A few diabetes papers of interest
“More than 30 years ago in Diabetes Care, Schmidt et al. (1) defined “dawn phenomenon,” the night-to-morning elevation of blood glucose (BG) before and, to a larger extent, after breakfast in subjects with type 1 diabetes (T1D). Shortly after, a similar observation was made in type 2 diabetes (T2D) (2), and the physiology of glucose homeostasis at night was studied in normal, nondiabetic subjects (3–5). Ever since the first description, the dawn phenomenon has been studied extensively with at least 187 articles published as of today (6). […] what have we learned from the last 30 years of research on the dawn phenomenon? What is the appropriate definition, the identified mechanism(s), the importance (if any), and the treatment of the dawn phenomenon in T1D and T2D?”
“Physiology of glucose homeostasis in normal, nondiabetic subjects indicates that BG and plasma insulin concentrations remain remarkably flat and constant overnight, with a modest, transient increase in insulin secretion just before dawn (3,4) to restrain hepatic glucose production (4) and prevent hyperglycemia. Thus, normal subjects do not exhibit the dawn phenomenon sensu strictiori because they secrete insulin to prevent it.
In T1D, the magnitude of BG elevation at dawn first reported was impressive and largely secondary to the decrease of plasma insulin concentration overnight (1), commonly observed with evening administration of NPH or lente insulins (8) (Fig. 1). Even in early studies with intravenous insulin by the “artificial pancreas” (Biostator) (2), plasma insulin decreased overnight because of progressive inactivation of insulin in the pump (9). This artifact exaggerated the dawn phenomenon, now defined as need for insulin to limit fasting hyperglycemia (2). When the overnight waning of insulin was prevented by continuous subcutaneous insulin infusion (CSII) […] or by the long-acting insulin analogs (LA-IAs) (8), it was possible to quantify the real magnitude of the dawn phenomenon — 15–25 mg/dL BG elevation from nocturnal nadir to before breakfast […]. Nocturnal spikes of growth hormone secretion are the most likely mechanism of the dawn phenomenon in T1D (13,14). The observation from early pioneering studies in T1D (10–12) that insulin sensitivity is higher after midnight until 3 a.m. as compared to the period 4–8 a.m., soon translated into use of more physiological replacement of basal insulin […] to reduce risk of nocturnal hypoglycemia while targeting fasting near-normoglycemia”.
“In T2D, identification of diurnal changes in BG goes back decades, but only quite recently fasting hyperglycemia has been attributed to a transient increase in hepatic glucose production (both glycogenolysis and gluconeogenesis) at dawn in the absence of compensatory insulin secretion (15–17). Monnier et al. (7) report on the overnight (interstitial) glucose concentration (IG), as measured by continuous ambulatory IG monitoring, in three groups of 248 subjects with T2D […] Importantly, the dawn phenomenon had an impact on mean daily IG and A1C (mean increase of 0.39% [4.3 mmol/mol]), which was independent of treatment. […] Two messages from the data of Monnier et al. (7) are important. First, the dawn phenomenon is confirmed as a frequent event across the heterogeneous population of T2D independent of (oral) treatment and studied in everyday life conditions, not only in the setting of specialized clinical research units. Second, the article reaffirms that the primary target of treatment in T2D is to reestablish near-normoglycemia before and after breakfast (i.e., to treat the dawn phenomenon) to lower mean daily BG and A1C (8). […] the dawn phenomenon induces hyperglycemia not only before, but, to a larger extent, after breakfast as well (7,18). Over the years, fasting (and postbreakfast) hyperglycemia in T2D worsens as result of progressively impaired pancreatic B-cell function on the background of continued insulin resistance primarily at dawn (8,15–18) and independently of age (19). Because it is an early metabolic abnormality leading over time to the vicious circle of “hyperglycemia begets hyperglycemia” by glucotoxicity and lipotoxicity, the dawn phenomenon in T2D should be treated early and appropriately before A1C continues to increase (20).”
“Oral medications do not adequately control the dawn phenomenon, even when given in combination (7,18). […] The evening replacement of basal insulin, which abolishes the dawn phenomenon by restraining hepatic glucose production and lipolysis (21), is an effective treatment as it mimics the physiology of glucose homeostasis in normal, nondiabetic subjects (4). Early use of basal insulin in T2D is an add-on option treatment after failure of metformin to control A1C <7.0% (20). However, […] it would be wise to consider initiation of basal insulin […] before — not after — A1C has increased well beyond 7.0%, as usually it is done in practice currently.”
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“Diabetic peripheral neuropathy (DPN) is among the most distressing of all the chronic complications of diabetes and is a cause of significant disability and poor quality of life (4). Depending on the patient population and diagnostic criteria, the prevalence of DPN among adults with diabetes ranges from 30 to 70% (5–7). However, there are insufficient data on the prevalence and predictors of DPN among the pediatric population. Furthermore, early detection and good glycemic control have been proven to prevent or delay adverse outcomes associated with DPN (5,8,9). Near-normal control of blood glucose beginning as soon as possible after the onset of diabetes may delay the development of clinically significant nerve impairment (8,9). […] The American Diabetes Association (ADA) recommends screening for DPN in children and adolescents with type 2 diabetes at diagnosis and 5 years after diagnosis for those with type 1 diabetes, followed by annual evaluations thereafter, using simple clinical tests (10). Since subclinical signs of DPN may precede development of frank neuropathic symptoms, systematic, preemptive screening is required in order to identify DPN in its earliest stages.
There are various measures that can be used for the assessment of DPN. The Michigan Neuropathy Screening Instrument (MNSI) is a simple, sensitive, and specific tool for the screening of DPN (11). It was validated in large independent cohorts (12,13) and has been widely used in clinical trials and longitudinal cohort studies […] The aim of this pilot study was to provide preliminary estimates of the prevalence of and factors associated with DPN among children and adolescents with type 1 and type 2 diabetes.”
“A total of 399 youth (329 with type 1 and 70 with type 2 diabetes) participated in the pilot study. Youth with type 1 diabetes were younger (mean age 15.7 ± 4.3 years) and had a shorter duration of diabetes (mean duration 6.2 ± 0.9 years) compared with youth with type 2 diabetes (mean age 21.6 ± 4.1 years and mean duration 7.6 ± 1.8 years). Participants with type 2 diabetes had a higher BMI z score and waist circumference, were more likely to be smokers, and had higher blood pressure and lipid levels than youth with type 1 diabetes (all P < 0.001). A1C, however, did not significantly differ between the two groups (mean A1C 8.8 ± 1.8% [73 ± 2 mmol/mol] for type 1 diabetes and 8.5 ± 2.9% [72 ± 3 mmol/mol] for type 2 diabetes; P = 0.5) but was higher than that recommended by the ADA for this age-group (A1C ≤7.5%) (10). The prevalence of DPN (defined as the MNSIE score >2) was 8.2% among youth with type 1 diabetes and 25.7% among those with type 2 diabetes. […] Youth with DPN were older and had a longer duration of diabetes, greater central obesity (increased waist circumference), higher blood pressure, an atherogenic lipid profile (low HDL cholesterol and marginally high triglycerides), and microalbuminuria. A1C […] was not significantly different between those with and without DPN (9.0% ± 2.0 […] vs. 8.8% ± 2.1 […], P = 0.58). Although nearly 37% of youth with type 2 diabetes came from lower-income families with annual income <25,000 USD per annum (as opposed to 11% for type 1 diabetes), socioeconomic status was not significantly associated with DPN (P = 0.77).”
“In the unadjusted logistic regression model, the odds of having DPN was nearly four times higher among those with type 2 diabetes compared with youth with type 1 diabetes (odds ratio [OR] 3.8 [95% CI 1.9–7.5, P < 0.0001). This association was attenuated, but remained significant, after adjustment for age and sex (OR 2.3 [95% CI 1.1–5.0], P = 0.03). However, this association was no longer significant (OR 2.1 [95% CI 0.3–15.9], P = 0.47) when additional covariates […] were added to the model […] The loss of the association between diabetes type and DPN with addition of covariates in the fully adjusted model could be due to power loss, given the small number of youth with DPN in the sample, or indicative of stronger associations between these covariates and DPN such that conditioning on them eliminates the observed association between DPN and diabetes type.”
“The prevalence of DPN among type 1 diabetes youth in our pilot study is lower than that reported by Eppens et al. (15) among 1,433 Australian adolescents with type 1 diabetes assessed by thermal threshold testing and VPT (prevalence of DPN 27%; median age and duration 15.7 and 6.8 years, respectively). A much higher prevalence was also reported among Danish (62.5%) and Brazilian (46%) cohorts of type 1 diabetes youth (16,17) despite a younger age (mean age among Danish children 13.7 years and Brazilian cohort 12.9 years). The prevalence of DPN among youth with type 2 diabetes (26%) found in our study is comparable to that reported among the Australian cohort (21%) (15). The wide ranges in the prevalence estimates of DPN among the young cannot solely be attributed to the inherent racial/ethnic differences in this population but could potentially be due to the differing criteria and diagnostic tests used to define and characterize DPN.”
“In our study, the duration of diabetes was significantly longer among those with DPN, but A1C values did not differ significantly between the two groups, suggesting that a longer duration with its sustained impact on peripheral nerves is an important determinant of DPN. […] Cho et al. (22) reported an increase in the prevalence of DPN from 14 to 28% over 17 years among 819 Australian adolescents with type 1 diabetes aged 11–17 years at baseline, despite improvements in care and minor improvements in A1C (8.2–8.7%). The prospective Danish Study Group of Diabetes in Childhood also found no association between DPN (assessed by VPT) and glycemic control (23).”
“In conclusion, our pilot study found evidence that the prevalence of DPN in adolescents with type 2 diabetes approaches rates reported in adults with diabetes. Several CVD risk factors such as central obesity, elevated blood pressure, dyslipidemia, and microalbuminuria, previously identified as predictors of DPN among adults with diabetes, emerged as independent predictors of DPN in this young cohort and likely accounted for the increased prevalence of DPN in youth with type 2 diabetes.“
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“Type 1 diabetes appears to be a risk factor for the development of disturbed eating behavior (DEB) (1,2). Estimates of the prevalence of DEB among individuals with type 1 diabetes range from 10 to 49% (3,4), depending on methodological issues such as the definition and measurement of DEB. Some studies only report the prevalence of full-threshold diagnoses of anorexia nervosa, bulimia nervosa, and eating disorders not otherwise specified, whereas others also include subclinical eating disorders (1). […] Although different terminology complicates the interpretation of prevalence rates across studies, the findings are sufficiently robust to indicate that there is a higher prevalence of DEB in type 1 diabetes compared with healthy controls. A meta-analysis reported a three-fold increase of bulimia nervosa, a two-fold increase of eating disorders not otherwise specified, and a two-fold increase of subclinical eating disorders in patients with type 1 diabetes compared with controls (2). No elevated rates of anorexia nervosa were found.”
“When DEB and type 1 diabetes co-occur, rates of morbidity and mortality are dramatically increased. A Danish study of comorbid type 1 diabetes and anorexia nervosa showed that the crude mortality rate at 10-year follow-up was 2.5% for type 1 diabetes and 6.5% for anorexia nervosa, but the rate increased to 34.8% when occurring together (the standardized mortality rates were 4.06, 8.86, and 14.5, respectively) (9). The presence of DEB in general also can severely impair metabolic control and advance the onset of long-term diabetes complications (4). Insulin reduction or omission is an efficient weight loss strategy uniquely available to patients with type 1 diabetes and has been reported in up to 37% of patients (10–12). Insulin restriction is associated with poorer metabolic control, and previous research has found that self-reported insulin restriction at baseline leads to a three-fold increased risk of mortality at 11-year follow-up (10).
Few population-based studies have specifically investigated the prevalence of and relationship between DEBs and insulin restriction. The generalizability of existing research remains limited by relatively small samples and a lack of males. Further, many studies have relied on generic measures of DEBs, which may not be appropriate for use in individuals with type 1 diabetes. The Diabetes Eating Problem Survey–Revised (DEPS-R) is a newly developed and diabetes-specific screening tool for DEBs. A recent study demonstrated satisfactory psychometric properties of the Norwegian version of the DEPS-R among children and adolescents with type 1 diabetes 11–19 years of age (13). […] This study aimed to assess young patients with type 1 diabetes to assess the prevalence of DEBs and frequency of insulin omission or restriction, to compare the prevalence of DEB between males and females across different categories of weight and age, and to compare the clinical features of participants with and without DEBs and participants who restrict and do not restrict insulin. […] The final sample consisted of 770 […] children and adolescents with type 1 diabetes 11–19 years of age. There were 380 (49.4%) males and 390 (50.6%) females.”
“27.7% of female and 9% of male children and adolescents with type 1 diabetes receiving intensified insulin treatment scored above the predetermined cutoff on the DEPS-R, suggesting a level of disturbed eating that warrants further attention by treatment providers. […] Significant differences emerged across age and weight categories, and notable sex-specific trends were observed. […] For the youngest (11–13 years) and underweight (BMI <18.5) categories, the proportion of DEB was <10% for both sexes […]. Among females, the prevalence of DEB increased dramatically with age to ∼33% among 14 to 16 year olds and to nearly 50% among 17 to 19 year olds. Among males, the rate remained low at 7% for 14 to 16 year olds and doubled to ∼15% for 17 to 19 year olds.
A similar sex-specific pattern was detected across weight categories. Among females, the prevalence of DEB increased steadily and significantly from 9% among the underweight category to 23% for normal weight, 42% for overweight, and 53% for the obese categories, respectively. Among males, ∼6–7% of both the underweight and normal weight groups reported DEB, with rates increasing to ∼15% for both the overweight and obese groups. […] When separated by sex, females scoring above the cutoff on the DEPS-R had significantly higher HbA1c (9.2% [SD, 1.9]) than females scoring below the cutoff (8.4% [SD, 1.3]; P < 0.001). The same trend was observed among males (9.2% [SD, 1.6] vs. 8.4% [SD, 1.3]; P < 0.01). […] A total of 31.6% of the participants reported using less insulin and 6.9% reported skipping their insulin dose entirely at least occasionally after overeating. When assessing the sexes separately, we found that 36.8% of females reported restricting and 26.2% reported skipping insulin because of overeating. The rates for males were 9.4 and 4.5%, respectively.”
“The finding that DEBs are common in young patients with type 1 diabetes is in line with previous literature (2). However, because of different assessment methods and different definitions of DEB, direct comparison with other studies is complicated, especially because this is the first study to have used the DEPS-R in a prevalence study. However, two studies using the original DEPS have reported similar results, with 37.9% (23) and 53.8% (24) of the participants reporting engaging in unhealthy weight control practices. In our study, females scored significantly higher than males, which is not surprising given previous studies demonstrating an increased risk of development of DEB in nondiabetic females compared with males. In addition, the prevalence rates increased considerably by increasing age and weight. A relationship between eating pathology and older age and higher BMI also has been demonstrated in previous research conducted in both diabetic and nondiabetic adolescent populations.”
“Consistent with existent literature (10–12,27), we found a high frequency of insulin restriction. For example, Bryden et al. (11) assessed 113 males and females (aged 17–25 years) with type 1 diabetes and found that a total of 37% of the females (no males) reported a history of insulin omission or reduction for weight control purposes. Peveler et al. (12) investigated 87 females with type 1 diabetes aged 11–25 years, and 36% reported intentionally reducing or omitting their insulin doses to control their weight. Finally, Goebel-Fabbri et al. (10) examined 234 females 13–60 years of age and found that 30% reported insulin restriction. Similarly, 36.8% of the participants in our study reported reducing their insulin doses occasionally or more often after overeating.”
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“Despite good-quality evidence of tight glycemic control, particularly early in the disease trajectory (3), people with type 2 diabetes often do not reach recommended glycemic targets. Baseline characteristics in observational studies indicate that both insulin-experienced and insulin-naïve people may have mean HbA1c above the recommended target levels, reflecting the existence of patients with poor glycemic control in routine clinical care (8–10). […] U.K. data, based on an analysis reflecting previous NICE guidelines, show that it takes a mean of 7.7 years to initiate insulin after the start of the last OAD [oral antidiabetes drugs] (in people taking two or more OADs) and that mean HbA1c is ~10% (86 mmol/mol) at the time of insulin initiation (12). […] This failure to intensify treatment in a timely manner has been termed clinical inertia; however, data are lacking on clinical inertia in the diabetes-management pathway in a real-world primary care setting, and studies that have been carried out are, relatively speaking, small in scale (13,14). This retrospective cohort analysis investigates time to intensification of treatment in people with type 2 diabetes treated with OADs and the associated levels of glycemic control, and compares these findings with recommended treatment guidelines for diabetes.”
“We used the Clinical Practice Research Datalink (CPRD) database. This is the world’s largest computerized database, representing the primary care longitudinal records of >13 million patients from across the U.K. The CPRD is representative of the U.K. general population, with age and sex distributions comparable with those reported by the U.K. National Population Census (15). All information collected in the CPRD has been subjected to validation studies and been proven to contain consistent and high-quality data (16).”
“50,476 people taking one OAD, 25,600 people taking two OADs, and 5,677 people taking three OADs were analyzed. Mean baseline HbA1c (the most recent measurement within 6 months before starting OADs) was 8.4% (68 mmol/mol), 8.8% (73 mmol/mol), and 9.0% (75 mmol/mol) in people taking one, two, or three OADs, respectively. […] In people with HbA1c ≥7.0% (≥53 mmol/mol) taking one OAD, median time to intensification with an additional OAD was 2.9 years, whereas median time to intensification with insulin was >7.2 years. Median time to insulin intensification in people with HbA1c ≥7.0% (≥53 mmol/mol) taking two or three OADs was >7.2 and >7.1 years, respectively. In people with HbA1c ≥7.5% or ≥8.0% (≥58 or ≥64 mmol/mol) taking one OAD, median time to intensification with an additional OAD was 1.9 or 1.6 years, respectively; median time to intensification with insulin was >7.1 or >6.9 years, respectively. In those people with HbA1c ≥7.5% or ≥8.0% (≥58 or ≥64 mmol/mol) and taking two OADs, median time to insulin was >7.2 and >6.9 years, respectively; and in those people taking three OADs, median time to insulin intensification was >6.1 and >6.0 years, respectively.”
“By end of follow-up, treatment of 17.5% of people with HbA1c ≥7.0% (≥53 mmol/mol) taking three OADs was intensified with insulin, treatment of 20.6% of people with HbA1c ≥7.5% (≥58 mmol/mol) taking three OADs was intensified with insulin, and treatment of 22.0% of people with HbA1c ≥8.0% (≥64 mmol/mol) taking three OADs was intensified with insulin. There were minimal differences in the proportion of patients intensified between the groups. […] In people taking one OAD, the probability of an additional OAD or initiation of insulin was 23.9% after 1 year, increasing to 48.7% by end of follow-up; in people taking two OADs, the probability of an additional OAD or initiation of insulin was 11.4% after 1 year, increasing to 30.1% after 2 years; and in people taking three OADs, the probability of an additional OAD or initiation of insulin was 5.7% after 1 year, increasing to 12.0% by the end of follow-up […] Mean ± SD HbA1c in patients taking one OAD was 8.7 ± 1.6% in those intensified with an additional OAD (n = 14,605), 9.4 ± 2.3% (n = 1,228) in those intensified with insulin, and 8.7 ± 1.7% (n = 15,833) in those intensified with additional OAD or insulin. Mean HbA1c in patients taking two OADs was 8.8 ± 1.5% (n = 3,744), 9.8 ± 1.9% (n = 1,631), and 9.1 ± 1.7% (n = 5,405), respectively. In patients taking three OADs, mean HbA1c at intensification with insulin was 9.7 ± 1.6% (n = 514).”
“This analysis shows that there is a delay in intensifying treatment in people with type 2 diabetes with suboptimal glycemic control, with patients remaining in poor glycemic control for >7 years before intensification of treatment with insulin. In patients taking one, two, or three OADs, median time from initiation of treatment to intensification with an additional OAD for any patient exceeded the maximum follow-up time of 7.2–7.3 years, dependent on subcohort. […] Despite having HbA1c levels for which diabetes guidelines recommend treatment intensification, few people appeared to undergo intensification (4,6,7). The highest proportion of people with clinical inertia was for insulin initiation in people taking three OADs. Consequently, these people experienced prolonged periods in poor glycemic control, which is detrimental to long-term outcomes.”
“Previous studies in U.K. general practice have shown similar findings. A retrospective study involving 14,824 people with type 2 diabetes from 154 general practice centers contributing to the Doctors Independent Network Database (DIN-LINK) between 1995 and 2005 observed that median time to insulin initiation for people prescribed multiple OADs was 7.7 years (95% CI 7.4–8.5 years); mean HbA1c before insulin was 9.85% (84 mmol/mol), which decreased by 1.34% (95% CI 1.24–1.44%) after therapy (12). A longitudinal observational study from health maintenance organization data in 3,891 patients with type 2 diabetes in the U.S. observed that, despite continued HbA1c levels >7% (>53 mmol/mol), people treated with sulfonylurea and metformin did not start insulin for almost 3 years (21). Another retrospective cohort study, using data from the Health Improvement Network database of 2,501 people with type 2 diabetes, estimated that only 25% of people started insulin within 1.8 years of multiple OAD failure, if followed for 5 years, and that 50% of people delayed starting insulin for almost 5 years after failure of glycemic control with multiple OADs (22). The U.K. cohort of a recent, 26-week observational study examining insulin initiation in clinical practice reported a large proportion of insulin-naïve people with HbA1c >9% (>75 mmol/mol) at baseline (64%); the mean HbA1c in the global cohort was 8.9% (74 mmol/mol) (10). Consequently, our analysis supports previous findings concerning clinical inertia in both U.K. and U.S. general practice and reflects little improvement in recent years, despite updated treatment guidelines recommending tight glycemic control.“
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“How hyperglycemia may cause damage to the nervous system is not fully understood. One consequence of hyperglycemia is the generation of advanced glycation end products (AGEs) that can form nonenzymatically between glucose, lipids, and amino groups. It is believed that AGEs are involved in the pathophysiology of neuropathy. AGEs tend to affect cellular function by altering protein function (11). One of the AGEs, N-ε-(carboxymethyl)lysine (CML), has been found in excessive amounts in the human diabetic peripheral nerve (12). High levels of methylglyoxal in serum have been found to be associated with painful peripheral neuropathy (13). In recent years, differentiation of affected nerves is possible by virtue of specific function tests to distinguish which fibers are damaged in diabetic polyneuropathy: large myelinated (Aα, Aβ), small thinly myelinated (Aδ), or small nonmyelinated (C) fibers. […] Our aims were to evaluate large- and small-nerve fiber function in long-term type 1 diabetes and to search for longitudinal associations with HbA1c and the AGEs CML and methylglyoxal-derived hydroimidazolone.”
“27 persons with type 1 diabetes of 40 ± 3 years duration underwent large-nerve fiber examinations, with nerve conduction studies at baseline and years 8, 17, and 27. Small-fiber functions were assessed by quantitative sensory thresholds (QST) and intraepidermal nerve fiber density (IENFD) at year 27. HbA1c was measured prospectively through 27 years. […] Fourteen patients (52%) reported sensory symptoms. Nine patients reported symptoms of a sensory neuropathy (reduced sensibility in feet or impaired balance), while three of these patients described pain. Five patients had symptoms compatible with carpal tunnel syndrome (pain or paresthesias within the innervation territory of the median nerve […]. An additional two had no symptoms but abnormal neurological tests with absent tendon reflexes and reduced sensibility. A total of 16 (59%) of the patients had symptoms or signs of neuropathy. […] No patient with symptoms of neuropathy had normal neurophysiological findings. […] Abnormal autonomic testing was observed in 7 (26%) of the patients and occurred together with neurophysiological signs of peripheral neuropathy. […] Twenty-two (81%) had small-fiber dysfunction by QST. Heat pain thresholds in the foot were associated with hydroimidazolone and HbA1c. IENFD was abnormal in 19 (70%) and significantly lower in diabetic patients than in age-matched control subjects (4.3 ± 2.3 vs. 11.2 ± 3.5 mm, P < 0.001). IENFD correlated negatively with HbA1c over 27 years (r = −0.4, P = 0.04) and CML (r = −0.5, P = 0.01). After adjustment for age, height, and BMI in a multiple linear regression model, CML was still independently associated with IENFD.”
“Our study shows that small-fiber dysfunction is more prevalent than large-fiber dysfunction in diabetic neuropathy after long duration of type 1 diabetes. Although large-fiber abnormalities were less common than small-fiber abnormalities, almost 60% of the participants had their large nerves affected after 40 years with diabetes. Long-term blood glucose estimated by HbA1c measured prospectively through 27 years and AGEs predict large- and small-nerve fiber function.”
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“Subarachnoid hemorrhage (SAH) is a life-threatening cerebrovascular event, which is usually caused by a rupture of a cerebrovascular aneurysm. These aneurysms are mostly found in relatively large-caliber (≥1 mm) vessels and can often be considered as macrovascular lesions. The overall incidence of SAH has been reported to be 10.3 per 100,000 person-years (1), even though the variation in incidence between countries is substantial (1). Notably, the population-based incidence of SAH is 35 per 100,000 person-years in the adult (≥25 years of age) Finnish population (2). The incidence of nonaneurysmal SAH is globally unknown, but it is commonly believed that 5–15% of all SAHs are of nonaneurysmal origin. Prospective, long-term, population-based SAH risk factor studies suggest that smoking (2–4), high blood pressure (2–4), age (2,3), and female sex (2,4) are the most important risk factors for SAH, whereas diabetes (both types 1 and 2) does not appear to be associated with an increased risk of SAH (2,3).
An increased risk of cardiovascular disease is well recognized in people with diabetes. There are, however, very few studies on the risk of cerebrovascular disease in type 1 diabetes since most studies have focused on type 2 diabetes alone or together with type 1 diabetes. Cerebrovascular mortality in the 20–39-year age-group of people with type 1 diabetes is increased five- to sevenfold in comparison with the general population but accounts only for 15% of all cardiovascular deaths (5). Of the cerebrovascular deaths in patients with type 1 diabetes, 23% are due to hemorrhagic strokes (5). However, the incidence of SAH in type 1 diabetes is unknown. […] In this prospective cohort study of 4,083 patients with type 1 diabetes, we aimed to determine the incidence and characteristics of SAH.”
“52% [of participants] were men, the mean age was 37.4 ± 11.8 years, and the duration of diabetes was 21.6 ± 12.1 years at enrollment. The FinnDiane Study is a nationwide multicenter cohort study of genetic, clinical, and environmental risk factors for microvascular and macrovascular complications in type 1 diabetes. […] all type 1 diabetic patients in the FinnDiane database with follow-up data and without a history of stroke at baseline were included. […] Fifteen patients were confirmed to have an SAH, and thus the crude incidence of SAH was 40.9 (95% CI 22.9–67.4) per 100,000 person-years. Ten out of these 15 SAHs were nonaneurysmal SAHs […] The crude incidence of nonaneurysmal SAH was 27.3 (13.1–50.1) per 100,000 person-years. None of the 10 nonaneurysmal SAHs were fatal. […] Only 3 out of 10 patients did not have verified diabetic microvascular or macrovascular complications prior to the nonaneurysmal SAH event. […] Four patients with type 1 diabetes had a fatal SAH, and all these patients died within 24 h after SAH.”
“The presented study results suggest that the incidence of nonaneurysmal SAH is high among patients with type 1 diabetes. […] It is of note that smoking type 1 diabetic patients had a significantly increased risk of nonaneurysmal and all-cause SAHs. Smoking also increases the risk of microvascular complications in insulin-treated diabetic patients, and these patients more often have retinal and renal microangiopathy than never-smokers (8). […] Given the high incidence of nonaneurysmal SAH in patients with type 1 diabetes and microvascular changes (i.e., diabetic retinopathy and nephropathy), the results support the hypothesis that nonaneurysmal SAH is a microvascular rather than macrovascular subtype of stroke.”
“Only one patient with type 1 diabetes had a confirmed aneurysmal SAH. Four other patients died suddenly due to an SAH. If these four patients with type 1 diabetes and a fatal SAH had an aneurysmal SAH, which, taking into account the autopsy reports and imaging findings, is very likely, aneurysmal SAH may be an exceptionally deadly event in type 1 diabetes. Population-based evidence suggests that up to 45% of people die during the first 30 days after SAH, and 18% die at emergency rooms or outside hospitals (9). […] Contrary to aneurysmal SAH, nonaneurysmal SAH is virtually always a nonfatal event (10–14). This also supports the view that nonaneurysmal SAH is a disease of small intracranial vessels, i.e., a microvascular disease. Diabetic retinopathy, a chronic microvascular complication, has been associated with an increased risk of stroke in patients with diabetes (15,16). Embryonically, the retina is an outgrowth of the brain and is similar in its microvascular properties to the brain (17). Thus, it has been suggested that assessments of the retinal vasculature could be used to determine the risk of cerebrovascular diseases, such as stroke […] Most interestingly, the incidence of nonaneurysmal SAH was at least two times higher than the incidence of aneurysmal SAH in type 1 diabetic patients. In comparison, the incidence of nonaneurysmal SAH is >10 times lower than the incidence of aneurysmal SAH in the general adult population (21).”
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vii. HbA1c and the Risks for All-Cause and Cardiovascular Mortality in the General Japanese Population.
Keep in mind when looking at these data that this is type 2 data. Type 1 diabetes is very rare in Japan and the rest of East Asia.
“The risk for cardiovascular death was evaluated in a large cohort of participants selected randomly from the overall Japanese population. A total of 7,120 participants (2,962 men and 4,158 women; mean age 52.3 years) free of previous CVD were followed for 15 years. Adjusted hazard ratios (HRs) and 95% CIs among categories of HbA1c (<5.0%, 5.0–5.4%, 5.5–5.9%, 6.0–6.4%, and ≥6.5%) for participants without treatment for diabetes and HRs for participants with diabetes were calculated using a Cox proportional hazards model.
RESULTS During the study, there were 1,104 deaths, including 304 from CVD, 61 from coronary heart disease, and 127 from stroke (78 from cerebral infarction, 25 from cerebral hemorrhage, and 24 from unclassified stroke). Relations to HbA1c with all-cause mortality and CVD death were graded and continuous, and multivariate-adjusted HRs for CVD death in participants with HbA1c 6.0–6.4% and ≥6.5% were 2.18 (95% CI 1.22–3.87) and 2.75 (1.43–5.28), respectively, compared with participants with HbA1c <5.0%. Similar associations were observed between HbA1c and death from coronary heart disease and death from cerebral infarction.
CONCLUSIONS High HbA1c levels were associated with increased risk for all-cause mortality and death from CVD, coronary heart disease, and cerebral infarction in general East Asian populations, as in Western populations.”
A few diabetes papers of interest
“The study examined long-term IHD-specific mortality in a Finnish population-based cohort of patients with early-onset (0–14 years) and late-onset (15–29 years) T1D (n = 17,306). […] Follow-up started from the time of diagnosis of T1D and ended either at the time of death or at the end of 2011. […] ICD codes used to define patients as having T1D were 2500B–2508B, E10.0–E10.9, or O24.0. […] The median duration of diabetes was 24.4 (interquartile range 17.6–32.2) years. Over a 41-year study period totaling 433,782 person-years of follow-up, IHD accounted for 27.6% of the total 1,729 deaths. Specifically, IHD was identified as the cause of death in 478 patients, in whom IHD was the primary cause of death in 303 and a contributory cause in 175. […] Within the early-onset cohort, the average crude mortality rate in women was 33.3% lower than in men (86.3 [95% CI 65.2–112.1] vs. 128.2 [104.2–156.1] per 100,000 person-years, respectively, P = 0.02). When adjusted for duration of diabetes and the year of diabetes diagnosis, the mortality RR between women and men of 0.64 was only of borderline significance (P = 0.05) […]. In the late-onset cohort, crude mortality in women was, on average, only one-half that of men (117.2 [92.0–147.1] vs. 239.7 [210.9–271.4] per 100,000 person-years, respectively, P < 0.0001) […]. An RR of 0.43 remained highly significant after adjustment for duration of diabetes and year of diabetes diagnosis. Every year of duration of diabetes increased the risk 10–13%”
“The number of deaths from IHD in the patients with T1D were compared with the number of deaths from IHD in the background population, and the SMRs were calculated. For the total cohort (early and late onset pooled), the SMR was 7.2 (95% CI 6.4–8.0) […]. In contrast to the crude mortality rates, the SMRs were higher in women (21.6 [17.2–27.0]) than in men (5.8 [5.1–6.6]). When stratified by the age at onset of diabetes, the SMR was considerably higher in patients with early onset (16.9 [13.5–20.9]) than in those with late onset (5.9 [5.2–6.8]). In both the late- and the early-onset cohorts, there was a striking difference in the SMRs between women and men, and this was especially evident in the early-onset cohort where the SMR for women was 52.8 (36.3–74.5) compared with 12.1 (9.2–15.8) for men. This higher risk of death from IHD compared with the background population was evident in all women, regardless of age. However, the most pronounced effect was seen in women in the early-onset cohort <40 years of age, who were 83 times more likely to die of IHD than the age-matched women in the background population. This compares with a 37 times higher risk of death from IHD in women aged >40 years. The corresponding SMRs for men aged <40 and ≥40 years were 19.4 and 8.5, respectively.”
“Overall, the 40-year cumulative mortality for IHD was 8.8% (95% CI 7.9–9.7%) in all patients […] The 40-year cumulative IHD mortality in the early-onset cohort was 6.3% (4.8–7.8%) for men and 4.5% (3.1–5.9%) for women (P = 0.009 by log-rank test) […]. In the late-onset cohort, the corresponding cumulative mortality rates were 16.6% (14.3–18.7%) in men and 8.5% (6.5–10.4%) in women (P < 0.0001 by log-rank test)”
“The major findings of the current study are that women with early-onset T1D are exceptionally vulnerable to dying from IHD, which is especially evident in those receiving a T1D diagnosis during the prepubertal and pubertal years. Crude mortality rates were similar for women compared with men, highlighting the loss of cardioprotection in women. […] Although men of all ages have greater crude mortality rates than women regardless of the age at onset of T1D, the current study shows that mortality from IHD attributable to diabetes is much more pronounced in women than in men. […] it is conceivable that one of the underlying reasons for the loss of female sex as a protective factor against the development of CVD in the setting of diabetes may be the loss of ovarian hormones. Indeed, women with T1D have been shown to have reduced levels of plasma estradiol compared with age-matched nondiabetic women (23) possibly because of idiopathic ovarian failure or dysregulation of the hypothalamic-pituitary-ovarian axis.”
“One of the novelties of the present study is that the risk of death from IHD highly depends on the age at onset of T1D. The data show that the SMR was considerably higher in early-onset (0–14 years) than in late-onset (15–29 years) T1D in both sexes. […] the risk of dying from IHD is high in both women and men receiving a diagnosis of T1D at a young age.“
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ii. Microalbuminuria as a Risk Predictor in Diabetes: The Continuing Saga.
“The term “microalbuminuria” (MA) originated in 1964 when Professor Harry Keen first used it to signify a small amount of albumin in the urine of patients with type 1 diabetes (1). […] Whereas early research focused on the relevance of MA as a risk factor for diabetic kidney disease, research over the past 2 decades has shifted to examine whether MA is a true risk factor. To appreciate fully the contribution of MA to overall cardiorenal risk, it is important to distinguish between a risk factor and risk marker. A risk marker is a variable that identifies a pathophysiological state, such as inflammation or infection, and is not necessarily involved, directly or causally, in the genesis of a specified outcome (e.g., association of a cardiovascular [CV] event with fever, high-sensitivity C-reactive protein [hs-CRP], or MA). Conversely, a risk factor is involved clearly and consistently with the cause of a specified event (e.g., a CV event associated with persistently elevated blood pressure or elevated levels of LDL). Both a risk marker and a risk factor can predict an adverse outcome, but only one lies within the causal pathway of a disease. Moreover, a reduction (or alteration in a beneficial direction) of a risk factor (i.e., achievement of blood pressure goal) generally translates into a reduction of adverse outcomes, such as CV events; this is not necessarily true for a risk marker.”
“The data sources included in this article were all PubMed-referenced articles in English-language peer-reviewed journals since 1964. Studies selected had to have a minimum follow-up of 1 year; include at least 100 participants; be either a randomized trial, a systematic review, a meta-analysis, or a large observational cohort study in patients with any type of diabetes; or be trials of high CV risk that included at least 50% of patients with diabetes. All studies had to assess changes in MA tied to CV or CKD outcomes and not purely reflect changes in MA related to blood pressure, unless they were mechanistic studies. On the basis of these inclusion criteria, 31 studies qualified and provide the data used for this review.”
“Early studies in patients with diabetes supported the concept that as MA increases to higher levels, the risk of CKD progression and CV risk also increases […]. Moreover, evidence from epidemiological studies in patients with diabetes suggested that the magnitude of urine albumin excretion should be viewed as a continuum of CV risk, with the lower the albumin excretion, the lower the CV risk (15,16). However, MA values can vary daily up to 100% (11). These large biological variations are a result of a variety of conditions, with a central core tied to inflammation associated with factors ranging from increased blood pressure variability, high blood glucose levels, high LDL cholesterol, and high uric acid levels to high sodium ingestion, smoking, and exercise (17) […]. Additionally, any febrile illness, regardless of etiology, will increase urine albumin excretion (18). Taken together, these data support the concept that MA is highly variable and that values over a short time period (i.e., 3–6 months) are meaningless in predicting any CV or kidney disease outcome.”
“Initial studies to understand the mechanisms of MA examined changes in glomerular membrane permeability as a key determinant in patients with diabetes […]. Many factors affect the genesis and level of MA, most of which are linked to inflammatory conditions […]. A good evidence base, however, supports the concept that MA directly reflects the amount of inflammation and vascular “leakiness” present in patients with diabetes (16,18,19).
More recent studies have found a number of other factors that affect glomerular permeability by modifying cytokines that affect permeability. Increased amounts of glycated albumin reduce glomerular nephrin and increase vascular endothelial growth factor (20). Additionally, increases in sodium intake (21) as well as intraglomerular pressure secondary to high protein intake or poorly controlled blood pressure (22,23) increase glomerular permeability in diabetes and, hence, MA levels.
In individuals with diabetes, albumin is glycated and associated with the generation of reactive oxygen species. In addition, many other factors such as advanced glycation end products, reactive oxygen species, and other cellular toxins contribute to vascular injury. Once such injury occurs, the effect of pressor hormones, such as angiotensin II, is magnified, resulting in a faster progression of vascular injury. The end result is direct injury to the vascular smooth muscle cells, endothelial cells, and visceral epithelial cells (podocytes) of the glomerular capillary wall membrane as well as to the proximal tubular cells and podocyte basement membrane of the nephron (20,24,25). All these contribute to the development of MA. […] better glycemic control is associated with far lower levels of inflammatory markers (31).”
“MA is accepted as a CV risk marker for myocardial infarction and stroke, regardless of diabetes status. […] there is good evidence in those with type 2 diabetes that the presence of MA >100 mg/day is associated with higher CV events and greater likelihood of kidney disease development (6). Evidence for this association comes from many studies and meta-analyses […] a meta-analysis by Perkovic et al. (37) demonstrated a dose-response relationship between the level of albuminuria and CV risk. In this meta-analysis, individuals with MA were at 50% greater risk of coronary heart disease (risk ratio 1.47 [95% CI 1.30–1.66]) than those without. Those with macroalbuminuria (i.e., >300 mg/day) had more than a twofold risk for coronary heart disease (risk ratio 2.17 [95% CI 1.87–2.52]) (37). Despite these data indicating a higher CV risk in patients with MA regardless of diabetes status and other CV risk factors, there is no consensus that the addition of MA to conventional CV risk stratification for the general population (e.g., Framingham or Reynolds scoring systems) is of any clinical value, and that includes patients with diabetes (38).”
“Given that MA was evaluated in a post hoc manner in almost all interventional studies, it is likely that the reduction in MA simply reflects the effects of either renin-angiotensin system (RAS) blockade on endothelial function or significant blood pressure reduction rather than the MA itself being implicated as a CV disease risk factor (18). […] associations of lowering MA with angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) does not prove a direct benefit on CV event lowering associated with MA reduction in diabetes. […] Four long-term, appropriately powered trials demonstrated an inverse relationship between reductions in MA and primary event rates for CV events […]. Taken together, these studies support the concept that MA is a risk marker in diabetes and is consistent with data of other inflammatory markers, such as hs-CRP [here’s a relevant link – US], such that the higher the level, the higher the risk (15,39,42). The importance of MA as a CV risk marker is exemplified further by another meta-analysis that showed that MA has a similar magnitude of CV risk as hs-CRP and is a better predictor of CV events (43). Thus, the data supporting MA as a risk marker for CV events are relatively consistent, clearly indicate that an association exists, and help to identify the presence of underlying inflammatory states, regardless of etiology.”
“In people with early stage nephropathy (i.e., stage 2 or 3a [GFR 45–89 mL/min/1.73 m2]) and MA, there is no clear benefit on slowing GFR decline by reducing MA with drugs that block the RAS independent of lowering blood pressure (16). This is exemplified by many trials […]. Thus, blood pressure lowering is the key goal for all patients with early stage nephropathy associated with normoalbuminuria or MA. […] When albuminuria levels are in the very high or macroalbuminuria range (i.e., >300 mg/day), it is accepted that the patient has CKD and is likely to progress ultimately to ESRD, unless they die of a CV event (39,52). However, only one prospective randomized trial evaluated the role of early intervention to reduce blood pressure with an ACE inhibitor versus a calcium channel blocker in CKD progression by assessing change in MA and creatinine clearance in people with type 2 diabetes (Appropriate Blood Pressure Control in Diabetes [ABCD] trial) (23). After >7 years of follow-up, there was no relationship between changes in MA and CKD progression. Moreover, there was regression to the mean of MA.”
“Many observational studies used development of MA as indicating the presence of early stage CKD. Early studies by the individual groups of Mogensen and Parving demonstrated a relationship between increases in MA and progression to nephropathy in type 1 diabetes. These groups also showed that use of ACE inhibitors, blood pressure reduction, and glucose control reduced MA (9,58,59). However, more recent studies in both type 1 and type 2 diabetes demonstrated that only a subgroup of patients progress from MA to >300 mg/day albuminuria, and this subgroup accounts for those destined to progress to ESRD (29,32,60–63). Thus, the presence of MA alone is not predictive of CKD progression. […] some patients with type 2 diabetes progress to ESRD without ever having developed albuminuria levels of ≥300 mg/day (67). […] Taken together, data from outcome trials, meta-analyses, and observations demonstrate that MA [Micro-Albuminuria] alone is not synonymous with the presence of clearly defined CKD [Chronic Kidney Disease] in diabetes, although it is used as part of the criteria for the diagnosis of CKD in the most recent CKD classification and staging (71). Note that only a subgroup of ∼25–30% of people with diabetes who also have MA will likely progress to more advanced stages of CKD. Predictors of progression to ESRD, apart from family history, and many years of poor glycemic and blood pressure control are still not well defined. Although there are some genetic markers, such as CUBN and APOL1, their use in practice is not well established.”
“In the context of the data presented in this article, MA should be viewed as a risk marker associated with an increase in CV risk and for kidney disease, but its presence alone does not indicate established kidney disease, especially if the eGFR is well above 60 mL/min/1.73 m2. Increases in MA, with blood pressure and other CV risk factors controlled, are likely but not proven to portend a poor prognosis for CKD progression over time. Achieving target blood pressure (<140/80 mmHg) and target HbA1c (<7%) should be priorities in treating patients with MA. Recent guidelines from both the American Diabetes Association and the National Kidney Foundation provide a strong recommendation for using agents that block the RAS, such as ACE inhibitors and ARBs, as part of the regimen for those with albuminuria levels >300 mg/day but not MA (73). […] maximal antialbuminuric effects will [however] not be achieved with these agents unless a low-sodium diet is strictly followed.”
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iii. The SEARCH for Diabetes in Youth Study: Rationale, Findings, and Future Directions.
“The SEARCH for Diabetes in Youth (SEARCH) study was initiated in 2000, with funding from the Centers for Disease Control and Prevention and support from the National Institute of Diabetes and Digestive and Kidney Diseases, to address major knowledge gaps in the understanding of childhood diabetes. SEARCH is being conducted at five sites across the U.S. and represents the largest, most diverse study of diabetes among U.S. youth. An active registry of youth diagnosed with diabetes at age <20 years allows the assessment of prevalence (in 2001 and 2009), annual incidence (since 2002), and trends by age, race/ethnicity, sex, and diabetes type. Prevalence increased significantly from 2001 to 2009 for both type 1 and type 2 diabetes in most age, sex, and race/ethnic groups. SEARCH has also established a longitudinal cohort to assess the natural history and risk factors for acute and chronic diabetes-related complications as well as the quality of care and quality of life of persons with diabetes from diagnosis into young adulthood. […] This review summarizes the study methods, describes key registry and cohort findings and their clinical and public health implications, and discusses future directions.”
“SEARCH includes a registry and a cohort study […]. The registry study identifies incident cases each year since 2002 through the present with ∼5.5 million children <20 years of age (∼6% of the U.S. population <20 years) under surveillance annually. Approximately 3.5 million children <20 years of age were under surveillance in 2001 at the six SEARCH recruitment centers, with approximately the same number at the five centers under surveillance in 2009.”
“The prevalence of all types of diabetes was 1.8/1,000 youth in 2001 and was 2.2/1,000 youth in 2009, which translated to at least 154,000 children/youth in the U.S. with diabetes in 2001 (5) and at least 192,000 in 2009 (6). Overall, between 2001 and 2009, prevalence of type 1 diabetes in youth increased by 21.1% (95% CI 15.6–27.0), with similar increases for boys and girls and in most racial/ethnic and age groups (2) […]. The prevalence of type 2 diabetes also increased significantly over the same time period by 30.5% (95% CI 17.3–45.1), with increases observed in both sexes, 10–14- and 15–19-year-olds, and among Hispanic and non-Hispanic white and African American youth (2). These data on changes in type 2 are consistent with smaller U.S. studies (7–11).”
“The incidence of diabetes […] in 2002 to 2003 was 24.6/100,000/year (12), representing ∼15,000 new patients every year with type 1 diabetes and 3,700 with type 2 diabetes, increasing to 18,436 newly diagnosed type 1 and 5,089 with type 2 diabetes in 2008 to 2009 (13). Among non-Hispanic white youth, the incidence of type 1 diabetes increased by 2.7% (95% CI 1.2–4.3) annually between 2002 and 2009. Significant increases were observed among all age groups except the youngest age group (0–4 years) (14). […] The underlying factors responsible for this increase have not yet been identified.”
“Over 50% of youth are hospitalized at diabetes onset, and ∼30% of children newly diagnosed with diabetes present with diabetic ketoacidosis (DKA) (19). Prevalence of DKA at diagnosis was three times higher among youth with type 1 diabetes (29.4%) compared with youth with type 2 diabetes (9.7%) and was lowest in Asian/Pacific Islanders (16.2%) and highest among Hispanics (27.0%).”
“A significant proportion of youth with diabetes, particularly those with type 2 diabetes, have very poor glycemic control […]: 17% of youth with type 1 diabetes and 27% of youth with type 2 diabetes had A1C levels ≥9.5% (≥80 mmol/mol). Minority youth were significantly more likely to have higher A1C levels compared with non-Hispanic white youth, regardless of diabetes type. […] Optimal care is an important component of successful long-term management for youth with diabetes. While there are high levels of adherence for some diabetes care indicators such as blood pressure checks (95%), urinary protein tests (83%), and lipid assessments (88%), approximately one-third of youth had no documentation of eye or A1C values at appropriate intervals and therefore were not meeting the American Diabetes Association (ADA)-recommended screening for diabetic control and complications (40). Participants ≥18 years old, particularly those with type 2 diabetes, and minority youth with type 1 diabetes had fewer tests of all kinds performed. […] Despite current treatment options, the prevalence of poor glycemic control is high, particularly among minority youth. Our initial findings suggest that a substantial number of youth with diabetes will develop serious, debilitating complications early in life, which is likely to have significant implications for their quality of life, as well as economic and health care implications.”
“Because recognition of the broader spectrum of diabetes in children and adolescents is recent, there are no gold-standard definitions for differentiating the types of diabetes in this population, either for research or clinical purposes or for public health surveillance. The ADA classification of diabetes as type 1 and type 2 does not include operational definitions for the specific etiologic markers of diabetes type, such as types and numbers of diabetes autoantibodies or measures of insulin resistance, hallmarks of type 1 and 2 diabetes, respectively (43). Moreover, obese adolescents with a clinical phenotype suggestive of type 2 diabetes can present with ketoacidosis (44) or have evidence of autoimmunity (45).”
“Using the ADA framework (43), we operationalized definitions of two main etiologic markers, autoimmunity and insulin sensitivity, to identify four etiologic subgroups based on the presence or absence of markers. Autoimmunity was based on presence of one or more diabetes autoantibodies (GAD65 and IA2). Insulin sensitivity was estimated using clinical variables (A1C, triglyceride level, and waist circumference) from a formula that was highly associated with estimated insulin sensitivity measured using a euglycemic-hyperinsulinemic clamp among youth with type 1 and 2 and normal control subjects (46). Participants were categorized as insulin resistant […] and insulin sensitive (47). Using this approach, 54.5% of SEARCH cases were classified as typical type 1 (autoimmune, insulin-sensitive) diabetes, while 15.9% were classified as typical type 2 (nonautoimmune, insulin-resistant) diabetes. Cases that were classified as autoimmune and insulin-resistant likely represent individuals with type 1 autoimmune diabetes and concomitant obesity, a phenotype becoming more prevalent as a result of the recent increase in the frequency of obesity, but is unlikely to be a distinct etiologic entity.”
“Ten percent of SEARCH participants had no evidence of either autoimmunity or insulin resistance and thus require additional testing, including additional measurements of diabetes-related autoantibodies (only two antibodies were measured in SEARCH) as well as testing for monogenic forms of diabetes to clarify etiology. Among antibody-negative youth, 8% of those tested had a mutation in one or more of the hepatocyte nuclear factor-1α (HNF-1α), glucokinase, and HNF-4α genes, an estimated monogenic diabetes population prevalence of at least 1.2% (48).”
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The short answer is ‘yes, it does’. Some observations from the paper:
“Diabetic sensorimotor polyneuropathy (DSP) is a common complication of diabetes, affecting 28–55% of patients (1). A prospective Finnish study found evidence of probable or definite neuropathy in 8.3% of diabetic patients at the time of diagnosis, 16.7% after 5 years, and 41.9% after 10 years (2). Diabetes-related peripheral neuropathy results in serious morbidity, including chronic neuropathic pain, leg weakness and falls, sensory loss and foot ulceration, and amputation (3). Health care costs associated with diabetic neuropathy were estimated at $10.9 billion in the U.S. in 2003 (4). However, despite the high prevalence of diabetes and DSP, and the important public health implications, there is a lack of serum- or tissue-based biomarkers to diagnose and follow patients with DSP longitudinally. Moreover, numerous attempts at treatment have yielded negative results.”
“DSP is known to cause injury to both large-diameter, myelinated (Aα and Aβ) fibers and small-diameter, unmyelinated nerve (Aδ and C) fibers; however, the sequence of nerve fiber damage remains uncertain. While earlier reports seemed to indicate simultaneous loss of small- and large-diameter nerve fibers, with preserved small/large ratios (5), more recent studies have suggested the presence of early involvement of small-diameter Aδ and C fibers (6–11). Some suggest a temporal relationship of small-fiber impairment preceding that of large fibers. For example, impairment in the density of the small intraepidermal nerve fibers in symptomatic patients with impaired glucose tolerance (prediabetes) have been observed in the face of normal large-fiber function, as assessed by nerve conduction studies (NCSs) (9,10). In addition, surveys of patients with DSP have demonstrated an overwhelming predominance of sensory and autonomic symptoms, as compared with motor weakness. Again, this has been interpreted as indicative of preferential small-fiber dysfunction (12). Though longitudinal studies are limited, such studies have lead to the current prevailing hypothesis for the natural history of DSP that measures of small-fiber morphology and function decline prior to those of large fibers. One implication of this hypothesis is that small-fiber testing could serve as an earlier, subclinical primary end point in clinical trials investigating interventions for DSP (13).
The hypothesis described above has been investigated exclusively in type 2 diabetic or prediabetic patients. Through the study of a cohort of healthy volunteers and type 1 diabetic subjects […], we had the opportunity to evaluate in cross-sectional analysis the relationship between measures of large-fiber function and small-fiber structure and function. Under the hypothesis that small-fiber abnormalities precede large-fiber dysfunction in the natural history of DSP, we sought to determine if: 1) the majority of subjects who meet criteria for large-fiber dysfunction have concurrent evidence of small-fiber dysfunction and 2) the subset of patients without DSP includes a spectrum with normal small-fiber tests (indicating lack of initiation of nerve injury) as well as abnormal small-fiber tests (indicating incipient DSP).”
“Overall, 57 of 131 (43.5%) type 1 diabetic patients met DSP criteria, and 74 of 131 (56.5%) did not meet DSP criteria. Abnormality of CCM [link] was present in 30 of 57 (52.6%) DSP patients and 6 of 74 (8.1%) type 1 diabetic patients without DSP. Abnormality of CDT [Cooling Detection Thresholds, relevant link] was present in 47 of 56 (83.9%) DSP patients and 17 of 73 (23.3%) without DSP. Abnormality of LDIflare [laser Doppler imaging of heat-evoked flare] was present in 30 of 57 (52.6%) DSP patients and 20 of 72 (27.8%) without DSP. Abnormality of HRV [Heart Rate Variability] was present in 18 of 45 (40.0%) DSP patients and 6 of 70 (8.6%) without DSP. […] sensitivity analysis […] revealed that abnormality of any one of the four small-fiber measures was present in 55 of 57 (96.5%) DSP patients […] and 39 of 74 (52.7%) type 1 diabetic patients without DSP. Similarly, abnormality of any two of the four small-fiber measures was present in 43 of 57 (75.4%) DSP patients […] and 9 of 74 (12.2%) without DSP. Finally, abnormality of either CDT or CCM (with these two tests selected based on their high reliability) was noted in 53 of 57 (93.0%) DSP patients and 21 of 74 (28.4%) patients without DSP […] When DSP was defined based on symptoms and signs plus abnormal sural SNAP [sensory nerve action potential] amplitude or conduction velocity, there were 68 of 131 patients who met DSP criteria and 63 of 131 who did not. Abnormality of any one of the four small-fiber measures was present in 63 of 68 (92.6%) DSP patients and 31 of 63 (49.2%) type 1 diabetic patients without DSP. […] Finally, if DSP was defined based on clinical symptoms and signs alone, with TCNS ≥5, there were 68 of 131 patients who met DSP criteria and 63 of 131 who did not. Abnormality of any one of the four small-fiber measures was present in 62 of 68 (91.2%) DSP patients and 32 of 63 (50.8%) type 1 diabetic patients without DSP.”
“Qualitative analysis of contingency tables shows that the majority of patients with DSP have concurrent evidence of small-fiber dysfunction, and patients without DSP include a spectrum with normal small-fiber tests (indicating lack of initiation of nerve injury) as well as abnormal small-fiber tests. Evidence of isolated large-fiber injury was much less frequent […]. These findings suggest that small-fiber damage may herald the onset of DSP in type 1 diabetes. In addition, the above findings remained true when alternative definitions of DSP were explored in a sensitivity analysis. […] The second important finding was the linear relationships noted between small-fiber structure and function tests (CDT, CNFL, LDIflare, and HRV) […] and the number of NCS abnormalities (a marker of large-fiber function). This might indicate that once the process of large-fiber nerve injury in DSP has begun, damage to large and small nerve fibers occurs simultaneously.”
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v. Long-Term Complications and Mortality in Young-Onset Diabetes.
“Records from the Royal Prince Alfred Hospital Diabetes Clinical Database, established in 1986, were matched with the Australian National Death Index to establish mortality outcomes for all subjects until June 2011. Clinical and mortality outcomes in 354 patients with T2DM, age of onset between 15 and 30 years (T2DM15–30), were compared with T1DM in several ways but primarily with 470 patients with T1DM with a similar age of onset (T1DM15–30) to minimize the confounding effect of age on outcome.
RESULTS For a median observation period of 21.4 (interquartile range 14–30.7) and 23.4 (15.7–32.4) years for the T2DM and T1DM cohorts, respectively, 71 of 824 patients (8.6%) died. A significant mortality excess was noted in T2DM15–30 (11 vs. 6.8%, P = 0.03), with an increased hazard for death (hazard ratio 2.0 [95% CI 1.2–3.2], P = 0.003). Death for T2DM15–30 occurred after a significantly shorter disease duration (26.9 [18.1–36.0] vs. 36.5 [24.4–45.4] years, P = 0.01) and at a relatively young age. There were more cardiovascular deaths in T2DM15–30 (50 vs. 30%, P < 0.05). Despite equivalent glycemic control and shorter disease duration, the prevalence of albuminuria and less favorable cardiovascular risk factors were greater in the T2DM15–30 cohort, even soon after diabetes onset. Neuropathy scores and macrovascular complications were also increased in T2DM15–30 (P < 0.0001).
CONCLUSIONS Young-onset T2DM is the more lethal phenotype of diabetes and is associated with a greater mortality, more diabetes complications, and unfavorable cardiovascular disease risk factors when compared with T1DM.”
“Only a few previous studies have looked at comparative mortality in T1DM and T2DM onset in patients <30 years of age. In a Swedish study of patients with diabetes aged 15–34 years compared with a general population, the standardized mortality ratio was higher for the T2DM than for the T1DM cohort (2.9 vs. 1.8) (17). […] Recently, Dart et al. (19) examined survival in youth aged 1–18 years with T2DM versus T1DM. Kaplan-Meier analysis revealed a statistically significant lower survival probability for the youth with T2DM, although the number at risk was low after 10 year’s duration. Taken together, these findings are in keeping with the present observations and are supportive evidence for a higher mortality in young-onset T2DM than in T1DM. The majority of deaths appear to be from cardiovascular causes and significantly more so for young T2DM.”
“Although the age of onset of T1DM diabetes is usually in little doubt because of a more abrupt presentation, it is possible that the age of onset of T2DM was in fact earlier than recognized. With a previously published method for estimating time delay until diagnosis of T2DM (26) by plotting the prevalence of retinopathy against duration and extrapolating to a point of zero retinopathy, we found that there is no difference in the slope and intercept of this relationship between the T2DM and the T1DM cohorts […] delay in diagnosis is unlikely to be an explanation for the differences in observed outcome.”
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“Increased arterial stiffness independently predicts all-cause and CVD mortality (3), and higher pulse pressure predicts CVD mortality, incidence, and end-stage renal disease development among adults with type 1 diabetes (1,4,5). Several reports have shown that youth and adults with type 1 diabetes have elevated arterial stiffness, though the mechanisms are largely unknown (6). The etiology of advanced atherosclerosis in type 1 diabetes is likely multifactorial, involving metabolic, behavioral, and diabetes-specific cardiovascular (CV) risk factors. Aging, high blood pressure (BP), obesity, the metabolic syndrome (MetS), and type 2 diabetes are the main contributors of sustained increased arterial stiffness in adults (7,8). However, the natural history, the age-related progression, and the possible determinants of increased arterial stiffness in youth with type 1 diabetes have not been studied systematically. […] There are currently no data examining the impact of CV risk factors and their clustering in youth with type 1 diabetes on subsequent CVD morbidity and mortality […]. Thus, the aims of this report were: 1) to describe the progression of arterial stiffness, as measured by pulse wave velocity (PWV), over time, among youth with type 1 diabetes, and 2) to explore the association of CV risk factors and their clustering as MetS with PWV in this cohort.”
“Youth were age 14.5 years (SD 2.8) and had an average disease duration of 4.8 (3.8) years at baseline, 46.3% were female, and 87.6% were of NHW race/ethnicity. At baseline, 10.0% had high BP, 10.9% had a large waist circumference, 11.6% had HDL-c ≤40 mg/dL, 10.9% had a TG level ≥110 mg/dL, and 7.0% had at least two of the above CV risk factors (MetS). In addition, 10.3% had LDL-c ≥130 mg/dL, 72.0% had an HbA1c ≥7.5% (58 mmol/mol), and 9.2% had ACR ≥30 μg/mL. Follow-up measures were obtained on average at age 19.2 years, when the average duration of diabetes was 10.1 (3.9) years.”
“Over an average follow-up period of ∼5 years, there was a statistically significant increase of 0.7 m/s in PWV (from 5.2 to 5.9 m/s), representing an annual increase of 2.8% or 0.145 m/s. […] Based on our data, if this rate of change is stable over time, the estimated average PWV by the time these youth enter their third decade of life will be 11.3 m/s, which was shown to be associated with a threefold increased hazard for major CV events (26). There are no similar studies in youth to compare these findings. In adults, the rate of change in PWV was 0.081 m/s/year in nondiabetic normotensive patients, although it was higher in hypertensive adults (0.147 m/s/year) (7). We also showed that the presence of central adiposity and elevated BP at baseline, as well as clustering of at least two CV risk factors, was associated with significantly worse PWV over time, although these baseline factors did not significantly influence the rate of change in PWV over this period of time. Changes in CV risk factors, specifically increases in central adiposity, LDL-c levels, and worsening glucose control, were independently associated with worse PWV over time. […] Our inability to detect a difference in the rate of change in PWV in our youth with MetS (vs. those without MetS) may be due to several factors, including a combination of a relatively small sample size, short period of follow-up, and young age of the cohort (thus with lower baseline PWV levels).”
Acute Coronary Syndromes
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A few quotes from the lecture, as well as some links to related stuff:
“You might say: Why doesn’t coronary stenting prevent heart attacks? You got an 80 % blockage causing some angina and you stent it, why doesn’t that prevent a heart attack? And the answer is very curious. The plaques that are most likely to rupture are mild. They’re typically less than 50 %. They have a thin fibrous cap, a lot of lipid, and they rupture during stress. This has been the real confusion for my specialty over the last 30 years, starting to realize that, you know, when you get angina we find the blockage and we fix it and your angina’s better, but the lesions that were gonna cause next week’s heart attack often are not the lesion we fixed, but there’s 25 other moderate plaques in the coronary tree and one of them is heating up and it’s vulnerable. […] ACS, the whole thing here is the idea of a vulnerable plaque rupture. And it’s often not a severe narrowing.” (3-5 minutes in)
[One of the plaque rupture triggers of relevance is inflammatory cytokines…] “What’s a good example of that? Influenza. Right, influenza releases things like, IL-6 and other cytokines. What do they do? Well, they make you shake and shiver and feel like your muscles are dying. They also dissolve plaques. […] If you take a town like Ann Arbor and vaccinate everybody for influenza, we reduce heart attacks by a lot … 20-30 % during flu season.” (~11-12 minutes in)
“What happens to your systolic function as you get older? Any ideas? I’m happy to tell you it stays strong. […] What happens to diastole? […] As your myocardial cells die, a few die every day, […] those cells get replaced by fibrous tissue. So an aging heart becomes gradually stiffer [this is apparently termed ‘presbycardia’]. It beats well because the cells that are alive can overcome the fibrosis and squeeze, but it doesn’t relax as well. So left ventricular and diastolic pressure goes up. Older patients are much more likely to develop heart failure [in the ACS setting] because they already have impaired diastole from […] presbycardia.” (~1.14-1.15)
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Some links to coverage of topics covered during the lecture:
Acute Coronary Syndrome.
Unstable angina.
Pathology of Acute Myocardial Infarction.
Acute Coronary Syndrome Workup.
Acute Coronary Syndrome Treatment & Management.
The GRACE risk score.
Complications of Myocardial Infarction.
Early versus Delayed Invasive Intervention in Acute Coronary Syndromes (Mehta et al. 2009).
A few diabetes papers of interest
“Fatigue is a classical symptom of hyperglycemia, but the relationship between chronic fatigue and diabetes has not been systematically studied. […] glucose control [in diabetics] is often suboptimal with persistent episodes of hyperglycemia that may result in sustained fatigue. Fatigue may also sustain in diabetic patients because it is associated with the presence of a chronic disease, as has been demonstrated in patients with rheumatoid arthritis and various neuromuscular disorders (2,3).
It is important to distinguish between acute and chronic fatigue, because chronic fatigue, defined as severe fatigue that persists for at least 6 months, leads to substantial impairments in patients’ daily functioning (4,5). In contrast, acute fatigue can largely vary during the day and generally does not cause functional impairments.
Literature provides limited evidence for higher levels of fatigue in diabetic patients (6,7), but its chronicity, impact, and determinants are unknown. In various chronic diseases, it has been proven useful to distinguish between precipitating and perpetuating factors of chronic fatigue (3,8). Illness-related factors trigger acute fatigue, while other factors, often cognitions and behaviors, cause fatigue to persist. Sleep disturbances, low self-efficacy concerning fatigue, reduced physical activity, and a strong focus on fatigue are examples of these fatigue-perpetuating factors (8–10). An episode of hyperglycemia or hypoglycemia could trigger acute fatigue for diabetic patients (11,12). However, variations in blood glucose levels might also contribute to chronic fatigue, because these variations continuously occur.
The current study had two aims. First, we investigated the prevalence and impact of chronic fatigue in a large sample of type 1 diabetic (T1DM) patients and compared the results to a group of age- and sex-matched population-based controls. Secondly, we searched for potential determinants of chronic fatigue in T1DM.”
“A significantly higher percentage of T1DM patients were chronically fatigued (40%; 95% CI 34–47%) than matched controls (7%; 95% CI 3–10%). Mean fatigue severity was also significantly higher in T1DM patients (31 ± 14) compared with matched controls (17 ± 9; P < 0.001). T1DM patients with a comorbidity_mr [a comorbidity affecting patients’ daily functioning, based on medical records – US] or clinically relevant depressive symptoms [based on scores on the Beck Depression Inventory for Primary Care – US] were significantly more often chronically fatigued than patients without a comorbidity_mr (55 vs. 36%; P = 0.014) or without clinically relevant depressive symptoms (88 vs. 31%; P < 0.001). Patients who reported neuropathy, nephropathy, or cardiovascular disease as complications of diabetes were more often chronically fatigued […] Chronically fatigued T1DM patients were significantly more impaired compared with nonchronically fatigued T1DM patients on all aspects of daily functioning […]. Fatigue was the most troublesome symptom of the 34 assessed diabetes-related symptoms. The five most troublesome symptoms were overall sense of fatigue, lack of energy, increasing fatigue in the course of the day, fatigue in the morning when getting up, and sleepiness or drowsiness”.
“This study establishes that chronic fatigue is highly prevalent and clinically relevant in T1DM patients. While current blood glucose level was only weakly associated with chronic fatigue, cognitive behavioral factors were by far the strongest potential determinants.”
“Another study found that type 2 diabetic, but not T1DM, patients had higher levels of fatigue compared with healthy controls (7). This apparent discrepancy may be explained by the relatively small sample size of this latter study, potential selection bias (patients were not randomly selected), and the use of a different fatigue questionnaire.”
“Not only was chronic fatigue highly prevalent, fatigue also had a large impact on T1DM patients. Chronically fatigued T1DM patients had more functional impairments than nonchronically fatigued patients, and T1DM patients considered fatigue as the most burdensome diabetes-related symptom.
Contrary to what was expected, there was at best a weak relationship between blood glucose level and chronic fatigue. Chronically fatigued T1DM patients spent slightly less time in hypoglycemia, but average glucose levels, glucose variability, hyperglycemia, or HbA1c were not related to chronic fatigue. In type 2 diabetes mellitus also, no relationship was found between fatigue and HbA1c (7).”
“Regarding demographic characteristics, current health status, diabetes-related factors, and fatigue-related cognitions and behaviors as potential determinants of chronic fatigue, we found that sleeping problems, physical activity, self-efficacy concerning fatigue, age, depression, and pain were significantly associated with chronic fatigue in T1DM. Although depression was strongly related, it could not completely explain the presence of chronic fatigue (38), as 31% was chronically fatigued without having clinically relevant depressive symptoms.”
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Some comments may be worth adding here. It’s important to note to people who may not be aware of this that although chronic fatigue is a weird entity that’s hard to get a handle on (and, to be frank, is somewhat controversial), specific organic causes have been identified that greatly increases the risk. Many survivors of cancer experience chronic fatigue (see e.g. this paper, or wikipedia), and chronic fatigue is also not uncommon in a kidney failure setting (“The silence of renal disease creeps up on us (doctors and patients). Do not dismiss odd chronic symptoms such as fatigue or ‘not being quite with it’ without considering checking renal function” (Oxford Handbook of Clinical Medicine, 9th edition. My italics – US)). As observed above, linkage with RA and some neuromuscular disorders has also been observed. The brief discussion of related topics in Houghton & Grey made it clear to me that some people with chronic fatigue are almost certainly suffering from an organic illness which has not been diagnosed or treated. Here’s a relevant quote from that book’s coverage: “it is unusual to find a definite organic cause for fatigue. However, consider anaemia, thyroid dysfunction, Addison’s disease and hypopituitarism.” It’s sort of neat, if you think about the potential diabetes-fatigue link investigated by the guys above, that some of these diseases are likely to be relevant, as type 1 diabetics are more likely to develop them (anemia is not linked to diabetes, as far as I know, and I believe the relationship between autoimmune hypophysitis – which is a cause of hypopituitarism – and type 1 diabetes is at best unclear, but the others are definitely involved) due to their development being caused by some of the same genetic mutations which cause type 1 diabetes; the combinations of some of these diseases even have fancy names of their own, like ‘Type I Polyglandular Autoimmune Syndrome’ and ‘Schmidt Syndrome’ (if you’re interested here are a couple of medscape links). It’s noteworthy that although most of these diseases are uncommon in the general population, their incidence/prevalence is likely to be greatly increased in type 1 diabetics due to the common genetic pathways at play (variants regulating T-cell function seem to be important, but there’s no need to go into these details here). Sperling et al. note in their book that: “Hypothyroid or hyperthyroid AITD [autoimmune thyroid disease] has been observed in 10–24% of patients with type 1 diabetes”. In one series including 151 patients with APS [/PAS]-2, when they looked at disease combinations they found that: “Of combinations of the component diseases, [type 1] diabetes with thyroid disease was the most common, occurring in 33%. The second, diabetes with adrenal insufficiency, made up 15%” (same source).
It seems from estimates like these likely that a not unsubstantial proportion of type 1 diabetics over time go on to develop other health problems that might if unaddressed/undiagnosed cause fatigue, and this may in my opinion be a potentially much more important cause than direct metabolic effects such as hyperglycemia, or chronic inflammation. If this is the case you’d however expect to see a substantial sex difference, as the autoimmune syndromes are in general much more likely to hit females than males. I’m not completely sure how to interpret a few of the results reported, but to me it doesn’t look like the sex differences in this study are anywhere near ‘large enough’ to support such an explanatory model, though. Another big problem is also that fatigue seems to be more common in young patients, which is weird; most long-term complications display significant (positive) duration dependence, and when diabetes is a component of an autoimmune syndrome diabetes tend to develop first, with other diseases hitting later, usually in middle age. Duration and age are strongly correlated, and a negative duration dependence in a diabetes complication setting is a surprising and unusual finding that needs to be explained, badly; it’s unexpected and may in my opinion be the sign of a poor disease model. It’d make more sense for disease-related fatigue to present late, rather than early, I don’t really know what to make of that negative age gradient. ‘More studies needed’ (preferably by people familiar with those autoimmune syndromes..), etc…
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ii. Risk for End-Stage Renal Disease Over 25 Years in the Population-Based WESDR Cohort.
“It is well known that diabetic nephropathy is the leading cause of end-stage renal disease (ESRD) in many regions, including the U.S. (1). Type 1 diabetes accounts for >45,000 cases of ESRD per year (2), and the incidence may be higher than in people with type 2 diabetes (3). Despite this, there are few population-based data available regarding the prevalence and incidence of ESRD in people with type 1 diabetes in the U.S. (4). A declining incidence of ESRD has been suggested by findings of lower incidence with increasing calendar year of diagnosis and in comparison with older reports in some studies in Europe and the U.S. (5–8). This is consistent with better diabetes management tools becoming available and increased renoprotective efforts, including the greater use of ACE inhibitors and angiotensin type II receptor blockers, over the past two to three decades (9). Conversely, no reduction in the incidence of ESRD across enrollment cohorts was found in a recent clinic-based study (9). Further, an increase in ESRD has been suggested for older but not younger people (9). Recent improvements in diabetes care have been suggested to delay rather than prevent the development of renal disease in people with type 1 diabetes (4).
A decrease in the prevalence of proliferative retinopathy by increasing calendar year of type 1 diabetes diagnosis was previously reported in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) cohort (10); therefore, we sought to determine if a similar pattern of decline in ESRD would be evident over 25 years of follow-up. Further, we investigated factors that may mediate a possible decline in ESRD as well as other factors associated with incident ESRD over time.”
“At baseline, 99% of WESDR cohort members were white and 51% were male. Individuals were 3–79 years of age (mean 29) with diabetes duration of 0–59 years (mean 15), diagnosed between 1922 and 1980. Four percent of individuals used three or more daily insulin injections and none used an insulin pump. Mean HbA1c was 10.1% (87 mmol/mol). Only 16% were using an antihypertensive medication, none was using an ACE inhibitor, and 3% reported a history of renal transplant or dialysis (ESRD). At 25 years, 514 individuals participated (52% of original cohort at baseline, n = 996) and 367 were deceased (37% of baseline). Mean HbA1c was much lower than at baseline (7.5%, 58 mmol/mol), the decline likely due to the improvements in diabetes care, with 80% of participants using intensive insulin management (three or more daily insulin injections or insulin pump). The decline in HbA1c was steady, becoming slightly steeper following the results of the DCCT (25). Overall, at the 25-year follow-up, 47% had proliferative retinopathy, 53% used aspirin daily, and 54% reported taking antihypertensive medications, with the majority (87%) using an ACE inhibitor. Thirteen percent reported a history of ESRD.”
“Prevalence of ESRD was negligible until 15 years of diabetes duration and then steadily increased with 5, 8, 10, 13, and 14% reporting ESRD by 15–19, 20–24, 25–29, 30–34, and 35+ years of diabetes duration, respectively. […] After 15 years of diagnosis, prevalence of ESRD increased with duration in people diagnosed from 1960 to 1980, with the lowest increase in people with the most recent diagnosis. People diagnosed from 1922 to 1959 had consistent rather than increasing levels of ESRD with duration of 20+ years. If not for their greater mortality (at the 25-year follow-up, 48% of the deceased had been diagnosed prior to 1960), an increase with duration may have also been observed.
From baseline, the unadjusted cumulative 25-year incidence of ESRD was 17.9% (95% CI 14.3–21.5) in males, 10.3% (7.4–13.2) in females, and 14.2% (11.9–16.5) overall. For those diagnosed in 1970–1980, the cumulative incidence at 14, 20, and 25 years of follow-up (or ∼15–25, 20–30, and 25–35 years diabetes duration) was 5.2, 7.9, and 9.3%, respectively. At 14, 20, and 25 years of follow-up (or 35, 40, and 45 up to 65+ years diabetes duration), the cumulative incidence in those diagnosed during 1922–1969 was 13.6, 16.3, and 18.8%, respectively, consistent with the greater prevalence observed for these diagnosis periods at longer duration of diabetes.”
“The unadjusted hazard of ESRD was reduced by 70% among those diagnosed in 1970–1980 as compared with those in 1922–1969 (HR 0.29 [95% CI 0.19–0.44]). Duration (by 10%) and HbA1c (by an additional 10%) partially mediated this association […] Blood pressure and antihypertensive medication use each further attenuated the association. When fully adjusted for these and [other risk factors included in the model], period of diagnosis was no longer significant (HR 0.89 [0.55–1.45]). Sensitivity analyses for the hazard of incident ESRD or death due to renal disease showed similar findings […] The most parsimonious model included diabetes duration, HbA1c, age, sex, systolic and diastolic blood pressure, and history of antihypertensive medication […]. A 32% increased risk for incident ESRD was found per increasing year of diabetes duration at 0–15 years (HR 1.32 per year [95% CI 1.16–1.51]). The hazard plateaued (1.01 per year [0.98–1.05]) after 15 years of duration of diabetes. Hazard of ESRD increased with increasing HbA1c (1.28 per 1% or 10.9 mmol/mol increase [1.14–1.45]) and blood pressure (1.51 per 10 mmHg increase in systolic pressure [1.35–1.68]; 1.12 per 5 mmHg increase in diastolic pressure [1.01–1.23]). Use of antihypertensive medications increased the hazard of incident ESRD nearly fivefold [this finding is almost certainly due to confounding by indication, as also noted by the authors later on in the paper – US], and males had approximately two times the risk as compared with females. […] Having proliferative retinopathy was strongly associated with increased risk (HR 5.91 [3.00–11.6]) and attenuated the association between sex and ESRD.”
“The current investigation […] sought to provide much-needed information on the prevalence and incidence of ESRD and associated risk specific to people with type 1 diabetes. Consistent with a few previous studies (5,7,8), we observed decreased prevalence and incidence of ESRD among individuals with type 1 diabetes diagnosed in the 1970s compared with prior to 1970. The Epidemiology of Diabetes Complications (EDC) Study, another large cohort of people with type 1 diabetes followed over a long period of time, reported cumulative incidence rates of 2–6% for those diagnosed after 1970 and with similar duration (7), comparable to our findings. Slightly higher cumulative incidence (7–13%) reported from older studies at slightly lower duration also supports a decrease in incidence of ESRD (28–30). Cumulative incidences through 30 years in European cohorts were even lower (3.3% in Sweden [6] and 7.8% in Finland [5]), compared with the 9.3% noted for those diagnosed during 1970–1980 in the WESDR cohort. The lower incidence could be associated with nationally organized care, especially in Sweden where a nationwide intensive diabetes management treatment program was implemented at least a decade earlier than recommendations for intensive care followed from the results of the DCCT in the U.S.”
“We noted an increased risk of incident ESRD in the first 15 years of diabetes not evident at longer durations. This pattern also demonstrated by others could be due to a greater earlier risk among people most genetically susceptible, as only a subset of individuals with type 1 diabetes will develop renal disease (27,28). The risk plateau associated with greater durations of diabetes and lower risk associated with increasing age may also reflect more death at longer durations and older ages. […] Because age and duration are highly correlated, we observed a positive association between age and ESRD only in univariate analyses, without adjustment for duration. The lack of adjustment for diabetes duration may have, in part, explained the increasing incidence of ESRD shown with age for some people in a recent investigation (9). Adjustment for both age and duration was found appropriate after testing for collinearity in the current analysis.”
“In conclusion, this U.S. population-based report showed a lower prevalence and incidence of ESRD among those more recently diagnosed, explained by improvements in glycemic and blood pressure control over the last several decades. Even lower rates may be expected for those diagnosed during the current era of diabetes care. Intensive diabetes management, especially for glycemic control, remains important even in long-standing diabetes as potentially delaying the development of ESRD.“
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iii. Earlier Onset of Complications in Youth With Type 2 Diabetes.
The prevalence of type 2 diabetes in youth is increasing worldwide, coinciding with the rising obesity epidemic (1,2). […] Diabetes is associated with both microvascular and macrovascular complications. The evolution of these complications has been well described in type 1 diabetes (6) and in adult type 2 diabetes (7), wherein significant complications typically manifest 15–20 years after diagnosis (8). Because type 2 diabetes is a relatively new disease in children (first described in the 1980s), long-term outcome data on complications are scant, and risk factors for the development of complications are incompletely understood. The available literature suggests that development of complications in youth with type 2 diabetes may be more rapid than in adults, thus afflicting individuals at the height of their individual and social productivity (9). […] A small but notable proportion of type 2 diabetes is associated with a polymorphism of hepatic nuclear factor (HNF)-1α, a transcription factor expressed in many tissues […] It is not yet known what effect the HNF-1α polymorphism has on the risk of complications associated with diabetes.”
“The main objective of the current study was to describe the time course and risk factors for microvascular complications (nephropathy, retinopathy, and neuropathy) and macrovascular complications (cardiac, cerebrovascular, and peripheral vascular diseases) in a large cohort of youth [diagnosed with type 2 diabetes] who have been carefully followed for >20 years and to compare this evolution with that of youth with type 1 diabetes. We also compared vascular complications in the youth with type 2 diabetes with nondiabetic control youth. Finally, we addressed the impact of HNF-1α G319S on the evolution of complications in young patients with type 2 diabetes.”
“All prevalent cases of type 2 diabetes and type 1 diabetes (control group 1) seen between January 1986 and March 2007 in the DER-CA for youth aged 1–18 years were included. […] The final type 2 diabetes cohort included 342 youth, and the type 1 diabetes control group included 1,011. The no diabetes control cohort comprised 1,710 youth matched to the type 2 diabetes cohort from the repository […] Compared with the youth with type 1 diabetes, the youth with type 2 diabetes were, on average, older at the time of diagnosis and more likely to be female. They were more likely to have a higher BMIz, live in a rural area, have a low SES, and have albuminuria at diagnosis. […] one-half of the type 2 diabetes group was either a heterozygote (GS) or a homozygote (SS) for the HNF-1α polymorphism […] At the time of the last available follow-up in the DER-CA, the youth with diabetes were, on average, between 15 and 16 years of age. […] The median follow-up times in the repository were 4.4 (range 0–27.4) years for youth with type 2 diabetes, 6.7 ( 0–28.2) years for youth with type 1 diabetes, and 6.0 (0–29.9) years for nondiabetic control youth.”
“After controlling for low SES, sex, and BMIz, the risk associated with type 2 versus type 1 diabetes of any complication was an HR of 1.47 (1.02–2.12, P = 0.04). […] In the univariate analysis, youth with type 2 diabetes were at significantly higher risk of developing any vascular (HR 6.15 [4.26–8.87], P < 0.0001), microvascular (6.26 [4.32–9.10], P < 0.0001), or macrovascular (4.44 [1.71–11.52], P < 0.0001) disease compared with control youth without diabetes. In addition, the youth with type 2 diabetes had an increased risk of opthalmologic (19.49 [9.75–39.00], P < 0.0001), renal (16.13 [7.66–33.99], P < 0.0001), and neurologic (2.93 [1.79–4.80], P ≤ 0.001) disease. There were few cardiovascular, cerebrovascular, and peripheral vascular disease events in all groups (five or fewer events per group). Despite this, there was still a statistically significant higher risk of peripheral vascular disease in the type 2 diabetes group (6.25 [1.68–23.28], P = 0.006).”
“Differences in renal and neurologic complications between the two diabetes groups began to occur before 5 years postdiagnosis, whereas differences in ophthalmologic complications began 10 years postdiagnosis. […] Both cardiovascular and cerebrovascular complications were rare in both groups, but peripheral vascular complications began to occur 15 years after diagnosis in the type 2 diabetes group […] The presence of HNF-1α G319S polymorphism in youth with type 2 diabetes was found to be protective of complications. […] Overall, major complications were rare in the type 1 diabetes group, but they occurred in 1.1% of the type 2 diabetes cohort at 10 years, in 26.0% at 15 years, and in 47.9% at 20 years after diagnosis (P < 0.001) […] youth with type 2 diabetes have a higher risk of any complication than youth with type 1 diabetes and nondiabetic control youth. […] The time to both renal and neurologic complications was significantly shorter in youth with type 2 diabetes than in control youth, whereas differences were not significant with respect to opthalmologic and cardiovascular complications between cohorts. […] The current study is consistent with the literature, which has shown high rates of cardiovascular risk factors in youth with type 2 diabetes. However, despite the high prevalence of risk, this study reports low rates of clinical events. Because the median follow-up time was between 5 and 8 years, it is possible that a longer follow-up period would be required to correctly evaluate macrovascular outcomes in young adults. Also possible is that diagnoses of mild disease are not being made because of a low index of suspicion in 20- and 30-year-old patients.”
“In conclusion, youth with type 2 diabetes have an increased risk of complications early in the course of their disease. Microvascular complications and cardiovascular risk factors are highly prevalent, whereas macrovascular complications are rare in young adulthood. HbA1c is an important modifiable risk factor; thus, optimizing glycemic control should remain an important goal of therapy.”
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iv. HbA1c and Coronary Heart Disease Risk Among Diabetic Patients.
“We prospectively investigated the association of HbA1c at baseline and during follow-up with CHD risk among 17,510 African American and 12,592 white patients with type 2 diabetes. […] During a mean follow-up of 6.0 years, 7,258 incident CHD cases were identified. The multivariable-adjusted hazard ratios of CHD associated with different levels of HbA1c at baseline (<6.0 [reference group], 6.0–6.9, 7.0–7.9, 8.0–8.9, 9.0–9.9, 10.0–10.9, and ≥11.0%) were 1.00, 1.07 (95% CI 0.97–1.18), 1.16 (1.04–1.31), 1.15 (1.01–1.32), 1.26 (1.09–1.45), 1.27 (1.09–1.48), and 1.24 (1.10–1.40) (P trend = 0.002) for African Americans and 1.00, 1.04 (0.94–1.14), 1.15 (1.03–1.28), 1.29 (1.13–1.46), 1.41 (1.22–1.62), 1.34 (1.14–1.57), and 1.44 (1.26–1.65) (P trend <0.001) for white patients, respectively. The graded association of HbA1c during follow-up with CHD risk was observed among both African American and white diabetic patients (all P trend <0.001). Each one percentage increase of HbA1c was associated with a greater increase in CHD risk in white versus African American diabetic patients. When stratified by sex, age, smoking status, use of glucose-lowering agents, and income, this graded association of HbA1c with CHD was still present. […] The current study in a low-income population suggests a graded positive association between HbA1c at baseline and during follow-up with the risk of CHD among both African American and white diabetic patients with low socioeconomic status.”
A few more observations from the conclusions:
“Diabetic patients experience high mortality from cardiovascular causes (2). Observational studies have confirmed the continuous and positive association between glycemic control and the risk of cardiovascular disease among diabetic patients (4,5). But the findings from RCTs are sometimes uncertain. Three large RCTs (7–9) designed primarily to determine whether targeting different glucose levels can reduce the risk of cardiovascular events in patients with type 2 diabetes failed to confirm the benefit. Several reasons for the inconsistency of these studies can be considered. First, small sample sizes, short follow-up duration, and few CHD cases in some RCTs may limit the statistical power. Second, most epidemiological studies only assess a single baseline measurement of HbA1c with CHD risk, which may produce potential bias. The recent analysis of 10 years of posttrial follow-up of the UKPDS showed continued reductions for myocardial infarction and death from all causes despite an early loss of glycemic differences (10). The scientific evidence from RCTs was not sufficient to generate strong recommendations for clinical practice. Thus, consensus groups (AHA, ACC, and ADA) have provided a conservative endorsement (class IIb recommendation, level of evidence A) for the cardiovascular benefits of glycemic control (11). In the absence of conclusive evidence from RCTs, observational epidemiological studies might provide useful information to clarify the relationship between glycemia and CHD risk. In the current study with 30,102 participants with diabetes and 7,258 incident CHD cases during a mean follow-up of 6.0 years, we found a graded positive association by various HbA1c intervals of clinical relevance or by using HbA1c as a continuous variable at baseline and during follow-up with CHD risk among both African American and white diabetic patients. Each one percentage increase in baseline and follow-up HbA1c was associated with a 2 and 5% increased risk of CHD in African American and 6 and 11% in white diabetic patients. Each one percentage increase of HbA1c was associated with a greater increase in CHD risk in white versus African American diabetic patients.”
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v. Blood Viscosity in Subjects With Normoglycemia and Prediabetes.
“Blood viscosity (BV) is the force that counteracts the free sliding of the blood layers within the circulation and depends on the internal cohesion between the molecules and the cells. Abnormally high BV can have several negative effects: the heart is overloaded to pump blood in the vascular bed, and the blood itself, more viscous, can damage the vessel wall. Furthermore, according to Poiseuille’s law (1), BV is inversely related to flow and might therefore reduce the delivery of insulin and glucose to peripheral tissues, leading to insulin resistance or diabetes (2–5).
It is generally accepted that BV is increased in diabetic patients (6–8). Although the reasons for this alteration are still under investigation, it is believed that the increase in osmolarity causes increased capillary permeability and, consequently, increased hematocrit and viscosity (9). It has also been suggested that the osmotic diuresis, consequence of hyperglycemia, could contribute to reduce plasma volume and increase hematocrit (10).
Cross-sectional studies have also supported a link between BV, hematocrit, and insulin resistance (11–17). Recently, a large prospective study has demonstrated that BV and hematocrit are risk factors for type 2 diabetes. Subjects in the highest quartile of BV were >60% more likely to develop diabetes than their counterparts in the lowest quartile (18). This finding confirms previous observations obtained in smaller or selected populations, in which the association between hemoglobin or hematocrit and occurrence of type 2 diabetes was investigated (19–22).
These observations suggest that the elevation in BV may be very early, well before the onset of diabetes, but definite data in subjects with normal glucose or prediabetes are missing. In the current study, we evaluated the relationship between BV and blood glucose in subjects with normal glucose or prediabetes in order to verify whether alterations in viscosity are appreciable in these subjects and at which blood glucose concentration they appear.”
“According to blood glucose levels, participants were divided into three groups: group A, blood glucose <90 mg/dL; group B, blood glucose between 90 and 99 mg/dL; and group C, blood glucose between 100 and 125 mg/dL. […] Hematocrit (P < 0.05) and BV (P between 0.01 and 0.001) were significantly higher in subjects with prediabetes and in those with blood glucose ranging from 90 to 99 mg/dL compared with subjects with blood glucose <90 mg/dL. […] The current study shows, for the first time, a direct relationship between BV and blood glucose in nondiabetic subjects. It also suggests that, even within glucose values considered completely normal, individuals with higher blood glucose levels have increases in BV comparable with those observed in subjects with prediabetes. […] Overall, changes in viscosity in diabetic patients are accepted as common and as a result of the disease. However, the relationship between blood glucose, diabetes, and viscosity may be much more complex. […] the main finding of the study is that BV significantly increases already at high-normal blood glucose levels, independently of other common determinants of hemorheology. Intervention studies are needed to verify whether changes in BV can influence the development of type 2 diabetes.”
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“Type 1 diabetes and multiple sclerosis (MS) are organ-specific inflammatory diseases, which result from an autoimmune attack against either pancreatic β-cells or the central nervous system; a combined appearance has been described repeatedly (1–3). For children and adolescents below the age of 21 years, the prevalence of type 1 diabetes in Germany and Austria is ∼19.4 cases per 100,000 population, and for MS it is 7–10 per 100,000 population (4–6). A Danish cohort study revealed a three times higher risk for the development of MS in patients with type 1 diabetes (7). Further, an Italian study conducted in Sardinia showed a five times higher risk for the development of type 1 diabetes in MS patients (8,9). An American study on female adults in whom diabetes developed before the age of 21 years yielded an up to 20 times higher risk for the development of MS (10).
These findings support the hypothesis of clustering between type 1 diabetes and MS. The pathogenesis behind this association is still unclear, but T-cell cross-reactivity was discussed as well as shared disease associations due to the HLA-DRB1-DQB1 gene loci […] The aim of this study was to evaluate the prevalence of MS in a diabetic population and to look for possible factors related to the co-occurrence of MS in children and adolescents with type 1 diabetes using a large multicenter survey from the Diabetes Patienten Verlaufsdokumentation (DPV) database.”
“We used a large database of pediatric and adolescent type 1 diabetic patients to analyze the RR of MS co-occurrence. The DPV database includes ∼98% of the pediatric diabetic population in Germany and Austria below the age of 21 years. In children and adolescents, the RR for MS in type 1 diabetes was estimated to be three to almost five times higher in comparison with the healthy population.”
A few diabetes papers of interest
i. The Pharmacogenetics of Type 2 Diabetes: A Systematic Review.
“We performed a systematic review to identify which genetic variants predict response to diabetes medications.
RESEARCH DESIGN AND METHODS We performed a search of electronic databases (PubMed, EMBASE, and Cochrane Database) and a manual search to identify original, longitudinal studies of the effect of diabetes medications on incident diabetes, HbA1c, fasting glucose, and postprandial glucose in prediabetes or type 2 diabetes by genetic variation.
RESULTS Of 7,279 citations, we included 34 articles (N = 10,407) evaluating metformin (n = 14), sulfonylureas (n = 4), repaglinide (n = 8), pioglitazone (n = 3), rosiglitazone (n = 4), and acarbose (n = 4). […] Significant medication–gene interactions for glycemic outcomes included 1) metformin and the SLC22A1, SLC22A2, SLC47A1, PRKAB2, PRKAA2, PRKAA1, and STK11 loci; 2) sulfonylureas and the CYP2C9 and TCF7L2 loci; 3) repaglinide and the KCNJ11, SLC30A8, NEUROD1/BETA2, UCP2, and PAX4 loci; 4) pioglitazone and the PPARG2 and PTPRD loci; 5) rosiglitazone and the KCNQ1 and RBP4 loci; and 5) acarbose and the PPARA, HNF4A, LIPC, and PPARGC1A loci. Data were insufficient for meta-analysis.
CONCLUSIONS We found evidence of pharmacogenetic interactions for metformin, sulfonylureas, repaglinide, thiazolidinediones, and acarbose consistent with their pharmacokinetics and pharmacodynamics.”
“In this systematic review, we identified 34 articles on the pharmacogenetics of diabetes medications, with several reporting statistically significant interactions between genetic variants and medications for glycemic outcomes. Most pharmacogenetic interactions were only evaluated in a single study, did not use a control group, and/or did not report enough information to judge internal validity. However, our results do suggest specific, biologically plausible, gene–medication interactions, and we recommend confirmation of the biologically plausible interactions as a priority, including those for drug transporters, metabolizers, and targets of action. […] Given the number of comparisons reported in the included studies and the lack of accounting for multiple comparisons in approximately 53% of studies, many of the reported findings may [however] be false positives.”
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ii. Insights Offered by Economic Analyses.
“This issue of Diabetes Care includes three economic analyses. The first describes the incremental costs of diabetes over a lifetime and highlights how interventions to prevent diabetes may reduce lifetime costs (1). The second demonstrates that although an expensive, intensive lifestyle intervention for type 2 diabetes does not reduce adverse cardiovascular outcomes over 10 years, it significantly reduces the costs of non-intervention−related medical care (2). The third demonstrates that although the use of the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria for the screening and diagnosis of gestational diabetes mellitus (GDM) results in a threefold increase in the number of people labeled as having GDM, it reduces the risk of maternal and neonatal adverse health outcomes and reduces costs (3). The first report highlights the enormous potential value of intervening in adults at high risk for type 2 diabetes to prevent its development. The second illustrates the importance of measuring economic outcomes in addition to standard clinical outcomes to fully assess the value of new treatments. The third demonstrates the importance of rigorously weighing the costs of screening and treatment against the costs of health outcomes when evaluating new approaches to care.”
“The costs of diabetes monitoring and treatment accrue as of function of the duration of diabetes, so adults who are younger at diagnosis are more likely to survive to develop the late, expensive complications of diabetes, thus they incur higher lifetime costs attributable to diabetes. Zhuo et al. report that people with diabetes diagnosed at age 40 spend approximately $125,000 more for medical care over their lifetimes than people without diabetes. For people diagnosed with diabetes at age 50, the discounted lifetime excess medical spending is approximately $91,000; for those diagnosed at age 60, it is approximately $54,000; and for those diagnosed at age 65, it is approximately $36,000 (1).
These results are very consistent with results reported by the Diabetes Prevention Program (DPP) Research Group, which assessed the cost-effectiveness of diabetes prevention. […] In the simulated lifetime economic analysis [included in that study] the lifestyle intervention was more cost-effective in younger participants than in older participants (5). By delaying the onset of type 2 diabetes, the lifestyle intervention delayed or prevented the need for diabetes monitoring and treatment, surveillance of diabetic microvascular and neuropathic complications, and treatment of the late, expensive complications and comorbidities of diabetes, including end-stage renal disease and cardiovascular disease (5). Although this finding was controversial at the end of the randomized, controlled clinical trial, all but 1 of 12 economic analyses published by 10 research groups in nine countries have demonstrated that lifestyle intervention for the prevention of type 2 diabetes is very cost-effective, if not cost-saving, compared with a placebo intervention (6).
Empiric, within-trial economic analyses of the DPP have now demonstrated that the incremental costs of the lifestyle intervention are almost entirely offset by reductions in the costs of medical care outside the study, especially the cost of self-monitoring supplies, prescription medications, and outpatient and inpatient care (7). Over 10 years, the DPP intensive lifestyle intervention cost only ∼$13,000 per quality-adjusted life-year gained when the analysis used an intent-to-treat approach (7) and was even more cost-effective when the analysis assessed outcomes and costs among adherent participants (8).”
“The American Diabetes Association has reported that although institutional care (hospital, nursing home, and hospice care) still account for 52% of annual per capita health care expenditures for people with diabetes, outpatient medications and supplies now account for 30% of expenditures (9). Between 2007 and 2012, annual per capita expenditures for inpatient care increased by 2%, while expenditures for medications and supplies increased by 51% (9). As the costs of diabetes medications and supplies continue to increase, it will be even more important to consider cost savings arising from the less frequent use of medications when evaluating the benefits of nonpharmacologic interventions.”
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iii. The Lifetime Cost of Diabetes and Its Implications for Diabetes Prevention. (This is the Zhuo et al. paper mentioned above.)
“We aggregated annual medical expenditures from the age of diabetes diagnosis to death to determine lifetime medical expenditure. Annual medical expenditures were estimated by sex, age at diagnosis, and diabetes duration using data from 2006–2009 Medical Expenditure Panel Surveys, which were linked to data from 2005–2008 National Health Interview Surveys. We combined survival data from published studies with the estimated annual expenditures to calculate lifetime spending. We then compared lifetime spending for people with diabetes with that for those without diabetes. Future spending was discounted at 3% annually. […] The discounted excess lifetime medical spending for people with diabetes was $124,600 ($211,400 if not discounted), $91,200 ($135,600), $53,800 ($70,200), and $35,900 ($43,900) when diagnosed with diabetes at ages 40, 50, 60, and 65 years, respectively. Younger age at diagnosis and female sex were associated with higher levels of lifetime excess medical spending attributed to diabetes.
CONCLUSIONS Having diabetes is associated with substantially higher lifetime medical expenditures despite being associated with reduced life expectancy. If prevention costs can be kept sufficiently low, diabetes prevention may lead to a reduction in long-term medical costs.”
The selection criteria employed in this paper are not perfect; they excluded all individuals below the age of 30 “because they likely had type 1 diabetes”, which although true is only ‘mostly true’. Some of those individuals had(/have) type 2, but if you’re evaluating prevention schemes it probably makes sense to error on the side of caution (better to miss some type 2 patients than to include some type 1s), assuming the timing of the intervention is not too important. This gets more complicated if prevention schemes are more likely to have large and persistent effects in young people – however I don’t think that’s the case, as a counterpoint drug adherence studies often seem to find that young people aren’t particularly motivated to adhere to their treatment schedules compared to their older counterparts (who might have more advanced disease and so are more likely to achieve symptomatic relief by adhering to treatments).
A few more observations from the paper:
“The prevalence of participants with diabetes in the study population was 7.4%, of whom 54% were diagnosed between the ages of 45 and 64 years. The mean age at diagnosis was 55 years, and the mean length of time since diagnosis was 9.4 years (39% of participants with diabetes had been diagnosed for ≤5 years, 32% for 6–15 years, and 27% for ≥16 years). […] The observed annual medical spending for people with diabetes was $13,966—more than twice that for people without diabetes.”
“Regardless of diabetes status, the survival-adjusted annual medical spending decreased after age 60 years, primarily because of a decreasing probability of survival. Because the probability of survival decreased more rapidly in people with diabetes than in those without, corresponding spending declined as people died and no longer accrued medical costs. For example, among men diagnosed with diabetes at age 40 years, 34% were expected to survive to age 80 years; among men of the same age who never developed diabetes, 55% were expected to survive to age 80 years. The expected annual expenditure for a person diagnosed with diabetes at age 40 years declined from $8,500 per year at age 40 years to $3,400 at age 80 years, whereas the expenses for a comparable person without diabetes declined from $3,900 to $3,200 over that same interval. […] People diagnosed with diabetes at age 40 years lived with the disease for an average of 34 years after diagnosis. Those diagnosed when older lived fewer years and, therefore, lost fewer years of life. […] The annual excess medical spending attributed to diabetes […] was smaller among people who were diagnosed at older ages. For men diagnosed at age 40 years, annual medical spending was $3,700 higher than that of similar men without diabetes; spending was $2,900 higher for those diagnosed at age 50 years; $2,200 higher for those diagnosed at age 60 years; and $2,000 higher for those diagnosed at age 65 years. Among women diagnosed with diabetes, the excess annual medical spending was consistently higher than for men of the same age at diagnosis.”
“Regardless of age at diagnosis, people with diabetes spent considerably more on health care after age 65 years than their nondiabetic counterparts. Health care spending attributed to diabetes after age 65 years ranged from $23,900 to $40,900, depending on sex and age at diagnosis. […] Of the total excess lifetime medical spending among an average diabetic patient diagnosed at age 50 years, prescription medications and inpatient care accounted for 44% and 35% of costs, respectively. Outpatient care and other medical care accounted for 17% and 4% of costs, respectively.”
“Our findings differed from those of studies of the lifetime costs of other chronic conditions. For instance, smokers have a lower average lifetime medical cost than nonsmokers (29) because of their shorter life spans. Smokers have a life expectancy about 10 years less than those who do not smoke (30); life expectancy is 16 years less for those who develop smoking-induced cancers (31). As a result, smoking cessation leads to increased lifetime spending (32). Studies of the lifetime costs for an obese person relative to a person with normal body weight show mixed results: estimated excess lifetime medical costs for people with obesity range from $3,790 less to $39,000 more than costs for those who are nonobese (33,34). […] obesity, when considered alone, results in much lower annual excess medical costs than diabetes (–$940 to $1,150 for obesity vs. $2,000 to $4,700 for diabetes) when compared with costs for people who are nonobese (33,34).”
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iv. Severe Hypoglycemia and Mortality After Cardiovascular Events for Type 1 Diabetic Patients in Sweden.
“This study examines factors associated with all-cause mortality after cardiovascular complications (myocardial infarction [MI] and stroke) in patients with type 1 diabetes. In particular, we aim to determine whether a previous history of severe hypoglycemia is associated with increased mortality after a cardiovascular event in type 1 diabetic patients.
Hypoglycemia is the most common and dangerous acute complication of type 1 diabetes and can be life threatening if not promptly treated (1). The average individual with type 1 diabetes experiences about two episodes of symptomatic hypoglycemia per week, with an annual prevalence of 30–40% for hypoglycemic episodes requiring assistance for recovery (2). We define severe hypoglycemia to be an episode of hypoglycemia that requires hospitalization in this study. […] Patients with type 1 diabetes are more susceptible to hypoglycemia than those with type 2 diabetes, and therefore it is potentially of greater relevance if severe hypoglycemia is associated with mortality (6).”
“This study uses a large linked data set comprising health records from the Swedish National Diabetes Register (NDR), which were linked to administrative records on hospitalization, prescriptions, and national death records. […] [The] study is based on data from four sources: 1) risk factor data from the Swedish NDR […], 2) hospital records of inpatient episodes from the National Inpatients Register (IPR) […], 3) death records […], and 4) prescription data records […]. A study comparing registered diagnoses in the IPR with information in medical records found positive predictive values of IPR diagnoses were 85–95% for most diagnoses (8). In terms of NDR coverage, a recent study found that 91% of those aged 18–34 years and with type 1 diabetes in the Prescribed Drug Register could be matched with those in the NDR for 2007–2009 (9).”
“The outcome of the study was all-cause mortality after a major cardiovascular complication (MI or stroke). Our sample for analysis included patients with type 1 diabetes who visited a clinic after 2002 and experienced a major cardiovascular complication after this clinic visit. […] We define type 1 diabetes as diabetes diagnosed under the age of 30 years, being reported as being treated with insulin only at some clinic visit, and when alive, having had at least one prescription for insulin filled per year between 2006 and 2010 […], and not having filled a prescription for metformin at any point between July 2005 and December 2010 (under the assumption that metformin users were more likely to be type 2 diabetes patients).”
“Explanatory variables included in both models were type of complication (MI or stroke), age at complication, duration of diabetes, sex, smoking status, HbA1c, BMI, systolic blood pressure, diastolic blood pressure, chronic kidney disease status based on estimated glomerular filtration rate, microalbuminuria and macroalbuminuria status, HDL, LDL, total–to–HDL cholesterol ratio, triglycerides, lipid medication status, clinic visits within the year prior to the CVD event, and prior hospitalization events: hypoglycemia, hyperglycemia, MI, stroke, heart failure, AF, amputation, PVD, ESRD, IHD/unstable angina, PCI, and CABG. The last known value for each clinical risk factor, prior to the cardiovascular complication, was used for analysis. […] Initially, all explanatory variables were included and excluded if the variable was not statistically significant at a 5% level (P < 0.05) via stepwise backward elimination.” [Aaaaaaargh! – US. These guys are doing a lot of things right, but this is not one of them. Just to mention this one more time: “Generally, hypothesis testing is a very poor basis for model selection […] There is no statistical theory that supports the notion that hypothesis testing with a fixed α level is a basis for model selection.” (Burnham & Anderson)]
“Patients who had prior hypoglycemic events had an estimated HR for mortality of 1.79 (95% CI 1.37–2.35) in the first 28 days after a CVD event and an estimated HR of 1.25 (95% CI 1.02–1.53) of mortality after 28 days post CVD event in the backward regression model. The univariate analysis showed a similar result compared with the backward regression model, with prior hypoglycemic events having an estimated HR for mortality of 1.79 (95% CI 1.38–2.32) and 1.35 (95% CI 1.11–1.65) in the logistic and Cox regressions, respectively. Even when all explanatory factors were included in the models […], the mortality increase associated with a prior severe hypoglycemic event was still significant, and the P values and SE are similar when compared with the backward stepwise regression. Similarly, when explanatory factors were included individually, the mortality increase associated with a prior severe hypoglycemic event was also still significant.” [Again, this sort of testing scheme is probably not a good approach to getting at a good explanatory model, but it’s what they did – US]
“The 5-year cumulative estimated mortality risk for those without complications after MI and stroke were 40.1% (95% CI 35.2–45.1) and 30.4% (95% CI 26.3–34.6), respectively. Patients with prior heart failure were at the highest estimated 5-year cumulative mortality risk, with those who suffered an MI and stroke having a 56.0% (95% CI 47.5–64.5) and 44.0% (95% CI 35.8–52.2) 5-year cumulative mortality risk, respectively. Patients who had a prior severe hypoglycemic event and suffered an MI had an estimated 5-year cumulative mortality risk at age 60 years of 52.4% (95% CI 45.3–59.5), and those who suffered a stroke had a 5-year cumulative mortality risk of 39.8% (95% CI 33.4–46.3). Patients at age 60 years who suffer a major CVD complication have over twofold risk of 5-year mortality compared with the general type 1 diabetic Swedish population, who had an estimated 5-year mortality risk of 13.8% (95% CI 12.0–16.1).”
“We found evidence that prior severe hypoglycemia is associated with reduced survival after a major CVD event but no evidence that prior severe hypoglycemia is associated with an increased risk of a subsequent CVD event.
Compared with the general type 1 diabetic Swedish population, a major CVD complication increased 5-year mortality risk at age 60 years by >25% and 15% in patients with an MI and stroke, respectively. Patients with a history of a hypoglycemic event had an even higher mortality after a major CVD event, with approximately an additional 10% being dead at the 5-year mark. This risk was comparable with that in those with late-stage kidney disease. This information is useful in determining the prognosis of patients after a major cardiovascular event and highlights the need to include this as a risk factor in simulation models (18) that are used to improve decision making (19).”
“This is the first study that has found some evidence of a dose-response relationship, where patients who experienced two or more severe hypoglycemic events had higher mortality after a cardiovascular event compared with those who experienced one severe hypoglycemic event. A lack of statistical power prevented us from investigating this further when we tried to stratify by number of prior severe hypoglycemic events in our regression models. There was no evidence of a dose-response relationship between repeated episodes of severe hypoglycemia and vascular outcomes or death in previous type 2 diabetes studies (5).”
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v. Alterations in White Matter Structure in Young Children With Type 1 Diabetes.
“Careful regulation of insulin dosing, dietary intake, and activity levels are essential for optimal glycemic control in individuals with type 1 diabetes. However, even with optimal treatment many children with type 1 diabetes have blood glucose levels in the hyperglycemic range for more than half the day and in the hypoglycemic range for an hour or more each day (1). Brain cells may be especially sensitive to aberrant blood glucose levels, as glucose is the brain’s principal substrate for its energy needs.
Research in animal models has shown that white matter (WM) may be especially sensitive to dysglycemia-associated insult in diabetes (2–4). […] Early childhood is a period of rapid myelination and brain development (6) and of increased sensitivity to insults affecting the brain (6,7). Hence, study of the developing brain is particularly important in type 1 diabetes.”
“WM structure can be measured with diffusion tensor imaging (DTI), a method based on magnetic resonance imaging (MRI) that uses the movement of water molecules to characterize WM brain structure (8,9). Results are commonly reported in terms of mathematical scalars (representing vectors in vector space) such as fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). FA reflects the degree of diffusion anisotropy of water (how diffusion varies along the three axes) within a voxel (three-dimensional pixel) and is determined by fiber diameter and density, myelination, and intravoxel fiber-tract coherence (increases in which would increase FA), as well as extracellular diffusion and interaxonal spacing (increases in which would decrease FA) (10). AD, a measure of water diffusivity along the main axis of diffusion within a voxel, is thought to reflect fiber coherence and structure of axonal membranes (increases in which would increase AD), as well as microtubules, neurofilaments, and axonal branching (increases in which would decrease AD) (11,12). RD, the mean of the diffusivities perpendicular to the vector with the largest eigenvalue, is thought to represent degree of myelination (13,14) (more myelin would decrease RD values) and axonal “leakiness” (which would increase RD). Often, however, a combination of these WM characteristics results in opposing contributions to the final observed FA/AD/RD value, and thus DTI scalars should not be interpreted globally as “good” or “bad” (15). Rather, these scalars can show between-group differences and relationships between WM structure and clinical variables and are suggestive of underlying histology. Definitive conclusions about histology of WM can only be derived from direct microscopic examination of biological tissue.”
“Children (ages 4 to <10 years) with type 1 diabetes (n = 127) and age-matched nondiabetic control subjects (n = 67) had diffusion weighted magnetic resonance imaging scans in this multisite neuroimaging study. Participants with type 1 diabetes were assessed for HbA1c history and lifetime adverse events, and glucose levels were monitored using a continuous glucose monitor (CGM) device and standardized measures of cognition.
RESULTS Between-group analysis showed that children with type 1 diabetes had significantly reduced axial diffusivity (AD) in widespread brain regions compared with control subjects. Within the type 1 diabetes group, earlier onset of diabetes was associated with increased radial diffusivity (RD) and longer duration was associated with reduced AD, reduced RD, and increased fractional anisotropy (FA). In addition, HbA1c values were significantly negatively associated with FA values and were positively associated with RD values in widespread brain regions. Significant associations of AD, RD, and FA were found for CGM measures of hyperglycemia and glucose variability but not for hypoglycemia. Finally, we observed a significant association between WM structure and cognitive ability in children with type 1 diabetes but not in control subjects. […] These results suggest vulnerability of the developing brain in young children to effects of type 1 diabetes associated with chronic hyperglycemia and glucose variability.”
“The profile of reduced overall AD in type 1 diabetes observed here suggests possible axonal damage associated with diabetes (30). Reduced AD was associated with duration of type 1 diabetes suggesting that longer exposure to diabetes worsens the insult to WM structure. However, measures of hyperglycemia and glucose variability were either not associated or were positively associated with AD values, suggesting that these measures did not contribute to the observed decreased AD in the type 1 diabetes group. A possible explanation for these observations is that several biological processes influence WM structure in type 1 diabetes. Some processes may be related to insulin insufficiency or C-peptide levels independent of glucose levels (31,32) and may affect WM coherence (and reduce AD values as observed in the between-group results). Other processes related to hyperglycemia and glucose variability may target myelin (resulting in reduced FA and increased RD) as well as reduced axonal branching (both would result in increased AD values). Alternatively, these seemingly conflicting AD observations may be due to a dominant effect of age, which could overshadow effects from dysglycemia.
Early age of onset is one of the most replicable risk factors for cognitive impairments in type 1 diabetes (33,34). It has been hypothesized that young children are especially vulnerable to brain insults resulting from episodes of chronic hyperglycemia, hypoglycemia, and acute hypoglycemic complications of type 1 diabetes (seizures and severe hypoglycemic episodes). In addition, fear of hypoglycemia often results in caregivers maintaining relatively higher blood glucose to avoid lows altogether (1), especially in very young children. However, our study suggests that this approach of aggressive hypoglycemia avoidance resulting in hyperglycemia may not be optimal and may be detrimental to WM structure in young children.
Neuronal damage (reflected in altered WM structure) may affect neuronal signal transfer and, thus, cognition (35). Cognitive domains commonly reported to be affected in children with type 1 diabetes include general intellectual ability, visuospatial abilities, attention, memory, processing speed, and executive function (36–38). In our sample, even though the duration of illness was relatively short (2.9 years on average), there were modest but significant cognitive differences between children with type 1 diabetes and control subjects (24).”
“In summary, we present results from the largest study to date investigating WM structure in very young children with type 1 diabetes. We observed significant and widespread brain differences in the WM microstructure of children with type 1 diabetes compared with nondiabetic control subjects and significant associations between WM structure and measures of hyperglycemia, glucose variability, and cognitive ability in the type 1 diabetic population.”
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“Polyneuropathy is a common complication in diabetes. The prevalence of neuropathy in patients with diabetes is ∼30%. During the course of the disease, up to 50% of the patients will eventually develop neuropathy (1). Its clinical features are characterized by numbness, tingling, or burning sensations and typically extend in a distinct stocking and glove pattern. Prevention plays a key role since poor glucose control is a major risk factor in the development of diabetic polyneuropathy (DPN) (1,2).
There is no clear definition for the onset of painful diabetic neuropathy. Different hypotheses have been formulated.
Hyperglycemia in diabetes can lead to osmotic swelling of the nerves, related to increased glucose conversion into sorbitol by the enzyme aldose reductase (2,3). High sorbitol concentrations might also directly cause axonal degeneration and demyelination (2). Furthermore, stiffening and thickening of ligamental structures and the plantar fascia make underlying structures more prone to biomechanical compression (4–6). A thicker and stiffer retinaculum might restrict movements and lead to alterations of the nerve in the tarsal tunnel.
Both swelling of the nerve and changes in the tarsal tunnel might lead to nerve damage through compression.
Furthermore, vascular changes may diminish endoneural blood flow and oxygen distribution. Decreased blood supply in the (compressed) nerve might lead to ischemic damage as well as impaired nerve regeneration.
Several studies suggest that surgical decompression of nerves at narrow anatomic sites, e.g., the tarsal tunnel, is beneficial and has a positive effect on pain, sensitivity, balance, long-term risk of ulcers and amputations, and quality of life (3,7–10). Since the effect of decompression of the tibial nerve in patients with DPN has not been proven with a randomized clinical trial, its contribution as treatment for patients with painful DPN is still controversial. […] In this study, we compare the mean CSA and any changes in shape of the tibial nerve before and after decompression of the tarsal tunnel using ultrasound in order to test the hypothesis that the tarsal tunnel leads to compression of the tibial nerve in patients with DPN.”
“This study, with a large sample size and standardized sonographic imaging procedure with a good reliability, is the first randomized controlled trial that evaluates the effect of decompression of the tibial nerve on the CSA. Although no effect on CSA after surgery was found, this study using ultrasound demonstrates a larger and swollen tibial nerve and thicker flexor retinaculum at the ankle in patients with DPN compared with healthy control subjects.”
I would have been interested to know if there were any observable changes in symptom relief measures post-surgery, even if such variables are less ‘objective’ than measures like CSA (less objective, but perhaps more relevant to the patient…), but the authors did not look at those kinds of variables.
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“Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions worldwide (1). Up to 30% of adults in the U.S. and Europe have NAFLD, and the prevalence of this disease is much higher in people with diabetes (1,2). Indeed, the prevalence of NAFLD on ultrasonography ranges from ∼50 to 70% in patients with type 2 diabetes (3–5) and ∼40 to 50% in patients with type 1 diabetes (6,7). Notably, patients with diabetes and NAFLD are also more likely to develop more advanced forms of NAFLD that may result in end-stage liver disease (8). However, accumulating evidence indicates that NAFLD is associated not only with liver-related morbidity and mortality but also with an increased risk of developing cardiovascular disease (CVD) and other serious extrahepatic complications (8–10).”
“Increasing evidence indicates that NAFLD is strongly associated with an increased risk of CKD [chronic kidney disease, US] in people with and without diabetes (11). Indeed, we have previously shown that NAFLD is associated with an increased prevalence of CKD in patients with both type 1 and type 2 diabetes (15–17), and that NAFLD independently predicts the development of incident CKD in patients with type 2 diabetes (18). However, many of the risk factors for CKD are different in patients with type 1 and type 2 diabetes, and to date, it is uncertain whether NAFLD is an independent risk factor for incident CKD in type 1 diabetes or whether measurement of NAFLD improves risk prediction for CKD, taking account of traditional risk factors for CKD.
Therefore, the aim of the current study was to investigate 1) whether NAFLD is associated with an increased incidence of CKD and 2) whether measurement of NAFLD improves risk prediction for CKD, adjusting for traditional risk factors, in type 1 diabetic patients.”
“Using a retrospective, longitudinal cohort study design, we have initially identified from our electronic database all Caucasian type 1 diabetic outpatients with preserved kidney function (i.e., estimated glomerular filtration rate [eGFR] ≥60 mL/min/1.73 m2) and with no macroalbuminuria (n = 563), who regularly attended our adult diabetes clinic between 1999 and 2001. Type 1 diabetes was diagnosed by the typical presentation of disease, the absolute dependence on insulin treatment for survival, the presence of undetectable fasting C-peptide concentrations, and the presence of anti–islet cell autoantibodies. […] Overall, 261 type 1 diabetic outpatients were included in the final analysis and were tested for the development of incident CKD during the follow-up period […] All participants were periodically seen (every 3–6 months) for routine medical examinations of glycemic control and chronic complications of diabetes. No participants were lost to follow-up. […] For this study, the development of incident CKD was defined as occurrence of eGFR <60 mL/min/1.73 m2 and/or macroalbuminuria (21). Both of these outcome measures were confirmed in all participants in a least two consecutive occasions (within 3–6 months after the first examination).”
“At baseline, the mean eGFRMDRD was 92 ± 23 mL/min/1.73 m2 (median 87.9 [IQR 74–104]), or eGFREPI was 98.6 ± 19 mL/min/1.73 m2 (median 99.7 [84–112]). Most patients (n = 234; 89.7%) had normal albuminuria, whereas 27 patients (10.3%) had microalbuminuria. NAFLD was present in 131 patients (50.2%). […] At baseline, patients who developed CKD at follow-up were older, more likely to be female and obese, and had a longer duration of diabetes than those who did not. These patients also had higher values of systolic blood pressure, A1C, triglycerides, serum GGT, and urinary ACR and lower values of eGFRMDRD and eGFREPI. Moreover, there was a higher percentage of patients with hypertension, metabolic syndrome, microalbuminuria, and some degree of diabetic retinopathy in patients who developed CKD at follow-up compared with those remaining free from CKD. The proportion using antihypertensive drugs (that always included the use of ACE inhibitors or angiotensin receptor blockers) was higher in those who progressed to CKD. Notably, […] this patient group also had a substantially higher frequency of NAFLD on ultrasonography.”
“During follow-up (mean duration 5.2 ± 1.7 years, range 2–10), 61 patients developed CKD using the MDRD study equation to estimate eGFR (i.e., ∼4.5% of participants progressed every year to eGFR <60 mL/min/1.73 m2 or macroalbuminuria). Of these, 28 developed an eGFRMDRD <60 mL/min/1.73 m2 with abnormal albuminuria (micro- or macroalbuminuria), 21 developed a reduced eGFRMDRD with normal albuminuria (but 9 of them had some degree of diabetic retinopathy at baseline), and 12 developed macroalbuminuria alone. None of them developed kidney failure requiring chronic dialysis. […] The annual eGFRMDRD decline for the whole cohort was 2.68 ± 3.5 mL/min/1.73 m2 per year. […] NAFLD patients had a greater annual decline in eGFRMDRD than those without NAFLD at baseline (3.28 ± 3.8 vs. 2.10 ± 3.0 mL/min/1.73 m2 per year, P < 0.005). Similarly, the frequency of a renal functional decline (arbitrarily defined as ≥25% loss of baseline eGFRMDRD) was greater among those with NAFLD than among those without the disease (26 vs. 11%, P = 0.005). […] Interestingly, BMI was not significantly associated with CKD.”
“Our novel findings indicate that NAFLD is strongly associated with an increased incidence of CKD during a mean follow-up of 5 years and that measurement of NAFLD improves risk prediction for CKD, independently of traditional risk factors (age, sex, diabetes duration, A1C, hypertension, baseline eGFR, and microalbuminuria [i.e., the last two factors being the strongest known risk factors for CKD]), in type 1 diabetic adults. Additionally, although NAFLD was strongly associated with obesity, obesity (or increased BMI) did not explain the association between NAFLD and CKD. […] The annual cumulative incidence rate of CKD in our cohort of patients (i.e., ∼4.5% per year) was essentially comparable to that previously described in other European populations with type 1 diabetes and similar baseline characteristics (∼2.5–9% of patients who progressed every year to CKD) (25,26). In line with previously published information (25–28), we also found that hypertension, microalbuminuria, and lower eGFR at baseline were strong predictors of incident CKD in type 1 diabetic patients.”
“There is a pressing and unmet need to determine whether NAFLD is associated with a higher risk of CKD in people with type 1 diabetes. It has only recently been recognized that NAFLD represents an important burden of disease for type 2 diabetic patients (11,17,18), but the magnitude of the problem of NAFLD and its association with risk of CKD in type 1 diabetes is presently poorly recognized. Although there is clear evidence that NAFLD is closely associated with a higher prevalence of CKD both in those without diabetes (11) and in those with type 1 and type 2 diabetes (15–17), only four prospective studies have examined the association between NAFLD and risk of incident CKD (18,29–31), and only one of these studies was published in patients with type 2 diabetes (18). […] The underlying mechanisms responsible for the observed association between NAFLD and CKD are not well understood. […] The possible clinical implication for these findings is that type 1 diabetic patients with NAFLD may benefit from more intensive surveillance or early treatment interventions to decrease the risk for CKD. Currently, there is no approved treatment for NAFLD. However, NAFLD and CKD share numerous cardiometabolic risk factors, and treatment strategies for NAFLD and CKD should be similar and aimed primarily at modifying the associated cardiometabolic risk factors.”
A few diabetes papers of interest
i. Burden of Diabetic Foot Ulcers for Medicare and Private Insurers.
Some observations from the paper (my bold):
“According to the American Diabetes Association, the annual cost of diabetes, which affects 22.3 million people in the U.S., was $245 billion in 2012: $176 billion in excess health care expenditures and $69 billion in reduced workforce productivity (1). While much of the excess health care cost is attributable to treatment of diabetes itself, a substantial amount of the cost differential arises via treatment of chronic complications such as those related to the heart, kidneys, and nervous system (1).
One common complication of diabetes is the development of foot ulcers. Historically, foot ulcers have been estimated to affect 1–4% of patients with diabetes annually (2,3) and as many as 25% of the patients with diabetes over their lifetimes (2). More recently, Margolis et al. (3) have estimated that the annual incidence of foot ulcers among patients with diabetes may be as high as 6%. Treatment of diabetic foot ulcers (DFUs) includes conventional wound management (e.g., debridement, moist dressings, and offloading areas of high pressure or friction) as well as more sophisticated treatments such as bioengineered cellular technologies and hyperbaric oxygen therapy (HBO) (4).
DFUs often require extensive healing time and are associated with increased risk for infections and other sequelae that can result in severe and costly outcomes (4). […] DFU patients have a low survival prognosis, with a 3-year cumulative mortality rate of 28% (6) and rates among amputated patients approaching 50% (7).”
“While DFU patients can require substantial amounts of resource use, little is known about the burden of DFUs imposed on the U.S. health care system and payers. In fact, we are aware of only two studies to date that have estimated the incremental medical resource use and costs of DFU beyond that of diabetes alone (6,8). Neither of these analyses, however, accounted for the many underlying differences between DFU and non-DFU patient populations, such as disproportionate presence of costly underlying comorbid conditions among DFU patients […] Other existing literature on the burden of DFUs in the U.S. calculated the overall health care costs (as opposed to incremental) without reference to a non-DFU control population (9–11). As a result of the variety of data and methodologies used, it is not surprising that the burden of DFUs reported in the literature is wide-ranging, with the average per-patient costs, for example, ranging from $4,595 per episode (9) to over $35,000 annually for all services (6).
The objective of this study was to expand and improve on previous research to provide a more robust, current estimate of incremental clinical and economic burden of DFUs. To do so, this analysis examined the differences in medical resource use and costs between patients with DFUs during a recent time period (January 2007–September 2011) and a matched control population with diabetes but without DFUs, using administrative claims records from nationally representative databases for Medicare and privately insured populations. […] [Our] criteria resulted in a final analytic sample of 231,438 Medicare patients, with 29,681 (12.8%) identified as DFU patients and the remaining 201,757 comprising the potential control population of non-DFU diabetic patients. For private insurance, 119,018 patients met the sample selection criteria, with 5,681 (4.8%) DFU patients and 113,337 potential controls (Fig. 1).”
“Prior to matching, DFU patients were statistically different from the non-DFU control population on nearly every dimension examined during the 12-month preindex period. […] The matching process resulted in the identification of 27,878 pairs of DFU and control patients for Medicare and 4,536 pairs for private insurance that were very similar with regards to preindex patient characteristics […] [I]mportantly, the matched DFU and control groups had comparable health care costs during the 12 months prior to the index date (Medicare, $17,744 DFU and controls; private insurance, $14,761 DFU vs. $14,766 controls). […] Despite having matched the groups to ensure similar patient characteristics, DFU patients used significantly (P < 0.0001) more medical resources during the 12-month follow-up period than did the matched controls […]. Among matched Medicare patients, DFU patients had 138.2% more days hospitalized, 85.4% more days of home health care, 40.6% more ED visits, and 35.1% more outpatient/physician office visits. The results were similar for the privately insured DFU patients, who had 173.5% more days hospitalized, 230.0% more days of home health care, 109.0% more ED visits, and 42.5% more outpatient/physician office visits than matched controls. […] The rate of lower limb amputations was 3.8% among matched Medicare DFU patients and 5.0% among matched privately insured DFU patients. In contrast, observed lower limb amputation rates among diabetic patients without foot ulcer were only 0.04% in Medicare and 0.02% in private insurance.”
“Increased medical resource utilization resulted in DFU patients having approximately twice the costs as the matched non-DFU controls […], with annual incremental per-patient medical costs ranging from $11,710 for Medicare ($28,031 vs. $16,320; P < 0.0001) to $15,890 for private insurance ($26,881 vs. $10,991; P < 0.0001). All places of service (i.e., inpatient, ED, outpatient/physician office, home health care, and other) contributed approximately equally to the cost differential among Medicare patients. For the privately insured, however, increased inpatient costs ($17,061 vs. $6,501; P < 0.0001) were responsible for nearly two-thirds of the overall cost differential, […] resulting in total incremental direct health care (i.e., medical + prescription drug) costs of $16,883 ($31,419 vs. $14,536; P < 0.0001). Substantial proportions of the incremental medical costs were attributable to claims with DFU-related diagnoses or procedures for both Medicare (45.1%) and privately insured samples (60.3%).”
“Of the 4,536 matched pairs of privately insured patients, work-loss information was available for 575 DFU patients and 857 controls. DFU patients had $3,259 in excess work-loss costs ($6,311 vs. $3,052; P < 0.0001) compared with matched controls, with disability and absenteeism comprising $1,670 and $1,589 of the overall differential, respectively […] The results indicate that compared with diabetic patients without foot ulcers, DFU patients miss more days of work due to medical-related absenteeism and to disability, imposing additional burden on employers.”
“These estimates indicate that DFU imposes substantial burden on payers beyond that required to treat diabetes itself. For example, prior research has estimated annual per-patient incremental health care expenditures for patients with diabetes (versus those without diabetes) of approximately $7,900 (1). The estimates of this analysis suggest that the presence of DFU further compounds these incremental treatment costs by adding $11,710 to $16,883 per patient. Stated differently, the results indicate that the excess health care costs of DFU are approximately twice that attributable to treatment of diabetes itself, and that the presence of DFU approximately triples the excess cost differential versus a population of patients without diabetes.“
“Using estimates of the total U.S. diabetes population (22.3 million) (1) and the midpoint (3.5%) of annual DFU incidence estimates (1–6%) (2,3), the results of this analysis suggest an annual incremental payer burden of DFU ranging from $9.1 billion (22.3 million patients with diabetes × 3.5% DFU incidence × $11,710 Medicare cost differential) to $13.2 billion (22.3 million patients with diabetes × 3.5% DFU incidence × $16,883 private insurance cost differential). These estimates, moreover, likely understate the actual burden of DFU because the incremental costs referenced in this calculation do not include excess work-loss costs described above, prescription drug costs for Medicare patients, out-of-pocket costs paid by the patient, costs borne by supplemental insurers, and other (non-work loss) indirect costs such as those associated with premature mortality, reduced quality of life, and informal caregiving.”
…
ii. Contributors to Mortality in High-Risk Diabetic Patients in the Diabetes Heart Study.
“Rates of cardiovascular disease (CVD) are two- to fourfold greater in individuals with type 2 diabetes compared with nondiabetic individuals, and up to 65% of all-cause mortality among individuals with type 2 diabetes is attributed to CVD (1,2). However, the risk profile is not uniform for all individuals affected by diabetes (3–5). Coronary artery calcified plaque (CAC), determined using computed tomography, is a measure of CVD burden (6,7). CAC scores have been shown to be an independent predictor of CVD outcomes and mortality in population-based studies (8–10) and a powerful predictor of all-cause and CVD mortality in individuals affected by type 2 diabetes (4,11–15).
In the Diabetes Heart Study (DHS), individuals with CAC >1,000 were found to have greater than 6-fold (16) and 11-fold (17) increased risk for all-cause mortality and CVD mortality, respectively, after 7 years of follow-up. With this high risk for adverse outcomes, it is noteworthy that >50% of the DHS sample with CAC >1,000 have lived with this CVD burden for (now) an average of over 12 years. This suggests that outcomes vary in the type 2 diabetic patient population, even among individuals with the highest risk. This study examined the subset of DHS participants with CAC >1,000 and evaluated whether differences in a range of clinical factors and measurements, including modifiable CVD risk factors, provided further insights into risk for mortality.”
“This investigation focused on 371 high-risk participants (from 260 families) […] The goal of this analysis was to identify clinical and other characteristics that influence risk for all-cause mortality in high-risk (baseline CAC >1,000) DHS participants. […] a predominance of traditional CVD risk factors, including older age, male sex, elevated BMI, and high rates of dyslipidemia and hypertension, was evident in this high-risk subgroup (Table 1). These participants were followed for 8.2 ± 3.0 years (mean ± SD), over which time 41% died. […] a number of indices continued to significantly predict outcome following adjustment for other CVD risk factors (including age, sex, and medication use) […]. Higher cholesterol and LDL concentrations were associated with an increased risk (∼1.3-fold) for mortality […] Slightly larger increases in risk for mortality were observed with changes in kidney function (1.3- to 1.4-fold) and elevated CRP (∼1.4-fold) […] use of cholesterol-lowering medication was less common among the deceased participants; those reporting no use of cholesterol-lowering medication at baseline were at a 1.4-fold increased risk of mortality […] these results confirm that, even among this high-risk group, heterogeneity in known CVD risk factors and associations with adverse outcomes are still observed and support their ongoing consideration as useful tools for individual risk assessment. Finally, the data presented here suggest that use of cholesterol-lowering medication was strongly associated with protection, supporting the known beneficial effects of cholesterol management on CVD risk (28,29). […] data suggest that cholesterol-lowering medications may be used less than recommended and need to be more aggressively targeted as a critical modifiable risk factor.”
…
“Patients aged 6–18 years with and without DKA at diagnosis were studied at four time points: <48 h, 5 days, 28 days, and 6 months postdiagnosis. Patients underwent magnetic resonance imaging (MRI) and spectroscopy with cognitive assessment at each time point. Relationships between clinical characteristics at presentation and MRI and neurologic outcomes were examined using multiple linear regression, repeated-measures, and ANCOVA analyses.”
“With DKA, cerebral white matter showed the greatest alterations with increased total white matter volume and higher mean diffusivity in the frontal, temporal, and parietal white matter. Total white matter volume decreased over the first 6 months. For gray matter in DKA patients, total volume was lower at baseline and increased over 6 months. […] Of note, although changes in total and regional brain volumes over the first 5 days resolved, they were associated with poorer delayed memory recall and poorer sustained and divided attention at 6 months. Age at time of presentation and pH level were predictors of neuroimaging and functional outcomes.
CONCLUSIONS DKA at type 1 diabetes diagnosis results in morphologic and functional brain changes. These changes are associated with adverse neurocognitive outcomes in the medium term.”
“This study highlights the common nature of transient focal cerebral edema and associated impaired mental state at presentation with new-onset type 1 diabetes in children. We demonstrate that alterations occur most markedly in cerebral white matter, particularly in the frontal lobes, and are most prominent in the youngest children with the most dramatic acidemia. […] early brain changes were associated with persisting alterations in attention and memory 6 months later. Children with DKA did not differ in age, sex, SES, premorbid need for school assistance/remediation, or postdiagnosis clinical trajectory. Earlier diagnosis of type 1 diabetes in children may avoid the complication of DKA and the neurological consequences documented in this study and is worthy of a major public health initiative.”
“In relation to clinical risk factors, the degree of acidosis and younger age appeared to be the greatest risk factors for alterations in cerebral structure. […] cerebral volume changes in the frontal, temporal, and parietal regions in the first week after diagnosis were associated with lower attention and memory scores 6 months later, suggesting that functional information processing difficulties persist after resolution of tissue water increases in cerebral white matter. These findings have not been reported to date but are consistent with the growing concern over academic performance in children with diabetes (2). […] Brain injury should no longer be considered a rare complication of DKA. This study has shown that it is both frequent and persistent.” (my bold)
…
“High blood pressure (BP) is a risk factor for coronary artery disease, heart failure, and stroke, as well as for chronic kidney disease. Furthermore, hypertension has been estimated to affect ∼30% of patients with type 1 diabetes (1,2) and both parallels and precedes the worsening of kidney disease in these patients (3–5). […] Despite strong evidence that intensive treatment of elevated BP reduces the risk of cardiovascular disease and microvascular complications, as well as improves the prognosis of patients with diabetic nephropathy (especially with the use of ACE inhibitors [ACEIs] and angiotensin II antagonists [angiotensin receptor blockers, ARBs]) (1,9–11), treatment targets and recommendations seem difficult to meet in clinical practice (12–15). This suggests that the patients might either show poor adherence to the treatment and lifestyle changes or have a suboptimal drug regimen. It is evident that most patients with hypertension might require multiple-drug therapy to reach treatment goals (16). However, certain subgroups of the patients have been considered to have resistant hypertension (RH). RH is defined as office BP that remains above target even after using a minimum of three antihypertensive drugs at maximal tolerated doses, from different classes, one of which is a diuretic. Also, patients with controlled BP using four or more antihypertensive drugs are considered resistant to treatment (17).”
“The true prevalence of RH is unknown, but clinical trials suggest a share between 10 and 30% of the hypertensive patients in the general population (18). […] Only a few studies have considered BP control and treatment in patients with type 1 diabetes (2,15,22). Typically these studies have been limited to a small number of participants, which has not allowed stratifying of the patients according to the nephropathy status. The rate of RH is therefore unknown in patients with type 1 diabetes in general and with respect to different stages of diabetic nephropathy. Therefore, we estimated to what extent patients with type 1 diabetes meet the BP targets proposed by the ADA guidelines. We also evaluated the use of antihypertensive medication and the prevalence of RH in the patients stratified by stage of diabetic nephropathy.”
“[A]ll adult patients with type 1 diabetes from >80 hospitals and primary healthcare centers across Finland were asked to participate. Type 1 diabetes was defined by age at onset of diabetes <40 years, C-peptide ≤0.3 nmol/L, and insulin treatment initiated within 1 year of diagnosis, if C-peptide was not measured. […] we used two different ADA BP targets: <130/85 mmHg, which was the target until 2000 (6), and <130/80 mmHg, which was the target between 2001 and 2012 (7). Patients were divided into groups based on whether their BP had reached the target or not and whether the antihypertensive drug was in use or not. […] uncontrolled hypertension was defined as failure to achieve target BP, based on these two different ADA guidelines, despite use of antihypertensive medication. RH was defined as failure to achieve the goal BP (<130/85 mmHg) even after using a minimum of three antihypertensive drugs, from different classes, one of which was a diuretic. […] On the basis of eGFR (mL/min/1.73 m2) level, patients were classified into five groups according to the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines: stage 1 eGFR ≥90, stage 2 eGFR 60–89, stage 3 eGFR 30–59, stage 4 eGFR 15–29, and stage 5 eGFR <15. Patients who were on dialysis were classified into stage 5. […] A total of 3,678 patients with complete data on systolic and diastolic BP and nephropathy status were identified from the FinnDiane database. […] The mean age was 38.0 ± 12.0 and mean duration of diabetes 22.1 ± 12.3 years. […] The patients with advanced diabetic nephropathy had higher BP, worse dyslipidemia, poorer glycemic control, and more insulin resistance and macrovascular complications. BMI values were lower in the dialysis patients, probably due to renal cachexia.”
“Of all patients, 60.9% did not reach the BP target <130/85 mmHg, and the proportion was 70.3% with the target of <130/80 mmHg. […] The patients who were not on target had higher age and longer duration of diabetes and were more likely to be men. They also had poorer glycemic and lipid control as well as more micro- and macrovascular complications. […] Based on the BP target <130/85 mmHg, more than half of the patients in the normoalbuminuria group did not reach the BP target, and the share increased along with the worsening of nephropathy; two-thirds of the patients in the microalbuminuria group and four–fifths in the macroalbuminuria group were not on target, while even 90% of the dialysis and kidney transplant patients did not reach the target (Fig. 1A). Based on the stricter BP target of <130/80 mmHg, the numbers were obviously worse, but the trend was the same (Fig. 1B).”
“About 37% of the FinnDiane patients had antihypertensive treatment […] Whereas 14.1% of the patients with normal AER [Albumin Excretion Rate] had antihypertensive treatment, the proportions were 60.5% in the microalbuminuric, 90.3% in the macroalbuminuric, 88.6% in the dialysis, and 91.2% in the kidney transplant patients. However, in all groups, only a minority of the patients had BP values on target with the antihypertensive drug treatment they were prescribed […] The mean numbers of antihypertensive drugs varied within the nephropathy groups between those who had BP on target and those who did not […]. However, only in the micro- (P = 0.02) and macroalbuminuria (P = 0.003) groups were the mean numbers of the drugs higher if the BP was not on target, compared with those who had reached the targets. Notably, among the patients with normoalbuminuria who had not reached the BP target, 58% and, of the patients with microalbuminuria, 61% were taking only one antihypertensive drug. In contrast, more than half of the dialysis and 40% of the macroalbuminuric and transplanted patients, who had not reached the targets, had at least three drugs in their regimen. Moreover, one-fifth of the dialysis, 15% of the macroalbuminuric, and 10% of the transplanted patients had at least four antihypertensive drugs in use without reaching the target (Table 2). Almost all patients treated with antihypertensive drugs in the normo-, micro-, and macroalbuminuria groups (76% of normo-, 93% of micro-, and 89% of macrolbuminuric patients) had ACEIs or ARBs in the regimen. The proportions were lower in the ESRD groups: 42% of the dialysis and 29% of the transplanted patients were taking these drugs.”
“In general, the prevalence of RH was 7.9% for all patients with type 1 diabetes (n = 3,678) and 21.2% for the antihypertensive drug–treated patients (n = 1,370). The proportion was higher in men than in women (10.0 vs. 5.7%, P < 0.0001) […] When the patients were stratified by nephropathy status, the figures changed; in the normoalbuminuria group, the prevalence of RH was 1.2% of all and 8.7% of the drug treated patients. The corresponding numbers were 4.7 and 7.8% for the microalbuminuric patients, 28.1 and 31.2% for the macroalbuminuric patients, 36.6 and 41.3% for the patients on dialysis, and 26.3 and 28.8% for the kidney-transplanted patients, respectively […] The prevalence of RH also increased along with the worsening of renal function. The share was 1.4% for all and 7.4% for drug-treated patients at KDOQI stage 1. The corresponding numbers were 3.8 and 10.0% for the patients at stage 2, 26.6 and 30.0% for the patients at stage 3, 54.8 and 56.0% for the patients at stage 4, and 48.0 and 52.1% for those at stage 5, when kidney transplantation patients were excluded. […] In a multivariate logistic regression analysis, higher age, lower eGFR, higher waist-to-hip ratio, higher triglycerides, as well as microalbuminuria and macroalbuminuria, when normoalbuminuria was the reference category, were independently associated with RH […] A separate analysis also showed that dietary sodium intake, based on urinary sodium excretion rate, was independently associated with RH.”
“The current study shows that the prevalence of RH in patients with type 1 diabetes increases alongside the worsening of diabetic nephropathy. Whereas less than one-tenth of the antihypertensive drug–treated patients with normo- or microalbuminuria met the criteria for RH, the proportions were substantially higher among the patients with overt nephropathy: one-third of the patients with macroalbuminuria or a transplanted kidney and even 40% of the patients on dialysis. […] the prevalence of RH for the drug-treated patients was even higher (56%) in patients at the predialysis stage (eGFR 15–29). The findings are consistent with other studies that have demonstrated that chronic kidney disease is a strong predictor of failure to achieve BP targets despite the use of three or more different types of antihypertensive drugs in the general hypertensive population (26).”
“The prevalence of RH was 21.2% of the patients treated with antihypertensive drugs. Previous studies have indicated a prevalence of RH of 13% among patients being treated for hypertension (19–21,27). […] the prevalence [of RH] seems to be […] higher among the drug-treated type 1 diabetic patients. These figures can only partly be explained by the use of a lower treatment target for BP, as recommended for patients with diabetes (6), since even when we used the BP target recommended for hypertensive patients (<140/90 mmHg), our data still showed a higher prevalence of RH (17%).”
“The study also confirmed previous findings that a large number of patients with type 1 diabetes do not achieve the recommended BP targets. Although the prevalence of RH increased with the severity of diabetic nephropathy, our data also suggest that patients with normo- and microalbuminuria might have a suboptimal drug regimen, since the majority of those who had not reached the BP target were taking only one antihypertensive drug. […] There is therefore an urgent need to improve antihypertensive treatment, not only in patients with overt nephropathy but also in those who have elevated BP without complications or early signs of renal disease. Moreover, further emphasis should be placed on the transplanted patients, since it is well known that hypertension affects both graft and patient survival negatively (30).” (my bold)
…
“Neuropathy is a chronic complication that includes a number of distinct syndromes and autonomic dysfunctions and contributes to increase morbidity and mortality in the diabetic population. In particular, cardiovascular autonomic neuropathy (CAN) is an independent risk factor for mortality in type 1 diabetes and is associated with poor prognosis and poor quality of life (1–3). Cardiovascular (CV) autonomic regulation rests upon a balance between sympathetic and parasympathetic innervation of the heart and blood vessels controlling heart rate and vascular dynamics. CAN encompasses several clinical manifestations, from resting tachycardia to fatal arrhythmia and silent myocardial infarction (4).
The mechanisms responsible for altered neural function in diabetes are not fully understood, and it is assumed that multiple mutually perpetuating pathogenic mechanisms may concur. These include dysmetabolic injury, neurovascular insufficiency, deficiency of neurotrophic growth factors and essential fatty acids, advanced glycosylation products (5,6), and autoimmune damage. Independent cross-sectional and prospective (7–13) studies identified circulating autoantibodies to autonomic nervous structures and hypothesized that immune determinants may be involved in autonomic nerve damage in type 1 diabetes. […] However, demonstration of a cause–effect relationship between antibodies (Ab) and diabetic autonomic neuropathy awaits confirmation.”
“We report on a 16-year follow-up study specifically designed to prospectively examine a cohort of patients with type 1 diabetes and aimed at assessing whether the presence of circulating Ab to autonomic nervous structures is associated with increased risk and predictive value of developing CAN. This, in turn, would be highly suggestive of the involvement of autoimmune mechanisms in the pathogenesis of this complication.”
“The present prospective study, conducted in young patients without established autonomic neuropathy at recruitment and followed for over 16 years until adulthood, strongly indicates that a cause–effect relationship may exist between auto-Ab to autonomic nervous tissues and development of diabetic autonomic neuropathy. Incipient or established CAN (22) reached a prevalence of 68% among the Ab-positive patients, significantly higher compared with the Ab-negative patients. […] Logistic regression analysis indicates that auto-Ab carry an almost 15-fold increased RR of developing an abnormal DB [deep breathing] test over 16 years and an almost sixfold increase of developing at least one abnormal CV [cardiovascular] test, independent of other variables. […] Circulating Ab to autonomic structures are associated with the development of autonomic dysfunction in young diabetic patients independent of glycemic control. […] autoimmune mechanisms targeting sympathetic and parasympathetic structures may play a primary etiologic role in the development and progression of autonomic dysfunction in type 1 diabetes in the long term. […] positivity for auto-Ab had a high positive predictive value for the later development of autonomic neuropathy.”
“Diabetic autonomic neuropathy, possibly the least recognized and most overlooked of diabetes complications, has increasingly gained attention as an independent predictor of silent myocardial ischemia and mortality, as consistently indicated by several cross-sectional studies (2,3,33). The pooled prevalence rate risk for silent ischemia is estimated at 1.96 by meta-analysis studies (5). In this report, established CAN (22) was detected in nearly 20% of young adult patients with acceptable metabolic control, after over approximately 23 years of diabetes duration, against 12% of patients of the same cohort with subtle asymptomatic autonomic dysfunction (one abnormal CV test) a decade earlier, in line with other studies in type 1 diabetes (2,24). Approximately 30% of the patients developed signs of peripheral somatic neuropathy not associated with autonomic dysfunction. This discrepancy suggests the participation of pathogenic mechanisms different from metabolic control and a distinct clinical course, as indicated by the DCCT study, where hyperglycemia had a less robust relationship with autonomic than somatic neuropathy (6).”
“Furthermore, this study shows that autonomic neuropathy, together with female sex and the occurrence of severe hypoglycemia, is a major determinant for poor quality of life in patients with type 1 diabetes. This is in agreement with previous reports (35) and linked to such invalidating symptoms as orthostatic hypotension and chronic diarrhea. […] In conclusion, the current study provides persuasive evidence for a primary pathogenic role of autoimmunity in the development of autonomic diabetic neuropathy. However, the mechanisms through which auto-Ab impair their target organ function, whether through classical complement action, proapoptotic effects of complement, enhanced antigen presentation, or channelopathy (26,39,40), remain to be elucidated.” (my bold)
…
“According to current understanding, the pathophysiology of type 2 diabetes is different in Japanese compared with Caucasians in the sense that Japanese are unable to compensate insulin resistance with increased insulin secretion to the same extent as Caucasians. Prediabetes and early stage diabetes in Japanese are characterized by reduced β-cell function combined with lower degree of insulin resistance compared with Caucasians (8–10). In a prospective, cross-sectional study of individuals with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT), it was demonstrated that Japanese in Japan were more insulin sensitive than Mexican Americans in the U.S. and Arabs in Israel (11). The three populations also differed with regards to β-cell response, whereas the disposition index — a measure of insulin secretion relative to insulin resistance — was similar across ethnicities for NGT and IGT participants. These studies suggest that profound differences in type 2 diabetes pathophysiology exist between different populations. However, few attempts have been made to establish the underlying demographic or lifestyle-related factors such as body composition, physical fitness, and physical activity leading to these differences.”
“The current study aimed at comparing Japanese and Caucasians at various glucose tolerance states, with respect to 1) insulin sensitivity and β-cell response and 2) the role of demographic, genetic, and lifestyle-related factors as underlying predictors for possible ethnic differences in insulin sensitivity and β-cell response. […] In our study, glucose profiles from OGTTs [oral glucose tolerance tests] were similar in Japanese and Caucasians, whereas insulin and C-peptide responses were lower in Japanese participants compared with Caucasians. In line with these observations, measures of β-cell response were generally lower in Japanese, who simultaneously had higher insulin sensitivity. Moreover, β-cell response relative to the degree of insulin resistance as measured by disposition indices was virtually identical in the two populations. […] We […] confirmed the existence of differences in insulin sensitivity and β-cell response between Japanese and Caucasians and showed for the first time that a major part of these differences can be explained by differences in body composition […]. On the basis of these results, we propose a similar pathophysiology of type 2 diabetes in Caucasians and Japanese with respect to insulin sensitivity and β-cell function.”
A few diabetes papers of interest
“Children and youth with type 1 diabetes are at risk for developing neurocognitive dysfunction, especially in the areas of psychomotor speed, attention/executive functioning, and visuomotor integration (1,2). Most research suggests that deficits emerge over time, perhaps in response to the cumulative effect of glycemic extremes (3–6). However, the idea that cognitive changes emerge gradually has been challenged (7–9). Ryan (9) argued that if diabetes has a cumulative effect on cognition, cognitive test performance should be positively correlated with illness duration. Yet he found comparable deficits in psychomotor speed (the most commonly noted area of deficit) in adolescents and young adults with illness duration ranging from 6 to 25 years. He therefore proposed a diathesis model in which cognitive declines in diabetes are especially likely to occur in more vulnerable patients, at crucial periods, in response to illness-related events (e.g., severe hyperglycemia) known to have an impact on the central nervous system (CNS) (8). This model accounts for the finding that cognitive deficits are more likely in children with early-onset diabetes, and for the accelerated cognitive aging seen in diabetic individuals later in life (7). A third hypothesized crucial period is the time leading up to diabetes diagnosis, during which severe fluctuations in blood glucose and persistent hyperglycemia often occur. Concurrent changes in blood-brain barrier permeability could result in a flood of glucose into the brain, with neurotoxic effects (9).”
“In the current study, we report neuropsychological test findings for children and adolescents tested within 3 days of diabetes diagnosis. The purpose of the study was to determine whether neurocognitive impairments are detectable at diagnosis, as predicted by the diathesis hypothesis. We hypothesized that performance on tests of psychomotor speed, visuomotor integration, and attention/executive functioning would be significantly below normative expectations, and that differences would be greater in children with earlier disease onset. We also predicted that diabetic ketoacidosis (DKA), a primary cause of diabetes-related neurological morbidity (12) and a likely proxy for severe peri-onset hyperglycemia, would be associated with poorer performance.”
“Charts were reviewed for 147 children/adolescents aged 5–18 years (mean = 10.4 ± 3.2 years) who completed a short neuropsychological screening during their inpatient hospitalization for new-onset type 1 diabetes, as part of a pilot clinical program intended to identify patients in need of further neuropsychological evaluation. Participants were patients at a large urban children’s hospital in the southwestern U.S. […] Compared with normative expectations, children/youth with type 1 diabetes performed significantly worse on GPD, GPN, VMI, and FAS (P < 0.0001 in all cases), with large decrements evident on all four measures (Fig. 1). A small but significant effect was also evident in DSB (P = 0.022). High incidence of impairment was evident on all neuropsychological tasks completed by older participants (aged 9–18 years) except DSF/DSB (Fig. 2).”
“Deficits in neurocognitive functioning were evident in children and adolescents within days of type 1 diabetes diagnosis. Participants performed >1 SD below normative expectations in bilateral psychomotor speed (GP) and 0.7–0.8 SDs below expected performance in visuomotor integration (VMI) and phonemic fluency (FAS). Incidence of impairment was much higher than normative expectations on all tasks except DSF/DSB. For example, >20% of youth were impaired in dominant hand fine-motor control, and >30% were impaired with their nondominant hand. These findings provide provisional support for Ryan’s hypothesis (7–9) that the peri-onset period may be a time of significant cognitive vulnerability.
Importantly, deficits were not evident on all measures. Performance on measures of attention/executive functioning (TMT-A, TMT-B, DSF, and DSB) was largely consistent with normative expectations, as was reading ability (WRAT-4), suggesting that the below-average performance in other areas was not likely due to malaise or fatigue. Depressive symptoms at diagnosis were associated with performance on TMT-B and FAS, but not on other measures. Thus, it seems unlikely that depressive symptoms accounted for the observed motor slowing.
Instead, the findings suggest that the visual-motor system may be especially vulnerable to early effects of type 1 diabetes. This interpretation is especially compelling given that psychomotor impairment is the most consistently reported long-term cognitive effect of type 1 diabetes. The sensitivity of the visual-motor system at diabetes diagnosis is consistent with a growing body of neuroimaging research implicating posterior white matter tracts and associated gray matter regions (particularly cuneus/precuneus) as areas of vulnerability in type 1 diabetes (30–32). These regions form part of the neural system responsible for integrating visual inputs with motor outputs, and in adults with type 1 diabetes, structural pathology in these regions is directly correlated to performance on GP [grooved pegboard test] (30,31). Arbelaez et al. (33) noted that these brain areas form part of the “default network” (34), a system engaged during internally focused cognition that has high resting glucose metabolism and may be especially vulnerable to glucose variability.”
“It should be noted that previous studies (e.g., Northam et al. [3]) have not found evidence of neurocognitive dysfunction around the time of diabetes diagnosis. This may be due to study differences in measures, outcomes, and/or time frame. We know of no other studies that completed neuropsychological testing within days of diagnosis. Given our time frame, it is possible that our findings reflect transient effects rather than more permanent changes in the CNS. Contrary to predictions, we found no association between DKA at diagnosis and neurocognitive performance […] However, even transient effects could be considered potential indicators of CNS vulnerability. Neurophysiological changes at the time of diagnosis have been shown to persist under certain circumstances or for some patients. […] [Some] findings suggest that some individuals may be particularly susceptible to the effects of glycemic extremes on neurocognitive function, consistent with a large body of research in developmental neuroscience indicating individual differences in neurobiological vulnerability to adverse events. Thus, although it is possible that the neurocognitive impairments observed in our study might resolve with euglycemia, deficits at diagnosis could still be considered a potential marker of CNS vulnerability to metabolic perturbations (both acute and chronic).”
“In summary, this study provides the first demonstration that type 1 diabetes–associated neurocognitive impairment can be detected at the time of diagnosis, supporting the possibility that deficits arise secondary to peri-onset effects. Whether these effects are transient markers of vulnerability or represent more persistent changes in CNS awaits further study.”
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“Cardiovascular autonomic neuropathy (CAN) is a chronic complication of diabetes and an independent predictor of cardiovascular disease (CVD) morbidity and mortality (1–3). The mechanisms of CAN are complex and not fully understood. It can be assessed by simple cardiovascular reflex tests (CARTs) and heart rate variability (HRV) studies that were shown to be sensitive, noninvasive, and reproducible (3,4).”
“HbA1c fails to capture information on the daily fluctuations in blood glucose levels, termed glycemic variability (GV). Recent observations have fostered the notion that GV, independent of HbA1c, may confer an additional risk for the development of micro- and macrovascular diabetes complications (8,9). […] the relationship between GV and chronic complications, specifically CAN, in patients with type 1 diabetes has not been systematically studied. In addition, limited data exist on the relationship between hypoglycemic components of the GV and measures of CAN among subjects with type 1 diabetes (11,12). Therefore, we have designed a prospective study to evaluate the impact and the possible sustained effects of GV on measures of cardiac autonomic function and other cardiovascular complications among subjects with type 1 diabetes […] In the present communication, we report cross-sectional analyses at baseline between indices of hypoglycemic stress on measures of cardiac autonomic function.”
“The following measures of CAN were predefined as outcomes of interests and analyzed: expiration-to-inspiration ratio (E:I), Valsalva ratio, 30:15 ratios, low-frequency (LF) power (0.04 to 0.15 Hz), high-frequency (HF) power (0.15 to 0.4 Hz), and LF/HF at rest and during CARTs. […] We found that LBGI [low blood glucose index] and AUC [area under the curve] hypoglycemia were associated with reduced LF and HF power of HRV [heart rate variability], suggesting an impaired autonomic function, which was independent of glucose control as assessed by the HbA1c.”
“Our findings are in concordance with a recent report demonstrating attenuation of the baroreflex sensitivity and of the sympathetic response to various cardiovascular stressors after antecedent hypoglycemia among healthy subjects who were exposed to acute hypoglycemic stress (18). Similar associations […] were also reported in a small study of subjects with type 2 diabetes (19). […] higher GV and hypoglycemic stress may have an acute effect on modulating autonomic control with inducing a sympathetic/vagal imbalance and a blunting of the cardiac vagal control (18). The impairment in the normal counter-regulatory autonomic responses induced by hypoglycemia on the cardiovascular system could be important in healthy individuals but may be particularly detrimental in individuals with diabetes who have hitherto compromised cardiovascular function and/or subclinical CAN. In these individuals, hypoglycemia may also induce QT interval prolongation, increase plasma catecholamine levels, and lower serum potassium (19,20). In concert, these changes may lower the threshold for serious arrhythmia (19,20) and could result in an increased risk of cardiovascular events and sudden cardiac death. Conversely, the presence of CAN may increase the risk of hypoglycemia through hypoglycemia unawareness and subsequent impaired ability to restore euglycemia (21) through impaired sympathoadrenal response to hypoglycemia or delayed gastric emptying. […] A possible pathogenic role of GV/hypoglycemic stress on CAN development and progressions should be also considered. Prior studies in healthy and diabetic subjects have found that higher exposure to hypoglycemia reduces the counter-regulatory hormone (e.g., epinephrine, glucagon, and adrenocorticotropic hormone) and blunts autonomic nervous system responses to subsequent hypoglycemia (21). […] Our data […] suggest that wide glycemic fluctuations, particularly hypoglycemic stress, may increase the risk of CAN in patients with type 1 diabetes.”
“In summary, in this cohort of relatively young and uncomplicated patients with type 1 diabetes, GV and higher hypoglycemic stress were associated with impaired HRV reflective of sympathetic/parasympathetic dysfunction with potential important clinical consequences.”
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“In the last decade, several studies have been published that suggest a close association between diabetes and depression. Patients with diabetes have a high prevalence of depression (1) […] and a high prevalence of complications (3). In addition, depression is associated with mortality in these patients (4). […] Because of this strong association, several recent studies have suggested the possibility of a common biological pathway such as inflammation as an underlying mechanism of the association between depression and diabetes (5). […] Multiple mechanisms are involved in the association between diabetes and inflammation, including modulation of lipolysis, alteration of glucose uptake by adipose tissue, and an indirect mechanism involving an increase in free fatty acid levels blocking the insulin signaling pathway (10). Psychological stress can also cause inflammation via innervation of cytokine-producing cells and activation of the sympathetic nervous systems and adrenergic receptors on macrophages (11). Depression enhances the production of inflammatory cytokines (12–14). Overproduction of inflammatory cytokines may stimulate corticotropin-releasing hormone production, a mechanism that leads to hypothalamic-pituitary axis activity. Conversely, cytokines induce depressive-like behaviors; in studies where healthy participants were given endotoxin infusions to trigger cytokine release, the participants developed classic depressive symptoms (15). Based on this evidence, it could be hypothesized that inflammation is the common biological pathway underlying the association between diabetes and depression.”
“[F]ew studies have examined the clinical role of inflammation and depression as biological correlates in patients with diabetes. […] In this study, we hypothesized that high CRP [C-reactive protein] levels were associated with the high prevalence of depression in patients with diabetes and that this association may be modified by obesity or glycemic control. […] Patient data were derived from the second-year survey of a diabetes registry at Tenri Hospital, a regional tertiary care teaching hospital in Japan. […] 3,573 patients […] were included in the study. […] Overall, mean age, HbA1c level, and BMI were 66.0 years, 7.4% (57.8 mmol/mol), and 24.6 kg/m2, respectively. Patients with major depression tended to be relatively young […] and female […] with a high BMI […], high HbA1c levels […], and high hs-CRP levels […]; had more diabetic nephropathy […], required more insulin therapy […], and exercised less […]”.
“In conclusion, we observed that hs-CRP levels were associated with a high prevalence of major depression in patients with type 2 diabetes with a BMI of ≥25 kg/m2. […] In patients with a BMI of <25 kg/m2, no significant association was found between hs-CRP quintiles and major depression […] We did not observe a significant association between hs-CRP and major depression in either of HbA1c subgroups. […] Our results show that the association between hs-CRP and diabetes is valid even in an Asian population, but it might not be extended to nonobese subjects. […] several factors such as obesity and glycemic control may modify the association between inflammation and depression. […] Obesity is strongly associated with chronic inflammation.”
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iv. A Novel Association Between Nondipping and Painful Diabetic Polyneuropathy.
“Sleep problems are common in painful diabetic polyneuropathy (PDPN) (1) and contribute to the effect of pain on quality of life. Nondipping (the absence of the nocturnal fall in blood pressure [BP]) is a recognized feature of diabetic cardiac autonomic neuropathy (CAN) and is attributed to the abnormal prevalence of nocturnal sympathetic activity (2). […] This study aimed to evaluate the relationship of the circadian pattern of BP with both neuropathic pain and pain-related sleep problems in PDPN […] Investigating the relationship between PDPN and BP circadian pattern, we found patients with PDPN exhibited impaired nocturnal decrease in BP compared with those without neuropathy, as well as higher nocturnal systolic BP than both those without DPN and with painless DPN. […] in multivariate analysis including comorbidities and most potential confounders, neuropathic pain was an independent determinant of ∆ in BP and nocturnal systolic BP.”
“PDPN could behave as a marker for the presence and severity of CAN. […] PDPN should increasingly be regarded as a condition of high cardiovascular risk.”
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I think a potentially important take-away from this paper, which they don’t really talk about, is that when you’re analyzing time series data in research contexts where the HbA1c variable is available at the individual level at some base frequency and you then encounter individuals for whom the HbA1c variable is unobserved in such a data set for some time periods/is not observed at the frequency you’d expect, such (implicit) missing values may not be missing at random (for more on these topics see e.g. this post). More specifically, in light of the findings of this paper I think it would make a lot of sense to default to an assumption of missing values being an indicator of worse-than-average metabolic control during the unobserved period of the time series in question when doing time-to-event analyses, especially in contexts where the values are missing for an extended period of time.
The authors of the paper consider metabolic control an outcome to be explained by the testing frequency. That’s one way to approach these things, but it’s not the only one and I think it’s also important to keep in mind that some patients also sometimes make a conscious decision not to show up for their appointments/tests; i.e. the testing frequency is not necessarily fully determined by the medical staff, although they of course have an important impact on this variable.
Some observations from the paper:
“We examined repeat HbA1c tests (400,497 tests in 79,409 patients, 2008–2011) processed by three U.K. clinical laboratories. We examined the relationship between retest interval and 1) percentage change in HbA1c and 2) proportion of cases showing a significant HbA1c rise. The effect of demographics factors on these findings was also explored. […] Figure 1 shows the relationship between repeat requesting interval (categorized in 1-month intervals) and percentage change in HbA1c concentration in the total data set. From 2 months onward, there was a direct relationship between retesting interval and control. A testing frequency of >6 months was associated with deterioration in control. The optimum testing frequency in order to maximize the downward trajectory in HbA1c between two tests was approximately four times per year. Our data also indicate that testing more frequently than 2 months has no benefit over testing every 2–4 months. Relative to the 2–3 month category, all other categories demonstrated statistically higher mean change in HbA1c (all P < 0.001). […] similar patterns were observed for each of the three centers, with the optimum interval to improvement in overall control at ∼3 months across all centers.”
“[I]n patients with poor control, the pattern was similar to that seen in the total group, except that 1) there was generally a more marked decrease or more modest increase in change of HbA1c concentration throughout and, consequently, 2) a downward trajectory in HbA1c was observed when the interval between tests was up to 8 months, rather than the 6 months as seen in the total group. In patients with a starting HbA1c of <6% (<42 mmol/mol), there was a generally linear relationship between interval and increase in HbA1c, with all intervals demonstrating an upward change in mean HbA1c. The intermediate group showed a similar pattern as those with a starting HbA1c of <6% (<42 mmol/mol), but with a steeper slope.”
“In order to examine the potential link between monitoring frequency and the risk of major deterioration in control, we then assessed the relationship between testing interval and proportion of patients demonstrating an increase in HbA1c beyond the normal biological and analytical variation in HbA1c […] Using this definition of significant increase as a ≥9.9% rise in subsequent HbA1c, our data show that the proportion of patients showing this magnitude of rise increased month to month, with increasing intervals between tests for each of the three centers. […] testing at 2–3-monthly intervals would, at a population level, result in a marked reduction in the proportion of cases demonstrating a significant increase compared with annual testing […] irrespective of the baseline HbA1c, there was a generally linear relationship between interval and the proportion demonstrating a significant increase in HbA1c, though the slope of this relationship increased with rising initial HbA1c.”
“Previous data from our and other groups on requesting patterns indicated that relatively few patients in general practice were tested annually (5,6). […] Our data indicate that for a HbA1c retest interval of more than 2 months, there was a direct relationship between retesting interval and control […], with a retest frequency of greater than 6 months being associated with deterioration in control. The data showed that for diabetic patients as a whole, the optimum repeat testing interval should be four times per year, particularly in those with poorer diabetes control (starting HbA1c >7% [≥53 mmol/mol]). […] The optimum retest interval across the three centers was similar, suggesting that our findings may be unrelated to clinical laboratory factors, local policies/protocols on testing, or patient demographics.”
It might be important to mention that there are important cross-country differences in terms of how often people with diabetes get HbA1c measured – I’m unsure of whether or not standards have changed since then, but at least in Denmark a specific treatment goal of the Danish Regions a few years ago was whether or not 95% of diabetics had had their HbA1c measured within the last year (here’s a relevant link to some stuff I wrote about related topics a while back).
Type 1 Diabetes Mellitus and Cardiovascular Disease
“Despite the known higher risk of cardiovascular disease (CVD) in individuals with type 1 diabetes mellitus (T1DM), the pathophysiology underlying the relationship between cardiovascular events, CVD risk factors, and T1DM is not well understood. […] The present review will focus on the importance of CVD in patients with T1DM. We will summarize recent observations of potential differences in the pathophysiology of T1DM compared with T2DM, particularly with regard to atherosclerosis. We will explore the implications of these concepts for treatment of CVD risk factors in patients with T1DM. […] The statement will identify gaps in knowledge about T1DM and CVD and will conclude with a summary of areas in which research is needed.”
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The above quote is from this paper: Type 1 Diabetes Mellitus and Cardiovascular Disease: A Scientific Statement From the American Heart Association and American Diabetes Association.
I originally intended to cover this one in one of my regular diabetes posts, but I decided in the end that there was simply too much stuff to cover here for it to make sense not to devote an entire post to it. I have quoted extensively from the paper/statement below and I also decided to bold a few of the observations I found particularly important/noteworthy(/worth pointing out to people reading along?).
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“T1DM has strong human leukocyte antigen associations to the DQA, DQB, and DRB alleles (2). One or more autoantibodies, including islet cell, insulin, glutamic acid decarboxylase 65 (GAD65), zinc transporter 8 (3), and tyrosine phosphatase IA-2β and IA-2β antibodies, can be detected in 85–90% of individuals on presentation. The rate of β-cell destruction varies, generally occurring more rapidly at younger ages. However, T1DM can also present in adults, some of whom can have enough residual β-cell function to avoid dependence on insulin until many years later. When autoantibodies are present, this is referred to as latent autoimmune diabetes of adulthood. Infrequently, T1DM can present without evidence of autoimmunity but with intermittent episodes of ketoacidosis; between episodes, the need for insulin treatment can come and go. This type of DM, called idiopathic diabetes (1) or T1DM type B, occurs more often in those of African and Asian ancestry (4). Because of the increasing prevalence of obesity in the United States, there are also obese individuals with T1DM, particularly children. Evidence of insulin resistance (such as acanthosis nigricans); fasting insulin, glucose, and C-peptide levels; and the presence of islet cell, insulin, glutamic acid decarboxylase, and phosphatase autoantibodies can help differentiate between T1DM and T2DM, although both insulin resistance and insulin insufficiency can be present in the same patient (5), and rarely, T2DM can present at an advanced stage with low C-peptide levels and minimal islet cell function.”
“Overall, CVD events are more common and occur earlier in patients with T1DM than in nondiabetic populations; women with T1DM are more likely to have a CVD event than are healthy women. CVD prevalence rates in T1DM vary substantially based on duration of DM, age of cohort, and sex, as well as possibly by race/ethnicity (8,11,12). The Pittsburgh Epidemiology of Diabetes Complications (EDC) study demonstrated that the incidence of major coronary artery disease (CAD) events in young adults (aged 28–38 years) with T1DM was 0.98% per year and surpassed 3% per year after age 55 years, which makes it the leading cause of death in that population (13). By contrast, incident first CVD in the nondiabetic population ranges from 0.1% in 35- to 44-year-olds to 7.4% in adults aged 85–94 years (14). An increased risk of CVD has been reported in other studies, with the age-adjusted relative risk (RR) for CVD in T1DM being ≈10 times that of the general population (15–17). One of the most robust analyses of CVD risk in this disease derives from the large UK General Practice Research Database (GPRD), comprising data from >7,400 patients with T1DM with a mean ± SD age of 33 ± 14.5 years and a mean DM duration of 15 ± 12 years (8). CVD events in the UK GPRD study occurred on average 10 to 15 years earlier than in matched nondiabetic control subjects.”
“When types of CVD are reported separately, CHD [coronary heart disease] predominates […] The published cumulative incidence of CHD ranges between 2.1% (18) and 19% (19), with most studies reporting cumulative incidences of ≈15% over ≈15 years of follow-up (20–22). […] Although stroke is less common than CHD in T1DM, it is another important CVD end point. Reported incidence rates vary but are relatively low. […] the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) reported an incidence rate of 5.9% over 20 years (≈0.3%) (21); and the European Diabetes (EURODIAB) Study reported a 0.74% incidence of cerebrovascular disease per year (18). These incidence rates are for the most part higher than those reported in the general population […] PAD [peripheral artery disease] is another important vascular complication of T1DM […] The rate of nontraumatic amputation in T1DM is high, occurring at 0.4–7.2% per year (28). By 65 years of age, the cumulative probability of lower-extremity amputation in a Swedish administrative database was 11% for women with T1DM and 20.7% for men (10). In this Swedish population, the rate of lower-extremity amputation among those with T1DM was nearly 86-fold that of the general population.”
“Abnormal vascular findings associated with atherosclerosis are also seen in patients with T1DM. Coronary artery calcification (CAC) burden, an accepted noninvasive assessment of atherosclerosis and a predictor of CVD events in the general population, is greater in people with T1DM than in nondiabetic healthy control subjects […] With regard to subclinical carotid disease, both carotid intima-media thickness (cIMT) and plaque are increased in children, adolescents, and adults with T1DM […] compared with age- and sex-matched healthy control subjects […] Endothelial function is altered even at a very early stage of T1DM […] Taken together, these data suggest that preclinical CVD can be seen more frequently and to a greater extent in patients with T1DM, even at an early age. Some data suggest that its presence may portend CVD events; however, how these subclinical markers function as end points is not clear.”
“Neuropathy in T1DM can lead to abnormalities in the response of the coronary vasculature to sympathetic stimulation, which may manifest clinically as resting tachycardia or bradycardia, exercise intolerance, orthostatic hypotension, loss of the nocturnal decline in BP, or silent myocardial ischemia on cardiac testing. These abnormalities can lead to delayed presentation of CVD. An early indicator of cardiac autonomic neuropathy is reduced heart rate variability […] Estimates of the prevalence of cardiac autonomic neuropathy in T1DM vary widely […] Cardiac neuropathy may affect as many as ≈40% of individuals with T1DM (45).”
“CVD events occur much earlier in patients with T1DM than in the general population, often after 2 decades of T1DM, which in some patients may be by age 30 years. Thus, in the EDC study, CVD was the leading cause of death in T1DM patients after 20 years of disease duration, at rates of >3% per year (13). Rates of CVD this high fall into the National Cholesterol Education Program’s high-risk category and merit intensive CVD prevention efforts (48). […] CVD events are not generally expected to occur during childhood, even in the setting of T1DM; however, the atherosclerotic process begins during childhood. Children and adolescents with T1DM have subclinical CVD abnormalities even within the first decade of DM diagnosis according to a number of different methodologies”.
“Rates of CVD are lower in premenopausal women than in men […much lower: “Cardiovascular disease develops 7 to 10 years later in women than in men” – US]. In T1DM, these differences are erased. In the United Kingdom, CVD affects men and women with T1DM equally at <40 years of age (23), although after age 40 years, men are affected more than women (51). Similar findings on CVD mortality rates were reported in a large Norwegian T1DM cohort study (52) and in the Allegheny County (PA) T1DM Registry (13), which reported the relative impact of CVD compared with the general population was much higher for women than for men (standardized mortality ratio [SMR] 13.2 versus 5.0 for total mortality and 24.7 versus 8.8 for CVD mortality, women versus men). […] Overall, T1DM appears to eliminate most of the female sex protection seen in the nondiabetic population.”
“The data on atherosclerosis in T1DM are limited. A small angiographic study compared 32 individuals with T1DM to 31 nondiabetic patients matched for age and symptoms (71). That study found atherosclerosis in the setting of T1DM was characterized by more severe (tighter) stenoses, more extensive involvement (multiple vessels), and more distal coronary findings than in patients without DM. A quantitative coronary angiographic study in T1DM suggested more severe, distal disease and an overall increased burden compared with nondiabetic patients (up to fourfold higher) (72).”
“In the general population, inflammation is a central pathological process of atherosclerosis (79). Limited pathology data suggest that inflammation is more prominent in patients with DM than in nondiabetic control subjects (70), and those with T1DM in particular are affected. […] Knowledge of the clinical role of inflammatory markers in T1DM and CVD prediction and management is in its infancy, but early data suggest a relationship with preclinical atherosclerosis. […] Studies showed C-reactive protein is elevated within the first year of diagnosis of T1DM (80), and interleukin-6 and fibrinogen levels are high in individuals with an average disease duration of 2 years (81), independent of adiposity and glycemia (82). Other inflammatory markers such as soluble interleukin-2 receptor (83) and CD40 ligand (84,85) are higher in patients with T1DM than in nondiabetic subjects. Inflammation is evident in youth, even soon after the diagnosis of T1DM. […] The mechanisms by which inflammation operates in T1DM are likely multiple but may include hyperglycemia and hypoglycemia, excess adiposity or altered body fat distribution, thrombosis, and adipokines. Several recent studies have demonstrated a relationship between acute hypoglycemia and indexes of systemic inflammation […] These studies suggest that acute hypoglycemia in T1DM produces complex vascular effects involved in the activation of proinflammatory, prothrombotic, and proatherogenic mechanisms. […] Fibrinogen, a prothrombotic acute phase reactant, is increased in T1DM and is associated with premature CVD (109), and it may be important in vessel thrombosis at later stages of CVD.”
“Genetic polymorphisms appear to influence the progression and prognosis of CVD in T1DM […] Like fibrinogen, haptoglobin is an acute phase protein that inhibits hemoglobin-induced oxidative tissue damage by binding to free hemoglobin (110). […] In humans, there are 2 classes of alleles at the haptoglobin locus, giving rise to 3 possible genotypes: haptoglobin 1-1, haptoglobin 2-1, and haptoglobin 2-2. […] In T1DM, there is an independent twofold increased incidence of CAD in haptoglobin 2-2 carriers compared with those with the haptoglobin 1-1 genotype (117); the 2-1 genotype is associated with an intermediate effect of increased CVD risk. More recently, an independent association was reported in T1DM between the haptoglobin 2-2 genotype and early progression to end-stage renal disease (ESRD) (118). In the CACTI study group, the presence of the haptoglobin 2-2 genotype also doubled the risk of CAC [coronary artery calcification] in patients free from CAC at baseline, after adjustment for traditional CVD risk factors (119). […] At present, genetic testing for polymorphisms in T1DM [however] has no clear clinical utility in CVD prediction or management.”
“Dysglycemia is often conceived of as a vasculopathic process. Preclinical atherosclerosis and epidemiological studies generally support this relationship. Clinical trial data from the DCCT supplied definitive findings strongly in favor of beneficial effects of better glycemic control on CVD outcomes. Glycemia is associated with preclinical atherosclerosis in studies that include tests of endothelial function, arterial stiffness, cIMT, autonomic neuropathy, and left ventricular (LV) function in T1DM […] LV mass and function improve with better glycemic control (126,135,136). Epidemiological evidence generally supports the relationship between hyperglycemia and clinical CHD events in T1DM. […] A large Swedish database review recently reported a reasonably strong association between HbA1c and CAD in T1DM (HR, 1.3 per 1% HbA1c increase) (141). […] findings support the recommendation that early optimal glycemic control in T1DM will have long-term benefits for CVD reduction.”
“Obesity is a known independent risk factor for CVD in nondiabetic populations, but the impact of obesity in T1DM has not been fully established. Traditionally, T1DM was a condition of lean individuals, yet the prevalence of overweight and obesity in T1DM has increased significantly […] This is related to epidemiological shifts in the population overall, tighter glucose control leading to less glucosuria, more frequent/greater caloric intake to fend off real and perceived hypoglycemia, and the specific effects of intensive DM therapy, which has been shown to increase the prevalence of obesity (152). Indeed, several clinical trials, including the DCCT, demonstrate that intensive insulin therapy can lead to excessive weight gain in a subset of patients with T1DM (152). […] No systematic evaluation has been conducted to assess whether improving insulin sensitization lowers rates of CVD. Ironically, the better glycemic control associated with insulin therapy may lead to weight gain, with a superimposed insulin resistance, which may be approached by giving higher doses of insulin. However, some evidence from the EDC study suggests that weight gain in the presence of improved glycemic control is associated with an improved CVD risk profile (162). […] Although T1DM is characteristically a disease of absolute insulin deficiency (154), insulin resistance appears to contribute to CHD risk in patients with T1DM. For example, having a family history of T2DM, which suggests a genetic predisposition for insulin resistance, has been associated with an increased CVD risk in patients with T1DM (155).”
“In general, the lipid levels of adults with well-controlled T1DM are similar to those of individuals without DM […] Worse glycemic control, higher weight (164), and more insulin resistance as measured by euglycemic clamp (165) are associated with a more atherogenic cholesterol distribution in men and women with T1DM […] Studies in pediatric and young adult populations suggest higher lipid values than in youth without T1DM, with glycemic control being a significant contributor (148). […] Most studies show that as is true for the general population, dyslipidemia is a risk factor for CVD in T1DM. Qualitative differences in lipid and lipoprotein fractions are being investigated to determine whether abnormal lipid function may contribute to this. The HDL-C fraction has been of particular interest because the metabolism of HDL-C in T1DM may be altered because of abnormal lipoprotein lipase and hepatic lipase activities related to exogenously administered insulin […] Additionally, as noted earlier, the less efficient handling of heme by the haptoglobin 2-2 genotype in patients with T1DM leaves these complexes less capable of being removed by macrophages, which allows them to associate with HDL, which renders it less functional (116). […] Conventionally, pharmacotherapy is used more aggressively for patients with T1DM and lipid disorders than for nondiabetic patients; however, recommendations for treatment are mostly extrapolated from interventional trials in adults with T2DM, in which rates of CVD events are equivalent to those in secondary prevention populations. Whether this is appropriate for T1DM is not clear […] Awareness of CVD risk and screening for hypercholesterolemia in T1DM have increased over time, yet recent data indicate that control is suboptimal, particularly in younger patients who have not yet developed long-term complications and might therefore benefit from prevention efforts (173). Adults with T1DM who have abnormal lipids and additional risk factors for CVD (e.g., hypertension, obesity, or smoking) who have not developed CVD should be treated with statins. Adults with CVD and T1DM should also be treated with statins, regardless of whether they have additional risk factors.”
“Diabetic kidney disease (DKD) is a complication of T1DM that is strongly linked to CVD. DKD can present as microalbuminuria or macroalbuminuria, impaired GFR, or both. These represent separate but complementary manifestations of DKD and are often, but not necessarily, sequential in their presentation. […] the risk of all-cause mortality increased with the severity of DKD, from microalbuminuria to macroalbuminuria to ESRD. […] Microalbuminuria is likely an indicator of diffuse vascular injury. […] Microalbuminuria is highly correlated with CVD (49,180–182). In the Steno Diabetes Center (Gentofte, Denmark) cohort, T1DM patients with isolated microalbuminuria had a 4.2-fold increased risk of CVD (49,180). In the EDC study, microalbuminuria was associated with mortality risk, with an SMR of 6.4. In the FinnDiane study, mortality risk was also increased with microalbuminuria (SMR, 2.8). […] A recent review summarized these data. In patients with T1DM and microalbuminuria, there was an RR of all-cause mortality of 1.8 (95% CI, 1.5–2.1) that was unaffected by adjustment for confounders (183). Similar RRs were found for mortality from CVD (1.9; 95% CI, 1.3–2.9), CHD (2.1; 95% CI, 1.2–3.5), and aggregate CVD mortality (2.0; 95% CI, 1.5–2.6).”
“Macroalbuminuria represents more substantial kidney damage and is also associated with CVD. Mechanisms may be more closely related to functional consequences of kidney disease, such as higher LDL-C and lower HDL-C. Prospective data from Finland indicate the RR for CVD is ≈10 times greater in patients with macroalbuminuria than in those without macroalbuminuria (184). Historically, in the [Danish] Steno cohort, patients with T1DM and macroalbuminuria had a 37-fold increased risk of CVD mortality compared with the general population (49,180); however, a more recent report from EURODIAB suggests a much lower RR (8.7; 95% CI, 4.03–19.0) (185). […] In general, impaired GFR is a risk factor for CVD, independent of albuminuria […] ESRD [end-stage renal disease, US], the extreme form of impaired GFR, is associated with the greatest risk of CVD of all varieties of DKD. In the EDC study, ESRD was associated with an SMR for total mortality of 29.8, whereas in the FinnDiane study, it was 18.3. It is now clear that GFR loss and the development of eGFR <60 mL · min−1 · 1.73 m−2 can occur without previous manifestation of microalbuminuria or macroalbuminuria (177,178). In T1DM, the precise incidence, pathological basis, and prognosis of this phenotype remain incompletely described.”
“Prevention of DKD remains challenging. Although microalbuminuria and macroalbuminuria are attractive therapeutic targets for CVD prevention, there are no specific interventions directed at the kidney that prevent DKD. Inhibition of the renin-angiotensin-aldosterone system is an attractive option but has not been demonstrated to prevent DKD before it is clinically apparent. […] In contrast to prevention efforts, treatment of DKD with agents that inhibit the renin-angiotensin-aldosterone system is effective. […] angiotensin-converting enzyme (ACE) inhibitors reduce the progression of DKD and death in T1DM (200). Thus, once DKD develops, treatment is recommended to prevent progression and to reduce or minimize other CVD risk factors, which has a positive effect on CVD risk. All patients with T1DM and hypertension or albuminuria should be treated with an ACE inhibitor. If an ACE inhibitor is not tolerated, an angiotensin II receptor blocker (ARB) is likely to have similar efficacy, although this has not been studied specifically in patients with T1DM. Optimal dosing for ACE inhibitors or ARBs in the setting of DKD is not well defined; titration may be guided by BP, albuminuria, serum potassium, and creatinine. Combination therapy of ACE and ARB blockade cannot be specifically recommended at this time.”
“Hypertension is more common in patients with T1DM and is a powerful risk factor for CVD, regardless of whether an individual has DKD. In the CACTI [Coronary Artery Calcification in Type 1 Diabetes] study, hypertension was much more common in patients with T1DM than in age- and sex-matched control subjects (43% versus 15%, P < 0.001); in fact, only 42% of all T1DM patients met the Joint National Commission 7 goal (BP <130/80 mmHg) (201). Hypertension also affects youth with T1DM. The SEARCH trial of youth aged 3–17 years with T1DM (n = 3,691) found the prevalence of elevated BP was 5.9% […] Abnormalities in BP can stem from DKD or obesity. Hyperglycemia may also contribute to hypertension over the long term. In the DCCT/EDIC cohort, higher HbA1c was strongly associated with increased risk of hypertension, and intensive DM therapy reduced the long-term risk of hypertension by 24% (203). […] There are few published trials about the ideal pharmacotherapeutic agent(s) for hypertension in T1DM.”
“Smoking is a major risk factor for CVD, particularly PAD (213); however, there is little information on the prevalence or effects of smoking in T1DM. […] The added CVD risk of smoking may be particularly important in patients with DM, who are already vulnerable. In patients with T1DM, cigarette smoking [has been shown to increase] the risk of DM nephropathy, retinopathy, and neuropathy (214,215) […] Smoking increases CVD risk factors in T1DM via deterioration in glucose metabolism, lipids, and endothelial function (216). Unfortunately, smoking cessation can result in weight gain, which may deter smokers with DM from quitting (217). […] Smoking cessation should be strongly recommended to all patients with T1DM as part of an overall strategy to lower CVD, in particular PAD.”
“CVD risk factors are more common in children with T1DM than in the general pediatric population (218). Population-based studies estimate that 14–45% of children with T1DM have ≥2 CVD risk factors (219–221). As with nondiabetic children, the prevalence of CVD risk factors increases with age (221). […] The American Academy of Pediatrics, the American Heart Association, and the ADA recognize patients with DM, and particularly T1DM, as being in a higher-risk group who should receive more aggressive risk factor screening and treatment than nondiabetic children […] The available data suggest many children and adolescents with T1DM do not receive the recommended treatment for their dyslipidemia and hypertension (220,222).”
“There are no CVD risk-prediction algorithms for patients with T1DM in widespread use. […] Use of the Framingham Heart Study and UK Prospective Diabetes Study (UKPDS) algorithms in the EDC study population did not provide good predictive results, which suggests that neither general or T2DM risk algorithms are sufficient for risk prediction in T1DM (235). On the basis of these findings, a model has been developed with the use of EDC cohort data (236) that incorporates measures outside the Framingham construct (white blood cell count, albuminuria, DM duration). Although this algorithm was validated in the EURODIAB Study cohort (237), it has not been widely adopted, and diagnostic and therapeutic decisions are often based on global CVD risk-estimation methods (i.e., Framingham risk score or T2DM-specific UKPDS risk engine [http://www.dtu.ox.ac.uk/riskengine/index.php]). Other options for CVD risk prediction in patients with T1DM include the ADA risk-assessment tool (http://main.diabetes.org/dorg/mha/main_en_US.html?loc=dorg-mha) and the Atherosclerosis Risk in Communities (ARIC) risk predictor (http://www.aricnews.net/riskcalc/html/RC1.html), but again, accuracy for T1DM is not clear.”
A few diabetes papers of interest
“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 (3–12). 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 (13–16).
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 (3–12,22).”
“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 (9,26–30). 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).”
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“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 (2–6). Type 2 diabetes also has recently been associated with lower brain volume, particularly gray matter volume (7–9).
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 (3,12–14), 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.]
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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 (4–6) 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.”
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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.”
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“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.”
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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 (1–5). Immune cells have been identified in the pancreases of phenotypic T2D patients (3–5). Moreover, treatment with interleukin-1 receptor agonist improves β-cell function in T2D patients (6–8). 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 (9–15). 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 (14,16–20).”
“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.”
A few diabetes papers of interest
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 (22,28–30). […] 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.”
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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.”
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“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 (3–14). 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 (5–7,10–12,24) 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.]
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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.”
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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.”
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vi. Diabetic Peripheral Neuropathy Compromises Balance During Daily Activities.
“Patients with diabetic peripheral neuropathy (DPN) have an altered gait strategy (1–3) and a fivefold increased risk of falling (4–6). 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.”
Type 1 Diabetes Is Associated With an Increased Risk of Fracture Across the Life Span
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:
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“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 (12–19). 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.”
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 (10–12) 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.
…
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.”
…
“[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) (3–6). 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.”
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“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 (3–6). 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).”
…
“Empiric data are limited on best practices in transition care, especially in the U.S. (10,13–16). Prior research, largely from the patient perspective, has highlighted challenges in the transition process, including gaps in care (13,17–19); suboptimal pediatric transition preparation (13,20); increased post-transition hospitalizations (21); and patient dissatisfaction with the transition experience (13,17–19). […] 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.”
How Species Interact
There are multiple reasons why I have not covered Arditi and Ginzburg’s book before, but none of them are related to the quality of the book’s coverage. It’s a really nice book. However the coverage is somewhat technical and model-focused, which makes it harder to blog than other kinds of books. Also, the version of the book I read was a hardcover ‘paper book’ version, and ‘paper books’ take a lot more work for me to cover than do e-books.
I should probably get it out of the way here at the start of the post that if you’re interested in ecology, predator-prey dynamics, etc., this book is a book you would be well advised to read; or, if you don’t read the book, you should at least familiarize yourself with the ideas therein e.g. through having a look at some of Arditi & Ginzburg’s articles on these topics. I should however note that I don’t actually think skipping the book and having a look at some articles instead will necessarily be a labour-saving strategy; the book is not particularly long and it’s to the point, so although it’s not a particularly easy read their case for ratio dependence is actually somewhat easy to follow – if you take the effort – in the sense that I believe how different related ideas and observations are linked is quite likely better expounded upon in the book than they might have been in their articles. The presumably wrote the book precisely in order to provide a concise yet coherent overview.
I have had some trouble figuring out how to cover this book, and I’m still not quite sure what might be/have been the best approach; when covering technical books I’ll often skip a lot of detail and math and try to stick to what might be termed ‘the main ideas’ when quoting from such books, but there’s a clear limit as to how many of the technical details included in a book like this it is possible to skip if you still want to actually talk about the stuff covered in the work, and this sometimes make blogging such books awkward. These authors spend a lot of effort talking about how different ecological models work and which sort of conclusions these different models may lead to in different contexts, and this kind of stuff is a very big part of the book. I’m not sure if you strictly need to have read an ecology textbook or two before you read this one in order to be able to follow the coverage, but I know that I personally derived some benefit from having read Gurney & Nisbet’s ecology text in the past and I did look up stuff in that book a few times along the way, e.g. when reminding myself what a Holling type 2 functional response is and how models with such a functional response pattern behave. ‘In theory’ I assume one might argue that you could theoretically look up all the relevant concepts along the way without any background knowledge of ecology – assuming you have a decent understanding of basic calculus/differential equations, linear algebra, equilibrium dynamics, etc. (…systems analysis? It’s hard for me to know and outline exactly which sources I’ve read in the past which helped make this book easier to read than it otherwise would have been, but suffice it to say that if you look at the page count and think that this will be an quick/easy read, it will be that only if you’ve read more than a few books on ‘related topics’, broadly defined, in the past), but I wouldn’t advise reading the book if all you know is high school math – the book will be incomprehensible to you, and you won’t make it. I ended up concluding that it would simply be too much work to try to make this post ‘easy’ to read for people who are unfamiliar with these topics and have not read the book, so although I’ve hardly gone out of my way to make the coverage hard to follow, the blog coverage that is to follow is mainly for my own benefit.
First a few relevant links, then some quotes and comments.
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Lotka–Volterra equations.
Ecosystem model.
Arditi–Ginzburg equations. (Yep, these equations are named after the authors of this book).
Nicholson–Bailey model.
Functional response.
Monod equation.
Rosenzweig-MacArthur predator-prey model.
Trophic cascade.
Underestimation of mutual interference of predators.
Coupling in predator-prey dynamics: Ratio Dependence.
Michaelis–Menten kinetics.
Trophic level.
Advection–diffusion equation.
Paradox of enrichment. [Two quotes from the book: “actual systems do not behave as Rosensweig’s model predict” + “When ecologists have looked for evidence of the paradox of enrichment in natural and laboratory systems, they often find none and typically present arguments about why it was not observed”]
Predator interference emerging from trophotaxis in predator–prey systems: An individual-based approach.
Directed movement of predators and the emergence of density dependence in predator-prey models.
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“Ratio-dependent predation is now covered in major textbooks as an alternative to the standard prey-dependent view […]. One of this book’s messages is that the two simple extreme theories, prey dependence and ratio dependence, are not the only alternatives: they are the ends of a spectrum. There are ecological domains in which one view works better than the other, with an intermediate view also being a possible case. […] Our years of work spent on the subject have led us to the conclusion that, although prey dependence might conceivably be obtained in laboratory settings, the common case occurring in nature lies close to the ratio-dependent end. We believe that the latter, instead of the prey-dependent end, can be viewed as the “null model of predation.” […] we propose the gradual interference model, a specific form of predator-dependent functional response that is approximately prey dependent (as in the standard theory) at low consumer abundances and approximately ratio dependent at high abundances. […] When density is low, consumers do not interfere and prey dependence works (as in the standard theory). When consumers density is sufficiently high, interference causes ratio dependence to emerge. In the intermediate densities, predator-dependent models describe partial interference.”
“Studies of food chains are on the edge of two domains of ecology: population and community ecology. The properties of food chains are determined by the nature of their basic link, the interaction of two species, a consumer and its resource, a predator and its prey.1 The study of this basic link of the chain is part of population ecology while the more complex food webs belong to community ecology. This is one of the main reasons why understanding the dynamics of predation is important for many ecologists working at different scales.”
“We have named predator-dependent the functional responses of the form g = g(N,P), where the predator density P acts (in addition to N [prey abundance, US]) as an independent variable to determine the per capita kill rate […] predator-dependent functional response models have one more parameter than the prey-dependent or the ratio-dependent models. […] The main interest that we see in these intermediate models is that the additional parameter can provide a way to quantify the position of a specific predator-prey pair of species along a spectrum with prey dependence at one end and ratio dependence at the other end:
g(N) <- g(N,P) -> g(N/P) (1.21)
In the Hassell-Varley and Arditi-Akçakaya models […] the mutual interference parameter m plays the role of a cursor along this spectrum, from m = 0 for prey dependence to m = 1 for ratio dependence. Note that this theory does not exclude that strong interference goes “beyond ratio dependence,” with m > 1.2 This is also called overcompensation. […] In this book, rather than being interested in the interference parameters per se, we use predator-dependent models to determine, either parametrically or nonparametrically, which of the ends of the spectrum (1.21) better describes predator-prey systems in general.”
“[T]he fundamental problem of the Lotka-Volterra and the Rosensweig-MacArthur dynamic models lies in the functional response and in the fact that this mathematical function is assumed not to depend on consumer density. Since this function measures the number of prey captured per consumer per unit time, it is a quantity that should be accessible to observation. This variable could be apprehended either on the fast behavioral time scale or on the slow demographic time scale. These two approaches need not necessarily reveal the same properties: […] a given species could display a prey-dependent response on the fast scale and a predator-dependent response on the slow scale. The reason is that, on a very short scale, each predator individually may “feel” virtually alone in the environment and react only to the prey that it encounters. On the long scale, the predators are more likely to be affected by the presence of conspecifics, even without direct encounters. In the demographic context of this book, it is the long time scale that is relevant. […] if predator dependence is detected on the fast scale, then it can be inferred that it must be present on the slow scale; if predator dependence is not detected on the fast scale, it cannot be inferred that it is absent on the slow scale.”
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Some related thoughts. A different way to think about this – which they don’t mention in the book, but which sprang to mind to me as I was reading it – is to think about this stuff in terms of a formal predator territorial overlap model and then asking yourself this question: Assume there’s zero territorial overlap – does this fact mean that the existence of conspecifics does not matter? The answer is of course no. The sizes of the individual patches/territories may be greatly influenced by the predator density even in such a context. Also, the territorial area available to potential offspring (certainly a fitness-relevant parameter) may be greatly influenced by the number of competitors inhabiting the surrounding territories. In relation to the last part of the quote it’s easy to see that in a model with significant territorial overlap you don’t need direct behavioural interaction among predators for the overlap to be relevant; even if two bears never meet, if one of them eats a fawn the other one would have come across two days later, well, such indirect influences may be important for prey availability. Of course as prey tend to be mobile, even if predator territories are static and non-overlapping in a geographic sense, they might not be in a functional sense. Moving on…
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“In [chapter 2 we] attempted to assess the presence and the intensity of interference in all functional response data sets that we could gather in the literature. Each set must be trivariate, with estimates of the prey consumed at different values of prey density and different values of predator densities. Such data sets are not very abundant because most functional response experiments present in the literature are simply bivariate, with variations of the prey density only, often with a single predator individual, ignoring the fact that predator density can have an influence. This results from the usual presentation of functional responses in textbooks, which […] focus only on the influence of prey density.
Among the data sets that we analyzed, we did not find a single one in which the predator density did not have a significant effect. This is a powerful empirical argument against prey dependence. Most systems lie somewhere on the continuum between prey dependence (m=0) and ratio dependence (m=1). However, they do not appear to be equally distributed. The empirical evidence provided in this chapter suggests that they tend to accumulate closer to the ratio-dependent end than to the prey-dependent end.”
“Equilibrium properties result from the balanced predator-prey equations and contain elements of the underlying dynamic model. For this reason, the response of equilibria to a change in model parameters can inform us about the structure of the underlying equations. To check the appropriateness of the ratio-dependent versus prey-dependent views, we consider the theoretical equilibrium consequences of the two contrasting assumptions and compare them with the evidence from nature. […] According to the standard prey-dependent theory, in reference to [an] increase in primary production, the responses of the populations strongly depend on their level and on the total number of trophic levels. The last, top level always responds proportionally to F [primary input]. The next to the last level always remains constant: it is insensitive to enrichment at the bottom because it is perfectly controled [sic] by the last level. The first, primary producer level increases if the chain length has an odd number of levels, but declines (or stays constant with a Lotka-Volterra model) in the case of an even number of levels. According to the ratio-dependent theory, all levels increase proportionally, independently of how many levels are present. The present purpose of this chapter is to show that the second alternative is confirmed by natural data and that the strange predictions of the prey-dependent theory are unsupported.”
“If top predators are eliminated or reduced in abundance, models predict that the sequential lower trophic levels must respond by changes of alternating signs. For example, in a three-level system of plants-herbivores-predators, the reduction of predators leads to the increase of herbivores and the consequential reduction in plant abundance. This response is commonly called the trophic cascade. In a four-level system, the bottom level will increase in response to harvesting at the top. These predicted responses are quite intuitive and are, in fact, true for both short-term and long-term responses, irrespective of the theory one employs. […] A number of excellent reviews have summarized and meta-analyzed large amounts of data on trophic cascades in food chains […] In general, the cascading reaction is strongest in lakes, followed by marine systems, and weakest in terrestrial systems. […] Any theory that claims to describe the trophic chain equilibria has to produce such cascading when top predators are reduced or eliminated. It is well known that the standard prey-dependent theory supports this view of top-down cascading. It is not widely appreciated that top-down cascading is likewise a property of ratio-dependent trophic chains. […] It is [only] for equilibrial responses to enrichment at the bottom that predictions are strikingly different according to the two theories”.
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As the book does spend a little time on this I should perhaps briefly interject here that the above paragraph should not be taken to indicate that the two types of models provide identical predictions in the top-down cascading context in all cases; both predict cascading, but there are even so some subtle differences between the models here as well. Some of these differences are however quite hard to test.
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“[T]he traditional Lotka-Volterra interaction term […] is nothing other than the law of mass action of chemistry. It assumes that predator and prey individuals encounter each other randomly in the same way that molecules interact in a chemical solution. Other prey-dependent models, like Holling’s, derive from the same idea. […] an ecological system can only be described by such a model if conspecifics do not interfere with each other and if the system is sufficiently homogeneous […] we will demonstrate that spatial heterogeneity, be it in the form of a prey refuge or in the form of predator clusters, leads to emergence of gradual interference or of ratio dependence when the functional response is observed at the population level. […] We present two mechanistic individual-based models that illustrate how, with gradually increasing predator density and gradually increasing predator clustering, interference can become gradually stronger. Thus, a given biological system, prey dependent at low predator density, can gradually become ratio dependent at high predator density. […] ratio dependence is a simple way of summarizing the effects induced by spatial heterogeneity, while the prey dependent [models] (e.g., Lotka-Volterra) is more appropriate in homogeneous environments.”
“[W]e consider that a good model of interacting species must be fundamentally invariant to a proportional change of all abundances in the system. […] Allowing interacting populations to expand in balanced exponential growth makes the laws of ecology invariant with respect to multiplying interacting abundances by the same constant, so that only ratios matter. […] scaling invariance is required if we wish to preserve the possibility of joint exponential growth of an interacting pair. […] a ratio-dependent model allows for joint exponential growth. […] Neither the standard prey-dependent models nor the more general predator-dependent models allow for balanced growth. […] In our view, communities must be expected to expand exponentially in the presence of unlimited resources. Of course, limiting factors ultimately stop this expansion just as they do for a single species. With our view, it is the limiting resources that stop the joint expansion of the interacting populations; it is not directly due to the interactions themselves. This partitioning of the causes is a major simplification that traditional theory implies only in the case of a single species.”
Epilepsy Diagnosis & Treatment – 5 New Things Every Physician Should Know
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Links to related stuff:
i. Sudden unexpected death in epilepsy (SUDEP).
ii. Status epilepticus.
iii. Epilepsy surgery.
iv. Temporal lobe epilepsy.
v. Lesional epilepsy surgery.
vi. Nonlesional neocortical epilepsy.
vii. A Randomized, Controlled Trial of Surgery for Temporal-Lobe Epilepsy.
viii. Stereoelectroencephalography.
ix. Accuracy of intracranial electrode placement for stereoencephalography: A systematic review and meta-analysis. (The results of the review is not discussed in the lecture, for obvious reasons – lecture is a few years old, this review is brand new – but seemed relevant to me.)
x. MRI-guided laser ablation in epilepsy treatment.
xi. Laser thermal therapy: real-time MRI-guided and computer-controlled procedures for metastatic brain tumors.
xii. Critical review of the responsive neurostimulator system for epilepsy (Again, not covered but relevant).
xiii. A Multicenter, Prospective Pilot Study of Gamma Knife Radiosurgery for Mesial Temporal Lobe Epilepsy: Seizure Response, Adverse Events, and Verbal Memory.
xiv. Gamma Knife radiosurgery for recurrent or residual seizures after anterior temporal lobectomy in mesial temporal lobe epilepsy patients with hippocampal sclerosis: long-term follow-up results of more than 4 years (Not covered but relevant).
Detecting Cosmic Neutrinos with IceCube at the Earth’s South Pole
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I thought there were a bit too many questions/interruptions for my taste, mainly because you can’t really hear the questions posed by the members of the audience, but aside from that it’s a decent lecture. I’ve added a few links below which covers some of the topics discussed in the lecture.
Neutrino astronomy.
Antarctic Impulse Transient Antenna (ANITA).
Hydrophone.
Neutral pion decays.
IceCube Neutrino Observatory.
Evidence for High-Energy Extraterrestrial Neutrinos at the IceCube Detector (Science).
Atmospheric and astrophysical neutrinos above 1 TeV interacting in IceCube.
Notes on isotropy.
Measuring the flavor ratio of astrophysical neutrinos.
Blazar.
Supernova 1987A neutrino emissions.
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.”
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“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.”
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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 (1–4). 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 (5–7). 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.”
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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 (10–16), 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.”
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“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 (1–3). 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 (5–7), 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.”
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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 (1–4), 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 (12–14). 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 (16,18–20) 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 (22–25).”
“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 (9,25,36–38). 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.”
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“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 (8–13). 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.”
A few diabetes papers of interest
“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.
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“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.”
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“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]).”
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“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.”
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“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.”
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“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.”
A few papers
A couple of data points and observations from the paper:
“There is an increasing number of youth in Japan who are dropping out of society and isolating themselves in their bedrooms from years to decades at a time. According to Japan’s Ministry of Health, Labor and Welfare’s first official 2003 guidelines on this culture-bound syndrome, hikikomori (social isolation syndrome) has the following specific diagnostic criteria: (1) no motivation to participate in school or work; (2) no signs of schizophrenia or any other known psychopathologies; and (3) persistence of social withdrawal for at least six months.”
“One obvious dilemma in studying hikikomori is that most of those suffering from hikikomori, by definition, do not seek treatment. More importantly, social isolation itself is not even a symptom of any of the DSM diagnosis often assigned to an individual afflicted with hikikomori […] The motivation for isolating oneself among a hikikomori is simply to avoid possible social interactions with others who might know or judge them (Zielenziger, 2006).”
“Saito’s (2010) and Sakai and colleagues’ (2011) data suggest that 10% to 15% of the hikikomori population suffer from an autism spectrum disorder. […] in the first epidemiological study conducted on hikikomori that was as close to a nation-wide random sample as possible, Koyama and colleagues (2010) conducted a face-to-face household survey, including a structured diagnostic interview, by randomly picking households and interviewing 4,134 individuals. They confirmed a hikikomori lifetime prevalence rate of 1.2% in their nationwide sample. Among these hikikomori individuals, the researchers found that only half suffered from a DSM-IV diagnosis. However, and more importantly, there was no particular diagnosis that was systematically associated with hikikomori. […] the researchers concluded that any DSM diagnosis was an epiphenomenon to hikikomori at best and that hikikomori is rather a “psychopathology characterized by impaired motivation” p. 72).”
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“Purpose
To explore whether the ‘hikikomori’ syndrome (social withdrawal) described in Japan exists in other countries, and if so, how patients with the syndrome are diagnosed and treated.
Methods
Two hikikomori case vignettes were sent to psychiatrists in Australia, Bangladesh, India, Iran, Japan, Korea, Taiwan, Thailand and the USA. Participants rated the syndrome’s prevalence in their country, etiology, diagnosis, suicide risk, and treatment.
Results
Out of 247 responses to the questionnaire (123 from Japan and 124 from other countries), 239 were enrolled in the analysis. Respondents’ felt the hikikomori syndrome is seen in all countries examined and especially in urban areas. Biopsychosocial, cultural, and environmental factors were all listed as probable causes of hikikomori, and differences among countries were not significant. Japanese psychiatrists suggested treatment in outpatient wards and some did not think that psychiatric treatment is necessary. Psychiatrists in other countries opted for more active treatment such as hospitalization.
Conclusions
Patients with the hikikomori syndrome are perceived as occurring across a variety of cultures by psychiatrists in multiple countries. Our results provide a rational basis for study of the existence and epidemiology of hikikomori in clinical or community populations in international settings.”
“Our results extend rather than clarify the debate over diagnosis of hikikomori. In our survey, a variety of diagnoses, such as psychosis, depression anxiety and personality disorders, were proffered. Opinions as to whether hikikomori cases can be diagnosed using ICD-10/DSV-IV criteria differed depending on the participants’ countries and the cases’ age of onset. […] a recent epidemiological survey in Japan reported approximately a fifty-fifty split between hikikomori who had experienced a psychiatric disorder and had not [14]. These data and other studies that have not been able to diagnose all cases of hikikomori may suggest the existence of ‘primary hikikomori’ that is not an expression of any other psychiatric disorder [28,8,9,5,29]. In order to clarify differences between ‘primary hikikomori’ (social withdrawal not associated with any underlying psychiatric disorder) and ‘secondary hikikomori’ (social withdrawal caused by an established psychiatric disorder), further epidemiological and psychopathological studies are needed. […] Even if all hikikomori cases prove to be within some kind of psychiatric disorders, it is valuable to continue to focus on the hikikomori phenomenon because of its associated morbidity, similar to how suicidality is examined in various fields of psychiatry [30]. Reducing the burden of hikikomori symptoms, regardless of what psychiatric disorders patients may have, may provide a worthwhile improvement in their quality of life, and this suggests another direction of future hikikomori research.”
“Our case vignette survey indicates that the hikikomori syndrome, previously thought to exist only in Japan, is perceived by psychiatrists to exist in many other countries. It is particularly perceived as occurring in urban areas and might be associated with rapid global sociocultural changes. There is no consensus among psychiatrists within or across countries about the causes, diagnosis and therapeutic interventions for hikikomori yet.”
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iii. Hikikomori: clinical and psychopathological issues (review). A poor paper, but it did have a little bit of data of interest:
“The prevalence of hikikomori is difficult to assess […]. In Japan, more than one million cases have been estimated by experts, but there is no population-based study to confirm these data (9). […] In 2008, Kiyota et al. summarized 3 population-based studies involving 12 cities and 3951 subjects, highlighting that a percentage comprised between 0.9% and 3.8% of the sample had an hikikomori history in anamnesis (11). The typical hikikomori patient is male (4:1 male-to-female ratio) […] females constitute a minor fraction of the reported cases, and usually their period of social isolation is limited.”
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iv. Interpreting results of ethanol analysis in postmortem specimens: A review of the literature.
A few observations from the paper:
“A person’s blood-alcohol concentration (BAC) and state of inebriation at the time of death is not always easy to establish owing to various postmortem artifacts. The possibility of alcohol being produced in the body after death, e.g. via microbial contamination and fermentation is a recurring issue in routine casework. If ethanol remains unabsorbed in the stomach at the time of death, this raises the possibility of continued local diffusion into surrounding tissues and central blood after death. Skull trauma often renders a person unconscious for several hours before death, during which time the BAC continues to decrease owing to metabolism in the liver. Under these circumstances blood from an intracerebral or subdural clot is a useful specimen for determination of ethanol. Bodies recovered from water are particular problematic to deal with owing to possible dilution of body fluids, decomposition, and enhanced risk of microbial synthesis of ethanol. […] Alcoholics often die at home with zero or low BAC and nothing more remarkable at autopsy than a fatty liver. Increasing evidence suggests that such deaths might be caused by a pronounced ketoacidosis.”
“The concentrations of ethanol measured in blood drawn from different sampling sites tend to vary much more than expected from inherent variations in the analytical methods used [49]. Studies have shown that concentrations of ethanol and other drugs determined in heart blood are generally higher than in blood from a peripheral vein although in any individual case there are likely to be considerable variations [50–53].”
“The BAC necessary to cause death is often an open question and much depends on the person’s age, drinking experience and degree of tolerance development [78]. The speed of drinking plays a role in alcohol toxicity as does the kind of beverage consumed […] Drunkenness and hypothermia represent a dangerous combination and deaths tend to occur at a lower BAC when people are exposed to cold, such as, when an alcoholic sleeps outdoors in the winter months [78]. Drinking large amounts of alcohol to produce stupor and unconsciousness combined with positional asphyxia or inhalation of vomit are common causes of death in intoxicated individuals who die of suffocation [81–83]. The toxicity of ethanol is often considerably enhanced by the concomitant use of other drugs with their site of action in the brain, especially opiates, propoxyphene, antidepressants and some sedative hypnotics [84]. […] It seems reasonable to assume that the BAC at autopsy will almost always be lower than the maximum BAC reached during a drinking binge, owing to metabolism of ethanol taking place up until the moment of death [85–87]. During the time after discontinuation of drinking until death, the BAC might decrease appreciably depending on the speed of alcohol elimination from blood, which in heavy drinkers could exceed 20 or 30 mg/100 mL per h (0.02 or 0.03 g% per h) [88].”
“When the supply of oxygen to the body ends, the integrity of cell membranes and tissue compartments gradually disintegrate through the action of various digestive enzymes. This reflects the process of autolysis (self digestion) resulting in a softening and liquefaction of the tissue (freezing the body prevents autolysis). During this process, bacteria from the bowel invade the surrounding tissue and vascular system and the rate of infiltration depends on many factors including the ambient temperature, position of the body and whether death was caused by bacterial infection. Glucose concentrations increase in blood after death and this sugar is probably the simplest substrate for microbial synthesis of ethanol [20,68]. […] Extensive trauma to a body […] increases the potential for spread of bacteria and heightens the risk of ethanol production after death [217]. Blood-ethanol concentrations as high as 190 mg/100 mL have been reported in postmortem blood after particularly traumatic events such as explosions and when no evidence existed to support ingestion of ethanol before the disaster [218].”
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v. Interventions based on the Theory of Mind cognitive model for autism spectrum disorder (ASD) (Cochrane review).
“The ‘Theory of Mind’ (ToM) model suggests that people with autism spectrum disorder (ASD) have a profound difficulty understanding the minds of other people – their emotions, feelings, beliefs, and thoughts. As an explanation for some of the characteristic social and communication behaviours of people with ASD, this model has had a significant influence on research and practice. It implies that successful interventions to teach ToM could, in turn, have far-reaching effects on behaviours and outcome.”
“Twenty-two randomised trials were included in the review (N = 695). Studies were highly variable in their country of origin, sample size, participant age, intervention delivery type, and outcome measures. Risk of bias was variable across categories. There were very few studies for which there was adequate blinding of participants and personnel, and some were also judged at high risk of bias in blinding of outcome assessors. There was also evidence of some bias in sequence generation and allocation concealment.”
“Studies were grouped into four main categories according to intervention target/primary outcome measure. These were: emotion recognition studies, joint attention and social communication studies, imitation studies, and studies teaching ToM itself. […] There was very low quality evidence of a positive effect on measures of communication based on individual results from three studies. There was low quality evidence from 11 studies reporting mixed results of interventions on measures of social interaction, very low quality evidence from four studies reporting mixed results on measures of general communication, and very low quality evidence from four studies reporting mixed results on measures of ToM ability. […] While there is some evidence that ToM, or a precursor skill, can be taught to people with ASD, there is little evidence of maintenance of that skill, generalisation to other settings, or developmental effects on related skills. Furthermore, inconsistency in findings and measurement means that evidence has been graded of ‘very low’ or ‘low’ quality and we cannot be confident that suggestions of positive effects will be sustained as high-quality evidence accumulates. Further longitudinal designs and larger samples are needed to help elucidate both the efficacy of ToM-linked interventions and the explanatory value of the ToM model itself.”
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“The Finnish Prenatal Study of Autism and Autism Spectrum Disorders used a population-based cohort that included children born from January 1, 1987, to December 31, 2005, who received a diagnosis of ASD by December 31, 2007. Each case was individually matched to 4 control participants by sex and date and place of birth. […] Among the 3578 cases with ASD (2841 boys [79.4%]) and 11 775 controls (9345 boys [79.4%]), 1319 cases (36.9%) and 2052 controls (17.4%) had at least 1 sibling diagnosed with any psychiatric or neurodevelopmental disorder (adjusted RR, 2.5; 95% CI, 2.3-2.6).”
“Conclusions and Relevance Psychiatric and neurodevelopmental disorders cluster among siblings of probands with ASD. For etiologic research, these findings provide further evidence that several psychiatric and neurodevelopmental disorders have common risk factors.”
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vii. Treatment for epilepsy in pregnancy: neurodevelopmental outcomes in the child (Cochrane review).
“Accumulating evidence suggests an association between prenatal exposure to antiepileptic drugs (AEDs) and increased risk of both physical anomalies and neurodevelopmental impairment. Neurodevelopmental impairment is characterised by either a specific deficit or a constellation of deficits across cognitive, motor and social skills and can be transient or continuous into adulthood. It is of paramount importance that these potential risks are identified, minimised and communicated clearly to women with epilepsy.”
“Twenty-two prospective cohort studies were included and six registry based studies. Study quality varied. […] the IQ of children exposed to VPA [sodium valproate] (n = 112) was significantly lower than for those exposed to CBZ [carbamazepine] (n = 191) (MD [mean difference] 8.69, 95% CI 5.51 to 11.87, P < 0.00001). […] IQ was significantly lower for children exposed to VPA (n = 74) versus LTG [lamotrigine] (n = 84) (MD -10.80, 95% CI -14.42 to -7.17, P < 0.00001). DQ [developmental quotient] was higher in children exposed to PHT (n = 80) versus VPA (n = 108) (MD 7.04, 95% CI 0.44 to 13.65, P = 0.04). Similarly IQ was higher in children exposed to PHT (n = 45) versus VPA (n = 61) (MD 9.25, 95% CI 4.78 to 13.72, P < 0.0001). A dose effect for VPA was reported in six studies, with higher doses (800 to 1000 mg daily or above) associated with a poorer cognitive outcome in the child. We identified no convincing evidence of a dose effect for CBZ, PHT or LTG. Studies not included in the meta-analysis were reported narratively, the majority of which supported the findings of the meta-analyses.”
“The most important finding is the reduction in IQ in the VPA exposed group, which are sufficient to affect education and occupational outcomes in later life. However, for some women VPA is the most effective drug at controlling seizures. Informed treatment decisions require detailed counselling about these risks at treatment initiation and at pre-conceptual counselling. We have insufficient data about newer AEDs, some of which are commonly prescribed, and further research is required. Most women with epilepsy should continue their medication during pregnancy as uncontrolled seizures also carries a maternal risk.”
Do take note of the effect sizes reported here. To take an example, the difference between being treated with valproate and lamotrigine might equal 10 IQ points in the child – these are huge effects.
Harnessing phenotypic heterogeneity to design better therapies
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Unlike many of the IAS lectures I’ve recently blogged this one is a new lecture – it was uploaded earlier this week. I have to say that I was very surprised – and disappointed – that the treatment strategy discussed in the lecture had not already been analyzed in a lot of detail and been implemented in clinical practice for some time. Why would you not expect the composition of cancer cell subtypes in the tumour microenvironment to change when you start treatment – in any setting where a subgroup of cancer cells has a different level of responsiveness to treatment than ‘the average’, that would to me seem to be the expected outcome. And concepts such as drug holidays and dose adjustments as treatment responses to evolving drug resistance/treatment failure seem like such obvious approaches to try out here (…the immunologists dealing with HIV infection have been studying such things for decades). I guess ‘better late than never’.
A few papers mentioned/discussed in the lecture:
Impact of Metabolic Heterogeneity on Tumor Growth, Invasion, and Treatment Outcomes.
Adaptive vs continuous cancer therapy: Exploiting space and trade-offs in drug scheduling.
Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer.
A few papers
“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.”
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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.
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ii. Exercise for depression (Cochrane review).
“Background
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.
Objectives
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.”
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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.”
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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.)
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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.”
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