneous disease, with multiple risk factors, etiologies, and clinical features. It has been recognized for many years that there is a significant relationship between the presence of depression and cerebrovascular disease in older adults [1, 2]. This subtype of LLD was eventually termed “vascular depression.” […] There have been a multitude of studies associating white matter abnormalities with depression in older adults using MRI technology to visualize lesions, or what appear as hyperintensities in the white matter on T2-weighted scans. A systematic review concluded that white matter hyperintensities (WMH) are more common and severe among older adults with depression compared to their non-depressed peers [9]. […] WMHs are associated with older age [13] and cerebrovascular risk factors, including diabetes, heart disease, and hypertension [14–17]. White matter severity and extent of WMH volume has been related to the severity of depression in late life [18, 19]. For example, among 639 older, community-dwelling adults, white matter lesion (WML) severity was found to predict depressive episodes and symptoms over a 3-year period [19]. […] Another way of investigating white matter integrity is with diffusion tensor imaging (DTI), which measures the diffusion of water in tissues and allows for indirect evidence of the microstructure of white matter, most commonly represented as fractional anisotropy (FA) and mean diffusivity (MD). DTI may be more sensitive to white matter pathology than is quantification of WMH […] A number of studies have found lower FA in widespread regions among individuals with LLD relative to controls [34, 36, 37]. […] lower FA has been associated with poorer performance on measures of cognitive functioning among patients with LLD [35, 38–40] and with measures of cerebrovascular risk severity. […] It is important to recognize that FA reflects the organization of fiber tracts, including fiber density, axonal diameter, or myelination in white matter. Thus, lower FA can result from multiple pathophysiological sources [42, 43]. […] Together, the aforementioned studies provide support for the vascular depression hypothesis. They demonstrate that white matter integrity is reduced in patients with LLD relative to controls, is somewhat specific to regions important for cognitive and emotional functioning, and is associated with cognitive functioning and depression severity. […] There is now a wealth of evidence to support the association between vascular pathology and depression in older age. While the etiology of depression in older age is multifactorial, from the epidemiological, neuroimaging, behavioral, and genetic evidence available, we can conclude that vascular depression represents one important subtype of LLD. The mechanisms underlying the relationship between vascular pathology and depression are likely multifactorial, and may include disrupted connections between key neural regions, reduced perfusion of blood to key brain regions integral to affective and cognitive processing, and inflammatory processes.”
“Cognitive changes associated with depression have been the focus of research for decades. Results have been inconsistent, likely as a result of methodological differences in how depression is diagnosed and cognitive functioning measured, as well as the effects of potential subtypes and the severity of depression […], though deficits in executive functioning, learning and memory, and attention have been associated with depression in most studies [75]. In older adults, additional confounding factors include the potential presence of primary degenerative disorders, such as Alzheimer’s disease, which can pose a challenge to differential diagnosis in its early stages. […] LLD with cognitive dysfunction has been shown to result in greater disability than depressive symptoms alone [6], and MCI [mild cognitive impairment, US] with co-occurring LLD has been shown to double the risk of developing Alzheimer’s disease (AD) compared to MCI alone [86]. The conversion from MCI to AD also appears to occur earlier in patients with cooccurring depressive symptoms, as demonstrated by Modrego & Ferrandez [86] in their prospective cohort study of 114 outpatients diagnosed with amnestic MCI. […] Given accruing evidence for abnormal functioning of a number of cortical and subcortical networks in geriatric depression, of particular interest is whether these abnormalities are a reflection of the actively depressed state, or whether they may persist following successful resolution of symptoms. To date, studies have investigated this question through either longitudinal investigation of adults with geriatric depression, or comparison of depressed elders who are actively depressed versus those who have achieved symptom remission. Of encouragement, successful treatment has been reliably associated with normalization of some aspects of disrupted network functioning. For example, successful antidepressant treatment is associated with reduction of the elevated cerebral glucose metabolism observed during depressed states (e.g., [71–74]), with greater symptom reduction associated with greater metabolic change […] Taken together, these studies suggest that although a subset of the functional abnormalities observed during the LLD state may resolve with successful treatment, other abnormalities persist and may be tied to damage to the structural connectivity in important affective and cognitive networks. […] studies suggest a chronic decrement in cognitive functioning associated with LLD that is not adequately addressed through improvement of depressive symptoms alone.”
“A review of the literature on evidence-based treatments for LLD found that about 50 % of patients improved on antidepressants, but that the number needed to treat (NNT) was quite high (NNT = 8, [139]) and placebo effects were significant [140]. Additionally, no difference was demonstrated in the effectiveness of one antidepressant drug class over another […], and in one-third of patients, depression was resistant to monotherapy [140]. The addition of medications or switching within or between drug classes appears to result in improved treatment response for these patients [140, 141]. A meta-analysis of patient-level variables demonstrated that duration of depressive symptoms and baseline depression severity significantly predicts response to antidepressant treatment in LLD, with chronically depressed older patients with moderate-to-severe symptoms at baseline experiencing more improvement in symptoms than mildly and acutely depressed patients [142]. Pharmacological treatment response appears to range from incomplete to poor in LLD with co-occurring cognitive impairment.”
“[C]ompared to other formulations of prevention, such as primary, secondary, or tertiary — in which interventions are targeted at the level of disease/stage of disease — the IOM conceptual framework involves interventions that are targeted at the level of risk in the population [2]. […] [S]elective prevention studies have an important “numbers” advantage — similar to that of indicated prevention trials: the relatively high incidence of depression among persons with key risk markers enables investigator to test interventions with strong statistical power, even with somewhat modest sample sizes. This fact was illustrated by Schoevers and colleagues [3], in which the authors were able to account for nearly 50 % of total risk of late-life depression with consideration of only a handful of factors. Indeed, research, largely generated by groups in the Netherlands and the USA, has identified that selective prevention may be one of the most efficient approaches to late-life depression prevention, as they have estimated that targeting persons at high risk for depression — based on risk markers such as medical comorbidity, low social support, or physical/functional disability — can yield theoretical numbers needed to treat (NNTs) of approximately 5–7 in primary care settings [4–7]. […] compared to the findings from selective prevention trials targeting older persons with general health/medical problems, […] trials targeting older persons based on sociodemographic risk factors have been more mixed and did not reveal as consistent a pattern of benefits for selective prevention of depression.”
“Few of the studies in the existing literature that involve interventions to prevent depression and/or reduce depressive symptoms in older populations have included economic evaluations [13]. The identification of cost-effective interventions to provide to groups at high risk for depression is an important public health goal, as such treatments may avert or reduce a significant amount of the disease burden. […] A study by Katon and colleagues [8] showed that elderly patients with either subsyndromal or major depression had significantly higher medical costs during the previous 6 months than those without depression; total healthcare costs were $1,045 to $1,700 greater, and total outpatient/ambulatory costs ranged from being $763 to $979 more, on average. Depressed patients had greater usage of health resources in every category of care examined, including those that are not mental health-related, such as emergency department visits. No difference in excess costs was found between patients with a DSM-IV depressive disorder and those with depressive symptoms only, however, as mean total costs were 51 % higher in the subthreshold depression group (95 % CI = 1.39–1.66) and 49 % higher in the MDD/dysthymia group (95 % CI = 1.28–1.72) than in the nondepressed group [8]. In a similar study, the usage of various types of health services by primary care patients in the Netherlands was assessed, and average costs were determined to be €1,403 more in depressed individuals versus control patients [21]. Study investigators once again observed that patients with depression had greater utilization of both non-mental and mental healthcare services than controls.”
“In order for routine depression screening in the elderly to be cost-effective […] appropriate follow-up measures must be taken with those who screen positive, including a diagnostic interview and/or referral to a mental health professional [this – the necessity/requirement of proper follow-up following screens in order for screening to be cost-effective – is incidentally a standard result in screening contexts, see also Juth & Munthe’s book – US] [23, 25]. For example, subsequent steps may include initiation of psychotherapy or antidepressant treatment. Thus, one reason that the USPSTF does not recommend screening for depression in settings where proper mental health resources do not exist is that the evidence suggests that outcomes are unlikely to improve without effective follow-up care […] as per the USPSTF suggestion, Medicare will only cover the screening when the appropriate supports for proper diagnosis and treatment are available […] In order to determine which interventions to prevent and treat depression should be provided to those who screen positive for depressive symptoms and to high-risk populations in general, cost-effectiveness analyses must be completed for a variety of different treatments and preventive measures. […] questions remain regarding whether annual versus other intervals of screening are most cost-effective. With respect to preventive interventions, the evidence to date suggests that these are cost-effective in settings where those at the highest risk are targeted.”
Endocrinology (part 4 – reproductive endocrinology)
Some observations from chapter 4 of the book below.
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“*♂. The whole process of spermatogenesis takes approximately 74 days, followed by another 12-21 days for sperm transport through the epididymis. This means that events which may affect spermatogenesis may not be apparent for up to three months, and successful induction of spermatogenesis treatment may take 2 years. *♀. From primordial follicle to primary follicle, it takes about 180 days (a continuous process). It is then another 60 days to form a preantral follicle which then proceeds to ovulation three menstrual cycles later. Only the last 2-3 weeks of this process is under gonadotrophin drive, during which time the follicle grows from 2 to 20mm.”
“Hirsutism (not a diagnosis in itself) is the presence of excess hair growth in ♀ as a result of ↑ androgen production and ↑ skin sensitivity to androgens. […] In ♀, testosterone is secreted primarily by the ovaries and adrenal glands, although a significant amount is produced by the peripheral conversion of androstenedione and DHEA. Ovarian androgen production is regulated by luteinizing hormone, whereas adrenal production is ACTH-dependent. The predominant androgens produced by the ovaries are testosterone and androstenedione, and the adrenal glands are the main source of DHEA. Circulating testosterone is mainly bound to sex hormone-binding globulin (SHBG), and it is the free testosterone which is biologically active. […] Slowly progressive hirsutism following puberty suggests a benign cause, whereas rapidly progressive hirsutism of recent onset requires further immediate investigation to rule out an androgen-secreting neoplasm. [My italics, US] […] Serum testosterone should be measured in all ♀ presenting with hirsutism. If this is <5nmol/L, then the risk of a sinister cause for her hirsutism is low.”
“Polycystic ovary syndrome (PCOS) *A heterogeneous clinical syndrome characterized by hyperandrogenism, mainly of ovarian origin, menstrual irregularity, and hyperinsulinaemia, in which other causes of androgen excess have been excluded […] *A distinction is made between polycystic ovary morphology on ultrasound (PCO which also occurs in congenital adrenal hyperplasia, acromegaly, Cushing’s syndrome, and testesterone-secreting tumours) and PCOS – the syndrome. […] PCOS is the most common endocrinopathy in ♀ of reproductive age; >95% of ♀ presenting to outpatients with hirsutism have PCOS. *The estimated prevalence of PCOS ranges from 5 to 10% on clinical criteria. Polycystic ovaries on US alone are present in 20-25% of ♀ of reproductive age. […] family history of type 2 diabetes mellitus is […] more common in ♀ with PCOS. […] Approximately 70% of ♀ with PCOS are insulin-resistant, depending on the definition. […] Type 2 diabetes mellitus is 2-4 x more common in ♀ with PCOS. […] Hyperinsulinaemia is exacerbated by obesity but can also be present in lean ♀ with PCOS. […] Insulin […] inhibits SHBG synthesis by the liver, with a consequent rise in free androgen levels. […] Symptoms often begin around puberty, after weight gain, or after stopping the oral contraceptive pill […] Oligo-/amenorrhoea [is present in] 70% […] Hirsutism [is present in] 66% […] Obesity [is present in] 50% […] *Infertility (30%). PCOS accounts for 75% of cases of anovulatory infertility. The risk of spontaneous miscarriage is also thought to be higher than the general population, mainly because of obesity. […] The aims of investigations [of PCOS] are mainly to exclude serious underlying disorders and to screen for complications, as the diagnosis is primarily clinical […] Studies have uniformly shown that weight reduction in obese ♀ with PCOS will improve insulin sensitivity and significantly reduce hyperandrogenaemia. Obese ♀ are less likely to respond to antiandrogens and infertility treatment.”
“Androgen-secreting tumours [are] [r]are tumours of the ovary or adrenal gland which may be benign or malignant, which cause virilization in ♀ through androgen production. […] Virilization […] [i]ndicates severe hyperandrogenism, is associated with clitoromegaly, and is present in 98% of ♀ with androgen-producing tumours. Not usually a feature of PCOS. […] Androgen-secreting ovarian tumours[:] *75% develop before the age of 40 years. *Account for 0.4% of all ovarian tumours; 20% are malignant. *Tumours are 5-25cm in size. The larger they are, the more likely they are to be malignant. They are rarely bilateral. […] Androgen-secreting adrenal tumours[:] *50% develop before the age of 50 years. *Larger tumours […] are more likely to be malignant. *Usually with concomitant cortisol secretion as a variant of Cushing’s syndrome. […] Symptoms and signs of Cushing’s syndrome are present in many of ♀ with adrenal tumours. […] Onset of symptoms. Usually recent onset of rapidly progressive symptoms. […] Malignant ovarian and adrenal androgen-secreting tumours are usually resistant to chemotherapy and radiotherapy. […] *Adrenal tumours. 20% 5-year survival. Most have metastatic disease at the time of surgery. *Ovarian tumours. 30% disease-free survival and 40% overall survival at 5 years. […] Benign tumours. *Prognosis excellent. *Hirsutism improves post-operatively, but clitoromegaly, male pattern balding, and deep voice may persist.”
*Oligomenorrhoea is defined as the reduction in the frequency of menses to <9 periods a year. *1° amenorrhoea is the failure of menarche by the age of 16 years. Prevalence ~0.3% *2° amenorrhoea refers to the cessation of menses for >6 months in ♀ who had previously menstruated. Prevalence ~3%. […] Although the list of causes is long […], the majority of cases of secondary amenorrhoea can be accounted for by four conditions: *Polycystic ovary syndrome. *Hypothalamic amenorrhoea. *Hyperprolactinaemia. *Ovarian failure. […] PCOS is the only common endocrine cause of amenorrhoea with normal oestrogenization – all other causes are oestrogen-deficient. Women with PCOS, therefore, are at risk of endometrial hyperplasia, and all others are at risk of osteoporosis. […] Anosmia may indicate Kallman’s syndrome. […] In routine practice, a common differential diagnosis is between mild version of PCOS and hypothalamic amenorrhoea. The distinction between these conditions may require repeated testing, as a single snapshot may not discriminate. The reason to be precise is that PCOS is oestrogen-replete and will, therefore, respond to clomiphene citrate (an antioestrogen) for fertility. HA will be oestrogen-deficient and will need HRT and ovulation induction with pulsatile GnRH or hMG [human Menopausal Gonadotropins – US]. […] […] 75% of ♀ who develop 2° amenorrhoea report hot flushes, night sweats, mood changes, fatigue, or dyspareunia; symptoms may precede the onset of menstrual disturbances.”
“POI [Premature Ovarian Insufficiency] is a disorder characterized by amenorrhoea, oestrogen deficiency, and elevated gonadotrophins, developing in ♀ <40 years, as a result of loss of ovarian follicular function. […] *Incidence – 0.1% of ♀ <30 years and 1% of those <40 years. *Accounts for 10% of all cases of 2° amenorrhoea. […] POI is the result of accelerated depletion of ovarian germ cells. […] POI is usually permanent and progressive, although a remitting course is also experienced and cannot be fully predicted, so all women must know that pregnancy is possible, even though fertility treatments are not effective (often a difficult paradox to describe). Spontaneous pregnancy has been reported in 5%. […] 80% of [women with Turner’s syndrome] have POI. […] All ♀ presenting with hypergonadotrophic amenorrhoea below age 40 should be karyotyped.”
“The menopause is the permanent cessation of menstruation as a result of ovarian failure and is a retrospective diagnosis made after 12 months of amenorrhoea. The average age of ♀ at the time of the menopause is ~50 years, although smokers reach the menopause ~2 years earlier. […] Cycles gradually become increasingly anovulatory and variable in length (often shorter) from about 4 years prior to the menopause. Oligomenorrhoea often precedes permanent amenorrhoea. in 10% of ♀, menses cease abruptly, with no preceding transitional period. […] During the perimenopausal period, there is an accelerated loss of bone mineral density (BMD), rendering post-menopausal ♀ more susceptible to osteoporotic fractures. […] Post-menopausal ♀ are 2-3 x more likely to develop IHD [ischaemic heart disease] than premenopausal ♀, even after age adjustments. The menopause is associated with an increase in risk factors for atherosclerosis, including less favourable lipid profile, ↓ insulin sensitivity, and an ↑ thrombotic tendency. […] ♀ are 2-3 x more likely to develop Alzheimer’s disease than ♂. It is suggested that oestrogen deficiency may play a role in the development of dementia. […] The aim of treatment of perimenopausal ♀ is to alleviate menopausal symptoms and optimize quality of life. The majority of women with mild symptoms require no HRT. […] There is an ↑ risk of breast cancer in HRT users which is related to the duration of use. The risk increases by 35%, following 5 years of use (over the age of 50), and falls to never-used risk 5 years after discontinuing HRT. For ♀ aged 50 not using HRT, about 45 in every 1,000 will have cancer diagnosed over the following 20 years. This number increases to 47/1,000 ♀ using HRT for 5 years, 51/1,000 using HRT for 10 years, and 57/1,000 after 15 years of use. The risk is highest in ♀ on combined HRT compared with oestradiol alone. […] Oral HRT increases the risk [of venous thromboembolism] approximately 3-fold, resulting in an extra two cases/10,000 women-years. This risk is markedly ↑ in ♀ who already have risk factors for DVT, including previous DVT, cardiovascular disease, and within 90 days of hospitalization. […] Data from >30 observational studies suggest that HRT may reduce the risk of developing CVD [cardiovascular disease] by up to 50%. However, randomized placebo-controlled trials […] have failed to show that HRT protects against IHD. Currently, HRT should not be prescribed to prevent cardiovascular disease.”
“Any chronic illness may affect testicular function, in particular chronic renal failure, liver cirrhosis, and haemochromatosis. […] 25% of ♂ who develop mumps after puberty have associated orchitis, and 25-50% of these will develop 1° testicular failure. […] Alcohol excess will also cause 1° testicular failure. […] Cytotoxic drugs, particularly alkylating agents, are gonadotoxic. Infertility occurs in 50% of patients following chemotherapy, and a significant number of ♂ require androgen replacement therapy because of low testosterone levels. […] Testosterone has direct anabolic effects on skeletal muscle and has been shown to increase muscle mass and strength when given to hypogonadal men. Lean body mass is also ↑ with a reduction in fat mass. […] Hypogonadism is a risk factor for osteoporosis. Testosterone inhibits bone resorption, thereby reducing bone turnover. Its administration to hypogonadal ♂ has been shown to improve bone mineral density and reduce the risk of developing osteoporosis. […] *Androgens stimulate prostatic growth, and testosterone replacement therapy may therefore induce symptoms of bladder outflow obstruction in ♂ with prostatic hypertrophy. *It is unlikely that testosterone increases the risk of developing prostrate cancer, but it may promote the growth of an existing cancer. […] Testosterone replacement therapy may cause a fall in both LDL and HDL cholesterol levels, the significance of which remains unclear. The effect of androgen replacement therapy on the risk of developing coronary artery disease is unknown.”
“Erectile dysfunction [is] [t]he consistent inability to achieve or maintain an erect penis sufficient for satisfactory sexual intercourse. Affects approximately 10% of ♂ and >50% of ♂>70 years. […] Erectile dysfunction may […] occur as a result of several mechanisms: *Neurological damage. *Arterial insufficiency. *Venous incompetence. *Androgen deficiency. *Penile abnormalities. […] *Abrupt onset of erectile dysfunction which is intermittent is often psychogenic in origin. *Progressive and persistent dysfunction indicates an organic cause. […] Absence of morning erections suggests an organic cause of erectile dysfunction.”
“*Infertility, defined as failure of pregnancy after 1 year of unprotected regular (2 x week) sexual intercourse, affects ~10% of all couples. *Couples who fail to conceive after 1 years of regular unprotected sexual intercourse should be investigated. […] Causes[:] *♀ factors (e.g. PCOS, tubal damage) 35%. *♂ factors (idiopathic gonadal failure in 60%) 25%. *Combined factors 25%. *Unexplained infertility 15%. […] [♀] Fertility declines rapidly after the age of 36 years. […] Each episode of acute PID causes infertility in 10-15% of cases. *Trachomatis is responsible for half the cases of PID in developed countries. […] Unexplained infertility [is] [i]nfertility despite normal sexual intercourse occurring at least twice weakly, normal semen analysis, documentation of ovulation in several cycles, and normal patent tubes (by laparoscopy). […] 30-50% will become pregnant within 3 years of expectant management. If not pregnant by then, chances that spontaneous pregnancy will occur are greatly reduced, and ART should be considered. In ♀>34 years of age, then expectant management is not an option, and up to six cycles of IUI or IVF should be considered.”
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