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

Gastrointestinal Function in Diabetes Mellitus (III)

Below some observations from chapters 5 and 6.

“The major functions of the small intestine are to digest and absorb nutrients, while those of the large bowel are to extract water and process faeces before expulsion. Diabetes mellitus may be associated with both small intestinal and colonic dysfunction, potentially resulting in a wide range of clinical manifestations, including gastrointestinal symptoms, poor nutritional status and impaired glycaemic control. […] The prevalence of small intestinal and colonic dysfunction in diabetes has not been formally evaluated and remains uncertain. However, small intestinal motor abnormalities are evident in about 80% of patients with diabetic gastroparesis, suggesting that the prevalence of intestinal dysmotility is likely to be comparable to the prevalence of gastroparesis in diabetic patients, i.e. 30–50% of unselected patients [1–6]. […] symptoms resulting from intestinal dysfunction are not cause-specific and are heterogeneous, potentially giving rise to diverse complaints, including anorexia, nausea, vomiting, constipation, diarrhoea and abdominal pain or discomfort. […] Transport of chyme through the small intestine is closely linked to intraluminal digestion and absorption of nutrients. The efficacy of absorption of nutrients is, therefore, potentially affected by dysmotility of the small intestine observed in diabetes, and by alterations in the transport mechanisms facilitating nutrient uptake across the intestinal membrane.”

“After meal ingestion, food is initially stored in the proximal stomach, then triturated in the distal stomach, and finally transported to the small intestine […]. The major functions of the small intestine are to mix and propel food particles in order to optimise intraluminal digestion and absorption. Those food particles that escape absorption, as well as indigestible solids, are transported to the colon, where water is extracted and faeces processed before expulsion. The motility patterns of the small intestine and colon are designed to efficiently serve these functions of controlled mixing and transport. When the small intestine is not exposed to nutrients, it exhibits a cyclic pattern of motility […] termed the migrating motor complex (MMC). […] The major function of the colon is to absorb water and electrolytes in order to concentrate and solidify the intraluminal content. Colonic motility plays an important role in these processes. In contrast to small intestinal motility, colonic motility follows a diurnal rhythm, with relative motor quiescence during sleep [55,56]. […] Transit and absorption of intestinal contents are regulated by the autonomic and enteric nervous systems. […] Numerous neuropeptides have been shown to play an important role in controlling the smooth muscle function of the small intestine and colon […] studies using experimental animal models of diabetes have shown altered activity of many neurotransmitters known to be of importance in preserving the integrity of intestinal motility […] Recently, the so-called interstitial cells of Cajal have been identified in the gastrointestinal tract [64–66] and appear to be responsible for the generation of the slow wave activity present in the entire gastrointestinal tract. […] The interplay between the enteric nervous system and the interstitial cells of Cajal is essential for normal gut motility.”

“[N]europathy of the autonomic (vagal and sympathetic) and enteric nerves may result in intestinal dysmotility. Autonomic neuropathy at the level of the gut can be assessed using cardiac autonomic nerve (CAN) function tests as a surrogate marker […] at present CAN function tests are the best tests available in the clinical situation. Studies using CAN function tests to assess involvement of the autonomic nerve system indicate that in patients with CAN the prevalence and severity of dysmotility of the small intestine and colon is substantially greater when compared to patients with normal CAN function. […] there is evidence that intestinal secretion may be abnormal in diabetes, due to increased secretion of fluids in response to a meal, rather than an increased basal secretory state [176]. […] These observations suggest that progressive neuropathy of the enteric and autonomic nervous system is likely to be responsible for the impaired intestinal secretion, rather than hyperglycaemia.”

“Studies that have investigated small intestinal motility in diabetes mellitus have revealed a wide spectrum of motor patterns, ranging from normal to grossly abnormal motility […] Postprandial small intestinal motor abnormalities include early recurrence of phase III and burst activity […] Both […] are thought to indicate neuropathic changes in either the intrinsic or extrinsic innervation of the gut. […] The data relating to colonic function in patients with diabetes mellitus are even more limited than those that exist for the small intestine […] [Some results suggest that] symptoms may not be a good indicator of the presence or absence of delayed colonic transit in diabetic patients.”

“There is little or no evidence that diabetes per se affects protein absorption to a clinically relevant extent. However, when diabetes mellitus is associated with severe pancreatic insufficiency […], coeliac disease […] or bacterial overgrowth, malabsorption of protein may occur. […] Since lipid absorption is dependent on the interplay of several organs (small intestine, pancreas, liver, gall bladder), diabetes mellitus has the potential to be associated with fat malabsorption […] Although it is not known whether small intestinal dysmotility per se can lead to fat malabsorption, it certainly can when the dysmotility is associated with bacterial overgrowth [160,161]. […] Recently, drug-induced malabsorption of fat has become a treatment option in diabetes mellitus. The inhibition of pancreatic lipase activity by orlistat prevents the hydrolysis of triglycerides, resulting in fat malabsorption. This approach has been reported to improve glycaemic control in type 2 diabetes”.

“The superior and inferior mesenteric arteries supply blood to the small and large intestine, while the superior, middle and inferior rectal arteries provide the arterial blood supply of the rectum. About 25% of the cardiac output in the fasting state circulates through the splanchnic arteries […] Animal models of diabetes are associated with abnormalities of neurotransmitters in the mesenteric veins and arteries […] Human diabetes may be associated with abnormalities in mesenteric blood flow. In diabetic patients with autonomic neuropathy, preprandial superior mesenteric arterial blood flow is greater than that in both control subjects and patients without autonomic neuropathy […] patients with autonomic dysfunction […] are at particular risk of postprandial hypotension and often exhibit a marked fall in systemic blood pressure after a meal […] the magnitude of the postprandial fall in blood pressure is dependent on meal composition (glucose has the greatest effect) and the rate of nutrient entry into the small intestine [196]. […] Patients with diabetes mellitus also frequently report symptoms attributable to orthostatic hypotension. A large survey of type 1 diabetes mellitus reported that the frequency of feeling faint on standing was 18% [200]. Symptomatic orthostatic hypotension in diabetic patients has been shown to be related to cardiovascular autonomic neuropathy”.

“Disordered defaecation, characterised by incontinence, constipation and diarrhoea, occurs frequently in patients with diabetes mellitus [1–3] but is often overlooked as a cause of morbidity. For example, in a study of 136 unselected diabetic outpatients referred to a tertiary centre, Feldman and Schiller found that constipation occurred in 60%, diarrhoea in 22% and faecal incontinence in 20% of their patients [1]. […] Disordered defaecation appears to be less common among patients with diabetes attending secondary referral centres [4,5], where constipation has been reported in about 20% and faecal incontinence in about 9% [5].”

“[D]efaecation and the preservation of continence are both complex territorial behaviours in humans. They are generated in the cerebral cortex and are […] markedly influenced by psychosocial factors. The multiple physiological functions required to control the passage of faeces are under the influence of a control centre in the pontine brain stem and orchestrated by the neuronal activity in the terminal expansion of the spinal cord. The instructions are conveyed via pelvic parasympathetic nerves, lumbar sympathetic nerves and sacral somatic nerves, influencing the function of the enteric nervous system and visceral smooth muscle and also the muscles of the pelvic floor. […] the muscles of the colon, abdominal wall and pelvic floor must be able to contract with sufficient power to propel faeces or resist that propulsion. But more important, the arrival of faeces in the rectum or even quite small increases in intra-abdominal pressure need to be detected immediately, so that appropriate responses can be rapidly triggered through spinal and enteric reflexes. These actions can be influenced at many levels by the diabetic process. […] Impairment of neural function caused by diabetic microangiopathy can affect to a lesser or greater extent all the mechanisms involved in the maintenance of faecal continence. So whether a person develops faecal incontinence or not depends on the interplay between all of these. Physiological studies have demonstrated that cohorts of patients with long-standing diabetes have an abnormally low anal tone, weak squeeze pressures and impaired rectal sensation [58–60]. […] Patients with long-standing diabetes mellitus are more likely to be afflicted by the shame of nocturnal incontinence of faeces than non-diabetics with faecal incontinence. […] Faecal incontinence in diabetic patients is also often associated with urinary incontinence [63]. […] Patients with faecal incontinence may only rarely be ‘cured’ — the major aim of treatment is to improve symptoms to a level where there is minimal impact on lifestyle.”

“It is important to recognise that the most common factor responsible for pudendal neuropathy in women is […] damage to the pelvic floor sustained during childbirth. […] Endo-anal ultrasonography has shown that 35% of primiparous women tested after delivery had sustained sphincter damage that persisted for at least 6 months [66]. The percentages are higher in those who had undergone forceps delivery and for multiparous women […] Diabetic women, especially those with less than optimal diabetic control, are more liable to suffer from obstetric complications, such as traumatic disruption of the anal sphincter or weakness of the pelvic floor, leading to chronic stretching of the pudendal nerve. This is because diabetics tend to give birth to large babies when glycaemic control is poor, and are more likely to experience long and difficult labours and require assisted delivery with forceps or ventouse [67].”


September 10, 2017 Posted by | Books, Diabetes, Gastroenterology, Neurology | Leave a comment

Gastrointestinal Function in Diabetes (II)

Some more observations from the book below.

“In comparison with other parts of the gastrointestinal tract, the human oesophagus is a relatively simple organ with relatively simple functions. Despite this simplicity, disordered oesophageal function is not uncommon. […] The human oesophagus is a muscular tube that connects the pharyngeal cavity to the stomach. […] The most important functions of the human oesophagus and its sphincters are to propel swallowed food boluses to the stomach and to prevent gastro-oesophageal and oesophagopharyngeal reflux. […] Whereas the passage of liquid and solid food boluses through the oesophagus, and even acid gastrooesophageal reflux, are usually not perceived, the likelihood of perception is greater under pathological circumstances […] However, the relationship between oesophageal perception and stimulation is highly variable, e.g. patients with severe oesophagitis may deny any oesophageal symptom, while others with an endoscopically normal oesophagus may suffer from severe reflux symptoms.”

“While it is clear that oesophageal dysfunction occurs frequently in diabetes mellitus, there is considerable variation in the reported prevalence between different studies. […] Numerous studies have shown that oesophageal transit, as measured with radionuclide techniques, is slower in patients with diabetes than in age- and sex-matched healthy controls […] oesophageal transit appears to be delayed in 40–60% of patients with long-standing diabetes […] Although information relating to the prevalence of manometric abnormalities of the oesophagus [relevant link] is limited, the available data indicate that these are evident in approximately 50% of patients with diabetes […] A variety of oesophageal motor abnormalities has been demonstrated in patients with diabetes mellitus […]. These include a decreased amplitude […] and number […] of peristaltic contractions […], and an increased incidence of simultaneous […] and nonpropagated [10] contractions, as well as abnormal wave forms [17,30,32]. […] there is unequivocal evidence of damage to the extrinsic nerve supply to the oesophagus in diabetes mellitus. The results of examination of the oesophagus in 20 patients who died from diabetes disclosed histologic abnormalities in 18 of them […] The available information indicates that the prevalence of gastro-oesophageal reflux disease is higher in diabetes. Murray and co-workers studied 20 diabetic patients (14 type 1, six type 2), of whom nine (45%) were found to have excessive gastro-oesophageal acid reflux […] In a larger study of 50 type 1 diabetic patients without symptoms or history of gastro-oesophageal disease, abnormal gastro-oesophageal reflux, defined as a percentage of time with esophageal pH < 4 exceeding 3.5%, was detected in 14 patients (28%) [37].”

“Several studies have shown that the gastrointestinal motor responses to various stimuli are impaired during acute hyperglycaemia in both healthy subjects and diabetic patients […] acute hyperglycaemia reduces LOS [lower oesophageal sphincter, US] pressure and impairs oesophageal motility […] Several studies have shown that abnormal oesophageal motility is more frequent in diabetic patients who have evidence of peripheral or autonomic neuropathy than in those without […] In one of the largest studies that focused on the relationship between neuropathy and disordered oesophageal function, 50 […] insulin-requiring diabetics were stratified into three groups: (a) patients without peripheral neuropathy (n = 18); (b) patients with peripheral neuropathy but no autonomic neuropathy (n = 20); and (c) patients with both peripheral and autonomic neuropathy (n = 12). Radionuclide oesophageal emptying was found to be abnormal in 55%, 70% and 83% of patients in groups A, B and C, respectively [17]. […] It must be emphasised, however, that although several studies have provided evidence for the existence of a relationship between disordered oesophageal function and diabetic autonomic neuropathy, this relationship is relatively weak [13,14,17,27,37,49].”

“There is considerable disagreement in the literature as to the prevalence of symptoms of oesophageal dysfunction in diabetes mellitus. Some publications indicate that patients with diabetes mellitus usually do not complain about oesophageal symptoms, even when severe oesophageal dysfunction is present. […] However, in other studies a high prevalence of oesophageal symptoms in diabetics has been documented. For example, 27% of 137 unselected diabetics attending an outpatient clinic admitted to having dysphagia when specifically asked […] The poor association between oesophageal dysfunction and symptoms in patients with diabetes may reflect impaired perception of oesophageal stimuli caused by neuropathic abnormalities in afferent pathways. The development of symptoms and signs of gastro-oesophageal reflux disease in diabetics may in part be counteracted by a decrease in gastric acid secretion [59]. […] [However] oesophageal acid exposure is increased in about 40% of diabetics and it is known that the absence of reflux symptoms does not exclude the presence of severe oesophagitis and/or Barrett’s metaplasia. Due to impaired oesophageal perception, the proportion of asymptomatic patients with reflux disease may be higher in the presence of diabetes than when diabetes is absent. It might, therefore, be argued that a screening upper gastrointestinal endoscopy should be performed in diabetic patients, even when no oesophageal or gastric symptoms are reported. However, [a] more cost-effective
and realistic approach may be to perform endoscopy in diabetics with other risk factors for reflux disease, in particular severe obesity.
[…] Since upper gastrointestinal symptoms correlate poorly with objective abnormalities of gastrointestinal motor function in diabetes, the symptomatic benefit that could be expected from correction of these motor abnormalities is questionable. […] Little or nothing is known about the prognosis of disordered oesophageal function in diabetes. Long-term follow-up studies are lacking.

“Abnormally delayed gastric emptying, or gastroparesis, was once considered to be a rare sequela of diabetes mellitus, occurring occasionally in patients who had long-standing diabetes complicated by symptomatic autonomic neuropathy, and inevitably associated with both intractable upper gastrointestinal symptoms and a poor prognosis [1]. Consequent upon the development of a number of techniques to quantify gastric motility […] and the rapid expansion of knowledge relating to both normal and disordered gastric motor function in humans over the last ∼ 20 years, it is now recognised that these concepts are incorrect. […] Delayed gastric emptying represents a frequent, and clinically important, complication of diabetes mellitus. […] Cross-sectional studies […] have established that gastric emptying of solid, or nutrient liquid, meals is abnormally slow in some 30–50% of outpatients with longstanding type 1 [7–20] or type 2 [20–26] diabetes […]. Early studies, using insensitive barium contrast techniques to quantify gastric emptying, clearly underestimated the prevalence substantially [1,27]. The reported prevalence of delayed gastric emptying is highest when gastric emptying of both solid and nutrient-containing liquids (or semi-solids) are measured, either simultaneously or on separate occasions [17,28,29], as there is a relatively poor correlation between gastric emptying of solids and liquids in diabetes [28–30]. […] It is now recognised that delayed gastric emptying also occurs frequently (perhaps about 30%) in children and adolescents with type 1 diabetes [37–39]. […] intragastric meal distribution is also frequently abnormal in outpatients with diabetes, with increased retention of food in both the proximal and distal stomach [31,33]. The former may potentially be important in the aetiology of gastro-oesophageal reflux [34], which appears to occur more frequently in patients with diabetes […] Diabetic gastroparesis is often associated with motor dysfunction in other areas of the gut, e.g. oesophageal transit is delayed in some 50% of patients with long-standing diabetes [8].”

“Overall patterns of gastric emptying are critically dependent on the physical and chemical composition of a meal, so that there are substantial differences between solids, semi-solids, nutrient liquids and non-nutrient liquids [70]. […] The major factor regulating gastric emptying of nutrients (liquids and ‘liquefied’ solids) is feedback inhibition, triggered by receptors that are distributed throughout the small intestine [72]; as a result of this inhibition, nutrient-containing liquids usually empty from the stomach at an overall rate of about 2 kcal/min, after an initial emptying phase that may be somewhat faster [73]. These small intestinal receptors also respond to pH, osmolality and distension, as well as nutrient content. […] While the differential emptying rates of solids, nutrient and non-nutrient liquids when ingested alone is well established, there is much less information about the interaction between different meal components. When liquids and solids are consumed together, liquids empty preferentially (∼ 80% before the solid starts to empty) […] and the presence of a solid meal results in an overall slowing of a simultaneously ingested liquid [71,75,76]. Therefore, while it is clear that the stomach can, to some extent, regulate the emptying of liquids and solids separately, the mechanisms by which this is accomplished remain poorly defined. Extracellular fat has a much lower density than water and is liquid at body temperature. The pattern of gastric emptying of fat, and its effects on emptying of other meal components are, therefore, dependent on posture — in the left lateral posture oil accumulates in the stomach and empties early, which markedly delays emptying of a nutrient liquid [77]. Gastric emptying is also influenced by patterns of previous nutrient intake. In healthy young and older subjects, supplementation of the diet with glucose is associated with acceleration of gastric emptying of glucose [78,79], while short-term starvation slows gastric emptying”.

“[I]n animal models of diabetes a number of morphological changes are evident in the autonomic nerves supplying the gut and the myenteric plexus, including a reduction in the number of myelinated axons in the vagosympathetic trunk and neurons in the dorsal root ganglia, abnormalities in neurotransmitters […] as well as a reduced number of interstitial cells of Cajal in the fundus and antrum [89–92]. In contrast, there is hitherto little evidence of a fixed pathological process in the neural tissue of humans with diabetes […] While a clear-cut association between disordered gastrointestinal function in diabetes mellitus and the presence of autonomic neuropathy remains to be established, it is now recognised that acute changes in the blood glucose concentration have a substantial, and reversible, effect on gastric (as well as oesophageal, intestinal, gallbladder and anorectal) motility, in both healthy subjects and patients with diabetes […] Marked hyperglycaemia (blood glucose concentration ∼ 15 mmol/l) affects motility in every region of the gastrointestinal tract [103]. […] In healthy subjects [114] and patients with uncomplicated type 1 diabetes […] gastric emptying is accelerated markedly during hypoglycaemia […] this response is likely to be important in the counterregulation of hypoglycaemia. It is not known whether the magnitude of the effect of hypoglycaemia on gastric emptying is influenced by gastroparesis and/or autonomic neuropathy. Recent studies have established that changes in the blood glucose concentration within the normal postprandial range also influence gastric emptying and motility [104–106]; emptying of solids and nutrient-containing liquids is slower at a blood glucose of 8 mmol/l than at 4 mmol/l in both healthy subjects and patients with type 1 diabetes […] Recent studies suggest that the rate of gastric emptying is a significant factor in postprandial hypotension. The latter, which may lead to syncope and falls, is an important clinical problem, particularly in the elderly and patients with autonomic dysfunction (usually diabetes mellitus), occurring more frequently than orthostatic hypotension [154].”

“Gastric emptying is potentially an important determinant of oral drug absorption; most orally administered drugs (including alcohol) are absorbed more slowly from the stomach than from the small intestine because the latter has a much greater surface area [179,180]. Thus, delayed gastric emptying (particularly that of tablets or capsules, which are not degraded easily in the stomach) and a reduction in antral phase 3 activity, may potentially lead to fluctuations in the serum concentrations of orally administered drugs. This may be particularly important when a rapid onset of drug effect is desirable, as with some oral hypoglycaemic drugs […]. There is relatively little information about drug absorption in patients with diabetic gastroparesis [179] and additional studies are required.”

“Glycated haemoglobin is influenced by both fasting and postprandial glucose levels; while their relative contributions have not been defined precisely [181], it is clear that improved overall glycaemic control, as assessed by glycated haemoglobin, can be achieved by lowering postprandial blood glucose concentrations, even at the expense of higher fasting glucose levels [182]. Accordingly, the control of postprandial blood glucose levels, as opposed to glycated haemoglobin, now represents a specific target for treatment […] It remains to be established whether postprandial glycaemia per se, including the magnitude of postprandial hyperglycaemic spikes, has a distinct role in the pathogenesis of diabetic complications, but there is increasing data to support this concept [181,183,184]. It is also possible that the extent of blood glucose fluctuations is an independent determinant of the risk for long-term diabetic complications [184]. […] postprandial blood glucose levels are potentially determined by a number of factors, including preprandial glucose concentrations, the glucose content of a meal, small intestinal delivery and absorption of nutrients, insulin secretion, hepatic glucose metabolism and peripheral insulin sensitivity. Although the relative contribution of these factors remains controversial, and is likely to vary with time after a meal, it is now recognised that gastric emptying accounts for at least 35% of the variance in peak glucose levels after oral glucose (75 g) in both healthy individuals and patients with type 2 diabetes […] It is also clear that even modest perturbations in gastric emptying of carbohydrate have a major effect on postprandial glycaemia [76,79]. […] it appears that much of the observed variation in the glycaemic response to different food types (‘glycaemic indices’) in both normal subjects and patients with diabetes is attributable to differences in rates of gastric emptying [103]. […] In type 1 patients with gastroparesis […] less insulin is initially required to maintain euglycaemia after a meal when compared to those with normal gastric emptying [187]. […] There are numerous uncontrolled reports supporting the concept […] that in type 1 patients gastroparesis is a risk factor for poor glycaemic control.”

“The potential for the modulation of gastric emptying, by dietary or pharmacological means, to minimise postprandial glucose excursions and optimise glycaemic control, represents a novel approach to the optimisation of glycaemic control in diabetes, which is now being explored actively. It is important to appreciate that the underlying strategies are likely to differ fundamentally between type 1 and type 2 diabetes. In type 1 diabetes, interventions that improve the coordination between nutrient absorption and the action of exogenous insulin are likely to be beneficial, even in those patients who have delayed gastric emptying, i.e. by accelerating or even slowing gastric emptying, so that the rate of nutrient delivery (and hence absorption) is more predictable. In contrast, in type 2 diabetes, it may be anticipated that slowing of the absorption of nutrients would be desirable […] In the treatment of type 2 diabetes mellitus, dietary modifications potentially represent a more attractive and cost-effective approach than drugs […] A number of dietary strategies may slow carbohydrate absorption […] an increase in dietary fibre […] Fat is a potent inhibitor of gastric emptying and […] these effects may be dependent on posture [77]; there is the potential for relatively small quantities of fat given immediately before consumption of, or with, a meal to slow gastric emptying of other meal components, so that the postprandial rise in blood glucose is minimised [210] (this is analogous to the slowing of alcohol absorption and liquid gastric emptying when alcohol is ingested after a solid meal, rather than in the fasted state [75]). […] there is evidence that the suppression of subsequent food intake by the addition of fat to a meal may exceed the caloric value of the fat load [212]. In the broadest sense, the glycaemic response to a meal is also likely to be critically dependent on whether food from the previous meal is still present in the stomach and/or small intestine at the time of its ingestion, so that glucose tolerance may be expected to be worse in the fasted state […] than after a meal.”

“At present it is not known whether normalisation of gastric emptying in either type 1 or type 2 patients with gastroparesis improves glycaemic control. […] prokinetic drugs would not be expected to have a beneficial effect on glycaemic control in type 2 patients who are not using insulin. Erythromycin may, however, as a result of its interaction with motilin receptors, also stimulate insulin secretion (and potentially improve glycaemic control by this mechanism) in type 2 diabetes [220] […] It should […] be recognised that any drug that slows gastric emptying has the potential to induce or exacerbate upper gastrointestinal symptoms, delay oral drug absorbtion and impair the counter-regulation of glycaemia. […] At present, the use of prokinetic drugs (mainly cisapride, domperidone, metoclopramide and erythromycin) forms the mainstay of therapy [167,244–259], and most patients will require drug treatment. In general, these drugs all result in dose-related improvements in gastric emptying after acute administration […] The response to prokinetic therapy (magnitude of acceleration in gastric emptying) tends to be greater when gastric emptying is more delayed. It should be recognised that relatively few controlled studies have evaluated the effects of ‘prolonged’ (> 8 weeks) prokinetic therapy, that in many studies the sample sizes have been small, and that the assessments of gastrointestinal symptoms have, not infrequently, been suboptimal; furthermore, the results of some of these studies have been negative [32]. There have hitherto been relatively few randomised controlled trials of high quality, and those that are available differ substantially in design. […] In general, there is a poor correlation between effects on symptoms and gastric emptying — prokinetic drugs may improve symptoms by effects unrelated to acceleration of gastric emptying or central anti-emetic properties [254].”

“Autoimmune factors are well recognised to play a role in the aetiology of type 1 diabetes [316,317]. In such patients there is an increased prevalence of autoimmune aggression against non-endocrine tissues, including the gastric mucosa. The reported prevalence of parietal cell antibodies in patients with type 1 diabetes is in the range 5–28%, compared to 1.4–12% in non-diabetic controls […] The autoimmune response to parietal cell antibodies may lead to atrophic gastritis, pernicious anaemia and iron deficiency anaemia […] Parietal cell antibodies can inhibit the secretion of intrinsic factor, which is necessary for the absorption of vitamin B12, potentially resulting in pernicious anaemia. The prevalence of latent and overt pernicious anaemia in type 1 diabetes has been reported to be 1.6–4% and 0.4%, respectively […] screening for parietal cell antibodies in patients with type 1 diabetes currently appears inappropriate. However, there should be a low threshold for further investigation in those patients presenting with anaemia”.


September 1, 2017 Posted by | Books, Diabetes, Gastroenterology, Immunology, Medicine, Neurology | Leave a comment

Gastrointestinal Function in Diabetes (I)

“During the last 15–20 years, primarily as a result of the application of novel investigative techniques, there has been a rapid expansion of knowledge relating to the function of the gastrointestinal tract in diabetes mellitus. These insights have been substantial and have led to the recognition that gastrointestinal function represents a hitherto inappropriately neglected, as well as important, aspect of diabetes management. In particular, disordered gastrointestinal motor and sensory function occur frequently in both type 1 and type 2 diabetes and may be associated with significant clinical sequelae. Recent epidemiological studies have established that there is a high prevalence of gastrointestinal symptoms in the diabetic population and that these are associated with impaired quality of life. Furthermore, upper gastrointestinal motility, even when normal, is central to the regulation of postprandial blood glucose concentrations. Hence, diabetes and the gastrointestinal tract are inextricably linked. […] This book, which to our knowledge represents the first of its kind, was stimulated by the need to consolidate these advances, to illuminate an area that is perceived as increasingly important, but somewhat difficult to understand. […] The book aims to be comprehensive and to present the relevant information in context for both the clinician and clinical researcher. There are nine chapters: five are organ-specific, relating to oesophageal, gastric, intestinal, anorectal and hepatobiliary function; the four other chapters address epidemiological aspects of gastrointestinal function in diabetes, the effects of diabetes mellitus on gastrointestinal function in animal models, the impact of gastrointestinal function on glycaemic control, and the evaluation of gastrointestinal autonomic function. All of the authors are recognised internationally for their expertise in the field”.

I added this book to my list of favourite books on goodreads – it’s a great book, from which I learned a lot.

I have added some more quotes and observations from the book below, as well as a few comments.

“Population-based studies of gastrointestinal symptoms in diabetic patients have been relatively few and the results conflicting […] To date, a total of nine population-based studies have been undertaken evaluating gastrointestinal symptoms in subjects with diabetes mellitus […] Depending on the population studied, the prevalence of symptoms has varied considerably in patients with both type 1 and type 2 diabetes mellitus. […] there is evidence that gastrointestinal symptoms are linked with diabetes mellitus, but the prevalence over and above the general population is at most only modestly increased. Some studies have failed to detect an association between diabetes and gastrointestinal symptoms, but several confounders may have obscured the findings. For example, it is well documented that chronic gastrointestinal symptoms are common in non-diabetics in the community, presumably due to functional gastrointestinal disorders such as the irritable bowel syndrome [33,34]. Moreover, the presence of diabetic complications and possibly long-term glycaemic control appear to be important factors in symptom onset [31,32]. This may explain the difficulty in establishing a firm link between diabetes and chronic gastrointestinal complaints in population-based studies.”

It is perhaps important to interpose already at this early stage of the coverage that diabetes seems to be related to many changes in gastrointestinal function that do not necessarily cause symptoms which lead to patient complaints, but which even so may still affect individuals with the disease in a variety of ways. For example drug metabolism may be altered in diabetics secondary to hyperglycemia-induced delayed gastric emptying, which can naturally be very important in some situations (drugs don’t work, or don’t work when they’re supposed to). Symptomatic disease is important to observe and address, but there are many other aspects that may be relevant as well. The symptomatology of diabetes-related gastrointestinal changes is of course complicated by the fact that nervous system involvement is an important player, and a player we know from other contexts may both generate symptoms (in this setting you’d e.g. think of altered peristalsis in severe neuropathy, causing constipation) and may also lead to an absence of symptoms in settings where symptoms would otherwise have been present (‘silent ischemia‘ is common in diabetics). I may or may not go much more into these topics, there’s a lot of interesting stuff in this book.

“In patients with long-standing type 1 and type 2 diabetes, the prevalence of delayed gastric emptying of a nutrient meal is reported to range from 27% to 40% [40–42] and the prevalence is similar in insulin-dependent and non-insulindependent diabetes mellitus […]. In a minority of patients (less than 10%) with long-standing diabetes, gastric emptying is accelerated [42–44]. […] A number of studies have shown that acute changes in blood glucose concentrations can have a profound effect on motor function throughout the gastrointestinal tract in both normal subjects and patients with diabetes mellitus [54]. Recent studies have demonstrated that the blood glucose concentration may also modulate the perception of sensations arising from the gastrointestinal tract [56–58]. However, there is relatively little information about the mechanisms mediating the effects of the blood glucose concentration on gastrointestinal motility. While some studies have implicated impaired glycaemic control in the genesis of chronic gastrointestinal symptoms [24,31], this remains controversial.”

“As part of the Medical Outcomes Study, that determined the impact of nine different chronic illnesses upon HRQL [Health-Related Quality of Life, US], Stewart et al. [90] used the Short Form (SF-20) of the General Health Survey to evaluate HRQL ratings in 9385 patients, 844 of whom had diabetes […] gastrointestinal disorders had a more negative impact on HRQL than all other conditions with the exception of heart disease [90]. Others have reported similar findings [120,121]. […] A study of diabetic patients undergoing transplantation [122] indicated that, of all the factors likely to compromise HRQL, the single most important one was gastrointestinal dysfunction.”

“In animal studies of gastrointestinal function in diabetes mellitus, most information has been generated using insulinopenic rats with severe hyperglycaemia; around one-third of the literature has been generated using BB rats (autoimmune spontaneous diabetic) and two-thirds using streptozotocin (STZ; chemically-induced) diabetic models. In the choice of these animal models, an assumption appears to have been often made that hyperglycaemia per se, or at least some aspect of the metabolic disturbance secondary to insulin lack, is the aetiopathologic insult. A common hypothesis is that neurotoxicity of the autonomic nervous system, secondary to this metabolic insult, is responsible for the gastrointestinal effects of diabetes. This hypothesis is described here as the ‘autonomic neuropathic’ hypothesis.”

“Central nervous structures, especially those in the brain stem […] are implicated in the normal autonomic control of gastrointestinal function […] over two-thirds of the literature regarding gastrointestinal dysfunction in diabetes is derived from chemically-induced models in which, alarmingly, much of the reported gut dysfunction could be an artifact of selective damage to central structures. It is now recognised that there are major differences in gastrointestinal function between animals in which β-cell damage was caused by chemical means and those in which damage was a result of an autoimmune process. These differences prompt an examination of the extent to which gastrointestinal dysfunction in some models is a consequence of diabetes per se, perhaps applicable to human disease, as opposed to being a consequence of damage to specific central structures.”

“The […] most accepted hypothesis in the past to explain gastrointestinal dysfunction in diabetes has been the proposal that autonomic neuropathy has disturbed the normal regulation of gut function. But there are recently identified disturbances in several of the neurohormones found in gut in different diabetic states. Several of these, including amylin, GLP-1 and PYY have effects on gut function, and should now be considered in explanations of diabetes-associated changes in gut function. […] A ‘neurocrine’ alternative to the neuropathic hypothesis focuses on the possibility that absolute or relative deficiency of the pancreatic β-cell hormone, amylin, may be of importance in the aetiology of disordered gastrointestinal function in diabetes. […] STZ diabetic rats most often show increased gastric acid secretion [63,64] and increased rates of ulceration [65–71]. This effect is exacerbated by fasting [67] and is reversed by hyperglycaemia [72] but not by insulin replacement [73]. It thus appears that insulin lack is not the ulcerogenic stimulus, and raises the possibility that absence of gastric-inhibitory factors (e.g. amylin, PYY, GLP-1), which may be absent or reduced in diabetes, could be implicated. […] autoimmune type 1 diabetic BB rats [76] and autoimmune non-obese diabetic (NOD) mice [77] in which the gastric mucosa is not an immune target, also show a marked increase in gastric erosions. The constancy of findings of acid hypersecretion and ulceration in insulinopenic diabetes invoked by diverse insults (chemical and autoimmune) indicates that this gastrointestinal disturbance is a direct consequence of the diabetes, and perhaps of β-cell deficiency. […] Amylin […] is a potent inhibitor of gastric acid secretion [88], independent of changes in plasma glucose [89] and prevents gastric erosion in response to a number of irritants [90–92]. These effects appear to be specific to amylin […] It is possible that amylin deficiency could be implicated in a propensity to ulceration in some forms of diabetes. It is unclear whether such a propensity exists in type 1 diabetic adults. However, type 1 diabetic children are reported to have a three- to four-fold elevation in rate of peptic disease [93].”

“Changes in intestinal mucosal function are observed in diabetic rodents, but it is unclear whether these are intrinsic and contributory to the disease process, or are secondary to the disease. […] It […] appears likely […] that diabetes-associated changes in gut enzyme expression represent a response to some aspect of the diabetic state, since they occur in both chemically-induced and genetic models, and are reversible with vigorous treatment of the diabetes. […] While there appear to be no reports that quantify the relationship between acid secretion and rates of nutrient assimilation, there is evidence that type 1 diabetes, in animal models at least, is characterised by disturbed acid regulation.”

“[D]isordered gastrointestinal motility has long been recognised as a frequent feature in diabetic patients who also exhibit neuropathy [125]. Disturbances in gastrointestinal function have been estimated by some to have a prevalence of ∼ 30% (range 5–60% [126–128]). Both peripheral and autonomic [126–128] neuropathy are frequent complications of diabetes mellitus. Since the autonomic nervous system (ANS) plays a prominent role in the regulation of gut motility, a prevailing hypothesis has been that autonomic neuropathic dysfunction could account for much of this disturbance. […] Motor disturbances associated with autonomic neuropathy include dilation of the oesophagus, gastrointestinal stasis, accumulation of digesta and constipation, mainly signs associated with vagal (parasympathetic) dysfunction. There are also reports of faecal incontinence, related to decreased sphincter pressure, and diarrhoea.”

“The best-characterised signs of damage to the autonomic nervous system during diabetes are morphological […] For example, the number of myelinated axons in the vagosympathetic trunk is decreased in diabetic rats [131], as is the number of neurones in dorsal root ganglia and peripheral postganglionic sympathetic nerves. […] In addition to alterations in numbers and morphology of axons, the tissue around the axons is also often disturbed. […] It is of interest that autonomic neuropathy can be prevented or partially reversed by rigorous glycaemic control [137], suggesting that hyperglycaemia per se is of major aetiological importance in autonomic neuropathy. […] Morphological evidence of neuropathy in BB rats includes axonal degeneration, irregularity of myelin sheaths and Mullerian degeneration […] It has been proposed that periodic hypoglycaemia in BB rats may induce Wallerian degeneration and reduced conduction velocity […] while abnormalities associated with chronic hyperglycaemia include sensory (afferent) axonopathy […] The secretion of a number of neuroendocrine substances may be decreased in diabetes. Glucagon, pancreatic polypeptide, gastrin, somatostatin and gastric inhibitory peptide levels are reportedly reduced in the gastrointestinal tract of diabetic patients […] In addition to peripheral autonomic neuropathy, neurons within the central nervous system are also reported to be damaged in animal models of diabetes, including areas […] which are important in controlling those parts of the autonomic nervous system that innervate the gut.”

“Despite ample evidence of morphologic and functional changes in nerves of rodent models of type 1 diabetes mellitus, it is not clear to what extent these changes underly the gastrointestinal dysfunction evident in these animals. Coincidence of neuropathic and gastrointestinal changes does not necessarily prove a causal association between autonomic neuropathy and gastrointestinal dysfunction in diabetes. […] recently recognised neuroendocrine disturbances in diabetes, especially of the β-cell hormone amylin, provide an alternative to the neuropathic hypothesis […] In considering primary endocrine changes associated with type 1 diabetes mellitus, it should be recognised that the central pathogenic event is a selective and near-absolute autoimmune destruction of pancreatic β-cells. Other cell types in the islets, and other tissues, are preserved. The only confirmed hormones currently known to be specific to pancreatic β-cells are insulin and amylin [251]. Recent evidence also suggests that C-peptide, cleaved from proinsulin during intracellular processing and co-secreted with insulin, may also be biologically active [252] […] It is therefore only insulin, C-peptide and amylin that disappear following the selective destruction of β-cells. The implications of this statement are profound; all diabetes-associated sequelae are somehow related to the absence of these (and/or other possibly undiscovered) hormones, whether directly or indirectly […]. Since insulin has minimal direct effect on gut function, until recently the most plausible explanation linking β-cell destruction to changes in gastrointestinal functions was a neuropathic effect secondary to hyperglycaemia. With the recent discovery of multiple physiological gastrointestinal effects of the second β-cell hormone, amylin [255], a plausible alternate explanation of gut dysfunction following β-cell loss has emerged. That is, instead of being due to insulin lack, some gut dysfunction in insulinopenic diabetes may instead be due to the loss of its co-secreted partner, amylin. […] While insulin and amylin are essentially absent in type 1 diabetes, in states of impaired glucose tolerance and early type 2 diabetes, each of these hormones may in fact be hypersecreted […] The ZDF rat is a model of insulin resistance, with some strains developing type 2 diabetes. These animals, which hypersecrete from pancreatic β-cells, exhibit both hyperinsulinaemia and hyperamylinaemia.”

If amylin is hypersecreted in type 2 diabetics and the hormone is absent in type 1 and you do population studies on mixed populations of type 1 and type 2 patients and try to figure out what is going on, you’re going to have some potential issues. The picture seems not too dissimilar to what you see when you look at bone disease in diabetes; type 1s have a high fracture risk, type 2s also have a higher than normal fracture risk, but ‘the effect of diabetes’ is in fact very different in the two groups (in part – but certainly not only – because most type 2s are overweight or obese, and overweight decreases the fracture risk). Some of the relevant pathways of pathophysiological interest are identical in the two patient populations (this is also the case here; acute hyperglycemia is known to cause delayed gastric emptying even in non-diabetics), some are completely different – it’s a mess. This is one reason why I don’t think the confusing results of some of the population studies included early in the book’s coverage – which I decided not to cover in detail here – are necessarily all that surprising.

“Many gastrointestinal reflexes are glucose-sensitive, reflecting their often unrecognised glucoregulatory (restricting elevations of glucose during hyperglycaemia) and counter-regulatory functions (promoting elevation of glucose during hypoglycaemia). Glucose-sensitive effects include inhibition of food intake, control of gastric emptying rate, and regulation of gastric acid secretion and pancreatic enzyme secretion […] Some gastrointestinal manifestations of diabetes may therefore be secondary, and compensatory, to markedly disturbed plasma glucose concentrations. […] It has emerged in recent years that several of the most potent of nearly 60 reported biological actions of amylin [286] are gastrointestinal effects that appear to collectively restrict nutrient influx and promote glucose tolerance. These include inhibition of gastric emptying, inhibition of food intake, inhibition of digestive functions (pancreatic enzyme secretion, gastric acid secretion and bile ejection), and inhibition of nutrient-stimulated glucagon secretion. […] In rats, amylin is the most potent of any known mammalian peptide in slowing gastric emptying […] An amylin agonist (pramlintide), several GLP-1 agonists and exendin-4 are being explored as potential therapies for the treatment of diabetes, with inhibition of gastric emptying being recognised as a mode of therapeutic action. […] The concept of the gut as an organ of metabolic control is yet to be widely accepted, and antidiabetic drugs that moderate nutrient uptake as a mode of therapy have only begun to emerge. A potential advantage such therapies hold over those that enhance insulin action, is their general glucose dependence and low propensity to (per se) induce hypoglycaemia.”


August 29, 2017 Posted by | Books, Diabetes, Gastroenterology, Medicine, Neurology | Leave a comment

A few diabetes papers of interest

i. Eating Disorders in Girls and Women With Type 1 Diabetes: A Longitudinal Study of Prevalence, Onset, Remission, and Recurrence.

If these results can be trusted, then the prevalence of eating disorders in young female diabetics is disturbingly high. Some quotes:

“The prevalence, clinical characteristics, and medical consequences of disturbed eating behavior (DEB) and eating disorders (EDs) in individuals with type 1 diabetes has received increasing attention since case reports of this dangerous combination were first published in the 1980s (1,2). Although the specificity of this association was initially unclear, systematic research has demonstrated that teenage girls and women with type 1 diabetes are at significantly increased risk of DEB compared with their nondiabetic peers (3). Such DEB includes dieting, fasting, binge-eating, and a range of compensatory and purging behaviors that can directly interfere with optimal diabetes management. […] Deliberately underdosing or omitting insulin to induce hyperglycemia and loss of glucose in the urine, and thereby control weight, is a unique purging behavior to control weight that is available to individuals with type 1 diabetes (4). This is an important mediator of the association of DEB and EDs with poorer metabolic control (5,6) and contributes to an increased risk of a range of short-term and long-term diabetes-related medical complications. These include abnormal lipid profiles (7), diabetic ketoacidosis (6), retinopathy (8), neuropathy (9), and nephropathy (10), as well as higher than expected mortality (11).”

“Bryden et al. (13) assessed a group of individuals with type 1 diabetes in adolescence and then again in early adulthood. […] They found EDs or other significant eating problems in 26% of participants, as well as significant associations between eating problems, insulin misuse, and microvascular complications (14). Goebel-Fabbri et al. (15) assessed 234 adult women with type 1 diabetes twice over an 11-year period. They found insulin omission for weight control to be very common (reported by 30% at baseline). Insulin omission frequently persisted over the lengthy follow-up period and was associated with higher rates of diabetes-related medical complications and tripled risk of mortality.”

“This study describes the longitudinal course of disturbed eating behavior (DEB) and EDs in a cohort with type 1 diabetes. […] A total of 126 girls with type 1 diabetes receiving care for diabetes at The Hospital for Sick Children in Toronto participated in a series of seven interview-based assessments of ED behavior and psychopathology over a 14-year period, beginning in late childhood. […] Mean age was 11.8 ± 1.5 years at time 1 and 23.7 ± 2.1 years at time 7. At time 7, 32.4% (23/71) met the criteria for a current ED, and an additional 8.5% (6/71) had a subthreshold ED. Mean age at ED onset (full syndrome or below the threshold) was 22.6 years (95% CI 21.6–23.5), and the cumulative probability of onset was 60% by age 25 years. […] The average time between remission of ED and subsequent recurrence was 6.5 years (95% CI 4.4–8.6), and the cumulative probability of recurrence was 53% by 6 years after remission.”

“In this longitudinal study, EDs were common and persistent, and new onset of ED was documented well into adulthood. […] [The] rates provide evidence that disordered eating is a common and serious concern among girls and young women with type 1 diabetes. Although adolescent and adult women in the general population also frequently report dieting, rates of more extreme weight loss behaviors and clinical eating disorders tend to be lower than those that occurred in this study (22,2830). […] The point prevalence for DEB and ED continued to increase across the study, largely because of marked increases in reported insulin omission for weight loss. Of particular concern, insulin omission as a weight control method was reported by 27% of participants at time 7. This dangerous method of purging directly compromises metabolic control and confers both short-term and long-term medical risk. Other researchers found it to be highly persistent among adult women with type 1 diabetes and associated with increased morbidity and mortality (10,15). […] In this study, both DEB and EDs tended to be persistent, with a mean time from observed onset to detected remission of 6.0 and 4.3 years, respectively, and significant estimated risk of recurrence among those whose eating disturbances initially remitted. […] The high prevalence of DEB and EDs among women with type 1 diabetes, in addition to high incidence of new ED cases continuing into the young adult years, suggests that sustained efforts at prevention, detection, and treatment of eating disturbances are needed across the adolescent and young adult years among women with type 1 diabetes.”

ii. Excess Risk of Dying From Infectious Causes in Those With Type 1 and Type 2 Diabetes.

“Individuals with type 1 and type 2 diabetes are widely considered to be more prone to infections than those without diabetes (1). […] The underlying pathology for an increased risk of infections among people with diabetes is not fully elucidated and is probably multifactorial. However, there are some data to suggest that it could, in part, relate to a compromised immune system. Short- and long-term hyperglycemia may disturb immune functions such as neutrophil bactericidal function (13), cellular immunity (14), and complement activation (15). These defects in the immune system, along with vascular insufficiency, render patients with diabetes at higher risk for a variety of severe or invasive infections compared with those without diabetes (16).”

“While there is a reasonably good understanding of the biological link between diabetes and infection, there are few data quantifying the excess risk of acquiring an infection or dying from infections associated with diabetes. […] the objective of this study was to examine the excess risk of death from several infectious causes in those with type 1 and type 2 diabetes compared with the general population and to see if this excess risk differs by age and over time. […] A total of 1,108,982 individuals with diabetes who were registered with the Australian Diabetes register between 2000 and 2010 were linked to the National Death Index. Mortality outcomes were defined as infection-relatedA-B death (ICD codes A99–B99), pneumonia (J12–J189), septicemia (A40 and A41), and osteomyelitis (M86). […] During a median follow-up of 6.7 years, there were 2,891, 2,158, 1,248, and 147 deaths from infection-relatedA-B causes, pneumonia, septicemia, or osteomyelitis, respectively. Crude mortality rates from infectionsA-B were 0.147 and 0.431 per 1,000 person-years in type 1 and type 2 diabetes, respectively. Standardized mortality ratios (SMRs) were higher in type 1 and type 2 diabetes for all outcomes after adjustment for age and sex. For infection-relatedA-B mortality, SMRs were 4.42 (95% CI 3.68–5.34) and 1.47 (1.42–1.53) for type 1 and type 2 diabetes (P < 0.001), respectively. For pneumonia in type 1 diabetes, SMRs were approximately 5 and 6 in males and females, respectively, while the excess risk was ∼20% for type 2 (both sexes). For septicemia, SMRs were approximately 10 and 2 for type 1 and type 2 diabetes, respectively, and similar by sex. For osteomyelitis in type 1 diabetes, SMRs were 16 and 58 in males and females, respectively, and ∼3 for type 2 diabetes (both sexes).”

“This prospective study of more than one million people with diabetes provides evidence that individuals with type 1 and type 2 diabetes are more likely to die of infection-related death, in particular death due to pneumonia, septicemia, and osteomyelitis, compared with the general population. These data show that infection in those with diabetes is an important cause of mortality. […] the increased risk appears to be greater for type 1 than type 2 diabetes. […] Patients with diabetes have a higher case fatality from infections than those without diabetes (17,30), which is both due to altered host immunity and due to having a higher prevalence of comorbidities, which places them at increased risk of death.”

iii. Effects of Acute Hypoglycemia on Working Memory and Language Processing in Adults With and Without Type 1 Diabetes.

“Cognitive function is impaired during acute hypoglycemia and frequently affects people with type 1 diabetes (1,2); elucidation of which cognitive domains are affected and by how much is of practical importance. Although cognitive domains do not function independently of each other, it is pertinent to design studies that investigate how everyday activities are affected by hypoglycemia as this has direct relevance to people with diabetes. Previous studies have demonstrated the effects of hypoglycemia on specific cognitive domains, including memory, attention, nonverbal intelligence, visual and auditory information processing, psychomotor function, spatial awareness, and executive functioning (314). However, the effects of hypoglycemia on language processing have seldom been explored.”

“Slurred speech and language difficulties are recognized features of hypoglycemia, but to our knowledge, the effects of hypoglycemia on linguistic processing have not been studied systematically. The current study used transient insulin-induced hypoglycemia in adults with and without type 1 diabetes to examine its effects on three aspects of language: the relationship between working memory and language (reading span), grammatical decoding (self-paced reading), and grammatical encoding (producing subject-verb agreement). Tests of these issues have been used extensively to understand the nature of language processing and its relationship to other cognitive abilities, specifically working memory (17).”

“Forty adults were studied (20 with type 1 diabetes and 20 healthy volunteers) using a hyperinsulinemic glucose clamp to lower blood glucose to 2.5 mmol/L (45 mg/dL) (hypoglycemia) for 60 min, or to maintain blood glucose at 4.5 mmol/L (81 mg/dL) (euglycemia), on separate occasions. Language tests were applied to assess the effects of hypoglycemia on the relationship between working memory and language (reading span), grammatical decoding (self-paced reading), and grammatical encoding (subject-verb agreement). […] Hypoglycemia caused a significant deterioration in reading span (P < 0.001; η2 = 0.37; Cohen d = 0.65) and a fall in correct responses (P = 0.005; η2 = 0.19; Cohen d = 0.41). On the self-paced reading test, the reading time for the first sentence fragment increased during hypoglycemia (P = 0.039; η2 = 0.11; Cohen d = 0.25). […] Hypoglycemia caused a deterioration of subject-verb agreement (correct responses: P = 0.011; η2 = 0.159; Cohen d = 0.31).”

“[We] demonstrated a significant deterioration in the accuracy of subject-verb agreement and also in reading span, a measure of working memory. This latter finding is compatible with the results of a previous study by our group (14) that used a different cognitive test battery but had an identical study design. In the current study, performance in the TMB and DST was significantly impaired during hypoglycemia, consistent with previous observations (57,1012,24) and confirming that adequate hypoglycemia had been achieved to impair cognitive function. […] Different mental functions have been shown to vary in their sensitivity to neuroglycopenia. […] higher-level skills are more vulnerable to hypoglycemia than simple cognitive tasks (1). In addition, during hypoglycemia, speed is usually killed in order to preserve accuracy (1). […] results strongly suggest that hypoglycemia induces difficulties in seemingly easy linguistic tasks such as correctly reading aloud a simple sentence fragment and its completion. Compared with other clamp studies exploring the effects of hypoglycemia on cognitive function, this was a large study that recruited both participants with and participants without diabetes. The fact that similar results were obtained in both groups suggests that these effects on language relate to acute hypoglycemia rather than to a chronic alternation of glycemic status in diabetes.” [My bold – US. These observations seem to corroborate observations I’ve made myself in the past.]

iv. Current State of Type 1 Diabetes Treatment in the U.S.: Updated Data From the T1D Exchange Clinic Registry.

Figure 1 from this paper is the sort of image which is worth a 1000 words.

Some observations from the paper:

“Data from 16,061 participants updated between 1 September 2013 and 1 December 2014 were compared with registry enrollment data collected from 1 September 2010 to 1 August 2012. […] The overall average HbA1c was 8.2% (66 mmol/mol) at enrollment and 8.4% (68 mmol/mol) at the most recent update. During childhood, mean HbA1c decreased from 8.3% (67 mmol/mol) in 2–4-year-olds to 8.1% (65 mmol/mol) at 7 years of age, followed by an increase to 9.2% (77 mmol/mol) in 19-year-olds. Subsequently, mean HbA1c values decline gradually until ∼30 years of age, plateauing at 7.5–7.8% (58–62 mmol/mol) beyond age 30 until a modest drop in HbA1c below 7.5% (58 mmol/mol) in those 65 years of age. Severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) remain all too common complications of treatment, especially in older (SH) and younger patients (DKA). […] Although the T1D Exchange registry findings are not population based and could be biased, it is clear that there remains considerable room for improving outcomes of treatment of type 1 diabetes across all age-groups.”

“[M]ean HbA1c values showed a gradual decline until ∼30 years of age, plateauing at a level of 7.5–7.8% (58–62 mmol/mol) beyond age 30 until a modest drop in HbA1c below 7.5% (58 mmol/mol) after 65 years of age. The ADA HbA1c goal of <7.5% (58 mmol/mol) was achieved by only a small percentage of children and adolescents <18 years of age (17–23%), and even fewer 18–25-year-olds (14%) met the ADA goal for adults of <7.0% (53 mmol/mol); this percentage increased to ∼30% in older adults […] across all age-groups, HbA1c was highest among non-Hispanic black participants, participants with lower annual household income, and those who performed SMBG less than four times per day […] On average, participants using an insulin pump or continuous glucose monitor tended to have lower HbA1c values [….] Among the subset of 2,561 participants who completed the participant questionnaire, 6% reported having had a seizure or loss of consciousness due to hypoglycemia in the prior 3 months, with the highest occurrence being among those who were 50 years old or older.”

“The most troubling aspect of the data is that the mean HbA1c level of 9.0% (75 mmol/mol) in 13–17-year-olds in the registry is only slightly lower than the 9.5% (80 mmol/mol) seen in 13–17-year-olds at the start of the DCCT in the 1980s (15). Clearly, advances in diabetes management over the past two decades have been less successful in overcoming the special challenges in managing teenagers than adults with type 1 diabetes. Our data also indicate that the majority of “emerging adults” in their 20s do not fully emerge with regard to glycemic control until they reach 30 years of age. […] In a cross-sectional comparison, the average HbA1c at the most recent update was higher than at enrollment (8.4 vs. 8.2% [68 vs. 66 mmol/mol]), suggesting a worsening in glycemic control over time. The greatest increase in HbA1c was observed in the 13–17 (9.0 vs. 8.7% [75 vs. 72 mmol/mol]) and 18–26-year-old (8.7 vs. 8.3% [72 vs. 67 mmol/mol]) groups. Although this could reflect differences in age and type 1 diabetes duration, the results nevertheless indicate that there certainly is no indication of improving glycemic control in these age-groups.”

v. Prevention and Reversal of Type 1 Diabetes — Past Challenges and Future Opportunities.

“Over the past three decades there have been a number of clinical trials directed at interdicting the type 1 diabetes (T1D) disease process in an attempt to prevent the development of the disease in those at increased risk or to stabilize — potentially even reverse — the disease in people with T1D, usually of recent onset. Unfortunately, to date there has been no prevention trial that has resulted in delay or prevention of T1D. […] Since the completion of the early trials, particularly during the past decade, a number of additional randomized, double-masked, adequately powered, controlled clinical trials have been conducted using many different immunological strategies. For the most part, these have been disappointing, with none showing unambiguous benefit in preserving β-cell function. […] [M]ost immune intervention trials in T1D have either failed to achieve success in preserving β-cell function or have met that hurdle but have nonetheless shown only a transient effect.”

vi. Diabetic Peripheral Neuropathy Compromises Balance During Daily Activities.

“Patients with diabetic peripheral neuropathy (DPN) have an altered gait strategy (13) and a fivefold increased risk of falling (46). Falling is a major health risk in many developed countries; for example, in the general U.K. population, over a quarter of accidents that required hospital treatment were the result of a fall (7). A fall is preceded by loss of balance, which may be recoverable in some individuals, but requires rapid responses and a high level of strength from the lower-limb muscles (8,9). Nevertheless, the more likely an individual is to lose balance, the more likely they will at some point experience a fall. Therefore, quantifying balance control during every day gait activities may be considered one of the closest proxies for the risk of falling.”

“During walking activities, when an individual transfers their weight from one limb to another there are brief periods of large separation between the center of mass and the center of pressure. High levels of muscular strength are required to maintain balance during these periods. These large separations between the center of mass and center of pressure experienced during the single stance periods of dynamic gait activities may be a contributing factor toward understanding why the risk of falling during gait activities is much greater than during quiet standing. Few studies, however, have attempted to address the issue of balance during walking in patients with diabetes, and none have addressed the much more physically challenging activities of stair ascent and descent, during which the risk of falling is known to be very high (7). We therefore investigated a more “dynamic” measure of balance during stair ascent, stair descent, and level walking — three activities with the highest risk of fall-related injury (7) — with the hypothesis that individuals with peripheral neuropathy would display greater separations between their center of mass and center of pressure (i.e., poorer balance), thereby contributing to explaining why they are at high risk of falls.”

“Gait analysis during level walking and stair negotiation was performed in 22 patients with diabetic neuropathy (DPN), 39 patients with diabetes without neuropathy (D), and 28 nondiabetic control subjects (C) using a motion analysis system and embedded force plates in a staircase and level walkway. Balance was assessed by measuring the separation between the body center of mass and center of pressure during level walking, stair ascent, and stair descent. […] DPN patients demonstrated greater (P < 0.05) maximum and range of separations of their center of mass from their center of pressure in the medial-lateral plane during stair descent, stair ascent, and level walking compared with the C group, as well as increased (P < 0.05) mean separation during level walking and stair ascent. The same group also demonstrated greater (P < 0.05) maximum anterior separations (toward the staircase) during stair ascent. […] Greater separations of the center of mass from the center of pressure present a greater challenge to balance. Therefore, the higher medial-lateral separations found in patients with DPN will require greater muscular demands to control upright posture. This may contribute to explaining why patients with DPN are more likely to fall, with the higher separations placing them at a higher risk of experiencing a sideways fall than nondiabetic control subjects. […] balance is markedly impaired in patients with DPN during the gait activities of level ground walking, stair ascent, and stair descent. […] During the gait tasks, we found no significant balance impairments in patients with diabetes without DPN, clearly emphasizing that the link between diabetes and instability is a symptom of peripheral neuropathy.”


August 26, 2017 Posted by | Diabetes, Infectious disease, Language, Neurology, Studies | 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

Type 1 Diabetes Is Associated With an Increased Risk of Fracture Across the Life Span

Type 1 Diabetes Is Associated With an Increased Risk of Fracture Across the Life Span: A Population-Based Cohort Study Using The Health Improvement Network (THIN).

I originally intended to include this paper in a standard diabetes post like this one, but the post gradually got more and more unwieldy as I added more stuff and so in the end I decided – like in this case – that it might be a better idea to just devote an entire post to the paper and then postpone my coverage of some of the other papers included in the post.

I’ve talked about this stuff before, but I’m almost certain the results of this paper were not included in Czernik and Fowlkes’ book as this paper was published at almost exactly the same time as was the book. It provides further support of some of the observations included in C&F’s publication. This is a very large and important study in the context of the relationship between type 1 diabetes and skeletal health. I have quoted extensively from the paper below, and also added some observations of my own along the way in order to provide a little bit of context where it might be needed:

“There is an emerging awareness that diabetes adversely affects skeletal health and that type 1 diabetes affects the skeleton more severely than type 2 diabetes (5). Studies in humans and animal models have identified a number of skeletal abnormalities associated with type 1 diabetes, including deficits in bone mineral density (BMD) (6,7) and bone structure (8), decreased markers of bone formation (9,10), and variable alterations in markers of bone resorption (10,11).

Previous studies and two large meta-analyses reported that type 1 diabetes is associated with an increased risk of fracture (1219). However, most of these studies were conducted in older adults and focused on hip fractures. Importantly, most affected individuals develop type 1 diabetes in childhood, before the attainment of peak bone mass, and therefore may be at increased risk of fracture throughout their life span. Moreover, because hip fractures are rare in children and young adults, studies limited to this outcome may underestimate the overall fracture burden in type 1 diabetes.

We used The Health Improvement Network (THIN) database to conduct a population-based cohort study to determine whether type 1 diabetes is associated with increased fracture incidence, to delineate age and sex effects on fracture risk, and to determine whether fracture site distribution is altered in participants with type 1 diabetes compared with participants without diabetes. […] 30,394 participants aged 0–89 years with type 1 diabetes were compared with 303,872 randomly selected age-, sex-, and practice-matched participants without diabetes. Cox regression analysis was used to determine hazard ratios (HRs) for incident fracture in participants with type 1 diabetes. […] A total of 334,266 participants, median age 34 years, were monitored for 1.9 million person-years. HR were lowest in males and females age <20 years, with HR 1.14 (95% CI 1.01–1.29) and 1.35 (95% CI 1.12–1.63), respectively. Risk was highest in men 60–69 years (HR 2.18 [95% CI 1.79–2.65]), and in women 40–49 years (HR 2.03 [95% CI 1.73–2.39]). Lower extremity fractures comprised a higher proportion of incident fractures in participants with versus those without type 1 diabetes (31.1% vs. 25.1% in males, 39.3% vs. 32% in females; P < 0.001). Secondary analyses for incident hip fractures identified the highest HR of 5.64 (95% CI 3.55–8.97) in men 60–69 years and the highest HR of 5.63 (95% CI 2.25–14.11) in women 30–39 years.”

“Conditions identified by diagnosis codes as covariates of interest were hypothyroidism, hyperthyroidism, adrenal insufficiency, celiac disease, inflammatory bowel disease, vitamin D deficiency, fracture before the start of the follow-up period, diabetic retinopathy, and diabetic neuropathy. All variables, with the exception of prior fracture, were treated as time-varying covariates. […] Multivariable Cox regression analysis was used to assess confounding by covariates of interest. Final models were stratified by age category (<20, 20–29, 30–39, 40–49, 50–59, 60–69, and ≥70 years) after age was found to be a significant predictor of fracture and to violate the assumption of proportionality of hazards […] Within each age stratum, models were again assessed for proportionality of hazards and further stratified where appropriate.”

A brief note on a few of those covariates. Some of them are obvious, other perhaps less so. Retinopathy is probably included mainly due to the associated vision issues, rather than some sort of direct pathophysiological linkage between the conditions; vision problems may increase the risk of falls, particularly in the elderly, and falls increase the fracture risk (they note this later on in the paper). Neuropathy could in my opinion affect risk in multiple ways, not only through an increased fall risk, but either way it certainly makes a lot of sense to include that variable if it’s available. Thyroid disorders can cause bone problems, and the incidence of thyroid disorders is elevated in type 1 – to the extent that e.g. the Oxford Handbook of Clinical Medicine recommends screening people with diabetes mellitus for abnormalities in thyroid function on the annual review. Both Addison‘s (adrenal insufficiency) and thyroid disorders in type 1 diabetics may be specific components of a more systemic autoimmune disease (relevant link here, see the last paragraph), by some termed autoimmune polyendocrine syndromes. When you treat people with Addison’s you give them glucocorticoids, and this treatment can have deleterious effects on bone density especially in the long run – they note in the paper that exposure to corticosteroids is a significant fracture predictor in their models, which is not surprising. In one of the chapters included in Horowitz & Samson‘s book (again, I hope to cover it in more detail later…) the authors note that the combination of coeliac disease and diabetes may lead to protein malabsorption (among other things), which can obviously affect bone health, and they also observe e.g. that common lab abnormalities found in patients with coeliac include “low levels of haemoglobin, albumin, calcium, potassium, magnesium and iron” and furthermore that “extra-intestinal symptoms [include] muscle cramps, bone pain due to osteoporotic fractures or osteomalacia” – coeliac is obviously relevant here, especially as the condition is much more common in type 1 diabetics than in non-diabetics (“The prevalence of coeliac disease in type 1 diabetic children varies from 1.0% to 3.5%, which is at least 15 times higher than the prevalence among children without diabetes” – also an observation from H&S’s book, chapter 5).

Moving on…

“During the study period, incident fractures occurred in 2,615 participants (8.6%) with type 1 diabetes compared with 18,624 participants (6.1%) without diabetes. […] The incidence in males was greatest in the 10- to 20-year age bracket, at 297.2 and 261.3 fractures per 10,000 person-years in participants with and without type 1 diabetes, respectively. The fracture incidence in women was greatest in the 80- to 90-year age bracket, at 549.1 and 333.9 fractures per 10,000 person-years in participants with and without type 1 diabetes, respectively.”

It’s important to note that the first percentages reported above (8.6% vs 6.1%) may be slightly misleading as the follow-up periods for the two groups were dissimilar; type 1s in the study were on average followed for a shorter amount of time than were the controls (4.7 years vs 3.89 years), meaning that raw incident fracture risk estimates like these cannot be translated directly into person-year estimates. The risk differential is thus at least slightly higher than these percentages would suggest. A good view of how the person-year risk difference evolves as a function of age/time are displayed in the paper’s figure 2.

“Hip fractures alone comprised 5.5% and 11.6% of all fractures in males and females with type 1 diabetes, compared with 4.1% and 8.6% in males and females without diabetes (P = 0.04 for males and P = 0.001 for females). Participants with type 1 diabetes with a lower extremity fracture were more likely to have retinopathy (30% vs. 22.5%, P < 0.001) and neuropathy (5.4% vs. 2.9%, P = 0.001) compared with those with fractures at other sites. The median average HbA1c did not differ between the two groups.”

I’ll reiterate this because it’s important: They care about lower-extremity fractures because some of those kinds of fractures, especially hip fractures, have a really poor prognosis. It’s not that it’s annoying and you’ll need a cast; I’ve seen estimates suggesting that roughly one-third of diabetics who sustain a hip fracture die within a year; a prognosis like that is much worse than many cancers. A few relevant observations from Czernik and Fowlkes:

“Together, [studies conducted during the last 15 years on type 1 diabetics] demonstrate an unequivocally increased fracture risk at the hip [compared to non-diabetic controls], with most demonstrating a six to ninefold increase in relative risk. […] type I DM patients have hip fractures at a younger age on average, with a mean of 43 for women and 41 for men in one study. Almost 7 % of people with type I DM can be expected to have sustained a hip fracture by age 65 [7] […] Patients with DM and hip fracture are at a higher risk of mortality than patients without DM, with 1-year rates as high as 32 % vs. 13 % of nondiabetic patients”.

Back to the paper:

“Incident hip fracture risk was increased in all age categories for female participants with type 1 diabetes, and in age categories >30 years in men. […] Type 1 diabetes remained significantly associated with fracture after adjustment for covariates in all previously significant sex and age strata, with the exception of women aged 40–49. […] Each 1% (11 mmol/mol) greater average HbA1c level was associated with a 5% greater risk of incident fracture in males and an 11% greater risk of fracture in females. Diabetic neuropathy was a significant risk factor for incident fracture in males (HR 1.33; 95% CI 1.03–1.72) and females (HR 1.52; 95% CI 1.19–1.92); however, diabetic retinopathy was significant only in males (HR 1.13; 95% CI 1.01–1.28). […] The presence of celiac disease was associated with an increased risk of fractures in females, with an HR of 1.8 (95% CI 1.18–2.76), but not in males. A higher BMI was protective against fracture. Smoking was a risk factor for fracture in males in the 13,763 participants with type 1 diabetes with smoking and BMI data available for analysis.”

The Hba1c-link was interesting to me because the relationships between glycemic control and fracture risk has in other contexts been somewhat unclear; one problem is that Hba1c levels in the lower ranges increase the risk of hypoglycemic episodes, and such episodes may increase the risk of fractures, so even if chronic hyperglycemia is bad for bone health if you don’t have access e.g. to event-level/-rate data on hypoglycemic episodes confounding may be an issue causing a (very plausible) chronic hyperglycemia-fracture risk link to perhaps be harder to detect than it otherwise might have been. It’s of note that these guys did not have access to data on hypoglycemic episodes. They observe later in the paper that: “If hypoglycemia was a major contributing factor, we might have expected a negative effect of HbA1c on fracture risk; our data indicated the opposite.” I don’t think you can throw out hypoglycemia as a contributing factor that easily.

Anyway, a few final observations from the paper:

CONCLUSIONS Type 1 diabetes was associated with increased risk of incident fracture that began in childhood and extended across the life span. Participants with type 1 diabetes sustained a disproportionately greater number of lower extremity fractures. These findings have important public health implications, given the increasing prevalence of type 1 diabetes and the morbidity and mortality associated with hip fractures.”

“To our knowledge, this is the first study to show that the increased fracture risk in type 1 diabetes begins in childhood. This finding has important implications for researchers planning future studies and for clinicians caring for patients in this population. Although peak bone mass is attained by the end of the third decade of life, peak bone accrual occurs in adolescence in conjunction with the pubertal growth spurt (31). This critical time for bone accrual may represent a period of increased skeletal vulnerability and also a window of opportunity for the implementation of therapies to improve bone formation (32). This is an especially important consideration in the population with type 1 diabetes, because the incidence of this disease peaks in early adolescence. Three-quarters of individuals will develop the condition before 18 years of age, and therefore before attainment of peak bone mass (33). The development and evaluation of therapies aimed at increasing bone formation and strength in adolescence may lead to a lifelong reduction in fracture risk.”

“The underlying mechanism for the increased fracture risk in patients with type 1 diabetes is not fully understood. Current evidence suggests that bone quantity and quality may both be abnormal in this condition. Clinical studies using dual-energy X-ray absorptiometry and peripheral quantitative computed tomography have identified mild to modest deficits in BMD and bone structure in both pediatric and adult participants with type 1 diabetes (6,8,34). Deficits in BMD are unlikely to be the only factor contributing to skeletal fragility in type 1 diabetes, however, as evidenced by a recent meta-analysis that found that the increased fracture risk seen in type 1 diabetes could not be explained by deficits in BMD alone (16). Recent cellular and animal models have shown that insulin signaling in osteoblasts and osteoblast progenitor cells promotes postnatal bone acquisition, suggesting that the insulin deficiency inherent in type 1 diabetes is a significant contributor to the pathogenesis of skeletal disease (35). Other proposed mechanisms contributing to skeletal fragility in type 1 diabetes include chronic hyperglycemia (36), impaired production of IGF-1 (37), and the accumulation of advanced glycation end products in bone (38). Our results showed that a higher average HbA1c was associated with an increased risk of fracture in participants with type 1 diabetes, supporting the hypothesis that chronic hyperglycemia and its sequelae contribute to skeletal fragility.”

“In summary, our study found that participants of all ages with type 1 diabetes were at increased risk of fracture. The adverse effect of type 1 diabetes on the skeleton is an underrecognized complication that is likely to grow into a significant public health burden given the increasing incidence and prevalence of this disease. […] Our novel finding that children with type 1 diabetes were already at increased risk of fracture suggests that therapeutic interventions aimed at children and adolescents may have an important effect on reducing lifelong fracture risk.”


August 15, 2017 Posted by | Diabetes, Epidemiology, Medicine, Studies | 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

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. Clinically Relevant Cognitive Impairment in Middle-Aged Adults With Childhood-Onset Type 1 Diabetes.

“Modest cognitive dysfunction is consistently reported in children and young adults with type 1 diabetes (T1D) (1). Mental efficiency, psychomotor speed, executive functioning, and intelligence quotient appear to be most affected (2); studies report effect sizes between 0.2 and 0.5 (small to modest) in children and adolescents (3) and between 0.4 and 0.8 (modest to large) in adults (2). Whether effect sizes continue to increase as those with T1D age, however, remains unknown.

A key issue not yet addressed is whether aging individuals with T1D have an increased risk of manifesting “clinically relevant cognitive impairment,” defined by comparing individual cognitive test scores to demographically appropriate normative means, as opposed to the more commonly investigated “cognitive dysfunction,” or between-group differences in cognitive test scores. Unlike the extensive literature examining cognitive impairment in type 2 diabetes, we know of only one prior study examining cognitive impairment in T1D (4). This early study reported a higher rate of clinically relevant cognitive impairment among children (10–18 years of age) diagnosed before compared with after age 6 years (24% vs. 6%, respectively) or a non-T1D cohort (6%).”

“This study tests the hypothesis that childhood-onset T1D is associated with an increased risk of developing clinically relevant cognitive impairment detectable by middle age. We compared cognitive test results between adults with and without T1D and used demographically appropriate published norms (1012) to determine whether participants met criteria for impairment for each test; aging and dementia studies have selected a score ≥1.5 SD worse than the norm on that test, corresponding to performance at or below the seventh percentile (13).”

“During 2010–2013, 97 adults diagnosed with T1D and aged <18 years (age and duration 49 ± 7 and 41 ± 6 years, respectively; 51% female) and 138 similarly aged adults without T1D (age 49 ± 7 years; 55% female) completed extensive neuropsychological testing. Biomedical data on participants with T1D were collected periodically since 1986–1988.  […] The prevalence of clinically relevant cognitive impairment was five times higher among participants with than without T1D (28% vs. 5%; P < 0.0001), independent of education, age, or blood pressure. Effect sizes were large (Cohen d 0.6–0.9; P < 0.0001) for psychomotor speed and visuoconstruction tasks and were modest (d 0.3–0.6; P < 0.05) for measures of executive function. Among participants with T1D, prevalent cognitive impairment was related to 14-year average A1c >7.5% (58 mmol/mol) (odds ratio [OR] 3.0; P = 0.009), proliferative retinopathy (OR 2.8; P = 0.01), and distal symmetric polyneuropathy (OR 2.6; P = 0.03) measured 5 years earlier; higher BMI (OR 1.1; P = 0.03); and ankle-brachial index ≥1.3 (OR 4.2; P = 0.01) measured 20 years earlier, independent of education.”

“Having T1D was the only factor significantly associated with the between-group difference in clinically relevant cognitive impairment in our sample. Traditional risk factors for age-related cognitive impairment, in particular older age and high blood pressure (24), were not related to the between-group difference we observed. […] Similar to previous studies of younger adults with T1D (14,26), we found no relationship between the number of severe hypoglycemic episodes and cognitive impairment. Rather, we found that chronic hyperglycemia, via its associated vascular and metabolic changes, may have triggered structural changes in the brain that disrupt normal cognitive function.”

Just to be absolutely clear about these results: The type 1 diabetics they recruited in this study were on average not yet fifty years old, yet more than one in four of them were cognitively impaired to a clinically relevant degree. This is a huge effect. As they note later in the paper:

“Unlike previous reports of mild/modest cognitive dysfunction in young adults with T1D (1,2), we detected clinically relevant cognitive impairment in 28% of our middle-aged participants with T1D. This prevalence rate in our T1D cohort is comparable to the prevalence of mild cognitive impairment typically reported among community-dwelling adults aged 85 years and older (29%) (20).”

The type 1 diabetics included in the study had had diabetes for roughly a decade more than I have. And the number of cognitively impaired individuals in that sample corresponds roughly to what you find when you test random 85+ year-olds. Having type 1 diabetes is not good for your brain.

ii. Comment on Nunley et al. Clinically Relevant Cognitive Impairment in Middle-Aged Adults With Childhood-Onset Type 1 Diabetes.

This one is a short comment to the above paper, below I’ve quoted ‘the meat’ of the comment:

“While the […] study provides us with important insights regarding cognitive impairment in adults with type 1 diabetes, we regret that depression has not been taken into account. A systematic review and meta-analysis published in 2014 identified significant objective cognitive impairment in adults and adolescents with depression regarding executive functioning, memory, and attention relative to control subjects (2). Moreover, depression is two times more common in adults with diabetes compared with those without this condition, regardless of type of diabetes (3). There is even evidence that the co-occurrence of diabetes and depression leads to additional health risks such as increased mortality and dementia (3,4); this might well apply to cognitive impairment as well. Furthermore, in people with diabetes, the presence of depression has been associated with the development of diabetes complications, such as retinopathy, and higher HbA1c values (3). These are exactly the diabetes-specific correlates that Nunley et al. (1) found.”

“We believe it is a missed opportunity that Nunley et al. (1) mainly focused on biological variables, such as hyperglycemia and microvascular disease, and did not take into account an emotional disorder widely represented among people with diabetes and closely linked to cognitive impairment. Even though severe or chronic cases of depression are likely to have been excluded in the group without type 1 diabetes based on exclusion criteria (1), data on the presence of depression (either measured through a diagnostic interview or by using a validated screening questionnaire) could have helped to interpret the present findings. […] Determining the role of depression in the relationship between cognitive impairment and type 1 diabetes is of significant importance. Treatment of depression might improve cognitive impairment both directly by alleviating cognitive depression symptoms and indirectly by improving treatment nonadherence and glycemic control, consequently lowering the risk of developing complications.”

iii. Prevalence of Diabetes and Diabetic Nephropathy in a Large U.S. Commercially Insured Pediatric Population, 2002–2013.

“[W]e identified 96,171 pediatric patients with diabetes and 3,161 pediatric patients with diabetic nephropathy during 2002–2013. We estimated prevalence of pediatric diabetes overall, by diabetes type, age, and sex, and prevalence of pediatric diabetic nephropathy overall, by age, sex, and diabetes type.”

“Although type 1 diabetes accounts for a majority of childhood and adolescent diabetes, type 2 diabetes is becoming more common with the increasing rate of childhood obesity and it is estimated that up to 45% of all new patients with diabetes in this age-group have type 2 diabetes (1,2). With the rising prevalence of diabetes in children, a rise in diabetes-related complications, such as nephropathy, is anticipated. Moreover, data suggest that the development of clinical macrovascular complications, neuropathy, and nephropathy may be especially rapid among patients with young-onset type 2 diabetes (age of onset <40 years) (36). However, the natural history of young patients with type 2 diabetes and resulting complications has not been well studied.”

I’m always interested in the identification mechanisms applied in papers like this one, and I’m a little confused about the high number of patients without prescriptions (almost one-third of patients); I sort of assume these patients do take (/are given) prescription drugs, but get them from sources not available to the researchers (parents get prescriptions for the antidiabetic drugs, and the researchers don’t have access to these data? Something like this..) but this is a bit unclear. The mechanism they employ in the paper is not perfect (no mechanism is), but it probably works:

“Patients who had one or more prescription(s) for insulin and no prescriptions for another antidiabetes medication were classified as having type 1 diabetes, while those who filled prescriptions for noninsulin antidiabetes medications were considered to have type 2 diabetes.”

When covering limitations of the paper, they observe incidentally in this context that:

“Klingensmith et al. (31) recently reported that in the initial month after diagnosis of type 2 diabetes around 30% of patients were treated with insulin only. Thus, we may have misclassified a small proportion of type 2 cases as type 1 diabetes or vice versa. Despite this, we found that 9% of patients had onset of type 2 diabetes at age <10 years, consistent with the findings of Klingensmith et al. (8%), but higher than reported by the SEARCH for Diabetes in Youth study (<3%) (31,32).”

Some more observations from the paper:

“There were 149,223 patients aged <18 years at first diagnosis of diabetes in the CCE database from 2002 through 2013. […] Type 1 diabetes accounted for a majority of the pediatric patients with diabetes (79%). Among these, 53% were male and 53% were aged 12 to <18 years at onset, while among patients with type 2 diabetes, 60% were female and 79% were aged 12 to <18 years at onset.”

“The overall annual prevalence of all diabetes increased from 1.86 to 2.82 per 1,000 during years 2002–2013; it increased on average by 9.5% per year from 2002 to 2006 and slowly increased by 0.6% after that […] The prevalence of type 1 diabetes increased from 1.48 to 2.32 per 1,000 during the study period (average increase of 8.5% per year from 2002 to 2006 and 1.4% after that; both P values <0.05). The prevalence of type 2 diabetes increased from 0.38 to 0.67 per 1,000 during 2002 through 2006 (average increase of 13.3% per year; P < 0.05) and then dropped from 0.56 to 0.49 per 1,000 during 2007 through 2013 (average decrease of 2.7% per year; P < 0.05). […] Prevalence of any diabetes increased by age, with the highest prevalence in patients aged 12 to <18 years (ranging from 3.47 to 5.71 per 1,000 from 2002 through 2013).” […] The annual prevalence of diabetes increased over the study period mainly because of increases in type 1 diabetes.”

“Dabelea et al. (8) reported, based on data from the SEARCH for Diabetes in Youth study, that the annual prevalence of type 1 diabetes increased from 1.48 to 1.93 per 1,000 and from 0.34 to 0.46 per 1,000 for type 2 diabetes from 2001 to 2009 in U.S. youth. In our study, the annual prevalence of type 1 diabetes was 1.48 per 1,000 in 2002 and 2.10 per 1,000 in 2009, which is close to their reported prevalence.”

“We identified 3,161 diabetic nephropathy cases. Among these, 1,509 cases (47.7%) were of specific diabetic nephropathy and 2,253 (71.3%) were classified as probable cases. […] The annual prevalence of diabetic nephropathy in pediatric patients with diabetes increased from 1.16 to 3.44% between 2002 and 2013; it increased by on average 25.7% per year from 2002 to 2005 and slowly increased by 4.6% after that (both P values <0.05).”

Do note that the relationship between nephropathy prevalence and diabetes prevalence is complicated and that you cannot just explain an increase in the prevalence of nephropathy over time easily by simply referring to an increased prevalence of diabetes during the same time period. This would in fact be a very wrong thing to do, in part but not only on account of the data structure employed in this study. One problem which is probably easy to understand is that if more children got diabetes but the same proportion of those new diabetics got nephropathy, the diabetes prevalence would go up but the diabetic nephropathy prevalence would remain fixed; when you calculate the diabetic nephropathy prevalence you implicitly condition on diabetes status. But this just scratches the surface of the issues you encounter when you try to link these variables, because the relationship between the two variables is complicated; there’s an age pattern to diabetes risk, with risk (incidence) increasing with age (up to a point, after which it falls – in most samples I’ve seen in the past peak incidence in pediatric populations is well below the age of 18). However diabetes prevalence increases monotonously with age as long as the age-specific death rate of diabetics is lower than the age-specific incidence, because diabetes is chronic, and then on top of that you have nephropathy-related variables, which display diabetes-related duration-dependence (meaning that although nephropathy risk is also increasing with age when you look at that variable in isolation, that age-risk relationship is confounded by diabetes duration – a type 1 diabetic at the age of 12 who’s had diabetes for 10 years has a higher risk of nephropathy than a 16-year old who developed diabetes the year before). When a newly diagnosed pediatric patient is included in the diabetes sample here this will actually decrease the nephropathy prevalence in the short run, but not in the long run, assuming no changes in diabetes treatment outcomes over time. This is because the probability that that individual has diabetes-related kidney problems as a newly diagnosed child is zero, so he or she will unquestionably only contribute to the denominator during the first years of illness (the situation in the middle-aged type 2 context is different; here you do sometimes have newly-diagnosed patients who have developed complications already). This is one reason why it would be quite wrong to say that increased diabetes prevalence in this sample is the reason why diabetic nephropathy is increasing as well. Unless the time period you look at is very long (e.g. you have a setting where you follow all individuals with a diagnosis until the age of 18), the impact of increasing prevalence of one condition may well be expected to have a negative impact on the estimated risk of associated conditions, if those associated conditions display duration-dependence (which all major diabetes complications do). A second factor supporting a default assumption of increasing incidence of diabetes leading to an expected decreasing rate of diabetes-related complications is of course the fact that treatment options have tended to increase over time, and especially if you take a long view (look back 30-40 years) the increase in treatment options and improved medical technology have lead to improved metabolic control and better outcomes.

That both variables grew over time might be taken to indicate that both more children got diabetes and that a larger proportion of this increased number of children with diabetes developed kidney problems, but this stuff is a lot more complicated than it might look and it’s in particular important to keep in mind that, say, the 2005 sample and the 2010 sample do not include the same individuals, although there’ll of course be some overlap; in age-stratified samples like this you always have some level of implicit continuous replacement, with newly diagnosed patients entering and replacing the 18-year olds who leave the sample. As long as prevalence is constant over time, associated outcome variables may be reasonably easy to interpret, but when you have dynamic samples as well as increasing prevalence over time it gets difficult to say much with any degree of certainty unless you crunch the numbers in a lot of detail (and it might be difficult even if you do that). A factor I didn’t mention above but which is of course also relevant is that you need to be careful about how to interpret prevalence rates when you look at complications with high mortality rates (and late-stage diabetic nephropathy is indeed a complication with high mortality); in such a situation improvements in treatment outcomes may have large effects on prevalence rates but no effect on incidence. Increased prevalence is not always bad news, sometimes it is good news indeed. Gleevec substantially increased the prevalence of CML.

In terms of the prevalence-outcomes (/complication risk) connection, there are also in my opinion reasons to assume that there may be multiple causal pathways between prevalence and outcomes. For example a very low prevalence of a condition in a given area may mean that fewer specialists are educated to take care of these patients than would be the case for an area with a higher prevalence, and this may translate into a more poorly developed care infrastructure. Greatly increasing prevalence may on the other hand lead to a lower level of care for all patients with the illness, not just the newly diagnosed ones, due to binding budget constraints and care rationing. And why might you have changes in prevalence; might they not sometimes rather be related to changes in diagnostic practices, rather than changes in the True* prevalence? If that’s the case, you might not be comparing apples to apples when you’re comparing the evolving complication rates. There are in my opinion many reasons to believe that the relationship between chronic conditions and the complication rates of these conditions is far from simple to model.

All this said, kidney problems in children with diabetes is still rare, compared to the numbers you see when you look at adult samples with longer diabetes duration. It’s also worth distinguishing between microalbuminuria and overt nephropathy; children rarely proceed to develop diabetes-related kidney failure, although poor metabolic control may mean that they do develop this complication later, in early adulthood. As they note in the paper:

“It has been reported that overt diabetic nephropathy and kidney failure caused by either type 1 or type 2 diabetes are uncommon during childhood or adolescence (24). In this study, the annual prevalence of diabetic nephropathy for all cases ranged from 1.16 to 3.44% in pediatric patients with diabetes and was extremely low in the whole pediatric population (range 2.15 to 9.70 per 100,000), confirming that diabetic nephropathy is a very uncommon condition in youth aged <18 years. We observed that the prevalence of diabetic nephropathy increased in both specific and unspecific cases before 2006, with a leveling off of the specific nephropathy cases after 2005, while the unspecific cases continued to increase.”

iv. Adherence to Oral Glucose-Lowering Therapies and Associations With 1-Year HbA1c: A Retrospective Cohort Analysis in a Large Primary Care Database.

“Between a third and a half of medicines prescribed for type 2 diabetes (T2DM), a condition in which multiple medications are used to control cardiovascular risk factors and blood glucose (1,2), are not taken as prescribed (36). However, estimates vary widely depending on the population being studied and the way in which adherence to recommended treatment is defined.”

“A number of previous studies have used retrospective databases of electronic health records to examine factors that might predict adherence. A recent large cohort database examined overall adherence to oral therapy for T2DM, taking into account changes of therapy. It concluded that overall adherence was 69%, with individuals newly started on treatment being significantly less likely to adhere (19).”

“The impact of continuing to take glucose-lowering medicines intermittently, but not as recommended, is unknown. Medication possession (expressed as a ratio of actual possession to expected possession), derived from prescribing records, has been identified as a valid adherence measure for people with diabetes (7). Previous studies have been limited to small populations in managed-care systems in the U.S. and focused on metformin and sulfonylurea oral glucose-lowering treatments (8,9). Further studies need to be carried out in larger groups of people that are more representative of the general population.

The Clinical Practice Research Database (CPRD) is a long established repository of routine clinical data from more than 13 million patients registered with primary care services in England. […] The Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) database is derived from integrated health records in Scotland with primary care, pharmacy, and hospital data on 9,400 patients with diabetes. […] We conducted a retrospective cohort study using [these databases] to examine the prevalence of nonadherence to treatment for type 2 diabetes and investigate its potential impact on HbA1c reduction stratified by type of glucose-lowering medication.”

“In CPRD and GoDARTS, 13% and 15% of patients, respectively, were nonadherent. Proportions of nonadherent patients varied by the oral glucose-lowering treatment prescribed (range 8.6% [thiazolidinedione] to 18.8% [metformin]). Nonadherent, compared with adherent, patients had a smaller HbA1c reduction (0.4% [4.4 mmol/mol] and 0.46% [5.0 mmol/mol] for CPRD and GoDARTs, respectively). Difference in HbA1c response for adherent compared with nonadherent patients varied by drug (range 0.38% [4.1 mmol/mol] to 0.75% [8.2 mmol/mol] lower in adherent group). Decreasing levels of adherence were consistently associated with a smaller reduction in HbA1c.”

“These findings show an association between adherence to oral glucose-lowering treatment, measured by the proportion of medication obtained on prescription over 1 year, and the corresponding decrement in HbA1c, in a population of patients newly starting treatment and continuing to collect prescriptions. The association is consistent across all commonly used oral glucose-lowering therapies, and the findings are consistent between the two data sets examined, CPRD and GoDARTS. Nonadherent patients, taking on average <80% of the intended medication, had about half the expected reduction in HbA1c. […] Reduced medication adherence for commonly used glucose-lowering therapies among patients persisting with treatment is associated with smaller HbA1c reductions compared with those taking treatment as recommended. Differences observed in HbA1c responses to glucose-lowering treatments may be explained in part by their intermittent use.”

“Low medication adherence is related to increased mortality (20). The mean difference in HbA1c between patients with MPR <80% and ≥80% is between 0.37% and 0.55% (4 mmol/mol and 6 mmol/mol), equivalent to up to a 10% reduction in death or an 18% reduction in diabetes complications (21).”

v. Health Care Transition in Young Adults With Type 1 Diabetes: Perspectives of Adult Endocrinologists in the U.S.

“Empiric data are limited on best practices in transition care, especially in the U.S. (10,1316). Prior research, largely from the patient perspective, has highlighted challenges in the transition process, including gaps in care (13,1719); suboptimal pediatric transition preparation (13,20); increased post-transition hospitalizations (21); and patient dissatisfaction with the transition experience (13,1719). […] Young adults with type 1 diabetes transitioning from pediatric to adult care are at risk for adverse outcomes. Our objective was to describe experiences, resources, and barriers reported by a national sample of adult endocrinologists receiving and caring for young adults with type 1 diabetes.”

“We received responses from 536 of 4,214 endocrinologists (response rate 13%); 418 surveys met the eligibility criteria. Respondents (57% male, 79% Caucasian) represented 47 states; 64% had been practicing >10 years and 42% worked at an academic center. Only 36% of respondents reported often/always reviewing pediatric records and 11% reported receiving summaries for transitioning young adults with type 1 diabetes, although >70% felt that these activities were important for patient care.”

“A number of studies document deficiencies in provider hand-offs across other chronic conditions and point to the broader relevance of our findings. For example, in two studies of inflammatory bowel disease, adult gastroenterologists reported inadequacies in young adult transition preparation (31) and infrequent receipt of medical histories from pediatric providers (32). In a study of adult specialists caring for young adults with a variety of chronic diseases (33), more than half reported that they had no contact with the pediatric specialists.

Importantly, more than half of the endocrinologists in our study reported a need for increased access to mental health referrals for young adult patients with type 1 diabetes, particularly in nonacademic settings. Report of barriers to care was highest for patient scenarios involving mental health issues, and endocrinologists without easy access to mental health referrals were significantly more likely to report barriers to diabetes management for young adults with psychiatric comorbidities such as depression, substance abuse, and eating disorders.”

“Prior research (34,35) has uncovered the lack of mental health resources in diabetes care. In the large cross-national Diabetes Attitudes, Wishes and Needs (DAWN) study (36) […] diabetes providers often reported not having the resources to manage mental health problems; half of specialist diabetes physicians felt unable to provide psychiatric support for patients and one-third did not have ready access to outside expertise in emotional or psychiatric matters. Our results, which resonate with the DAWN findings, are particularly concerning in light of the vulnerability of young adults with type 1 diabetes for adverse medical and mental health outcomes (4,34,37,38). […] In a recent report from the Mental Health Issues of Diabetes conference (35), which focused on type 1 diabetes, a major observation included the lack of trained mental health professionals, both in academic centers and the community, who are knowledgeable about the mental health issues germane to diabetes.”


August 3, 2017 Posted by | Diabetes, Epidemiology, Medicine, Nephrology, Neurology, Pharmacology, Psychiatry, Psychology, Statistics, Studies | Leave a comment

A New Classification System for Diabetes: Rationale and Implications of the β-Cell–Centric Classification Schema

When I started writing this post I intended to write a standard diabetes post covering a variety of different papers, but while I was covering one of the papers I intended to include in the post I realized that I felt like I had to cover that paper in a lot of detail, and I figured I might as well make a separate post about it. Here’s a link to the paper: The Time Is Right for a New Classification System for Diabetes: Rationale and Implications of the β-Cell–Centric Classification Schema.

I have frequently discussed the problem of how best to think about and -categorize the various disorders of glucose homeostasis which are currently lumped together into the various discrete diabetes categories, both online and offline, see e.g. the last few paragraphs of this recent post. I have frequently noted in such contexts that simplistic and very large ‘boxes’ like ‘type 1’ and ‘type 2’ leave out a lot of details, and that some of the details that are lost by employing such a categorization scheme might well be treatment-relevant in some contexts. Individualized medicine is however expensive, so I still consider it an open question to which extent valuable information – which is to say, information that could potentially be used cost-effectively in the treatment context – is lost on account of the current diagnostic practices, but information is certainly lost and treatment options potentially neglected. Relatedly, what’s not cost-effective today may well be tomorrow.

As I decided to devote an entire post to this paper, it is of course a must-read if you’re interested in these topics. I have quoted extensively from the paper below:

“The current classification system presents challenges to the diagnosis and treatment of patients with diabetes mellitus (DM), in part due to its conflicting and confounding definitions of type 1 DM, type 2 DM, and latent autoimmune diabetes of adults (LADA). The current schema also lacks a foundation that readily incorporates advances in our understanding of the disease and its treatment. For appropriate and coherent therapy, we propose an alternate classification system. The β-cell–centric classification of DM is a new approach that obviates the inherent and unintended confusions of the current system. The β-cell–centric model presupposes that all DM originates from a final common denominator — the abnormal pancreatic β-cell. It recognizes that interactions between genetically predisposed β-cells with a number of factors, including insulin resistance (IR), susceptibility to environmental influences, and immune dysregulation/inflammation, lead to the range of hyperglycemic phenotypes within the spectrum of DM. Individually or in concert, and often self-perpetuating, these factors contribute to β-cell stress, dysfunction, or loss through at least 11 distinct pathways. Available, yet underutilized, treatments provide rational choices for personalized therapies that target the individual mediating pathways of hyperglycemia at work in any given patient, without the risk of drug-related hypoglycemia or weight gain or imposing further burden on the β-cells.”

“The essential function of a classification system is as a navigation tool that helps direct research, evaluate outcomes, establish guidelines for best practices for prevention and care, and educate on all of the above. Diabetes mellitus (DM) subtypes as currently categorized, however, do not fit into our contemporary understanding of the phenotypes of diabetes (16). The inherent challenges of the current system, together with the limited knowledge that existed at the time of the crafting of the current system, yielded definitions for type 1 DM, type 2 DM, and latent autoimmune diabetes in adults (LADA) that are not distinct and are ambiguous and imprecise.”

“Discovery of the role played by autoimmunity in the pathogenesis of type 1 DM created the assumption that type 1 DM and type 2 DM possess unique etiologies, disease courses, and, consequently, treatment approaches. There exists, however, overlap among even the most “typical” patient cases. Patients presenting with otherwise classic insulin resistance (IR)-associated type 2 DM may display hallmarks of type 1 DM. Similarly, obesity-related IR may be observed in patients presenting with “textbook” type 1 DM (7). The late presentation of type 1 DM provides a particular challenge for the current classification system, in which this subtype of DM is generally termed LADA. Leading diabetes organizations have not arrived at a common definition for LADA (5). There has been little consensus as to whether this phenotype constitutes a form of type 2 DM with early or fast destruction of β-cells, a late manifestation of type 1 DM (8), or a distinct entity with its own genetic footprint (5). Indeed, current parameters are inadequate to clearly distinguish any of the subforms of DM (Fig. 1).

The use of IR to define type 2 DM similarly needs consideration. The fact that many obese patients with IR do not develop DM indicates that IR is insufficient to cause type 2 DM without predisposing factors that affect β-cell function (9).”

“The current classification schema imposes unintended constraints on individualized medicine. Patients diagnosed with LADA who retain endogenous insulin production may receive “default” insulin therapy as treatment of choice. This decision is guided largely by the categorization of LADA within type 1 DM, despite the capacity for endogenous insulin production. Treatment options that do not pose the risks of hypoglycemia or weight gain might be both useful and preferable for LADA but are typically not considered beyond use in type 2 DM (10). […] We believe that there is little rationale for limiting choice of therapy solely on the current definitions of type 1 DM, type 2 DM, and LADA. We propose that choice of therapy should be based on the particular mediating pathway(s) of hyperglycemia present in each individual patient […] the issue is not “what is LADA” or any clinical presentation of DM under the current system. The issue is the mechanisms and rate of destruction of β-cells at work in all DM. We present a model that provides a more logical approach to classifying DM: the β-cell–centric classification of DM. In this schema, the abnormal β-cell is recognized as the primary defect in DM. The β-cell–centric classification system recognizes the interplay of genetics, IR, environmental factors, and inflammation/immune system on the function and mass of β-cells […]. Importantly, this model is universal for the characterization of DM. The β-cell–centric concept can be applied to DM arising in genetically predisposed β-cells, as well as in strongly genetic IR syndromes, such as the Rabson-Mendenhall syndrome (28), which may exhaust nongenetically predisposed β-cells. Finally, the β-cell–centric classification of all DM supports best practices in the management of DM by identifying mediating pathways of hyperglycemia that are operative in each patient and directing treatment to those specific dysfunctions.”

“A key premise is that the mediating pathways of hyperglycemia are common across prediabetes, type 1 DM, type 2 DM, and other currently defined forms of DM. Accordingly, we believe that the current antidiabetes armamentarium has broader applicability across the spectrum of DM than is currently utilized.

The ideal treatment paradigm would be one that uses the least number of agents possible to target the greatest number of mediating pathways of hyperglycemia operative in the given patient. It is prudent to use agents that will help patients reach target A1C levels without introducing drug-related hypoglycemia or weight gain. Despite the capacity of insulin therapy to manage glucotoxicity, there is a concern for β-cell damage due to IR that has been exacerbated by exogenous insulin-induced hyperinsulinemia and weight gain (41).”

“We propose that the β-cell–centric model is a conceptual framework that could help optimize processes of care for DM. A1C, fasting blood glucose, and postprandial glucose testing remain the basis of DM diagnosis and monitoring. Precision medicine in the treatment of DM could be realized by additional diagnostic testing that could include C-peptide (1), islet cell antibodies or other markers of inflammation (1,65), measures of IR, improved assays for β-cell mass, and markers of environmental damage and by the development of markers for the various mediating pathways of hyperglycemia.

We uphold that there is, and will increasingly be, a place for genotyping in DM standard of care. Pharmacogenomics could help direct patient-level care (6669) and holds the potential to spur on research through the development of DM gene banks for analyzing genetic distinctions between type 1 DM, LADA, type 2 DM, and maturity-onset diabetes of the young. The cost for genotyping has become increasingly affordable.”

“The ideal treatment regimens should not be potentially detrimental to the long-term integrity of the β-cells. Specifically, sulfonylureas and glinides should be ardently avoided. Any benefits associated with sulfonylureas and glinides (including low cost) are not enduring and are far outweighed by their attendant risks (and associated treatment costs) of hypoglycemia and weight gain, high rate of treatment failure and subsequent enhanced requirements for antihyperglycemic management, potential for β-cell exhaustion (42), increased risk of cardiovascular events (74), and potential for increased risk of mortality (75,76). Fortunately, there are a large number of classes now available that do not pose these risks.”

“Newer agents present alternatives to insulin therapy, including in patients with “advanced” type 2 DM with residual insulin production. Insulin therapy induces hypoglycemia, weight gain, and a range of adverse consequences of hyperinsulinemia with both short- and long-term outcomes (77–85). Newer antidiabetes classes may be used to delay insulin therapy in candidate patients with endogenous insulin production (19). […] When insulin therapy is needed, we suggest it be incorporated as add-on therapy rather than as substitution for noninsulin antidiabetes agents. Outcomes research is needed to fully evaluate various combination therapeutic approaches, as well as the potential of newer agents to address drivers of β-cell dysfunction and loss.

The principles of the β-cell–centric model provide a rationale for adjunctive therapy with noninsulin regimens in patients with type 1 DM (7,1216). Thiazolidinedione (TZD) therapy in patients with type 1 DM presenting with IR, for example, is appropriate and can be beneficial (17). Clinical trials in type 1 DM show that incretins (20) or SGLT-2 inhibitors (25,88) as adjunctive therapy to exogenous insulin appear to reduce plasma glucose variability.”


July 24, 2017 Posted by | Diabetes, Medicine, Papers | 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 SSC comments

I recently left a few comments in an open thread on SSC, and I figured it might make sense to crosspost some of the comments made there here on the blog. I haven’t posted all my contributions to the debate here, rather I’ve just quoted some specific comments and observations which might be of interest. I’ve also added some additional remarks and comments which relate to the topics discussed. Here’s the main link (scroll down to get to my comments).

“One thing worth keeping in mind when evaluating pre-modern medicine characterizations of diabetes and the natural history of diabetes is incidentally that especially to the extent that one is interested in type 1 survivorship bias is a major problem lurking in the background. Prognostic estimates of untreated type 1 based on historical accounts of how long people could live with the disease before insulin are not in my opinion likely to be all that reliable, because the type of patients that would be recognized as (type 1) diabetics back then would tend to mainly be people who had the milder forms, because they were the only ones who lived long enough to reach a ‘doctor’; and the longer they lived, and the milder the sub-type, the more likely they were to be studied/’diagnosed’. I was a 2-year old boy who got unwell on a Tueday and was hospitalized three days later. Avicenna would have been unlikely to have encountered me, I’d have died before he saw me. (Similar lines of reasoning might lead to an argument that the incidence of diseases like type 1 diabetes may also today be underdiagnosed in developing countries with poorly developed health care systems.)”

Douglas Knight mentioned during our exchange that medical men of the far past might have been more likely to attend to patients with acute illnesses than patients with chronic conditions, making them more likely to attend to such cases than would otherwise be the case, a point I didn’t discuss in any detail during the exchange. I did however think it important to note here that information exchange was significantly slower, and transportation costs were much higher, in the past than they are today. This should make such a bias less relevant, all else equal. Avicenna and his colleagues couldn’t take a taxi, or learn by phone that X is sick. He might have preferentially attended to the acute cases he learned about, but given high transportation costs and inefficient communication channels he might often never arrive in time, or at all. A particular problem here is that there are no good data on the unobserved cases, because the only cases we know about today are the ones people like him have told us about.

Some more comments:

“One thing I was considering adding to my remarks about survivorship bias is that it is not in my opinion unlikely that what you might term the nature of the disease has changed over the centuries; indeed it might still be changing today. Globally the incidence of type 1 has been increasing for decades and nobody seems to know why, though there’s consensus about an environmental trigger playing a major role. Maybe incidence is not the only thing that’s changed, maybe e.g. the time course of the ‘average case’ has also changed? Maybe due to secondary factors; better nutritional status now equals slower progression of beta cell failure than was the case in the past? Or perhaps the other way around: Less exposure to bacterial agents the immune system throughout evolutionary time has been used to having to deal with today means that the autoimmune process is accelerated today, compared to in the far past where standards of hygiene were different. Who knows? […] Maybe survivorship bias wasn’t that big of a deal, but I think one should be very cautious about which assumptions one might implicitly be making along the way when addressing questions of this sort of nature. Some relevant questions will definitely be unknowable due to lack of good data which we will never be able to obtain.”

I should perhaps interpose here that even if survivorship bias ‘wasn’t that big of a deal’, it’s still sort of a big problem in the analytical setting because it seems perfectly plausible to me to be making the assumption that it might even so have been a big deal. These kinds of problems magnify our error bars and reduce confidence in our conclusions, regardless of the extent to which they actually played a role. When you know the exact sign and magnitude of a given moderating effect you can try to correct for it, but this is very difficult to do when a large range of moderator effect sizes might be considered plausible. It might also here be worth mentioning explicitly that biases such as the survivorship bias mentioned can of course impact a lot of things besides just the prognostic estimates; for example if a lot of cases never come to the attention of the medical people because these people were unavailable (due to distance, cost, lack of information, etc.) to the people who were sick, incidence and prevalence will also implicitly be underestimated. And so on. Back to the comments:

“Once you had me thinking that it might have been harder [for people in the past] to distinguish [between type 1 and type 2 diabetes] than […] it is today, I started wondering about this, and the comments below relate to this topic. An idea that came to mind in relation to the type 1/type 2 distinction and the ability of people in the past to make this distinction: I’ve worked on various identification problems present in the diabetes context before, and I know that people even today make misdiagnoses and e.g. categorize type 1 diabetics as type 2. I asked a diabetes nurse working in the local endocrinology unit about this at one point, and she told me they had actually had a patient not long before then who had been admitted a short while after having been diagnosed with type 2. Turned out he was type 1, so the treatment failed. Misdiagnoses happen for multiple reasons, one is that obese people also sometimes develop type 1, and if it’s an acute onset setting the weight loss is not likely to be very significant. Patient history should in such a case provide the doctor with the necessary clues, but if the guy making the diagnosis is a stressed out GP who’s currently treating a lot of obese patients for type 2, mistakes happen. ‘Pre-scientific method’ this sort of individual would have been inconvenient to encounter, because a ‘counter-example’ like that supposedly demonstrating that the obese/thin(/young/old, acute/protracted…) distinction was ‘invalid’ might have held a lot more weight than it hopefully would today in the age of statistical analysis. A similar problem would be some of the end-stage individuals: A type 1 pre-insulin would be unlikely to live long enough to develop long term complications of the disease, but would instead die of DKA. The problem is that some untreated type 2 patients also die of DKA, though the degree of ketosis varies in type 2 patients. DKA in type 2 could e.g. be triggered by a superimposed cardiovascular event or an infection, increasing metabolic demands to an extent that can no longer be met by the organism, and so might well present just as acutely as it would in a classic acute-onset type 1 case. Assume the opposite bias you mention is playing a role; the ‘doctor’ in the past is more likely to see the patients in such a life-threatening setting than in the earlier stages. He observes a 55 year old fat guy dying in a very similar manner to the way a 12 year old girl died a few months back – very characteristic symptoms, breath smells fruity, Kussmaul respiration, polyuria and polydipsia…). What does he conclude? Are these different diseases?”

Making the doctor’s decision problem even harder is of course the fact that type 2 diabetes even today often goes undiagnosed until complications arise. Some type 2 patients get their diagnosis only after they had their first heart attack as a result of their illness. So the hypothetical obese middle-aged guy presenting with DKA might not have been known by anyone to be ‘a potentially different kind of diabetic’.

‘The Nybbler’ asked this question in the thread: “Wouldn’t reduced selection pressure be a major reason for increase of Type I diabetes? Used to be if you had it, chance of surviving to reproduce was close to nil.”

I’ll mention here that I’ve encountered this kind of theorizing before, but that I’ve never really addressed it – especially the second part – explicitly, though I’ve sometimes felt like doing that. I figured this post might be a decent place to at least scratch the surface. The idea that there are more type 1 diabetics now than there used to be because type 1 diabetics used to die of their disease and now they don’t (…and so now they are able to transmit their faulty genes to subsequent generations, leading to more diabetic individuals over time) sounds sort of reasonable if you don’t know very much about diabetes, but it sounds less reasonable to people who do. Genes matter, and changed selection pressures have probably played a role, but I find it hard to believe this particular mechanism is a major factor. I have included both my of my replies to ‘Nybbler’ below:

First comment:

“I’m not a geneticist and this is sort-of-kind-of near the boundary area of where I feel comfortable providing answers (given that others may be more qualified to evaluate questions like this than I am). However a few observations which might be relevant are the following:

i) Although I’ll later go on to say that vertical transmission is low, I first have to point out that some people who developed type 1 diabetes in the past did in fact have offspring, though there’s no doubt about the condition being fitness-reducing to a very large degree. The median age of diagnosis of type 1 is somewhere in the teenage years (…today. Was it the same way 1000 years ago, or has the age profile changed over time? This again relates to questions asked elsewhere in this discussion…), but people above the age of 30 get type 1 too.

ii) Although type 1 display some level of familia[l] clustering, most cases of type 1 are not the result of diabetics having had children who then proceed to inherit their parents’ disease. To the extent that reduced selection is a driver of increased incidence, the main cause would be broad selection effects pertaining to immune system functioning in general in the total population at risk (i.e. children in general, including many children with what might be termed suboptimal immune system functioning, being more likely to survive and later develop type 1 diabetes), not effects derived from vertical transmission of the disease (from parent to child). Roughly 90% of newly diagnosed type 1 diabetics in population studies have a negative family history of the disease, and on average only 2% of the children of type 1 diabetic mothers, and 5% of the children of type 1 diabetic fathers, go on to develop type 1 diabetes themselves.

iii) Historically vertical transmission has even in modern times been low. On top of the quite low transmission rates mentioned above, until well into the 80es or 90es many type 1 diabetic females were explicitly advised by their medical care providers not to have children, not because of the genetic risk of disease transmission but because pregnancy outcomes were likely to be poor; and many of those who disregarded the advice gave birth to offspring who were at a severe fitness disadvantage from the start. Poorly controlled diabetes during pregnancy leads to a very high risk of birth defects and/or miscarriage, and may pose health risks to the mother as well through e.g. an increased risk of preeclampsia (relevant link). It is only very recently that we’ve developed the knowledge and medical technology required to make pregnancy a reasonably safe option for female diabetics. You still had some diabetic females who gave birth before developing diabetes, like in the far past, and the situation was different for males, but either way I feel reasonably confident claiming that if you look for genetic causes of increasing incidence, vertical transmission should not be the main factor to consider.

iv) You need to be careful when evaluating questions like these to keep a distinction between questions relating to drivers of incidence and questions relating to drivers of prevalence at the back of your mind. These two sets of questions are not equivalent.

v) If people are interested to know more about the potential causes of increased incidence of type 1 diabetes, here’s a relevant review paper.”

I followed up with a second comment a while later, because I figured a few points of interest might not have been sufficiently well addressed in my first comment:


A few additional remarks.

i) “Temporal trends in chronic disease incidence rates are almost certainly environmentally induced. If one observes a 50% increase in the incidence of a disorder over 20 yr, it is most likely the result of changes in the environment because the gene pool cannot change that rapidly. Type 1 diabetes is a very dynamic disease. […] results clearly demonstrate that the incidence of type 1 diabetes is rising, bringing with it a large public health problem. Moreover, these findings indicate that something in our environment is changing to trigger a disease response. […] With the exception of a possible role for viruses and infant nutrition, the specific environmental determinants that initiate or precipitate the onset of type 1 diabetes remain unclear.” (Type 1 Diabetes, Etiology and Treatment. Just to make it perfectly clear that although genes matter, environmental factors are the most likely causes of the rising levels of incidence we’ve seen in recent times.)

ii. Just as you need to always keep incidence and prevalence in mind when analyzing these things (for example low prevalence does not mean incidence is necessarily low, or was low in the past; low prevalence could also be a result of a combination of high incidence and high case mortality. I know from experience that even diabetes researchers tend to sometimes overlook stuff like this), you also need to keep the distinction between genotype and phenotype in mind. Given the increased importance of one or more environmental triggers in modern times, penetrance is likely to have changed over time. This means for example that ‘a diabetic genotype’ may have been less fitness reducing in the past than it is today, even if the associated ‘diabetic phenotype’ may on the other hand have been much more fitness reducing than it is now; people who developed diabetes died, but many of the people who might in the current environment be considered high-risk cases may not have been high risk in the far past, because the environmental trigger causing disease was absent, or rarely encountered. Assessing genetic risk for diabetes is complicated, and there’s no general formula for calculating this risk either in the type 1 or type 2 case; monogenic forms of diabetes do exist, but they account for a very small proportion of cases (1-5% of diabetes in young individuals) – most cases are polygenic and display variable levels of penetrance. Note incidentally that a story of environmental factors becoming more important over time is actually implicitly also, to the extent that diabetes is/has been fitness-reducing, a story of selection pressures against diabetic genotypes potentially increasing over time, rather than the opposite (which seems to be the default assumption when only taking into account stuff like the increased survival rates of type 1 diabetics over time). This stuff is complicated.”

I wasn’t completely happy with my second comment (I wrote it relatively fast and didn’t have time to go over it in detail after I’d written it), so I figured it might make sense to add a few details here. One key idea here is of course that you need to distinguish between people who are ‘vulnerable’ to developing type 1 diabetes, and people who actually do develop the disease. If fewer people who today would be considered ‘vulnerable’ developed the disease in the past than is the case now, selection against the ‘vulnerable’ genotype would – all else equal – have been lower throughout evolutionary time than it is today.

All else is not equal because of insulin treatment. But a second key point is that when you’re interested in fitness effects, mortality is not the only variable of interest; many diabetic women who were alive because of insulin during the 20th century but who were also being discouraged from having children may well have left no offspring. Males who committed suicide or died from kidney failure in their twenties likely also didn’t leave many offspring. Another point related to the mortality variable is that although diabetes mortality might in the past have been approximated reasonably well by a simple binary outcome variable/process (no diabetes = alive, diabetes = dead), type 1 diabetes has had large effects on mortality rates also throughout the chunk of history during which insulin has been a treatment option; mortality rates 3 or 4 times higher than those of non-diabetics are common in population studies, and such mortality rates add up over time even if base rates are low, especially in a fitness context, as they for most type 1 diabetics are at play throughout the entire fertile period of the life history. Type 2 diabetes is diagnosed mainly in middle-aged individuals, many of whom have already completed their reproductive cycle, but type 1 diabetes is very different in that respect. Of course there are multiple indirect effects at play as well here, e.g. those of mate choice; which is the more attractive potential partner, the individual with diabetes or the one without? What if the diabetic also happens to be blind?

A few other quotes from the comments:

“The majority of patients on insulin in the US are type 2 diabetics, and it is simply wrong that type 2 diabetics are not responsive to insulin treatment. They were likely found to be unresponsive in early trials because of errors of dosage, as they require higher levels of the drug to obtain the same effect as will young patients diagnosed with type 1 (the primary group on insulin in the 30es). However, insulin treatment is not the first-line option in the type 2 context because the condition can usually be treated with insulin-sensitizing agents for a while, until they fail (those drugs will on average fail in something like ~50% of subjects within five years of diagnosis, which is the reason – combined with the much (order(/s, depending on where you are) of magnitude) higher prevalence of type 2 – why a majority of patients on insulin have type 2), and these tend to a) be more acceptable to the patients (a pill vs an injection) and b) have fewer/less severe side effects on average. One reason which also played a major role in delaying the necessary use of insulin to treat type 2 diabetes which could not be adequately controlled via other means was incidentally the fact that insulin ca[u]ses weight gain, and the obesity-type 2 link was well known.”

“Type 1 is autoimmune, and most cases of type 2 are not, but some forms of type 2 seem to have an autoimmune component as well (“the overall autoantibody frequency in type 2 patients varies between 6% and 10%” – source) (these patients, who can be identified through genetic markers, will on average proceed to insulin dependence because of treatment failure in the context of insulin sensitizing-agents much sooner than is usually the case in patients with type 2). In general type 1 is caused by autoimmune beta cell destruction and type 2 mainly by insulin resistance, but combinations of the two are also possible […], and patients with type 1 can develop insulin resistance just as patients with type 2 can lose beta cells via multiple pathways. The major point here being that the sharp diagnostic distinction between type 1 and type 2 is a major simplification of what’s really going on, and it’s hiding a lot of heterogeneity in both samples. Some patients with type 1 will develop diabetes acutely or subacutely, within days or hours, whereas others will have elevated blood glucose levels for months before medical attention is received and a diagnosis is made (you can tell whether or not blood glucose has been elevated pre-diagnosis by looking at one of the key diagnostic variables, Hba1c, which is a measure of the average blood glucose over the entire lifetime of a red blood cell (~3-4 months) – in some newly diagnosed type 1s, this variable is elevated, in others it is not. Some type 1 patients will develop other autoimmune conditions later on, whereas others will not, and some will be more likely to develop complications than others who have the same level of glycemic control.

Type 1 and type 2 diabetes are quite different conditions, but in terms of many aspects of the diseases there are significant degrees of overlap (for example they develop many of the same complications, for similar (pathophysiological) reasons), yet they are both called diabetes. You don’t want to treat a type 2 diabetic with insulin if he can be treated with metformin, and treating a type 1 with metformin will not help – so different treatments are required.”

“In terms of whether it’s ideal to have one autistic diagnostic group or two (…or three, or…) [this question was a starting point for the debate from which I quote, but I decided not to go much into this topic here], I maintain that to a significant extent the answer to that question relates to what the diagnosis is supposed to accomplish. If it makes sense for researchers to be able to distinguish, which it probably does, but it is not necessary for support organizers/providers to know the subtype in order to provide aid, then you might end up with one ‘official’ category and two (or more) ‘research categories’. I would be fine with that (but again I don’t find this discussion interesting). Again a parallel might be made to diabetes research: Endocrinologists are well aware that there’s a huge amount of variation in both the type 1 and type 2 samples, to the extent that it’s sort of silly to even categorize these illnesses using the same name, but they do it anyway for reasons which are sort of obvious. If you’re type 1 diabetic and you have an HLA mutation which made you vulnerable to diabetes and you developed diabetes at the age of 5, well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. If on the other hand you’re an adult guy who due to a very different genetic vulnerability developed type 1 diabetes at the age of 30 (and later on Graves’ disease at the age of 40, due to the same mutation), well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. The only thing type 1 diabetics have in common is the fact that their beta cells die due to some autoimmune processes. But it could easily be conceived of instead as literally hundreds of different diseases. Currently the distinctions between the different disease-relevant pathophysiological processes don’t matter very much in the treatment context, but they might do that at some point in the future, and if that happens the differences will start to become more important. People might at that point start to talk about type 1a diabetes, which might be the sort you can delay or stop with gene therapy, and type 1b which you can’t delay or stop (…yet). Lumping ‘different’ groups together into one diagnostic category is bad if it makes you overlook variation which is important, and this may be a problem in the autism context today, but regardless of the sizes of the diagnostic groups you’ll usually still end up with lots of residual (‘unexplained’) variation.”

I can’t recall to which extent I’ve discussed this last topic – the extent to which type 1 diabetes is best modeled as one illness or many – but it’s an important topic to keep at the back of your mind when you’re reading the diabetes literature. I’m assuming that in some contexts the subgroup heterogeneities, e.g. in terms of treatment response, will be much more important than in other contexts, so you probably need specific subject matter knowledge to make any sort of informed decision about to which extent potential unobserved heterogeneities may be important in a specific setting, but even if you don’t have that ‘a healthy skepticism’, derived from keeping the potential for these factors to play a role in mind, is likely to be more useful than the alternative. In that context I think the (poor, but understandable) standard practice of lumping together type 1 and type 2 diabetics in studies may lead many people familiar with the differences between the two conditions to think along the lines that as long as you know the type, you’re good to go – ‘at least this study only looked at type 1 individuals, not like those crappy studies which do not distinguish between type 1 and type 2, so I can definitely trust these results to apply to the subgroup of type 1 diabetics in which I’m interested’ – and I think this tendency, to the extent that it exists, is unfortunate.


July 8, 2017 Posted by | autism, Diabetes, Epidemiology, Genetics, Medicine, Psychology | 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

Neurology Grand Rounds – Typical and Atypical Diabetic Neuropathy

The lecture is not particularly easy to follow if you’re not a neurologist, and/but I assume even neurologists might have difficulties with Liewluck’s (? the second guy’s…) contribution because that guy’s English pronunciation is not great. But if you’re the sort of person who watches neurology lectures online it’s well worth watching.

Said noted in his book on these topics that: “In general pharmacological treatments will not cause anywhere near complete pain relief: “For patients receiving pharmacological treatment, the average pain reduction is about 20-30%, and only 20-35% of patients will achieve at least a 50% pain reduction with available drugs. […] often only partial pain relief from neuropathic pain can be expected, and […] sensory deficits are unlikely to respond to treatment.” Treatment of neuropathic pain is often a trial-and-error process.”

These guys make an even stronger point than Said did: Diabetics who develop painful neuropathies do not get rid of the pain even with treatment – the pain can be managed, but it’s permanent in (…almost? …a few young type 1 diabetics, maybe? But the 60-year old neurologist had never encountered one of those, so odds are against you being one of the lucky ones…) every single case. This of course has some consequences for how patients should be managed – for example you want to devote some time and effort to managing expectations, so people don’t get/have unrealistic ideas about what the treatments which are available may actually accomplish. Another aspect related to this is which sort of treatment options to consider in such a setting, as also noted in the lecture – tolerance development is for example an easily foreseeable problem with opiate treatment which is likely to cause problems down the line if not addressed (but as I pointed out a few years ago, my impression is that: “‘it may not work particularly well in the long run, and there are a lot of side-effects’ is a better argument against [chronic opioid treatment] than the potential for addiction”).


June 23, 2017 Posted by | Diabetes, Lectures, Medicine, Neurology, Pharmacology | Leave a comment

A few papers

i. Quality of life of adolescents with autism spectrum disorders: comparison to adolescents with diabetes.

“The goals of our study were to clarify the consequences of autistic disorder without mental retardation on […] adolescents’ daily lives, and to consider them in comparison with the impact of a chronic somatic disease (diabetes) […] Scores for adolescents with ASD were significantly lower than those of the control and the diabetic adolescents, especially for friendships, leisure time, and affective and sexual relationships. On the other hand, better scores were obtained for the relationships with parents and teachers and for self-image. […] For subjects with autistic spectrum disorders and without mental retardation, impairment of quality of life is significant in adolescence and young adulthood. Such adolescents are dissatisfied with their relationships, although they often have real motivation to succeed with them.”

As someone who has both conditions, that paper was quite interesting. A follow-up question of some personal interest to me would of course be this: How do the scores/outcomes of these two groups compare to the scores of the people who have both conditions simultaneously? This question is likely almost impossible to answer in any confident manner, certainly if the conditions are not strongly dependent (unlikely), considering the power issues; global prevalence of autism is around 0.6% (link), and although type 1 prevalence is highly variable across countries, the variation just means that in some countries almost nobody gets it whereas in other countries it’s just rare; prevalence varies from 0.5 per 100.000 to 60 per 100.000 children aged 0-15 years. Assuming independence, if you look at combinations of the sort of conditions which affect one in a hundred people with those affecting one in a thousand, you’ll need on average in the order of 100.000 people to pick up just one individual with both of the conditions of interest. It’s bothersome to even try to find people like that, and good luck doing any sort of sensible statistics on that kind of sample. Of course type 1 diabetes prevalence increases with age in a way that autism does not because people continue to be diagnosed with it into late adulthood, whereas most autistics are diagnosed as children, so this makes the rarity of the condition less of a problem in adult samples, but if you’re looking at outcomes it’s arguable whether it makes sense to not differentiate between someone diagnosed with type 1 diabetes as a 35 year old and someone diagnosed as a 5 year old (are these really comparable diseases, and which outcomes are you interested in?). At least that is the case for developed societies where people with type 1 diabetes have high life expectancies; in less developed societies there may be stronger linkage between incidence and prevalence because of high mortality in the patient group (because people who get type 1 diabetes in such countries may not live very long because of inadequate medical care, which means there’s a smaller disconnect between how many new people get the disease during each time period and how many people in total have the disease than is the case for places where the mortality rates are lower). You always need to be careful about distinguishing between incidence and prevalence when dealing with conditions like T1DM with potential high mortality rates in settings where people have limited access to medical care because differential cross-country mortality patterns may be important.

ii. Exercise for depression (Cochrane review).


Depression is a common and important cause of morbidity and mortality worldwide. Depression is commonly treated with antidepressants and/or psychological therapy, but some people may prefer alternative approaches such as exercise. There are a number of theoretical reasons why exercise may improve depression. This is an update of an earlier review first published in 2009.


To determine the effectiveness of exercise in the treatment of depression in adults compared with no treatment or a comparator intervention. […]

Selection criteria 

Randomised controlled trials in which exercise (defined according to American College of Sports Medicine criteria) was compared to standard treatment, no treatment or a placebo treatment, pharmacological treatment, psychological treatment or other active treatment in adults (aged 18 and over) with depression, as defined by trial authors. We included cluster trials and those that randomised individuals. We excluded trials of postnatal depression.

Thirty-nine trials (2326 participants) fulfilled our inclusion criteria, of which 37 provided data for meta-analyses. There were multiple sources of bias in many of the trials; randomisation was adequately concealed in 14 studies, 15 used intention-to-treat analyses and 12 used blinded outcome assessors.For the 35 trials (1356 participants) comparing exercise with no treatment or a control intervention, the pooled SMD for the primary outcome of depression at the end of treatment was -0.62 (95% confidence interval (CI) -0.81 to -0.42), indicating a moderate clinical effect. There was moderate heterogeneity (I² = 63%).

When we included only the six trials (464 participants) with adequate allocation concealment, intention-to-treat analysis and blinded outcome assessment, the pooled SMD for this outcome was not statistically significant (-0.18, 95% CI -0.47 to 0.11). Pooled data from the eight trials (377 participants) providing long-term follow-up data on mood found a small effect in favour of exercise (SMD -0.33, 95% CI -0.63 to -0.03). […]

Authors’ conclusions

Exercise is moderately more effective than a control intervention for reducing symptoms of depression, but analysis of methodologically robust trials only shows a smaller effect in favour of exercise. When compared to psychological or pharmacological therapies, exercise appears to be no more effective, though this conclusion is based on a few small trials.”

iii. Risk factors for suicide in individuals with depression: A systematic review.

“The search strategy identified 3374 papers for potential inclusion. Of these, 155 were retrieved for a detailed evaluation. Thirty-two articles fulfilled the detailed eligibility criteria. […] Nineteen studies (28 publications) were included. Factors significantly associated with suicide were: male gender (OR = 1.76, 95% CI = 1.08–2.86), family history of psychiatric disorder (OR = 1.41, 95% CI= 1.00–1.97), previous attempted suicide (OR = 4.84, 95% CI = 3.26–7.20), more severe depression (OR = 2.20, 95% CI = 1.05–4.60), hopelessness (OR = 2.20, 95% CI = 1.49–3.23) and comorbid disorders, including anxiety (OR = 1.59, 95% CI = 1.03–2.45) and misuse of alcohol and drugs (OR = 2.17, 95% CI = 1.77–2.66).
Limitations: There were fewer studies than suspected. Interdependence between risk factors could not be examined.”

iv. Cognitive behaviour therapy for social anxiety in autism spectrum disorder: a systematic review.

“Individuals who have autism spectrum disorders (ASD) commonly experience anxiety about social interaction and social situations. Cognitive behaviour therapy (CBT) is a recommended treatment for social anxiety (SA) in the non-ASD population. Therapy typically comprises cognitive interventions, imagery-based work and for some individuals, behavioural interventions. Whether these are useful for the ASD population is unclear. Therefore, we undertook a systematic review to summarise research about CBT for SA in ASD.”

I mostly include this review here to highlight how reviews aren’t everything – I like them, but you can’t do reviews when a field hasn’t been studied. This is definitely the case here. The review was sort of funny, but also depressing. So much work for so little insight. Here’s the gist of it:

“Using a priori criteria, we searched for English-language peer-reviewed empirical studies in five databases. The search yielded 1364 results. Titles, abstracts and relevant publications were independently screened by two reviewers. Findings: Four single case studies met the review inclusion criteria; data were synthesised narratively. Participants (three adults and one child) were diagnosed with ASD and social anxiety disorder.”

You search the scientific literature systematically, you find more than a thousand results, and you carefully evaluate which ones of them should be included in this kind of study …and what you end up with is 4 individual case studies…

(I won’t go into the results of the study as they’re pretty much worthless.)

v. Immigrant Labor Market Integration across Admission Classes.

“We examine patterns of labor market integration across immigrant groups. The study draws on Norwegian longitudinal administrative data covering labor earnings and social insurance claims over a 25‐year period and presents a comprehensive picture of immigrant‐native employment and social insurance differentials by admission class and by years since entry.”

Some quotes from the paper:

“A recent study using 2011 administrative data from Sweden finds an average employment gap to natives of 30 percentage points for humanitarian migrants (refugees) and 26 percentage point for family immigrants (Luik et al., 2016).”

“A considerable fraction of the immigrants leaves the country after just a few years. […] this is particularly the case for immigrants from the old EU and for students and work-related immigrants from developing countries. For these groups, fewer than 50 percent remain in the country 5 years after entry. For refugees and family migrants, the picture is very different, and around 80 percent appear to have settled permanently in the country. Immigrants from the new EU have a settlement pattern somewhere in between, with approximately 70 percent settled on a permanent basis. An implication of such differential outmigration patterns is that the long-term labor market performance of refugees and family immigrants is of particular economic and fiscal importance. […] the varying rates of immigrant inflows and outflows by admission class, along with other demographic trends, have changed the composition of the adult (25‐66) population between 1990 and 2015. In this population segment, the overall immigrant share increased from 4.9 percent in 1990 to 18.7 percent in 2015 — an increase by a factor of 3.8 over 25 years. […] Following the 2004 EU enlargement, the fraction of immigrants in Norway has increased by a steady rate of approximately one percentage point per year.”

“The trends in population and employment shares varies considerably across admission classes, with employment shares of refugees and family immigrants lagging their growth in population shares. […] In 2014, refugees and family immigrants accounted for 12.8 percent of social insurance claims, compared to 5.7 percent of employment (and 7.7 percent of the adult population). In contrast, the two EU groups made up 9.3 percent of employment (and 8.8 percent of the adult population) but only 3.6 percent of social insurance claimants. Although these patterns do illuminate the immediate (short‐term) fiscal impacts of immigration at each particular point in time, they are heavily influenced by each year’s immigrant composition – in terms of age, years since migration, and admission classes – and therefore provide little information about long‐term consequences and impacts of fiscal sustainability. To assess the latter, we need to focus on longer‐term integration in the Norwegian labor market.”

Which they then proceed to do in the paper. From the results of those analyses:

“For immigrant men, the sample average share in employment (i.e., whose main source of income is work) ranges from 58 percent for refugees to 89 percent for EU immigrants, with family migrants somewhere between (around 80 percent). The average shares with social insurance as the main source of income ranges from only four percent for EU immigrants to as much as 38 percent for refugees. The corresponding shares for native men are 87 percent in employment and 12 percent with social insurance as their main income source. For women, the average shares in employment vary from 46 percent for refugees to 85 percent for new EU immigrants, whereas the average shares in social insurance vary from five percent for new EU immigrants to 42 percent for refugees. The corresponding rates for native women are 80 percent in employment and 17 percent with social insurance as their main source of income.”

“The profiles estimated for refugees are particularly striking. For men, we find that the native‐immigrant employment gap reaches its minimum value at 20 percentage points after five to six years of residence. The gap then starts to increase quite sharply again, and reaches 30 percentage points after 15 years. This development is mirrored by a corresponding increase in social insurance dependency. For female refugees, the employment differential reaches its minimum of 30 percentage points after 5‐9 years of residence. The subsequent decline is less dramatic than what we observe for men, but the differential stands at 35 percentage points 15 years after admission. […] The employment difference between refugees from Bosnia and Somalia is fully 22.2 percentage points for men and 37.7 points for women. […] For immigrants from the old EU, the employment differential is slightly in favor of immigrants regardless of years since migration, and the social insurance differentials remain consistently negative. In other words, employment of old EU immigrants is almost indistinguishable from that of natives, and they are less likely to claim social insurance benefits.”

vi. Glucose Peaks and the Risk of Dementia and 20-Year Cognitive Decline.

“Hemoglobin A1c (HbA1c), a measure of average blood glucose level, is associated with the risk of dementia and cognitive impairment. However, the role of glycemic variability or glucose excursions in this association is unclear. We examined the association of glucose peaks in midlife, as determined by the measurement of 1,5-anhydroglucitol (1,5-AG) level, with the risk of dementia and 20-year cognitive decline.”

“Nearly 13,000 participants from the Atherosclerosis Risk in Communities (ARIC) study were examined. […] Over a median time of 21 years, dementia developed in 1,105 participants. Among persons with diabetes, each 5 μg/mL decrease in 1,5-AG increased the estimated risk of dementia by 16% (hazard ratio 1.16, P = 0.032). For cognitive decline among participants with diabetes and HbA1c <7% (53 mmol/mol), those with glucose peaks had a 0.19 greater z score decline over 20 years (P = 0.162) compared with those without peaks. Among participants with diabetes and HbA1c ≥7% (53 mmol/mol), those with glucose peaks had a 0.38 greater z score decline compared with persons without glucose peaks (P < 0.001). We found no significant associations in persons without diabetes.

CONCLUSIONS Among participants with diabetes, glucose peaks are a risk factor for cognitive decline and dementia. Targeting glucose peaks, in addition to average glycemia, may be an important avenue for prevention.”

vii. Gaze direction detection in autism spectrum disorder.

“Detecting where our partners direct their gaze is an important aspect of social interaction. An atypical gaze processing has been reported in autism. However, it remains controversial whether children and adults with autism spectrum disorder interpret indirect gaze direction with typical accuracy. This study investigated whether the detection of gaze direction toward an object is less accurate in autism spectrum disorder. Individuals with autism spectrum disorder (n = 33) and intelligence quotients–matched and age-matched controls (n = 38) were asked to watch a series of synthetic faces looking at objects, and decide which of two objects was looked at. The angle formed by the two possible targets and the face varied following an adaptive procedure, in order to determine individual thresholds. We found that gaze direction detection was less accurate in autism spectrum disorder than in control participants. Our results suggest that the precision of gaze following may be one of the altered processes underlying social interaction difficulties in autism spectrum disorder.”

“Where people look at informs us about what they know, want, or attend to. Atypical or altered detection of gaze direction might thus lead to impoverished acquisition of social information and social interaction. Alternatively, it has been suggested that abnormal monitoring of inner states […], or the lack of social motivation […], would explain the reduced tendency to follow conspecific gaze in individuals with ASD. Either way, a lower tendency to look at the eyes and to follow the gaze would provide fewer opportunities to practice GDD [gaze direction detection – US] ability. Thus, impaired GDD might either play a causal role in atypical social interaction, or conversely be a consequence of it. Exploring GDD earlier in development might help disentangle this issue.”


June 1, 2017 Posted by | Diabetes, Economics, Epidemiology, Medicine, Neurology, Psychiatry, 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

i. Association Between Blood Pressure and Adverse Renal Events in Type 1 Diabetes.

“The Joint National Committee and American Diabetes Association guidelines currently recommend a blood pressure (BP) target of <140/90 mmHg for all adults with diabetes, regardless of type (13). However, evidence used to support this recommendation is primarily based on data from trials of type 2 diabetes (46). The relationship between BP and adverse outcomes in type 1 and type 2 diabetes may differ, given that the type 1 diabetes population is typically much younger at disease onset, hypertension is less frequently present at diagnosis (3), and the basis for the pathophysiology and disease complications may differ between the two populations.

Prior prospective cohort studies (7,8) of patients with type 1 diabetes suggested that lower BP levels (<110–120/70–80 mmHg) at baseline entry were associated with a lower risk of adverse renal outcomes, including incident microalbuminuria. In one trial of antihypertensive treatment in type 1 diabetes (9), assignment to a lower mean arterial pressure (MAP) target of <92 mmHg (corresponding to ∼125/75 mmHg) led to a significant reduction in proteinuria compared with a MAP target of 100–107 mmHg (corresponding to ∼130–140/85–90 mmHg). Thus, it is possible that lower BP (<120/80 mmHg) reduces the risk of important renal outcomes, such as proteinuria, in patients with type 1 diabetes and may provide a synergistic benefit with intensive glycemic control on renal outcomes (1012). However, fewer studies have examined the association between BP levels over time and the risk of more advanced renal outcomes, such as stage III chronic kidney disease (CKD) or end-stage renal disease (ESRD)”.

“The primary objective of this study was to determine whether there is an association between lower BP levels and the risk of more advanced diabetic nephropathy, defined as macroalbuminuria or stage III CKD, within a background of different glycemic control strategies […] We included 1,441 participants with type 1 diabetes between the ages of 13 and 39 years who had previously been randomized to receive intensive versus conventional glycemic control in the Diabetes Control and Complications Trial (DCCT). The exposures of interest were time-updated systolic BP (SBP) and diastolic BP (DBP) categories. Outcomes included macroalbuminuria (>300 mg/24 h) or stage III chronic kidney disease (CKD) […] During a median follow-up time of 24 years, there were 84 cases of stage III CKD and 169 cases of macroalbuminuria. In adjusted models, SBP in the 2 (95% CI 1.05–1.21), and a 1.04 times higher risk of ESRD (95% CI 0.77–1.41) in adjusted Cox models. Every 10 mmHg increase in DBP was associated with a 1.17 times higher risk of microalbuminuria (95% CI 1.03–1.32), a 1.15 times higher risk of eGFR decline to 2 (95% CI 1.04–1.29), and a 0.80 times higher risk of ESRD (95% CI 0.47–1.38) in adjusted models. […] Because these data are observational, they cannot prove causation. It remains possible that subtle kidney disease may lead to early elevations in BP, and we cannot rule out the potential for reverse causation in our findings. However, we note similar trends in our data even when imposing a 7-year lag between BP and CKD ascertainment.”

CONCLUSIONS A lower BP (<120/70 mmHg) was associated with a substantially lower risk of adverse renal outcomes, regardless of the prior assigned glycemic control strategy. Interventional trials may be useful to help determine whether the currently recommended BP target of 140/90 mmHg may be too high for optimal renal protection in type 1 diabetes.”

It’s important to keep in mind when interpreting these results that endpoints like ESRD and stage III CKD are not the only relevant outcomes in this setting; even mild-stage kidney disease in diabetics significantly increase the risk of death from cardiovascular disease, and a substantial proportion of patients may die from cardiovascular disease before reaching a late-stage kidney disease endpoint (here’s a relevant link).

Identifying Causes for Excess Mortality in Patients With Diabetes: Closer but Not There Yet.

“A number of epidemiological studies have quantified the risk of death among patients with diabetes and assessed the causes of death (26), with highly varying results […] Overall, the studies to date have confirmed that diabetes is associated with an increased risk of all-cause mortality, but the magnitude of this excess risk is highly variable, with the relative risk ranging from 1.15 to 3.15. Nevertheless, all studies agree that mortality is mainly attributable to cardiovascular causes (26). On the other hand, studies of cancer-related death have generally been lacking despite the diabetes–cancer association and a number of plausible biological mechanisms identified to explain this link (8,9). In fact, studies assessing the specific causes of noncardiovascular death in diabetes have been sparse. […] In this issue of Diabetes Care, Baena-Díez et al. (10) report on an observational study of the association between diabetes and cause-specific death. This study involved 55,292 individuals from 12 Spanish population cohorts with no prior history of cardiovascular disease, aged 35 to 79 years, with a 10-year follow-up. […] This study found that individuals with diabetes compared with those without diabetes had a higher risk of cardiovascular death, cancer death, and noncardiovascular noncancer death with similar estimates obtained using the two statistical approaches. […] Baena-Díez et al. (10) showed that individuals with diabetes have an approximately threefold increased risk of cardiovascular mortality, which is much higher than what has been reported by recent studies (5,6). While this may be due to the lack of adjustment for important confounders in this study, there remains uncertainty regarding the magnitude of this increase.”

“[A]ll studies of excess mortality associated with diabetes, including the current one, have produced highly variable results. The reasons may be methodological. For instance, it may be that because of the wide range of age in these studies, comparing the rates of death between the patients with diabetes and those without diabetes using a measure based on the ratio of the rates may be misleading because the ratio can vary by age [it almost certainly does vary by age, US]. Instead, a measure based on the difference in rates may be more appropriate (16). Another issue relates to the fact that the studies include patients with longstanding diabetes of variable duration, resulting in so-called prevalent cohorts that can result in muddled mortality estimates since these are necessarily based on a mix of patients at different stages of disease (17). Thus, a paradigm change may be in order for future observational studies of diabetes and mortality, in the way they are both designed and analyzed. With respect to cancer, such studies will also need to tease out the independent contribution of antidiabetes treatments on cancer incidence and mortality (1820). It is thus clear that the quantification of the excess mortality associated with diabetes per se will need more accurate tools.”

iii. Risk of Cause-Specific Death in Individuals With Diabetes: A Competing Risks Analysis. This is the paper some of the results of which were discussed above. I’ll just include the highlights here:

RESULTS We included 55,292 individuals (15.6% with diabetes and overall mortality of 9.1%). The adjusted hazard ratios showed that diabetes increased mortality risk: 1) cardiovascular death, CSH = 2.03 (95% CI 1.63–2.52) and PSH = 1.99 (1.60–2.49) in men; and CSH = 2.28 (1.75–2.97) and PSH = 2.23 (1.70–2.91) in women; 2) cancer death, CSH = 1.37 (1.13–1.67) and PSH = 1.35 (1.10–1.65) in men; and CSH = 1.68 (1.29–2.20) and PSH = 1.66 (1.25–2.19) in women; and 3) noncardiovascular noncancer death, CSH = 1.53 (1.23–1.91) and PSH = 1.50 (1.20–1.89) in men; and CSH = 1.89 (1.43–2.48) and PSH = 1.84 (1.39–2.45) in women. In all instances, the cumulative mortality function was significantly higher in individuals with diabetes.

CONCLUSIONS Diabetes is associated with premature death from cardiovascular disease, cancer, and noncardiovascular noncancer causes.”


Diabetes is associated with premature death from cardiovascular diseases (coronary heart disease, stroke, and heart failure), several cancers (liver, colorectal, and lung), and other diseases (chronic obstructive pulmonary disease and liver and kidney disease). In addition, the cause-specific cumulative mortality for cardiovascular, cancer, and noncardiovascular noncancer causes was significantly higher in individuals with diabetes, compared with the general population. The dual analysis with CSH and PSH methods provides a comprehensive view of mortality dynamics in the population with diabetes. This approach identifies the individuals with diabetes as a vulnerable population for several causes of death aside from the traditionally reported cardiovascular death.”

iv. Disability-Free Life-Years Lost Among Adults Aged ≥50 Years With and Without Diabetes.

RESEARCH DESIGN AND METHODS Adults (n = 20,008) aged 50 years and older were followed from 1998 to 2012 in the Health and Retirement Study, a prospective biannual survey of a nationally representative sample of adults. Diabetes and disability status (defined by mobility loss, difficulty with instrumental activities of daily living [IADL], and/or difficulty with activities of daily living [ADL]) were self-reported. We estimated incidence of disability, remission to nondisability, and mortality. We developed a discrete-time Markov simulation model with a 1-year transition cycle to predict and compare lifetime disability-related outcomes between people with and without diabetes. Data represent the U.S. population in 1998.

RESULTS From age 50 years, adults with diabetes died 4.6 years earlier, developed disability 6–7 years earlier, and spent about 1–2 more years in a disabled state than adults without diabetes. With increasing baseline age, diabetes was associated with significant (P < 0.05) reductions in the number of total and disability-free life-years, but the absolute difference in years between those with and without diabetes was less than at younger baseline age. Men with diabetes spent about twice as many of their remaining years disabled (20–24% of remaining life across the three disability definitions) as men without diabetes (12–16% of remaining life across the three disability definitions). Similar associations between diabetes status and disability-free and disabled years were observed among women.

CONCLUSIONS Diabetes is associated with a substantial reduction in nondisabled years, to a greater extent than the reduction of longevity. […] Using a large, nationally representative cohort of Americans aged 50 years and older, we found that diabetes is associated with a substantial deterioration of nondisabled years and that this is a greater number of years than the loss of longevity associated with diabetes. On average, a middle-aged adult with diabetes has an onset of disability 6–7 years earlier than one without diabetes, spends 1–2 more years with disability, and loses 7 years of disability-free life to the condition. Although other nationally representative studies have reported large reductions in complications (9) and mortality among the population with diabetes in recent decades (1), these studies, akin to our results, suggest that diabetes continues to have a substantial impact on morbidity and quality of remaining years of life.”

v. Association Between Use of Lipid-Lowering Therapy and Cardiovascular Diseases and Death in Individuals With Type 1 Diabetes.

“People with type 1 diabetes have a documented shorter life expectancy than the general population without diabetes (1). Cardiovascular disease (CVD) is the main cause of the excess morbidity and mortality, and despite advances in management and therapy, individuals with type 1 diabetes have a markedly elevated risk of cardiovascular events and death compared with the general population (2).

Lipid-lowering treatment with hydroxymethylglutaryl-CoA reductase inhibitors (statins) prevents major cardiovascular events and death in a broad spectrum of patients (3,4). […] We hypothesized that primary prevention with lipid-lowering therapy (LLT) can reduce the incidence of cardiovascular morbidity and mortality in individuals with type 1 diabetes. The aim of the study was to examine this in a nationwide longitudinal cohort study of patients with no history of CVD. […] A total of 24,230 individuals included in 2006–2008 NDR with type 1 diabetes without a history of CVD were followed until 31 December 2012; 18,843 were untreated and 5,387 treated with LLT [Lipid-Lowering Therapy] (97% statins). The mean follow-up was 6.0 years. […] Hazard ratios (HRs) for treated versus untreated were as follows: cardiovascular death 0.60 (95% CI 0.50–0.72), all-cause death 0.56 (0.48–0.64), fatal/nonfatal stroke 0.56 (0.46–0.70), fatal/nonfatal acute myocardial infarction 0.78 (0.66–0.92), fatal/nonfatal coronary heart disease 0.85 (0.74–0.97), and fatal/nonfatal CVD 0.77 (0.69–0.87).

CONCLUSIONS This observational study shows that LLT is associated with 22–44% reduction in the risk of CVD and cardiovascular death among individuals with type 1 diabetes without history of CVD and underlines the importance of primary prevention with LLT to reduce cardiovascular risk in type 1 diabetes.”

vi. Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes—A Recursive Partitioning Analysis.

“In many prognostic factor studies, multivariate analyses using the Cox proportional hazards model are applied to identify independent prognostic factors. However, the coefficient estimates derived from the Cox proportional hazards model may be biased as a result of violating assumptions of independence. […] RPA [Recursive Partitioning Analysis] classification is a useful tool that could prioritize the prognostic factors and divide the subjects into distinctive groups. RPA has an advantage over the proportional hazards model in identifying prognostic factors because it does not require risk factor independence and, as a nonparametric technique, makes no requirement on the underlying distributions of the variables considered. Hence, it relies on fewer modeling assumptions. Also, because the method is designed to divide subjects into groups based on the length of survival, it defines groupings for risk classification, whereas Cox regression models do not. Moreover, there is no need to explicitly include covariate interactions because of the recursive splitting structure of tree model construction.”

“This is the first study that characterizes the risk factors associated with the transition from one preclinical stage to the next following a recommended staging classification system (9). The tree-structured prediction model reveals that the risk parameters are not the same across each transition. […] Based on the RPA classification, the subjects at younger age and with higher GAD65Ab [an important biomarker in the context of autoimmune forms of diabetes, US – here’s a relevant link] titer are at higher risk for progression to multiple positive autoantibodies from a single autoantibody (seroconversion). Approximately 70% of subjects with a single autoantibody were positive for GAD65Ab, much higher than for insulin autoantibody (24%) and IA-2A [here’s a relevant link – US] (5%). Our study results are consistent with those of others (2224) in that seroconversion is age related. Previous studies in infants and children at an early age have shown that progression from single to two or more autoantibodies occurs more commonly in children 25). The subjects ≤16 years of age had almost triple the 5-year risk compared with subjects >16 years of age at the same GAD65Ab titer level. Hence, not all individuals with a single islet autoantibody can be thought of as being at low risk for disease progression.”

“This is the first study that identifies the risk factors associated with the timing of transitions from one preclinical stage to the next in the development of T1D. Based on RPA risk parameters, we identify the characteristics of groups with similar 5-year risks for advancing to the next preclinical stage. It is clear that individuals with one or more autoantibodies or with dysglycemia are not homogeneous with regard to the risk of disease progression. Also, there are differences in risk factors at each stage that are associated with increased risk of progression. The potential benefit of identifying these groups allows for a more informed discussion of diabetes risk and the selective enrollment of individuals into clinical trials whose risk more appropriately matches the potential benefit of an experimental intervention. Since the risk levels in these groups are substantial, their definition makes possible the design of more efficient trials with target sample sizes that are feasible, opening up the field of prevention to additional at-risk cohorts. […] Our results support the evidence that autoantibody titers are strong predictors at each transition leading to T1D development. The risk of the development of multiple autoantibodies was significantly increased when the GAD65Ab titer level was elevated, and the risk of the development of dysglycemia was increased when the IA-2A titer level increased. These indicate that better risk prediction on the timing of transitions can be obtained by evaluating autoantibody titers. The results also suggest that an autoantibody titer should be carefully considered in planning prevention trials for T1D in addition to the number of positive autoantibodies and the type of autoantibody.”


May 17, 2017 Posted by | Diabetes, Epidemiology, Health Economics, Immunology, Medicine, Nephrology, Statistics, Studies | 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