Econstudentlog

A few diabetes papers of interest

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

Some observations from the paper (my bold):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the Brain (V)

I have blogged this book in some detail in the past, but I never really finished my intended coverage of the book. This post is an attempt to rectify this.

Below I have added some quotes and observations from some of the chapters I have not covered in my previous posts about the book. I bolded some key observations along the way.

A substantial number of studies have assessed the effect of type 2 diabetes on cognitive functioning with psychometric tests. The majority of these studies reported subtle decrements in individuals with type 2 diabetes relative to non-diabetic controls (2, 4). […] the majority of studies in patients with type 2 diabetes reported moderate reductions in neuropsychological test performance, mainly in memory, information-processing speed, and mental flexibility, a pattern that is also observed in aging-related cognitive decline. […] the observed cognitive decrements are relatively subtle and rather non-specific. […] All in all, disturbances in glucose and insulin metabolism and associated vascular risk factors are associated with modest reductions in cognitive performance in “pre-diabetic stages.” Consequently, it may well be that the cognitive decrements that can be observed in patients with type 2 diabetes also start to develop before the actual onset of the diabetes. […] Because the different vascular and metabolic risk factors that are clustered in the metabolic syndrome are strongly interrelated, the contribution of each of the individual factor will be difficult to assess.” 

“Aging-related changes on brain imaging include vascular lesions and focal and global atrophy. Vascular lesions include (silent) brain infarcts and white-matter hyperintensities (WMHs). WMHs are common in the general population and their prevalence increases with age, approaching 100% by the age of 85 (69). The prevalence of lacunar infarcts also increases with age, up to 5% for symptomatic infarcts and 30% for silent infarcts by the age of 80 (70). In normal aging, the brain gradually reduces in size, which becomes particularly evident after the age of 70 (71). This loss of brain volume is global […] age-related changes of the brain […] are often relatively more pronounced in older patients with type 2 […] A recent systematic review showed that patients with diabetes have a 2-fold increased risk of (silent) infarcts compared to non-diabetic persons (75). The relationship between type 2 diabetes and WMHs is subject to debate. […] there are now clear indications that diabetes is a risk factor for WMH progression (82). […] The presence of the APOE ε4 allele is a risk factor for the development of Alzheimer’s disease (99). Patients with type 2 diabetes who carry the APOE ε4 allele appeared to have a 2-fold increased risk of dementia compared to persons with either of these risk factors in isolation (100, 101).”

In adults with type 1 diabetes the occurrence of microvascular complications is associated with reduced cognitive performance (137) and accelerated cognitive decline (138). Moreover, type 1 diabetes is associated with decreased white-matter volume of the brain and diminished cognitive performance in particular in patients with retinopathy (139). Microvascular complications are also thought to play a role in the development of cognitive decline in patients with type 2 diabetes, but studies that have specifically examined this association are scarce. […] Currently there are no established specific treatment measures to prevent or ameliorate cognitive impairments in patients with diabetes.”

“Clinicians should be aware of the fact that cognitive decrements are relatively more common among patients with diabetes. […] it is important to note that cognitive complaints as spontaneously expressed by the patient are often a poor indicator of the severity of cognitive decrements. People with moderate disturbances may express marked complaints, while people with marked disturbances of cognition often do not complain at all. […] Diabetes is generally associated with relatively mild impairments, mainly in attention, memory, information-processing speed, and executive function. Rapid cognitive decline or severe cognitive impairment, especially in persons under the age of 60 is indicative of other underlying pathology. Potentially treatable causes of cognitive decline such as depression should be excluded. People who are depressed often present with complaints of concentration or memory.”

“Insulin resistance increases with age, and the organism maintains normal glucose levels as long as it can produce enough insulin (hyperinsulinemia). Some individuals are less capable than others to mount sustained hyperinsulinemia and will develop glucose intolerance and T2D (23). Other individuals with insulin resistance will maintain normal glucose levels at the expense of hyperinsulinemia but their pancreas will eventually “burn out,” will not be able to sustain hyperinsulinemia, and will develop glucose intolerance and diabetes (23). Others will continue having insulin resistance, may have or not have glucose intolerance, will not develop diabetes, but will have hyperinsulinemia and suffer its consequences. […] Elevations of adiposity result in insulin resistance, causing the pancreas to increase insulin to abnormal levels to sustain normal glucose, and if and when the pancreas can no longer sustain hyperinsulinemia, glucose intolerance and diabetes will ensue. However, the overlap between these processes is not complete (26). Not all persons with higher adiposity will develop insulin resistance and hyperinsulinemia, but most will. Not all persons with insulin resistance and hyperinsulinemia will develop glucose intolerance and diabetes, and this depends on genetic and other susceptibility factors that are not completely understood (25, 26). Some adults develop diabetes without going through insulin resistance and hyperinsulinemia, but it is thought that most will. The susceptibility to adiposity, that is, the risk of developing the above-described sequence in response to adiposity, varies by gender (4) and particularly by ethnicity. […] Chinese and Southeast Asians are more susceptible than Europeans to developing insulin resistance with comparable increases of adiposity (2).”

There is very strong evidence that adiposity, hyperinsulinemia, and T2D are related to cognitive impairment syndromes, whether AD [Alzheimer’s Disease], VD [Vascular Dementia], or MCI [Mild Cognitive Impairment], and whether the main mechanism is cerebrovascular disease or non-vascular mechanisms. However, more evidence is needed to establish causation. If the relation between these conditions and dementia were to be causal, the public health implications are enormous. […] Diabetes mellitus affects about 20% of adults older than 65 years of age […] two-thirds of the adult population in the United States are overweight or obese, and the short-term trend is for this to worsen. These trends are also being observed worldwide. […] We estimated that in New York City the presence of diabetes or hyperinsulinemia in elderly people could account for 39% of cases of AD (78).”

Psychiatric illnesses in general may be more common among persons with diabetes than in community-based samples, specifically affective and anxiety-related disorders (4). Persons with diabetes are twice as likely to have depression as non-diabetic persons (5). A review of 20 studies on the comorbidity of depression and diabetes found that the average prevalence was about 15%, and ranged from 8.5 to 40%, three times the rate of depressive disorders found in the general adult population of the United States (4–7). The rates of clinically significant depressive symptoms among persons with diabetes are even higher – ranging from 21.8 to 60.0% (8). Recent studies have indicated that persons with type II diabetes, accompanied by either major or minor depression, have significantly higher mortality rates than non-depressed persons with diabetes (9–10) […] A recent meta-analysis reported that patients with type 2 diabetes have a 2-fold increased risk of depression compared to non-diabetic persons (142). The prevalence of major depressive disorder in patients with type 2 diabetes was estimated at 11% and depressive symptoms were observed in 31% of the patients.” (As should be obvious from the above quotes the range of estimates vary a lot here, but the estimates tend to be high – US.)

Depression is an important risk factor for cardiovascular disease (Glassman, Maj & Sartorius is a decent book on these topics), and diabetes is also an established risk factor. Might this not lead to a hypothesis that diabetics who are depressed may do particularly poorly, with higher mortality rates and so on? Yes. …and it seems that this is also what people tend to find when they look at this stuff:

Persons with diabetes and depressive symptoms have mortality rates nearly twice as high as persons with diabetes and no depressive symptomatology (9). Persons with co-occurring medical illness and depression also have higher health care utilization leading to higher direct and indirect health care costs (12–13) […]. A meta-analysis of the relationship between depression and diabetes (types I and II) indicated that an increase in the number of depressive symptoms is associated with an increase in the severity and number of diabetic complications, including retinopathy, neuropathy, and nephropathy (15–17). Compared to persons with either diabetes or depression alone, individuals with co-occurring diabetes and depression have shown poorer adherence to dietary and physical activity recommendations, decreased adherence to hypoglycemic medication regimens, higher health care costs, increases in HgbA1c levels, poorer glycemic control, higher rates of retinopathy, and macrovascular complications such as stroke and myocardial infarction, higher ambulatory care use, and use of prescriptions (14, 18–22). Diabetes and depressive symptoms have been shown to have strong independent effects on physical functioning, and individuals experiencing either of these conditions will have worse functional outcomes than those with neither or only one condition (19–20). Nearly all of diabetes management is conducted by the patient and those with co-occurring depression may have poorer outcomes and increased risk of complications due to less adherence to glucose, diet, and medication regimens […] There is some evidence that treatment of depression with antidepressant and/or cognitive-behavioral therapies can improve glycemic control and glucose regulation without any change in the treatment for diabetes (27, 28) […] One important finding is [also] that treatment of depression seems to be able to halt atrophy of the hippocampus and may even lead to stimulation of neurogenesis of hippocampal cells (86).”

Diabetic neuropathy is a severe, disabling chronic condition that affects a significant number of individuals with diabetes. Long considered a disease of the peripheral nervous system, there is mounting evidence of central nervous system involvement. Recent advances in neuroimaging methods have led to a better understanding and refinement of how diabetic neuropathy affects the central nervous system. […] spinal cord atrophy is an early process being present not only in established-DPN [diabetic peripheral neuropathy] but also even in subjects with relatively modest impairments of nerve function (subclinical-DPN) […] findings […] show that the neuropathic process in diabetes is not confined to the peripheral nerve and does involve the spinal cord. Worryingly, this occurs early in the neuropathic process. Even at the early DPN stage, extensive and perhaps even irreversible damage may have occurred. […] it is likely that the insult of diabetes is generalised, concomitantly affecting the PNS and CNS. […] It is noteworthy that a variety of therapeutic interventions specifically targeted at peripheral nerve damage in DPN have thus far been ineffective, and it is possible that this may in part be due to inadequate appreciation of the full extent of CNS involvement in DPN.

Interestingly, if the CNS is also involved in the pathogenesis of (‘human’) diabetic neuropathy it may have some relevance to the complaint that some methods of diabetes-induction in animal models cause (secondary) damage to central structures in animal models – a complaint which I’ve previously made a note of e.g. in the context of my coverage of Horowitz & Samson’s book. The relevance of this depends quite a bit on whether it’s the same central structures that are affected in the animal models and in humans. It probably isn’t. These guys also discuss this stuff in some detail, though I won’t go into too much detail here. Some observations on related topics are however worth including here:

“Several studies examining behavioral learning have shown progressive deficits in diabetic rodents, whereas simple avoidance tasks are preserved. Impaired spatial learning and memory as assessed by the Morris water maze paradigm occur progressively in both the spontaneously diabetic BB/Worrat and STZ-induced diabetic rodents (1, 11, 12, 22, 41, 42). The cognitive components reflected by impaired Morris water maze performances involve problem-solving, enhanced attention and storage, and retrieval of information (43). […] Observations regarding cognition and plasticity in models characterized by hyperglycemia and insulin deficiency (i.e., alloxan or STZ-diabetes, BB/Wor rats, NOD-mice), often referred to as models of type 1 diabetes, are quite consistent. With respect to clinical relevance, it should be noted that the level of glycemia in these models markedly exceeds that observed in patients. Moreover, changes in cognition as observed in these models are much more rapid and severe than in adult patients with type 1 diabetes […], even if the relatively shorter lifespan of rodents is taken into account. […] In my view these models of “type 1 diabetes” may help to understand the pathophysiology of the effects of severe chronic hyperglycemia–hypoinsulinemia on the brain, but mimic the impact of type 1 diabetes on the brain in humans only to a limited extent.”

“Abnormalities in cognition and plasticity have also been noted in the majority of models characterized by insulin resistance, hyperinsulinemia, and (modest) hyperglycemia (e.g., Zucker fa/fa rat, Diabetic Zucker rat, db/db mouse, GK rat, OLETF rat), often referred to as models of type 2 diabetes. With regard to clinical relevance, it is important to note that although the endocrinological features of these models do mimic certain aspects of type 2 diabetes, the genetic defect that underlies each of them is not the primary defect encountered in humans with type 2 diabetes. Some of the genetic abnormalities that lead to a “diabetic phenotype” may also have a direct impact on the brain. […] some studies using these models report abnormalities in cognition and plasticity, even in the absence of hyperglycemia […] In addition, in the majority of available models insulin resistance and associated metabolic abnormalities develop at a relatively early age. Although this is practical for research purposes it needs to be acknowledged that type 2 diabetes is typically a disease of older age in humans. […] It is therefore still too early to determine the clinical significance of the available models in understanding the impact of type 2 diabetes on the brain. Further efforts into the development of a valid model are warranted.”

[A] key problem in clinical studies is the complexity and multifactorial nature of cerebral complications in relation to diabetes. Metabolic factors in patients (e.g., glucose levels, insulin levels, insulin sensitivity) are strongly interrelated and related to other factors that may affect the brain (e.g., blood pressure, lipids, inflammation, oxidative stress). Derangements in these factors in the periphery and the brain may be dissociated, for example, through the role of the blood–brain barrier, or adaptations of transport across this barrier, or through differences in receptor functions and post-receptor signaling cascades in the periphery and the brain. The different forms of treatments that patients receive add to the complexity. A key contribution of animal studies may be to single out individual components and study them in isolation or in combination with a limited number of other factors in a controlled fashion.

October 9, 2017 Posted by | Books, Cardiology, Diabetes, Epidemiology, Medicine, Neurology, Pharmacology | Leave a comment

A few diabetes papers of interest

i. Neurocognitive Functioning in Children and Adolescents at the Time of Type 1 Diabetes Diagnosis: Associations With Glycemic Control 1 Year After Diagnosis.

“Children and youth with type 1 diabetes are at risk for developing neurocognitive dysfunction, especially in the areas of psychomotor speed, attention/executive functioning, and visuomotor integration (1,2). Most research suggests that deficits emerge over time, perhaps in response to the cumulative effect of glycemic extremes (36). However, the idea that cognitive changes emerge gradually has been challenged (79). Ryan (9) argued that if diabetes has a cumulative effect on cognition, cognitive test performance should be positively correlated with illness duration. Yet he found comparable deficits in psychomotor speed (the most commonly noted area of deficit) in adolescents and young adults with illness duration ranging from 6 to 25 years. He therefore proposed a diathesis model in which cognitive declines in diabetes are especially likely to occur in more vulnerable patients, at crucial periods, in response to illness-related events (e.g., severe hyperglycemia) known to have an impact on the central nervous system (CNS) (8). This model accounts for the finding that cognitive deficits are more likely in children with early-onset diabetes, and for the accelerated cognitive aging seen in diabetic individuals later in life (7). A third hypothesized crucial period is the time leading up to diabetes diagnosis, during which severe fluctuations in blood glucose and persistent hyperglycemia often occur. Concurrent changes in blood-brain barrier permeability could result in a flood of glucose into the brain, with neurotoxic effects (9).”

“In the current study, we report neuropsychological test findings for children and adolescents tested within 3 days of diabetes diagnosis. The purpose of the study was to determine whether neurocognitive impairments are detectable at diagnosis, as predicted by the diathesis hypothesis. We hypothesized that performance on tests of psychomotor speed, visuomotor integration, and attention/executive functioning would be significantly below normative expectations, and that differences would be greater in children with earlier disease onset. We also predicted that diabetic ketoacidosis (DKA), a primary cause of diabetes-related neurological morbidity (12) and a likely proxy for severe peri-onset hyperglycemia, would be associated with poorer performance.”

“Charts were reviewed for 147 children/adolescents aged 5–18 years (mean = 10.4 ± 3.2 years) who completed a short neuropsychological screening during their inpatient hospitalization for new-onset type 1 diabetes, as part of a pilot clinical program intended to identify patients in need of further neuropsychological evaluation. Participants were patients at a large urban children’s hospital in the southwestern U.S. […] Compared with normative expectations, children/youth with type 1 diabetes performed significantly worse on GPD, GPN, VMI, and FAS (P < 0.0001 in all cases), with large decrements evident on all four measures (Fig. 1). A small but significant effect was also evident in DSB (P = 0.022). High incidence of impairment was evident on all neuropsychological tasks completed by older participants (aged 9–18 years) except DSF/DSB (Fig. 2).”

“Deficits in neurocognitive functioning were evident in children and adolescents within days of type 1 diabetes diagnosis. Participants performed >1 SD below normative expectations in bilateral psychomotor speed (GP) and 0.7–0.8 SDs below expected performance in visuomotor integration (VMI) and phonemic fluency (FAS). Incidence of impairment was much higher than normative expectations on all tasks except DSF/DSB. For example, >20% of youth were impaired in dominant hand fine-motor control, and >30% were impaired with their nondominant hand. These findings provide provisional support for Ryan’s hypothesis (79) that the peri-onset period may be a time of significant cognitive vulnerability.

Importantly, deficits were not evident on all measures. Performance on measures of attention/executive functioning (TMT-A, TMT-B, DSF, and DSB) was largely consistent with normative expectations, as was reading ability (WRAT-4), suggesting that the below-average performance in other areas was not likely due to malaise or fatigue. Depressive symptoms at diagnosis were associated with performance on TMT-B and FAS, but not on other measures. Thus, it seems unlikely that depressive symptoms accounted for the observed motor slowing.

Instead, the findings suggest that the visual-motor system may be especially vulnerable to early effects of type 1 diabetes. This interpretation is especially compelling given that psychomotor impairment is the most consistently reported long-term cognitive effect of type 1 diabetes. The sensitivity of the visual-motor system at diabetes diagnosis is consistent with a growing body of neuroimaging research implicating posterior white matter tracts and associated gray matter regions (particularly cuneus/precuneus) as areas of vulnerability in type 1 diabetes (3032). These regions form part of the neural system responsible for integrating visual inputs with motor outputs, and in adults with type 1 diabetes, structural pathology in these regions is directly correlated to performance on GP [grooved pegboard test] (30,31). Arbelaez et al. (33) noted that these brain areas form part of the “default network” (34), a system engaged during internally focused cognition that has high resting glucose metabolism and may be especially vulnerable to glucose variability.”

“It should be noted that previous studies (e.g., Northam et al. [3]) have not found evidence of neurocognitive dysfunction around the time of diabetes diagnosis. This may be due to study differences in measures, outcomes, and/or time frame. We know of no other studies that completed neuropsychological testing within days of diagnosis. Given our time frame, it is possible that our findings reflect transient effects rather than more permanent changes in the CNS. Contrary to predictions, we found no association between DKA at diagnosis and neurocognitive performance […] However, even transient effects could be considered potential indicators of CNS vulnerability. Neurophysiological changes at the time of diagnosis have been shown to persist under certain circumstances or for some patients. […] [Some] findings suggest that some individuals may be particularly susceptible to the effects of glycemic extremes on neurocognitive function, consistent with a large body of research in developmental neuroscience indicating individual differences in neurobiological vulnerability to adverse events. Thus, although it is possible that the neurocognitive impairments observed in our study might resolve with euglycemia, deficits at diagnosis could still be considered a potential marker of CNS vulnerability to metabolic perturbations (both acute and chronic).”

“In summary, this study provides the first demonstration that type 1 diabetes–associated neurocognitive impairment can be detected at the time of diagnosis, supporting the possibility that deficits arise secondary to peri-onset effects. Whether these effects are transient markers of vulnerability or represent more persistent changes in CNS awaits further study.”

ii. Association Between Impaired Cardiovascular Autonomic Function and Hypoglycemia in Patients With Type 1 Diabetes.

“Cardiovascular autonomic neuropathy (CAN) is a chronic complication of diabetes and an independent predictor of cardiovascular disease (CVD) morbidity and mortality (13). The mechanisms of CAN are complex and not fully understood. It can be assessed by simple cardiovascular reflex tests (CARTs) and heart rate variability (HRV) studies that were shown to be sensitive, noninvasive, and reproducible (3,4).”

“HbA1c fails to capture information on the daily fluctuations in blood glucose levels, termed glycemic variability (GV). Recent observations have fostered the notion that GV, independent of HbA1c, may confer an additional risk for the development of micro- and macrovascular diabetes complications (8,9). […] the relationship between GV and chronic complications, specifically CAN, in patients with type 1 diabetes has not been systematically studied. In addition, limited data exist on the relationship between hypoglycemic components of the GV and measures of CAN among subjects with type 1 diabetes (11,12). Therefore, we have designed a prospective study to evaluate the impact and the possible sustained effects of GV on measures of cardiac autonomic function and other cardiovascular complications among subjects with type 1 diabetes […] In the present communication, we report cross-sectional analyses at baseline between indices of hypoglycemic stress on measures of cardiac autonomic function.”

“The following measures of CAN were predefined as outcomes of interests and analyzed: expiration-to-inspiration ratio (E:I), Valsalva ratio, 30:15 ratios, low-frequency (LF) power (0.04 to 0.15 Hz), high-frequency (HF) power (0.15 to 0.4 Hz), and LF/HF at rest and during CARTs. […] We found that LBGI [low blood glucose index] and AUC [area under the curve] hypoglycemia were associated with reduced LF and HF power of HRV [heart rate variability], suggesting an impaired autonomic function, which was independent of glucose control as assessed by the HbA1c.”

“Our findings are in concordance with a recent report demonstrating attenuation of the baroreflex sensitivity and of the sympathetic response to various cardiovascular stressors after antecedent hypoglycemia among healthy subjects who were exposed to acute hypoglycemic stress (18). Similar associations […] were also reported in a small study of subjects with type 2 diabetes (19). […] higher GV and hypoglycemic stress may have an acute effect on modulating autonomic control with inducing a sympathetic/vagal imbalance and a blunting of the cardiac vagal control (18). The impairment in the normal counter-regulatory autonomic responses induced by hypoglycemia on the cardiovascular system could be important in healthy individuals but may be particularly detrimental in individuals with diabetes who have hitherto compromised cardiovascular function and/or subclinical CAN. In these individuals, hypoglycemia may also induce QT interval prolongation, increase plasma catecholamine levels, and lower serum potassium (19,20). In concert, these changes may lower the threshold for serious arrhythmia (19,20) and could result in an increased risk of cardiovascular events and sudden cardiac death. Conversely, the presence of CAN may increase the risk of hypoglycemia through hypoglycemia unawareness and subsequent impaired ability to restore euglycemia (21) through impaired sympathoadrenal response to hypoglycemia or delayed gastric emptying. […] A possible pathogenic role of GV/hypoglycemic stress on CAN development and progressions should be also considered. Prior studies in healthy and diabetic subjects have found that higher exposure to hypoglycemia reduces the counter-regulatory hormone (e.g., epinephrine, glucagon, and adrenocorticotropic hormone) and blunts autonomic nervous system responses to subsequent hypoglycemia (21). […] Our data […] suggest that wide glycemic fluctuations, particularly hypoglycemic stress, may increase the risk of CAN in patients with type 1 diabetes.”

“In summary, in this cohort of relatively young and uncomplicated patients with type 1 diabetes, GV and higher hypoglycemic stress were associated with impaired HRV reflective of sympathetic/parasympathetic dysfunction with potential important clinical consequences.”

iii. Elevated Levels of hs-CRP Are Associated With High Prevalence of Depression in Japanese Patients With Type 2 Diabetes: The Diabetes Distress and Care Registry at Tenri (DDCRT 6).

“In the last decade, several studies have been published that suggest a close association between diabetes and depression. Patients with diabetes have a high prevalence of depression (1) […] and a high prevalence of complications (3). In addition, depression is associated with mortality in these patients (4). […] Because of this strong association, several recent studies have suggested the possibility of a common biological pathway such as inflammation as an underlying mechanism of the association between depression and diabetes (5). […] Multiple mechanisms are involved in the association between diabetes and inflammation, including modulation of lipolysis, alteration of glucose uptake by adipose tissue, and an indirect mechanism involving an increase in free fatty acid levels blocking the insulin signaling pathway (10). Psychological stress can also cause inflammation via innervation of cytokine-producing cells and activation of the sympathetic nervous systems and adrenergic receptors on macrophages (11). Depression enhances the production of inflammatory cytokines (1214). Overproduction of inflammatory cytokines may stimulate corticotropin-releasing hormone production, a mechanism that leads to hypothalamic-pituitary axis activity. Conversely, cytokines induce depressive-like behaviors; in studies where healthy participants were given endotoxin infusions to trigger cytokine release, the participants developed classic depressive symptoms (15). Based on this evidence, it could be hypothesized that inflammation is the common biological pathway underlying the association between diabetes and depression.”

“[F]ew studies have examined the clinical role of inflammation and depression as biological correlates in patients with diabetes. […] In this study, we hypothesized that high CRP [C-reactive protein] levels were associated with the high prevalence of depression in patients with diabetes and that this association may be modified by obesity or glycemic control. […] Patient data were derived from the second-year survey of a diabetes registry at Tenri Hospital, a regional tertiary care teaching hospital in Japan. […] 3,573 patients […] were included in the study. […] Overall, mean age, HbA1c level, and BMI were 66.0 years, 7.4% (57.8 mmol/mol), and 24.6 kg/m2, respectively. Patients with major depression tended to be relatively young […] and female […] with a high BMI […], high HbA1c levels […], and high hs-CRP levels […]; had more diabetic nephropathy […], required more insulin therapy […], and exercised less […]”.

“In conclusion, we observed that hs-CRP levels were associated with a high prevalence of major depression in patients with type 2 diabetes with a BMI of ≥25 kg/m2. […] In patients with a BMI of <25 kg/m2, no significant association was found between hs-CRP quintiles and major depression […] We did not observe a significant association between hs-CRP and major depression in either of HbA1c subgroups. […] Our results show that the association between hs-CRP and diabetes is valid even in an Asian population, but it might not be extended to nonobese subjects. […] several factors such as obesity and glycemic control may modify the association between inflammation and depression. […] Obesity is strongly associated with chronic inflammation.”

iv. A Novel Association Between Nondipping and Painful Diabetic Polyneuropathy.

“Sleep problems are common in painful diabetic polyneuropathy (PDPN) (1) and contribute to the effect of pain on quality of life. Nondipping (the absence of the nocturnal fall in blood pressure [BP]) is a recognized feature of diabetic cardiac autonomic neuropathy (CAN) and is attributed to the abnormal prevalence of nocturnal sympathetic activity (2). […] This study aimed to evaluate the relationship of the circadian pattern of BP with both neuropathic pain and pain-related sleep problems in PDPN […] Investigating the relationship between PDPN and BP circadian pattern, we found patients with PDPN exhibited impaired nocturnal decrease in BP compared with those without neuropathy, as well as higher nocturnal systolic BP than both those without DPN and with painless DPN. […] in multivariate analysis including comorbidities and most potential confounders, neuropathic pain was an independent determinant of ∆ in BP and nocturnal systolic BP.”

“PDPN could behave as a marker for the presence and severity of CAN. […] PDPN should increasingly be regarded as a condition of high cardiovascular risk.”

v. Reduced Testing Frequency for Glycated Hemoglobin, HbA1c, Is Associated With Deteriorating Diabetes Control.

I think a potentially important take-away from this paper, which they don’t really talk about, is that when you’re analyzing time series data in research contexts where the HbA1c variable is available at the individual level at some base frequency and you then encounter individuals for whom the HbA1c variable is unobserved in such a data set for some time periods/is not observed at the frequency you’d expect, such (implicit) missing values may not be missing at random (for more on these topics see e.g. this post). More specifically, in light of the findings of this paper I think it would make a lot of sense to default to an assumption of missing values being an indicator of worse-than-average metabolic control during the unobserved period of the time series in question when doing time-to-event analyses, especially in contexts where the values are missing for an extended period of time.

The authors of the paper consider metabolic control an outcome to be explained by the testing frequency. That’s one way to approach these things, but it’s not the only one and I think it’s also important to keep in mind that some patients also sometimes make a conscious decision not to show up for their appointments/tests; i.e. the testing frequency is not necessarily fully determined by the medical staff, although they of course have an important impact on this variable.

Some observations from the paper:

“We examined repeat HbA1c tests (400,497 tests in 79,409 patients, 2008–2011) processed by three U.K. clinical laboratories. We examined the relationship between retest interval and 1) percentage change in HbA1c and 2) proportion of cases showing a significant HbA1c rise. The effect of demographics factors on these findings was also explored. […] Figure 1 shows the relationship between repeat requesting interval (categorized in 1-month intervals) and percentage change in HbA1c concentration in the total data set. From 2 months onward, there was a direct relationship between retesting interval and control. A testing frequency of >6 months was associated with deterioration in control. The optimum testing frequency in order to maximize the downward trajectory in HbA1c between two tests was approximately four times per year. Our data also indicate that testing more frequently than 2 months has no benefit over testing every 2–4 months. Relative to the 2–3 month category, all other categories demonstrated statistically higher mean change in HbA1c (all P < 0.001). […] similar patterns were observed for each of the three centers, with the optimum interval to improvement in overall control at ∼3 months across all centers.”

“[I]n patients with poor control, the pattern was similar to that seen in the total group, except that 1) there was generally a more marked decrease or more modest increase in change of HbA1c concentration throughout and, consequently, 2) a downward trajectory in HbA1c was observed when the interval between tests was up to 8 months, rather than the 6 months as seen in the total group. In patients with a starting HbA1c of <6% (<42 mmol/mol), there was a generally linear relationship between interval and increase in HbA1c, with all intervals demonstrating an upward change in mean HbA1c. The intermediate group showed a similar pattern as those with a starting HbA1c of <6% (<42 mmol/mol), but with a steeper slope.”

“In order to examine the potential link between monitoring frequency and the risk of major deterioration in control, we then assessed the relationship between testing interval and proportion of patients demonstrating an increase in HbA1c beyond the normal biological and analytical variation in HbA1c […] Using this definition of significant increase as a ≥9.9% rise in subsequent HbA1c, our data show that the proportion of patients showing this magnitude of rise increased month to month, with increasing intervals between tests for each of the three centers. […] testing at 2–3-monthly intervals would, at a population level, result in a marked reduction in the proportion of cases demonstrating a significant increase compared with annual testing […] irrespective of the baseline HbA1c, there was a generally linear relationship between interval and the proportion demonstrating a significant increase in HbA1c, though the slope of this relationship increased with rising initial HbA1c.”

“Previous data from our and other groups on requesting patterns indicated that relatively few patients in general practice were tested annually (5,6). […] Our data indicate that for a HbA1c retest interval of more than 2 months, there was a direct relationship between retesting interval and control […], with a retest frequency of greater than 6 months being associated with deterioration in control. The data showed that for diabetic patients as a whole, the optimum repeat testing interval should be four times per year, particularly in those with poorer diabetes control (starting HbA1c >7% [≥53 mmol/mol]). […] The optimum retest interval across the three centers was similar, suggesting that our findings may be unrelated to clinical laboratory factors, local policies/protocols on testing, or patient demographics.”

It might be important to mention that there are important cross-country differences in terms of how often people with diabetes get HbA1c measured – I’m unsure of whether or not standards have changed since then, but at least in Denmark a specific treatment goal of the Danish Regions a few years ago was whether or not 95% of diabetics had had their HbA1c measured within the last year (here’s a relevant link to some stuff I wrote about related topics a while back).

October 2, 2017 Posted by | Cardiology, Diabetes, Immunology, Medicine, Neurology, Psychology, Statistics, Studies | Leave a comment

Type 1 Diabetes Mellitus and Cardiovascular Disease

“Despite the known higher risk of cardiovascular disease (CVD) in individuals with type 1 diabetes mellitus (T1DM), the pathophysiology underlying the relationship between cardiovascular events, CVD risk factors, and T1DM is not well understood. […] The present review will focus on the importance of CVD in patients with T1DM. We will summarize recent observations of potential differences in the pathophysiology of T1DM compared with T2DM, particularly with regard to atherosclerosis. We will explore the implications of these concepts for treatment of CVD risk factors in patients with T1DM. […] The statement will identify gaps in knowledge about T1DM and CVD and will conclude with a summary of areas in which research is needed.”

The above quote is from this paper: Type 1 Diabetes Mellitus and Cardiovascular Disease: A Scientific Statement From the American Heart Association and American Diabetes Association.

I originally intended to cover this one in one of my regular diabetes posts, but I decided in the end that there was simply too much stuff to cover here for it to make sense not to devote an entire post to it. I have quoted extensively from the paper/statement below and I also decided to bold a few of the observations I found particularly important/noteworthy(/worth pointing out to people reading along?).

“T1DM has strong human leukocyte antigen associations to the DQA, DQB, and DRB alleles (2). One or more autoantibodies, including islet cell, insulin, glutamic acid decarboxylase 65 (GAD65), zinc transporter 8 (3), and tyrosine phosphatase IA-2β and IA-2β antibodies, can be detected in 85–90% of individuals on presentation. The rate of β-cell destruction varies, generally occurring more rapidly at younger ages. However, T1DM can also present in adults, some of whom can have enough residual β-cell function to avoid dependence on insulin until many years later. When autoantibodies are present, this is referred to as latent autoimmune diabetes of adulthood. Infrequently, T1DM can present without evidence of autoimmunity but with intermittent episodes of ketoacidosis; between episodes, the need for insulin treatment can come and go. This type of DM, called idiopathic diabetes (1) or T1DM type B, occurs more often in those of African and Asian ancestry (4). Because of the increasing prevalence of obesity in the United States, there are also obese individuals with T1DM, particularly children. Evidence of insulin resistance (such as acanthosis nigricans); fasting insulin, glucose, and C-peptide levels; and the presence of islet cell, insulin, glutamic acid decarboxylase, and phosphatase autoantibodies can help differentiate between T1DM and T2DM, although both insulin resistance and insulin insufficiency can be present in the same patient (5), and rarely, T2DM can present at an advanced stage with low C-peptide levels and minimal islet cell function.”

Overall, CVD events are more common and occur earlier in patients with T1DM than in nondiabetic populations; women with T1DM are more likely to have a CVD event than are healthy women. CVD prevalence rates in T1DM vary substantially based on duration of DM, age of cohort, and sex, as well as possibly by race/ethnicity (8,11,12). The Pittsburgh Epidemiology of Diabetes Complications (EDC) study demonstrated that the incidence of major coronary artery disease (CAD) events in young adults (aged 28–38 years) with T1DM was 0.98% per year and surpassed 3% per year after age 55 years, which makes it the leading cause of death in that population (13). By contrast, incident first CVD in the nondiabetic population ranges from 0.1% in 35- to 44-year-olds to 7.4% in adults aged 85–94 years (14). An increased risk of CVD has been reported in other studies, with the age-adjusted relative risk (RR) for CVD in T1DM being ≈10 times that of the general population (1517). One of the most robust analyses of CVD risk in this disease derives from the large UK General Practice Research Database (GPRD), comprising data from >7,400 patients with T1DM with a mean ± SD age of 33 ± 14.5 years and a mean DM duration of 15 ± 12 years (8). CVD events in the UK GPRD study occurred on average 10 to 15 years earlier than in matched nondiabetic control subjects.”

“When types of CVD are reported separately, CHD [coronary heart disease] predominates […] The published cumulative incidence of CHD ranges between 2.1% (18) and 19% (19), with most studies reporting cumulative incidences of ≈15% over ≈15 years of follow-up (2022). […] Although stroke is less common than CHD in T1DM, it is another important CVD end point. Reported incidence rates vary but are relatively low. […] the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) reported an incidence rate of 5.9% over 20 years (≈0.3%) (21); and the European Diabetes (EURODIAB) Study reported a 0.74% incidence of cerebrovascular disease per year (18). These incidence rates are for the most part higher than those reported in the general population […] PAD [peripheral artery disease] is another important vascular complication of T1DM […] 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.

“Abnormal vascular findings associated with atherosclerosis are also seen in patients with T1DM. Coronary artery calcification (CAC) burden, an accepted noninvasive assessment of atherosclerosis and a predictor of CVD events in the general population, is greater in people with T1DM than in nondiabetic healthy control subjects […] With regard to subclinical carotid disease, both carotid intima-media thickness (cIMT) and plaque are increased in children, adolescents, and adults with T1DM […] compared with age- and sex-matched healthy control subjects […] Endothelial function is altered even at a very early stage of T1DM […] Taken together, these data suggest that preclinical CVD can be seen more frequently and to a greater extent in patients with T1DM, even at an early age. Some data suggest that its presence may portend CVD events; however, how these subclinical markers function as end points is not clear.”

“Neuropathy in T1DM can lead to abnormalities in the response of the coronary vasculature to sympathetic stimulation, which may manifest clinically as resting tachycardia or bradycardia, exercise intolerance, orthostatic hypotension, loss of the nocturnal decline in BP, or silent myocardial ischemia on cardiac testing. These abnormalities can lead to delayed presentation of CVD. An early indicator of cardiac autonomic neuropathy is reduced heart rate variability […] Estimates of the prevalence of cardiac autonomic neuropathy in T1DM vary widely […] Cardiac neuropathy may affect as many as ≈40% of individuals with T1DM (45).”

CVD events occur much earlier in patients with T1DM than in the general population, often after 2 decades of T1DM, which in some patients may be by age 30 years. Thus, in the EDC study, CVD was the leading cause of death in T1DM patients after 20 years of disease duration, at rates of >3% per year (13). Rates of CVD this high fall into the National Cholesterol Education Program’s high-risk category and merit intensive CVD prevention efforts (48). […] CVD events are not generally expected to occur during childhood, even in the setting of T1DM; however, the atherosclerotic process begins during childhood. Children and adolescents with T1DM have subclinical CVD abnormalities even within the first decade of DM diagnosis according to a number of different methodologies”.

Rates of CVD are lower in premenopausal women than in men […much lower: “Cardiovascular disease develops 7 to 10 years later in women than in men” – US]. In T1DM, these differences are erased. In the United Kingdom, CVD affects men and women with T1DM equally at <40 years of age (23), although after age 40 years, men are affected more than women (51). Similar findings on CVD mortality rates were reported in a large Norwegian T1DM cohort study (52) and in the Allegheny County (PA) T1DM Registry (13), which reported the relative impact of CVD compared with the general population was much higher for women than for men (standardized mortality ratio [SMR] 13.2 versus 5.0 for total mortality and 24.7 versus 8.8 for CVD mortality, women versus men). […] Overall, T1DM appears to eliminate most of the female sex protection seen in the nondiabetic population.”

“The data on atherosclerosis in T1DM are limited. A small angiographic study compared 32 individuals with T1DM to 31 nondiabetic patients matched for age and symptoms (71). That study found atherosclerosis in the setting of T1DM was characterized by more severe (tighter) stenoses, more extensive involvement (multiple vessels), and more distal coronary findings than in patients without DM. A quantitative coronary angiographic study in T1DM suggested more severe, distal disease and an overall increased burden compared with nondiabetic patients (up to fourfold higher) (72).”

“In the general population, inflammation is a central pathological process of atherosclerosis (79). Limited pathology data suggest that inflammation is more prominent in patients with DM than in nondiabetic control subjects (70), and those with T1DM in particular are affected. […] Knowledge of the clinical role of inflammatory markers in T1DM and CVD prediction and management is in its infancy, but early data suggest a relationship with preclinical atherosclerosis. […] Studies showed C-reactive protein is elevated within the first year of diagnosis of T1DM (80), and interleukin-6 and fibrinogen levels are high in individuals with an average disease duration of 2 years (81), independent of adiposity and glycemia (82). Other inflammatory markers such as soluble interleukin-2 receptor (83) and CD40 ligand (84,85) are higher in patients with T1DM than in nondiabetic subjects. Inflammation is evident in youth, even soon after the diagnosis of T1DM. […] The mechanisms by which inflammation operates in T1DM are likely multiple but may include hyperglycemia and hypoglycemia, excess adiposity or altered body fat distribution, thrombosis, and adipokines. Several recent studies have demonstrated a relationship between acute hypoglycemia and indexes of systemic inflammation […] These studies suggest that acute hypoglycemia in T1DM produces complex vascular effects involved in the activation of proinflammatory, prothrombotic, and proatherogenic mechanisms. […] Fibrinogen, a prothrombotic acute phase reactant, is increased in T1DM and is associated with premature CVD (109), and it may be important in vessel thrombosis at later stages of CVD.”

“Genetic polymorphisms appear to influence the progression and prognosis of CVD in T1DM […] Like fibrinogen, haptoglobin is an acute phase protein that inhibits hemoglobin-induced oxidative tissue damage by binding to free hemoglobin (110). […] In humans, there are 2 classes of alleles at the haptoglobin locus, giving rise to 3 possible genotypes: haptoglobin 1-1, haptoglobin 2-1, and haptoglobin 2-2. […] In T1DM, there is an independent twofold increased incidence of CAD in haptoglobin 2-2 carriers compared with those with the haptoglobin 1-1 genotype (117); the 2-1 genotype is associated with an intermediate effect of increased CVD risk. More recently, an independent association was reported in T1DM between the haptoglobin 2-2 genotype and early progression to end-stage renal disease (ESRD) (118). In the CACTI study group, the presence of the haptoglobin 2-2 genotype also doubled the risk of CAC [coronary artery calcification] in patients free from CAC at baseline, after adjustment for traditional CVD risk factors (119). […] At present, genetic testing for polymorphisms in T1DM [however] has no clear clinical utility in CVD prediction or management.”

“Dysglycemia is often conceived of as a vasculopathic process. Preclinical atherosclerosis and epidemiological studies generally support this relationship. Clinical trial data from the DCCT supplied definitive findings strongly in favor of beneficial effects of better glycemic control on CVD outcomes. Glycemia is associated with preclinical atherosclerosis in studies that include tests of endothelial function, arterial stiffness, cIMT, autonomic neuropathy, and left ventricular (LV) function in T1DM […] LV mass and function improve with better glycemic control (126,135,136). Epidemiological evidence generally supports the relationship between hyperglycemia and clinical CHD events in T1DM. […] A large Swedish database review recently reported a reasonably strong association between HbA1c and CAD in T1DM (HR, 1.3 per 1% HbA1c increase) (141). […] findings support the recommendation that early optimal glycemic control in T1DM will have long-term benefits for CVD reduction.”

“Obesity is a known independent risk factor for CVD in nondiabetic populations, but the impact of obesity in T1DM has not been fully established. Traditionally, T1DM was a condition of lean individuals, yet the prevalence of overweight and obesity in T1DM has increased significantly […] This is related to epidemiological shifts in the population overall, tighter glucose control leading to less glucosuria, more frequent/greater caloric intake to fend off real and perceived hypoglycemia, and the specific effects of intensive DM therapy, which has been shown to increase the prevalence of obesity (152). Indeed, several clinical trials, including the DCCT, demonstrate that intensive insulin therapy can lead to excessive weight gain in a subset of patients with T1DM (152). […] No systematic evaluation has been conducted to assess whether improving insulin sensitization lowers rates of CVD. Ironically, the better glycemic control associated with insulin therapy may lead to weight gain, with a superimposed insulin resistance, which may be approached by giving higher doses of insulin. However, some evidence from the EDC study suggests that weight gain in the presence of improved glycemic control is associated with an improved CVD risk profile (162). […] Although T1DM is characteristically a disease of absolute insulin deficiency (154), insulin resistance appears to contribute to CHD risk in patients with T1DM. For example, having a family history of T2DM, which suggests a genetic predisposition for insulin resistance, has been associated with an increased CVD risk in patients with T1DM (155).”

“In general, the lipid levels of adults with well-controlled T1DM are similar to those of individuals without DM […] Worse glycemic control, higher weight (164), and more insulin resistance as measured by euglycemic clamp (165) are associated with a more atherogenic cholesterol distribution in men and women with T1DM […] Studies in pediatric and young adult populations suggest higher lipid values than in youth without T1DM, with glycemic control being a significant contributor (148). […] Most studies show that as is true for the general population, dyslipidemia is a risk factor for CVD in T1DM. Qualitative differences in lipid and lipoprotein fractions are being investigated to determine whether abnormal lipid function may contribute to this. The HDL-C fraction has been of particular interest because the metabolism of HDL-C in T1DM may be altered because of abnormal lipoprotein lipase and hepatic lipase activities related to exogenously administered insulin […] Additionally, as noted earlier, the less efficient handling of heme by the haptoglobin 2-2 genotype in patients with T1DM leaves these complexes less capable of being removed by macrophages, which allows them to associate with HDL, which renders it less functional (116). […] Conventionally, pharmacotherapy is used more aggressively for patients with T1DM and lipid disorders than for nondiabetic patients; however, recommendations for treatment are mostly extrapolated from interventional trials in adults with T2DM, in which rates of CVD events are equivalent to those in secondary prevention populations. Whether this is appropriate for T1DM is not clear […] Awareness of CVD risk and screening for hypercholesterolemia in T1DM have increased over time, yet recent data indicate that control is suboptimal, particularly in younger patients who have not yet developed long-term complications and might therefore benefit from prevention efforts (173). Adults with T1DM who have abnormal lipids and additional risk factors for CVD (e.g., hypertension, obesity, or smoking) who have not developed CVD should be treated with statins. Adults with CVD and T1DM should also be treated with statins, regardless of whether they have additional risk factors.”

“Diabetic kidney disease (DKD) is a complication of T1DM that is strongly linked to CVD. DKD can present as microalbuminuria or macroalbuminuria, impaired GFR, or both. These represent separate but complementary manifestations of DKD and are often, but not necessarily, sequential in their presentation. […] the risk of all-cause mortality increased with the severity of DKD, from microalbuminuria to macroalbuminuria to ESRD. […] Microalbuminuria is likely an indicator of diffuse vascular injury. […] Microalbuminuria is highly correlated with CVD (49,180182). In the Steno Diabetes Center (Gentofte, Denmark) cohort, T1DM patients with isolated microalbuminuria had a 4.2-fold increased risk of CVD (49,180). In the EDC study, microalbuminuria was associated with mortality risk, with an SMR of 6.4. In the FinnDiane study, mortality risk was also increased with microalbuminuria (SMR, 2.8). […] A recent review summarized these data. In patients with T1DM and microalbuminuria, there was an RR of all-cause mortality of 1.8 (95% CI, 1.5–2.1) that was unaffected by adjustment for confounders (183). Similar RRs were found for mortality from CVD (1.9; 95% CI, 1.3–2.9), CHD (2.1; 95% CI, 1.2–3.5), and aggregate CVD mortality (2.0; 95% CI, 1.5–2.6).”

“Macroalbuminuria represents more substantial kidney damage and is also associated with CVD. Mechanisms may be more closely related to functional consequences of kidney disease, such as higher LDL-C and lower HDL-C. Prospective data from Finland indicate the RR for CVD is ≈10 times greater in patients with macroalbuminuria than in those without macroalbuminuria (184). Historically, in the [Danish] Steno cohort, patients with T1DM and macroalbuminuria had a 37-fold increased risk of CVD mortality compared with the general population (49,180); however, a more recent report from EURODIAB suggests a much lower RR (8.7; 95% CI, 4.03–19.0) (185). […] In general, impaired GFR is a risk factor for CVD, independent of albuminuria […] ESRD [end-stage renal disease, US], the extreme form of impaired GFR, is associated with the greatest risk of CVD of all varieties of DKD. In the EDC study, ESRD was associated with an SMR for total mortality of 29.8, whereas in the FinnDiane study, it was 18.3. It is now clear that GFR loss and the development of eGFR <60 mL · min−1 · 1.73 m−2 can occur without previous manifestation of microalbuminuria or macroalbuminuria (177,178). In T1DM, the precise incidence, pathological basis, and prognosis of this phenotype remain incompletely described.”

“Prevention of DKD remains challenging. Although microalbuminuria and macroalbuminuria are attractive therapeutic targets for CVD prevention, there are no specific interventions directed at the kidney that prevent DKD. Inhibition of the renin-angiotensin-aldosterone system is an attractive option but has not been demonstrated to prevent DKD before it is clinically apparent. […] In contrast to prevention efforts, treatment of DKD with agents that inhibit the renin-angiotensin-aldosterone system is effective. […] angiotensin-converting enzyme (ACE) inhibitors reduce the progression of DKD and death in T1DM (200). Thus, once DKD develops, treatment is recommended to prevent progression and to reduce or minimize other CVD risk factors, which has a positive effect on CVD risk. All patients with T1DM and hypertension or albuminuria should be treated with an ACE inhibitor. If an ACE inhibitor is not tolerated, an angiotensin II receptor blocker (ARB) is likely to have similar efficacy, although this has not been studied specifically in patients with T1DM. Optimal dosing for ACE inhibitors or ARBs in the setting of DKD is not well defined; titration may be guided by BP, albuminuria, serum potassium, and creatinine. Combination therapy of ACE and ARB blockade cannot be specifically recommended at this time.”

“Hypertension is more common in patients with T1DM and is a powerful risk factor for CVD, regardless of whether an individual has DKD. In the CACTI [Coronary Artery Calcification in Type 1 Diabetes] study, hypertension was much more common in patients with T1DM than in age- and sex-matched control subjects (43% versus 15%, P < 0.001); in fact, only 42% of all T1DM patients met the Joint National Commission 7 goal (BP <130/80 mmHg) (201). Hypertension also affects youth with T1DM. The SEARCH trial of youth aged 3–17 years with T1DM (n = 3,691) found the prevalence of elevated BP was 5.9% […] Abnormalities in BP can stem from DKD or obesity. Hyperglycemia may also contribute to hypertension over the long term. In the DCCT/EDIC cohort, higher HbA1c was strongly associated with increased risk of hypertension, and intensive DM therapy reduced the long-term risk of hypertension by 24% (203). […] There are few published trials about the ideal pharmacotherapeutic agent(s) for hypertension in T1DM.”

“Smoking is a major risk factor for CVD, particularly PAD (213); however, there is little information on the prevalence or effects of smoking in T1DM. […] The added CVD risk of smoking may be particularly important in patients with DM, who are already vulnerable. In patients with T1DM, cigarette smoking [has been shown to increase] the risk of DM nephropathy, retinopathy, and neuropathy (214,215) […] Smoking increases CVD risk factors in T1DM via deterioration in glucose metabolism, lipids, and endothelial function (216). Unfortunately, smoking cessation can result in weight gain, which may deter smokers with DM from quitting (217). […] Smoking cessation should be strongly recommended to all patients with T1DM as part of an overall strategy to lower CVD, in particular PAD.”

“CVD risk factors are more common in children with T1DM than in the general pediatric population (218). Population-based studies estimate that 14–45% of children with T1DM have ≥2 CVD risk factors (219221). As with nondiabetic children, the prevalence of CVD risk factors increases with age (221). […] The American Academy of Pediatrics, the American Heart Association, and the ADA recognize patients with DM, and particularly T1DM, as being in a higher-risk group who should receive more aggressive risk factor screening and treatment than nondiabetic children […] The available data suggest many children and adolescents with T1DM do not receive the recommended treatment for their dyslipidemia and hypertension (220,222).”

“There are no CVD risk-prediction algorithms for patients with T1DM in widespread use. […] Use of the Framingham Heart Study and UK Prospective Diabetes Study (UKPDS) algorithms in the EDC study population did not provide good predictive results, which suggests that neither general or T2DM risk algorithms are sufficient for risk prediction in T1DM (235). On the basis of these findings, a model has been developed with the use of EDC cohort data (236) that incorporates measures outside the Framingham construct (white blood cell count, albuminuria, DM duration). Although this algorithm was validated in the EURODIAB Study cohort (237), it has not been widely adopted, and diagnostic and therapeutic decisions are often based on global CVD risk-estimation methods (i.e., Framingham risk score or T2DM-specific UKPDS risk engine [http://www.dtu.ox.ac.uk/riskengine/index.php]). Other options for CVD risk prediction in patients with T1DM include the ADA risk-assessment tool (http://main.diabetes.org/dorg/mha/main_en_US.html?loc=dorg-mha) and the Atherosclerosis Risk in Communities (ARIC) risk predictor (http://www.aricnews.net/riskcalc/html/RC1.html), but again, accuracy for T1DM is not clear.”

September 25, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Medicine, Nephrology, Neurology, Pharmacology, Studies | Leave a comment

A few diabetes papers of interest

i. Glycated Hemoglobin and All-Cause and Cause-Specific Mortality in Singaporean Chinese Without Diagnosed Diabetes: The Singapore Chinese Health Study.

“Previous studies have reported that elevated levels of HbA1c below the diabetes threshold (<6.5%) are associated with an increased risk for cardiovascular morbidity and mortality (312). Yet, this research base is not comprehensive, and data from Chinese populations are scant, especially in those without diabetes. This gap in the literature is important since Southeast Asian populations are experiencing epidemic rates of type 2 diabetes and related comorbidities with a substantial global health impact (1316).

Overall, there are few cohort studies that have examined the etiologic association between HbA1c levels and all-cause and cause-specific mortality. There is even lesser insight on the nature of the relationship between HbA1c and significant clinical outcomes in Southeast Asian populations. Therefore, we examined the association between HbA1c and all-cause and cause-specific mortality in the Singapore Chinese Health Study (SCHS).”

“The design of the SCHS has been previously summarized (17). Briefly, the cohort was drawn from men and women, aged 45–74 years, who belonged to one of the major dialect groups (Hokkien or Cantonese) of Chinese in Singapore. […] Between April 1993 and December 1998, 63,257 individuals completed an in-person interview that included questions on usual diet, demographics, height and weight, use of tobacco, usual physical activity, menstrual and reproductive history (women only), medical history including history of diabetes diagnosis by a physician, and family history of cancer. […] At the follow-up interview (F1), which occurred in 1999–2004, subjects were asked to update their baseline interview information. […] The study population derived from 28,346 participants of the total 54,243 who were alive and participated at F1, who provided consent at F1 to collect subsequent blood samples (a consent rate of ∼65%). The participants for this study were a random selection of individuals from the full study population who did not report a history of diabetes or CVD at the baseline or follow-up interview and reported no history of cancer.”

“During 74,890 person-years of follow-up, there were 888 total deaths, of which 249 were due to CVD, 388 were due to cancer, and 169 were recorded as respiratory mortality. […] There was a positive association between HbA1c and age, BMI, and prevalence of self-reported hypertension, while an inverse association was observed between educational attainment and HbA1c. […] The crude mortality rate was 1,186 deaths per 100,000 person-years. The age- and sex-standardized mortality rates for all-cause, CVD, and cerebrovascular each showed a J-shaped pattern according to HbA1c level. The CHD and cancer mortality rates were higher for HbA1c ≥6.5% (≥48 mmol/mol) and otherwise displayed no apparent pattern. […] There was no association between any level of HbA1c and respiratory causes of death.”

“Chinese men and women with no history of cancer, reported diabetes, or CVD with an HbA1c level ≥6.5% (≥48 mmol/mol) were at a significant increased risk of mortality during follow-up relative to their peers with an HbA1c of 5.4–5.6% (36–38 mmol/mol). No other range of HbA1c was significantly associated with risk of mortality during follow-up, and in secondary analyses, when the HbA1c level ≥6.5% (≥48 mmol/mol) was divided into four categories, this increased risk was observed in all four categories; thus, these data represent a clear threshold association between HbA1c and mortality in this population. These results are consistent with previous prospective cohort studies identifying chronically high HbA1c, outside of diabetes, to be associated with increased risk for all-cause and CVD-related mortality (312,22).”

“Hyperglycemia is a known risk factor for CVD, not limited to individuals with diabetes. This may be in part due to the vascular damage caused by oxidative stress in periods of hypo- and hyperglycemia (23,24). For individuals with impaired fasting glucose and impaired glucose tolerance, increased oxidative stress and endothelial dysfunction are present before the onset of diabetes (25). The association between chronically high levels of HbA1c and development of and death from cancer is not as well defined (9,2630). Abnormal metabolism may play a role in cancer development and death. This is important, considering cancer is the leading cause of death in Singapore for adults 15–59 years of age (31). Increased risk for cancer mortality was found in individuals with impaired glucose tolerance (30). […] Hyperinsulinemia and IGF-I are associated with increased cancer risk, possibly through mitogenic effects and tumor formation (27,28,37). This is the basis for the insulin-cancer hypothesis. Simply put, chronic levels of hyperinsulinemia reduce the production of IGF binding proteins 1 and 2. The absence of these proteins results in excess bioactive IGF-I, supporting tumor development (38). Chronic hyperglycemia, indicating high levels of insulin and IGF-I, may explain inhibition of cell apoptosis, increased cell proliferation, and increased cancer risk (39).”

ii. The Cross-sectional and Longitudinal Associations of Diabetic Retinopathy With Cognitive Function and Brain MRI Findings: The Action to Control Cardiovascular Risk in Diabetes (ACCORD) Trial.

“Brain imaging studies suggest that type 2 diabetes–related microvascular disease may affect the central nervous system in addition to its effects on other organs, such as the eye and kidney. Histopathological evidence indicates that microvascular disease in the brain can lead to white matter lesions (WMLs) visible with MRI of the brain (1), and risk for them is often increased by type 2 diabetes (26). Type 2 diabetes also has recently been associated with lower brain volume, particularly gray matter volume (79).

The association between diabetic retinopathy and changes in brain tissue is of particular interest because retinal and cerebral small vessels have similar anatomy, physiology, and embryology (10). […] the preponderance of evidence suggests diabetic retinopathy is associated with increased WML burden (3,1214), although variation exists. While cross-sectional studies support a correlation between diabetic retinopathy and WMLs (2,3,6,15), diabetic retinopathy and brain atrophy (16), diabetic retinopathy and psychomotor speed (17,18), and psychomotor speed and WMLs (5,19,20), longitudinal evidence demonstrating the assumed sequence of disease development, for example, vascular damage of eye and brain followed by cognitive decline, is lacking.

Using Action to Control Cardiovascular Risk in Diabetes (ACCORD) data, in which a subset of participants received longitudinal measurements of diabetic retinopathy, cognition, and MRI variables, we analyzed the 1) cross-sectional associations between diabetic retinopathy and evidence of brain microvascular disease and 2) determined whether baseline presence or severity of diabetic retinopathy predicts 20- or 40-month changes in cognitive performance or brain microvascular disease.”

“The ACCORD trial (21) was a multicenter randomized trial examining the effects of intensive glycemic control, blood pressure, and lipids on cardiovascular disease events. The 10,251 ACCORD participants were aged 40–79 years, had poorly controlled type 2 diabetes (HbA1c > 7.5% [58.5 mmol/mol]), and had or were at high risk for cardiovascular disease. […] The ACCORD-Eye sample comprised 3,472 participants who did not report previous vitrectomy or photocoagulation surgery for proliferative diabetic retinopathy at baseline […] ACCORD-MIND included a subset of 2,977 ACCORD participants who completed a 30-min cognitive testing battery, 614 of whom also had useable scans from the MRI substudy (23,24). […] ACCORD-MIND had visits at three time points: baseline, 20 months, and 40 months. MRI of the brain was completed at baseline and the 40-month time point.”

“Baseline diabetic retinopathy was associated with more rapid 40-month declines in DSST and MMSE [Mini-Mental State Examination] when adjusting for demographics and lifestyle factors in model 1 […]. Moreover, increasing severity of diabetic retinopathy was associated with increased amounts of decline in DSST [Digit Symbol Substitution Test] performance (−1.30, −1.76, and −2.81 for no, mild, and moderate/severe NPDR, respectively; P = 0.003) […Be careful about how to interpret that p-value – see below, US] . The associations remained virtually unchanged after further adjusting for vascular and diabetes risk factors, depression, and visual acuity using model 2.”

“This longitudinal study provides new evidence that diabetic retinopathy is associated with future cognitive decline in persons with type 2 diabetes and confirms the finding from the Edinburgh Type 2 Diabetes Study derived from cross-sectional data that lifetime cognitive decline is associated with diabetic retinopathy (32). We found that the presence of diabetic retinopathy, independent of visual acuity, predicts greater declines in global cognitive function measured with the MMSE and that the magnitude of decline in processing speed measured with the DSST increased with increasing severity of baseline diabetic retinopathy. The association with psychomotor speed is consistent with prior cross-sectional findings in community-based samples of middle-aged (18) and older adults (17), as well as prospective studies of a community-based sample of middle-aged adults (33) and patients with type 1 diabetes (34) showing that retinopathy with different etiologies predicted a subsequent decline in psychomotor speed. This study extends these findings to patients with type 2 diabetes.”

“we tested a number of different associations but did not correct P values for multiple testing” [Aargh!, US.]

iii. Incidence of Remission in Adults With Type 2 Diabetes: The Diabetes & Aging Study.

(Note to self before moving on to the paper: these people identified type 1 diabetes by self-report or diabetes onset at <30 years of age, treated with insulin only and never treated with oral agents).

“It is widely believed that type 2 diabetes is a chronic progressive condition, which at best can be controlled, but never cured (1), and that once treatment with glucose-lowering medication is initiated, it is required indefinitely and is intensified over time (2,3). However, a growing body of evidence from clinical trials and case-control studies (46) has reported the remission of type 2 diabetes in certain populations, most notably individuals who received bariatric surgery. […] Despite the clinical relevance and importance of remission, little is known about the incidence of remission in community settings (11,12). Studies to date have focused largely on remission after gastric bypass or relied on data from clinical trials, which have limited generalizability. Therefore, we conducted a retrospective cohort study to describe the incidence rates and variables associated with remission among adults with type 2 diabetes who received usual care, excluding bariatric surgery, in a large, ethnically diverse population. […] 122,781 individuals met our study criteria, yielding 709,005 person-years of total follow-up time.”

“Our definitions of remission were based on the 2009 ADA consensus statement (10). “Partial remission” of diabetes was defined as having two or more consecutive subdiabetic HbA1c measurements, all of which were in the range of 5.7–6.4% [39–46 mmol/mol] over a period of at least 12 months. “Complete remission” was defined as having two or more consecutive normoglycemic HbA1c measurements, all of which were <5.7% [<39 mmol/mol] over a period of at least 12 months. “Prolonged remission” was defined as having two or more consecutive normoglycemic HbA1c measurements, all of which were <5.7% [<39 mmol/mol] over a period of at least 60 months. Each definition of remission requires the absence of pharmacologic treatment during the defined observation period.”

“The average age of participants was 62 years, 47.1% were female, and 51.6% were nonwhite […]. The mean (SD) interval between HbA1c tests in the remission group was 256 days (139 days). The mean interval (SD) between HbA1c tests among patients not in the remission group was 212 days (118 days). The median time since the diagnosis of diabetes in our cohort was 5.9 years, and the average baseline HbA1c level was 7.4% [57 mmol/mol]. The 18,684 individuals (15.2%) in the subset with new-onset diabetes, defined as ≤2 years since diagnosis, were younger, were more likely to have their diabetes controlled by diet, and had fewer comorbidities […] The incidence densities of partial, complete, and prolonged remission in the full cohort were 2.8 (95% CI 2.6–2.9), 0.24 (95% CI 0.20–0.28), and 0.04 (95% CI 0.01–0.06) cases per 1,000 person-years, respectively […] The 7-year cumulative incidences of partial, complete, and prolonged remission were 1.5% (95% CI 1.4–1.5%), 0.14% (95% CI 0.12–0.16%), and 0.01% (95% CI 0.003–0.02%), respectively. The 7-year cumulative incidence of any remission decreased with longer time since diagnosis from a high of 4.6% (95% CI 4.3–4.9%) for individuals diagnosed with diabetes in the past 2 years to a low of 0.4% (95% CI 0.3–0.5%) in those diagnosed >10 years ago. The 7-year cumulative incidence of any remission was much lower for individuals using insulin (0.05%; 95% CI 0.03–0.1%) or oral agents (0.3%; 95% CI 0.2–0.3%) at baseline compared with diabetes patients not using medication at baseline (12%; 95% CI 12–13%).”

“In this large cohort of insured adults with type 2 diabetes not treated with bariatric surgery, we found that 1.5% of individuals with recent evidence of clinical diabetes achieved at least partial remission over a 7-year period. If these results were generalized to the 25.6 million U.S. adults living with type 2 diabetes in 2010 (25), they would suggest that 384,000 adults could experience remission over the next 7 years. However, the rate of prolonged remission was extremely rare (0.007%), translating into only 1,800 adults in the U.S. experiencing remission lasting at least 5 years. To provide context, 1.7% of the cohort died, while only 0.8% experienced any level of remission, during the calendar year 2006. Thus, the chances of dying were higher than the chances of any remission. […] Although remission of type 2 diabetes is uncommon, it does occur in patients who have not undergone surgical interventions. […] Our analysis shows that remission is rare and variable. The likelihood of remission is more common among individuals with early-onset diabetes and those not treated with glucose-lowering medications at the point of diabetes diagnosis. Although rare, remission can also occur in individuals with more severe diabetes and those previously treated with insulin.”

iv. Blood pressure control for diabetic retinopathy (Cochrane review).

“Diabetic retinopathy is a common complication of diabetes and a leading cause of visual impairment and blindness. Research has established the importance of blood glucose control to prevent development and progression of the ocular complications of diabetes. Simultaneous blood pressure control has been advocated for the same purpose, but findings reported from individual studies have supported varying conclusions regarding the ocular benefit of interventions on blood pressure. […] The primary aim of this review was to summarize the existing evidence regarding the effect of interventions to control or reduce blood pressure levels among diabetics on incidence and progression of diabetic retinopathy, preservation of visual acuity, adverse events, quality of life, and costs. A secondary aim was to compare classes of anti-hypertensive medications with respect to the same outcomes.”

“We included 15 RCTs, conducted primarily in North America and Europe, that had enrolled 4157 type 1 and 9512 type 2 diabetic participants, ranging from 16 to 2130 participants in individual trials. […] Study designs, populations, interventions, and lengths of follow-up (range one to nine years) varied among the included trials. Overall, the quality of the evidence for individual outcomes was low to moderate.”

“The evidence from these trials supported a benefit of more intensive blood pressure control intervention with respect to 4- to 5-year incidence of diabetic retinopathy (estimated risk ratio (RR) 0.80; 95% confidence interval (CI) 0.71 to 0.92) and the combined outcome of incidence and progression (estimated RR 0.78; 95% CI 0.63 to 0.97). The available evidence provided less support for a benefit with respect to 4- to 5-year progression of diabetic retinopathy (point estimate was closer to 1 than point estimates for incidence and combined incidence and progression, and the CI overlapped 1; estimated RR 0.88; 95% CI 0.73 to 1.05). The available evidence regarding progression to proliferative diabetic retinopathy or clinically significant macular edema or moderate to severe loss of best-corrected visual acuity did not support a benefit of intervention on blood pressure: estimated RRs and 95% CIs 0.95 (0.83 to 1.09) and 1.06 (0.85 to 1.33), respectively, after 4 to 5 years of follow-up. Findings within subgroups of trial participants (type 1 and type 2 diabetics; participants with normal blood pressure levels at baseline and those with elevated levels) were similar to overall findings.”

“The available evidence supports a beneficial effect of intervention to reduce blood pressure with respect to preventing diabetic retinopathy for up to 4 to 5 years. However, the lack of evidence to support such intervention to slow progression of diabetic retinopathy or to prevent other outcomes considered in this review, along with the relatively modest support for the beneficial effect on incidence, weakens the conclusion regarding an overall benefit of intervening on blood pressure solely to prevent diabetic retinopathy.”

v. Early Atherosclerosis Relates to Urinary Albumin Excretion and Cardiovascular Risk Factors in Adolescents With Type 1 Diabetes: Adolescent Type 1 Diabetes cardio-renal Intervention Trial (AdDIT).

“Children with type 1 diabetes are at greatly increased risk for the development of both renal and cardiovascular disease in later life (1,2). Evidence is accumulating that these two complications may have a common pathophysiology, with endothelial dysfunction a key early event.

Microalbuminuria is a recognized marker of endothelial damage (3) and predicts progression to proteinuria and diabetic nephropathy, as well as to atherosclerosis (4) and increased cardiovascular risk (5). It is, however, rare in adolescents with type 1 diabetes who more often have higher urinary albumin excretion rates within the normal range, which are associated with later progression to microalbuminuria and proteinuria (6).”

“The Adolescent Type 1 Diabetes cardio-renal Intervention Trial (AdDIT) (10) is designed to examine the impact of minor differences in albumin excretion in adolescents on the initiation and progression of cardiovascular and renal disease. The primary cardiovascular end point in AdDIT is carotid intima-media thickness (cIMT). Subclinical atherosclerosis can be detected noninvasively using high-resolution ultrasound to measure the intima-media thickness (IMT) of the carotid arteries, which predicts cardiovascular morbidity and mortality (11,12). […] The primary aim of this study was to examine the relationship of increased urinary albumin excretion and cardiovascular risk factors in adolescents with type 1 diabetes with structural arterial wall changes. We hypothesized that even minor increases in albumin excretion would be associated with early atherosclerosis but that this would be detectable only in the abdominal aorta. […] A total of 406 adolescents, aged 10–16 years, with type 1 diabetes for more than 1 year, recruited in five centers across Australia, were enrolled in this cross-sectional study”.

“Structural changes in the aorta and carotid arteries could be detected in >50% of adolescents with type 1 diabetes […] The difference in aIMT [aortic intima-media thickness] between type 1 diabetic patients and age- and sex-matched control subjects was equivalent to that seen with a 5- to 6-year age increase in the type 1 diabetic patients. […] Aortic IMT was […] able to better differentiate adolescents with type 1 diabetes from control subjects than was carotid wall changes. Aortic IMT enabled detection of the very early wall changes that are present with even small differences in urinary albumin excretion. This not only supports the concept of early intervention but provides a link between renal and cardiovascular disease.

The independent relationship between aIMT and urinary albumin excretion extends our knowledge of the pathogenesis of cardiovascular and renal disease in type 1 diabetes by showing that the first signs of the development of cardiovascular disease and diabetic nephropathy are related. The concept that microalbuminuria is a marker of a generalized endothelial damage, as well as a marker of renal disease, has been recognized for >20 years (3,20,21). Endothelial dysfunction is the first critical step in the development of atherosclerosis (22). Early rises in urinary albumin excretion precede the development of microalbuminuria and proteinuria (23). It follows that the first structural changes of atherosclerosis could relate to the first biochemical changes of diabetic nephropathy. To our knowledge, this is the first study to provide evidence of this.”

“In conclusion, atherosclerosis is detectable from early adolescence in type 1 diabetes. Its early independent associations are male sex, age, systolic blood pressure, LDL cholesterol, and, importantly, urinary albumin excretion. […] Early rises in urinary albumin excretion during adolescence not only are important for determining risk of progression to microalbuminuria and diabetic nephropathy but also may alert the clinician to increased risk of cardiovascular disease.”

vi. Impact of Islet Autoimmunity on the Progressive β-Cell Functional Decline in Type 2 Diabetes.

“Historically, type 2 diabetes (T2D) has not been considered to be immune mediated. However, many notable discoveries in recent years have provided evidence to support the concept of immune system involvement in T2D pathophysiology (15). Immune cells have been identified in the pancreases of phenotypic T2D patients (35). Moreover, treatment with interleukin-1 receptor agonist improves β-cell function in T2D patients (68). These studies suggest that β-cell damage/destruction mediated by the immune system may be a component of T2D pathophysiology.

Although the β-cell damage and destruction in autoimmune diabetes is most likely T-cell mediated (T), immune markers of autoimmune diabetes have primarily centered on the presence of circulating autoantibodies (Abs) to various islet antigens (915). Abs commonly positive in type 1 diabetes (T1D), especially GAD antibody (GADA) and islet cell Abs (ICA), have been shown to be more common in patients with T2D than in nondiabetic control populations, and the presence of multiple islet Abs, such as GADA, ICA, and tyrosine phosphatase-2 (insulinoma-associated protein 2 [IA-2]), have been demonstrated to be associated with an earlier need for insulin treatment in adult T2D patients (14,1620).”

“In this study, we observed development of islet autoimmunity, measured by islet Abs and islet-specific T-cell responses, in 61% of the phenotypic T2D patients. We also observed a significant association between positive islet-reactive T-cell responses and a more rapid decline in β-cell function as assessed by FCP and glucagon-SCP responses. […] The results of this pilot study led us to hypothesize that islet autoimmunity is present or will develop in a large portion of phenotypic T2D patients and that the development of islet autoimmunity is associated with a more rapid decline in β-cell function. Moreover, the prevalence of islet autoimmunity in most previous studies is grossly underestimated because these studies have not tested for islet-reactive T cells in T2D patients but have based the presence of autoimmunity on antibody testing alone […] The results of this pilot study suggest important changes to our understanding of T2D pathogenesis by demonstrating that the prevalence of islet autoimmune development is not only more prevalent in T2D patients than previously estimated but may also play an important role in β-cell dysfunction in the T2D disease process.”

September 18, 2017 Posted by | Cancer/oncology, Cardiology, Diabetes, Epidemiology, Immunology, Medicine, Nephrology, Neurology, Ophthalmology, Studies | Leave a comment

A few diabetes papers of interest

i. Impact of Parental Socioeconomic Status on Excess Mortality in a Population-Based Cohort of Subjects With Childhood-Onset Type 1 Diabetes.

“Numerous reports have shown that individuals with lower SES during childhood have increased morbidity and all-cause mortality at all ages (10–14). Although recent epidemiological studies have shown that all-cause mortality in patients with T1D increases with lower SES in the individuals themselves (15,16), the association between parental SES and mortality among patients with childhood-onset T1D has not been reported to the best of our knowledge. Our hypothesis was that low parental SES additionally increases mortality in subjects with childhood-onset T1D. In this study, we used large population-based Swedish databases to 1) explore in a population-based study how parental SES affects mortality in a patient with childhood-onset T1D, 2) describe and compare how the effect differs among various age-at-death strata, and 3) assess whether the adult patient’s own SES affects mortality independently of parental SES.”

“The Swedish Childhood Diabetes Registry (SCDR) is a dynamic population-based cohort reporting incident cases of T1D since 1 July 1977, which to date has collected >16,000 prospective cases. […] All patients recorded in the SCDR from 1 January 1978 to 31 December 2008 were followed until death or 31 December 2010. The cohort was subjected to crude analyses and stratified analyses by age-at-death groups (0–17, 18–24, and ≥25 years). Time at risk was calculated from date of birth until death or 31 December 2010. Kaplan-Meier analyses and log-rank tests were performed to compare the effect of low maternal educational level, low paternal educational level, and family income support (any/none). Cox regression analyses were performed to estimate and compare the hazard ratios (HRs) for the socioeconomic variables and to adjust for the potential confounding variables age at onset and sex.”

“The study included 14,647 patients with childhood-onset T1D. A total of 238 deaths (male 154, female 84) occurred in 349,762 person-years at risk. The majority of mortalities occurred among the oldest age-group (≥25 years of age), and most of the deceased subjects had onset of T1D at the ages of 10–14.99 years […]. Mean follow-up was 23.9 years and maximum 46.5 years. The overall standardized mortality ratio up to the age of 47 years was 2.3 (95% CI 1.35–3.63); for females, it was 2.6 (1.28–4.66) and for males, 2.1 (1.27–3.49). […] Analyses on the effect of low maternal educational level showed an increased mortality for male patients (HR 1.43 [95% CI 1.01–2.04], P = 0.048) and a nonsignificant increased mortality for female patients (1.21 [0.722–2.018], P = 0.472). Paternal educational level had no significant effect on mortality […] Having parents who ever received income support was associated with an increased risk of death in both males (HR 1.89 [95% CI 1.36–2.64], P < 0.001) and females (2.30 [1.43–3.67], P = 0.001) […] Excluding the 10% of patients with the highest accumulated income support to parents during follow-up showed that having parents who ever received income support still was a risk factor for mortality.”

“A Cox model including maternal educational level together with parental income support, adjusting for age at onset and sex, showed that having parents who received income support was associated with a doubled mortality risk (HR 1.96 [95% CI 1.49–2.58], P < 0.001) […] In a Cox model including the adult patient’s own SES, having parents who received income support was still an independent risk factor in the younger age-at-death group (18–24 years). Among those who died at age ≥25 years of age, the patient’s own SES was a stronger predictor for mortality (HR 2.46 [95% CI 1.54–3.93], P < 0.001)”

“Despite a well-developed health-care system in Sweden, overall mortality up to the age of 47 years is doubled in both males and females with childhood-onset T1D. These results are in accordance with previous Swedish studies and reports from other comparable countries […] Previous studies indicated that low SES during childhood is associated with low glycemic control and diabetes-related morbidity in patients with T1D (8,9), and the current study implies that mortality in adulthood is also affected by parental SES. […] The findings, when stratified by age-at-death group, show that adult patients’ own need of income support independently predicted mortality in those who died at ≥25 years of age, whereas among those who died in the younger age-group (18–24 years), parental requirement of income support was still a strong independent risk factor. None of the present SES measures seem to predict mortality in the ages 0–17 years perhaps due to low numbers and, thus, power.”

ii. Exercise Training Improves but Does Not Normalize Left Ventricular Systolic and Diastolic Function in Adolescents With Type 1 Diabetes.

“Adults and adolescents with type 1 diabetes have reduced exercise capacity (810), which increases their risk for cardiovascular morbidity and mortality (11). The causes for this reduced exercise capacity are unclear. However, recent studies have shown that adolescents with type 1 diabetes have lower stroke volume during exercise, which has been attributed to alterations in left ventricular function (9,10). Reduced left ventricular compliance resulting in an inability to fill the left ventricle appropriately during exercise has been shown to contribute to the lower stroke volume during exercise in both adults and adolescents with type 1 diabetes (12).

Exercise training is recommended as part of the management of type 1 diabetes. However, the effects of exercise training on left ventricular function at rest and during exercise in adolescents with type 1 diabetes have not been investigated. In particular, it is unclear whether exercise training improves cardiac hemodynamics during exercise in adolescents with diabetes. Therefore, we aimed to assess left ventricular volumes at rest and during exercise in a group of adolescents with type 1 diabetes compared with adolescents without diabetes before and after a 20-week exercise-training program. We hypothesized that exercise training would improve exercise capacity and exercise stroke volume in adolescents with diabetes.”

RESEARCH DESIGN AND METHODS Fifty-three adolescents with type 1 diabetes (aged 15.6 years) were divided into two groups: exercise training (n = 38) and nontraining (n = 15). Twenty-two healthy adolescents without diabetes (aged 16.7 years) were included and, with the 38 participants with type 1 diabetes, participated in a 20-week exercise-training intervention. Assessments included VO2max and body composition. Left ventricular parameters were obtained at rest and during acute exercise using MRI.

RESULTS Exercise training improved aerobic capacity (10%) and stroke volume (6%) in both trained groups, but the increase in the group with type 1 diabetes remained lower than trained control subjects. […]

CONCLUSIONS These data demonstrate that in adolescents, the impairment in left ventricular function seen with type 1 diabetes can be improved, although not normalized, with regular intense physical activity. Importantly, diastolic dysfunction, a common mechanism causing heart failure in older subjects with diabetes, appears to be partially reversible in this age group.”

“This study confirms that aerobic capacity is reduced in [diabetic] adolescents and that this, at least in part, can be attributed to impaired left ventricular function and a blunted cardiac response to exercise (9). Importantly, although an aerobic exercise-training program improved the aerobic capacity and cardiac function in adolescents with type 1 diabetes, it did not normalize them to the levels seen in the training group without diabetes. Both left ventricular filling and contractility improved after exercise training in adolescents with diabetes, suggesting that aerobic fitness may prevent or delay the well-described impairment in left ventricular function in diabetes (9,10).

The increase in peak aerobic capacity (∼12%) seen in this study was consistent with previous exercise interventions in adults and adolescents with diabetes (14). However, the baseline peak aerobic capacity was lower in the participants with diabetes and improved with training to a level similar to the baseline observed in the participants without diabetes; therefore, trained adolescents with diabetes remained less fit than equally trained adolescents without diabetes. This suggests there are persistent differences in the cardiovascular function in adolescents with diabetes that are not overcome by exercise training.”

“Although regular exercise potentially could improve HbA1c, the majority of studies have failed to show this (3134). Exercise training improved aerobic capacity in this study without affecting glucose control in the participants with diabetes, suggesting that the effects of glycemic status and exercise training may work independently to improve aerobic capacity.”

….

iii. Change in Medical Spending Attributable to Diabetes: National Data From 1987 to 2011.

“Diabetes care has changed substantially in the past 2 decades. We examined the change in medical spending and use related to diabetes between 1987 and 2011. […] Using the 1987 National Medical Expenditure Survey and the Medical Expenditure Panel Surveys in 2000–2001 and 2010–2011, we compared per person medical expenditures and uses among adults ≥18 years of age with or without diabetes at the three time points. Types of medical services included inpatient care, emergency room (ER) visits, outpatient visits, prescription drugs, and others. We also examined the changes in unit cost, defined by the expenditure per encounter for medical services.”

RESULTS The excess medical spending attributed to diabetes was $2,588 (95% CI, $2,265 to $3,104), $4,205 ($3,746 to $4,920), and $5,378 ($5,129 to $5,688) per person, respectively, in 1987, 2000–2001, and 2010–2011. Of the $2,790 increase, prescription medication accounted for 55%; inpatient visits accounted for 24%; outpatient visits accounted for 15%; and ER visits and other medical spending accounted for 6%. The growth in prescription medication spending was due to the increase in both the volume of use and unit cost, whereas the increase in outpatient expenditure was almost entirely driven by more visits. In contrast, the increase in inpatient and ER expenditures was caused by the rise of unit costs. […] The increase was observed across all components of medical spending, with the greatest absolute increase in the spending on prescription medications ($1,528 increase), followed by inpatient visits ($680 increase) and outpatient visits ($430 increase). The absolute change in the spending on ER and other medical services use was relatively small. In relative terms, the spending on ER visits grew more than five times, faster than that of prescription medication and other medical components. […] Among the total annual diabetes-attributable medical spending, the spending on inpatient and outpatient visits dropped from 40% and 23% to 31% and 19%, respectively, between 1987 and 2011, whereas spending on prescription medication increased from 27% to 41%.”

“The unit costs rose universally in all five measures of medical care in adults with and without diabetes. For each hospital admission, diabetes patients spent significantly more than persons without diabetes. The gap increased from $1,028 to $1,605 per hospital admission between 1987 and 2001, and dropped slightly to $1,360 per hospital admission in 2011. Diabetes patients also had higher spending per ER visit and per purchase of prescription medications.”

“From 1999 to 2011, national data suggest that growth in the use and price of prescription medications in the general population is 2.6% and 3.6% per year, respectively; and the growth has decelerated in recent years (22). Our analysis suggests that the growth rates in the use and prices of prescription medications for diabetes patients are considerably higher. The higher rate of growth is likely, in part, due to the growing emphasis on achieving glycemic targets, the use of newer medications, and the use of multidrug treatment strategies in modern diabetes care practice (23,24). In addition, the growth of medication spending is fueled by the rising prices per drug, particularly the drugs that are newly introduced in the market. For example, the prices for newer drug classes such as glitazones, dipeptidyl peptidase-4 inhibitors, and incretins have been 8 to 10 times those of sulfonylureas and 5 to 7 times those of metformin (9).”

“Between 1987 and 2011, medical spending increased both in persons with and in persons without diabetes; and the increase was substantially greater among persons with diabetes. As a result, the medical spending associated with diabetes nearly doubled. The growth was primarily driven by the spending in prescription medications. Further studies are needed to assess the cost-effectiveness of increased spending on drugs.”

iv. Determinants of Adherence to Diabetes Medications: Findings From a Large Pharmacy Claims Database.

“Adults with type 2 diabetes are often prescribed multiple medications to treat hyperglycemia, diabetes-associated conditions such as hypertension and dyslipidemia, and other comorbidities. Medication adherence is an important determinant of outcomes in patients with chronic diseases. For those with diabetes, adherence to medications is associated with better control of intermediate risk factors (14), lower odds of hospitalization (3,57), lower health care costs (5,79), and lower mortality (3,7). Estimates of rates of adherence to diabetes medications vary widely depending on the population studied and how adherence is defined. One review found that adherence to oral antidiabetic agents ranged from 36 to 93% across studies and that adherence to insulin was ∼63% (10).”

“Using a large pharmacy claims database, we assessed determinants of adherence to oral antidiabetic medications in >200,000 U.S. adults with type 2 diabetes. […] We selected a cohort of members treated for diabetes with noninsulin medications (oral agents or GLP-1 agonists) in the second half of 2010 who had continuous prescription benefits eligibility through 2011. Each patient was followed for 12 months from their index diabetes claim date identified during the 6-month targeting period. From each patient’s prescription history, we collected the date the prescription was filled, how many days the supply would last, the National Drug Code number, and the drug name. […] Given the difficulty in assessing insulin adherence with measures such as medication possession ratio (MPR), we excluded patients using insulin when defining the cohort.”

“We looked at a wide range of variables […] Predictor variables were defined a priori and grouped into three categories: 1) patient factors including age, sex, education, income, region, past exposure to therapy (new to diabetes therapy vs. continuing therapy), and concurrent chronic conditions; 2) prescription factors including refill channel (retail vs. mail order), total pill burden per day, and out of pocket costs; and 3) prescriber factors including age, sex, and specialty. […] Our primary outcome of interest was adherence to noninsulin antidiabetic medications. To assess adherence, we calculated an MPR for each patient. The ratio captures how often patients refill their medications and is a standard metric that is consistent with the National Quality Forum’s measure of adherence to medications for chronic conditions. MPR was defined as the proportion of days a patient had a supply of medication during a calendar year or equivalent period. We considered patients to be adherent if their MPR was 0.8 or higher, implying that they had their medication supplies for at least 80% of the days. An MPR of 0.8 or above is a well-recognized index of adherence (11,12). Studies have suggested that patients with chronic diseases need to achieve at least 80% adherence to derive the full benefits of their medications (13). […] [W]e [also] determined whether a patient was persistent, that is whether they had not discontinued or had at least a 45-day gap in their targeted therapy.”

“Previous exposure to diabetes therapy had a significant impact on adherence. Patients new to therapy were 61% less likely to be adherent to their diabetes medication. There was also a clear age effect. Patients 25–44 years of age were 49% less likely to be adherent when compared with patients 45–64 years of age. Patients aged 65–74 years were 27% more likely to be adherent, and those aged 75 years and above were 41% more likely to be adherent when compared with the 45–64 year age-group. Men were significantly more likely to be adherent than women […I dislike the use of the word ‘significant’ in such contexts; there is a difference in the level of adherence, but it is not large in absolute terms; the male vs female OR is 1.14 (CI 1.12-1.16) – US]. Education level and household income were both associated with adherence. The higher the estimated academic achievement, the more likely the patient was to be adherent. Patients completing graduate school were 41% more likely to be adherent when compared with patients with a high school equivalent education. Patients with an annual income >$60,000 were also more likely to be adherent when compared with patients with a household income <$30,000.”

“The largest effect size was observed for patients obtaining their prescription antidiabetic medications by mail. Patients using the mail channel were more than twice as likely to be adherent to their antidiabetic medications when compared with patients filling their prescriptions at retail pharmacies. Total daily pill burden was positively associated with antidiabetic medication adherence. For each additional pill a patient took per day, adherence to antidiabetic medications increased by 22%. Patient out-of-pocket costs were negatively associated with adherence. For each additional $15 in out-of-pocket costs per month, diabetes medication adherence decreased by 11%. […] We found few meaningful differences in patient adherence according to prescriber factors.”

“In our study, characteristics that suggest a “healthier” patient (being younger, new to diabetes therapy, and taking few other medications) were all associated with lower odds of adherence to antidiabetic medications. This suggests that acceptance of a chronic illness diagnosis and the potential consequences may be an important, but perhaps overlooked, determinant of medication-taking behavior. […] Our findings regarding income and costs are important reminders that prescribers should consider the impact of medication costs on patients with diabetes. Out-of-pocket costs are an important determinant of adherence to statins (26) and a self-reported cause of underuse of medications in one in seven insured patients with diabetes (27). Lower income has previously been shown to be associated with poor adherence to diabetes medications (15) and a self-reported cause of cost-related medication underuse (27).”

v. The Effect of Alcohol Consumption on Insulin Sensitivity and Glycemic Status: A Systematic Review and Meta-analysis of Intervention Studies.

“Moderate alcohol consumption, compared with abstaining and heavy drinking, is related to a reduced risk of type 2 diabetes (1,2). Although the risk is reduced with moderate alcohol consumption in both men and women, the association may differ for men and women. In a meta-analysis, consumption of 24 g alcohol/day reduced the risk of type 2 diabetes by 40% among women, whereas consumption of 22 g alcohol/day reduced the risk by 13% among men (1).

The association of alcohol consumption with type 2 diabetes may be explained by increased insulin sensitivity, anti-inflammatory effects, or effects of adiponectin (3). Several intervention studies have examined the effect of moderate alcohol consumption on these potential underlying pathways. A meta-analysis of intervention studies by Brien et al. (4) showed that alcohol consumption significantly increased adiponectin levels but did not affect inflammatory factors. Unfortunately, the effect of alcohol consumption on insulin sensitivity has not been summarized quantitatively. A review of cross-sectional studies by Hulthe and Fagerberg (5) suggested a positive association between moderate alcohol consumption and insulin sensitivity, although the three intervention studies included in their review did not show an effect (68). Several other intervention studies also reported inconsistent results (9,10). Consequently, consensus is lacking about the effect of moderate alcohol consumption on insulin sensitivity. Therefore, we aimed to conduct a systematic review and meta-analysis of intervention studies investigating the effect of alcohol consumption on insulin sensitivity and other relevant glycemic measures.”

“22 articles met criteria for inclusion in the qualitative synthesis. […] Of the 22 studies, 15 used a crossover design and 7 a parallel design. The intervention duration of the studies ranged from 2 to 12 weeks […] Of the 22 studies, 2 were excluded from the meta-analysis because they did not include an alcohol-free control group (14,19), and 4 were excluded because they did not have a randomized design […] Overall, 14 studies were included in the meta-analysis”

“A random-effects model was used because heterogeneity was present (P < 0.01, I2 = 91%). […] For HbA1c, a random-effects model was used because the I2 statistic indicated evidence for some heterogeneity (I2 = 30%).” [Cough, you’re not supposed to make these decisions that way, coughUS. This is not the first time I’ve seen this approach applied, and I don’t like it; it’s bad practice to allow the results of (frequently under-powered) heterogeneity tests to influence model selection decisions. As Bohrenstein and Hedges point out in their book, “A report should state the computational model used in the analysis and explain why this model was selected. A common mistake is to use the fixed-effect model on the basis that there is no evidence of heterogeneity. As [already] explained […], the decision to use one model or the other should depend on the nature of the studies, and not on the significance of this test”]

“This meta-analysis shows that moderate alcohol consumption did not affect estimates of insulin sensitivity or fasting glucose levels, but it decreased fasting insulin concentrations and HbA1c. Sex-stratified analysis suggested that moderate alcohol consumption may improve insulin sensitivity and decrease fasting insulin concentrations in women but not in men. The meta-regression suggested no influence of dosage and duration on the results. However, the number of studies may have been too low to detect influences by dosage and duration. […] The primary finding that alcohol consumption does not influence insulin sensitivity concords with the intervention studies included in the review of Hulthe and Fagerberg (5). This is in contrast with observational studies suggesting a significant association between moderate alcohol consumption and improved insulin sensitivity (34,35). […] We observed lower levels of HbA1c in subjects consuming moderate amounts of alcohol compared with abstainers. This has also been shown in several observational studies (39,43,44). Alcohol may decrease HbA1c by suppressing the acute rise in blood glucose after a meal and increasing the early insulin response (45). This would result in lower glucose concentrations over time and, thus, lower HbA1c concentrations. Unfortunately, the underlying mechanism of glycemic control by alcohol is not clearly understood.”

vi. Predictors of Lower-Extremity Amputation in Patients With an Infected Diabetic Foot Ulcer.

“Infection is a frequent complication of diabetic foot ulcers, with up to 58% of ulcers being infected at initial presentation at a diabetic foot clinic, increasing to 82% in patients hospitalized for a diabetic foot ulcer (1). These diabetic foot infections (DFIs) are associated with poor clinical outcomes for the patient and high costs for both the patient and the health care system (2). Patients with a DFI have a 50-fold increased risk of hospitalization and 150-fold increased risk of lower-extremity amputation compared with patients with diabetes and no foot infection (3). Among patients with a DFI, ∼5% will undergo a major amputation and 20–30% a minor amputation, with the presence of peripheral arterial disease (PAD) greatly increasing amputation risk (46).”

“As infection of a diabetic foot wound heralds a poor outcome, early diagnosis and treatment are important. Unfortunately, systemic signs of inflammation such as fever and leukocytosis are often absent even with a serious foot infection (10,11). As local signs and symptoms of infection are also often diminished, because of concomitant peripheral neuropathy and ischemia (12), diagnosing and defining resolution of infection can be difficult.”

“The system developed by the International Working Group on the Diabetic Foot (IWGDF) and the Infectious Diseases Society of America (IDSA) provides criteria for the diagnosis of infection of ulcers and classifies it into three categories: mild, moderate, or severe. The system was validated in three relatively small cohorts of patients […] The European Study Group on Diabetes and the Lower Extremity (Eurodiale) prospectively studied a large cohort of patients with a diabetic foot ulcer (17), enabling us to determine the prognostic value of the IWGDF system for clinically relevant lower-extremity amputations. […] We prospectively studied 575 patients with an infected diabetic foot ulcer presenting to 1 of 14 diabetic foot clinics in 10 European countries. […] Among these patients, 159 (28%) underwent an amputation. […] Patients were followed monthly until healing of the foot ulcer(s), major amputation, or death — up to a maximum of 1 year.”

“One hundred and ninety-nine patients had a grade 2 (mild) infection, 338 a grade 3 (moderate), and 38 a grade 4 (severe). Amputations were performed on 159 (28%) patients (126 minor and 33 major) within the year of follow-up; 103 patients (18%) underwent amputations proximal to and including the hallux. […] The independent predictors of any amputation were as follows: periwound edema, HR 2.01 (95% CI 1.33–3.03); foul smell, HR 1.74 (1.17–2.57); purulent and nonpurulent exudate, HR 1.67 (1.17–2.37) and 1.49 (1.02–2.18), respectively; deep ulcer, HR 3.49 (1.84–6.60); positive probe-to-bone test, HR 6.78 (3.79–12.15); pretibial edema, HR 1.53 (1.02–2.31); fever, HR 2.00 (1.15–3.48); elevated CRP levels but less than three times the upper limit of normal, HR 2.74 (1.40–5.34); and elevated CRP levels more than three times the upper limit, HR 3.84 (2.07–7.12). […] In comparison with mild infection, the presence of a moderate infection increased the hazard for any amputation by a factor of 2.15 (95% CI 1.25–3.71) and 3.01 (1.51–6.01) for amputations excluding the lesser toes. For severe infection, the hazard for any amputation increased by a factor of 4.12 (1.99–8.51) and for amputations excluding the lesser toes by a factor of 5.40 (2.20–13.26). Larger ulcer size and presence of PAD were also independent predictors of both any amputation and amputations excluding the lesser toes, with HRs between 1.81 and 3 (and 95% CIs between 1.05 and 6.6).”

“Previously published studies that have aimed to identify independent risk factors for lower-extremity amputation in patients with a DFI have noted an association with older age (5,22), the presence of fever (5), elevated acute-phase reactants (5,22,23), higher HbA1c levels (24), and renal insufficiency (5,22).”

“The new risk scores we developed for any amputation, and amputations excluding the lesser toes had higher prognostic capability, based on the area under the ROC curve (0.80 and 0.78, respectively), than the IWGDF system (0.67) […] which is currently the only one in use for infected diabetic foot ulcers. […] these Eurodiale scores were developed based on the available data of our cohort, and they will need to be validated in other populations before any firm conclusions can be drawn. The advantage of these newly developed scores is that they are easier for clinicians to perform […] These newly developed risk scores can be readily used in daily clinical practice without the necessity of obtaining additional laboratory testing.”

September 12, 2017 Posted by | Cardiology, Diabetes, Economics, Epidemiology, Health Economics, Infectious disease, Medicine, Microbiology, Statistics | Leave a comment

A few diabetes papers of interest

i. Rates of Diabetic Ketoacidosis: International Comparison With 49,859 Pediatric Patients With Type 1 Diabetes From England, Wales, the U.S., Austria, and Germany.

“Rates of DKA in youth with type 1 diabetes vary widely nationally and internationally, from 15% to 70% at diagnosis (4) to 1% to 15% per established patient per year (911). However, data from systematic comparisons between countries are limited. To address this gap in the literature, we analyzed registry and audit data from three organizations: the Prospective Diabetes Follow-up Registry (DPV) in Germany and Austria, the National Paediatric Diabetes Audit (NPDA) in England and Wales, and the T1D Exchange (T1DX) in the U.S. These countries have similarly advanced, yet differing, health care systems in which data on DKA and associated factors are collected. Our goal was to identify indicators of risk for DKA admissions in pediatric patients with >1-year duration of disease with an aim to better understand where targeted preventive programs might lead to a reduction in the frequency of this complication of management of type 1 diabetes.”

RESULTS The frequency of DKA was 5.0% in DPV, 6.4% in NPDA, and 7.1% in T1DX […] Mean HbA1c was lowest in DPV (63 mmol/mol [7.9%]), intermediate in T1DX (69 mmol/mol [8.5%]), and highest in NPDA (75 mmol/mol [9.0%]). […] In multivariable analyses, higher odds of DKA were found in females (odds ratio [OR] 1.23, 99% CI 1.10–1.37), ethnic minorities (OR 1.27, 99% CI 1.11–1.44), and HbA1c ≥7.5% (≥58 mmol/mol) (OR 2.54, 99% CI 2.09–3.09 for HbA1c from 7.5 to <9% [58 to <75 mmol/mol] and OR 8.74, 99% CI 7.18–10.63 for HbA1c ≥9.0% [≥75 mmol/mol]).”

Poor metabolic control is obviously very important, but it’s important to remember that poor metabolic control is in itself an outcome that needs to be explained. I would note that the mean HbA1c values here, especially that 75 mmol/mol one, seem really high; this is not a very satisfactory level of glycemic control and corresponds to an average glucose level of 12 mmol/l. And that’s a population average, meaning that many individuals have values much higher than this. Actually the most surprising thing to me about these data is that the DKA event rates are not much higher than they are, considering the level of metabolic control achieved. Another slightly surprising finding is that teenagers (13-17 yrs) were not actually all that much more likely to have experienced DKA than small children (0-6 yrs); the OR is only ~1.5. Of course this can not be taken as an indication that DKA in teenagers do not make up a substantial proportion of the total amount of DKA events in pediatric samples, as the type 1 prevalence is much higher in teenagers than in small children (incidence peaks in adolescence).

“In 2004–2009 in the U.S., the mean hospital cost per pediatric DKA admission was $7,142 (range $4,125–11,916) (6), and insurance claims data from 2007 reported an excess of $5,837 in annual medical expenditures for youth with insulin-treated diabetes with DKA compared with those without DKA (7). In Germany, pediatric patients with diabetes with DKA had diabetes-related costs that were up to 3.6-fold higher compared with those without DKA (8).”

“DKA frequency was lower in pump users than in injection users (OR 0.84, 99% CI 0.76–0.93). Heterogeneity in the association with DKA between registries was seen for pump use and age category, and the overall rate should be interpreted accordingly. A lower rate of DKA in pump users was only found in T1DX, in contrast to no association of pump use with DKA in DPV or NPDA. […] In multivariable analyses […], age, type 1 diabetes duration, and pump use were not significantly associated with DKA in the fully adjusted model. […] pump use was associated with elevated odds of DKA in the <6-year-olds and in the 6- to <13-year-olds but with reduced odds of DKA in the 13- to <18-year-olds.”

Pump use should probably all else equal increase the risk of DKA, but all else is never equal and in these data pump users actually had a lower DKA event rate than did diabetics treated with injections. One should not conclude from this finding that pump use decreases the risk of DKA, selection bias and unobserved heterogeneities are problems which it is almost impossible to correct for in an adequate way – I find it highly unlikely that selection bias is only a potential problem in the US (see below). There are many different ways selection bias can be a relevant problem, financial- and insurance-related reasons (relevant particularly in the US and likely the main factors the authors are considering) are far from the only potential problems; I could thus easily imagine selection dynamics playing a major role even in a hypothetical setting where all new-diagnosed children were started on pump therapy as a matter of course. In such a setting you might have a situation where very poorly controlled individuals would have 10 DKA events in a short amount of time because they didn’t take the necessary amount of blood glucose tests/disregarded alarms/forgot or postponed filling up the pump when it’s near-empty/failed to switch the battery in time/etc. etc., and then what might happen would be that the diabetologist/endocrinologist would then proceed to recommend these patients doing very poorly on pump treatment to switch to injection therapy, and what you would end up with would be a compliant/motivated group of patients on pump therapy and a noncompliant/poorly motivated group on injection therapy. This would happen even if everybody started on pump therapy and so pump therapy exposure was completely unrelated to outcomes. Pump therapy requires more of the patient than does injection therapy, and if the patient is unwilling/unable to put in the work required that treatment option will fail. In my opinion the default here should be that these treatment groups are (‘significantly’) different, not that they are similar.

A few more quotes from the paper:

“The major finding of these analyses is high rates of pediatric DKA across the three registries, even though DKA events at the time of diagnosis were not included. In the prior 12 months, ∼1 in 20 (DPV), 1 in 16 (NPDA), and 1 in 14 (T1DX) pediatric patients with a duration of diabetes ≥1 year were diagnosed with DKA and required treatment in a health care facility. Female sex, ethnic minority status, and elevated HbA1c were consistent indicators of risk for DKA across all three registries. These indicators of increased risk for DKA are similar to previous reports (10,11,18,19), and our rates of DKA are within the range in the pediatric diabetes literature of 1–15% per established patient per year (10,11).

Compared with patients receiving injection therapy, insulin pump use was associated with a lower risk of DKA only in the U.S. in the T1DX, but no difference was seen in the DPV or NPDA. Country-specific factors on the associations of risk factors with DKA require further investigation. For pump use, selection bias may play a role in the U.S. The odds of DKA in pump users was not increased in any registry, which is a marked difference from some (10) but not all historic data (20).”

ii. Effect of Long-Acting Insulin Analogs on the Risk of Cancer: A Systematic Review of Observational Studies.

NPH insulin has been the mainstay treatment for type 1 diabetes and advanced type 2 diabetes since the 1950s. However, this insulin is associated with an increased risk of nocturnal hypoglycemia, and its relatively short half-life requires frequent administration (1,2). Consequently, structurally modified insulins, known as long-acting insulin analogs (glargine and detemir), were developed in the 1990s to circumvent these limitations. However, there are concerns that long-acting insulin analogs may be associated with an increased risk of cancer. Indeed, some laboratory studies showed long-acting insulin analogs were associated with cancer cell proliferation and protected against apoptosis via their higher binding affinity to IGF-I receptors (3,4).

In 2009, four observational studies associated the use of insulin glargine with an increased risk of cancer (58). These studies raised important concerns but were also criticized for important methodological shortcomings (913). Since then, several observational studies assessing the association between long-acting insulin analogs and cancer have been published but yielded inconsistent findings (1428). […] Several meta-analyses of observational studies have investigated the association between insulin glargine and cancer risk (3437). These meta-analyses assessed the quality of included studies, but the methodological issues particular to pharmacoepidemiologic research were not fully considered. In addition, given the presence of important heterogeneity in this literature, the appropriateness of pooling the results of these studies remains unclear. We therefore conducted a systematic review of observational studies examining the association between long-acting insulin analogs and cancer incidence, with a particular focus on methodological strengths and weaknesses of these studies.”

“[W]e assessed the quality of studies for key components, including time-related biases (immortal time, time-lag, and time-window), inclusion of prevalent users, inclusion of lag periods, and length of follow-up between insulin initiation and cancer incidence.

Immortal time bias is defined by a period of unexposed person-time that is misclassified as exposed person-time or excluded, resulting in the exposure of interest appearing more favorable (40,41). Time-lag bias occurs when treatments used later in the disease management process are compared with those used earlier for less advanced stages of the disease. Such comparisons can result in confounding by disease duration or severity of disease if duration and severity of disease are not adequately considered in the design or analysis of the study (29). This is particularly true for chronic disease with dynamic treatment processes such as type 2 diabetes. Currently, American and European clinical guidelines suggest using basal insulin (e.g., NPH, glargine, and detemir) as a last line of treatment if HbA1c targets are not achieved with other antidiabetic medications (42). Therefore, studies that compare long-acting insulin analogs to nonbasal insulin may introduce confounding by disease duration. Time-window bias occurs when the opportunity for exposure differs between case subjects and control subjects (29,43).

The importance of considering a lag period is necessary for latency considerations (i.e., a minimum time between treatment initiation and the development of cancer) and to minimize protopathic and detection bias. Protopathic bias, or reverse causation, is present when a medication (exposure) is prescribed for early symptoms related to the outcome of interest, which can lead to an overestimation of the association. Lagging the exposure by a predefined time window in cohort studies or excluding exposures in a predefined time window before the event in case-control studies is a means of minimizing this bias (44). Detection bias is present when the exposure leads to higher detection of the outcome of interest due to the increased frequency of clinic visits (e.g., newly diagnosed patients with type 2 diabetes or new users of another antidiabetic medication), which also results in an overestimation of risk (45). Thus, including a lag period, such as starting follow-up after 1 year of the initiation of a drug, simultaneously considers a latency period while also minimizing protopathic and detection bias.”

“We systematically searched MEDLINE and EMBASE from 2000 to 2014 to identify all observational studies evaluating the relationship between the long-acting insulin analogs and the risk of any and site-specific cancers (breast, colorectal, prostate). […] 16 cohort and 3 case-control studies were included in this systematic review (58,1428). All studies evaluated insulin glargine, with four studies also investigating insulin detemir (15,17,25,28). […] The study populations ranged from 1,340 to 275,164 patients […]. The mean or median durations of follow-up and age ranged from 0.9 to 7.0 years and from 52.3 to 77.4 years, respectively. […] Thirteen of 15 studies reported no association between insulin glargine and detemir and any cancer. Four of 13 studies reported an increased risk of breast cancer with insulin glargine. In the quality assessment, 7 studies included prevalent users, 11 did not consider a lag period, 6 had time-related biases, and 16 had short (<5 years) follow-up.”

“Of the 19 studies in this review, immortal time bias may have been introduced in one study based on the time-independent exposure and cohort entry definitions that were used in this cohort study […] Time-lag bias may have occurred in four studies […] A variation of time-lag bias was observed in a cohort study of new insulin users (28). For the exposure definition, highest duration since the start of insulin use was compared with the lowest. It is expected that the risk of cancer would increase with longer duration of insulin use; however, the opposite was reported (with RRs ranging from 0.50 to 0.90). The protective association observed could be due to competing risks (e.g., death from cardiovascular-related events) (47,48). Patients with diabetes have a higher risk of cardiovascular-related deaths compared with patients with no diabetes (49,50). Therefore, patients with diabetes who die of cardiovascular-related events do not have the opportunity to develop cancer, resulting in an underestimation of the risk of cancer. […] Time-window bias was observed in two studies (18,22). […] HbA1c and diabetes duration were not accounted for in 15 of the 19 studies, resulting in likely residual confounding (7,8,1418,2026,28). […] Seven studies included prevalent users of insulin (8,15,18,20,21,23,25), which is problematic because of the corresponding depletion of susceptible subjects in other insulin groups compared with long-acting insulin analogs. Protopathic or detection bias could have resulted in 11 of the 19 studies because a lag period was not incorporated in the study design (6,7,1416,1821,23,28).”

CONCLUSIONS The observational studies examining the risk of cancer associated with long-acting insulin analogs have important methodological shortcomings that limit the conclusions that can be drawn. Thus, uncertainty remains, particularly for breast cancer risk.”

iii. Impact of Socioeconomic Status on Cardiovascular Disease and Mortality in 24,947 Individuals With Type 1 Diabetes.

“Socioeconomic status (SES) is a powerful predictor of cardiovascular disease (CVD) and death. We examined the association in a large cohort of patients with type 1 diabetes. […] Clinical data from the Swedish National Diabetes Register were linked to national registers, whereby information on income, education, marital status, country of birth, comorbidities, and events was obtained. […] Type 1 diabetes was defined on the basis of epidemiologic data: treatment with insulin and a diagnosis at the age of 30 years or younger. This definition has been validated as accurate in 97% of the cases listed in the register (14).”

“We included 24,947 patients. Mean (SD) age and follow-up was 39.1 (13.9) and 6.0 (1.0) years. Death and fatal/nonfatal CVD occurred in 926 and 1378 individuals. Compared with being single, being married was associated with 50% lower risk of death, cardiovascular (CV) death, and diabetes-related death. Individuals in the two lowest quintiles had twice as great a risk of fatal/nonfatal CVD, coronary heart disease, and stroke and roughly three times as great a risk of death, diabetes-related death, and CV death as individuals in the highest income quintile. Compared with having ≤9 years of education, individuals with a college/university degree had 33% lower risk of fatal/nonfatal stroke.”

“Individuals with 10–12 years of education were comparable at baseline (considering distribution of age and sex) with those with a college/university degree […]. Individuals with a college/university degree had higher income, had 5 mmol/mol lower HbA1c, were more likely to be married/cohabiting, used insulin pump more frequently (17.5% vs. 14.5%), smoked less (5.8% vs. 13.1%), and had less albuminuria (10.8% vs. 14.2%). […] Women had substantially lower income and higher education, were more often married, used insulin pump more frequently, had less albuminuria, and smoked more frequently than men […] Individuals with high income were more likely to be married/cohabiting, had lower HbA1c, and had lower rates of smoking as well as albuminuria”.

CONCLUSIONS Low SES increases the risk of CVD and death by a factor of 2–3 in type 1 diabetes.”

“The effect of SES was striking despite rigorous adjustments for risk factors and confounders. Individuals in the two lowest income quintiles had two to three times higher risk of CV events and death than those in the highest income quintile. Compared with low educational level, having high education was associated with ∼30% lower risk of stroke. Compared with being single, individuals who were married/cohabiting had >50% lower risk of death, CV death, and diabetes-related death. Immigrants had 20–40% lower risk of fatal/nonfatal CVD, all-cause death, and diabetes-related death. Additionally, we show that males had 44%, 63%, and 29% higher risk of all-cause death, CV death, and diabetes-related death, respectively.

Despite rigorous adjustments for covariates and equitable access to health care at a negligible cost (20,21), SES and sex were robust predictors of CVD disease and mortality in type 1 diabetes; their effect was comparable with that of smoking, which represented an HR of 1.56 (95% CI 1.29–1.91) for all-cause death. […] Our study shows that men with type 1 diabetes are at greater risk of CV events and death compared with women. This should be viewed in the light of a recent meta-analysis of 26 studies, which showed higher excess risk in women compared with men. Overall, women had 40% greater excess risk of all-cause mortality, and twice the excess risk of fatal/nonfatal vascular events, compared with men (29). Thus, whereas the excess risk (i.e., the risk of patients with diabetes compared with the nondiabetic population) of vascular disease is higher in women with diabetes, we show that men with diabetes are still at substantially greater risk of all-cause death, CV death, and diabetes death compared with women with diabetes. Other studies are in line with our findings (10,11,13,3032).”

iv. Interventions That Restore Awareness of Hypoglycemia in Adults With Type 1 Diabetes: A Systematic Review and Meta-analysis.

“Hypoglycemia remains the major limiting factor toward achieving good glycemic control (1). Recurrent hypoglycemia reduces symptomatic and hormone responses to subsequent hypoglycemia (2), associated with impaired awareness of hypoglycemia (IAH). IAH occurs in up to one-third of adults with type 1 diabetes (T1D) (3,4), increasing their risk of severe hypoglycemia (SH) sixfold (3) and contributing to substantial morbidity, with implications for employment (5), driving (6), and mortality. Distribution of risk of SH is skewed: one study showed that 5% of subjects accounted for 54% of all SH episodes, with IAH one of the main risk factors (7). “Dead-in-bed,” related to nocturnal hypoglycemia, is a leading cause of death in people with T1D <40 years of age (8).”

“This systematic review assessed the clinical effectiveness of treatment strategies for restoring hypoglycemia awareness (HA) and reducing SH risk in those with IAH and performed a meta-analysis, where possible, for different approaches in restoring awareness in T1D adults. Interventions to restore HA were broadly divided into three categories: educational (inclusive of behavioral), technological, and pharmacotherapeutic. […] Forty-three studies (18 randomized controlled trials, 25 before-and-after studies) met the inclusion criteria, comprising 27 educational, 11 technological, and 5 pharmacological interventions. […] A meta-analysis for educational interventions on change in mean SH rates per person per year was performed. Combining before-and-after and RCT studies, six studies (n = 1,010 people) were included in the meta-analysis […] A random-effects meta-analysis revealed an effect size of a reduction in SH rates of 0.44 per patient per year with 95% CI 0.253–0.628. [here’s the forest plot, US] […] Most of the educational interventions were observational and mostly retrospective, with few RCTs. The overall risk of bias is considered medium to high and the study quality moderate. Most, if not all, of the RCTs did not use double blinding and lacked information on concealment. The strength of association of the effect of educational interventions is moderate. The ability of educational interventions to restore IAH and reduce SH is consistent and direct with educational interventions showing a largely positive outcome. There is substantial heterogeneity between studies, and the estimate is imprecise, as reflected by the large CIs. The strength of evidence is moderate to high.”

v. Trends of Diagnosis-Specific Work Disability After Newly Diagnosed Diabetes: A 4-Year Nationwide Prospective Cohort Study.

“There is little evidence to show which specific diseases contribute to excess work disability among those with diabetes. […] In this study, we used a large nationwide register-based data set, which includes information on work disability for all working-age inhabitants of Sweden, in order to investigate trends of diagnosis-specific work disability (sickness absence and disability pension) among people with diabetes for 4 years directly after the recorded onset of diabetes. We compared work disability trends among people with diabetes with trends among those without diabetes. […] The register data of diabetes medication and in- and outpatient hospital visits were used to identify all recorded new diabetes cases among the population aged 25–59 years in Sweden in 2006 (n = 14,098). Data for a 4-year follow-up of ICD-10 physician-certified sickness absence and disability pension days (2007‒2010) were obtained […] Comparisons were made using a random sample of the population without recorded diabetes (n = 39,056).”

RESULTS The most common causes of work disability were mental and musculoskeletal disorders; diabetes as a reason for disability was rare. Most of the excess work disability among people with diabetes compared with those without diabetes was owing to mental disorders (mean difference adjusted for confounding factors 18.8‒19.8 compensated days/year), musculoskeletal diseases (12.1‒12.8 days/year), circulatory diseases (5.9‒6.5 days/year), diseases of the nervous system (1.8‒2.0 days/year), and injuries (1.0‒1.2 days/year).”

CONCLUSIONS The increased risk of work disability among those with diabetes is largely attributed to comorbid mental, musculoskeletal, and circulatory diseases. […] Diagnosis of diabetes as the cause of work disability was rare.”

August 19, 2017 Posted by | Cancer/oncology, Cardiology, Diabetes, Health Economics, Medicine, Statistics | Leave a comment

Depression and Heart Disease (II)

Below I have added some more observations from the book, which I gave four stars on goodreads.

“A meta-analysis of twin (and family) studies estimated the heritability of adult MDD around 40% [16] and this estimate is strikingly stable across different countries [17, 18]. If measurement error due to unreliability is taken into account by analysing MDD assessed on two occasions, heritability estimates increase to 66% [19]. Twin studies in children further show that there is already a large genetic contribution to depressive symptoms in youth, with heritability estimates varying between 50% and 80% [20–22]. […] Cardiovascular research in twin samples has suggested a clear-cut genetic contribution to hypertension (h2 = 61%) [30], fatal stroke (h2 = 32%) [31] and CAD (h2 = 57% in males and 38% in females) [32]. […] A very important, and perhaps underestimated, source of pleiotropy in the association of MDD and CAD are the major behavioural risk factors for CAD: smoking and physical inactivity. These factors are sometimes considered ‘environmental’, but twin studies have shown that such behaviours have a strong genetic component [33–35]. Heritability estimates for [many] established risk factors [for CAD – e.g. BMI, smoking, physical inactivity – US] are 50% or higher in most adult twin samples and these estimates remain remarkably similar across the adult life span [41–43].”

“The crucial question is whether the genetic factors underlying MDD also play a role in CAD and CAD risk factors. To test for an overlap in the genetic factors, a bivariate extension of the structural equation model for twin data can be used [57]. […] If the depressive symptoms in a twin predict the IL-6 level in his/her co-twin, this can only be explained by an underlying factor that affects both depression and IL-6 levels and is shared by members of a family. If the prediction is much stronger in MZ than in DZ twins, this signals that the underlying factor is their shared genetic make-up, rather than their shared (family) environment. […] It is important to note clearly here that genetic correlations do not prove the existence of pleiotropy, because genes that influence MDD may, through causal effects of MDD on CAD risk, also become ‘CAD genes’. The absence of a genetic correlation, however, can be used to falsify the existence of genetic pleiotropy. For instance, the hypothesis that genetic pleiotropy explains part of the association between depressive symptoms and IL-6 requires the genetic correlation between these traits to be significantly different from zero. [Furthermore,] the genetic correlation should have a positive value. A negative genetic correlation would signal that genes that increase the risk for depression decrease the risk for higher IL-6 levels, which would go against the genetic pleiotropy hypothesis. […] Su et al. [26] […] tested pleiotropy as a possible source of the association of depressive symptoms with Il-6 in 188 twin pairs of the Vietnam Era Twin (VET) Registry. The genetic correlation between depressive symptoms and IL-6 was found to be positive and significant (RA = 0.22, p = 0.046)”

“For the association between MDD and physical inactivity, the dominant hypothesis has not been that MDD causes a reduction in regular exercise, but instead that regular exercise may act as a protective factor against mood disorders. […] we used the twin method to perform a rigorous test of this popular hypothesis [on] 8558 twins and their family members using their longitudinal data across 2-, 4-, 7-, 9- and 11-year follow-up periods. In spite of sufficient statistical power, we found only the genetic correlation to be significant (ranging between *0.16 and *0.44 for different symptom scales and different time-lags). The environmental correlations were essentially zero. This means that the environmental factors that cause a person to take up exercise do not cause lower anxiety or depressive symptoms in that person, currently or at any future time point. In contrast, the genetic factors that cause a person to take up exercise also cause lower anxiety or depressive symptoms in that person, at the present and all future time points. This pattern of results falsifies the causal hypothesis and leaves genetic pleiotropy as the most likely source for the association between exercise and lower levels of anxiety and depressive symptoms in the population at large. […] Taken together, [the] studies support the idea that genetic pleiotropy may be a factor contributing to the increased risk for CAD in subjects suffering from MDD or reporting high counts of depressive symptoms. The absence of environmental correlations in the presence of significant genetic correlations for a number of the CAD risk factors (CFR, cholesterol, inflammation and regular exercise) suggests that pleiotropy is the sole reason for the association between MDD and these CAD risk factors, whereas for other CAD risk factors (e.g. smoking) and CAD incidence itself, pleiotropy may coexist with causal effects.”

“By far the most tested polymorphism in psychiatric genetics is a 43-base pair insertion or deletion in the promoter region of the serotonin transporter gene (5HTT, renamed SLC6A4). About 55% of Caucasians carry a long allele (L) with 16 repeat units. The short allele (S, with 14 repeat units) of this length polymorphism repeat (LPR) reduces transcriptional efficiency, resulting in decreased serotonin transporter expression and function [83]. Because serotonin plays a key role in one of the major theories of MDD [84], and because the most prescribed antidepressants act directly on this transporter, 5HTT is an obvious candidate gene for this disorder. […] The dearth of studies attempting to associate the 5HTTLPR to MDD or related personality traits tells a revealing story about the fate of most candidate genes in psychiatric genetics. Many conflicting findings have been reported, and the two largest studies failed to link the 5HTTLPR to depressive symptoms or clinical MDD [85, 86]. Even at the level of reviews and meta-analyses, conflicting conclusions have been drawn about the role of this polymorphism in the development of MDD [87, 88]. The initially promising explanation for discrepant findings – potential interactive effects of the 5HTTLPR and stressful life events [89] – did not survive meta-analysis [90].”

“Across the board, overlooking the wealth of candidate gene studies on MDD, one is inclined to conclude that this approach has failed to unambiguously identify genetic variants involved in MDD […]. Hope is now focused on the newer GWA [genome wide association] approach. […] At the time of writing, only two GWA studies had been published on MDD [81, 95]. […] In theory, the strategy to identify potential pleiotropic genes in the MDD–CAD relationship is extremely straightforward. We simply select the genes that occur in the lists of confirmed genes from the GWA studies for both traits. In practice, this is hard to do, because genetics in psychiatry is clearly lagging behind genetics in cardiology and diabetes medicine. […] What is shown by the reviewed twin studies is that some genetic variants may influence MDD and CAD risk factors. This can occur through one of three mechanisms: (a) the genetic variants that increase the risk for MDD become part of the heritability of CAD through a causal effect of MDD on CAD risk factors (causality); (b) the genetic variants that increase the risk for CAD become part of the heritability of MDD through a direct causal effect of CAD on MDD (reverse causality); (c) the genetic variants influence shared risk factors that independently increase the risk for MDD as well as CAD (pleiotropy). I suggest that to fully explain the MDD–CAD association we need to be willing to be open to the possibility that these three mechanisms co-exist. Even in the presence of true pleiotropic effects, MDD may influence CAD risk factors, and having CAD in turn may worsen the course of MDD.”

“Patients with depression are more likely to exhibit several unhealthy behaviours or avoid other health-promoting ones than those without depression. […] Patients with depression are more likely to have sleep disturbances [6]. […] sleep deprivation has been linked with obesity, diabetes and the metabolic syndrome [13]. […] Physical inactivity and depression display a complex, bidirectional relationship. Depression leads to physical inactivity and physical inactivity exacerbates depression [19]. […] smoking rates among those with depression are about twice that of the general population [29]. […] Poor attention to self-care is often a problem among those with major depressive disorder. In the most severe cases, those with depression may become inattentive to their personal hygiene. One aspect of this relationship that deserves special attention with respect to cardiovascular disease is the association of depression and periodontal disease. […] depression is associated with poor adherence to medical treatment regimens in many chronic illnesses, including heart disease. […] There is some evidence that among patients with an acute coronary syndrome, improvement in depression is associated with improvement in adherence. […] Individuals with depression are often socially withdrawn or isolated. It has been shown that patients with heart disease who are depressed have less social support [64], and that social isolation or poor social support is associated with increased mortality in heart disease patients [65–68]. […] [C]linicians who make recommendations to patients recovering from a heart attack should be aware that low levels of social support and social isolation are particularly common among depressed individuals and that high levels of social support appear to protect patients from some of the negative effects of depression [78].”

“Self-efficacy describes an individual’s self-confidence in his/her ability to accomplish a particular task or behaviour. Self-efficacy is an important construct to consider when one examines the psychological mechanisms linking depression and heart disease, since it influences an individual’s engagement in behaviour and lifestyle changes that may be critical to improving cardiovascular risk. Many studies on individuals with chronic illness show that depression is often associated with low self-efficacy [95–97]. […] Low self-efficacy is associated with poor adherence behaviour in patients with heart failure [101]. […] Much of the interest in self-efficacy comes from the fact that it is modifiable. Self-efficacy-enhancing interventions have been shown to improve cardiac patients’ self-efficacy and thereby improve cardiac health outcomes [102]. […] One problem with targeting self-efficacy in depressed heart disease patients is [however] that depressive symptoms reduce the effects of self-efficacy-enhancing interventions [105, 106].”

“Taken together, [the] SADHART and ENRICHD [studies] suggest, but do not prove, that antidepressant drug therapy in general, and SSRI treatment in particular, improve cardiovascular outcomes in depressed post-acute coronary syndrome (ACS) patients. […] even large epidemiological studies of depression and antidepressant treatment are not usually informative, because they confound the effects of depression and antidepressant treatment. […] However, there is one Finnish cohort study in which all subjects […] were followed up through a nationwide computerised database [17]. The purpose of this study was not to examine the relationship between depression and cardiac mortality, but rather to look at the relationship between antidepressant use and suicide. […] unexpectedly, ‘antidepressant use, and especially SSRI use, was associated with a marked reduction in total mortality (=49%, p < 0.001), mostly attributable to a decrease in cardiovascular deaths’. The study involved 15 390 patients with a mean follow-up of 3.4 years […] One of the marked differences between the SSRIs and the earlier tricyclic antidepressants is that the SSRIs do not cause cardiac death in overdose as the tricyclics do [41]. There has been literature that suggested that tricyclics even at therapeutic doses could be cardiotoxic and more problematic than SSRIs [42, 43]. What has been surprising is that both in the clinical trial data from ENRICHD and the epidemiological data from Finland, tricyclic treatment has also been associated with a decreased risk of mortality. […] Given that SSRI treatment of depression in the post-ACS period is safe, effective in reducing depressed mood, able to improve health behaviours and may reduce subsequent cardiac morbidity and mortality, it would seem obvious that treating depression is strongly indicated. However, the vast majority of post-ACS patients will not see a psychiatrically trained professional and many cases are not identified [33].”

“That depression is associated with cardiovascular morbidity and mortality is no longer open to question. Similarly, there is no question that the risk of morbidity and mortality increases with increasing severity of depression. Questions remain about the mechanisms that underlie this association, whether all types of depression carry the same degree of risk and to what degree treating depression reduces that risk. There is no question that the benefits of treating depression associated with coronary artery disease far outweigh the risks.”

“Two competing trends are emerging in research on psychotherapy for depression in cardiac patients. First, the few rigorous RCTs that have been conducted so far have shown that even the most efficacious of the current generation of interventions produce relatively modest outcomes. […] Second, there is a growing recognition that, even if an intervention is highly efficacious, it may be difficult to translate into clinical practice if it requires intensive or extensive contacts with a highly trained, experienced, clinically sophisticated psychotherapist. It can even be difficult to implement such interventions in the setting of carefully controlled, randomised efficacy trials. Consequently, there are efforts to develop simpler, more efficient interventions that can be delivered by a wider variety of interventionists. […] Although much more work remains to be done in this area, enough is already known about psychotherapy for comorbid depression in heart disease to suggest that a higher priority should be placed on translation of this research into clinical practice. In many cases, cardiac patients do not receive any treatment for their depression.”

August 14, 2017 Posted by | Books, Cardiology, Diabetes, Genetics, Medicine, Pharmacology, Psychiatry, Psychology | Leave a comment

Depression and Heart Disease (I)

I’m currently reading this book. It’s a great book, with lots of interesting observations.

Below I’ve added some quotes from the book.

“Frasure-Smith et al. [1] demonstrated that patients diagnosed with depression post MI [myocardial infarction, US] were more than five times more likely to die from cardiac causes by 6 months than those without major depression. At 18 months, cardiac mortality had reached 20% in patients with major depression, compared with only 3% in non-depressed patients [5]. Recent work has confirmed and extended these findings. A meta-analysis of 22 studies of post-MI subjects found that post-MI depression was associated with a 2.0–2.5 increased risk of negative cardiovascular outcomes [6]. Another meta-analysis examining 20 studies of subjects with MI, coronary artery bypass graft (CABG), angioplasty or angiographically documented CAD found a twofold increased risk of death among depressed compared with non-depressed patients [7]. Though studies included in these meta-analyses had substantial methodological variability, the overall results were quite similar [8].”

“Blumenthal et al. [31] published the largest cohort study (N = 817) to date on depression in patients undergoing CABG and measured depression scores, using the CES-D, before and at 6 months after CABG. Of those patients, 26% had minor depression (CES-D score 16–26) and 12% had moderate to severe depression (CES-D score =27). Over a mean follow-up of 5.2 years, the risk of death, compared with those without depression, was 2.4 (HR adjusted; 95% CI 1.4, 4.0) in patients with moderate to severe depression and 2.2 (95% CI 1.2, 4.2) in those whose depression persisted from baseline to follow-up at 6 months. This is one of the few studies that found a dose response (in terms of severity and duration) between depression and death in CABG in particular and in CAD in general.”

“Of the patients with known CAD but no recent MI, 12–23% have major depressive disorder by DSM-III or DSM-IV criteria [20, 21]. Two studies have examined the prognostic association of depression in patients whose CAD was confirmed by angiography. […] In [Carney et al.], a diagnosis of major depression by DSM-III criteria was the best predictor of cardiac events (MI, bypass surgery or death) at 1 year, more potent than other clinical risk factors such as impaired left ventricular function, severity of coronary disease and smoking among the 52 patients. The relative risk of a cardiac event was 2.2 times higher in patients with major depression than those with no depression.[…] Barefoot et al. [23] provided a larger sample size and longer follow-up duration in their study of 1250 patients who had undergone their first angiogram. […] Compared with non-depressed patients, those who were moderately to severely depressed had 69% higher odds of cardiac death and 78% higher odds of all-cause mortality. The mildly depressed had a 38% higher risk of cardiac death and a 57% higher risk of all-cause mortality than non-depressed patients.”

“Ford et al. [43] prospectively followed all male medical students who entered the Johns Hopkins Medical School from 1948 to 1964. At entry, the participants completed questionnaires about their personal and family history, health status and health behaviour, and underwent a standard medical examination. The cohort was then followed after graduation by mailed, annual questionnaires. The incidence of depression in this study was based on the mailed surveys […] 1190 participants [were included in the] analysis. The cumulative incidence of clinical depression in this population at 40 years of follow-up was 12%, with no evidence of a temporal change in the incidence. […] In unadjusted analysis, clinical depression was associated with an almost twofold higher risk of subsequent CAD. This association remained after adjustment for time-dependent covariates […]. The relative risk ratio for CAD development with versus without clinical depression was 2.12 (95% CI 1.24, 3.63), as was their relative risk ratio for future MI (95% CI 1.11, 4.06), after adjustment for age, baseline serum cholesterol level, parental MI, physical activity, time-dependent smoking, hypertension and diabetes. The median time from the first episode of clinical depression to first CAD event was 15 years, with a range of 1–44 years.”

“In the Women’s Ischaemia Syndrome Evaluation (WISE) study, 505 women referred for coronary angiography were followed for a mean of 4.9 years and completed the BDI [46]. Significantly increased mortality and cardiovascular events were found among women with elevated BDI scores, even after adjustment for age, cholesterol, stenosis score on angiography, smoking, diabetes, education, hyper-tension and body mass index (RR 3.1; 95% CI 1.5, 6.3). […] Further compelling evidence comes from a meta-analysis of 28 studies comprising almost 80 000 subjects [47], which demonstrated that, despite heterogeneity and differences in study quality, depression was consistently associated with increased risk of cardiovascular diseases in general, including stroke.”

“The preponderance of evidence strongly suggests that depression is a risk factor for CAD [coronary artery disease, US] development. […] In summary, it is fair to conclude that depression plays a significant role in CAD development, independent of conventional risk factors, and its adverse impact endures over time. The impact of depression on the risk of MI is probably similar to that of smoking [52]. […] Results of longitudinal cohort studies suggest that depression occurs before the onset of clinically significant CAD […] Recent brain imaging studies have indicated that lesions resulting from cerebrovascular insufficiency may lead to clinical depression [54, 55]. Depression may be a clinical manifestation of atherosclerotic lesions in certain areas of the brain that cause circulatory deficits. The depression then exacerbates the onset of CAD. The exact aetiological mechanism of depression and CAD development remains to be clarified.”

“Rutledge et al. [65] conducted a meta-analysis in 2006 in order to better understand the prevalence of depression among patients with CHF and the magnitude of the relationship between depression and clinical outcomes in the CHF population. They found that clinically significant depression was present in 21.5% of CHF patients, varying by the use of questionnaires versus diagnostic interview (33.6% and 19.3%, respectively). The combined results suggested higher rates of death and secondary events (RR 2.1; 95% CI 1.7, 2.6), and trends toward increased health care use and higher rates of hospitalisation and emergency room visits among depressed patients.”

“In the past 15 years, evidence has been provided that physically healthy subjects who suffer from depression are at increased risk for cardiovascular morbidity and mortality [1, 2], and that the occurrence of depression in patients with either unstable angina [3] or myocardial infarction (MI) [4] increases the risk for subsequent cardiac death. Moreover, epidemiological studies have proved that cardiovascular disease is a risk factor for depression, since the prevalence of depression in individuals with a recent MI or with coronary artery disease (CAD) or congestive heart failure has been found to be significantly higher than in the general population [5, 6]. […] findings suggest a bidirectional association between depression and cardiovascular disease. The pathophysiological mechanisms underlying this association are, at present, largely unclear, but several candidate mechanisms have been proposed.”

“Autonomic nervous system dysregulation is one of the most plausible candidate mechanisms underlying the relationship between depression and ischaemic heart disease, since changes of autonomic tone have been detected in both depression and cardiovascular disease [7], and autonomic imbalance […] has been found to lower the threshold for ventricular tachycardia, ventricular fibrillation and sudden cardiac death in patients with CAD [8, 9]. […] Imbalance between prothrombotic and antithrombotic mechanisms and endothelial dysfunction have [also] been suggested to contribute to the increased risk of cardiac events in both medically well patients with depression and depressed patients with CAD. Depression has been consistently associated with enhanced platelet activation […] evidence has accumulated that selective serotonin reuptake inhibitors (SSRIs) reduce platelet hyperreactivity and hyperaggregation of depressed patients [39, 40] and reduce the release of the platelet/endothelial biomarkers ß-thromboglobulin, P-selectin and E-selectin in depressed patients with acute CAD [41]. This may explain the efficacy of SSRIs in reducing the risk of mortality in depressed patients with CAD [42–44].”

“[S]everal studies have shown that reduced endothelium-dependent flow-mediated vasodilatation […] occurs in depressed adults with or without CAD [48–50]. Atherosclerosis with subsequent plaque rupture and thrombosis is the main determinant of ischaemic cardiovascular events, and atherosclerosis itself is now recognised to be fundamentally an inflammatory disease [56]. Since activation of inflammatory processes is common to both depression and cardiovascular disease, it would be reasonable to argue that the link between depression and ischaemic heart disease might be mediated by inflammation. Evidence has been provided that major depression is associated with a significant increase in circulating levels of both pro-inflammatory cytokines, such as IL-6 and TNF-a, and inflammatory acute phase proteins, especially the C-reactive protein (CRP) [57, 58], and that antidepressant treatment is able to normalise CRP levels irrespective of whether or not patients are clinically improved [59]. […] Vaccarino et al. [79] assessed specifically whether inflammation is the mechanism linking depression to ischaemic cardiac events and found that, in women with suspected coronary ischaemia, depression was associated with increased circulating levels of CRP and IL-6 and was a strong predictor of ischaemic cardiac events”

“Major depression has been consistently associated with hyperactivity of the HPA axis, with a consequent overstimulation of the sympathetic nervous system, which in turn results in increased circulating catecholamine levels and enhanced serum cortisol concentrations [68–70]. This may cause an imbalance in sympathetic and parasympathetic activity, which results in elevated heart rate and blood pressure, reduced HRV [heart rate variability], disruption of ventricular electrophysiology with increased risk of ventricular arrhythmias as well as an increased risk of atherosclerotic plaque rupture and acute coronary thrombosis. […] In addition, glucocorticoids mobilise free fatty acids, causing endothelial inflammation and excessive clotting, and are associated with hypertension, hypercholesterolaemia and glucose dysregulation [88, 89], which are risk factors for CAD.”

“Most of the literature on [the] comorbidity [between major depressive disorder (MDD) and coronary artery disease (CAD), US] has tended to favour the hypothesis of a causal effect of MDD on CAD, but reversed causality has also been suggested to contribute. Patients with severe CAD at baseline, and consequently a worse prognosis, may simply be more prone to report mood disturbances than less severely ill patients. Furthermore, in pre-morbid populations, insipid atherosclerosis in cerebral vessels may cause depressive symptoms before the onset of actual cardiac or cerebrovascular events, a variant of reverse causality known as the ‘vascular depression’ hypothesis [2]. To resolve causality, comorbidity between MDD and CAD has been addressed in longitudinal designs. Most prospective studies reported that clinical depression or depressive symptoms at baseline predicted higher incidence of heart disease at follow-up [1], which seems to favour the hypothesis of causal effects of MDD. We need to remind ourselves, however […] [that] [p]rospective associations do not necessarily equate causation. Higher incidence of CAD in depressed individuals may reflect the operation of common underlying factors on MDD and CAD that become manifest in mental health at an earlier stage than in cardiac health. […] [T]he association between MDD and CAD may be due to underlying genetic factors that lead to increased symptoms of anxiety and depression, but may also independently influence the atherosclerotic process. This phenomenon, where low-level biological variation has effects on multiple complex traits at the organ and behavioural level, is called genetic ‘pleiotropy’. If present in a time-lagged form, that is if genetic effects on MDD risk precede effects of the same genetic variants on CAD risk, this phenomenon can cause longitudinal correlations that mimic a causal effect of MDD.”

 

August 12, 2017 Posted by | Books, Cardiology, Genetics, Medicine, Neurology, Pharmacology, Psychiatry, Psychology | Leave a comment

A few diabetes papers of interest

i. Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis.

“This systematic review and meta-analysis evaluates the association between HbA1c variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes. […] Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 [95% CI 1.08–2.25], two studies), cardiovascular events (1.98 [1.39–2.82]), and retinopathy (2.11 [1.54–2.89]). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 [1.15–1.57], two studies), macrovascular events (1.21 [1.06–1.38]), ulceration/gangrene (1.50 [1.06–2.12]), cardiovascular disease (1.27 [1.15–1.40]), and mortality (1.34 [1.18–1.53]). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability.

CONCLUSIONS HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment.”

Two observations related to the paper: One, although only a relatively small number of studies were included in the review, the number of patients included in some of those included studies was rather large – the 7 type 1 studies thus included 44,021 participants, and the 13 type 2 studies included in total 43,620 participants. Two, it’s noteworthy that some of the associations already look at least reasonably strong, despite interest in HbA1c variability being a relatively recent phenomenon. Confounding might be an issue, but then again it almost always might be, and to give an example, out of 11 studies analyzing the association between renal disease and HbA1c variability included in the review, ten of them support a link and the only one which does not was a small study on pediatric patients which was almost certainly underpowered to investigate such a link in the first place (the base rate of renal complications is, as mentioned before here on this blog quite recently (link 3), quite low in pediatric samples).

ii. Risk of Severe Hypoglycemia in Type 1 Diabetes Over 30 Years of Follow-up in the DCCT/EDIC Study.

(I should perhaps note here that I’m already quite familiar with the context of the DCCT/EDIC study/studies, and although readers may not be, and although background details are included in the paper, I decided not to cover such details here although they would make my coverage of the paper easier to understand. I instead decided to limit my coverage of the paper to a few observations which I myself found to be of interest.)

“During the DCCT, the rates of SH [Severe Hypoglycemia, US], including episodes with seizure or coma, were approximately threefold greater in the intensive treatment group than in the conventional treatment group […] During EDIC, the frequency of SH increased in the former conventional group and decreased in the former intensive group so that the difference in SH event rates between the two groups was no longer significant (36.6 vs. 40.8 episodes per 100 patient-years, respectively […] By the end of DCCT, with an average of 6.5 years of follow-up, 65% of the intensive group versus 35% of the conventional group experienced at least one episode of SH. In contrast, ∼50% of participants within each group reported an episode of SH during the 20 years of EDIC.”

“Of [the] participants reporting episodes of SH, during the DCCT, 54% of the intensive group and 30% of the conventional group experienced four or more episodes, whereas in EDIC, 37% of the intensive group and 33% of the conventional group experienced four or more events […]. Moreover, a subset of participants (14% [99 of 714]) experienced nearly one-half of all SH episodes (1,765 of 3,788) in DCCT, and a subset of 7% (52 of 709) in EDIC experienced almost one-third of all SH episodes (888 of 2,813) […] Fifty-one major accidents occurred during the 6.5 years of DCCT and 143 during the 20 years of EDIC […] The most frequent type of major accident was that involving a motor vehicle […] Hypoglycemia played a role as a possible, probable, or principal cause in 18 of 28 operator-caused motor vehicle accidents (MVAs) during DCCT […] and in 23 of 54 operator-caused MVAs during EDIC”.

“The T1D Exchange Clinic Registry recently reported that 8% of 4,831 adults with T1D living in the U.S. had a seizure or coma event during the 3 months before their most recent annual visit (11). During EDIC, we observed that 27% of the cohort experienced a coma or seizure event over the 20 years of 3-month reporting intervals (∼1.4% per year), a much lower annual risk than in the T1D Exchange Clinic Registry. In part, the open enrollment of patients into the T1D Exchange may be reflected without the exclusion of participants with a history of SH as in the DCCT and other clinical trials. The current data support the clinical perception that a small subset of individuals is more susceptible to SH (7% of patients with 11 or more SH episodes during EDIC, which represents 32% of all SH episodes in EDIC) […] a history of SH during DCCT and lower current HbA1c levels were the two major factors associated with an increased risk of SH during EDIC. Safety concerns were the reason why a history of frequent SH events was an exclusion criterion for enrollment in DCCT. […] Of note, we found that participants who entered the DCCT as adolescents were more likely to experience SH during EDIC.”

“In summary, although event rates in the DCCT/EDIC cohort seem to have fallen and stabilized over time, SH remains an ever-present threat for patients with T1D who use current technology, occurring at a rate of ∼36–41 episodes per 100 patient-years, even among those with longer diabetes duration. Having experienced one or more such prior events is the strongest predictor of a future SH episode.”

I didn’t actually like that summary. If a history of severe hypoglycemia was an exclusion criterion in the DCCT trial, which it was, then the event rate you’d get from this data set is highly likely to provide a biased estimator of the true event rate, as the Exchange Clinic Registry data illustrate. The true population event rate in unselected samples is higher.

Another note which may also be important to add is that many diabetics who do not have a ‘severe event’ during a specific time period might still experience a substantial number of hypoglycemic episodes; ‘severe events’ (which require the assistance of another individual) is a somewhat blunt instrument in particular for assessing quality-of-life aspects of hypoglycemia.

iii. The Presence and Consequence of Nonalbuminuric Chronic Kidney Disease in Patients With Type 1 Diabetes.

“This study investigated the prevalence of nonalbuminuric chronic kidney disease in type 1 diabetes to assess whether it increases the risk of cardiovascular and renal outcomes as well as all-cause mortality. […] This was an observational follow-up of 3,809 patients with type 1 diabetes from the Finnish Diabetic Nephropathy Study. […] mean age was 37.6 ± 11.8 years and duration of diabetes 21.2 ± 12.1 years. […] During 13 years of median follow-up, 378 developed end-stage renal disease, 415 suffered an incident cardiovascular event, and 406 died. […] At baseline, 78 (2.0%) had nonalbuminuric chronic kidney disease. […] Nonalbuminuric chronic kidney disease did not increase the risk of albuminuria (hazard ratio [HR] 2.0 [95% CI 0.9–4.4]) or end-stage renal disease (HR 6.4 [0.8–53.0]) but did increase the risk of cardiovascular events (HR 2.0 [1.4–3.5]) and all-cause mortality (HR 2.4 [1.4–3.9]). […] ESRD [End-Stage Renal Disease] developed during follow-up in 0.3% of patients with nonalbuminuric non-CKD [CKD: Chronic Kidney Disease], in 1.3% of patients with nonalbuminuric CKD, in 13.9% of patients with albuminuric non-CKD, and in 63.0% of patients with albuminuric CKD (P < 0.001).”

CONCLUSIONS Nonalbuminuric chronic kidney disease is not a frequent finding in patients with type 1 diabetes, but when present, it is associated with an increased risk of cardiovascular morbidity and all-cause mortality but not with renal outcomes.”

iv. Use of an α-Glucosidase Inhibitor and the Risk of Colorectal Cancer in Patients With Diabetes: A Nationwide, Population-Based Cohort Study.

This one relates closely to stuff covered in Horowitz & Samsom’s book about Gastrointestinal Function in Diabetes Mellitus which I just finished (and which I liked very much). Here’s a relevant quote from chapter 7 of that book (which is about ‘Hepato-biliary and Pancreatic Function’):

“Several studies have provided evidence that the risk of pancreatic cancer is increased in patients with type 1 and type 2 diabetes mellitus [136,137]. In fact, diabetes has been associated with an increased risk of several cancers, including those of the pancreas, liver, endometrium and kidney [136]. The pooled relative risk of pancreatic cancer for diabetics vs. non-diabetics in a meta-analysis was 2.1 (95% confidence interval 1.6–2.8). Patients presenting with diabetes mellitus within a period of 12 months of the diagnosis of pancreatic cancer were excluded because in these cases diabetes may be an early presenting sign of pancreatic cancer rather than a risk factor [137]”.

They don’t mention colon cancer there, but it’s obvious from the research which has been done – and which is covered extensively in that book – that diabetes has the potential to cause functional changes in a large number of components of the digestive system (and I hope to cover this kind of stuff in a lot more detail later on) so the fact that some of these changes may lead to neoplastic changes should hardly be surprising. However evaluating causal pathways is more complicated here than it might have been, because e.g. pancreatic diseases may also themselves cause secondary diabetes in some patients. Liver pathologies like hepatitis B and C also display positive associations with diabetes, although again causal pathways here are not completely clear; treatments used may be a contributing factor (interferon-treatment may induce diabetes), but there are also suggestions that diabetes should be considered one of the extrahepatic manifestations of hepatitis. This stuff is complicated.

The drug mentioned in the paper, acarbose, is incidentally a drug also discussed in some detail in the book. It belongs to a group of drugs called alpha glucosidase inhibitors, and it is ‘the first antidiabetic medication designed to act through an influence on intestinal functions.’ Anyway, some quotes from the paper:

“We conducted a nationwide, population-based study using a large cohort with diabetes in the Taiwan National Health Insurance Research Database. Patients with newly diagnosed diabetes (n = 1,343,484) were enrolled between 1998 and 2010. One control subject not using acarbose was randomly selected for each subject using acarbose after matching for age, sex, diabetes onset, and comorbidities. […] There were 1,332 incident cases of colorectal cancer in the cohort with diabetes during the follow-up period of 1,487,136 person-years. The overall incidence rate was 89.6 cases per 100,000 person-years. Patients treated with acarbose had a 27% reduction in the risk of colorectal cancer compared with control subjects. The adjusted HRs were 0.73 (95% CI 0.63–0.83), 0.69 (0.59–0.82), and 0.46 (0.37–0.58) for patients using >0 to <90, 90 to 364, and ≥365 cumulative defined daily doses of acarbose, respectively, compared with subjects who did not use acarbose (P for trend < 0.001).

CONCLUSIONS Acarbose use reduced the risk of incident colorectal cancer in patients with diabetes in a dose-dependent manner.”

It’s perhaps worth mentioning that the prevalence of type 1 is relatively low in East Asian populations and that most of the patients included were type 2 (this is also clearly indicated by this observation from the paper: “The median age at the time of the initial diabetes diagnosis was 54.1 years, and the median diabetes duration was 8.9 years.”). Another thing worth mentioning is that colon cancer is a very common type of cancer, and so even moderate risk reductions here at the individual level may translate into a substantial risk reduction at the population level. A third thing, noted in Horowitz & Samsom’s coverage, is that the side effects of acarbose are quite mild, so widespread use of the drug is not out of the question, at least poor tolerance is not likely to be an obstacle; the drug may cause e.g. excessive flatulence and something like 10% of patients may have to stop treatment because of gastrointestinal side effects, but although the side effects are annoying and may be unacceptable to some patients, they are not dangerous; it’s a safe drug which can be used even in patients with renal failure (a context where some of the other oral antidiabetic treatments available are contraindicated).

v. Diabetes, Lower-Extremity Amputation, and Death.

“Worldwide, every 30 s, a limb is lost to diabetes (1,2). Nearly 2 million people living in the U.S. are living with limb loss (1). According to the World Health Organization, lower-extremity amputations (LEAs) are 10 times more common in people with diabetes than in persons who do not have diabetes. In the U.S. Medicare population, the incidence of diabetic foot ulcers is ∼6 per 100 individuals with diabetes per year and the incidence of LEA is 4 per 1,000 persons with diabetes per year (3). LEA in those with diabetes generally carries yearly costs between $30,000 and $60,000 and lifetime costs of half a million dollars (4). In 2012, it was estimated that those with diabetes and lower-extremity wounds in the U.S. Medicare program accounted for $41 billion in cost, which is ∼1.6% of all Medicare health care spending (47). In 2012, in the U.K., it was estimated that the National Health Service spent between £639 and 662 million on foot ulcers and LEA, which was approximately £1 in every £150 spent by the National Health Service (8).”

“LEA does not represent a traditional medical complication of diabetes like myocardial infarction (MI), renal failure, or retinopathy in which organ failure is directly associated with diabetes (2). An LEA occurs because of a disease complication, usually a foot ulcer that is not healing (e.g., organ failure of the skin, failure of the biomechanics of the foot as a unit, nerve sensory loss, and/or impaired arterial vascular supply), but it also occurs at least in part as a consequence of a medical plan to amputate based on a decision between health care providers and patients (9,10). […] 30-day postoperative mortality can approach 10% […]. Previous reports have estimated that the 1-year post-LEA mortality rate in people with diabetes is between 10 and 50%, and the 5-year mortality rate post-LEA is between 30 and 80% (4,1315). More specifically, in the U.S. Medicare population mortality within a year after an incident LEA was 23.1% in 2006, 21.8% in 2007, and 20.6% in 2008 (4). In the U.K., up to 80% will die within 5 years of an LEA (8). In general, those with diabetes with an LEA are two to three times more likely to die at any given time point than those with diabetes who have not had an LEA (5). For perspective, the 5-year death rate after diagnosis of malignancy in the U.S. was 32% in 2010 (16).”

“Evidence on why individuals with diabetes and an LEA die is based on a few mainly small (e.g., <300 subjects) and often single center–based (13,1720) studies or <1 year duration of evaluation (11). In these studies, death is primarily associated with a previous history of cardiovascular disease and renal insufficiency, which are also major complications of diabetes; these complications are also associated with an increased risk of LEA. The goal of our study was to determine whether complications of diabetes well-known to be associated with death in those with diabetes such as cardiovascular disease and renal failure fully explain the higher rate of death in those who have undergone an LEA.”

“This is the largest and longest evaluation of the risk of death among those with diabetes and LEA […] Between 2003 and 2012, 416,434 individuals met the entrance criteria for the study. This cohort accrued an average of 9.0 years of follow-up and a total of 3.7 million diabetes person-years of follow-up. During this period of time, 6,566 (1.6%) patients had an LEA and 77,215 patients died (18.5%). […] The percentage of individuals who died within 30 days, 1 year, and by year 5 of their initial code for an LEA was 1.0%, 9.9%, and 27.2%, respectively. For those >65 years of age, the rates were 12.2% and 31.7%, respectively. For the full cohort of those with diabetes, the rate of death was 2.0% after 1 year of follow up and 7.3% after 5 years of follow up. In general, those with an LEA were more than three times more likely to die during a year of follow-up than an individual with diabetes who had not had an LEA. […] In any given year, >5% of those with diabetes and an LEA will die.”

“From 2003 to 2012, the HR [hazard rate, US] for death after an LEA was 3.02 (95% CI 2.90, 3.14). […] our a priori assumption was that the HR associating LEA with death would be fully diminished (i.e., it would become 1) when adjusted for the other risk factor variables. However, the fully adjusted LEA HR was diminished only ∼22% to 2.37 (95% CI 2.27, 2.48). With the exception of age >65 years, individual risk factors, in general, had minimal effect (<10%) on the HR of the association between LEA and death […] We conducted sensitivity analyses to determine the general statistical parameters of an unmeasured risk factor that could remove the association of LEA with death. We found that even if there existed a very strong risk factor with an HR of death of three, a prevalence of 10% in the general diabetes population, and a prevalence of 60% in those who had an LEA, LEA would still be associated with a statistically significant and clinically important risk of 1.30. These findings are describing a variable that would seem to be so common and so highly associated with death that it should already be clinically apparent. […] In summary, individuals with diabetes and an LEA are more likely to die at any given point in time than those who have diabetes but no LEA. While some of this variation can be explained by other known complications of diabetes, the amount that can be explained is small. Based on the results of this study, including a sensitivity analysis, it is highly unlikely that a “new” major risk factor for death exists. […] LEA is often performed because of an end-stage disease process like chronic nonhealing foot ulcer. By the time a patient has a foot ulcer and an LEA is offered, they are likely suffering from the end-stage consequence of diabetes. […] We would […] suggest that patients who have had an LEA require […] vigilant follow-up and evaluation to assure that their medical care is optimized. It is also important that GPs communicate to their patients about the risk of death to assure that patients have proper expectations about the severity of their disease.”

vi. Trends in Health Care Expenditure in U.S. Adults With Diabetes: 2002–2011.

Before quoting from the paper, I’ll remind people reading along here that ‘total medical expenditures’ != ‘total medical costs’. Lots of relevant medical costs are not included when you focus only on direct medical expenditures (sick days, early retirement, premature mortality and productivity losses associated therewith, etc., etc.). With that out of the way…

“This study examines trends in health care expenditures by expenditure category in U.S. adults with diabetes between 2002 and 2011. […] We analyzed 10 years of data representing a weighted population of 189,013,514 U.S. adults aged ≥18 years from the Medical Expenditure Panel Survey. […] Relative to individuals without diabetes ($5,058 [95% CI 4,949–5,166]), individuals with diabetes ($12,180 [11,775–12,586]) had more than double the unadjusted mean direct expenditures over the 10-year period. After adjustment for confounders, individuals with diabetes had $2,558 (2,266–2,849) significantly higher direct incremental expenditures compared with those without diabetes. For individuals with diabetes, inpatient expenditures rose initially from $4,014 in 2002/2003 to $4,183 in 2004/2005 and then decreased continuously to $3,443 in 2010/2011, while rising steadily for individuals without diabetes. The estimated unadjusted total direct expenditures for individuals with diabetes were $218.6 billion/year and adjusted total incremental expenditures were approximately $46 billion/year. […] in the U.S., direct medical costs associated with diabetes were $176 billion in 2012 (1,3). This is almost double to eight times the direct medical cost of other chronic diseases: $32 billion for COPD in 2010 (10), $93 billion for all cancers in 2008 (11), $21 billion for heart failure in 2012 (12), and $43 billion for hypertension in 2010 (13). In the U.S., total economic cost of diabetes rose by 41% from 2007 to 2012 (2). […] Our findings show that compared with individuals without diabetes, individuals with diabetes had significantly higher health expenditures from 2002 to 2011 and the bulk of the expenditures came from hospital inpatient and prescription expenditures.”

 

August 10, 2017 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Economics, Epidemiology, Gastroenterology, Health Economics, Medicine, Nephrology, Pharmacology | Leave a comment

A few diabetes papers of interest

i. Long-Acting C-Peptide and Neuropathy in Type 1 Diabetes: A 12-Month Clinical Trial.

“Lack of C-peptide in type 1 diabetes may be an important contributing factor in the development of microvascular complications. Replacement of native C-peptide has been shown to exert a beneficial influence on peripheral nerve function in type 1 diabetes. The aim of this study was to evaluate the efficacy and safety of a long-acting C-peptide in subjects with type 1 diabetes and mild to moderate peripheral neuropathy. […] C-peptide, an integral component of the insulin biosynthesis, is the 31-amino acid peptide that makes up the connecting segment between the parts of the proinsulin molecule that become the A and B chains of insulin. It is split off from proinsulin and secreted together with insulin in equimolar amounts. Much new information on C-peptide physiology has appeared during the past 20 years […] Studies in animal models of diabetes and early clinical trials in patients with type 1 diabetes (T1DM) demonstrate that C-peptide in physiological replacement doses elicits beneficial effects on early stages of diabetes-induced functional and structural abnormalities of the peripheral nerves, the autonomic nervous system, and the kidneys (9). Even though much is still to be learned about C-peptide and its mechanism of action, the available evidence presents the picture of a bioactive peptide with therapeutic potential.”

“This was a multicenter, phase 2b, randomized, double-blind, placebo-controlled, parallel-group study. The study screened 756 subjects and enrolled 250 at 32 clinical sites in the U.S. (n = 23), Canada (n = 2), and Sweden (n = 7). […] A total of 250 patients with type 1 diabetes and peripheral neuropathy received long-acting (pegylated) C-peptide in weekly dosages […] for 52 weeks. […] Once-weekly subcutaneous administration of long-acting C-peptide for 52 weeks did not improve SNCV [sural nerve conduction velocity], other electrophysiological variables, or mTCNS [modified Toronto Clinical Neuropathy Score] but resulted in marked improvement of VPT [vibration perception threshold] compared with placebo. […] During the course of the 12-month study period, there were no significant changes in fasting blood glucose. Levels of HbA1c remained stable and varied within the treatment groups on average less than 0.1% (0.9 mmol/mol) between baseline and 52 weeks. […] There was a gradual lowering of VPT, indicating improvement in subjects receiving PEG–C-peptide […] after 52 weeks, subjects in the low-dose group had lowered their VPT by an average of 31% compared with baseline; the corresponding value for the high-dose group was 19%. […] The difference in VPT response between the dose groups did not attain statistical significance. In contrast to the SNCV results, VPT in the placebo group changed very little from baseline during the study […] The mTCNS, pain, and sexual function scores did not change significantly during the study nor did subgroup analysis involving the subjects most affected at baseline reveal significant differences between subjects treated with PEG–C-peptide or placebo subjects.”

“Evaluation of the safety population showed that PEG–C-peptide was well tolerated and that there was a low and similar incidence of treatment-related adverse events (11.3–16.4%) in all three treatment groups […] A striking finding in the current study is the observation of a progressive improvement in VPT during the 12-month treatment with PEG–C-peptide […], despite nonsignificant changes in SNCV. This finding may reflect differences in the mechanisms of conduction versus transduction of neural impulses. Changes in transduction reflect membrane receptor characteristics limited to the distal extreme of specific subtypes of sensory axons. In the case of vibration, the principal receptor is Pacinian corpuscles in the skin that are innervated by Aβ fibers. Transduction takes place uniquely at the distal extreme of the axon and is largely influenced by the integrity of this limited segment. Studies have documented that the initial effect of toxic neuropathy is a loss of the surface area of the pseudopod extensions of the distal axon within the Pacinian corpuscle and a consequent diminution of transduction (30). In contrast, changes in the speed of conduction are largely a function of factors that influence the elongated tract of the nerve, including the cross-sectional diameter of axons, the degree of myelination, and the integrity of ion clusters at the nodes of Ranvier (31). Thus, it is reasonable that some aspects of distal sensory function may be influenced by a treatment option that has little or no direct effect on nerve conduction velocity. The alternative is the unsupported belief that any intervention in the onset and progression of a sensory neuropathy must alter conduction velocity.

The marked VPT improvement observed in the current study, although associated with nonsignificant changes in SNCV, other electrophysiological variables, or mTCNS, can be interpreted as targeted improvement in a key aspect of sensory function (e.g., the conversion of mechanical energy to neural signals — transduction). […] Because progressive deficits in sensation are often considered the hallmark of diabetic polyneuropathy, the observed effects of C-peptide in the current study are an important finding.”

ii. Hyperbaric Oxygen Therapy Does Not Reduce Indications for Amputation in Patients With Diabetes With Nonhealing Ulcers of the Lower Limb: A Prospective, Double-Blind, Randomized Controlled Clinical Trial.

“Hyperbaric oxygen therapy (HBOT) is used for the treatment of chronic diabetic foot ulcers (DFUs). The controlled evidence for the efficacy of this treatment is limited. The goal of this study was to assess the efficacy of HBOT in reducing the need for major amputation and improving wound healing in patients with diabetes and chronic DFUs.”

“Patients with diabetes and foot lesions (Wagner grade 2–4) of at least 4 weeks’ duration participated in this study. In addition to comprehensive wound care, participants were randomly assigned to receive 30 daily sessions of 90 min of HBOT (breathing oxygen at 244 kPa) or sham (breathing air at 125 kPa). Patients, physicians, and researchers were blinded to group assignment. At 12 weeks postrandomization, the primary outcome was freedom from meeting the criteria for amputation as assessed by a vascular surgeon. Secondary outcomes were measures of wound healing. […] One hundred fifty-seven patients were assessed for eligibility, with 107 randomly assigned and 103 available for end point adjudication. Criteria for major amputation were met in 13 of 54 patients in the sham group and 11 of 49 in the HBOT group (odds ratio 0.91 [95% CI 0.37, 2.28], P = 0.846). Twelve (22%) patients in the sham group and 10 (20%) in the HBOT group were healed (0.90 [0.35, 2.31], P = 0.823).”

CONCLUSIONS HBOT does not offer an additional advantage to comprehensive wound care in reducing the indication for amputation or facilitating wound healing in patients with chronic DFUs.”

iii. Risk Factors Associated With Severe Hypoglycemia in Older Adults With Type 1 Diabetes.

“Older adults with type 1 diabetes (T1D) are a growing but underevaluated population (14). Of particular concern in this age group is severe hypoglycemia, which, in addition to producing altered mental status and sometimes seizures or loss of consciousness, can be associated with cardiac arrhythmias, falls leading to fractures, and in some cases, death (57). In Medicare beneficiaries with diabetes, hospitalizations related to hypoglycemia are now more frequent than those for hyperglycemia and are associated with high 1-year mortality (6). Emergency department visits due to hypoglycemia also are common (5). […] The T1D Exchange clinic registry reported a remarkably high frequency of severe hypoglycemia resulting in seizure or loss of consciousness in older adults with long-standing T1D (9). One or more such events during the prior year was reported by 1 in 5 of 211 participants ≥65 years of age with ≥40 years’ duration of diabetes (9).”

“Despite the high frequency of severe hypoglycemia in older adults with long-standing T1D, little information is available about the factors associated with its occurrence. We conducted a case-control study in adults ≥60 years of age with T1D of ≥20 years’ duration to assess potential contributory factors for the occurrence of severe hypoglycemia, including cognitive and functional measurements, social support, depression, hypoglycemia unawareness, various aspects of diabetes management, residual insulin secretion (as measured by C-peptide levels), frequency of biochemical hypoglycemia, and glycemic control and variability. […] A case-control study was conducted at 18 diabetes centers in the T1D Exchange Clinic Network. […] Case subjects (n = 101) had at least one severe hypoglycemic event in the prior 12 months. Control subjects (n = 100), frequency-matched to case subjects by age, had no severe hypoglycemia in the prior 3 years.”

RESULTS Glycated hemoglobin (mean 7.8% vs. 7.7%) and CGM-measured mean glucose (175 vs. 175 mg/dL) were similar between case and control subjects. More case than control subjects had hypoglycemia unawareness: only 11% of case subjects compared with 43% of control subjects reported always having symptoms associated with low blood glucose levels (P < 0.001). Case subjects had greater glucose variability than control subjects (P = 0.008) and experienced CGM glucose levels <60 mg/dL for ≥20 min on 46% of days compared with 33% of days in control subjects (P = 0.10). […] When defining high glucose variability as a coefficient of variation greater than the study cohort’s 75th percentile (0.481), 38% of case and 12% of control subjects had high glucose variability (P < 0.001).”

CONCLUSIONS In older adults with long-standing type 1 diabetes, greater hypoglycemia unawareness and glucose variability are associated with an increased risk of severe hypoglycemia.”

iv. Type 1 Diabetes and Polycystic Ovary Syndrome: Systematic Review and Meta-analysis.

“Even though PCOS is mainly an androgen excess disorder, insulin resistance and compensatory endogenous hyperinsulinemia, in close association with obesity and abdominal adiposity, are implicated in the pathogenesis of PCOS in many patients (3,4). In agreement, women with PCOS are at high risk for developing type 2 diabetes and gestational diabetes mellitus (3). […] Type 1 diabetes is a disease produced by an autoimmune injury to the endocrine pancreas that results in the abolition of endogenous insulin secretion. We hypothesized 15 years ago that PCOS could be associated with type 1 diabetes (8). The rationale was that women with type 1 diabetes needed supraphysiological doses of subcutaneous insulin to reach insulin concentrations at the portal level capable of suppressing hepatic glucose secretion, thus leading to exogenous systemic hyperinsulinism. Exogenous hyperinsulinism could then contribute to androgen excess in predisposed women, leading to PCOS as happens in insulin-resistance syndromes.

We subsequently published the first report of the association of PCOS with type 1 diabetes consisting of the finding of a threefold increase in the prevalence of this syndrome compared with that of women from the general population […]. Of note, even though this association was confirmed by all of the studies that addressed the issue thereafter (1016), with prevalences of PCOS as high as 40% in some series (10,16), this syndrome is seldom diagnosed and treated in women with type 1 diabetes.

With the aim of increasing awareness of the frequent association of PCOS with type 1 diabetes, we have conducted a systematic review and meta-analysis of the prevalence of PCOS and associated hyperandrogenic traits in adolescent and adult women with type 1 diabetes. […] Nine primary studies involving 475 adolescent or adult women with type 1 diabetes were included. The prevalences of PCOS and associated traits in women with type 1 diabetes were 24% (95% CI 15–34) for PCOS, 25% (95% CI 17–33) for hyperandrogenemia, 25% (95% CI 16–36) for hirsutism, 24% (95% CI 17–32) for menstrual dysfunction, and 33% (95% CI 24–44) for PCOM. These figures are considerably higher than those reported earlier in the general population without diabetes.”

CONCLUSIONS PCOS and its related traits are frequent findings in women with type 1 diabetes. PCOS may contribute to the subfertility of these women by a mechanism that does not directly depend on glycemic/metabolic control among other negative consequences for their health. Hence, screening for PCOS and androgen excess should be included in current guidelines for the management of type 1 diabetes in women.”

v. Impaired Awareness of Hypoglycemia in Adults With Type 1 Diabetes Is Not Associated With Autonomic Dysfunction or Peripheral Neuropathy.

“Impaired awareness of hypoglycemia (IAH), defined as a diminished ability to perceive the onset of hypoglycemia, is associated with an increased risk of severe hypoglycemia in people with insulin-treated diabetes (13). Elucidation of the pathogenesis of IAH may help to minimize the risk of severe hypoglycemia.

The glycemic thresholds for counterregulatory responses, generation of symptoms, and cognitive impairment are reset at lower levels of blood glucose in people who have developed IAH (4). This cerebral adaptation appears to be induced by recurrent exposure to hypoglycemia, and failure of cerebral autonomic mechanisms may be implicated in the pathogenesis (4). Awareness may be improved by avoidance of hypoglycemia (57), but this is very difficult to achieve and does not restore normal awareness of hypoglycemia (NAH) in all people with IAH. Because the prevalence of IAH in adults with type 1 diabetes increases with progressive disease duration (2,8,9), mechanisms that involve diabetic complications have been suggested to underlie the development of IAH.

Because activation of the autonomic nervous system is a fundamental physiological response to hypoglycemia and provokes many of the symptoms of hypoglycemia, autonomic neuropathy was considered to be a cause of IAH for many years (10). […] Studies of people with type 1 diabetes that have examined the glycemic thresholds for symptom generation in those with and without autonomic neuropathy (13,14,16) have [however] found no differences, and autonomic symptom generation was not delayed. […] The aim of the current study was […] to evaluate a putative association between IAH and the presence of autonomic neuropathy using composite Z (cZ) scores based on a battery of contemporary methods, including heart rate variability during paced breathing, the cardiovascular response to tilting and the Valsalva maneuver, and quantitative light reflex measurements by pupillometry.”

“Sixty-six adults with type 1 diabetes were studied, 33 with IAH and 33 with normal awareness of hypoglycemia (NAH), confirmed by formal testing. Participants were matched for age, sex, and diabetes duration. […] The [study showed] no difference in measures of autonomic function between adults with long-standing type 1 diabetes who had IAH, and carefully matched adults with type 1 diabetes with NAH. In addition, no differences between IAH and NAH participants were found with respect to the NCS [nerve conduction studies], thermal thresholds, and clinical pain or neuropathy scores. Neither autonomic dysfunction nor somatic neuropathy was associated with IAH. We consider that this study provides considerable value and novelty in view of the rigorous methodology that has been used. Potential confounding variables have been controlled for by the use of well-matched groups of participants, validated methods for classification of awareness, a large battery of neurophysiological tests, and a novel statistical approach to provide very high sensitivity for the detection of between-group differences.”

vi. Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes.

“Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. […] Our primary message is that diabetes control is all about optimization and balance between two key markers — frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization […] Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components — amplitude and timing.”

“Although reducing hyperglycemia and targeting HbA1c values of 7% or less result in decreased risk of micro- and macrovascular complications (14), the risk for hypoglycemia increases with tightening glycemic control (5,6). […] Thus, patients with diabetes face a lifelong optimization problem: reducing average glycemic levels and postprandial hyperglycemia while simultaneously avoiding hypoglycemia. A strategy for achieving such an optimization can only be successful if it reduces glucose variability (GV). This is because bringing average glycemia down is only possible if GV is constrained — otherwise blood glucose (BG) fluctuations would inevitably enter the range of hypoglycemia (9).”

“In health, glucose metabolism is tightly controlled by a hormonal network including the gut, liver, pancreas, and brain to ensure stable fasting BG levels and transient postprandial glucose fluctuations. In other words, BG fluctuations in type 1 diabetes result from the activity of a complex metabolic system perturbed by behavioral challenges. The frequency and extent of these challenges and the ability of the person’s system to absorb them determine the stability of glycemic control. The degree of system destabilization depends on each individual’s physiological parameters of glucose–insulin kinetics, including glucose appearance from food, insulin secretion, insulin sensitivity, and counterregulatory response.”

“There is strong evidence that feeding behavior is abnormal in both uncontrolled diabetes and hypoglycemia and that feeding signals within the brain and hormones affecting feeding, such as leptin and ghrelin, are implicated in diabetes (1214). Insulin secretion and action vary with the type and duration of diabetes. In type 1 diabetes, insulin secretion is virtually absent, which destroys the natural insulin–glucagon feedback loop and thereby diminishes the dampening effect of glucagon on hypoglycemia. In addition, insulin is typically administered subcutaneously, which adds delays to insulin action and thereby amplifies the amplitude of glucose fluctuations. […] impaired hypoglycemia counterregulation and increased GV in the hypoglycemic range are particularly relevant to type 1 diabetes: It has been shown that glucagon response is impaired (15), and epinephrine response is typically attenuated as well (16). Antecedent hypoglycemia shifts down BG thresholds for autonomic and cognitive responses, thereby further impairing both the hormonal defenses and the detection of hypoglycemia (17). Studies have established relationships between intensive therapy, hypoglycemia unawareness, and impaired counterregulation (16,1820) and concluded that recurrent hypoglycemia spirals into a “vicious cycle” known as hyperglycemia-associated autonomic failure (HAAF) (21). Our studies showed that increased GV and the extent and frequency of low BG are major contributors to hypoglycemia and that such changes are detectable by frequent BG measurement (2225).”

“The traditional statistical calculation of BG includes standard deviation (SD) (27), coefficient of variation (CV), or other metrics, such as the M-value introduced in 1965 (28), the mean amplitude of glucose excursions (MAGE) introduced in 1970 (29), the glycemic lability index (30), or the mean absolute glucose (MAG) change (31,32). […] the low BG index (LBGI), high BG index (HBGI), and average daily risk range (ADRR) […] are [all] based on a transformation of the BG measurement scale […], which aims to correct the substantial asymmetry of the BG measurement scale. Numerically, the hypoglycemic range (BG <70 mg/dL) is much narrower than that in the hyperglycemic range (BG >180 mg/dL) (34). As a result, whereas SD, CV, MAGE, and MAG are inherently biased toward hyperglycemia and have a relatively weak association with hypoglycemia, the LBGI and ADRR account well for the risk of hypoglycemic excursions. […] The analytical form of the scale transformation […] was based on accepted clinical assumptions, not on a particular data set, and was fixed 17 years ago, which made the approach extendable to any data set (34). On the basis of this transformation, we have developed our theory of risk analysis of BG data (35), defining a computational risk space that proved to be very suitable for quantifying the extent and frequency of glucose excursions. The utility of the risk analysis has been repeatedly confirmed (9,25,3638). We first introduced the LBGI and HBGI, which were specifically designed to be sensitive only to the low and high end of the BG scale, respectively, accounting for hypo- and hyperglycemia without overlap (24). Then in 2006, we introduced the ADRR, a measure of GV that is equally sensitive to hypo- and hyperglycemic excursions and is predictive of extreme BG fluctuations (38). Most recently, corrections were introduced that allowed the LBGI and HBGI to be computed from CGM data with results directly comparable to SMBG [self-monitoring of BG] (39).”

“[A]lthough GV has richer information content than just average glucose (HbA1c), its quantitative assessment is not straightforward because glucose fluctuations carry two components: amplitude and timing.

The standard assessment of GV is measuring amplitude. However, when measuring amplitude we should be mindful that deviations toward hypoglycemia are not equal to deviations toward hyperglycemia—a 20 mg/dL decline in BG levels from 70 to 50 mg/dL is clinically more important than a 20 mg/dL raise of BG from 160 to 180 mg/dL. We explained how to fix that with a well-established rescaling of the BG axis introduced more than 15 years ago (34). […] In addition, we should be mindful of the timing of BG fluctuations. There are a number of measures assessing GV amplitude from routine SMBG, but the timing of readings is frequently ignored even if the information is available (42). Yet, contrary to widespread belief, BG fluctuations are a process in time and the speed of transition from one BG state to another is of clinical importance. With the availability of CGM, the assessment of GV timing became not only possible but also required (32). Responding to this necessity, we should keep in mind that the assessment of temporal characteristics of GV benefits from mathematical computations that go beyond basic arithmetic. Thus, some assistance from the theory and practice of time series and dynamical systems analysis would be helpful. Fortunately, these fields are highly developed, theoretically and computationally, and have been used for decades in other areas of science […] The computational methods are standardized and available in a number of software products and should be used for the assessment of GV. […] There is no doubt that the timing of glucose fluctuations is clinically important, but there is a price to pay for its accurate assessment—a bit higher level of mathematical complexity. This, however, should not be a deterrent.”

vii. Predictors of Increased Carotid Intima-Media Thickness in Youth With Type 1 Diabetes: The SEARCH CVD Study.

“Adults with childhood-onset type 1 diabetes are at increased risk for premature cardiovascular disease (CVD) morbidity and mortality compared with the general population (1). The antecedents of CVD begin in childhood (2), and early or preclinical atherosclerosis can be detected as intima-media thickening in the artery wall (3). Carotid intima-media thickness (IMT) is an established marker of atherosclerosis because of its associations with CVD risk factors (4,5) and CVD outcomes, such as myocardial infarction and stroke in adults (6,7).

Prior work […] has shown that youth with type 1 diabetes have higher carotid IMT than control subjects (813). In cross-sectional studies, risk factors associated with higher carotid IMT include younger age at diabetes onset, male sex, adiposity, higher blood pressure (BP) and hemoglobin A1c (HbA1c), and lower vitamin C levels (8,9,11). Only one study has evaluated CVD risk factors longitudinally and the association with carotid IMT progression in youth with type 1 diabetes (14). In a German cohort of 70 youth with type 1 diabetes, Dalla Pozza et al. (14) demonstrated that CVD risk factors, including BMI z score (BMIz), systolic BP, and HbA1c, worsened over time. They also found that baseline HbA1c and baseline and follow-up systolic BP were significant predictors of change in carotid IMT over 4 years.”

“Before the current study, no published reports had assessed the impact of changes in CVD risk factors and carotid IMT in U.S. adolescents with type 1 diabetes. […] Participants in this study were enrolled in SEARCH CVD, an ancillary study to the SEARCH for Diabetes in Youth that was conducted in two of the five SEARCH centers (Colorado and Ohio). […] This report includes 298 youth who completed both baseline and follow-up SEARCH CVD visits […] At the initial visit, youth with type 1 diabetes were a mean age of 13.3 ± 2.9 years (range 7.6–21.3 years) and had an average disease duration of 3.6 ± 3.3 years. […] Follow-up data were obtained at a mean age of 19.2 ± 2.7 years, when the average duration of type 1 diabetes was 10.1 ± 3.9 years. […] In the current study, we show that older age (at baseline) and male sex were significantly associated with follow-up IMT. By using AUC measurements, we also show that a higher BMIz exposure over ∼5 years was significantly associated with IMT at follow-up. From baseline to follow-up, the mean BMI increased from within normal limits (21.1 ± 4.3 kg/m2) to overweight (25.1 ± 4.8 kg/m2), defined as a BMI ≥25 kg/m2 in adults (26,27). This large change in BMI may explain why BMIz was the only modifiable risk factor to be associated with follow-up IMT in the final models. Whether the observed increase in BMIz over time is part of the natural evolution of diabetes, aging in an obesogenic society, or a consequence of intensive insulin therapy is not known.”

“Data from the DCCT/EDIC cohorts have suggested nontraditional risk factors, including acute phase reactants, thrombolytic factors, cytokines/adipokines (34), oxidized LDL, and advanced glycation end products (30) may be important biomarkers of increased CVD risk in adults with type 1 diabetes. However, many of these nontraditional risk factors […] were not found to associate with IMT until 8–12 years after the DCCT ended, at the time when traditional CVD risk factors were also found to predict IMT. Collectively, these findings suggest that many traditional and nontraditional risk factors are not identified as relevant until later in the atherosclerotic process and highlight the critical need to better identify risk factors that may influence carotid IMT early in the course of type 1 diabetes because these may be important modifiable CVD risk factors of focus in the adolescent population. […] Although BMIz was the only identified risk factor to predict follow-up IMT at this age [in our study], it is possible that increases in dyslipidemia, BP, smoking, and HbA1c are related to carotid IMT but only after longer duration of exposure.”

July 13, 2017 Posted by | Cardiology, Diabetes, Medicine, Neurology, Studies | Leave a comment

A few diabetes papers of interest

i. An Inverse Relationship Between Age of Type 2 Diabetes Onset and Complication Risk and Mortality: The Impact of Youth-Onset Type 2 Diabetes.

“This study compared the prevalence of complications in 354 patients with T2DM diagnosed between 15 and 30 years of age (T2DM15–30) with that in a duration-matched cohort of 1,062 patients diagnosed between 40 and 50 years (T2DM40–50). It also examined standardized mortality ratios (SMRs) according to diabetes age of onset in 15,238 patients covering a wider age-of-onset range.”

“After matching for duration, despite their younger age, T2DM15–30 had more severe albuminuria (P = 0.004) and neuropathy scores (P = 0.003). T2DM15–30 were as commonly affected by metabolic syndrome factors as T2DM40–50 but less frequently treated for hypertension and dyslipidemia (P < 0.0001). An inverse relationship between age of diabetes onset and SMR was seen, which was the highest for T2DM15–30 (3.4 [95% CI 2.7–4.2]). SMR plots adjusting for duration show that for those with T2DM15–30, SMR is the highest at any chronological age, with a peak SMR of more than 6 in early midlife. In contrast, mortality for older-onset groups approximates that of the background population.”

“Young people with type 2 diabetes are likely to be obese, with a clustering of unfavorable cardiometabolic risk factors all present at a very early age (3,4). In adolescents with type 2 diabetes, a 10–30% prevalence of hypertension and an 18–54% prevalence of dyslipidemia have been found, much greater than would be expected in a population of comparable age (4).”

CONCLUSIONS The negative effect of diabetes on morbidity and mortality is greatest for those diagnosed at a young age compared with T2DM of usual onset.”

It’s important to keep base rates in mind when interpreting the reported SMRs, but either way this is interesting.

ii. Effects of Sleep Deprivation on Hypoglycemia-Induced Cognitive Impairment and Recovery in Adults With Type 1 Diabetes.

OBJECTIVE To ascertain whether hypoglycemia in association with sleep deprivation causes greater cognitive dysfunction than hypoglycemia alone and protracts cognitive recovery after normoglycemia is restored.”

CONCLUSIONS Hypoglycemia per se produced a significant decrement in cognitive function; coexisting sleep deprivation did not have an additive effect. However, after restoration of normoglycemia, preceding sleep deprivation was associated with persistence of hypoglycemic symptoms and greater and more prolonged cognitive dysfunction during the recovery period. […] In the current study of young adults with type 1 diabetes, the impairment of cognitive function that was associated with hypoglycemia was not exacerbated by sleep deprivation. […] One possible explanation is that hypoglycemia per se exerts a ceiling effect on the degree of cognitive dysfunction as is possible to demonstrate with conventional tests.”

iii. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up.

“The DCCT randomly assigned 1,441 patients with type 1 diabetes to intensive versus conventional therapy for a mean of 6.5 years, after which 93% were subsequently monitored during the observational Epidemiology of Diabetes Interventions and Complications (EDIC) study. Cardiovascular disease (nonfatal myocardial infarction and stroke, cardiovascular death, confirmed angina, congestive heart failure, and coronary artery revascularization) was adjudicated using standardized measures.”

“During 30 years of follow-up in DCCT and EDIC, 149 cardiovascular disease events occurred in 82 former intensive treatment group subjects versus 217 events in 102 former conventional treatment group subjects. Intensive therapy reduced the incidence of any cardiovascular disease by 30% (95% CI 7, 48; P = 0.016), and the incidence of major cardiovascular events (nonfatal myocardial infarction, stroke, or cardiovascular death) by 32% (95% CI −3, 56; P = 0.07). The lower HbA1c levels during the DCCT/EDIC statistically account for all of the observed treatment effect on cardiovascular disease risk.”

CONCLUSIONS Intensive diabetes therapy during the DCCT (6.5 years) has long-term beneficial effects on the incidence of cardiovascular disease in type 1 diabetes that persist for up to 30 years.”

I was of course immediately thinking that perhaps they had not considered if this might just be the result of the Hba1c differences achieved during the trial being maintained long-term (during follow-up), and so what they were doing was not as much measuring the effect of the ‘metabolic memory’ component as they were just measuring standard population outcome differences resulting from long-term Hba1c differences. But they (of course) had thought about that, and that’s not what’s going on here, which is what makes it particularly interesting:

“Mean HbA1c during the average 6.5 years of DCCT intensive therapy was ∼2% (20 mmol/mol) lower than that during conventional therapy (7.2 vs. 9.1% [55.6 vs. 75.9 mmol/mol], P < 0.001). Subsequently during EDIC, HbA1c differences between the treatment groups dissipated. At year 11 of EDIC follow-up and most recently at 19–20 years of EDIC follow-up, there was only a trivial difference between the original intensive and conventional treatment groups in the mean level of HbA1c

They do admittedly find a statistically significant difference between the Hba1cs of the two groups when you look at (weighted) Hba1cs long-term, but that difference is certainly nowhere near large enough to explain the clinical differences in outcomes you observe. Another argument in favour of the view that what’s driving these differences is metabolic memory is the observation that the difference in outcomes between the treatment and control groups are smaller now than they were ten years ago (my default would probably be to if anything expect the outcomes of the two groups to converge long-term if the samples were properly randomized to start with, but this is not the only plausible model and it sort of depends on how you model the risk function, as they also talk about in the paper):

“[T]he risk reduction of any CVD with intensive therapy through 2013 is now less than that reported previously through 2004 (30% [P = 0.016] vs. 47% [P = 0.005]), and likewise, the risk reduction per 10% lower mean HbA1c through 2013 was also somewhat lower than previously reported but still highly statistically significant (17% [P = 0.0001] vs. 20% [P = 0.001]).”

iv. Commonly Measured Clinical Variables Are Not Associated With Burden of Complications in Long-standing Type 1 Diabetes: Results From the Canadian Study of Longevity in Diabetes.

“The Canadian Study of Longevity in Diabetes actively recruited 325 individuals who had T1D for 50 or more years (5). Subjects completed a questionnaire, and recent laboratory tests and eye reports were provided by primary care physicians and eye specialists, respectively. […] The 325 participants were 65.5 ± 8.5 years old with diagnosis at age 10 years (interquartile range [IQR] 6.0, 16) and duration of 54.9 ± 6.4 years.”

“In univariable analyses, the following were significantly associated with a greater burden of complications: presence of hypertension, statin, aspirin and ACE inhibitor or ARB use, higher Problem Areas in Diabetes (PAID) and Geriatric Depression Scale (GDS) scores, and higher levels of triglycerides and HbA1c. The following were significantly associated with a lower burden of complications: current physical activity, higher quality of life, and higher HDL cholesterol.”

“In the multivariable analysis, a higher PAID score was associated with a greater burden of complications (risk ratio [RR] 1.15 [95% CI 1.06–1.25] for each 10-point-higher score). Aspirin and statin use were also associated with a greater burden of complications (RR 1.24 [95% CI 1.01–1.52] and RR 1.34 [95% CI 1.05–1.70], respectively) (Table 1), whereas HbA1c was not.”

“Our findings indicate that in individuals with long-standing T1D, burden of complications is largely not associated with historical characteristics or simple objective measurements, as associations with statistical significance likely reflect reverse causality. Notably, HbA1c was not associated with burden of complications […]. This further confirms that other unmeasured variables such as genetic, metabolic, or physiologic characteristics may best identify mechanisms and biomarkers of complications in long-standing T1D.”

v. Cardiovascular Risk Factor Targets and Cardiovascular Disease Event Risk in Diabetes: A Pooling Project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study.

“Controlling cardiovascular disease (CVD) risk factors in diabetes mellitus (DM) reduces the number of CVD events, but the effects of multifactorial risk factor control are not well quantified. We examined whether being at targets for blood pressure (BP), LDL cholesterol (LDL-C), and glycated hemoglobin (HbA1c) together are associated with lower risks for CVD events in U.S. adults with DM. […] We studied 2,018 adults, 28–86 years of age with DM but without known CVD, from the Atherosclerosis Risk in Communities (ARIC) study, Multi-Ethnic Study of Atherosclerosis (MESA), and Jackson Heart Study (JHS). Cox regression examined coronary heart disease (CHD) and CVD events over a mean 11-year follow-up in those individuals at BP, LDL-C, and HbA1c target levels, and by the number of controlled risk factors.”

“Of 2,018 DM subjects (43% male, 55% African American), 41.8%, 32.1%, and 41.9% were at target levels for BP, LDL-C, and HbA1c, respectively; 41.1%, 26.5%, and 7.2% were at target levels for any one, two, or all three factors, respectively. Being at BP, LDL-C, or HbA1c target levels related to 17%, 33%, and 37% lower CVD risks and 17%, 41%, and 36% lower CHD risks, respectively (P < 0.05 to P < 0.0001, except for BP in CHD risk); those subjects with one, two, or all three risk factors at target levels (vs. none) had incrementally lower adjusted risks of CVD events of 36%, 52%, and 62%, respectively, and incrementally lower adjusted risks of CHD events of 41%, 56%, and 60%, respectively (P < 0.001 to P < 0.0001). Propensity score adjustment showed similar findings.”

“In our pooled analysis of subjects with DM in three large-scale U.S. prospective studies, the more factors among HbA1c, BP, and LDL-C that were at goal levels, the lower are the observed CHD and CVD risks (∼60% lower when all three factors were at goal levels compared with none). However, fewer than one-tenth of our subjects were at goal levels for all three factors. These findings underscore the value of achieving target or lower levels of these modifiable risk factors, especially in combination, among persons with DM for the future prevention of CHD and CVD events.”

In some studies you see very low proportions of patients reaching target variables because the targets are stupid (to be perfectly frank about it). The HbA1c target applied in this study was a level <53.0 mmol/mol (7%), which is definitely not crazy if the majority of the individuals included were type 2, which they almost certainly were. You can argue about the BP goal, but it’s obvious here that the authors are perfectly aware of the contentiousness of this variable.

It’s incidentally noteworthy – and the authors do take note of it, of course – that one of the primary results of this study (~60% lower risk when all risk factors reach the target goal), which includes a large proportion of African Americans in the study sample, is almost identical to the results of the Danish Steno-2 clinical trial, which included only Danish white patients (and the results of which I have discussed here on the blog before). In the Steno study, the result was “a 57% reduction in CVD death and a 59% reduction in CVD events.”

vi. Illness Identity in Adolescents and Emerging Adults With Type 1 Diabetes: Introducing the Illness Identity Questionnaire.

“The current study examined the utility of a new self-report questionnaire, the Illness Identity Questionnaire (IIQ), which assesses the concept of illness identity, or the degree to which type 1 diabetes is integrated into one’s identity. Four illness identity dimensions (engulfment, rejection, acceptance, and enrichment) were validated in adolescents and emerging adults with type 1 diabetes. Associations with psychological and diabetes-specific functioning were assessed.”

“A sample of 575 adolescents and emerging adults (14–25 years of age) with type 1 diabetes completed questionnaires on illness identity, psychological functioning, diabetes-related problems, and treatment adherence. Physicians were contacted to collect HbA1c values from patients’ medical records. Confirmatory factor analysis (CFA) was conducted to validate the IIQ. Path analysis with structural equation modeling was used to examine associations between illness identity and psychological and diabetes-specific functioning.”

“The first two identity dimensions, engulfment and rejection, capture a lack of illness integration, or the degree to which having diabetes is not well integrated as part of one’s sense of self. Engulfment refers to the degree to which diabetes dominates a person’s identity. Individuals completely define themselves in terms of their diabetes, which invades all domains of life (9). Rejection refers to the degree to which diabetes is rejected as part of one’s identity and is viewed as a threat or as unacceptable to the self. […] Acceptance refers to the degree to which individuals accept diabetes as a part of their identity, besides other social roles and identity assets. […] Enrichment refers to the degree to which having diabetes results in positive life changes, benefits one’s identity, and enables one to grow as a person (12). […] These changes can manifest themselves in different ways, including an increased appreciation for life, a change of life priorities, and a more positive view of the self (14).”

“Previous quantitative research assessing similar constructs has suggested that the degree to which individuals integrate their illness into their identity may affect psychological and diabetes-specific functioning in patients. Diabetes intruding upon all domains of life (similar to engulfment) [has been] related to more depressive symptoms and more diabetes-related problems […] In contrast, acceptance has been related to fewer depressive symptoms and diabetes-related problems and to better glycemic control (6,15). Similarly, benefit finding has been related to fewer depressive symptoms and better treatment adherence (16). […] The current study introduces the IIQ in individuals with type 1 diabetes as a way to assess all four illness identity dimensions.”

“The Cronbach α was 0.90 for engulfment, 0.84 for rejection, 0.85 for acceptance, and 0.90 for enrichment. […] CFA indicated that the IIQ has a clear factor structure, meaningfully differentiating four illness identity dimensions. Rejection was related to worse treatment adherence and higher HbA1c values. Engulfment was related to less adaptive psychological functioning and more diabetes-related problems. Acceptance was related to more adaptive psychological functioning, fewer diabetes-related problems, and better treatment adherence. Enrichment was related to more adaptive psychological functioning. […] the concept of illness identity may help to clarify why certain adolescents and emerging adults with diabetes show difficulties in daily functioning, whereas others succeed in managing developmental and diabetes-specific challenges.”

June 30, 2017 Posted by | Cardiology, Diabetes, Medicine, Psychology, Studies | Leave a comment

A few diabetes papers of interest

i. Cost-Effectiveness of Prevention and Treatment of the Diabetic Foot.

“A risk-based Markov model was developed to simulate the onset and progression of diabetic foot disease in patients with newly diagnosed type 2 diabetes managed with care according to guidelines for their lifetime. Mean survival time, quality of life, foot complications, and costs were the outcome measures assessed. Current care was the reference comparison. Data from Dutch studies on the epidemiology of diabetic foot disease, health care use, and costs, complemented with information from international studies, were used to feed the model.

RESULTS—Compared with current care, guideline-based care resulted in improved life expectancy, gain of quality-adjusted life-years (QALYs), and reduced incidence of foot complications. The lifetime costs of management of the diabetic foot following guideline-based care resulted in a cost per QALY gained of <$25,000, even for levels of preventive foot care as low as 10%. The cost-effectiveness varied sharply, depending on the level of foot ulcer reduction attained.

CONCLUSIONS—Management of the diabetic foot according to guideline-based care improves survival, reduces diabetic foot complications, and is cost-effective and even cost saving compared with standard care.”

I won’t go too deeply into the model setup and the results but some of the data they used to feed the model were actually somewhat interesting in their own right, and I have added some of these data below, along with some of the model results.

“It is estimated that 80% of LEAs [lower extremity amputations] are preceded by foot ulcers. Accordingly, it has been demonstrated that preventing the development of foot ulcers in patients with diabetes reduces the frequency of LEAs by 49–85% (6).”

“An annual ulcer incidence rate of 2.1% and an amputation incidence rate of 0.6% were among the reference country-specific parameters derived from this study and adopted in the model.”

“The health outcomes results of the cohort following standard care were comparable to figures reported for diabetic patients in the Netherlands. […] In the 10,000 patients followed until death, a total of 1,780 ulcer episodes occurred, corresponding to a cumulative ulcer incidence of 17.8% and an annual ulcer incidence of 2.2% (mean annual ulcer incidence for the Netherlands is 2.1%) (17). The number of amputations observed was 362 (250 major and 112 minor), corresponding to a cumulative incidence of 3.6% and an annual incidence of 0.4% (mean annual amputation incidence reported for the Netherlands is 0.6%) (17).”

“Cornerstones of guidelines-based care are intensive glycemic control (IGC) and optimal foot care (OFC). Although health benefits and economic efficiency of intensive blood glucose control (8) and foot care programs (914) have been individually reported, the health and economic outcomes and the cost-effectiveness of both interventions have not been determined. […] OFC according to guidelines includes professional protective foot care, education of patients and staff, regular inspection of the feet, identification of the high-risk patient, treatment of nonulcerative lesions, and a multidisciplinary approach to established foot ulcers. […] All cohorts of patients simulated for the different scenarios of guidelines care resulted in improved life expectancy, QALYs gained, and reduced incidence of foot ulcers and LEA compared with standard care. The largest effects on these outcomes were obtained when patients received IGC + OFC. When comparing the independent health effects of the two guidelines strategies, OFC resulted in a greater reduction in ulcer and amputation rates than IGC. Moreover, patients who received IGC + OFC showed approximately the same LEA incidence as patients who received OFC alone. The LEA decrease obtained was proportional to the level of foot ulcer reduction attained.”

“The mean total lifetime costs of a patient under either of the three guidelines care scenarios ranged from $4,088 to $4,386. For patients receiving IGC + OFC, these costs resulted in <$25,000 per QALY gained (relative to standard care). For patients receiving IGC alone, the ICER [here’s a relevant link – US] obtained was $32,057 per QALY gained, and for those receiving OFC alone, this ICER ranged from $12,169 to $220,100 per QALY gained, depending on the level of ulcer reduction attained. […] Increasing the effectiveness of preventive foot care in patients under OFC and IGC + OFC resulted in more QALYs gained, lower costs, and a more favorable ICER. The results of the simulations for the combined scenario (IGC + OFC) were rather insensitive to changes in utility weights and costing parameters. Similar results were obtained for parameter variations in the other two scenarios (IGC and OFC separately).”

“The results of this study suggest that IGC + OFC reduces foot ulcers and amputations and leads to an improvement in life expectancy. Greater health benefits are obtained with higher levels of foot ulcer prevention. Although care according to guidelines increases health costs, the cost per QALY gained is <$25,000, even for levels of preventive foot care as low as 10%. ICERs of this order are cost-effective according to the stratification of interventions for diabetes recently proposed (32). […] IGC falls into the category of a possibly cost-effective intervention in the management of the diabetic foot. Although it does not produce significant reduction in foot ulcers and LEA, its effectiveness resides in the slowing of neuropathy progression rates.

Extrapolating our results to a practical situation, if IGC + OFC was to be given to all diabetic patients in the Netherlands, with the aim of reducing LEA by 50% (St. Vincent’s declaration), the cost per QALY gained would be $12,165 and the cost for managing diabetic ulcers and amputations would decrease by 53 and 58%, respectively. From a policy perspective, this is clearly cost-effective and cost saving compared with current care.”

ii. Early Glycemic Control, Age at Onset, and Development of Microvascular Complications in Childhood-Onset Type 1 Diabetes.

“The aim of this work was to study the impact of glycemic control (HbA1c) early in disease and age at onset on the occurrence of incipient diabetic nephropathy (MA) and background retinopathy (RP) in childhood-onset type 1 diabetes.

RESEARCH DESIGN AND METHODS—All children, diagnosed at 0–14 years in a geographically defined area in northern Sweden between 1981 and 1992, were identified using the Swedish Childhood Diabetes Registry. From 1981, a nationwide childhood diabetes care program was implemented recommending intensified insulin treatment. HbA1c and urinary albumin excretion were analyzed, and fundus photography was performed regularly. Retrospective data on all 94 patients were retrieved from medical records and laboratory reports.

RESULTS—During the follow-up period, with a mean duration of 12 ± 4 years (range 5–19), 17 patients (18%) developed MA, 45 patients (48%) developed RP, and 52% had either or both complications. A Cox proportional hazard regression, modeling duration to occurrence of MA or RP, showed that glycemic control (reflected by mean HbA1c) during the follow-up was significantly associated with both MA and RP when adjusted for sex, birth weight, age at onset, and tobacco use as potential confounders. Mean HbA1c during the first 5 years of diabetes was a near-significant determinant for development of MA (hazard ratio 1.41, P = 0.083) and a significant determinant of RP (1.32, P = 0.036). The age at onset of diabetes significantly influenced the risk of developing RP (1.11, P = 0.021). Thus, in a Kaplan-Meier analysis, onset of diabetes before the age of 5 years, compared with the age-groups 5–11 and >11 years, showed a longer time to occurrence of RP (P = 0.015), but no clear tendency was seen for MA, perhaps due to lower statistical power.

CONCLUSIONS—Despite modern insulin treatment, >50% of patients with childhood-onset type 1 diabetes developed detectable diabetes complications after ∼12 years of diabetes. Inadequate glycemic control, also during the first 5 years of diabetes, seems to accelerate time to occurrence, whereas a young age at onset of diabetes seems to prolong the time to development of microvascular complications. […] The present study and other studies (15,54) indicate that children with an onset of diabetes before the age of 5 years may have a prolonged time to development of microvascular complications. Thus, the youngest age-groups, who are most sensitive to hypoglycemia with regard to risk of persistent brain damage, may have a relative protection during childhood or a longer time to development of complications.”

It’s important to note that although some people reading the study may think this is all ancient history (people diagnosed in the 80es?), to a lot of people it really isn’t. The study is of great personal interest to me, as I was diagnosed in ’87; if it had been a Danish study rather than a Swedish one I might well have been included in the analysis.

Another note to add in the context of the above coverage is that unlike what the authors of the paper seem to think/imply, hypoglycemia may not be the only relevant variable of interest in the context of the effect of childhood diabetes on brain development, where early diagnosis has been observed to tend to lead to less favourable outcomes – other variables which may be important include DKA episodes and perhaps also chronic hyperglycemia during early childhood. See this post for more stuff on these topics.

Some more stuff from the paper:

“The annual incidence of type 1 diabetes in northern Sweden in children 0–14 years of age is now ∼31/100,000. During the time period 1981–1992, there has been an increase in the annual incidence from 19 to 31/100,000 in northern Sweden. This is similar to the rest of Sweden […]. Seventeen (18%) of the 94 patients fulfilled the criteria for MA during the follow-up period. None of the patients developed overt nephropathy, elevated serum creatinine, or had signs of any other kidney disorder, e.g., hematuria, during the follow-up period. […] The mean time to diagnosis of MA was 9 ± 3 years (range 4–15) from diabetes onset. Forty-five (48%) of the 94 patients fulfilled the criteria for RP during the follow-up period. None of the patients developed proliferative retinopathy or were treated with photocoagulation. The mean time to diagnosis of RP was 11 ± 4 years (range 4–19) from onset of diabetes. Of the 45 patients with RP, 13 (29%) had concomitant MA, and thus 13 (76.5%) of the 17 patients with MA had concomitant RP. […] Altogether, among the 94 patients, 32 (34%) had isolated RP, 4 (4%) had isolated MA, and 13 (14%) had combined RP and MA. Thus, 49 (52%) patients had either one or both complications and, hence, 45 (48%) had neither of these complications.”

“When modeling MA as a function of glycemic level up to the onset of MA or during the entire follow-up period, adjusting for sex, birth weight, age at onset of diabetes, and tobacco use, only glycemic control had a significant effect. An increase in hazard ratio (HR) of 83% per one percentage unit increase in mean HbA1c was seen. […] The increase in HR of developing RP for each percentage unit rise in HbA1c during the entire follow-up period was 43% and in the early period 32%. […] Age at onset of diabetes was a weak but significant independent determinant for the development of RP in all regression models (P = 0.015, P = 0.018, and P = 0.010, respectively). […] Despite that this study was relatively small and had a retrospective design, we were able to show that the glycemic level already during the first 5 years may be an important predictor of later development of both MA and RP. This is in accordance with previous prospective follow-up studies (16,30).”

“Previously, male sex, smoking, and low birth weight have been shown to be risk factors for the development of nephropathy and retinopathy (6,4549). However, in this rather small retrospective study with a limited follow-up time, we could not confirm these associations”. This may just be because of lack of power, it’s a relatively small study. Again, this is/was of personal interest to me; two of those three risk factors apply to me, and neither of those risk factors are modifiable.

iii. Eighteen Years of Fair Glycemic Control Preserves Cardiac Autonomic Function in Type 1 Diabetes.

“Reduced cardiovascular autonomic function is associated with increased mortality in both type 1 and type 2 diabetes (14). Poor glycemic control plays an important role in the development and progression of diabetic cardiac autonomic dysfunction (57). […] Diabetic cardiovascular autonomic neuropathy (CAN) can be defined as impaired function of the peripheral autonomic nervous system. Exercise intolerance, resting tachycardia, and silent myocardial ischemia may be early signs of cardiac autonomic dysfunction (9).The most frequent finding in subclinical and symptomatic CAN is reduced heart rate variability (HRV) (10). […] No other studies have followed type 1 diabetic patients on intensive insulin treatment during ≥14-year periods and documented cardiac autonomic dysfunction. We evaluated the association between 18 years’ mean HbA1c and cardiac autonomic function in a group of type 1 diabetic patients with 30 years of disease duration.”

“A total of 39 patients with type 1 diabetes were followed during 18 years, and HbA1c was measured yearly. At 18 years follow-up heart rate variability (HRV) measurements were used to assess cardiac autonomic function. Standard cardiac autonomic tests during normal breathing, deep breathing, the Valsalva maneuver, and the tilt test were performed. Maximal heart rate increase during exercise electrocardiogram and minimal heart rate during sleep were also used to describe cardiac autonomic function.

RESULTS—We present the results for patients with mean HbA1c <8.4% (two lowest HbA1c tertiles) compared with those with HbA1c ≥8.4% (highest HbA1c tertile). All of the cardiac autonomic tests were significantly different in the high- and the low-HbA1c groups, and the most favorable scores for all tests were seen in the low-HbA1c group. In the low-HbA1c group, the HRV was 40% during deep breathing, and in the high-HbA1c group, the HRV was 19.9% (P = 0.005). Minimal heart rate at night was significantly lower in the low-HbA1c groups than in the high-HbA1c group (P = 0.039). With maximal exercise, the increase in heart rate was significantly higher in the low-HbA1c group compared with the high-HbA1c group (P = 0.001).

CONCLUSIONS—Mean HbA1c during 18 years was associated with cardiac autonomic function. Cardiac autonomic function was preserved with HbA1c <8.4%, whereas cardiac autonomic dysfunction was impaired in the group with HbA1c ≥8.4%. […] The study underlines the importance of good glycemic control and demonstrates that good long-term glycemic control is associated with preserved cardiac autonomic function, whereas a lack of good glycemic control is associated with cardiac autonomic dysfunction.”

These results are from Norway (Oslo), and again they seem relevant to me personally (‘from a statistical point of view’) – I’ve had diabetes for about as long as the people they included in the study.

iv. The Mental Health Comorbidities of Diabetes.

“Individuals living with type 1 or type 2 diabetes are at increased risk for depression, anxiety, and eating disorder diagnoses. Mental health comorbidities of diabetes compromise adherence to treatment and thus increase the risk for serious short- and long-term complications […] Young adults with type 1 diabetes are especially at risk for poor physical and mental health outcomes and premature mortality. […] we summarize the prevalence and consequences of mental health problems for patients with type 1 or type 2 diabetes and suggest strategies for identifying and treating patients with diabetes and mental health comorbidities.”

“Major advances in the past 2 decades have improved understanding of the biological basis for the relationship between depression and diabetes.2 A bidirectional relationship might exist between type 2 diabetes and depression: just as type 2 diabetes increases the risk for onset of major depression, a major depressive disorder signals increased risk for on set of type 2 diabetes.2 Moreover, diabetes distress is now recognized as an entity separate from major depressive disorder.2 Diabetes distress occurs because virtually all of diabetes care involves self-management behavior—requiring balance of a complex set of behavioral tasks by the person and family, 24 hours a day, without “vacation” days. […] Living with diabetes is associated with a broad range of diabetes-related distresses, such as feeling over-whelmed with the diabetes regimen; being concerned about the future and the possibility of serious complications; and feeling guilty when management is going poorly. This disease burden and emotional distress in individuals with type 1 or type 2 diabetes, even at levels of severity below the threshold for a psychiatric diagnosis of depression or anxiety, are associated with poor adherence to treatment, poor glycemic control, higher rates of diabetes complications, and impaired quality of life. […] Depression in the context of diabetes is […] associated with poor self-care with respect to diabetes treatment […] Depression among individuals with diabetes is also associated with increased health care use and expenditures, irrespective of age, sex, race/ethnicity, and health insurance status.3

“Women with type 1 diabetes have a 2-fold increased risk for developing an eating disorder and a 1.9-fold increased risk for developing subthreshold eating disorders than women without diabetes.6 Less is known about eating disorders in boys and men with diabetes. Disturbed eating behaviors in women with type 1 diabetes include binge eating and caloric purging through insulin restriction, with rates of these disturbed eating behaviors reported to occur in 31% to 40% of women with type 1 diabetes aged between 15 and 30 years.6 […] disordered eating behaviors persist and worsen over time. Women with type 1 diabetes and eating disorders have poorer glycemic control, with higher rates of hospitalizations and retinopathy, neuropathy, and premature death compared with similarly aged women with type 1 diabetes without eating disorders.6 […] few diabetes clinics provide mental health screening or integrate mental/behavioral health services in diabetes clinical care.4 It is neither practical nor affordable to use standardized psychiatric diagnostic interviews to diagnose mental health comorbidities in individuals with diabetes. Brief paper-and-pencil self-report measures such as the Beck Depression Inventory […] that screen for depressive symptoms are practical in diabetes clinical settings, but their use remains rare.”

The paper does not mention this, but it is important to note that there are multiple plausible biological pathways which might help to explain bidirectional linkage between depression and type 2 diabetes. Physiological ‘stress’ (think: inflammation) is likely to be an important factor, and so are the typical physiological responses to some of the pharmacological treatments used to treat depression (…as well as other mental health conditions); multiple drugs used in psychiatry, including tricyclic antidepressants, cause weight gain and have proven diabetogenic effects – I’ve covered these topics before here on the blog. I’ve incidentally also covered other topics touched briefly upon in the paper – here’s for example a more comprehensive post about screening for depression in the diabetes context, and here’s a post with some information about how one might go about screening for eating disorders; skin signs are important. I was a bit annoyed that the author of the above paper did not mention this, as observing whether or not Russell’s sign – which is a very reliable indicator of eating disorder – is present or not is easier/cheaper/faster than performing any kind of even semi-valid depression screen.

v. Diabetes, Depression, and Quality of Life. This last one covers topics related to the topics covered in the paper above.

“The study consisted of a representative population sample of individuals aged ≥15 years living in South Australia comprising 3,010 personal interviews conducted by trained health interviewers. The prevalence of depression in those suffering doctor-diagnosed diabetes and comparative effects of diabetic status and depression on quality-of-life dimensions were measured.

RESULTS—The prevalence of depression in the diabetic population was 24% compared with 17% in the nondiabetic population. Those with diabetes and depression experienced an impact with a large effect size on every dimension of the Short Form Health-Related Quality-of-Life Questionnaire (SF-36) as compared with those who suffered diabetes and who were not depressed. A supplementary analysis comparing both depressed diabetic and depressed nondiabetic groups showed there were statistically significant differences in the quality-of-life effects between the two depressed populations in the physical and mental component summaries of the SF-36.

CONCLUSIONS—Depression for those with diabetes is an important comorbidity that requires careful management because of its severe impact on quality of life.”

I felt slightly curious about the setup after having read this, because representative population samples of individuals should not in my opinion yield depression rates of either 17% nor 24%. Rates that high suggest to me that the depression criteria used in the paper are a bit ‘laxer’/more inclusive than what you see in some other contexts when reading this sort of literature – to give an example of what I mean, the depression screening post I link to above noted that clinical or major depression occurred in 11.4% of people with diabetes, compared to a non-diabetic prevalence of 5%. There’s a long way from 11% to 24% and from 5% to 17%. Another potential explanation for such a high depression rate could of course also be some sort of selection bias at the data acquisition stage, but that’s obviously not the case here. However 3000 interviews is a lot of interviews, so let’s read on…

“Several studies have assessed the impact of depression in diabetes in terms of the individual’s functional ability or quality of life (3,4,13). Brown et al. (13) examined preference-based time tradeoff utility values associated with diabetes and showed that those with diabetes were willing to trade a significant proportion of their remaining life in return for a diabetes-free health state.”

“Depression was assessed using the mood module of the Primary Care Evaluation of Mental Disorders questionnaire. This has been validated to provide estimates of mental disorder comparable with those found using structured and longer diagnostic interview schedules (16). The mental disorders examined in the questionnaire included major depressive disorder, dysthymia, minor depressive disorder, and bipolar disorder. [So yes, the depression criteria used in this study are definitely more inclusive than depression criteria including only people with MDD] […] The Short Form Health-Related Quality-of-Life Questionnaire (SF-36) was also included to assess the quality of life of the different population groups with and without diabetes. […] Five groups were examined: the overall population without diabetes and without depression; the overall diabetic population; the depression-only population; the diabetic population without depression; and the diabetic population with depression.”

“Of the population sample, 205 (6.8%) were classified as having major depression, 130 (4.3%) had minor depression, 105 (3.5%) had partial remission of major depression, 79 (2.6%) had dysthymia, and 5 (0.2%) had bipolar disorder (depressed phase). No depressive syndrome was detected in 2,486 (82.6%) respondents. The population point prevalence of doctor-diagnosed diabetes in this survey was 5.2% (95% CI 4.6–6.0). The prevalence of depression in the diabetic population was 23.6% (22.1–25.1) compared with 17.1% (15.8–18.4) in the nondiabetic population. This difference approached statistical significance (P = 0.06). […] There [was] a clear difference in the quality-of-life scores for the diabetic and depression group when compared with the diabetic group without depression […] Overall, the highest quality-of-life scores are experienced by those without diabetes and depression and the lowest by those with diabetes and depression. […] the standard scores of those with no diabetes have quality-of-life status comparable with the population mean or slightly better. At the other extreme those with diabetes and depression experience the most severe comparative impact on quality-of-life for every dimension. Between these two extremes, diabetes overall and the diabetes without depression groups have a moderate-to-severe impact on the physical functioning, role limitations (physical), and general health scales […] The results of the two-factor ANOVA showed that the interaction term was significant only for the PCS [Physical Component Score – US] scale, indicating a greater than additive effect of diabetes and depression on the physical health dimension.”

“[T]here was a significant interaction between diabetes and depression on the PCS but not on the MCS [Mental Component Score. Do note in this context that the no-interaction result is far from certain, because as they observe: “it may simply be sample size that has not allowed us to observe a greater than additive effect in the MCS scale. Although there was no significant interaction between diabetes and depression and the MCS scale, we did observe increases on the effect size for the mental health dimensions”]. One explanation for this finding might be that depression can influence physical outcomes, such as recovery from myocardial infarction, survival with malignancy, and propensity to infection. Various mechanisms have been proposed for this, including changes to the immune system (24). Other possibilities are that depression in diabetes may affect the capacity to maintain medication vigilance, maintain a good diet, and maintain other lifestyle factors, such as smoking and exercise, all of which are likely possible pathways for a greater than additive effect. Whatever the mechanism involved, these data indicate that the addition of depression to diabetes has a severe impact on quality of life, and this needs to be managed in clinical practice.”

May 25, 2017 Posted by | Cardiology, Diabetes, Health Economics, Medicine, Nephrology, Neurology, Ophthalmology, Papers, Personal, Pharmacology, Psychiatry, Psychology | Leave a comment

A few diabetes papers of interest

A couple of weeks ago I decided to cover some of the diabetes articles I’d looked at and bookmarked in the past, but there were a lot of articles and I did not get very far. This post will cover some more of these articles I had failed to cover here despite intending to do so at some point. Considering that I these days relatively regularly peruse e.g. the Diabetes Care archives I am thinking of making this sort of post a semi-regular feature of the blog.

i. Association Between Diabetes and Hippocampal Atrophy in Elderly Japanese: The Hisayama Study.

“A total of 1,238 community-dwelling Japanese subjects aged ≥65 years underwent brain MRI scans and a comprehensive health examination in 2012. Total brain volume (TBV), intracranial volume (ICV), and hippocampal volume (HV) were measured using MRI scans for each subject. We examined the associations between diabetes-related parameters and the ratios of TBV to ICV (an indicator of global brain atrophy), HV to ICV (an indicator of hippocampal atrophy), and HV to TBV (an indicator of hippocampal atrophy beyond global brain atrophy) after adjustment for other potential confounders.”

“The multivariable-adjusted mean values of the TBV-to-ICV, HV-to-ICV, and HV-to-TBV ratios were significantly lower in the subjects with diabetes compared with those without diabetes (77.6% vs. 78.2% for the TBV-to-ICV ratio, 0.513% vs. 0.529% for the HV-to-ICV ratio, and 0.660% vs. 0.676% for the HV-to-TBV ratio; all P < 0.01). These three ratios decreased significantly with elevated 2-h postload glucose (PG) levels […] Longer duration of diabetes was significantly associated with lower TBV-to-ICV, HV-to-ICV, and HV-to-TBV ratios. […] Our data suggest that a longer duration of diabetes and elevated 2-h PG levels, a marker of postprandial hyperglycemia, are risk factors for brain atrophy, particularly hippocampal atrophy.”

“Intriguingly, our findings showed that the subjects with diabetes had significantly lower mean HV-to-TBV ratio values, indicating […] that the hippocampus is predominantly affected by diabetes. In addition, in our subjects a longer duration and a midlife onset of diabetes were significantly associated with a lower HV, possibly suggesting that a long exposure of diabetes particularly worsens hippocampal atrophy.”

The reason why hippocampal atrophy is a variable of interest to these researchers is that hippocampal atrophy is a feature of Alzheimer’s Disease, and diabetics have an elevated risk of AD. This is incidentally far from the first study providing some evidence for the existence of potential causal linkage between impaired glucose homeostasis and AD (see e.g. also this paper, which I’ve previously covered here on the blog).

ii. A Population-Based Study of All-Cause Mortality and Cardiovascular Disease in Association With Prior History of Hypoglycemia Among Patients With Type 1 Diabetes.

“Although patients with T1DM may suffer more frequently from hypoglycemia than those with T2DM (8), very few studies have investigated whether hypoglycemia may also increase the risk of CVD (6,9,10) or death (1,6,7) in patients with T1DM; moreover, the results of these studies have been inconclusive (6,9,10) because of the dissimilarities in their methodological aspects, including their enrollment of populations with T1DM with different levels of glycemic control, application of different data collection methods, and adoption of different lengths of observational periods.”

“Only a few population-based studies have examined the potential cumulative effect of repeated severe hypoglycemia on all-cause mortality or CVD incidence in T1DM (9). The Action to Control Cardiovascular Risk in Diabetes (ACCORD) study of T2DM found a weakly inverse association between the annualized number of hypoglycemic episodes and the risk of death (11,12). By contrast, some studies find that repeated hypoglycemia may be an aggravating factor to atherosclerosis in T1DM (13,14). Studies on the compromised sympathetic-adrenal reaction in patients with repeated hypoglycemia have been inconclusive regarding whether such a reaction may further damage intravascular coagulation and thrombosis (15) or decrease the vulnerability of these patients to adverse health outcomes (12).

Apart from the lack of information on the potential dose–gradient effect associated with severe hypoglycemic events in T1DM from population-based studies, the risks of all-cause mortality/CVD incidence associated with severe hypoglycemia occurring at different periods before all-cause mortality/CVD incidence have never been examined. In this study, we used the population-based medical claims of a cohort of patients with T1DM to examine whether the risks of all-cause mortality/CVD incidence are associated with previous episodes of severe hypoglycemia in different periods and whether severe hypoglycemia may pose a dose–gradient effect on the risks of all-cause mortality/CVD incidence.”

“Two nested case-control studies with age- and sex-matched control subjects and using the time-density sampling method were performed separately within a cohort of 10,411 patients with T1DM in Taiwan. The study enrolled 564 nonsurvivors and 1,615 control subjects as well as 743 CVD case subjects and 1,439 control subjects between 1997 and 2011. History of severe hypoglycemia was identified during 1 year, 1–3 years, and 3–5 years before the occurrence of the study outcomes.”

“Prior severe hypoglycemic events within 1 year were associated with higher risks of all-cause mortality and CVD (adjusted OR 2.74 [95% CI 1.96–3.85] and 2.02 [1.35–3.01], respectively). Events occurring within 1–3 years and 3–5 years before death were also associated with adjusted ORs of 1.94 (95% CI 1.39–2.71) and 1.68 (1.15–2.44), respectively. Significant dose–gradient effects of severe hypoglycemia frequency on mortality and CVD were observed within 5 years. […] we found that a greater frequency of severe hypoglycemia occurring 1 year before death was significantly associated with a higher OR of all-cause mortality (1 vs. 0: 2.45 [95% CI 1.65–3.63]; ≥2 vs. 0: 3.49 [2.01–6.08], P < 0.001 for trend). Although the strength of the association was attenuated, a significant dose–gradient effect still existed for severe hypoglycemia occurring in 1–3 years (P < 0.001 for trend) and 3–5 years (P < 0.015 for trend) before death. […] Exposure to repeated severe hypoglycemic events can lead to higher risks of mortality and CVD.”

“Our findings are supported by two previous studies that investigated atherosclerosis risk in T1DM (13,14). The DCCT/EDIC project reported that the prevalence of coronary artery calcification, an established atherosclerosis marker, was linearly correlated with the incidence rate of hypoglycemia on the DCCT stage (14). Giménez et al. (13) also demonstrated that repeated episodes of hypoglycemia were an aggravating factor for preclinical atherosclerosis in T1DM. […] The mechanism of hypoglycemia that predisposes to all-cause mortality/CVD incidence remains unclear.”

iii. Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990 to 2010.

“On the basis of previous large-scale population-based studies and meta-analyses, diabetic retinopathy (DR) has been recognized as one of the most common and important causes for visual impairment and blindness (1–19). These studies in general showed that DR was the leading cause of blindness globally among working-aged adults and therefore has a significant socioeconomic impact (20–22).”

“A previous meta-analysis (21) summarizing 35 studies with more than 20,000 patients with diabetes estimated a prevalence of any DR of 34.6%, of diabetic macular edema of 6.8%, and of vision-threating DR of 10.2% within the diabetes population. […] Yau et al. (21) estimated that ∼93 million people had some DR and 28 million people had sight-threatening stages of DR. However, this meta-analysis did not address the prevalence of visual impairment and blindness due to DR and thus the impact of DR on the general population. […] We therefore conducted the present meta-analysis of all available population-based studies performed worldwide within the last two decades as part of the Global Burden of Disease Study 2010 (GBD) to estimate the number of people affected by blindness and visual impairment.”

“DR [Diabetic Retinopathy] ranks as the fifth most common cause of global blindness and of global MSVI [moderate and severe vision impairment] (25). […] this analysis estimates that, in 2010, 1 out of every 39 blind people had blindness due to DR and 1 out of every 52 people had visual impairment due to DR. […] Globally in 2010, out of overall 32.4 million blind and 191 million visually impaired people, 0.8 million were blind and 3.7 million were visually impaired because of DR, with an alarming increase of 27% and 64%, respectively, spanning the two decades from 1990 to 2010. DR accounted for 2.6% of all blindness in 2010 and 1.9% of all MSVI worldwide, increasing from 2.1% and 1.3%, respectively, in 1990. […] The number of persons with visual impairment due to DR worldwide is rising and represents an increasing proportion of all blindness/MSVI causes. Age-standardized prevalence of DR-related blindness/MSVI was higher in sub-Saharan Africa and South Asia.”

“Our data suggest that the percentage of blindness and MSVI attributable to DR was lower in low-income regions with younger populations than in high-income regions with older populations. There are several reasons that may explain this observation. First, low-income societies may have a higher percentage of unoperated cataract or undercorrected refractive error–related blindness and MSVI (25), which is probably related to access to visual and ocular health services. Therefore, the proportional increase in blindness and MSVI attributable to DR may be rising because of the decreasing proportion attributable to cataract (25) as a result of the increasing availability of cataract surgery in many parts of the world (29) during the past decade. Improved visualization of the fundus afforded by cataract surgery should also improve the detection of DR. The increase in the percentage of global blindness caused by DR within the last two decades took place in all world regions except Western Europe and high-income North America where there was a slight decrease. This decrease may reflect the effect of intensified prevention and treatment of DR possibly in part due to the introduction of intravitreal injections of steroids and anti-VEGF (vascular endothelial growth factor) drugs (30,31).

Second, in regions with poor medical infrastructure, patients with diabetes may not live long enough to experience DR (32). This reduces the number of patients with diabetes, and, furthermore, it reduces the number of patients with DR-related vision loss. Studies in the literature have reported that the prevalence of severe DR decreased from 1990 to 2010 (21) while the prevalence of diabetes simultaneously increased (27), which implies a reduction in the prevalence of severe DR per person with diabetes. […] Third, […] younger populations may have a lower prevalence of diabetes (33). […] Therefore, considering further economic development in rural regions, improvements in medical infrastructure, the general global demographic transition to elderly populations, and the association between increasing economic development and obesity, we project the increase in the proportion of DR-related blindness and MSVI to continue to rise in the future.”

iv. Do Patient Characteristics Impact Decisions by Clinicians on Hemoglobin A1c Targets?

“In setting hemoglobin A1c (HbA1c) targets, physicians must consider individualized risks and benefits of tight glycemic control (1,2) by recognizing that the risk-benefit ratio may become unfavorable in certain patients, including the elderly and/or those with multiple comorbidities (3,4). Customization of treatment goals based on patient characteristics is poorly understood, partly due to insufficient data on physicians’ decisions in setting targets. We used the National Health and Nutrition Examination Survey (NHANES) to analyze patient-reported HbA1c targets set by physicians and to test whether targets are correlated with patient characteristics.”

“we did not find any evidence that U.S. physicians systematically consider important patient-specific information when selecting the intensity of glycemic control. […] the lack of variation with patient characteristics suggests overreliance on a general approach, without consideration of individual variation in the risks and benefits (or patient preference) of tight control.”

v. Cardiovascular Autonomic Neuropathy, Sexual Dysfunction, and Urinary Incontinence in Women With Type 1 Diabetes.

“This study evaluated associations among cardiovascular autonomic neuropathy (CAN), female sexual dysfunction (FSD), and urinary incontinence (UI) in women with type I diabetes mellitus (T1DM). […] We studied 580 women with T1DM in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study (DCCT/EDIC).”

“At EDIC year 17, FSD was observed in 41% of women and UI in 30%. […] We found that CAN was significantly more prevalent among women with FSD and/or UI, because 41% of women with FSD and 44% with UI had positive measures of CAN compared with 30% without FSD and 38% without UI at EDIC year 16/17. We also observed bivariate associations between FSD and several measures of CAN […] In long-standing T1DM, CAN may predict development of FSD and may be a useful surrogate for generalized diabetic autonomic neuropathy.”

“Although autonomic dysfunction has been considered an important factor in the etiology of many diabetic complications, including constipation, exercise intolerance, bladder dysfunction, erectile dysfunction, orthostatic hypotension, and impaired neurovascular function, our study is among the first to systematically demonstrate a link between CAN and FSD in a large cohort of well-characterized patients with T1DM (14).”

vi. Correlates of Medication Adherence in the TODAY Cohort of Youth With Type 2 Diabetes.

“A total of 699 youth 10–17 years old with recent-onset type 2 diabetes and ≥80% adherence to metformin therapy for ≥8 weeks during a run-in period were randomized to receive one of three treatments. Participants took two study pills twice daily. Adherence was calculated by pill count from blister packs returned at visits. High adherence was defined as taking ≥80% of medication; low adherence was defined as taking <80% of medication.”

“In this low socioeconomic cohort, high and low adherence did not differ by sex, age, family income, parental education, or treatment group. Adherence declined over time (72% high adherence at 2 months, 56% adherence at 48 months, P < 0.0001). A greater percentage of participants with low adherence had clinically significant depressive symptoms at baseline (18% vs. 12%, P = 0.0415). No adherence threshold predicted the loss of glycemic control. […] Most pediatric type 1 diabetes studies (5–7) consistently document a correlation between adherence and race, ethnicity, and socioeconomic status, and studies of adults with type 2 diabetes (8,9) have documented that depressed patients are less adherent to their diabetes regimen. There is a dearth of information in the literature regarding adherence to medication in pediatric patients with type 2 diabetes.”

“In the cohort, the presence of baseline clinically significant depressive symptoms was associated with subsequent lower adherence. […] The TODAY cohort demonstrated deterioration in study medication adherence over time, irrespective of treatment group assignment. […] Contrary to expectation, demographic factors (sex, race-ethnicity, household income, and parental educational level) did not predict medication adherence. The lack of correlation with these factors in the TODAY trial may be explained by the limited income and educational range of the families in the TODAY trial. Nearly half of the families in the TODAY trial had an annual income of <$25,000, and, for over half of the families, the highest level of parental education was a high school degree or lower. In addition, our run-in criteria selected for more adherent subjects. All subjects had to have >80% adherence to M therapy for ≥8 weeks before they could be randomized. This may have limited variability in medication adherence postrandomization. It is also possible that selecting for more adherent subjects in the run-in period also selected for subjects with a lower frequency of depressive symptoms.”

“In the TODAY trial, baseline clinically significant depressive symptoms were more prevalent in the lower-adherence group, suggesting that regular screening for depressive symptoms should be undertaken to identify youth who were at high risk for poor medication adherence. […] Studies in adults with type 2 diabetes (2328) consistently report that depressed patients are less adherent to their diabetes regimen and experience more physical complications of diabetes. Identifying youth who are at risk for poor medication adherence early in the course of disease would make it possible to provide support and, if needed, specific treatment. Although we were not able to determine whether the treatment of depressive symptoms changed adherence over time, our findings support the current guidelines for psychosocial screening in youth with diabetes (29,30).”

vii. Increased Risk of Incident Chronic Kidney Disease, Cardiovascular Disease, and Mortality in Patients With Diabetes With Comorbid Depression.

Another depression-related paper, telling another part of the story. If depressed diabetics are less compliant/adherent, which seems – as per the above study – to be the case both in the context of the adult and pediatric patient population, then you might also expect this reduced compliance/adherence to ‘translate’ into this group having poorer metabolic control, and thus be at higher risk of developing microvascular complications such as nephropathy. This seems to be what we observe, at least according to the findings of this study:

“It is not known if patients with diabetes with depression have an increased risk of chronic kidney disease (CKD). We examined the association between depression and incident CKD, mortality, and incident cardiovascular events in U.S. veterans with diabetes.”

“Among a nationally representative prospective cohort of >3 million U.S. veterans with baseline estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2, we identified 933,211 patients with diabetes. Diabetes was ascertained by an ICD-9-CM code for diabetes, an HbA1c >6.4%, or receiving antidiabetes medication during the inclusion period. Depression was defined by an ICD-9-CM code for depression or by antidepressant use during the inclusion period. Incident CKD was defined as two eGFR levels 2 separated by ≥90 days and a >25% decline in baseline eGFR.”

“Depression was associated with 20% higher risk of incident CKD (adjusted hazard ratio [aHR] and 95% CI: 1.20 [1.19–1.21]). Similarly, depression was associated with increased all-cause mortality (aHR and 95% CI: 1.25 [1.24–1.26]). […] The presence of depression in patients with diabetes is associated with higher risk of developing CKD compared with nondepressed patients.”

It’s important to remember that the higher reported eGFRs in the depressed patient group may not be important/significant, and they should not be taken as an indication of relatively better kidney function in this patient population – especially in the type 2 context, the relationship between eGFR and kidney function is complicated. I refer to Bakris et al.‘s text on these topics for details (blog coverage here).

May 6, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Medicine, Nephrology, Neurology, Ophthalmology, Psychology, Studies | Leave a comment

Standing on the Shoulders of Mice: Aging T-cells

Most of the lecture is not about mice, but rather about stuff like this and this (both papers are covered in the lecture). I’ve read about related topics before (see e.g this), but if you haven’t some parts of the lecture will probably be too technical for you to follow.

May 3, 2017 Posted by | Cancer/oncology, Cardiology, Genetics, Immunology, Lectures, Medicine, Papers | Leave a comment

Random stuff

It’s been a long time since I last posted one of these posts, so a great number of links of interest has accumulated in my bookmarks. I intended to include a large number of these in this post and this of course means that I surely won’t cover each specific link included in this post in anywhere near the amount of detail it deserves, but that can’t be helped.

i. Autism Spectrum Disorder Grown Up: A Chart Review of Adult Functioning.

“For those diagnosed with ASD in childhood, most will become adults with a significant degree of disability […] Seltzer et al […] concluded that, despite considerable heterogeneity in social outcomes, “few adults with autism live independently, marry, go to college, work in competitive jobs or develop a large network of friends”. However, the trend within individuals is for some functional improvement over time, as well as a decrease in autistic symptoms […]. Some authors suggest that a sub-group of 15–30% of adults with autism will show more positive outcomes […]. Howlin et al. (2004), and Cederlund et al. (2008) assigned global ratings of social functioning based on achieving independence, friendships/a steady relationship, and education and/or a job. These two papers described respectively 22% and 27% of groups of higher functioning (IQ above 70) ASD adults as attaining “Very Good” or “Good” outcomes.”

“[W]e evaluated the adult outcomes for 45 individuals diagnosed with ASD prior to age 18, and compared this with the functioning of 35 patients whose ASD was identified after 18 years. Concurrent mental illnesses were noted for both groups. […] Comparison of adult outcome within the group of subjects diagnosed with ASD prior to 18 years of age showed significantly poorer functioning for those with co-morbid Intellectual Disability, except in the domain of establishing intimate relationships [my emphasis. To make this point completely clear, one way to look at these results is that apparently in the domain of partner-search autistics diagnosed during childhood are doing so badly in general that being intellectually disabled on top of being autistic is apparently conferring no additional disadvantage]. Even in the normal IQ group, the mean total score, i.e. the sum of the 5 domains, was relatively low at 12.1 out of a possible 25. […] Those diagnosed as adults had achieved significantly more in the domains of education and independence […] Some authors have described a subgroup of 15–27% of adult ASD patients who attained more positive outcomes […]. Defining an arbitrary adaptive score of 20/25 as “Good” for our normal IQ patients, 8 of thirty four (25%) of those diagnosed as adults achieved this level. Only 5 of the thirty three (15%) diagnosed in childhood made the cutoff. (The cut off was consistent with a well, but not superlatively, functioning member of society […]). None of the Intellectually Disabled ASD subjects scored above 10. […] All three groups had a high rate of co-morbid psychiatric illnesses. Depression was particularly frequent in those diagnosed as adults, consistent with other reports […]. Anxiety disorders were also prevalent in the higher functioning participants, 25–27%. […] Most of the higher functioning ASD individuals, whether diagnosed before or after 18 years of age, were functioning well below the potential implied by their normal range intellect.”

Related papers: Social Outcomes in Mid- to Later Adulthood Among Individuals Diagnosed With Autism and Average Nonverbal IQ as Children, Adults With Autism Spectrum Disorders.

ii. Premature mortality in autism spectrum disorder. This is a Swedish matched case cohort study. Some observations from the paper:

“The aim of the current study was to analyse all-cause and cause-specific mortality in ASD using nationwide Swedish population-based registers. A further aim was to address the role of intellectual disability and gender as possible moderators of mortality and causes of death in ASD. […] Odds ratios (ORs) were calculated for a population-based cohort of ASD probands (n = 27 122, diagnosed between 1987 and 2009) compared with gender-, age- and county of residence-matched controls (n = 2 672 185). […] During the observed period, 24 358 (0.91%) individuals in the general population died, whereas the corresponding figure for individuals with ASD was 706 (2.60%; OR = 2.56; 95% CI 2.38–2.76). Cause-specific analyses showed elevated mortality in ASD for almost all analysed diagnostic categories. Mortality and patterns for cause-specific mortality were partly moderated by gender and general intellectual ability. […] Premature mortality was markedly increased in ASD owing to a multitude of medical conditions. […] Mortality was significantly elevated in both genders relative to the general population (males: OR = 2.87; females OR = 2.24)”.

“Individuals in the control group died at a mean age of 70.20 years (s.d. = 24.16, median = 80), whereas the corresponding figure for the entire ASD group was 53.87 years (s.d. = 24.78, median = 55), for low-functioning ASD 39.50 years (s.d. = 21.55, median = 40) and high-functioning ASD 58.39 years (s.d. = 24.01, median = 63) respectively. […] Significantly elevated mortality was noted among individuals with ASD in all analysed categories of specific causes of death except for infections […] ORs were highest in cases of mortality because of diseases of the nervous system (OR = 7.49) and because of suicide (OR = 7.55), in comparison with matched general population controls.”

iii. Adhesive capsulitis of shoulder. This one is related to a health scare I had a few months ago. A few quotes:

Adhesive capsulitis (also known as frozen shoulder) is a painful and disabling disorder of unclear cause in which the shoulder capsule, the connective tissue surrounding the glenohumeral joint of the shoulder, becomes inflamed and stiff, greatly restricting motion and causing chronic pain. Pain is usually constant, worse at night, and with cold weather. Certain movements or bumps can provoke episodes of tremendous pain and cramping. […] People who suffer from adhesive capsulitis usually experience severe pain and sleep deprivation for prolonged periods due to pain that gets worse when lying still and restricted movement/positions. The condition can lead to depression, problems in the neck and back, and severe weight loss due to long-term lack of deep sleep. People who suffer from adhesive capsulitis may have extreme difficulty concentrating, working, or performing daily life activities for extended periods of time.”

Some other related links below:

The prevalence of a diabetic condition and adhesive capsulitis of the shoulder.
“Adhesive capsulitis is characterized by a progressive and painful loss of shoulder motion of unknown etiology. Previous studies have found the prevalence of adhesive capsulitis to be slightly greater than 2% in the general population. However, the relationship between adhesive capsulitis and diabetes mellitus (DM) is well documented, with the incidence of adhesive capsulitis being two to four times higher in diabetics than in the general population. It affects about 20% of people with diabetes and has been described as the most disabling of the common musculoskeletal manifestations of diabetes.”

Adhesive Capsulitis (review article).
“Patients with type I diabetes have a 40% chance of developing a frozen shoulder in their lifetimes […] Dominant arm involvement has been shown to have a good prognosis; associated intrinsic pathology or insulin-dependent diabetes of more than 10 years are poor prognostic indicators.15 Three stages of adhesive capsulitis have been described, with each phase lasting for about 6 months. The first stage is the freezing stage in which there is an insidious onset of pain. At the end of this period, shoulder ROM [range of motion] becomes limited. The second stage is the frozen stage, in which there might be a reduction in pain; however, there is still restricted ROM. The third stage is the thawing stage, in which ROM improves, but can take between 12 and 42 months to do so. Most patients regain a full ROM; however, 10% to 15% of patients suffer from continued pain and limited ROM.”

Musculoskeletal Complications in Type 1 Diabetes.
“The development of periarticular thickening of skin on the hands and limited joint mobility (cheiroarthropathy) is associated with diabetes and can lead to significant disability. The objective of this study was to describe the prevalence of cheiroarthropathy in the well-characterized Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) cohort and examine associated risk factors […] This cross-sectional analysis was performed in 1,217 participants (95% of the active cohort) in EDIC years 18/19 after an average of 24 years of follow-up. Cheiroarthropathy — defined as the presence of any one of the following: adhesive capsulitis, carpal tunnel syndrome, flexor tenosynovitis, Dupuytren’s contracture, or a positive prayer sign [related link] — was assessed using a targeted medical history and standardized physical examination. […] Cheiroarthropathy was present in 66% of subjects […] Cheiroarthropathy is common in people with type 1 diabetes of long duration (∼30 years) and is related to longer duration and higher levels of glycemia. Clinicians should include cheiroarthropathy in their routine history and physical examination of patients with type 1 diabetes because it causes clinically significant functional disability.”

Musculoskeletal disorders in diabetes mellitus: an update.
“Diabetes mellitus (DM) is associated with several musculoskeletal disorders. […] The exact pathophysiology of most of these musculoskeletal disorders remains obscure. Connective tissue disorders, neuropathy, vasculopathy or combinations of these problems, may underlie the increased incidence of musculoskeletal disorders in DM. The development of musculoskeletal disorders is dependent on age and on the duration of DM; however, it has been difficult to show a direct correlation with the metabolic control of DM.”

Rheumatic Manifestations of Diabetes Mellitus.

Prevalence of symptoms and signs of shoulder problems in people with diabetes mellitus.

Musculoskeletal Disorders of the Hand and Shoulder in Patients with Diabetes.
“In addition to micro- and macroangiopathic complications, diabetes mellitus is also associated with several musculoskeletal disorders of the hand and shoulder that can be debilitating (1,2). Limited joint mobility, also termed diabetic hand syndrome or cheiropathy (3), is characterized by skin thickening over the dorsum of the hands and restricted mobility of multiple joints. While this syndrome is painless and usually not disabling (2,4), other musculoskeletal problems occur with increased frequency in diabetic patients, including Dupuytren’s disease [“Dupuytren’s disease […] may be observed in up to 42% of adults with diabetes mellitus, typically in patients with long-standing T1D” – link], carpal tunnel syndrome [“The prevalence of [carpal tunnel syndrome, CTS] in patients with diabetes has been estimated at 11–30 % […], and is dependent on the duration of diabetes. […] Type I DM patients have a high prevalence of CTS with increasing duration of disease, up to 85 % after 54 years of DM” – link], palmar flexor tenosynovitis or trigger finger [“The incidence of trigger finger [/stenosing tenosynovitis] is 7–20 % of patients with diabetes comparing to only about 1–2 % in nondiabetic patients” – link], and adhesive capsulitis of the shoulder (5–10). The association of adhesive capsulitis with pain, swelling, dystrophic skin, and vasomotor instability of the hand constitutes the “shoulder-hand syndrome,” a rare but potentially disabling manifestation of diabetes (1,2).”

“The prevalence of musculoskeletal disorders was greater in diabetic patients than in control patients (36% vs. 9%, P < 0.01). Adhesive capsulitis was present in 12% of the diabetic patients and none of the control patients (P < 0.01), Dupuytren’s disease in 16% of diabetic and 3% of control patients (P < 0.01), and flexor tenosynovitis in 12% of diabetic and 2% of control patients (P < 0.04), while carpal tunnel syndrome occurred in 12% of diabetic patients and 8% of control patients (P = 0.29). Musculoskeletal disorders were more common in patients with type 1 diabetes than in those with type 2 diabetes […]. Forty-three patients [out of 100] with type 1 diabetes had either hand or shoulder disorders (37 with hand disorders, 6 with adhesive capsulitis of the shoulder, and 10 with both syndromes), compared with 28 patients [again out of 100] with type 2 diabetes (24 with hand disorders, 4 with adhesive capsulitis of the shoulder, and 3 with both syndromes, P = 0.03).”

Association of Diabetes Mellitus With the Risk of Developing Adhesive Capsulitis of the Shoulder: A Longitudinal Population-Based Followup Study.
“A total of 78,827 subjects with at least 2 ambulatory care visits with a principal diagnosis of DM in 2001 were recruited for the DM group. The non-DM group comprised 236,481 age- and sex-matched randomly sampled subjects without DM. […] During a 3-year followup period, 946 subjects (1.20%) in the DM group and 2,254 subjects (0.95%) in the non-DM group developed ACS. The crude HR of developing ACS for the DM group compared to the non-DM group was 1.333 […] the association between DM and ACS may be explained at least in part by a DM-related chronic inflammatory process with increased growth factor expression, which in turn leads to joint synovitis and subsequent capsular fibrosis.”

It is important to note when interpreting the results of the above paper that these results are based on Taiwanese population-level data, and type 1 diabetes – which is obviously the high-risk diabetes subgroup in this particular context – is rare in East Asian populations (as observed in Sperling et al., “A child in Helsinki, Finland is almost 400 times more likely to develop diabetes than a child in Sichuan, China”. Taiwanese incidence of type 1 DM in children is estimated at ~5 in 100.000).

iv. Parents who let diabetic son starve to death found guilty of first-degree murder. It’s been a while since I last saw one of these ‘boost-your-faith-in-humanity’-cases, but they in my impression do pop up every now and then. I should probably keep at hand one of these articles in case my parents ever express worry to me that they weren’t good parents; they could have done a lot worse…

v. Freedom of medicine. One quote from the conclusion of Cochran’s post:

“[I]t is surely possible to materially improve the efficacy of drug development, of medical research as a whole. We’re doing better than we did 500 years ago – although probably worse than we did 50 years ago. But I would approach it by learning as much as possible about medical history, demographics, epidemiology, evolutionary medicine, theory of senescence, genetics, etc. Read Koch, not Hayek. There is no royal road to medical progress.”

I agree, and I was considering including some related comments and observations about health economics in this post – however I ultimately decided against doing that in part because the post was growing unwieldy; I might include those observations in another post later on. Here’s another somewhat older Westhunt post I at some point decided to bookmark – I in particular like the following neat quote from the comments, which expresses a view I have of course expressed myself in the past here on this blog:

“When you think about it, falsehoods, stupid crap, make the best group identifiers, because anyone might agree with you when you’re obviously right. Signing up to clear nonsense is a better test of group loyalty. A true friend is with you when you’re wrong. Ideally, not just wrong, but barking mad, rolling around in your own vomit wrong.”

vi. Economic Costs of Diabetes in the U.S. in 2012.

“Approximately 59% of all health care expenditures attributed to diabetes are for health resources used by the population aged 65 years and older, much of which is borne by the Medicare program […]. The population 45–64 years of age incurs 33% of diabetes-attributed costs, with the remaining 8% incurred by the population under 45 years of age. The annual attributed health care cost per person with diabetes […] increases with age, primarily as a result of increased use of hospital inpatient and nursing facility resources, physician office visits, and prescription medications. Dividing the total attributed health care expenditures by the number of people with diabetes, we estimate the average annual excess expenditures for the population aged under 45 years, 45–64 years, and 65 years and above, respectively, at $4,394, $5,611, and $11,825.”

“Our logistic regression analysis with NHIS data suggests that diabetes is associated with a 2.4 percentage point increase in the likelihood of leaving the workforce for disability. This equates to approximately 541,000 working-age adults leaving the workforce prematurely and 130 million lost workdays in 2012. For the population that leaves the workforce early because of diabetes-associated disability, we estimate that their average daily earnings would have been $166 per person (with the amount varying by demographic). Presenteeism accounted for 30% of the indirect cost of diabetes. The estimate of a 6.6% annual decline in productivity attributed to diabetes (in excess of the estimated decline in the absence of diabetes) equates to 113 million lost workdays per year.”

vii. Total red meat intake of ≥0.5 servings/d does not negatively influence cardiovascular disease risk factors: a systemically searched meta-analysis of randomized controlled trials.

viii. Effect of longer term modest salt reduction on blood pressure: Cochrane systematic review and meta-analysis of randomised trials. Did I blog this paper at some point in the past? I could not find any coverage of it on the blog when I searched for it so I decided to include it here, even if I have a nagging suspicion I may have talked about these findings before. What did they find? The short version is this:

“A modest reduction in salt intake for four or more weeks causes significant and, from a population viewpoint, important falls in blood pressure in both hypertensive and normotensive individuals, irrespective of sex and ethnic group. Salt reduction is associated with a small physiological increase in plasma renin activity, aldosterone, and noradrenaline and no significant change in lipid concentrations. These results support a reduction in population salt intake, which will lower population blood pressure and thereby reduce cardiovascular disease.”

ix. Some wikipedia links:

Heroic Age of Antarctic Exploration (featured).

Wien’s displacement law.

Kuiper belt (featured).

Treason (one quote worth including here: “Currently, the consensus among major Islamic schools is that apostasy (leaving Islam) is considered treason and that the penalty is death; this is supported not in the Quran but in the Hadith.[42][43][44][45][46][47]“).

Lymphatic filariasis.

File:World map of countries by number of cigarettes smoked per adult per year.

Australian gold rushes.

Savant syndrome (“It is estimated that 10% of those with autism have some form of savant abilities”). A small sidenote of interest to Danish readers: The Danish Broadcasting Corporation recently featured a series about autistics with ‘special abilities’ – the show was called ‘The hidden talents’ (De skjulte talenter), and after multiple people had nagged me to watch it I ended up deciding to do so. Most of the people in that show presumably had some degree of ‘savantism’ combined with autism at the milder end of the spectrum, i.e. Asperger’s. I was somewhat conflicted about what to think about the show and did consider blogging it in detail (in Danish?), but I decided against it. However I do want to add here to Danish readers reading along who’ve seen the show that they would do well to repeatedly keep in mind that a) the great majority of autistics do not have abilities like these, b) many autistics with abilities like these presumably do quite poorly, and c) that many autistics have even greater social impairments than do people like e.g. (the very likeable, I have to add…) Louise Wille from the show).

Quark–gluon plasma.

Simo Häyhä.

Chernobyl liquidators.

Black Death (“Over 60% of Norway’s population died in 1348–1350”).

Renault FT (“among the most revolutionary and influential tank designs in history”).

Weierstrass function (“an example of a pathological real-valued function on the real line. The function has the property of being continuous everywhere but differentiable nowhere”).

W Ursae Majoris variable.

Void coefficient. (“a number that can be used to estimate how much the reactivity of a nuclear reactor changes as voids (typically steam bubbles) form in the reactor moderator or coolant. […] Reactivity is directly related to the tendency of the reactor core to change power level: if reactivity is positive, the core power tends to increase; if it is negative, the core power tends to decrease; if it is zero, the core power tends to remain stable. […] A positive void coefficient means that the reactivity increases as the void content inside the reactor increases due to increased boiling or loss of coolant; for example, if the coolant acts as a neutron absorber. If the void coefficient is large enough and control systems do not respond quickly enough, this can form a positive feedback loop which can quickly boil all the coolant in the reactor. This happened in the RBMK reactor that was destroyed in the Chernobyl disaster.”).

Gregor MacGregor (featured) (“a Scottish soldier, adventurer, and confidence trickster […] MacGregor’s Poyais scheme has been called one of the most brazen confidence tricks in history.”).

Stimming.

Irish Civil War.

March 10, 2017 Posted by | Astronomy, autism, Cardiology, Diabetes, Economics, Epidemiology, Health Economics, History, Infectious disease, Mathematics, Medicine, Papers, Physics, Psychology, Random stuff, Wikipedia | Leave a comment

Diabetes and the brain (IV)

Here’s one of my previous posts in the series about the book. In this post I’ll cover material dealing with two acute hyperglycemia-related diabetic complications (DKA and HHS – see below…) as well as multiple topics related to diabetes and stroke. I’ll start out with a few quotes from the book about DKA and HHS:

“DKA [diabetic ketoacidosis] is defined by a triad of hyperglycemia, ketosis, and acidemia and occurs in the absolute or near-absolute absence of insulin. […] DKA accounts for the bulk of morbidity and mortality in children with T1DM. National population-based studies estimate DKA mortality at 0.15% in the United States (4), 0.18–0.25% in Canada (4, 5), and 0.31% in the United Kingdom (6). […] Rates reach 25–67% in those who are newly diagnosed (4, 8, 9). The rates are higher in younger children […] The risk of DKA among patients with pre-existing diabetes is 1–10% annual per person […] DKA can present with mild-to-severe symptoms. […] polyuria and polydipsia […] patients may present with signs of dehydration, such as tachycardia and dry mucus membranes. […] Vomiting, abdominal pain, malaise, and weight loss are common presenting symptoms […] Signs related to the ketoacidotic state include hyperventilation with deep breathing (Kussmaul’s respiration) which is a compensatory respiratory response to an underlying metabolic acidosis. Acetonemia may cause a fruity odor to the breath. […] Elevated glucose levels are almost always present; however, euglycemic DKA has been described (19). Anion-gap metabolic acidosis is the hallmark of this condition and is caused by elevated ketone bodies.”

“Clinically significant cerebral edema occurs in approximately 1% of patients with diabetic ketoacidosis […] DKA-related cerebral edema may represent a continuum. Mild forms resulting in subtle edema may result in modest mental status abnormalities whereas the most severe manifestations result in overt cerebral injury. […] Cerebral edema typically presents 4–12 h after the treatment for DKA is started (28, 29), but can occur at any time. […] Increased intracranial pressure with cerebral edema has been recognized as the leading cause of morbidity and mortality in pediatric patients with DKA (59). Mortality from DKA-related cerebral edema in children is high, up to 90% […] and accounts for 60–90% of the mortality seen in DKA […] many patients are left with major neurological deficits (28, 31, 35).”

“The hyperosmolar hyperglycemic state (HHS) is also an acute complication that may occur in patients with diabetes mellitus. It is seen primarily in patients with T2DM and has previously been referred to as “hyperglycemic hyperosmolar non-ketotic coma” or “hyperglycemic hyperosmolar non-ketotic state” (13). HHS is marked by profound dehydration and hyperglycemia and often by some degree of neurological impairment. The term hyperglycemic hyperosmolar state is used because (1) ketosis may be present and (2) there may be varying degrees of altered sensorium besides coma (13). Like DKA, the basic underlying disorder is inadequate circulating insulin, but there is often enough insulin to inhibit free fatty acid mobilization and ketoacidosis. […] Up to 20% of patients diagnosed with HHS do not have a previous history of diabetes mellitus (14). […] Kitabchi et al. estimated the rate of hospital admissions due to HHS to be lower than DKA, accounting for less than 1% of all primary diabetic admissions (13). […] Glucose levels rise in the setting of relative insulin deficiency. The low levels of circulating insulin prevent lipolysis, ketogenesis, and ketoacidosis (62) but are unable to suppress hyperglycemia, glucosuria, and water losses. […] HHS typically presents with one or more precipitating factors, similar to DKA. […] Acute infections […] account for approximately 32–50% of precipitating causes (13). […] The mortality rates for HHS vary between 10 and 20% (14, 93).”

It should perhaps be noted explicitly that the mortality rates for these complications are particularly high in the settings of either very young individuals (DKA) or in elderly individuals (HHS) who might have multiple comorbidities. Relatedly HHS often develops acutely specifically in settings where the precipitating factor is something really unpleasant like pneumonia or a cardiovascular event, so a high-ish mortality rate is perhaps not that surprising. Nor is it surprising that very young brains are particularly vulnerable in the context of DKA (I already discussed some of the research on these matters in some detail in an earlier post about this book).

This post to some extent covered the topic of ‘stroke in general’, however I wanted to include here also some more data specifically on diabetes-related matters about this topic. Here’s a quote to start off with:

“DM [Diabetes Mellitus] has been consistently shown to represent a strong independent risk factor of ischemic stroke. […] The contribution of hyperglycemia to increased stroke risk is not proven. […] the relationship between hyperglycemia and stroke remains subject of debate. In this respect, the association between hyperglycemia and cerebrovascular disease is established less strongly than the association between hyperglycemia and coronary heart disease. […] The course of stroke in patients with DM is characterized by higher mortality, more severe disability, and higher recurrence rate […] It is now well accepted that the risk of stroke in individuals with DM is equal to that of individuals with a history of myocardial infarction or stroke, but no DM (24–26). This was confirmed in a recently published large retrospective study which enrolled all inhabitants of Denmark (more than 3 million people out of whom 71,802 patients with DM) and were followed-up for 5 years. In men without DM the incidence of stroke was 2.5 in those without and 7.8% in those with prior myocardial infarction, whereas in patients with DM it was 9.6 in those without and 27.4% in those with history of myocardial infarction. In women the numbers were 2.5, 9.0, 10.0, and 14.2%, respectively (22).

That study incidentally is very nice for me in particular to know about, given that I am a Danish diabetic. I do not here face any of the usual tiresome questions about ‘external validity’ and issues pertaining to ‘extrapolating out of sample’ – not only is it quite likely I’ve actually looked at some of the data used in that analysis myself, I also know that I am almost certainly one of the people included in the analysis. Of course you need other data as well to assess risk (e.g. age, see the previously linked post), but this is pretty clean as far as it goes. Moving on…

“The number of deaths from stroke attributable to DM is highest in low-and-middle-income countries […] the relative risk conveyed by DM is greater in younger subjects […] It is not well known whether type 1 or type 2 DM affects stroke risk differently. […] In the large cohort of women enrolled in the Nurses’ Health Study (116,316 women followed for up to 26 years) it was shown that the incidence of total stroke was fourfold higher in women with type 1 DM and twofold higher among women with type 2 DM than for non-diabetic women (33). […] The impact of DM duration as a stroke risk factor has not been clearly defined. […] In this context it is important to note that the actual duration of type 2 DM is difficult to determine precisely […and more generally: “the date of onset of a certain chronic disease is a quantity which is not defined as precisely as mortality“, as Yashin et al. put it – I also talked about this topic in my previous post, but it’s important when you’re looking at these sorts of things and is worth reiterating – US]. […] Traditional risk factors for stroke such as arterial hypertension, dyslipidemia, atrial fibrillation, heart failure, and previous myocardial infarction are more common in people with DM […]. However, the impact of DM on stroke is not just due to the higher prevalence of these risk factors, as the risk of mortality and morbidity remains over twofold increased after correcting for these factors (4, 37). […] It is informative to distinguish between factors that are non-specific and specific to DM. DM-specific factors, including chronic hyperglycemia, DM duration, DM type and complications, and insulin resistance, may contribute to an elevated stroke risk either by amplification of the harmful effect of other “classical” non-specific risk factors, such as hypertension, or by acting independently.”

More than a few variables are known to impact stroke risk, but the fact that many of the risk factors are related to each other (‘fat people often also have high blood pressure’) makes it hard to figure out which variables are most important, how they interact with each other, etc., etc. One might in that context perhaps conceptualize the metabolic syndrome (-MS) as a sort of indicator variable indicating whether a relatively common set of such related potential risk factors of interest are present or not – it is worth noting in that context that the authors include in the text the observation that: “it is yet uncertain if the whole concept of the MS entails more than its individual components. The clustering of risk factors complicates the assessment of the contribution of individual components to the risk of vascular events, as well as assessment of synergistic or interacting effects.” MS confers a two-threefold increased stroke risk, depending on the definition and the population analyzed, so there’s definitely some relevant stuff included in that box, but in the context of developing new treatment options and better assess risk it might be helpful to – to put it simplistically – know if variable X is significantly more important than variable Y (and how the variables interact, etc., etc.). But this sort of information is hard to get.

There’s more than one type of stroke, and the way diabetes modifies the risk of various stroke types is not completely clear:

“Most studies have consistently shown that DM is an important risk factor for ischemic stroke, while the incidence of hemorrhagic stroke in subjects with DM does not seem to be increased. Consequently, the ratio of ischemic to hemorrhagic stroke is higher in patients with DM than in those stroke patients without DM [recall the base rates I’ve mentioned before in the coverage of this book: 80% of strokes are ischemic strokes in Western countries, and 15 % hemorrhagic] […] The data regarding an association between DM and the risk of hemorrhagic stroke are quite conflicting. In the most series no increased risk of cerebral hemorrhage was found (10, 101), and in the Copenhagen Stroke Registry, hemorrhagic stroke was even six times less frequent in diabetic patients than in non-diabetic subjects (102). […] However, in another prospective population-based study DM was associated with an increased risk of primary intracerebral hemorrhage (103). […] The significance of DM as a risk factor of hemorrhagic stroke could differ depending on ethnicity of subjects or type of DM. In the large Nurses’ Health Study type 1 DM increased the risk of hemorrhagic stroke by 3.8 times while type 2 DM did not increase such a risk (96). […] It is yet unclear if DM predominantly predisposes to either large or small vessel ischemic stroke. Nevertheless, lacunar stroke (small, less than 15mm in diameter infarction, cyst-like, frequently multiple) is considered to be the typical type of stroke in diabetic subjects (105–107), and DM may be present in up to 28–43% of patients with cerebral lacunar infarction (108–110).”

The Danish results mentioned above might not be as useful to me as they were before if the type is important, because the majority of those diabetics included were type 2 diabetics. I know from personal experience that it is difficult to type-identify diabetics using the Danish registry data available if you want to work with population-level data, and any type of scheme attempting this will be subject to potentially large misidentification problems. Some subgroups can be presumably correctly identified using diagnostic codes, but a very large number of individuals will be left out of the analyses if you only rely on identification strategies where you’re (at least reasonably?) certain about the type. I’ve worked on these identification problems during my graduate work so perhaps a few more things are worth mentioning here. In the context of diabetic subgroup analyses, misidentification is in general a much larger problem in the context of type 1 results than in the context of type 2 results; unless the study design takes the large prevalence difference of the two conditions into account, the type 1 sample will be much smaller than the type 2 sample in pretty much all analytical contexts, so a small number of misidentified type 2 individuals can have large impacts on the results of the type 1 sample. Type 1s misidentified as type 2 individuals is in general to be expected to be a much smaller problem in terms of the validity of the type 2 analysis; misidentification of that type will cause a loss of power in the context of the type 1 subgroup analysis, which is already low to start with (and it’ll also make the type 1 subgroup analysis even more vulnerable to misidentified type 2s), but it won’t much change the results of the type 2 subgroup analysis in any significant way. Relatedly, even if enough type 2 patients are misidentified to cause problems with the interpretation of the type 1 subgroup analysis, this would not on its own be a good reason to doubt the results of the type 2 subgroup analysis. Another thing to note in terms of these things is that given that misidentification will tend to lead to ‘mixing’, i.e. it’ll make the subgroup results look similar, when outcomes are not similar in the type 1 and the type 2 individuals then this might be taken to be an indicator that something potentially interesting might be going on, because most analyses will struggle with some level of misidentification which will tend to reduce the power of tests of group differences.

What about stroke outcomes? A few observations were included on that topic above, but the book has a lot more stuff on that – some observations on this topic:

“DM is an independent risk factor of death from stroke […]. Tuomilehto et al. (35) calculated that 16% of all stroke mortality in men and 33% in women could be directly attributed to DM. Patients with DM have higher hospital and long-term stroke mortality, more pronounced residual neurological deficits, and more severe disability after acute cerebrovascular accidents […]. The 1-year mortality rate, for example, was twofold higher in diabetic patients compared to non-diabetic subjects (50% vs. 25%) […]. Only 20% of people with DM survive over 5 years after the first stroke and half of these patients die within the first year (36, 128). […] The mechanisms underlying the worse outcome of stroke in diabetic subjects are not fully understood. […] Regarding prevention of stroke in patients with DM, it may be less relevant than in non-DM subjects to distinguish between primary and secondary prevention as all patients with DM are considered to be high-risk subjects regardless of the history of cerebrovascular accidents or the presence of clinical and subclinical vascular lesions. […] The influence of the mode of antihyperglycemic treatment on the risk of stroke is uncertain.

Control of blood pressure is very important in the diabetic setting:

“There are no doubts that there is a linear relation between elevated systolic blood pressure and the risk of stroke, both in people with or without DM. […] Although DM and arterial hypertension represent significant independent risk factors for stroke if they co-occur in the same patient the risk increases dramatically. A prospective study of almost 50 thousand subjects in Finland followed up for 19 years revealed that the hazard ratio for stroke incidence was 1.4, 2.0, 2.5, 3.5, and 4.5 and for stroke mortality was 1.5, 2.6, 3.1, 5.6, and 9.3, respectively, in subjects with an isolated modestly elevated blood pressure (systolic 140–159/diastolic 90–94 mmHg), isolated more severe hypertension (systolic >159 mmHg, diastolic >94 mmHg, or use of antihypertensive drugs), with isolated DM only, with both DM and modestly elevated blood pressure, and with both DM and more severe hypertension, relative to subjects without either of the risk factors (168). […] it remains unclear whether some classes of antihypertensive agents provide a stronger protection against stroke in diabetic patients than others. […] effective antihypertensive treatment is highly beneficial for reduction of stroke risk in diabetic patients, but the advantages of any particular class of antihypertensive medications are not substantially proven.”

Treatment of dyslipidemia is also very important, but here it does seem to matter how you treat it:

“It seems that the beneficial effect of statins is dose-dependent. The lower the LDL level that is achieved the stronger the cardiovascular protection. […] Recently, the results of the meta-analysis of 14 randomized trials of statins in 18,686 patients with DM had been published. It was calculated that statins use in diabetic patients can result in a 21% reduction of the risk of any stroke per 1 mmol/l reduction of LDL achieved […] There is no evidence from trials that supports efficacy of fibrates for stroke prevention in diabetic patients. […] No reduction of stroke risk by fibrates was shown also in a meta-analysis of eight trials enrolled 12,249 patients with type 2 DM (204).”

Antiplatelets?

“Significant reductions in stroke risk in diabetic patients receiving antiplatelet therapy were found in large-scale controlled trials (205). It appears that based on the high incidence of stroke and prevalence of stroke risk factors in the diabetic population the benefits of routine aspirin use for primary and secondary stroke prevention outweigh its potential risk of hemorrhagic stroke especially in patients older than 30 years having at least one additional risk factor (206). […] both guidelines issued by the AHA/ADA or the ESC/EASD on the prevention of cardiovascular disease in patients with DM support the use of aspirin in a dose of 50–325 mg daily for the primary prevention of stroke in subjects older than 40 years of age and additional risk factors, such as DM […] The newer antiplatelet agent, clopidogrel, was more efficacious in prevention of ischemic stroke than aspirin with greater risk reduction in the diabetic cohort especially in those treated with insulin compared to non-diabetics in CAPRIE trial (209). However, the combination of aspirin and clopidogrel does not appear to be more efficacious and safe compared to clopidogrel or aspirin alone”.

When you treat all risk factors aggressively, it turns out that the elevated stroke risk can be substantially reduced. Again the data on this stuff is from Denmark:

“Gaede et al. (216) have shown in the Steno 2 study that intensive multifactorial intervention aimed at correction of hyperglycemia, hypertension, dyslipidemia, and microalbuminuria along with aspirin use resulted in a reduction of cardiovascular morbidity including non-fatal stroke […] recently the results of the extended 13.3 years follow-up of this study were presented and the reduction of cardiovascular mortality by 57% and morbidity by 59% along with the reduction of the number of non-fatal stroke (6 vs. 30 events) in intensively treated group was convincingly demonstrated (217). Antihypertensive, hypolipidemic treatment, use of aspirin should thus be recommended as either primary or secondary prevention of stroke for patients with DM.”

March 3, 2017 Posted by | Books, Cardiology, Diabetes, Epidemiology, Medicine, Neurology, Pharmacology, Statistics | Leave a comment

Biodemography of aging (I)

“The goal of this monograph is to show how questions about the connections between and among aging, health, and longevity can be addressed using the wealth of available accumulated knowledge in the field, the large volumes of genetic and non-genetic data collected in longitudinal studies, and advanced biodemographic models and analytic methods. […] This monograph visualizes aging-related changes in physiological variables and survival probabilities, describes methods, and summarizes the results of analyses of longitudinal data on aging, health, and longevity in humans performed by the group of researchers in the Biodemography of Aging Research Unit (BARU) at Duke University during the past decade. […] the focus of this monograph is studying dynamic relationships between aging, health, and longevity characteristics […] our focus on biodemography/biomedical demography meant that we needed to have an interdisciplinary and multidisciplinary biodemographic perspective spanning the fields of actuarial science, biology, economics, epidemiology, genetics, health services research, mathematics, probability, and statistics, among others.”

The quotes above are from the book‘s preface. In case this aspect was not clear from the comments above, this is the kind of book where you’ll randomly encounter sentences like these:

The simplest model describing negative correlations between competing risks is the multivariate lognormal frailty model. We illustrate the properties of such model for the bivariate case.

“The time-to-event sub-model specifies the latent class-specific expressions for the hazard rates conditional on the vector of biomarkers Yt and the vector of observed covariates X …”

…which means that some parts of the book are really hard to blog; it simply takes more effort to deal with this stuff here than it’s worth. As a result of this my coverage of the book will not provide a remotely ‘balanced view’ of the topics covered in it; I’ll skip a lot of the technical stuff because I don’t think it makes much sense to cover specific models and algorithms included in the book in detail here. However I should probably also emphasize while on this topic that although the book is in general not an easy read, it’s hard to read because ‘this stuff is complicated’, not because the authors are not trying. The authors in fact make it clear already in the preface that some chapters are more easy to read than are others and that some chapters are actually deliberately written as ‘guideposts and way-stations‘, as they put it, in order to make it easier for the reader to find the stuff in which he or she is most interested (“the interested reader can focus directly on the chapters/sections of greatest interest without having to read the entire volume“) – they have definitely given readability aspects some thought, and I very much like the book so far; it’s full of great stuff and it’s very well written.

I have had occasion to question a few of the observations they’ve made, for example I was a bit skeptical about a few of the conclusions they drew in chapter 6 (‘Medical Cost Trajectories and Onset of Age-Associated Diseases’), but this was related to what some would certainly consider to be minor details. In the chapter they describe a model of medical cost trajectories where the post-diagnosis follow-up period is 20 months; this is in my view much too short a follow-up period to draw conclusions about medical cost trajectories in the context of type 2 diabetes, one of the diseases included in the model, which I know because I’m intimately familiar with the literature on that topic; you need to look 7-10 years ahead to get a proper sense of how this variable develops over time – and it really is highly relevant to include those later years, because if you do not you may miss out on a large proportion of the total cost given that a substantial proportion of the total cost of diabetes relate to complications which tend to take some years to develop. If your cost analysis is based on a follow-up period as short as that of that model you may also on a related note draw faulty conclusions about which medical procedures and -subsidies are sensible/cost effective in the setting of these patients, because highly adherent patients may be significantly more expensive in a short run analysis like this one (they show up to their medical appointments and take their medications…) but much cheaper in the long run (…because they take their medications they don’t go blind or develop kidney failure). But as I say, it’s a minor point – this was one condition out of 20 included in the analysis they present, and if they’d addressed all the things that pedants like me might take issue with, the book would be twice as long and it would likely no longer be readable. Relatedly, the model they discuss in that chapter is far from unsalvageable; it’s just that one of the components of interest –  ‘the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity’ – in the case of at least one disease is highly unlikely to be correct (given the authors’ interpretation of the variable), because there’s some stuff of relevance which the model does not include. I found the model quite interesting, despite the shortcomings, and the results were definitely surprising. (No, the above does not in my opinion count as an example of coverage of a ‘specific model […] in detail’. Or maybe it does, but I included no equations. On reflection I probably can’t promise much more than that, sometimes the details are interesting…)

Anyway, below I’ve added some quotes from the first few chapters of the book and a few remarks along the way.

“The genetics of aging, longevity, and mortality has become the subject of intensive analyses […]. However, most estimates of genetic effects on longevity in GWAS have not reached genome-wide statistical significance (after applying the Bonferroni correction for multiple testing) and many findings remain non-replicated. Possible reasons for slow progress in this field include the lack of a biologically-based conceptual framework that would drive development of statistical models and methods for genetic analyses of data [here I was reminded of Burnham & Anderson’s coverage, in particular their criticism of mindless ‘Let the computer find out’-strategies – the authors of that chapter seem to share their skepticism…], the presence of hidden genetic heterogeneity, the collective influence of many genetic factors (each with small effects), the effects of rare alleles, and epigenetic effects, as well as molecular biological mechanisms regulating cellular functions. […] Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward fashion (Finch and Tanzi 1997; Martin 2007). Recent genome-wide association studies (GWAS) have supported this finding by showing that the traits in late life are likely controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny size (Stranger et al. 2011).”

I think this ties in well with what I’ve previously read on these and related topics – see e.g. the second-last paragraph quoted in my coverage of Richard Alexander’s book, or some of the remarks included in Roberts et al. Anyway, moving on:

“It is well known from epidemiology that values of variables describing physiological states at a given age are associated with human morbidity and mortality risks. Much less well known are the facts that not only the values of these variables at a given age, but also characteristics of their dynamic behavior during the life course are also associated with health and survival outcomes. This chapter [chapter 8 in the book, US] shows that, for monotonically changing variables, the value at age 40 (intercept), the rate of change (slope), and the variability of a physiological variable, at ages 40–60, significantly influence both health-span and longevity after age 60. For non-monotonically changing variables, the age at maximum, the maximum value, the rate of decline after reaching the maximum (right slope), and the variability in the variable over the life course may influence health-span and longevity. This indicates that such characteristics can be important targets for preventive measures aiming to postpone onsets of complex diseases and increase longevity.”

The chapter from which the quotes in the next two paragraphs are taken was completely filled with data from the Framingham Heart Study, and it was hard for me to know what to include here and what to leave out – so you should probably just consider the stuff I’ve included below as samples of the sort of observations included in that part of the coverage.

“To mediate the influence of internal or external factors on lifespan, physiological variables have to show associations with risks of disease and death at different age intervals, or directly with lifespan. For many physiological variables, such associations have been established in epidemiological studies. These include body mass index (BMI), diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), blood glucose (BG), serum cholesterol (SCH), hematocrit (H), and ventricular rate (VR). […] the connection between BMI and mortality risk is generally J-shaped […] Although all age patterns of physiological indices are non-monotonic functions of age, blood glucose (BG) and pulse pressure (PP) can be well approximated by monotonically increasing functions for both genders. […] the average values of body mass index (BMI) increase with age (up to age 55 for males and 65 for females), and then decline for both sexes. These values do not change much between ages 50 and 70 for males and between ages 60 and 70 for females. […] Except for blood glucose, all average age trajectories of physiological indices differ between males and females. Statistical analysis confirms the significance of these differences. In particular, after age 35 the female BMI increases faster than that of males. […] [When comparing women with less than or equal to 11 years of education [‘LE’] to women with 12 or more years of education [HE]:] The average values of BG for both groups are about the same until age 45. Then the BG curve for the LE females becomes higher than that of the HE females until age 85 where the curves intersect. […] The average values of BMI in the LE group are substantially higher than those among the HE group over the entire age interval. […] The average values of BG for the HE and LE males are very similar […] However, the differences between groups are much smaller than for females.”

They also in the chapter compared individuals with short life-spans [‘SL’, died before the age of 75] and those with long life-spans [‘LL’, 100 longest-living individuals in the relevant sample] to see if the variables/trajectories looked different. They did, for example: “trajectories for the LL females are substantially different from those for the SL females in all eight indices. Specifically, the average values of BG are higher and increase faster in the SL females. The entire age trajectory of BMI for the LL females is shifted to the right […] The average values of DBP [diastolic blood pressure, US] among the SL females are higher […] A particularly notable observation is the shift of the entire age trajectory of BMI for the LL males and females to the right (towards an older age), as compared with the SL group, and achieving its maximum at a later age. Such a pattern is markedly different from that for healthy and unhealthy individuals. The latter is mostly characterized by the higher values of BMI for the unhealthy people, while it has similar ages at maximum for both the healthy and unhealthy groups. […] Physiological aging changes usually develop in the presence of other factors affecting physiological dynamics and morbidity/mortality risks. Among these other factors are year of birth, gender, education, income, occupation, smoking, and alcohol use. An important limitation of most longitudinal studies is the lack of information regarding external disturbances affecting individuals in their day-today life.”

I incidentally noted while I was reading that chapter that a relevant variable ‘lurking in the shadows’ in the context of the male and female BMI trajectories might be changing smoking habits over time; I have not looked at US data on this topic, but I do know that the smoking patterns of Danish males and females during the latter half of the last century were markedly different and changed really quite dramatically in just a few decades; a lot more males than females smoked in the 60es, whereas the proportions of male- and female smokers today are much more similar, because a lot of males have given up smoking (I refer Danish readers to this blog post which I wrote some years ago on these topics). The authors of the chapter incidentally do look a little at data on smokers and they observe that smokers’ BMI are lower than non-smokers (not surprising), and that the smokers’ BMI curve (displaying the relationship between BMI and age) grows at a slower rate than the BMI curve of non-smokers (that this was to be expected is perhaps less clear, at least to me – the authors don’t interpret these specific numbers, they just report them).

The next chapter is one of the chapters in the book dealing with the SEER data I also mentioned not long ago in the context of my coverage of Bueno et al. Some sample quotes from that chapter below:

“To better address the challenge of “healthy aging” and to reduce economic burdens of aging-related diseases, key factors driving the onset and progression of diseases in older adults must be identified and evaluated. An identification of disease-specific age patterns with sufficient precision requires large databases that include various age-specific population groups. Collections of such datasets are costly and require long periods of time. That is why few studies have investigated disease-specific age patterns among older U.S. adults and there is limited knowledge of factors impacting these patterns. […] Information collected in U.S. Medicare Files of Service Use (MFSU) for the entire Medicare-eligible population of older U.S. adults can serve as an example of observational administrative data that can be used for analysis of disease-specific age patterns. […] In this chapter, we focus on a series of epidemiologic and biodemographic characteristics that can be studied using MFSU.”

“Two datasets capable of generating national level estimates for older U.S. adults are the Surveillance, Epidemiology, and End Results (SEER) Registry data linked to MFSU (SEER-M) and the National Long Term Care Survey (NLTCS), also linked to MFSU (NLTCS-M). […] The SEER-M data are the primary dataset analyzed in this chapter. The expanded SEER registry covers approximately 26 % of the U.S. population. In total, the Medicare records for 2,154,598 individuals are available in SEER-M […] For the majority of persons, we have continuous records of Medicare services use from 1991 (or from the time the person reached age 65 after 1990) to his/her death. […] The NLTCS-M data contain two of the six waves of the NLTCS: namely, the cohorts of years 1994 and 1999. […] In total, 34,077 individuals were followed-up between 1994 and 1999. These individuals were given the detailed NLTCS interview […] which has information on risk factors. More than 200 variables were selected”

In short, these data sets are very large, and contain a lot of information. Here are some results/data:

“Among studied diseases, incidence rates of Alzheimer’s disease, stroke, and heart failure increased with age, while the rates of lung and breast cancers, angina pectoris, diabetes, asthma, emphysema, arthritis, and goiter became lower at advanced ages. [..] Several types of age-patterns of disease incidence could be described. The first was a monotonic increase until age 85–95, with a subsequent slowing down, leveling off, and decline at age 100. This pattern was observed for myocardial infarction, stroke, heart failure, ulcer, and Alzheimer’s disease. The second type had an earlier-age maximum and a more symmetric shape (i.e., an inverted U-shape) which was observed for lung and colon cancers, Parkinson’s disease, and renal failure. The majority of diseases (e.g., prostate cancer, asthma, and diabetes mellitus among them) demonstrated a third shape: a monotonic decline with age or a decline after a short period of increased rates. […] The occurrence of age-patterns with a maximum and, especially, with a monotonic decline contradicts the hypothesis that the risk of geriatric diseases correlates with an accumulation of adverse health events […]. Two processes could be operative in the generation of such shapes. First, they could be attributed to the effect of selection […] when frail individuals do not survive to advanced ages. This approach is popular in cancer modeling […] The second explanation could be related to the possibility of under-diagnosis of certain chronic diseases at advanced ages (due to both less pronounced disease symptoms and infrequent doctor’s office visits); however, that possibility cannot be assessed with the available data […this is because the data sets are based on Medicare claims – US]”

“The most detailed U.S. data on cancer incidence come from the SEER Registry […] about 60 % of malignancies are diagnosed in persons aged 65+ years old […] In the U.S., the estimated percent of cancer patients alive after being diagnosed with cancer (in 2008, by current age) was 13 % for those aged 65–69, 25 % for ages 70–79, and 22 % for ages 80+ years old (compared with 40 % of those aged younger than 65 years old) […] Diabetes affects about 21 % of the U.S. population aged 65+ years old (McDonald et al. 2009). However, while more is known about the prevalence of diabetes, the incidence of this disease among older adults is less studied. […] [In multiple previous studies] the incidence rates of diabetes decreased with age for both males and females. In the present study, we find similar patterns […] The prevalence of asthma among the U.S. population aged 65+ years old in the mid-2000s was as high as 7 % […] older patients are more likely to be underdiagnosed, untreated, and hospitalized due to asthma than individuals younger than age 65 […] asthma incidence rates have been shown to decrease with age […] This trend of declining asthma incidence with age is in agreement with our results.”

“The prevalence and incidence of Alzheimer’s disease increase exponentially with age, with the most notable rise occurring through the seventh and eight decades of life (Reitz et al. 2011). […] whereas dementia incidence continues to increase beyond age 85, the rate of increase slows down [which] suggests that dementia diagnosed at advanced ages might be related not to the aging process per se, but associated with age-related risk factors […] Approximately 1–2 % of the population aged 65+ and up to 3–5 % aged 85+ years old suffer from Parkinson’s disease […] There are few studies of Parkinsons disease incidence, especially in the oldest old, and its age patterns at advanced ages remain controversial”.

“One disadvantage of large administrative databases is that certain factors can produce systematic over/underestimation of the number of diagnosed diseases or of identification of the age at disease onset. One reason for such uncertainties is an incorrect date of disease onset. Other sources are latent disenrollment and the effects of study design. […] the date of onset of a certain chronic disease is a quantity which is not defined as precisely as mortality. This uncertainty makes difficult the construction of a unified definition of the date of onset appropriate for population studies.”

“[W]e investigated the phenomenon of multimorbidity in the U.S. elderly population by analyzing mutual dependence in disease risks, i.e., we calculated disease risks for individuals with specific pre-existing conditions […]. In total, 420 pairs of diseases were analyzed. […] For each pair, we calculated age patterns of unconditional incidence rates of the diseases, conditional rates of the second (later manifested) disease for individuals after onset of the first (earlier manifested) disease, and the hazard ratio of development of the subsequent disease in the presence (or not) of the first disease. […] three groups of interrelations were identified: (i) diseases whose risk became much higher when patients had a certain pre-existing (earlier diagnosed) disease; (ii) diseases whose risk became lower than in the general population when patients had certain pre-existing conditions […] and (iii) diseases for which “two-tail” effects were observed: i.e., when the effects are significant for both orders of disease precedence; both effects can be direct (either one of the diseases from a disease pair increases the risk of the other disease), inverse (either one of the diseases from a disease pair decreases the risk of the other disease), or controversial (one disease increases the risk of the other, but the other disease decreases the risk of the first disease from the disease pair). In general, the majority of disease pairs with increased risk of the later diagnosed disease in both orders of precedence were those in which both the pre-existing and later occurring diseases were cancers, and also when both diseases were of the same organ. […] Generally, the effect of dependence between risks of two diseases diminishes with advancing age. […] Identifying mutual relationships in age-associated disease risks is extremely important since they indicate that development of […] diseases may involve common biological mechanisms.”

“in population cohorts, trends in prevalence result from combinations of trends in incidence, population at risk, recovery, and patients’ survival rates. Trends in the rates for one disease also may depend on trends in concurrent diseases, e.g., increasing survival from CHD contributes to an increase in the cancer incidence rate if the individuals who survived were initially susceptible to both diseases.”

March 1, 2017 Posted by | Biology, Books, Cancer/oncology, Cardiology, Demographics, Diabetes, Epidemiology, Genetics, Health Economics, Medicine, Nephrology, Neurology | Leave a comment

Diabetes and the Brain (III)

Some quotes from the book below.

Tests that are used in clinical neuropsychology in most cases examine one or more aspects of cognitive domains, which are theoretical constructs in which a multitude of cognitive processes are involved. […] By definition, a subdivision in cognitive domains is arbitrary, and many different classifications exist. […] for a test to be recommended, several criteria must be met. First, a test must have adequate reliability: the test must yield similar outcomes when applied over multiple test sessions, i.e., have good test–retest reliability. […] Furthermore, the interobserver reliability is important, in that the test must have a standardized assessment procedure and is scored in the same manner by different examiners. Second, the test must have adequate validity. Here, different forms of validity are important. Content validity is established by expert raters with respect to item formulation, item selection, etc. Construct validity refers to the underlying theoretical construct that the test is assumed to measure. To assess construct validity, both convergent and divergent validities are important. Convergent validity refers to the amount of agreement between a given test and other tests that measure the same function. In turn, a test with a good divergent validity correlates minimally with tests that measure other cognitive functions. Moreover, predictive validity (or criterion validity) is related to the degree of correlation between the test score and an external criterion, for example, the correlation between a cognitive test and functional status. […] it should be stressed that cognitive tests alone cannot be used as ultimate proof for organic brain damage, but should be used in combination with more direct measures of cerebral abnormalities, such as neuroimaging.”

“Intelligence is a theoretically ill-defined construct. In general, it refers to the ability to think in an abstract manner and solve new problems. Typically, two forms of intelligence are distinguished, crystallized intelligence (academic skills and knowledge that one has acquired during schooling) and fluid intelligence (the ability to solve new problems). Crystallized intelligence is better preserved in patients with brain disease than fluid intelligence (3). […] From a neuropsychological viewpoint, the concept of intelligence as a unitary construct (often referred to as g-factor) does not provide valuable information, since deficits in specific cognitive functions may be averaged out in the total IQ score. Thus, in most neuropsychological studies, intelligence tests are included because of specific subtests that are assumed to measure specific cognitive functions, and the performance profile is analyzed rather than considering the IQ measure as a compound score in isolation.”

“Attention is a concept that in general relates to the selection of relevant information from our environment and the suppression of irrelevant information (selective or “focused” attention), the ability to shift attention between tasks (divided attention), and to maintain a state of alertness to incoming stimuli over longer periods of time (concentration and vigilance). Many different structures in the human brain are involved in attentional processing and, consequently, disorders in attention occur frequently after brain disease or damage (21). […] Speed of information processing is not a localized cognitive function, but depends greatly on the integrity of the cerebral network as a whole, the subcortical white matter and the interhemispheric and intrahemispheric connections. It is one of the cognitive functions that clearly declines with age and it is highly susceptible to brain disease or dysfunction of any kind.”

“The MiniMental State Examination (MMSE) is a screening instrument that has been developed to determine whether older adults have cognitive impairments […] numerous studies have shown that the MMSE has poor sensitivity and specificity, as well as a low-test–retest reliability […] the MMSE has been developed to determine cognitive decline that is typical for Alzheimer’s dementia, but has been found less useful in determining cognitive decline in nondemented patients (44) or in patients with other forms of dementia. This is important since odds ratios for both vascular dementia and Alzheimer’s dementia are increased in diabetes (45). Notwithstanding this increased risk, most patients with diabetes have subtle cognitive deficits (46, 47) that may easily go undetected using gross screening instruments such as the MMSE. For research in diabetes a high sensitivity is thus especially important. […] ceiling effects in test performance often result in a lack of sensitivity. Subtle impairments are easily missed, resulting in a high proportion of false-negative cases […] In general, tests should be cognitively demanding to avoid ceiling effects in patients with mild cognitive dysfunction.[…] sensitive domains such as speed of information processing, (working) memory, attention, and executive function should be examined thoroughly in diabetes patients, whereas other domains such as language, motor function, and perception are less likely to be affected. Intelligence should always be taken into account, and confounding factors such as mood, emotional distress, and coping are crucial for the interpretation of the neuropsychological test results.”

“The life-time risk of any dementia has been estimated to be more than 1 in 5 for women and 1 in 6 for men (2). Worldwide, about 24 million people have dementia, with 4.6 million new cases of dementia every year (3). […] Dementia can be caused by various underlying diseases, the most common of which is Alzheimer’s disease (AD) accounting for roughly 70% of cases in the elderly. The second most common cause of dementia is vascular dementia (VaD), accounting for 16% of cases. Other, less common, causes include dementia with Lewy bodies (DLB) and frontotemporal lobar degeneration (FTLD). […] It is estimated that both the incidence and the prevalence [of AD] double with every 5-year increase in age. Other risk factors for AD include female sex and vascular risk factors, such as diabetes, hypercholesterolaemia and hypertension […] In contrast with AD, progression of cognitive deficits [in VaD] is mostly stepwise and with an acute or subacute onset. […] it is clear that cerebrovascular disease is one of the major causes of cognitive decline. Vascular risk factors such as diabetes mellitus and hypertension have been recognized as risk factors for VaD […] Although pure vascular dementia is rare, cerebrovascular pathology is frequently observed on MRI and in pathological studies of patients clinically diagnosed with AD […] Evidence exists that AD and cerebrovascular pathology act synergistically (60).”

“In type 1 diabetes the annual prevalence of severe hypoglycemia (requiring help for recovery) is 30–40% while the annual incidence varies depending on the duration of diabetes. In insulin-treated type 2 diabetes, the frequency is lower but increases with duration of insulin therapy. […] In normal health, blood glucose is maintained within a very narrow range […] The functioning of the brain is optimal within this range; cognitive function rapidly becomes impaired when the blood glucose falls below 3.0 mmol/l (54 mg/dl) (3). Similarly, but much less dramatically, cognitive function deteriorates when the brain is exposed to high glucose concentrations” (I did not know the latter for certain, but I certainly have had my suspicions for a long time).

“When exogenous insulin is injected into a non-diabetic adult human, peripheral tissues such as skeletal muscle and adipose tissue rapidly take up glucose, while hepatic glucose output is suppressed. This causes blood glucose to fall and triggers a series of counterregulatory events to counteract the actions of insulin; this prevents a progressive decline in blood glucose and subsequently reverses the hypoglycemia. In people with insulin-treated diabetes, many of the homeostatic mechanisms that regulate blood glucose are either absent or deficient. [If you’re looking for more details on these topics, it should perhaps be noted here that Philip Cryer’s book on these topics is very nice and informative]. […] The initial endocrine response to a fall in blood glucose in non-diabetic humans is the suppression of endogenous insulin secretion. This is followed by the secretion of the principal counterregulatory hormones, glucagon and epinephrine (adrenaline) (5). Cortisol and growth hormone also contribute, but have greater importance in promoting recovery during exposure to prolonged hypoglycemia […] Activation of the peripheral sympathetic nervous system and the adrenal glands provokes the release of a copious quantity of catecholamines, epinephrine, and norepinephrine […] Glucagon is secreted from the alpha cells of the pancreatic islets, apparently in response to localized neuroglycopenia and independent of central neural control. […] The large amounts of catecholamines that are secreted in response to hypoglycemia exert other powerful physiological effects that are unrelated to counterregulation. These include major hemodynamic actions with direct effects on the heart and blood pressure. […] regional blood flow changes occur during hypoglycemia that encourages the transport of substrates to the liver for gluconeogenesis and simultaneously of glucose to the brain. Organs that have no role in the response to acute stress, such as the spleen and kidneys, are temporarily under-perfused. The mobilisation and activation of white blood cells are accompanied by hemorheological effects, promoting increased viscosity, coagulation, and fibrinolysis and may influence endothelial function (6). In normal health these acute physiological changes probably exert no harmful effects, but may acquire pathological significance in people with diabetes of long duration.”

“The more complex and attention-demanding cognitive tasks, and those that require speeded responses are more affected by hypoglycemia than simple tasks or those that do not require any time restraint (3). The overall speed of response of the brain in making decisions is slowed, yet for many tasks, accuracy is preserved at the expense of speed (8, 9). Many aspects of mental performance become impaired when blood glucose falls below 3.0 mmol/l […] Recovery of cognitive function does not occur immediately after the blood glucose returns to normal, but in some cognitive domains may be delayed for 60 min or more (3), which is of practical importance to the performance of tasks that require complex cognitive functions, such as driving. […] [the] major changes that occur during hypoglycemia – counterregulatory hormone secretion, symptom generation, and cognitive dysfunction – occur as components of a hierarchy of responses, each being triggered as the blood glucose falls to its glycemic threshold. […] In nondiabetic individuals, the glycemic thresholds are fixed and reproducible (10), but in people with diabetes, these thresholds are dynamic and plastic, and can be modified by external factors such as glycemic control or exposure to preceding (antecedent) hypoglycemia (11). Changes in the glycemic thresholds for the responses to hypoglycemia underlie the effects of the acquired hypoglycemia syndromes that can develop in people with insulin-treated diabetes […] the incidence of severe hypoglycemia in people with insulin-treated type 2 diabetes increases steadily with duration of insulin therapy […], as pancreatic beta-cell failure develops. The under-recognized risk of severe hypoglycemia in insulin-treated type 2 diabetes is of great practical importance as this group is numerically much larger than people with type 1 diabetes and encompasses many older, and some very elderly, people who may be exposed to much greater danger because they often have co-morbidities such as macrovascular disease, osteoporosis, and general frailty.”

“Hypoglycemia occurs when a mismatch develops between the plasma concentrations of glucose and insulin, particularly when the latter is inappropriately high, which is common during the night. Hypoglycemia can result when too much insulin is injected relative to oral intake of carbohydrate or when a meal is missed or delayed after insulin has been administered. Strenuous exercise can precipitate hypoglycemia through accelerated absorption of insulin and depletion of muscle glycogen stores. Alcohol enhances the risk of prolonged hypoglycemia by inhibiting hepatic gluconeogenesis, but the hypoglycemia may be delayed for several hours. Errors of dosage or timing of insulin administration are common, and there are few conditions where the efficacy of the treatment can be influenced by so many extraneous factors. The time–action profiles of different insulins can be modified by factors such as the ambient temperature or the site and depth of injection and the person with diabetes has to constantly try to balance insulin requirement with diet and exercise. It is therefore not surprising that hypoglycemia occurs so frequently. […] The lower the median blood glucose during the day, the greater the frequency
of symptomatic and biochemical hypoglycemia […] Strict glycemic control can […] induce the acquired hypoglycemia syndromes, impaired awareness of hypoglycemia (a major risk factor for severe hypoglycemia), and counterregulatory hormonal deficiencies (which interfere with blood glucose recovery). […] Severe hypoglycemia is more common at the extremes of age – in very young children and in elderly people.
[…] In type 1 diabetes the frequency of severe hypoglycemia increases with duration of diabetes (12), while in type 2 diabetes it is associated with increasing duration of insulin treatment (18). […] Around one quarter of all episodes of severe hypoglycemia result in coma […] In 10% of episodes of severe hypoglycemia affecting people with type 1 diabetes and around 30% of those in people with insulin-treated type 2 diabetes, the assistance of the emergency medical services is required (23). However, most episodes (both mild and severe) are treated in the community, and few people require admission to hospital.”

“Severe hypoglycemia is potentially dangerous and has a significant mortality and morbidity, particularly in older people with insulin-treated diabetes who often have premature macrovascular disease. The hemodynamic effects of autonomic stimulation may provoke acute vascular events such as myocardial ischemia and infarction, cardiac failure, cerebral ischemia, and stroke (6). In clinical practice the cardiovascular and cerebrovascular consequences of hypoglycemia are frequently overlooked because the role of hypoglycemia in precipitating the vascular event is missed. […] The profuse secretion of catecholamines in response to hypoglycemia provokes a fall in plasma potassium and causes electrocardiographic (ECG) changes, which in some individuals may provoke a cardiac arrhythmia […]. A possible mechanism that has been observed with ECG recordings during hypoglycemia is prolongation of the QT interval […]. Hypoglycemia-induced arrhythmias during sleep have been implicated as the cause of the “dead in bed” syndrome that is recognized in young people with type 1 diabetes (40). […] Total cerebral blood flow is increased during acute hypoglycemia while regional blood flow within the brain is altered acutely. Blood flow increases in the frontal cortex, presumably as a protective compensatory mechanism to enhance the supply of available glucose to the most vulnerable part of the brain. These regional vascular changes become permanent in people who are exposed to recurrent severe hypoglycemia and in those with impaired awareness of hypoglycemia, and are then present during normoglycemia (41). This probably represents an adaptive response of the brain to recurrent exposure to neuroglycopenia. However, these permanent hypoglycemia-induced changes in regional cerebral blood flow may encourage localized neuronal ischemia, particularly if the cerebral circulation is already compromised by the development of cerebrovascular disease associated with diabetes. […] Hypoglycemia-induced EEG changes can persist for days or become permanent, particularly after recurrent severe hypoglycemia”.

“In the large British Diabetic Association Cohort Study of people who had developed type 1 diabetes before the age of 30, acute metabolic complications of diabetes were the greatest single cause of excess death under the age of 30; hypoglycemia was the cause of death in 18% of males and 6% of females in the 20–49 age group (47).”

“[The] syndromes of counterregulatory hormonal deficiencies and impaired awareness of hypoglycemia (IAH) develop over a period of years and ultimately affect a substantial proportion of people with type 1 diabetes and a lesser number with insulin-treated type 2 diabetes. They are considered to be components of hypoglycemia-associated autonomic failure (HAAF), through down-regulation of the central mechanisms within the brain that would normally activate glucoregulatory responses to hypoglycemia, including the release of counterregulatory hormones and the generation of warning symptoms (48). […] The glucagon secretory response to hypoglycemia becomes diminished or absent within a few years of the onset of insulin-deficient diabetes. With glucagon deficiency alone, blood glucose recovery from hypoglycemia is not noticeably affected because the secretion of epinephrine maintains counterregulation. However, almost half of those who have type 1 diabetes of 20 years duration have evidence of impairment of both glucagon and epinephrine in response to hypoglycemia (49); this seriously delays blood glucose recovery and allows progression to more severe and prolonged hypoglycemia when exposed to low blood glucose. People with type 1 diabetes who have these combined counterregulatory hormonal deficiencies have a 25-fold higher risk of experiencing severe hypoglycemia if they are subjected to intensive insulin therapy compared with those who have lost their glucagon response but have retained epinephrine secretion […] Impaired awareness is not an “all or none” phenomenon. “Partial” impairment of awareness may develop, with the individual being aware of some episodes of hypoglycemia but not others (53). Alternatively, the intensity or number of symptoms may be reduced, and neuroglycopenic symptoms predominate. […] total absence of any symptoms, albeit subtle, is very uncommon […] IAH affects 20–25% of patients with type 1 diabetes (11, 55) and less than 10% with type 2 diabetes (24), becomes more prevalent with increasing duration of diabetes (12) […], and predisposes the patient to a sixfold higher risk of severe hypoglycemia than people who retain normal awareness (56). When IAH is associated with strict glycemic control during intensive insulin therapy or has followed episodes of recurrent severe hypoglycemia, it may be reversible by relaxing glycemic control or by avoiding further hypoglycemia (11), but in many patients with type 1 diabetes of long duration, it appears to be a permanent defect. […] The modern management of diabetes strives to achieve strict glycemic control using intensive therapy to avoid or minimize the long-term complications of diabetes; this strategy tends to increase the risk of hypoglycemia and promotes development of the acquired hypoglycemia syndromes.”

February 5, 2017 Posted by | Books, Cardiology, Diabetes, Epidemiology, Medicine, Neurology, Psychology | Leave a comment

Diabetes and the Brain (II)

Here’s my first post about the book, which I recently finished – here’s my goodreads review. I added the book to my list of favourite books on goodreads, it’s a great textbook. Below some observations from the first few chapters of the book.

“Several studies report T1D [type 1 diabetes] incidence numbers of 0.1–36.8/100,000 subjects worldwide (2). Above the age of 15 years ketoacidosis at presentation occurs on average in 10% of the population; in children ketoacidosis at presentation is more frequent (3, 4). Overall, publications report a male predominance (1.8 male/female ratio) and a seasonal pattern with higher incidence in November through March in European countries. Worldwide, the incidence of T1D is higher in more developed countries […] After asthma, T1D is a leading cause of chronic disease in children. […]  twin studies show a low concordant prevalence of T1D of only 30–55%. […] Diabetes mellitus type 1 may be sporadic or associated with other autoimmune diseases […] The latter has been classified as autoimmune polyglandular syndrome type II (APS-II). APS-II is a polygenic disorder with a female preponderance which typically occurs between the ages of 20 and 40 years […] In clinical practice, anti-thyroxine peroxidase (TPO) positive hypothyroidism is the most frequent concomitant autoimmune disease in type 1 diabetic patients, therefore all type 1 diabetic patients should annually be screened for the presence of anti-TPO antibodies. Other frequently associated disorders are atrophic gastritis leading to vitamin B12 deficiency (pernicious anemia) and vitiligo. […] The normal human pancreas contains a superfluous amount of β-cells. In T1D, β-cell destruction therefore remains asymptomatic until a critical β-cell reserve is left. This destructive process takes months to years […] Only in a minority of type 1 diabetic patients does the disease begin with diabetic ketoacidosis, the majority presents with a milder course that may be mistaken as type 2 diabetes (7).”

“Insulin is the main regulator of glucose metabolism by stimulating glucose uptake in tissues and glycogen storage in liver and muscle and by inhibiting gluconeogenesis in the liver (11). Moreover, insulin is a growth factor for cells and cell differentiation, and acting as anabolic hormone insulin stimulates lipogenesis and protein synthesis. Glucagon is the counterpart of insulin and is secreted by the α-cells in the pancreatic islets in an inversely proportional quantity to the insulin concentration. Glucagon, being a catabolic hormone, stimulates glycolysis and gluconeogenesis in the liver as well as lipolysis and uptake of amino acids in the liver. Epinephrine and norepinephrine have comparable catabolic effects […] T1D patients lose the glucagon response to hypoglycemia after several years, when all β-cells are destructed […] The risk of hypoglycemia increases with improved glycemic control, autonomic neuropathy, longer duration of diabetes, and the presence of long-term complications (17) […] Long-term complications are prevalent in any population of type 1 diabetic patients with increasing prevalence and severity in relation to disease duration […] The pathogenesis of diabetic complications is multifactorial, complicated, and not yet fully elucidated.”

“Cataract is much more frequent in patients with diabetes and tends to become clinically significant at a younger age. Glaucoma is markedly increased in diabetes too.” (I was unaware of this).

“T1D should be considered as an independent risk factor for atherosclerosis […] An older study shows that the cumulative mortality of coronary heart disease in T1D was 35% by the age 55 (34). In comparison, the Framingham Heart Study showed a cardiovascular mortality of 8% of men and 4% of women without diabetes, respectively. […] Atherosclerosis is basically a systemic disease. Patients with one clinically apparent localization are at risk for other manifestations. […] Musculoskeletal disease in diabetes is best viewed as a systemic disorder with involvement of connective tissue. Potential pathophysiological mechanisms that play a role are glycosylation of collagen, abnormal cross-linking of collagen, and increased collagen hydration […] Dupuytren’s disease […] may be observed in up to 42% of adults with diabetes mellitus, typically in patients with long-standing T1D. Dupuytren’s is characterized by thickening of the palmar fascia due to fibrosis with nodule formation and contracture, leading to flexion contractures of the digits, most commonly affecting the fourth and fifth digits. […] Foot problems in diabetes are common and comprise ulceration, infection, and gangrene […] The lifetime risk of a foot ulcer for diabetic patients is about 15% (42). […] Wound depth is an important determinant of outcome (46, 47). Deep ulcers with cellulitis or abscess formation often involve osteomyelitis. […] Radiologic changes occur late in the course of osteomyelitis and negative radiographs certainly do not exclude it.”

“Education of people with diabetes is a comprehensive task and involves teamwork by a team that comprises at least a nurse educator, a dietician, and a physician. It is, however, essential that individuals with diabetes assume an active role in their care themselves, since appropriate self-care behavior is the cornerstone of the treatment of diabetes.” (for much more on these topics, see Simmons et al.)

“The International Diabetes Federation estimates that more than 245 million people around the world have diabetes (4). This total is expected to rise to 380 million within 20 years. Each year a further 7 million people develop diabetes. Diabetes, mostly type 2 diabetes (T2D), now affects 5.9% of the world’s adult population with almost 80% of the total in developing countries. […] According to […] 2007 prevalence data […] [a]lmost 25% of the population aged 60 years and older had diabetes in 2007. […] It has been projected that one in three Americans born in 2000 will develop diabetes, with the highest estimated lifetime risk among Latinos (males, 45.4% and females, 52.5%) (6). A rise in obesity rates is to blame for much of the increase in T2D (7). Nearly two-thirds of American adults are overweight or obese (8). [my bold, US]

“In the natural history of progression to diabetes, β-cells initially increase insulin secretion in response to insulin resistance and, for a period of time, are able to effectively maintain glucose levels below the diabetic range. However, when β-cell function begins to decline, insulin production is inadequate to overcome the insulin resistance, and blood glucose levels rise. […] Insulin resistance, once established, remains relatively stable over time. […] progression of T2D is a result of worsening β-cell function with pre-existing insulin resistance.”

“Lifestyle modification (i.e., weight loss through diet and increased physical activity) has proven effective in reducing incident T2D in high-risk groups. The Da Qing Study (China) randomly allocated 33 clinics (557 persons with IGT) to 1 of 4 study conditions: control, diet, exercise, or diet plus exercise (23). Compared with the control group, the incidence of diabetes was reduced in the three intervention groups by 31, 46, and 42%, respectively […] The Finnish Diabetes Prevention Study evaluated 522 obese persons with IGT randomly allocated on an individual basis to a control group or a lifestyle intervention group […] During the trial, the incidence of diabetes was reduced by 58% in the lifestyle group compared with the control group. The US Diabetes Prevention Program is the largest trial of primary prevention of diabetes to date and was conducted at 27 clinical centers with 3,234 overweight and obese participants with IGT randomly allocated to 1 of 3 study conditions: control, use of metformin, or intensive lifestyle intervention […] Over 3 years, the incidence of diabetes was reduced by 31% in the metformin group and by 58% in the lifestyle group; the latter value is identical to that observed in the Finnish Study. […] Metformin is recommended as first choice for pharmacologic treatment [of type 2 diabetes] and has good efficacy to lower HbA1c […] However, most patients will eventually require treatment with combinations of oral medications with different mechanisms of action simultaneously in order to attain adequate glycemic control.”

CVD [cardiovascular disease, US] is the cause of 65% of deaths in patients with T2D (31). Epidemiologic studies have shown that the risk of a myocardial infarction (MI) or CVD death in a diabetic individual with no prior history of CVD is comparable to that of an individual who has had a previous MI (32, 33). […] Stroke is the second leading cause of long-term disability in high-income countries and the second leading cause of death worldwide. […] Stroke incidence is highly age-dependent. The median stroke incidence in persons between 15 and 49 years of age is 10 per 100,000 per year, whereas this is 2,000 per 100,000 for persons aged 85 years or older. […] In Western communities, about 80% of strokes are caused by focal cerebral ischemia, secondary to arterial occlusion, 15% by intracerebral hemorrhage, and 5% by subarachnoid hemorrhage (2). […] Patients with ischemic stroke usually present with focal neurological deficit of sudden onset. […] Common deficits include dysphasia, dysarthria, hemianopia, weakness, ataxia, sensory loss, and cognitive disorders such as spatial neglect […] Mild-to-moderate headache is an accompanying symptom in about a quarter of all patients with ischemic stroke […] The risk of symptomatic intracranial hemorrhage after thrombolysis is higher with more severe strokes and higher age (21). [worth keeping in mind when in the ‘I-am-angry-and-need-someone-to-blame-for-the-death-of-individual-X-phase’ – if the individual died as a result of the treatment, the prognosis was probably never very good to start with..] […] Thirty-day case fatality rates for ischemic stroke in Western communities generally range between 10 and 17% (2). Stroke outcome strongly depends not only on age and comorbidity, but also on the type and cause of the infarct. Early case fatality can be as low as 2.5% in patients with lacunar infarcts (7) and as high as 78% in patients with space-occupying hemispheric infarction (8).”

“In the previous 20 years, ten thousands of patients with acute ischemic stroke have participated in hundreds of clinical trials of putative neuroprotective therapies. Despite this enormous effort, there is no evidence of benefit of a single neuroprotective agent in humans, whereas over 500 have been effective in animal models […] the failure of neuroprotective agents in the clinic may […] be explained by the fact that most neuroprotectants inhibit only a single step in the broad cascade of events that lead to cell death (9). Currently, there is no rationale for the use of any neuroprotective medication in patients with acute ischemic stroke.”

“Between 5 and 10% of patients with ischemic stroke suffer from epileptic seizures in the first week and about 3% within the first 24 h […] Post-stroke seizures are not associated with a higher mortality […] About 1 out of every 11 patient with an early epileptic seizure develops epilepsy within 10 years after stroke onset (51) […] In the first 12 h after stroke onset, plasma glucose concentrations are elevated in up to 68% of patients, of whom more than half are not known to have diabetes mellitus (53). An initially high blood glucose concentration in patients with acute stroke is a predictor of poor outcome (53, 54). […] Acute stroke is associated with a blood pressure higher than 170/110 mmHg in about two thirds of patients. Blood pressure falls spontaneously in the majority of patients during the first week after stroke. High blood pressure during the acute phase of stroke has been associated with a poor outcome (56). It is unclear how blood pressure should be managed during the acute phase of ischemic stroke. […] routine lowering of the blood pressure is not recommended in the first week after stroke, except for extremely elevated values on repeated measurements […] Urinary incontinence affects up to 60% of stroke patients admitted to hospital, with 25% still having problems on hospital discharge, and around 15% remaining incontinent at 1 year. […] Between 22 and 43% of patients develop fever or subfebrile temperatures during the first days after stroke […] High body temperature in the first days after stroke is associated with poor outcome (42, 67). There is currently no evidence from randomized trials to support the routine lowering of body temperature above 37◦C.”

December 28, 2016 Posted by | Books, Cardiology, Diabetes, Epidemiology, Immunology, Medicine, Neurology, Ophthalmology | Leave a comment