Econstudentlog

Depression and Heart Disease (I)

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

Below I’ve added some quotes from the book.

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

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

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

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

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

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

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

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

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

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

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

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

 

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

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some more observations from the paper:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Epilepsy Diagnosis & Treatment – 5 New Things Every Physician Should Know

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).

July 19, 2017 Posted by | Lectures, Medicine, Neurology, Studies | Leave a comment

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Neurology Grand Rounds – Typical and Atypical Diabetic Neuropathy

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

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

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

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

A few papers

i. To Conform or to Maintain Self-Consistency? Hikikomori Risk in Japan and the Deviation From Seeking Harmony.

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).”

ii. Does the ‘hikikomori’ syndrome of social withdrawal exist outside Japan?: A preliminary international investigation.

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.”

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.”

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].”

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.”

vi. Risk of Psychiatric and Neurodevelopmental Disorders Among Siblings of Probands With Autism Spectrum Disorders.

“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.”

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.

June 11, 2017 Posted by | Medicine, Neurology, Pharmacology, Psychiatry, Psychology, Studies | Leave a comment

A few papers

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

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

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

ii. Exercise for depression (Cochrane review).

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.”

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

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

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

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

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

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

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

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

v. Immigrant Labor Market Integration across Admission Classes.

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

Some quotes from the paper:

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

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

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

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

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

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

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

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

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

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

vii. Gaze direction detection in autism spectrum disorder.

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

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

June 1, 2017 Posted by | Diabetes, Economics, Epidemiology, Medicine, Neurology, Psychiatry, Psychology, Studies | Leave a comment

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some more stuff from the paper:

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

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

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

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

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

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

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

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

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

iv. The Mental Health Comorbidities of Diabetes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A few diabetes papers of interest

1. Cognitive Dysfunction in Older Adults With Diabetes: What a Clinician Needs to Know. I’ve talked about these topics before here on the blog (see e.g. these posts on related topics), but this is a good summary article. I have added some observations from the paper below:

“Although cognitive dysfunction is associated with both type 1 and type 2 diabetes, there are several distinct differences observed in the domains of cognition affected in patients with these two types. Patients with type 1 diabetes are more likely to have diminished mental flexibility and slowing of mental speed, whereas learning and memory are largely not affected (8). Patients with type 2 diabetes show decline in executive function, memory, learning, attention, and psychomotor efficiency (9,10).”

“So far, it seems that the risk of cognitive dysfunction in type 2 diabetes may be influenced by glycemic control, hypoglycemia, inflammation, depression, and macro- and microvascular pathology (14). The cumulative impact of these conditions on the vascular etiology may further decrease the threshold at which cognition is affected by other neurological conditions in the aging brain. In patients with type 1 diabetes, it seems as though diabetes has a lesser impact on cognitive dysfunction than those patients with type 2 diabetes. […] Thus, the cognitive decline in patients with type 1 diabetes may be mild and may not interfere with their functionality until later years, when other aging-related factors become important. […] However, recent studies have shown a higher prevalence of cognitive dysfunction in older patients (>60 years of age) with type 1 diabetes (5).”

“Unlike other chronic diseases, diabetes self-care involves many behaviors that require various degrees of cognitive pliability and insight to perform proper self-care coordination and planning. Glucose monitoring, medications and/or insulin injections, pattern management, and diet and exercise timing require participation from different domains of cognitive function. In addition, the recognition, treatment, and prevention of hypoglycemia, which are critical for the older population, also depend in large part on having intact cognition.

The reason a clinician needs to recognize different domains of cognition affected in patients with diabetes is to understand which self-care behavior will be affected in that individual. […] For example, a patient with memory problems may forget to take insulin doses, forget to take medications/insulin on time, or forget to eat on time. […] Cognitively impaired patients using insulin are more likely to not know what to do in the event of low blood glucose or how to manage medication on sick days (34). Patients with diminished mental flexibility and processing speed may do well with a simple regimen but may fail if the regimen is too complex. In general, older patients with diabetes with cognitive dysfunction are less likely to be involved in diabetes self-care and glucose monitoring compared with age-matched control subjects (35). […] Other comorbidities associated with aging and diabetes also add to the burden of cognitive impairment and its impact on self-care abilities. For example, depression is associated with a greater decline in cognitive function in patients with type 2 diabetes (36). Depression also can independently negatively impact the motivation to practice self-care.”

“Recently, there is an increasing discomfort with the use of A1C as a sole parameter to define glycemic goals in the older population. Studies have shown that A1C values in the older population may not reflect the same estimated mean glucose as in the younger population. Possible reasons for this discrepancy are the commonly present comorbidities that impact red cell life span (e.g., anemia, uremia, renal dysfunction, blood transfusion, erythropoietin therapy) (45,46). In addition, A1C level does not reflect glucose excursions and variability. […] Thus, it is prudent to avoid A1C as the sole measure of glycemic goal in this population. […] In patients who need insulin therapy, simplification, also known as de-intensification of the regimen, is generally recommended in all frail patients, especially if they have cognitive dysfunction (37,49). However, the practice has not caught up with the recommendations as shown by large observational studies showing unnecessary intensive control in patients with diabetes and dementia (50–52).”

“With advances in the past few decades, we now see a larger number of patients with type 1 diabetes who are aging successfully and facing the new challenges that aging brings. […] Patients with type 1 diabetes are typically proactive in their disease management and highly disciplined. Cognitive dysfunction in these patients creates significant distress for the first time in their lives; they suddenly feel a “lack of control” over the disease they have managed for many decades. The addition of autonomic dysfunction, gastropathy, or neuropathy may result in wider glucose excursions. These patients are usually more afraid of hyperglycemia than hypoglycemia — both of which they have managed for many years. However, cognitive dysfunction in older adults with type 1 diabetes has been found to be associated with hypoglycemic unawareness and glucose variability (5), which in turn increases the risk of severe hypoglycemia (54). The need for goal changes to avoid hypoglycemia and accept some hyperglycemia can be very difficult for many of these patients.”

2. Trends in Drug Utilization, Glycemic Control, and Rates of Severe Hypoglycemia, 2006–2013.

“From 2006 to 2013, use increased for metformin (from 47.6 to 53.5%), dipeptidyl peptidase 4 inhibitors (0.5 to 14.9%), and insulin (17.1 to 23.0%) but declined for sulfonylureas (38.8 to 30.8%) and thiazolidinediones (28.5 to 5.6%; all P < 0.001). […] The overall rate of severe hypoglycemia remained the same (1.3 per 100 person-years; P = 0.72), declined modestly among the oldest patients (from 2.9 to 2.3; P < 0.001), and remained high among those with two or more comorbidities (3.2 to 3.5; P = 0.36). […] During the recent 8-year period, the use of glucose-lowering drugs has changed dramatically among patients with T2DM. […] The use of older classes of medications, such as sulfonylureas and thiazolidinediones, declined. During this time, glycemic control of T2DM did not improve in the overall population and remained poor among nearly a quarter of the youngest patients. Rates of severe hypoglycemia remained largely unchanged, with the oldest patients and those with multiple comorbidities at highest risk. These findings raise questions about the value of the observed shifts in drug utilization toward newer and costlier medications.”

“Our findings are consistent with a prior study of drug prescribing in U.S. ambulatory practice conducted from 1997 to 2012 (2). In that study, similar increases in DPP-4 inhibitor and insulin analog prescribing were observed; these changes were accompanied by a 61% increase in drug expenditures (2). Our study extends these findings to drug utilization and demonstrates that these increases occurred in all age and comorbidity subgroups. […] In contrast, metformin use increased only modestly between 2006 and 2013 and remained relatively low among older patients and those with two or more comorbidities. Although metformin is recommended as first-line therapy (26), it may be underutilized as the initial agent for the treatment of T2DM (27). Its use may be additionally limited by coexisting contraindications, such as chronic kidney disease (28).”

“The proportion of patients with a diagnosis of diabetes who did not fill any glucose-lowering medications declined slightly (25.7 to 24.1%; P < 0.001).”

That is, one in four people who had a diagnosis of type 2 diabetes were not taking any prescription drugs for their health condition. I wonder how many of those people have read wikipedia articles like this one

“When considering treatment complexity, the use of oral monotherapy increased slightly (from 24.3 to 26.4%) and the use of multiple (two or more) oral agents declined (from 33.0 to 26.5%), whereas the use of insulin alone and in combination with oral agents increased (from 6.0 to 8.5% and from 11.1 to 14.6%, respectively; all P values <0.001).”

“Between 1987 and 2011, per person medical spending attributable to diabetes doubled (4). More than half of the increase was due to prescription drug spending (4). Despite these spending increases and greater utilization of newly developed medications, we showed no concurrent improvements in overall glycemic control or the rates of severe hypoglycemia in our study. Although the use of newer and more expensive agents may have other important benefits (44), further studies are needed to define the value and cost-effectiveness of current treatment options.”

iii. Among Low-Income Respondents With Diabetes, High-Deductible Versus No-Deductible Insurance Sharply Reduces Medical Service Use.

“Using the 2011–2013 Medical Expenditure Panel Survey, bivariate and regression analyses were conducted to compare demographic characteristics, medical service use, diabetes care, and health status among privately insured adult respondents with diabetes, aged 18–64 years (N = 1,461) by lower (<200% of the federal poverty level) and higher (≥200% of the federal poverty level) income and deductible vs. no deductible (ND), low deductible ($1,000/$2,400) (LD), and high deductible (>$1,000/$2,400) (HD). The National Health Interview Survey 2012–2014 was used to analyze differences in medical debt and delayed/avoided needed care among adult respondents with diabetes (n = 4,058) by income. […] Compared with privately insured respondents with diabetes with ND, privately insured lower-income respondents with diabetes with an LD report significant decreases in service use for primary care, checkups, and specialty visits (27%, 39%, and 77% lower, respectively), and respondents with an HD decrease use by 42%, 65%, and 86%, respectively. Higher-income respondents with an LD report significant decreases in specialty (28%) and emergency department (37%) visits.”

“The reduction in ambulatory visits made by lower-income respondents with ND compared with lower-income respondents with an LD or HD is far greater than for higher-income patients. […] The substantial reduction in checkup (preventive) and specialty visits by those with a lower income who have an HDHP [high-deductible health plan, US] implies a very different pattern of service use compared with lower-income respondents who have ND and with higher-income respondents. Though preventive visits require no out-of-pocket costs, reduced preventive service use with HDHPs is well established and might be the result of patients being unaware of this benefit or their concern about findings that could lead to additional expenses (31). Such sharply reduced service use by low-income respondents with diabetes may not be desirable. Patients with diabetes benefit from assessment of diabetes control, encouragement and reinforcement of behavior change and medication use, and early detection and treatment of diabetes complications or concomitant disease.”

iv. Long-term Mortality and End-Stage Renal Disease in a Type 1 Diabetes Population Diagnosed at Age 15–29 Years in Norway.

OBJECTIVE To study long-term mortality, causes of death, and end-stage renal disease (ESRD) in people diagnosed with type 1 diabetes at age 15–29 years.

RESEARCH DESIGN AND METHODS This nationwide, population-based cohort with type 1 diabetes diagnosed during 1978–1982 (n = 719) was followed from diagnosis until death, emigration, or September 2013. Linkages to the Norwegian Cause of Death Registry and the Norwegian Renal Registry provided information on causes of death and whether ESRD was present.

RESULTS During 30 years’ follow-up, 4.6% of participants developed ESRD and 20.6% (n = 148; 106 men and 42 women) died. Cumulative mortality by years since diagnosis was 6.0% (95% CI 4.5–8.0) at 10 years, 12.2% (10.0–14.8) at 20 years, and 18.4% (15.8–21.5) at 30 years. The SMR [standardized mortality ratio] was 4.4 (95% CI 3.7–5.1). Mean time from diagnosis of diabetes to ESRD was 23.6 years (range 14.2–33.5). Death was caused by chronic complications (32.2%), acute complications (20.5%), violent death (19.9%), or any other cause (27.4%). Death was related to alcohol in 15% of cases. SMR for alcohol-related death was 6.8 (95% CI 4.5–10.3), for cardiovascular death was 7.3 (5.4–10.0), and for violent death was 3.6 (2.3–5.3).

CONCLUSIONS The cumulative incidence of ESRD was low in this cohort with type 1 diabetes followed for 30 years. Mortality was 4.4 times that of the general population, and more than 50% of all deaths were caused by acute or chronic complications. A relatively high proportion of deaths were related to alcohol.”

Some additional observations from the paper:

“Studies assessing causes of death in type 1 diabetes are most frequently conducted in individuals diagnosed during childhood (17) or without evaluating the effect of age at diagnosis (8,9). Reports on causes of death in cohorts of patients diagnosed during late adolescence or young adulthood, with long-term follow-up, are less frequent (10). […] Adherence to treatment during this age is poor and the risk of acute diabetic complications is high (1316). Mortality may differ between those with diabetes diagnosed during this period of life and those diagnosed during childhood.”

“Mortality was between four and five times higher than in the general population […]. The excess mortality was similar for men […] and women […]. SMR was higher in the lower age bands — 6.7 (95% CI 3.9–11.5) at 15–24 years and 7.3 (95% CI 5.2–10.1) at 25–34 years — compared with the higher age bands: 3.7 (95% CI 2.7–4.9) at 45–54 years and 3.9 (95% CI 2.6–5.8) at 55–65 years […]. The Cox regression model showed that the risk of death increased significantly by age at diagnosis (HR 1.1; 95% CI 1.1–1.2; P < 0.001) and was eight to nine times higher if ESRD was present (HR 8.7; 95% CI 4.8–15.5; P < 0.0001). […] the underlying cause of death was diabetes in 57 individuals (39.0%), circulatory in 22 (15.1%), cancer in 18 (12.3%), accidents or intoxications in 20 (13.7%), suicide in 8 (5.5%), and any other cause in 21 (14.4%) […] In addition, diabetes contributed to death in 29.5% (n = 43) and CVD contributed to death in 10.9% (n = 29) of the 146 cases. Diabetes was mentioned on the death certificate for 68.2% of the cohort but for only 30.0% of the violent deaths. […] In 60% (88/146) of the cases the review committee considered death to be related to diabetes, whereas in 40% (58/146) the cause was unrelated to diabetes or had an unknown relation to diabetes. According to the clinical committee, acute complications caused death in 20.5% (30/146) of the cases; 20 individuals died as a result of DKA and 10 from hypoglycemia. […] Chronic complications caused the largest proportion of deaths (47/146; 32.2%) and increased with increasing duration of diabetes […]. Among individuals dying as a result of chronic complications (n = 47), CVD caused death in 94% (n = 44) and renal failure in 6% (n = 3). ESRD contributed to death in 22.7% (10/44) of those dying from CVD. Cardiovascular death occurred at mortality rates seven times higher than those in the general population […]. ESRD caused or contributed to death in 13 of 14 cases, when present.”

“Violence (intoxications, accidents, and suicides) was the leading cause of death before 10 years’ duration of diabetes; thereafter it was only a minor cause […] Insulin was used in two of seven suicides. […] According to the available medical records and autopsy reports, about 20% (29/146) of the deceased misused alcohol. In 15% (22/146) alcohol-related ICD-10 codes were listed on the death certificate (18% [19/106] of men and 8% [3/40] of women). In 10 cases the cause of death was uncertain but considered to be related to alcohol or diabetes […] The SMR for alcohol-related death was high when considering the underlying cause of death (5.0; 95% CI 2.5–10.0), and even higher when considering all alcohol-related ICD-10 codes listed on the death certificate (6.8; 95% CI 4.5–10.3). The cause of death was associated with alcohol in 21.8% (19/87) of those who died with less than 20 years’ diabetes duration. Drug abuse was noted on the death certificate in only two cases.”

“During follow-up, 33 individuals (4.6%; 22 men and 11 women) developed ESRD as a result of diabetic nephropathy. Mean time from diagnosis of diabetes to ESRD was 23.6 years (range 14.2–33.5 years). Cumulative incidence of ESRD by years since diagnosis of diabetes was 1.4% (95% CI 0.7–2.7) at 20 years and 4.8% (95% CI 3.4–6.9) at 30 years.”

“This study highlights three important findings. First, among individuals who were diagnosed with type 1 diabetes in late adolescence and early adulthood and had good access to health care, and who were followed for 30 years, mortality was four to five times that of the general population. Second, 15% of all deaths were associated with alcohol, and the SMR for alcohol-related deaths was 6.8. Third, there was a relatively low cumulative incidence of ESRD (4.8%) 30 years after the diagnosis of diabetes.

We report mortality higher than those from a large, population-based study from Finland that found cumulative mortality around 6% at 20 years’ and 15% at 30 years’ duration of diabetes among a population with age at onset and year of diagnosis similar to those in our cohort (10). The corresponding numbers in our cohort were 12% and 18%, respectively; the discrepancy was particularly high at 20 years. The SMR in the Finnish cohort was lower than that in our cohort (2.6–3.0 vs. 3.7–5.1), and those authors reported the SMR to be lower in late-onset diabetes (at age 15–29 years) compared with early-onset diabetes (at age 23). The differences between the Norwegian and Finnish data are difficult to explain since both reports are from countries with good access to health care and a high incidence of type 1 diabetes.”

However the reason for the somewhat different SMRs in these two reasonably similar countries may actually be quite simple – the important variable may be alcohol:

“Finland and Norway are appropriate to compare because they share important population and welfare characteristics. There are, however, significant differences in drinking levels and alcohol-related mortality: the Finnish population consumes more alcohol and the Norwegian population consumes less. The mortality rates for deaths related to alcohol are about three to four times higher in Finland than in Norway (30). […] The markedly higher SMR in our cohort can probably be explained by the lower mortality rates for alcohol-related mortality in the general population. […] In conclusion, the high mortality reported in this cohort with an onset of diabetes in late adolescence and young adulthood draws attention to people diagnosed during a vulnerable period of life. Both acute and chronic complications cause substantial premature mortality […] Our study suggests that increased awareness of alcohol-related death should be encouraged in clinics providing health care to this group of patients.”

April 23, 2017 Posted by | Diabetes, Economics, Epidemiology, Medicine, Nephrology, Neurology, Papers, Pharmacology, Psychology | Leave a comment

A few autism papers

i. The anterior insula in autism: Under-connected and under-examined.

“While the past decade has witnessed a proliferation of neuroimaging studies of autism, theoretical approaches for understanding systems-level brain abnormalities remain poorly developed. We propose a novel anterior insula-based systems-level model for investigating the neural basis of autism, synthesizing recent advances in brain network functional connectivity with converging evidence from neuroimaging studies in autism. The anterior insula is involved in interoceptive, affective and empathic processes, and emerging evidence suggests it is part of a “salience network” integrating external sensory stimuli with internal states. Network analysis indicates that the anterior insula is uniquely positioned as a hub mediating interactions between large-scale networks involved in externally- and internally-oriented cognitive processing. A recent meta-analysis identifies the anterior insula as a consistent locus of hypoactivity in autism. We suggest that dysfunctional anterior insula connectivity plays an important role in autism. […]

Increasing evidence for abnormal brain connectivity in autism comes from studies using functional connectivity measures […] These findings support the hypothesis that under-connectivity between specific brain regions is a characteristic feature of ASD. To date, however, few studies have examined functional connectivity within and between key large-scale canonical brain networks in autism […] The majority of published studies to date have examined connectivity of specific individual brain regions, without a broader theoretically driven systems-level approach.

We propose that a systems-level approach is critical for understanding the neurobiology of autism, and that the anterior insula is a key node in coordinating brain network interactions, due to its unique anatomy, location, function, and connectivity.”

ii. Romantic Relationships and Relationship Satisfaction Among Adults With Asperger Syndrome and High‐Functioning Autism.

“Participants, 31 recruited via an outpatient clinic and 198 via an online survey, were asked to answer a number of self-report questionnaires. The total sample comprised 229 high-functioning adults with ASD (40% males, average age: 35 years). […] Of the total sample, 73% indicated romantic relationship experience and only 7% had no desire to be in a romantic relationship. ASD individuals whose partner was also on the autism spectrum were significantly more satisfied with their relationship than those with neurotypical partners. Severity of autism, schizoid symptoms, empathy skills, and need for social support were not correlated with relationship status. […] Our findings indicate that the vast majority of high-functioning adults with ASD are interested in romantic relationships.”

Those results are very different from other results in the field – for example: “[a] meta-analysis of follow-up studies examining outcomes of ASD individuals revealed that, [o]n average only 14% of the individuals included in the reviewed studies were married or ha[d] a long-term, intimate relationship (Howlin, 2012)” – and one major reason is that they only include high-functioning autistics. I feel sort of iffy about the validity of the selection method used for procuring the online sample, this may also be a major factor (almost one third of them had a university degree so this is definitely not a random sample of high-functioning autistics; ‘high-functioning’ autistics are not that high-functioning in the general setting. Also, the sex ratio is very skewed as 60% of the participants in the study were female. A sex ratio like that may not sound like a big problem, but it is a major problem because a substantial majority of individuals with mild autism are males. Whereas the sex ratio is almost equal in the context of syndromic ASD, non-syndromic ASD is much more prevalent in males, with sex ratios approaching 1:7 in milder cases (link). These people are definitely looking at the milder cases, which means that a sample which skews female will not be remotely similar to most random samples of such individuals taken in the community setting. And this matters because females do better than males. A discussion can be had about to which extent women are under-diagnosed, but I have not seen data convincing me this is a major problem. It’s important to keep in mind in that context that the autism diagnosis is not based on phenotype alone, but on a phenotype-environment interaction; if you have what might be termed ‘an autistic phenotype’ but you are not suffering any significant ill effects as a result of this because you’re able to compensate relatively well (i.e. you are able to handle ‘the environment’ reasonably well despite the neurological makeup you’ve ended up with), you should not get an autism diagnosis – a diagnostic requirement is ‘clinically significant impairment in functioning’.

Anyway some more related data from the publication:

“Studies that analyze outcomes exclusively for ASD adults without intellectual impairment are rare. […] Engström, Ekström, and Emilsson (2003) recruited previous patients with an ASD diagnosis from four psychiatric clinics in Sweden. They reported that 5 (31%) of 16 adults with ASD had ”some form of relation with a partner.” Hofvander et al. (2009) analyzed data from 122 participants who had been referred to outpatient clinics for autism diagnosis. They found that 19 (16%) of all participants had lived in a long-term relationship.
Renty and Roeyers (2006) […] reported that at the time of the[ir] study 19% of 58 ASD adults had a romantic relationship and 8.6% were married or living with a partner. Cederlund, Hagberg, Billstedt, Gillberg, and Gillberg (2008) conducted a follow-up study of male individuals (aged 16–36 years) who had been diagnosed with Asperger syndrome at least 5 years before. […] at the time of the study, three (4%) [out of 76 male ASD individuals] of them were living in a long-term romantic relationship and 10 (13%) had had romantic relationships in the past.”

A few more data and observations from the study:

“A total of 166 (73%) of the 229 participants endorsed currently being in a romantic relationship or having a history of being in a relationship; 100 (44%) reported current involvement in a romantic relationship; 66 (29%) endorsed that they were currently single but have a history of involvement in a romantic relationship; and 63 (27%) participants did not have any experience with romantic relationships. […] Participants without any romantic relationship experience were significantly more likely to be male […] According to participants’ self-report, one fifth (20%) of the 100 participants who were currently involved in a romantic relationship were with an ASD partner. […] Of the participants who were currently single, 65% said that contact with another person was too exhausting for them, 61% were afraid that they would not be able to fulfil the expectations of a romantic partner, and 57% said that they did not know how they could find and get involved with a partner; and 50% stated that they did not know how a romantic relationship works or how they would be expected to behave in a romantic relationship”

“[P]revious studies that exclusively examined adults with ASD without intellectual impairment reported lower levels of romantic relationship experience than the current study, with numbers varying between 16% and 31% […] The results of our study can be best compared with the results of Hofvander et al. (2009) and Renty and Roeyers (2006): They selected their samples […] using methods that are comparable to ours. Hofvander et al. (2009) found that 16% of their participants have had romantic relationship experience in the past, compared to 29% in our sample; and Renty and Roeyers (2006) report that 28% of their participants were either married or engaged in a romantic relationship at the time of their study, compared to 44% in our study. […] Compared to typically developed individuals the percentage of ASD individuals with a romantic relationship partner is relatively low (Weimann, 2010). In the group aged 27–59 years, 68% of German males live together with a partner, 27% are single, and 5% still live with their parents. In the same age group, 73% of all females live with a partner, 26% live on their own, and 2% still live with their parents.”

“As our results show, it is not the case that male ASD individuals do not feel a need for romantic relationships. In fact, the contrary is true. Single males had a greater desire to be in a romantic relationship than single females, and males were more distressed than females about not being in a romantic relationship.” (…maybe in part because the females who were single were more likely than the males who were single to be single by choice?)

“Our findings showed that being with a partner who also has an ASD diagnosis makes a romantic relationship more satisfying for ASD individuals. None of the participants, who had been with a partner in the past but then separated, had been together with an ASD partner. This might indicate that once a person with ASD has found a partner who is also on the spectrum, a relationship might be very stable and long lasting.”

Reward Processing in Autism.

“The social motivation hypothesis of autism posits that infants with autism do not experience social stimuli as rewarding, thereby leading to a cascade of potentially negative consequences for later development. […] Here we use functional magnetic resonance imaging to examine social and monetary rewarded implicit learning in children with and without autism spectrum disorders (ASD). Sixteen males with ASD and sixteen age- and IQ-matched typically developing (TD) males were scanned while performing two versions of a rewarded implicit learning task. In addition to examining responses to reward, we investigated the neural circuitry supporting rewarded learning and the relationship between these factors and social development. We found diminished neural responses to both social and monetary rewards in ASD, with a pronounced reduction in response to social rewards (SR). […] Moreover, we show a relationship between ventral striatum activity and social reciprocity in TD children. Together, these data support the hypothesis that children with ASD have diminished neural responses to SR, and that this deficit relates to social learning impairments. […] When we examined the general neural response to monetary and social reward events, we discovered that only TD children showed VS [ventral striatum] activity for both reward types, whereas ASD children did not demonstrate a significant response to either monetary or SR. However, significant between-group differences were shown only for SR, suggesting that children with ASD may be specifically impaired on processing SR.”

I’m not quite sure I buy that the methodology captures what it is supposed to capture (“The SR feedback consisted of a picture of a smiling woman with the words “That’s Right!” in green text for correct trials and a picture of the same woman with a sad face along with the words “That’s Wrong” in red text for incorrect trials”) (this is supposed to be the ‘social reward feedback’), but on the other hand: “The chosen reward stimuli, faces and coins, are consistent with those used in previous studies of reward processing” (so either multiple studies are of dubious quality, or this kind of method actually ‘works’ – but I don’t know enough about the field to tell which of the two conclusions apply).

iv. The Social Motivation Theory of Autism.

“The idea that social motivation deficits play a central role in Autism Spectrum Disorders (ASD) has recently gained increased interest. This constitutes a shift in autism research, which has traditionally focused more intensely on cognitive impairments, such as Theory of Mind deficits or executive dysfunction, while granting comparatively less attention to motivational factors. This review delineates the concept of social motivation and capitalizes on recent findings in several research areas to provide an integrated picture of social motivation at the behavioral, biological and evolutionary levels. We conclude that ASD can be construed as an extreme case of diminished social motivation and, as such, provides a powerful model to understand humans’ intrinsic drive to seek acceptance and avoid rejection.”

v. Stalking, and Social and Romantic Functioning Among Adolescents and Adults with Autism Spectrum Disorder.

“We examine the nature and predictors of social and romantic functioning in adolescents and adults with ASD. Parental reports were obtained for 25 ASD adolescents and adults (13-36 years), and 38 typical adolescents and adults (13-30 years). The ASD group relied less upon peers and friends for social (OR = 52.16, p < .01) and romantic learning (OR = 38.25, p < .01). Individuals with ASD were more likely to engage in inappropriate courting behaviours (χ2 df = 19 = 3168.74, p < .001) and were more likely to focus their attention upon celebrities, strangers, colleagues, and ex-partners (χ2 df = 5 =2335.40, p < .001), and to pursue their target longer than controls (t = -2.23, df = 18.79, p < .05).”

“Examination of relationships the individuals were reported to have had with the target of their social or romantic interest, indicated that ASD adolescents and adults sought to initiate fewer social and romantic relationships but across a wider variety of people, such as strangers, colleagues, acquaintances, friends, ex-partners, and celebrities. […] typically developing peers […] were more likely to target colleagues, acquaintances, friends, and ex-partners in their relationship attempts, whilst the ASD group targeted these less frequently than expected, and attempted to initiate relationships significantly more frequently than is typical, with strangers and celebrities. […] In attempting to pursue and initiate social and romantic relationships, the ASD group were reported to display a much wider variety of courtship behaviours than the typical group. […] ASD adolescents and adults were more likely to touch the person of interest inappropriately, believe that the target must reciprocate their feelings, show obsessional interest, make inappropriate comments, monitor the person’s activities, follow them, pursue them in a threatening manner, make threats against the person, and threaten self-harm. ASD individuals displayed the majority of the behaviours indiscriminately across all types of targets. […] ASD adolescents and adults were also found […] to persist in their relationship pursuits for significantly longer periods of time than typical adolescents and adults when they received a negative or no response from the person or their family.”

April 4, 2017 Posted by | autism, Neurology, Papers, Psychology | Leave a comment

Diabetes and the brain (IV)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Antiplatelets?

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

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

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

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

Biodemography of aging (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Ageing Immune System and Health (II)

Here’s the first post about the book. I finished it a while ago but I recently realized I had not completed my intended coverage of the book here on the blog back then, and as some of the book’s material sort-of-kind-of relates to material encountered in a book I’m currently reading (Biodemography of Aging) I decided I might as well finish my coverage of the book now in order to review some things I might have forgot in the meantime, by providing coverage here of some of the material covered in the second half of the book. It’s a nice book with some interesting observations, but as I also pointed out in my first post it is definitely not an easy read. Below I have included some observations from the book’s second half.

Lungs:

“The aged lung is characterised by airspace enlargement similar to, but not identical with acquired emphysema [4]. Such tissue damage is detected even in non-smokers above 50 years of age as the septa of the lung alveoli are destroyed and the enlarged alveolar structures result in a decreased surface for gas exchange […] Additional problems are that surfactant production decreases with age [6] increasing the effort needed to expand the lungs during inhalation in the already reduced thoracic cavity volume where the weakened muscles are unable to thoroughly ventilate. […] As ageing is associated with respiratory muscle strength reduction, coughing becomes difficult making it progressively challenging to eliminate inhaled particles, pollens, microbes, etc. Additionally, ciliary beat frequency (CBF) slows down with age impairing the lungs’ first line of defence: mucociliary clearance [9] as the cilia can no longer repel invading microorganisms and particles. Consequently e.g. bacteria can more easily colonise the airways leading to infections that are frequent in the pulmonary tract of the older adult.”

“With age there are dramatic changes in neutrophil function, including reduced chemotaxis, phagocytosis and bactericidal mechanisms […] reduced bactericidal function will predispose to infection but the reduced chemotaxis also has consequences for lung tissue as this results in increased tissue bystander damage from neutrophil elastases released during migration […] It is currently accepted that alterations in pulmonary PPAR profile, more precisely loss of PPARγ activity, can lead to inflammation, allergy, asthma, COPD, emphysema, fibrosis, and cancer […]. Since it has been reported that PPARγ activity decreases with age, this provides a possible explanation for the increasing incidence of these lung diseases and conditions in older individuals [6].”

Cancer:

“Age is an important risk factor for cancer and subjects aged over 60 also have a higher risk of comorbidities. Approximately 50 % of neoplasms occur in patients older than 70 years […] a major concern for poor prognosis is with cancer patients over 70–75 years. These patients have a lower functional reserve, a higher risk of toxicity after chemotherapy, and an increased risk of infection and renal complications that lead to a poor quality of life. […] [Whereas] there is a difference in organs with higher cancer incidence in developed versus developing countries [,] incidence increases with ageing almost irrespective of country […] The findings from Surveillance, Epidemiology and End Results Program [SEERincidentally I likely shall at some point discuss this one in much more detail, as the aforementioned biodemography textbook covers this data in a lot of detail.. – US] [6] show that almost a third of all cancer are diagnosed after the age of 75 years and 70 % of cancer-related deaths occur after the age of 65 years. […] The traditional clinical trial focus is on younger and healthier patient, i.e. with few or no co-morbidities. These restrictions have resulted in a lack of data about the optimal treatment for older patients [7] and a poor evidence base for therapeutic decisions. […] In the older patient, neutropenia, anemia, mucositis, cardiomyopathy and neuropathy — the toxic effects of chemotherapy — are more pronounced […] The correction of comorbidities and malnutrition can lead to greater safety in the prescription of chemotherapy […] Immunosenescence is a general classification for changes occurring in the immune system during the ageing process, as the distribution and function of cells involved in innate and adaptive immunity are impaired or remodelled […] Immunosenescence is considered a major contributor to cancer development in aged individuals“.

Neurodegenerative diseases:

“Dementia and age-related vision loss are major causes of disability in our ageing population and it is estimated that a third of people aged over 75 are affected. […] age is the largest risk factor for the development of neurodegenerative diseases […] older patients with comorbidities such as atherosclerosis, type II diabetes or those suffering from repeated or chronic systemic bacterial and viral infections show earlier onset and progression of clinical symptoms […] analysis of post-mortem brain tissue from healthy older individuals has provided evidence that the presence of misfolded proteins alone does not correlate with cognitive decline and dementia, implying that additional factors are critical for neural dysfunction. We now know that innate immune genes and life-style contribute to the onset and progression of age-related neuronal dysfunction, suggesting that chronic activation of the immune system plays a key role in the underlying mechanisms that lead to irreversible tissue damage in the CNS. […] Collectively these studies provide evidence for a critical role of inflammation in the pathogenesis of a range of neurodegenerative diseases, but the factors that drive or initiate inflammation remain largely elusive.”

“The effect of infection, mimicked experimentally by administration of bacterial lipopolysaccharide (LPS) has revealed that immune to brain communication is a critical component of a host organism’s response to infection and a collection of behavioural and metabolic adaptations are initiated over the course of the infection with the purpose of restricting the spread of a pathogen, optimising conditions for a successful immune response and preventing the spread of infection to other organisms [10]. These behaviours are mediated by an innate immune response and have been termed ‘sickness behaviours’ and include depression, reduced appetite, anhedonia, social withdrawal, reduced locomotor activity, hyperalgesia, reduced motivation, cognitive impairment and reduced memory encoding and recall […]. Metabolic adaptation to infection include fever, altered dietary intake and reduction in the bioavailability of nutrients that may facilitate the growth of a pathogen such as iron and zinc [10]. These behavioural and metabolic adaptions are evolutionary highly conserved and also occur in humans”.

“Sickness behaviour and transient microglial activation are beneficial for individuals with a normal, healthy CNS, but in the ageing or diseased brain the response to peripheral infection can be detrimental and increases the rate of cognitive decline. Aged rodents exhibit exaggerated sickness and prolonged neuroinflammation in response to systemic infection […] Older people who contract a bacterial or viral infection or experience trauma postoperatively, also show exaggerated neuroinflammatory responses and are prone to develop delirium, a condition which results in a severe short term cognitive decline and a long term decline in brain function […] Collectively these studies demonstrate that peripheral inflammation can increase the accumulation of two neuropathological hallmarks of AD, further strengthening the hypothesis that inflammation i[s] involved in the underlying pathology. […] Studies from our own laboratory have shown that AD patients with mild cognitive impairment show a fivefold increased rate of cognitive decline when contracting a systemic urinary tract or respiratory tract infection […] Apart from bacterial infection, chronic viral infections have also been linked to increased incidence of neurodegeneration, including cytomegalovirus (CMV). This virus is ubiquitously distributed in the human population, and along with other age-related diseases such as cardiovascular disease and cancer, has been associated with increased risk of developing vascular dementia and AD [66, 67].”

Frailty:

“Frailty is associated with changes to the immune system, importantly the presence of a pro-inflammatory environment and changes to both the innate and adaptive immune system. Some of these changes have been demonstrated to be present before the clinical features of frailty are apparent suggesting the presence of potentially modifiable mechanistic pathways. To date, exercise programme interventions have shown promise in the reversal of frailty and related physical characteristics, but there is no current evidence for successful pharmacological intervention in frailty. […] In practice, acute illness in a frail person results in a disproportionate change in a frail person’s functional ability when faced with a relatively minor physiological stressor, associated with a prolonged recovery time […] Specialist hospital services such as surgery [15], hip fractures [16] and oncology [17] have now begun to recognise frailty as an important predictor of mortality and morbidity.

I should probably mention here that this is another area where there’s an overlap between this book and the biodemography text I’m currently reading; chapter 7 of the latter text is about ‘Indices of Cumulative Deficits’ and covers this kind of stuff in a lot more detail than does this one, including e.g. detailed coverage of relevant statistical properties of one such index. Anyway, back to the coverage:

“Population based studies have demonstrated that the incidence of infection and subsequent mortality is higher in populations of frail people. […] The prevalence of pneumonia in a nursing home population is 30 times higher than the general population [39, 40]. […] The limited data available demonstrates that frailty is associated with a state of chronic inflammation. There is also evidence that inflammageing predates a diagnosis of frailty suggesting a causative role. […] A small number of studies have demonstrated a dysregulation of the innate immune system in frailty. Frail adults have raised white cell and neutrophil count. […] High white cell count can predict frailty at a ten year follow up [70]. […] A recent meta-analysis and four individual systematic reviews have found beneficial evidence of exercise programmes on selected physical and functional ability […] exercise interventions may have no positive effect in operationally defined frail individuals. […] To date there is no clear evidence that pharmacological interventions improve or ameliorate frailty.”

Exercise:

“[A]s we get older the time and intensity at which we exercise is severely reduced. Physical inactivity now accounts for a considerable proportion of age-related disease and mortality. […] Regular exercise has been shown to improve neutrophil microbicidal functions which reduce the risk of infectious disease. Exercise participation is also associated with increased immune cell telomere length, and may be related to improved vaccine responses. The anti-inflammatory effect of regular exercise and negative energy balance is evident by reduced inflammatory immune cell signatures and lower inflammatory cytokine concentrations. […] Reduced physical activity is associated with a positive energy balance leading to increased adiposity and subsequently systemic inflammation [5]. […] Elevated neutrophil counts accompany increased inflammation with age and the increased ratio of neutrophils to lymphocytes is associated with many age-related diseases including cancer [7]. Compared to more active individuals, less active and overweight individuals have higher circulating neutrophil counts [8]. […] little is known about the intensity, duration and type of exercise which can provide benefits to neutrophil function. […] it remains unclear whether exercise and physical activity can override the effects of NK cell dysfunction in the old. […] A considerable number of studies have assessed the effects of acute and chronic exercise on measures of T-cell immunesenescence including T cell subsets, phenotype, proliferation, cytokine production, chemotaxis, and co-stimulatory capacity. […] Taken together exercise appears to promote an anti-inflammatory response which is mediated by altered adipocyte function and improved energy metabolism leading to suppression of pro-inflammatory cytokine production in immune cells.”

February 24, 2017 Posted by | Biology, Books, Cancer/oncology, Epidemiology, Immunology, Medicine, Neurology | Leave a comment

Diabetes and the Brain (III)

Some quotes from the book below.

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the Brain (II)

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the brain (I)

I recently learned that the probability that I have brain-damage as a result of my diabetes is higher than I thought it was.

I first took note of the fact that there might be a link between diabetes and brain development some years ago, but this is a topic I knew very little about before reading the book I’m currently reading. Below I have added some relevant quotes from chapters 10 and 11 of the book:

“Cognitive decrements [in adults with type 1 diabetes] are limited to only some cognitive domains and can best be characterised as a slowing of mental speed and a diminished mental flexibility, whereas learning and memory are generally spared. […] the cognitive decrements are mild in magnitude […] and seem neither to be progressive over time, nor to be substantially worse in older adults. […] neuroimaging studies […] suggest that type 1 diabetic patients have relatively subtle reductions in brain volume but these structural changes may be more pronounced in patients with an early disease onset.”

“With the rise of the subspecialty area ‘medical neuropsychology’ […] it has become apparent that many medical conditions may […] affect the structure and function of the central nervous system (CNS). Diabetes mellitus has received much attention in that regard, and there is now an extensive literature demonstrating that adults with type 1 diabetes have an elevated risk of CNS anomalies. This literature is no longer limited to small cross-sectional studies in relatively selected populations of young adults with type 1 diabetes, but now includes studies that investigated the pattern and magnitude of neuropsychological decrements and the associated neuroradiological changes in much more detail, with more sensitive measurements, in both younger and older patients.”

“Compared to non-diabetic controls, the type 1 diabetic group [in a meta-analysis including 33 studies] demonstrated a significant overall lowered performance, as well as impairment in the cognitive domains intelligence, implicit memory, speed of information processing, psychomotor efficiency, visual and sustained attention, cognitive flexibility, and visual perception. There was no difference in explicit memory, motor speed, selective attention, or language function. […] These results strongly support the hypothesis that there is a relationship between cognitive dysfunction and type 1 diabetes. Clearly, there is a modest, but statistically significant, lowered cognitive performance in patients with type 1 diabetes compared to non-diabetic controls. The pattern of cognitive findings does not suggest decline in all cognitive domains, but is characterised by a slowing of mental speed and a diminished mental flexibility. Patients with type 1 diabetes seem to be less able to flexibly apply acquired knowledge in a new situation. […] In all, the cognitive problems we see in type 1 diabetes mimics the patterns of cognitive ageing. […] One of the problems with much of this research is that it is conducted in patients who are seen in specialised medical centres where care is very good. Other aspects of population selection may also have affected the results. Persons who participate in research projects that include a detailed work-up at a hospital tend to be less affected than persons who refuse participation. Possibly, specific studies that recruit type 1 adults from the community, with individuals being in poorer health, would result in greater cognitive deficits”.

“[N]eurocognitive research suggests that type 1 diabetes is primarily associated with psychomotor slowing and reductions in mental efficiency. This pattern is more consistent with damage to the brain’s white matter than with grey-matter abnormalities. […] A very large neuroimaging literature indicates that adults with either type 1 or type 2 diabetes manifest structural changes in a number of brain regions […]. MRI changes in the brain of patients with type 1 diabetes are relatively subtle. In terms of effect sizes, these are at best large enough to distinguish the patient group from the control group, but not large enough to classify an individual subject as being patient or control.”

“[T]he subtle cognitive decrements in speed of information processing and mental flexibility found in diabetic patients are not merely caused by acute metabolic derangements or psychological factors, but point to end-organ damage in the central nervous system. Although some uncertainty remains about the exact pathogenesis, several mechanisms through which diabetes may affect the brain have now been identified […] The issue whether or not repeated episodes of severe hypoglycaemia result in permanent mild cognitive impairment has been debated extensively in the literature. […] The meta-analysis on the effect of type 1 diabetes on cognition (1) does not support the idea that there are important negative effects from recurrent episodes of severe hypoglycaemia on cognitive functioning, and large prospective studies did not confirm the earlier observations […] there is no evidence for a linear relationship between recurrent episodes of hypoglycaemia and permanent brain dysfunction in adults. […] Cerebral microvascular pathology in diabetes may result in a decrease of regional cerebral blood flow and an alteration in cerebral metabolism, which could partly explain the occurrence of cognitive impairments. It could be hypothesised that vascular pathology disrupts white-matter integrity in a way that is akin to what one sees in peripheral neuropathy and as such could perhaps affect the integrity of neurotransmitter systems and as a consequence limits cognitive efficiency. These effects are likely to occur diffusely across the brain. Indeed, this is in line with MRI findings and other reports.”

“[An] important issue is the interaction between different disease variables. In particular, patients with diabetes onset before the age of 5 […] and patients with advanced microangiopathy might be more sensitive to the effects of hypoglycaemic episodes or elevated HbA1c levels. […] decrements in cognitive function have been observed as early as 2 years after the diagnosis (63). It is important to consider the possibility that the developing brain is more vulnerable to the effect of diabetes […] Diabetes has a marked effect on brain function and structure in children and adolescents. As a group, diabetic children are more likely to perform more poorly than their nondiabetic peers in the classroom and earn lower scores on measures of academic achievement and verbal intelligence. Specialized neuropsychological testing reveals evidence of dysfunction in a variety of cognitive domains, including sustained attention, visuoperceptual skills, and psychomotor speed. Children diagnosed early in life – before 7 years of age – appear to be most vulnerable, showing impairments on virtually all types of cognitive tests, with learning and memory skills being particularly affected. Results from neurophysiological, cerebrovascular, and neuroimaging studies also show evidence of CNS anomalies. Earlier research attributed diabetes-associated brain dysfunction to episodes of recurrent hypoglycemia, but more recent studies have generally failed to find strong support for that view.”

“[M]ethodological issues notwithstanding, extant research on diabetic children’s brain function has identified a number of themes […]. All other things being equal, children diagnosed with type 1 diabetes early in life – within the first 5–7 years of age – have the greatest risk of manifesting neurocognitive dysfunction, the magnitude of which is greater than that seen in children with a later onset of diabetes. The development of brain dysfunction seems to occur within a relatively brief period of time, often appearing within the first 2–3 years following diagnosis. It is not limited to performance on neuropsychological tests, but is manifested on a wide range of electrophysiological measures as marked neural slowing. Somewhat surprisingly, the magnitude of these effects does not seem to worsen appreciably with increasing duration of diabetes – at least through early adulthood. […] As a group, diabetic children earn somewhat lower grades in school as compared to their nondiabetic classmates, are more likely to fail or repeat a grade, perform more poorly on formal tests of academic achievement, and have lower IQ scores, particularly on tests of verbal intelligence.”

The most compelling evidence for a link between diabetes and poorer school outcomes has been provided by a Swedish population-based register study involving 5,159 children who developed diabetes between July 1997 and July 2000 and 1,330,968 nondiabetic children […] Those who developed diabetes very early in life (diagnosis before 2 years of age) had a significantly increased risk of not completing school as compared to either diabetic patients diagnosed after that age or to the reference population. Small, albeit statistically reliable between-group differences were noted in school marks, with diabetic children, regardless of age at diagnosis, consistently earning somewhat lower grades. Of note is their finding that the diabetic sample had a significantly lower likelihood of getting a high mark (passed with distinction or excellence) in two subjects and was less likely to take more advanced courses. The authors conclude that despite universal access to active diabetes care, diabetic children – particularly those with a very early disease onset – had a greatly increased risk of somewhat lower educational achievement […] Similar results have been reported by a number of smaller studies […] in the prospective Melbourne Royal Children’s Hospital (RCH) cohort study (22), […] only 68% of [the] diabetic sample completed 12 years of school, as compared to 85% of the nondiabetic comparison group […] Children with diabetes, especially those with an earlier onset, have also been found to require more remedial educational services and to be more likely to repeat a grade (25–28), to earn lower school grades over time (29), to experience somewhat greater school absenteeism (28, 30–32), to have a two to threefold increase in rates of depression (33– 35), and to manifest more externalizing behavior problems (25).”

“Children with diabetes have a greatly increased risk of manifesting mild neurocognitive dysfunction. This is an incontrovertible fact that has emerged from a large body of research conducted over the past 60 years […]. There is, however, less agreement about the details. […] On standardized tests of academic achievement, diabetic children generally perform somewhat worse than their healthy peers […] Performance on measures of verbal intelligence – particularly those that assess vocabulary knowledge and general information about the world – is frequently compromised in diabetic children (9, 14, 26, 40) and in adults (41) with a childhood onset of diabetes. The few studies that have followed subjects over time have noted that verbal IQ scores tend to decline as the duration of diabetes increases (13, 15, 29). These effects appear to be more pronounced in boys and in those children with an earlier onset of diabetes. Whether this phenomenon is a marker of cognitive decline or whether it reflects a delay in cognitive development cannot yet be determined […] it is possible, but remains unproven, that psychosocial processes (e.g., school absence, depression, distress, externalizing problems) (42), and/or multiple and prolonged periods of classroom inattention and reduced motivation secondary to acute and prolonged episodes of hypoglycemia (43–45) may be contributing to the poor academic outcomes characteristic of children with diabetes. Although it may seem more reasonable to attribute poorer school performance and lower IQ scores to diabetes-associated disruption of specific neurocognitive processes (e.g., attention, learning, memory) secondary to brain dysfunction, there is little compelling evidence to support that possibility at the present time.”

“Children and adults who develop diabetes within the first 5–7 years of life may show moderate cognitive dysfunction that can affect all cognitive domains, although the specific pattern varies, depending both on the cognitive domain assessed and on the child’s age at assessment. Data from a recent meta-analysis of 19 pediatric studies have indicated that effect sizes tend to range between ∼ 0.4 and 0.5 for measures of learning, memory, and attention, but are lower for other cognitive domains (47). For the younger child with an early onset of diabetes, decrements are particularly pronounced on visuospatial tasks that require copying complex designs, solving jigsaw puzzles, or using multi-colored blocks to reproduce designs, with girls more likely to earn lower scores than boys (8). By adolescence and early adulthood, gender differences are less apparent and deficits occur on measures of attention, mental efficiency, learning, memory, eye–hand coordination, and “executive functioning” (13, 26, 40, 48–50). Not only do children with an early onset of diabetes often – but not invariably – score lower than healthy comparison subjects, but a subset earn scores that fall into the “clinically impaired” range […]. According to one estimate, the prevalence of clinically significant impairment is approximately four times higher in those diagnosed within the first 6 years of life as compared to either those diagnosed after that age or to nondiabetic peers (25 vs. 6%) (49). Nevertheless, it is important to keep in mind that not all early onset diabetic children show cognitive dysfunction, and not all tests within a particular cognitive domain differentiate diabetic from nondiabetic subjects.”

“Slowed neural activity, measured at rest by electroencephalogram (EEG) and in response to sensory stimuli, is common in children with diabetes. On tests of auditory- or visual-evoked potentials (AEP; VEP), children and adolescents with more than a 2-year history of diabetes show significant slowing […] EEG recordings have also demonstrated abnormalities in diabetic adolescents in very good metabolic control. […] EEG abnormalities have also been associated with childhood diabetes. One large study noted that 26% of their diabetic subjects had abnormal EEG recordings, as compared to 7% of healthy controls […] diabetic children with EEG abnormalities recorded at diagnosis may be more likely to experience a seizure or coma (i.e., a severe hypoglycemic event) when blood glucose levels subsequently fall […] This intriguing possibility – that seizures occur in some diabetic children during hypoglycemia because of the presence of pre-existing brain dysfunction – requires further study.” 

“A very large body of research on adults with diabetes now demonstrates that the risk of developing a wide range of neurocognitive changes – poorer cognitive function, slower neural functioning, abnormalities in cerebral blood flow and brain metabolites, and reductions or alterations in gray and white-brain matter – is associated with chronically elevated blood glucose values […] Taken together, the limited animal research on this topic […] provides quite compelling support for the view that even relatively brief bouts of chronically elevated blood glucose values can induce structural and functional changes to the brain. […] [One pathophysiological model proposed is] the “diathesis” or vulnerability model […] According to this model, in the very young child diagnosed with diabetes, chronically elevated blood glucose levels interfere with normal brain maturation at a time when those neurodevelopmental processes are particularly labile, as they are during the first 5–7 years of life […]. The resulting alterations in brain organization that occur during this “sensitive period” will not only lead to delayed cognitive development and lasting cognitive dysfunction, but may also induce a predisposition or diathesis that increases the individual’s sensitivity to subsequent insults to the brain, as could be initiated by the prolonged neuroglycopenia that occurs during an episode of hypoglycemia. Data from most, but not all, research are consistent with that view. […] Research is only now beginning to focus on plausible pathophysiological mechanisms.”

After having read these chapters, I’m now sort-of-kind-of wondering to which extent my autism was/is also at least partly diabetes-mediated. There’s no evidence linking autism and diabetes presented in the chapters, but you do start to wonder even so – the central nervous system is complicated.. If diabetes did play a role there, that would probably be an argument for not considering potential diabetes-mediated brain changes in me as ‘minor’ despite my somewhat higher than average IQ (just to be clear, a high observed IQ in an individual does not preclude the possibility that diabetes had a negative IQ-effect – we don’t observe the counterfactual – but a high observed IQ does make a potential IQ-lowering effect less likely to have happened, all else equal).

December 21, 2016 Posted by | Books, Diabetes, Epidemiology, Medicine, Neurology, Personal | Leave a comment

Role of Biomarkers in Medicine

“The use of biomarkers in basic and clinical research has become routine in many areas of medicine. They are accepted as molecular signatures that have been well characterized and repeatedly shown to be capable of predicting relevant disease states or clinical outcomes. In Role of Biomarkers in Medicine, expert researchers in their individual field have reviewed many biomarkers or potential biomarkers in various types of diseases. The topics address numerous aspects of medicine, demonstrating the current conceptual status of biomarkers as clinical tools and as surrogate endpoints in clinical research.”

The above quote is from the preface of the book. Here’s my goodreads review. I have read about biomarkers before – for previous posts on this topic, see this link. I added the link in part because the coverage provided in this book is in my opinion generally of a somewhat lower quality than is the coverage that has been provided in some of the other books I’ve read on these topics. However the fact that the book is not amazing should probably not keep me from sharing some observations of interest from the book, which I have done in this post.

we suggest more precise studies to establish the exact role of this hormone […] additional studies are necessary […] there are conflicting results […] require further investigation […] more intervention studies with long-term follow-up are required. […] further studies need to be conducted […] further research is needed (There are a lot of comments like these in the book, I figured I should include a few in my coverage…)

“Cancer biomarkers (CB) are biomolecules produced either by the tumor cells or by other cells of the body in response to the tumor, and CB could be used as screening/early detection tool of cancer, diagnostic, prognostic, or predictor for the overall outcome of a patient. Moreover, cancer biomarkers may identify subpopulations of patients who are most likely to respond to a given therapy […] Unfortunately, […] only very few CB have been approved by the FDA as diagnostic or prognostic cancer markers […] 25 years ago, the clinical usefulness of CB was limited to be an effective tool for patient’s prognosis, surveillance, and therapy monitoring. […] CB have [since] been reported to be used also for screening of general population or risk groups, for differential diagnosis, and for clinical staging or stratification of cancer patients. Additionally, CB are used to estimate tumor burden and to substitute for a clinical endpoint and/or to measure clinical benefit, harm or lack of benefit, or harm [4, 18, 30]. Among commonly utilized biomarkers in clinical practice are PSA, AFP, CA125, and CEA.”

“Bladder cancer (BC) is the second most common malignancy in the urologic field. Preoperative predictive biomarkers of cancer progression and prognosis are imperative for optimizing […] treatment for patients with BC. […] Approximately 75–85% of BC cases are diagnosed as nonmuscle-invasive bladder cancer (NMIBC) […] NMIBC has a tendency to recur (50–70%) and may progress (10–20%) to a higher grade and/or muscle-invasive BC (MIBC) in time, which can lead to high cancer-specific mortality [2]. Histological tumor grade is one of the clinical factors associated with outcomes of patients with NMIBC. High-grade NMIBC generally exhibits more aggressive behavior than low-grade NMIBC, and it increases the risk of a poorer prognosis […] Cystoscopy and urine cytology are commonly used techniques for the diagnosis and surveillance of BC. Cystoscopy can identify […] most papillary and solid lesions, but this is highly invasive […] urine cytology is limited by examiner experience and low sensitivity. For these reasons, some tumor markers have been investigated […], but their sensitivity and specificity are limited [5] and they are unable to predict the clinical outcome of BC patients. […] Numerous efforts have been made to identify tumor markers. […] However, a serum marker that can serve as a reliable detection marker for BC has yet to be identified.”

“Endometrial cancer (EmCa) is the most common type of gynecological cancer. EmCa is the fourth most common cancer in the United States, which has been linked to increased incidence of obesity. […] there are no reliable biomarker tests for early detection of EmCa and treatment effectiveness. […] Approximately 75% of women with EmCa are postmenopausal; the most common symptom is postmenopausal bleeding […] Approximately 15% of women diagnosed with EmCa are younger than 50 years of age, while 5% are diagnosed before the age of 40 [29]. […] Roughly, half of the EmCa cases are linked to obesity. Obese women are four times more likely to develop EmCa when compared to normal weight women […] Obese individuals oftentimes exhibit resistance to leptin and show high levels of the adipokine in blood, which is known as leptin resistance […] prolonged exposure of leptin damages the hypothalamus causing it to become insensitive to the effects of leptin […] Evidence shows that leptin is an important pro-inflammatory, pro-angiogenic, and mitogenic factor for cancer. Leptin produced by cancer cells acts in an autocrine and paracrine manner to promote tumor cell proliferation, migration and invasion, pro-inflammation, and angiogenesis [58, 70]. High levels of leptin […] are associated with metastasis and decreased survival rates in breast cancer patients [58]. […] Metabolic syndrome including obesity, hypertension, insulin resistance, diabetes, and dyslipidemia increase the risk of developing multiple malignancies, particularly EmCa [30]. Younger women diagnosed with EmCa are usually obese, and their carcinomas show a well-differentiated histology [20].

“Normally, tumor suppressor genes act to inhibit or arrest cell proliferation and tumor development [37]. However; when mutated, tumor suppressors become inactive, thus permitting tumor growth. For example, mutations in p53 have been determined in various cancers such as breast, colon, lung, endometrium, leukemias, and carcinomas of many tissues. These p53 mutations are found in approximately 50% of all cancers [38]. Roughly 10–20% of endometrial carcinomas exhibit p53 mutations [37]. […] overexpression of mutated tumor suppressor p53 has been associated with Type II EmCa (poor histologic grade, non-endometrioid histology, advanced stage, and poor survival).”

“Increasing data indicate that oxidative stress is involved in the development of DR [diabetic retinopathy] [16–19]. The retina has a high content of polyunsaturated fatty acids and has the highest oxygen uptake and glucose oxidation relative to any other tissue. This phenomenon renders the retina more susceptible to oxidative stress [20]. […] Since long-term exposure to oxidative stress is strongly implicated in the pathogenesis of diabetic complications, polymorphic genes of detoxifying enzymes may be involved in the development of DR. […] A meta-analysis comprising 17 studies, including type 1 and type 2 diabetic patients from different ethnic origins, implied that the C (Ala) allele of the C47T polymorphism in the MnSOD gene had a significant protective effect against microvascular complications (DR and diabetic nephropathy) […] In the development of DR, superoxide levels are elevated in the retina, antioxidant defense system is compromised, MnSOD is inhibited, and mitochondria are swollen and dysfunctional [77,87–90]. Overexpression of MnSOD protects [against] diabetes-induced mitochondrial damage and the development of DR [19,91].”

Continuous high level of blood glucose in diabetes damages micro and macro blood vessels throughout the body by altering the endothelial cell lining of the blood vessels […] Diabetes threatens vision, and patients with diabetes develop cataracts at an earlier age and are nearly twice as likely to get glaucoma compared to non-diabetic[s] [3]. More than 75% of patients who have had diabetes mellitus for more than 20 years will develop diabetic retinopathy (DR) [4]. […] DR is a slow progressive retinal disease and occurs as a consequence of longstanding accumulated functional and structural impairment of the retina by diabetes. It is a multifactorial condition arising from the complex interplay between biochemical and metabolic abnormalities occurring in all cells of the retina. DR has been classically regarded as a microangiopathy of the retina, involving changes in the vascular wall leading to capillary occlusion and thereby retinal ischemia and leakage. And more recently, the neural defects in the retina are also being appreciated […]. Recently, various clinical investigators [have detected] neuronal dysfunction at very early stages of diabetes and numerous abnormalities in the retina can be identified even before the vascular pathology appears [76, 77], thus suggesting a direct effect of diabetes on the neural retina. […] An emerging issue in DR research is the focus on the mechanistic link between chronic low-grade inflammation and angiogenesis. Recent evidence has revealed that extracellular high-mobility group box-1 (HMGB1) protein acts as a potent proinflammatory cytokine that triggers inflammation and recruits leukocytes to the site of tissue damage, and exhibits angiogenic effects. The expression of HMGB1 is upregulated in epiretinal membranes and vitreous fluid from patients with proliferative DR and in the diabetic retina. […] HMGB1 may be a potential biomarker [for diabetic retinopathy] […] early blockade of HMGB1 may be an effective strategy to prevent the progression of DR.”

“High blood pressure is one of the leading risk factors for global mortality and is estimated to have caused 9.4 million deaths in 2010. A meta‐analysis which includes 1 million individuals has indicated that death from both CHD [coronary heart disease] and stroke increase progressively and linearly from BP levels as low as 115 mmHg systolic and 75 mmHg diastolic upwards [138]. The WHO [has] pointed out that a “reduction in systolic blood pressure of 10 mmHg is associated with a 22% reduction in coronary heart disease, 41% reduction in stroke in randomized trials, and a 41–46% reduction in cardiometabolic mortality in epidemiological studies” [139].”

Several reproducible studies have ascertained that individuals with autism demonstrate an abnormal brain 5-HT system […] peripheral alterations in the 5-HT system may be an important marker of central abnormalities in autism. […] In a recent study, Carminati et al. [129] tested the therapeutic efficacy of venlafaxine, an antidepressant drug that inhibits the reuptake of 5-HT, and [found] that venlafaxine at a low dose [resulted in] a substantial improvement in repetitive behaviors, restricted interests, social impairment, communication, and language. Venlafaxine probably acts via serotonergic mechanisms  […] OT [Oxytocin]-related studies in autism have repeatedly reported lower blood OT level in autistic patients compared to age- and gender-matched control subjects […] autistic patients demonstrate an altered neuroinflammatory response throughout their lives; they also show increased astrocyte and microglia inflammatory response in the cortex and the cerebellum  [47, 48].”

November 3, 2016 Posted by | autism, Books, Cancer/oncology, Cardiology, Diabetes, Epidemiology, Genetics, Immunology, Medicine, Neurology, Pharmacology | Leave a comment

A couple of lectures and a little bit of random stuff

i. Two lectures from the Institute for Advanced Studies:

The IAS has recently uploaded a large number of lectures on youtube, and the ones I blog here are a few of those where you can actually tell from the title what the lecture is about; I find it outright weird that these people don’t include the topic covered in the lecture in their lecture titles.

As for the video above, as usual for the IAS videos it’s annoying that you can’t hear the questions asked by the audience, but the sound quality of this video is at least quite a bit better than the sound quality of the video below (which has a couple of really annoying sequences, in particular around the 15-16 minutes mark (it gets better), where the image is also causing problems, and in the last couple of minutes of the Q&A things are also not exactly optimal as the lecturer leaves the area covered by the camera in order to write something on the blackboard – but you don’t know what he’s writing and you can’t see the lecturer, because the camera isn’t following him). I found most of the above lecture easier to follow than I did the lecture posted below, though in either case you’ll probably not understand all of it unless you’re an astrophysicist – you definitely won’t in case of the latter lecture. I found it helpful to look up a few topics along the way, e.g. the wiki articles about the virial theorem (/also dealing with virial mass/radius), active galactic nucleus (this is the ‘AGN’ she refers to repeatedly), and the Tully–Fisher relation.

Given how many questions are asked along the way it’s really annoying that you in most cases can’t hear what people are asking about – this is definitely an area where there’s room for improvement in the context of the IAS videos. The lecture was not easy to follow but I figured along the way that I understood enough of it to make it worth watching the lecture to the end (though I’d say you’ll not miss much if you stop after the lecture – around the 1.05 hours mark – and skip the subsequent Q&A). I’ve relatively recently read about related topics, e.g. pulsar formation and wave- and fluid dynamics, and if I had not I probably would not have watched this lecture to the end.

ii. A vocabulary.com update. I’m slowly working my way up to the ‘Running Dictionary’ rank (I’m only a walking dictionary at this point); here’s some stuff from my progress page:

Vocab
I recently learned from a note added to a list that I’ve actually learned a very large proportion of all words available on vocabulary.com, which probably also means that I may have been too harsh on the word selection algorithm in past posts here on the blog; if there aren’t (/m)any new words left to learn it should not be surprising that the algorithm presents me with words I’ve already mastered, and it’s not the algorithm’s fault that there aren’t more words available for me to learn (well, it is to the extent that you’re of the opinion that questions should be automatically created by the algorithm as well, but I don’t think we’re quite there yet at this point). The aforementioned note was added in June, and here’s the important part: “there are words on your list that Vocabulary.com can’t teach yet. Vocabulary.com can teach over 12,000 words, but sadly, these aren’t among them”. ‘Over 12.000’ – and I’ve mastered 11.300. When the proportion of mastered words is this high, not only will the default random word algorithm mostly present you with questions related to words you’ve already mastered; but it actually also starts to get hard to find lists with many words you’ve not already mastered – I’ll often load lists with one hundred words and then realize that I’ve mastered every word on the list. This is annoying if you have a desire to continually be presented with both new words as well as old ones. Unless vocabulary.com increases the rate with which they add new words I’ll run out of new words to learn, and if that happens I’m sure it’ll be much more difficult for me to find motivation to use the site.

With all that stuff out of the way, if you’re not a regular user of the site I should note – again – that it’s an excellent resource if you desire to increase your vocabulary. Below is a list of words I’ve encountered on the site in recent weeks(/months?):

Copaceticfrumpyelisiontermagantharridanquondam, funambulist, phantasmagoriaeyelet, cachinnate, wilt, quidnunc, flocculent, galoot, frangible, prevaricate, clarion, trivet, noisome, revenant, myrmidon (I have included this word once before in a post of this type, but it is in my opinion a very nice word with which more people should be familiar…), debenture, teeter, tart, satiny, romp, auricular, terpsichorean, poultice, ululation, fusty, tangy, honorarium, eyas, bumptious, muckraker, bayou, hobble, omphaloskepsis, extemporize, virago, rarefaction, flibbertigibbet, finagle, emollient.

iii. I don’t think I’d do things exactly the way she’s suggesting here, but the general idea/approach seems to me appealing enough for it to be worth at least keeping in mind if I ever decide to start dating/looking for a partner.

iv. Some wikipedia links:

Tarrare (featured). A man with odd eating habits and an interesting employment history (“Dr. Courville was keen to continue his investigations into Tarrare’s eating habits and digestive system, and approached General Alexandre de Beauharnais with a suggestion that Tarrare’s unusual abilities and behaviour could be put to military use.[9] A document was placed inside a wooden box which was in turn fed to Tarrare. Two days later, the box was retrieved from his excrement, with the document still in legible condition.[9][17] Courville proposed to de Beauharnais that Tarrare could thus serve as a military courier, carrying documents securely through enemy territory with no risk of their being found if he were searched.” Yeah…).

Cauda equina syndromeCastleman’s disease, Astereognosis, Familial dysautonomia, Homonymous hemianopsia, Amaurosis fugax. All of these are of course related to content covered in the Handbook.

1740 Batavia massacre (featured).

v. I am also fun.

October 30, 2015 Posted by | Astronomy, History, Immunology, language, Lectures, Medicine, Neurology, Personal, Physics, Random stuff, Wikipedia | Leave a comment

Peripheral Neuropathy & Neuropathic Pain: Into the light (II)

Here’s my first post about the book. As I mentioned in that post, I figured I should limit detailed coverage to the parts of the book dealing with stuff related to diabetic/metabolic neuropathies. There’s a chapter specifically about ‘diabetic and uraemic neuropathies’ in the book and most of the coverage below relates to content covered in that chapter, but I have also included some related observations from other parts of the book as they seemed relevant.

It is noted in the book’s coverage that diabetes is the commonest cause of neuropathy in industrialized countries. There are many ways in which diabetes can affect the nervous system, and not all diabetes-related neuropathies affect peripheral nerves. Apart from distal symmetric polyneuropathy, which can probably in this context be thought of as ‘classic diabetic neuropathy’, focal or multifocal involvement of the peripheral nervous system is also common, and so is autonomic neuropathy. Diabetics are also at increased risk of inflammatory neuropathies such as CIDP – chronic inflammatory demyelinating polyneuropathy (about which the book also has a chapter). Late stage complications of diabetes usually relate to some extent to vessel wall abnormalities and their effects, and the blood vessels supplying the peripheral nerves can be affected just like all other blood vessels; in that context it is of interest to note that the author mentions elsewhere in the book that “tissue ischaemia is more likely to be symptomatic in nerves than in most other organs”. According to the author there isn’t really a great way to classify all the various manifestations of diabetic neuropathy, but most of them fall into one of three groups – distal symmetrical sensorimotor (length-dependent) polyneuropathy (DSSP); autonomic neuropathy; and focal- and multifocal neuropathy. The first one of these is by far the most common, and it is predominantly a sensory neuropathy (‘can you feel this?’ ‘does this hurt?’ ‘Is this water hot or cold?’ – as opposed to motor neuropathy: ‘can you move your arm?’) with no motor deficit.

Neuropathies in diabetics are common – how common? The author notes that the prevalence in several population-based surveys has been found to be around 30% “in studies using restrictive definitions”. The author does not mention this, but given that diabetic neuropathy usually has an insidious onset and given that diabetes-related sensory neuropathy “can be totally asymptomatic”, survey-based measures are if anything likely to underestimate prevalence. Risk increases with age and duration of diabetes; the prevalence of diabetic peripheral neuropathy is more than 50% in type 1 diabetics above the age of 60.

DSSP may lead to numbness, burning feet, a pins and needles sensation and piercing/stabbing pain in affected limbs. The ‘symmetric’ part of the abbreviation means that it usually affects both sides of the body, instead of e.g. just one foot or hand. The length-dependence mentioned in the parenthesis earlier relates in a way to the pathophysiological process. The axons of the peripheral nervous system lack ribosomes, and this means that essential proteins and enzymes needed in distal regions of the nervous system need to be transported great distances through the axons – which again means that neurons with long axons are particularly vulnerable to toxic or metabolic disturbances (introducing a length-dependence aspect in terms of which nerves are affected) which may lead to so-called dying-back axonal degeneration. The sensory loss can be restricted to the toes, extend over the feet, or it can migrate even further up the limbs – when sensory loss extends above the knee, signs and symptoms of nerve damage will usually also be observed in the fingers/hands/forearms. In generalized neuropathies a distinction can be made in terms of which type of nerve fibres are predominantly involved. When small fibres are most affected, sensory effects relating to pain- and temperature perception predominate, whereas light touch, position and vibratory senses are relatively preserved; on the other hand abnormalities of proprioception and sensitivity to light touch, often accompanied by motor deficits, will predominate if larger myelinated fibres are involved. DSSP is a small fibre neuropathy.

One of the ‘problems’ in diabetic neuropathy is actually that whereas sensation is affected, motor function often is not. This might be considered much better than the alternative, but unimpaired motor function actually relates closely to how damage often occurs. Wounds/ulcers developing on the soles of the feet (plantar ulcers) are very common in conditions in which there is sensation loss but no motor involvement/loss of strength; people with absent pain sensation will not know when their feet get hurt, e.g. because of a stone in the shoe or other forms of micro-trauma, but they’re still able to walk around relatively unimpaired and the absence of protective sensation in the limbs can thus lead to overuse of joints and accidental self-injury. A substantial proportion of diabetics with peripheral neuropathy also have lower limb ischaemia from peripheral artery disease, which further increases risk, but even in the absence of ischaemia things can go very wrong (for more details, see Edmonds, Foster, and Sanders – I should perhaps warn that the picture in that link is not a great appetite-stimulant). Of course one related problem here is that you can’t just stop moving around in order to avoid these problems once you’re aware that you have peripheral sensory neuropathy; inactivity will lead to muscle atrophy and ischaemia, and that’s not good for your feet either. The neuropathy may not ‘just’ lead to ulcers, but may also lead to the foot becoming deformed – the incidence of neuroarthropathy is approximately 2%/year in diabetics with peripheral neuropathy. Foot deformity is sometimes of acute onset and may be completely painless, despite leading to (painless) fractures and disorganization of joints. In the context of ulcers it is important that foot ulcers often take a *very* long time to heal, and so they provide excellent entry points for bacteria which among other things can cause chronic osteomyelitis (infection and inflammation of the bone and bone marrow). Pronounced motor involvement is as mentioned often absent in DSSP, but it does sometimes occur, usually at a late stage.

The author notes repeatedly in the text that peripheral neuropathy is sometimes the presenting symptom in type 2 diabetes, and I thought I should include that observation here as well. The high blood glucose may not be what leads the patient to see a doctor – sometimes the fact that he can no longer feel his toes is. At that point the nerve damage which has already occurred will of course usually be irreversible.

When the autonomic nervous system is affected (this is called Diabetic Autonomic Neuropathy, -DAN), this can lead to a variety of different symptoms. Effects of orthostatic hypotension (-OH) are frequent complaints; blackouts, faintness and dizziness or visual obscuration on standing are not always due to side effects of blood pressure medications. The author notes that OH can be aggravated by tricyclic antidepressants which are often used for treating chronic neuropathic pain (diabetics with autonomous nervous system disorder will often have, sometimes painful, peripheral neuropathy as well). Neurogenic male impotence seems to be “extremely common”; this leads to the absence of an erection at any time under any circumstances. The bladder may also be involved, which can lead to increased intervals between voiding and residual urine in the bladder after voiding, which can lead to UTIs. It is noted that retrograde ejaculation is frequent in people with bladder atony. The gastrointestinal system can be affected; this is often asymptomatic, but may lead to diarrhea and constipation causing weight loss and malnutrition. Associated diarrhea may be accompanied by fecal incontinence. DAN can lead to hypoglycemia unawareness, making glycemic control more difficult to accomplish. Sweating disorders are common in the feet. When a limb is affected by neuropathy the limb may lose its ability to sweat, and this may lead to other parts of the body (e.g. the head or upper trunk) engaging in ‘compensatory sweating’ to maintain temperature control. Abnormal pupil responses, e.g. in the form of reduced light reflexes and constricted pupils (miosis), are common in diabetics.

Focal (one nerve) and occasionally also multi-focal (more than one nerve) neuropathic syndromes also occur in the diabetic setting. The book spends quite a bit of time talking about what different nerves do and what happens when they stop working, so it’s hard to paint a broad picture of how these types of problems may present – it all depends on which nerve(s) is (are) affected. Usually in the setting of these disorders the long-term prognosis is good, or at least better than in the setting of DSSP; nerve damage is often not permanent. It seems that in terms of cranial nerve involvement, oculomotor nerve palsies are the most common, but still quite rare, affecting 1-2% of diabetics. Symptoms are rapid onset pain followed by double vision, and “spontaneous and complete recovery invariably occurs within 2-3 months” – I would like to note that as far as diabetes complications go, this is probably about as good as it gets… In so-called proximal diabetic neuropathy (-PDN), another type of mononeuropathy/focal neuropathy, the thighs are involved, with numbness or pain, often of a burning character which is worse at night, as well as muscle wasting. That syndrome progresses over weeks or months, after which the condition usually stabilizes and the pain improves, though residual muscle weakness seems to be common. Unlike in the case of DSSP, deficits in PDN are usually asymmetric, and both motor involvement and gradual recovery is common – it’s important to note in this context that DSSP virtually never improves spontaneously and often has a progressive course. Multi-focal neuropathies affect only a small proportion of diabetics, and in terms of outcome patterns they might be said to lie somewhere in between mononeuropathies and DSSP; outcomes are better than in the case of DSSP, but long-term sequelae are common.

Diabetics are at increased risk of developing pressure palsies in general. According to the author carpal tunnel syndrome occurs in 12% of diabetic patients, and “the incidence of ulnar neuropathy due to microlesions at the elbow level is high”.

In diabetics with renal failure caused by diabetic nephropathy (or presumably for that matter renal failure caused by other things as well, but most diabetics with kidney failure will have diabetic nephropathy) neuropathy is common and often severe. Renal failure impairs nerve function and is responsible for sometimes severe motor deficits in these patients. “Recovery from motor deficits is usually good after kidney transplant”. Carpal tunnel syndrome is very common in patients on long-term dialysis; 20 to 50 % of patients dialysed for 10 years or more are reported to have carpal tunnel syndrome. The presence of neuropathy in renal patients is closely related to renal function; the lower renal function, the more likely neurological symptoms become.

As you’ll learn from this book, a lot of things can cause peripheral neuropathies – and so the author notes that “In focal neuropathy occurring in diabetic patients, a neuropathy of another origin must always be excluded.” It’s not always diabetes, and sometimes missing the true cause can be a really bad thing; for example cancer-associated paraneoplastic syndromes are often associated with neuropathy (“paraneoplastic syndromes affect the PNS [Peripheral Nervous System] in up to one third of patients with solid tumors”), and so missing ‘the true cause’ in the case of a focal neuropathy may mean missing a growing tumour.

In terms of treatment options, “There is no specific treatment for distal symmetric polyneuropathy.” Complications can be treated/ideally prevented, but we have no drugs the primary effects of which are to specifically stop the nerves from dying. Treatment of autonomic neuropathy mostly relates to treating symptoms, in particular symptomatic OH. Treatment of proximal diabetic neuropathy, which is often very painful, relates only to pain management. Multifocal diabetic neuropathy can be treated with corticosteroids, minimizing inflammation.

Due to how common diabetic neuropathy is, most controlled studies on treatment options for neuropathic pain have involved patients with distal diabetic polyneuropathy. Various treatment options exist in the context of peripheral neuropathies, including antidepressants, antiepileptic drugs and opioids, as well as topical patches. In general pharmacological treatments will not cause anywhere near complete pain relief: “For patients receiving pharmacological treatment, the average pain reduction is about 20-30%, and only 20-35% of patients will achieve at least a 50% pain reduction with available drugs. […] often only partial pain relief from neuropathic pain can be expected, and […] sensory deficits are unlikely to respond to treatment.” Treatment of neuropathic pain is often a trial-and-error process.

October 17, 2015 Posted by | Books, Cancer/oncology, Diabetes, Epidemiology, Medicine, Neurology, Pharmacology | Leave a comment