Econstudentlog

Ophthalmology – National EM Board Review Course

The lecture covers a lot of different stuff. Some links:

Blepharitis.
Dacryocystitis.
Dacryoadenitis.
Chalazion.
Orbital Cellulitis.
Cranial Nerves III, IV, and VI: The Oculomotor System.
Argyll Robertson pupil.
Marcus Gunn pupil.
Horner syndrome.
Third nerve palsy.
Homonymous hemianopsia.
Central Retinal Artery Occlusion.
Central Retinal Vein Occlusion.
Optic Neuritis.
Retinal detachment.
Temporal Arteritis.
Conjunctivitis.
Epidemic Keratoconjunctivitis (EKC).
Uveitis.
Hypopyon.
Keratitis.
Herpes Zoster Ophthalmicus.
Subconjunctival Hemorrhage.
Corneal Abrasion.
Corneal Laceration.
Globe Rupture.
Acute Angle-Closure Glaucoma.
Hyphema.
Endophthalmitis.
Retrobulbar hemorrhage.

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September 15, 2017 Posted by | Lectures, Medicine, Ophthalmology, Pharmacology | Leave a comment

Depression and Heart Disease (II)

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

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

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

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

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

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

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

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

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

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

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

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

Depression and Heart Disease (I)

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

Below I’ve added some quotes from the book.

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

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

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

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

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

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

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

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

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

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

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

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

 

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

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

Infectious Disease Surveillance (I)

Concepts and Methods in Infectious Disease Surveillance […] familiarizes the reader with basic surveillance concepts; the legal basis for surveillance in the United States and abroad; and the purposes, structures, and intended uses of surveillance at the local, state, national, and international level. […] A desire for a readily accessible, concise resource that detailed current methods and challenges in disease surveillance inspired the collaborations that resulted in this volume. […] The book covers major topics at an introductory-to-intermediate level and was designed to serve as a resource or class text for instructors. It can be used in graduate level courses in public health, human and veterinary medicine, as well as in undergraduate programs in public health–oriented disciplines. We hope that the book will be a useful primer for frontline public health practitioners, hospital epidemiologists, infection-control practitioners, laboratorians in public health settings, infectious disease researchers, and medical informatics specialists interested in a concise overview of infectious disease surveillance.”

I thought the book was sort of okay, but not really all that great. I assume part of the reason I didn’t like it as much as I might have is that someone like me don’t really need to know all the details about, say, the issues encountered in Florida while they were trying to implement electronic patient records, or whether or not the mandated reporting requirements for brucellosis in, say, Texas are different from those of, say, Florida – but the book has a lot of that kind of information. Useful knowledge if you work with this stuff, but if you don’t and you’re just curious about the topic ‘in a general way’ those kinds of details can subtract a bit from the experience. A lot of chapters cover similar topics and don’t seem all that well coordinated, in the sense that details which could easily have been left out of specific chapters without any significant information loss (because those details were covered elsewhere in the publication) are included anyway; we are probably told at least ten times what is the difference between active and passive surveillance. It probably means that the various chapters can be read more or less independently (you don’t need to read chapter 5 to understand the coverage in chapter 11), but if you’re reading the book from cover to cover the way I was that sort of approach is not ideal. However in terms of the coverage included in the individual chapters and the content in general, I feel reasonably confident that if you’re actually working in public health or related fields and so a lot of this stuff might be ‘work-relevant’ (especially if you’re from the US), it’s probably a very useful book to keep around/know about. I didn’t need to know how many ‘NBS-states’ there are, and whether or not South Carolina is such a state, but some people might.

As I’ve pointed out before, a two star goodreads rating on my part (which is the rating I gave this publication) is not an indication that I think a book is terrible, it’s an indication that the book is ‘okay’.

Below I’ve added some quotes and observations from the book. The book is an academic publication but it is not a ‘classic textbook’ with key items in bold etc.; I decided to use bold to highlight key concepts and observations below, to make the post easier to navigate later on (none of the bolded words below were in bold in the original text), but aside from that I have made no changes to the quotes included in this post. I would note that given that many of the chapters included in the book are not covered by copyright (many chapters include this observation: “Materials appearing in this chapter are prepared by individuals as part of their official duties as United States government employees and are not covered by the copyright of the book, and any views expressed herein do not necessarily represent the views of the United States government.”) I may decide to cover the book in a bit more detail than I otherwise would have.

“The methods used for infectious disease surveillance depend on the type of disease. Part of the rationale for this is that there are fundamental differences in etiology, mode of transmission, and control measures between different types of infections. […] Despite the fact that much of surveillance is practiced on a disease-specific basis, it is worth remembering that surveillance is a general tool used across all types of infectious and, noninfectious conditions, and, as such, all surveillance methods share certain core elements. We advocate the view that surveillance should not be regarded as a public health “specialty,” but rather that all public health practitioners should understand the general principles underlying surveillance.”

“Control of disease spread is achieved through public health actions. Public health actions resulting from information gained during the investigation usually go beyond what an individual physician can provide to his or her patients presenting in a clinical setting. Examples of public health actions include identifying the source of infection […] identifying persons who were in contact with the index case or any infected person who may need vaccines or antiinfectives to prevent them from developing the infection; closure of facilities implicated in disease spread; or isolation of sick individuals or, in rare circumstances, quarantining those exposed to an infected person. […] Monitoring surveillance data enables public health authorities to detect sudden changes in disease occurrence and distribution, identify changes in agents or host factors, and detect changes in healthcare practices […] The primary use of surveillance data at the local and state public health level is to identify cases or outbreaks in order to implement immediate disease control and prevention activities. […] Surveillance data are also used by states and CDC to monitor disease trends, demonstrate the need for public health interventions such as vaccines and vaccine policy, evaluate public health activities, and identify future research priorities. […] The final and most-important link in the surveillance chain is the application of […] data to disease prevention and control. A surveillance system includes a functional capacity for data collection, analysis, and dissemination linked to public health programs [6].

“The majority of reportable disease surveillance is conducted through passive surveillance methods. Passive surveillance means that public health agencies inform healthcare providers and other entities of their reporting requirements, but they do not usually conduct intensive efforts to solicit all cases; instead, the public health agency waits for the healthcare entities to submit case reports. Because passive surveillance is often incomplete, public health agencies may use hospital discharge data, laboratory testing records, mortality data, or other sources of information as checks on completeness of reporting and to identify additional cases. This is called active surveillance. Active surveillance usually includes intensive activities on the part of the public health agency to identify all cases of a specific reportable disease or group of diseases. […] Because it can be very labor intensive, active surveillance is usually conducted for a subset of reportable conditions, in a defined geographic locale and for a defined period of time.”

“Active surveillance may be conducted on a routine basis or in response to an outbreak […]. When an outbreak is suspected or identified, another type of surveillance known as enhanced passive surveillance may also be initiated. In enhanced passive surveillance methods, public health may improve communication with the healthcare community, schools, daycare centers, and other facilities and request that all suspected cases be reported to public health. […] Case-based surveillance is supplemented through laboratory-based surveillance activities. As opposed to case-based surveillance, the focus is on laboratory results themselves, independent of whether or not an individual’s result is associated with a “case” of illness meeting the surveillance case definition. Laboratory-based surveillance is conducted by state public health laboratories as well as the healthcare community (e.g., hospital, private medical office, and commercial laboratories). […] State and local public health entities participate in sentinel surveillance activities. With sentinel methods, surveillance is conducted in a sample of reporting entities, such as healthcare providers or hospitals, or in a specific population known to be an early indicator of disease activity (e.g., pediatric). However, because the goal of sentinel surveillance is not to identify every case, it is not necessarily representative of the underlying population of interest; and results should be interpreted accordingly.”

Syndromic surveillance identifies unexpected changes in prediagnostic information from a variety of sources to detect potential outbreaks [56]. Sources include work- or school-absenteeism records, pharmacy sales for over-the-counter pharmaceuticals, or emergency room admission data [51]. During the 2009 H1N1 pandemic, syndromic surveillance of emergency room visits for influenza-like illness correlated well with laboratory diagnosed cases of influenza [57]. […] According to a 2008 survey of U.S. health departments, 88% of respondents reported that they employ syndromic-based approaches as part of routine surveillance [21].

“Public health operated for many decades (and still does to some extent) using stand-alone, case-based information systems for collection of surveillance data that do not allow information sharing between systems and do not permit the ability to track the occurrences of different diseases in a specific person over time. One of the primary objectives of NEDSS [National Electronic Disease Surveillance System] is to promote person-based surveillance and integrated and interoperable surveillance systems. In an integrated person-based system, information is collected to create a public health record for a given person for different diseases over time. This enables public health to track public health conditions associated with a person over time, allowing analyses of public health events and comorbidities, as well as more robust public health interventions. An interoperable system can exchange information with other systems. For example, data are shared between surveillance systems or between other public health or clinical systems, such as an electronic health record or outbreak management system. Achieving the goal of establishing a public health record for an individual over time does not require one monolithic system that supports all needs; this can, instead, be achieved through integration and/or interoperability of systems.

“For over a decade, public health has focused on automation of reporting of laboratory results to public health from clinical laboratories and healthcare providers. Paper-based submission of laboratory results to public health for reportable conditions results in delays in receipt of information, incomplete ascertainment of possible cases, and missing information on individual reports. All of these aspects are improved through automation of the process [39–43].”

“During the pre-vaccine era, rotavirus infected nearly every unvaccinated child before their fifth birthday. In the absence of vaccine, multiple rotavirus infections may occur during infancy and childhood. Rotavirus causes severe diarrhea and vomiting (acute gastroenteritis [AGE]), which can lead to dehydration, electrolyte depletion, complications of viremia, shock, and death. Nearly one-half million children around the world die of rotavirus infections each year […] [In the US] this virus was responsible for 40–50% of hospitalizations because of acute gastroenteritis during the winter months in the era before vaccines were introduced. […] Because first infections have been shown to induce strong immunity against severe rotavirus reinfections [3] and because vaccination mimics such first infections without causing illness, vaccination was identified as the optimal strategy for decreasing the burden associated with severe and fatal rotavirus diarrhea. Any changes that may be later attributed to vaccination effects require knowledge of the pre-licensure (i.e., baseline) rates and trends in the target disease as a reference […] Efforts to obtain baseline data are necessary before a vaccine is licensed and introduced [13]. […] After the first year of widespread rotavirus vaccination coverage in 2008, very large and consistent decreases in rotavirus hospitalizations were noted around the country. Many of the decreases in childhood hospitalizations resulting from rotavirus were 90% or more, compared with the pre-licensure, baseline period.”

There is no single perfect data source for assessing any VPD [Vaccine-Preventable Disease, US]. Meaningful surveillance is achieved by the much broader approach of employing diverse datasets. The true impact of a vaccine or the accurate assessment of disease trends in a population is more likely the result of evaluating many datasets having different strengths and weaknesses. Only by understanding these strengths and weaknesses can a public health practitioner give the appropriate consideration to the findings derived from these data. […] In a Phase III clinical trial, the vaccine is typically administered to large numbers of people who have met certain inclusionary and exclusionary criteria and are then randomly selected to receive either the vaccine or a placebo. […] Phase III trials represent the “best case scenario” of vaccine protection […] Once the Phase III trials show adequate protection and safety, the vaccine may be licensed by the FDA […] When the vaccine is used in routine clinical practice, Phase IV trials (called post-licensure studies or post-marketing studies) are initiated. These are the evaluations conducted during the course of VPD surveillance that delineate additional performance information in settings where strict controls on who receives the vaccine are not present. […] Often, measuring vaccine performance in the broader population yields slightly lower protective results compared to Phase III clinical trials […] During these post-licensure Phase IV studies, it is not the vaccine’s efficacy but its effectiveness that is assessed. […] Administrative datasets may be created by research institutions, managed-care organizations, or national healthcare utilization repositories. They are not specifically created for VPD surveillance and may contain coded data […] on health events. They often do not provide laboratory confirmation of specific diseases, unlike passive and active VPD surveillance. […] administrative datasets offer huge sample sizes, which allow for powerful inferences within the confines of any data limitations.”

August 6, 2017 Posted by | Books, Epidemiology, Infectious disease, Medicine, Pharmacology | Leave a comment

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some more observations from the paper:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some more stuff from the paper:

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

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

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

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

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

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

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

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

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

iv. The Mental Health Comorbidities of Diabetes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Today’s Landscape of Pharmaceutical Research in Cancer

It’s been a while since I watched this lecture so I don’t remember the details very well, but I usually add notes in my bookmarks when I watch lectures so that I know what details to include in my comments here on the blog, and I have added the details from the bookmark notes below.

It is a short lecture, the lecture itself lasts only roughly 30 minutes; it doesn’t really start until roughly the 9 minutes and 30 seconds mark, and it finishes around the 44 min mark (the rest is Q&A – I skipped some of the introduction, but watched the Q&A). The lecture is not very technical, I think the content is perfectly understandable also to people without a medical background. One data point from the lecture which I thought worth including in these comments is this: According to Sigal, “roughly 30 per cent of the biopharmaceutical industry’s portfolio … is focused on research in oncology.”

May 17, 2017 Posted by | Cancer/oncology, Health Economics, Immunology, Lectures, Medicine, Pharmacology | 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, Health Economics, Medicine, Nephrology, Neurology, Papers, Pharmacology, Psychology | Leave a comment

Biodemography of aging (II)

In my first post about the book I included a few general remarks about the book and what it’s about. In this post I’ll continue my coverage of the book, starting with a few quotes from and observations related to the content in chapter 4 (‘Evidence for Dependence Among Diseases‘).

“To compare the effects of public health policies on a population’s characteristics, researchers commonly estimate potential gains in life expectancy that would result from eradication or reduction of selected causes of death. For example, Keyfitz (1977) estimated that eradication of cancer would result in 2.265 years of increase in male life expectancy at birth (or by 3 % compared to its 1964 level). Lemaire (2005) found that the potential gain in the U.S. life expectancy from cancer eradication would not exceed 3 years for both genders. Conti et al. (1999) calculated that the potential gain in life expectancy from cancer eradication in Italy would be 3.84 years for males and 2.77 years for females. […] All these calculations assumed independence between cancer and other causes of death. […] for today’s populations in developed countries, where deaths from chronic non-communicable diseases are in the lead, this assumption might no longer be valid. An important feature of such chronic diseases is that they often develop in clusters manifesting positive correlations with each other. The conventional view is that, in a case of such dependence, the effect of cancer eradication on life expectancy would be even smaller.”

I think the great majority of people you asked would have assumed that the beneficial effect of hypothetical cancer eradication in humans on human life expectancy would be much larger than this, but that’s just an impression. I’ve seen estimates like these before, so I was not surprised – but I think many people would be if they knew this. A very large number of people die as a result of developing cancer today, but the truth of the matter is that if they hadn’t died from cancer they’d have died anyway, and on average probably not really all that much later. I linked to Richard Alexander’s comments on this topic in my last post about the book, and again his observations apply so I thought I might as well add the relevant quote from the book here:

“In the course of working against senescence, selection will tend to remove, one by one, the most frequent sources of mortality as a result of senescence. Whenever a single cause of mortality, such as a particular malfunction of any vital organ, becomes the predominant cause of mortality, then selection will more effectively reduce the significance of that particular defect (meaning those who lack it will outreproduce) until some other achieves greater relative significance. […] the result will be that all organs and systems will tend to deteriorate together. […] The point is that as we age, and as senescence proceeds, large numbers of potential sources of mortality tend to lurk ever more malevolently just “below the surface,”so that, unfortunately, the odds are very high against any dramatic lengthening of the maximum human lifetime through technology.”

Remove one cause of death and there are plenty of others standing in line behind it. We already knew that; two hundred years ago one out of every four deaths in England was the result of tuberculosis, but developing treatments for tuberculosis and other infectious diseases did not mean that English people stopped dying; these days they just die from cardiovascular disease and cancer instead. Do note in the context of that quote that Alexander is talking about the maximum human lifetime, not average life expectancy; again, we know and have known for a long time that human technology can have a dramatic effect on the latter variable. Of course a shift in one distribution will be likely to have spill-over effects on the other (if more people are alive at the age of 70, the potential group of people also living on to reach e.g. 100 years is higher, even if the mortality rate for the 70-100 year old group did not change) the point is just that these effects are secondary effects and are likely to be marginal at best.

Anyway, some more stuff from the chapter. Just like the previous chapter in the book did, this one also includes analyses of very large data sets:

The Multiple Cause of Death (MCD) data files contain information about underlying and secondary causes of death in the U.S. during 1968–2010. In total, they include more than 65 million individual death certificate records. […] we used data for the period 1979–2004.”

There’s some formal modelling stuff in the chapter which I won’t go into in detail here, this is the chapter in which I encountered the comment about ‘the multivariate lognormal frailty model’ I included in my first post about the book. One of the things the chapter looks at are the joint frequencies of deaths from cancer and other fatal diseases; it turns out that there are multiple diseases that are negatively related with cancer as a cause of death when you look at the population-level data mentioned above. The chapter goes into some of the biological mechanisms which may help explain why these associations look the way they do, and I’ll quote a little from that part of the coverage. A key idea here is (as always..?) that there are tradeoffs at play; some genetic variants may help protect you against e.g. cancer, but at the same time increase the risk of other diseases for the same reason that they protect you against cancer. In the context of the relationship between cancer deaths and deaths from other diseases they note in the conclusion that: “One potential biological mechanism underlying the negative correlation among cancer and other diseases could be related to the differential role of apoptosis in the development of these diseases.” The chapter covers that stuff in significantly more detail, and I decided to add some observations from the chapter on these topics below:

“Studying the role of the p53 gene in the connection between cancer and cellular aging, Campisi (2002, 2003) suggested that longevity may depend on a balance between tumor suppression and tissue renewal mechanisms. […] Although the mechanism by which p53 regulates lifespan remains to be determined, […] findings highlight the possibility that careful manipulation of p53 activity during adult life may result in beneficial effects on healthy lifespan. Other tumor suppressor genes are also involved in regulation of longevity. […] In humans, Dumont et al. (2003) demonstrated that a replacement of arginine (Arg) by proline (Pro) at position 72 of human p53 decreases its ability to initiate apoptosis, suggesting that these variants may differently affect longevity and vulnerability to cancer. Van Heemst et al. (2005) showed that individuals with the Pro/Pro genotype of p53 corresponding to reduced apoptosis in cells had significantly increased overall survival (by 41%) despite a more than twofold increased proportion of cancer deaths at ages 85+, together with a decreased proportion of deaths from senescence related causes such as COPD, fractures, renal failure, dementia, and senility. It was suggested that human p53 may protect against cancer but at a cost of longevity. […] Other biological factors may also play opposing roles in cancer and aging and thus contribute to respective trade-offs […]. E.g., higher levels of IGF-1 [have been] linked to both cancer and attenuation of phenotypes of physical senescence, such as frailty, sarcopenia, muscle atrophy, and heart failure, as well as to better muscle regeneration”.

“The connection between cancer and longevity may potentially be mediated by trade-offs between cancer and other diseases which do not necessarily involve any basic mechanism of aging per se. In humans, it could result, for example, from trade-offs between vulnerabilities to cancer and AD, or to cancer and CVD […] There may be several biological mechanisms underlying the negative correlation among cancer and these diseases. One can be related to the differential role of apoptosis in their development. For instance, in stroke, the number of dying neurons following brain ischemia (and thus probability of paralysis or death) may be less in the case of a downregulated apoptosis. As for cancer, the downregulated apoptosis may, conversely, mean a higher risk of the disease because more cells may survive damage associated with malignant transformation. […] Also, the role of the apoptosis may be different or even opposite in the development of cancer and Alzheimer’s disease (AD). Indeed, suppressed apoptosis is a hallmark of cancer, while increased apoptosis is a typical feature of AD […]. If so, then chronically upregulated apoptosis (e.g., due to a genetic polymorphism) may potentially be protective against cancer, but be deleterious in relation to AD. […] Increased longevity can be associated not only with increased but also with decreased chances of cancer. […] The most popular to-date “anti-aging” intervention, caloric restriction, often results in increased maximal life span along with reduced tumor incidence in laboratory rodents […] Because the rate of apoptosis was significantly and consistently higher in food restricted mice regardless of age, James et al. (1998) suggested that caloric restriction may have a cancer-protective effect primarily due to the upregulated apoptosis in these mice.”

Below I’ll discuss content covered in chapter 5, which deals with ‘Factors That May Increase Vulnerability to Cancer and Longevity in Modern Human Populations’. I’ll start out with a few quotes:

“Currently, the overall cancer incidence rate (age-adjusted) in the less developed world is roughly half that seen in the more developed world […] For countries with similar levels of economic development but different climate and ethnic characteristics […], the cancer rate patterns look much more similar than for the countries that share the same geographic location, climate, and ethnic distribution, but differ in the level of economic development […]. This suggests that different countries may share common factors linked to economic prosperity that could be primarily responsible for the modern increases in overall cancer risk. […] Population aging (increases in the proportion of older people) may […] partly explain the rise in the global cancer burden […]; however, it cannot explain increases in age-specific cancer incidence rates over time […]. Improved diagnostics and elevated exposures to carcinogens may explain increases in rates for selected cancer sites, but they cannot fully explain the increase in the overall cancer risk, nor incidence rate trends for most individual cancers (Jemal et al. 2008, 2013).”

“[W]e propose that the association between the overall cancer risk and the economic progress and spread of the Western lifestyle could in part be explained by the higher proportion of individuals more susceptible to cancer in the populations of developed countries, and discuss several mechanisms of such an increase in the proportion of the vulnerable. […] mechanisms include but are not limited to: (i) Improved survival of frail individuals. […] (ii) Avoiding or reducing traditional exposures. Excessive disinfection and hygiene typical of the developed world can diminish exposure to some factors that were abundant in the past […] Insufficiently or improperly trained immune systems may be less capable of resisting cancer. (iii) Burden of novel exposures. Some new medicines, cleaning agents, foods, etc., that are not carcinogenic themselves may still affect the natural ways of processing carcinogens in the body, and through this increase a person’s susceptibility to established carcinogens. [If this one sounds implausible to you, I’ll remind you that drug metabolism is complicatedUS] […] (iv) Some of the factors linked to economic prosperity and the Western lifestyle (e.g., delayed childbirth and food enriched with growth factors) may antagonistically influence aging and cancer risk.”

They provide detailed coverage of all of these mechanisms in the chapter, below I have included a few select observations from that part of the coverage.

“There was a dramatic decline in infant and childhood mortality in developed countries during the last century. For example, the infant mortality rate in the United States was about 6 % of live births in 1935, 3 % in 1950, 1.3 % in 1980, and 0.6 % in 2010. That is, it declined tenfold over the course of 75 years […] Because almost all children (including those with immunity deficiencies) survive, the proportion of the children who are inherently more vulnerable could be higher in the more developed countries. This is consistent with a typically higher proportion of children with chronic inflammatory immune disorders such as asthma and allergy in the populations of developed countries compared to less developed ones […] Over-reduction of such traditional exposures may result in an insufficiently/improperly trained immune system early in life, which could make it less able to resist diseases, including cancer later in life […] There is accumulating evidence of the important role of these effects in cancer risk. […] A number of studies have connected excessive disinfection and lack of antigenic stimulation (especially in childhood) of the immune system in Westernized communities with increased risks of both chronic inflammatory diseases and cancer […] The IARC data on migrants to Israel […] allow for comparison of the age trajectories of cancer incidence rates between adult Jews who live in Israel but were born in other countries […] [These data] show that Jews born in less developed regions (Africa and Asia) have overall lower cancer risk than those born in the more developed regions (Europe and America).  The discrepancy is unlikely to be due to differences in cancer diagnostics because at the moment of diagnosis all these people were citizens of the same country with the same standard of medical care. These results suggest that surviving childhood and growing up in a less developed country with diverse environmental exposures might help form resistance to cancer that lasts even after moving to a high risk country.”

I won’t go much into the ‘burden of novel exposures’ part, but I should note that exposures that may be relevant include factors like paracetamol use and antibiotics for treatment of H. pylori. Paracetamol is not considered carcinogenic by the IARC, but we know from animal studies that if you give rats paratamol and then expose them to an established carcinogen (with the straightforward name N-nitrosoethyl-N-hydroxyethylamine), the number of rats developing kidney cancer goes up. In the context of H. pylori, we know that these things may cause stomach cancer, but when you treat rats with metronidazol (which is used to treat H. pylori) and expose them to an established carcinogen, they’re more likely to develop colon cancer. The link between colon cancer and antibiotics use has been noted in other contexts as well; decreased microbial diversity after antibiotics use may lead to suppression of the bifidobacteria and promotion of E. coli in the colon, the metabolic products of which may lead to increased cancer risk. Over time an increase in colon cancer risk and a decrease in stomach cancer risk has been observed in developed societies, but aside from changes in diet another factor which may play a role is population-wide exposure to antibiotics. Colon and stomach cancers are incidentally not the only ones of interest in this particular context; it has also been found that exposure to chloramphenicol, a broad-spectrum antibiotic used since the 40es, increases the risk of lymphoma in mice when the mice are exposed to a known carcinogen, despite the drug itself again not being clearly carcinogenic on its own.

Many new exposures aside from antibiotics are of course relevant. Two other drug-related ones that might be worth mentioning are hormone replacement therapy and contraceptives. HRT is not as commonly used today as it was in the past, but to give some idea of the scope here, half of all women in the US aged 50-65 are estimated to have been on HRT at the peak of its use, around the turn of the millennium, and HRT is assumed to be partly responsible for the higher incidence of hormone-related cancers observed in female populations living in developed countries. It’s of some note that the use of HRT dropped dramatically shortly after this peak (from 61 million prescriptions in 2001 to 21 million in 2004), and that the incidence of estrogen-receptor positive cancers subsequently dropped. As for oral contraceptives, these have been in use since the 1960s, and combined hormonal contraceptives are known to increase the risk of liver- and breast cancer, while seemingly also having a protective effect against endometrial cancer and ovarian cancer. The authors speculate that some of the cancer incidence changes observed in the US during the latter half of the last century, with a decline in female endometrial and ovarian cancer combined with an increase in breast- and liver cancer, could in part be related to widespread use of these drugs. An estimated 10% of all women of reproductive age alive in the world, and 16% of those living in the US, are estimated to be using combined hormonal contraceptives. In the context of the protective effect of the drugs, it should perhaps be noted that endometrial cancer in particular is strongly linked to obesity so if you are not overweight you are relatively low-risk.

Many ‘exposures’ in a cancer context are not drug-related. For example women in Western societies tend to go into menopause at a higher age, and higher age of menopause has been associated with hormone-related cancers; but again the picture is not clear in terms of how the variable affects longevity, considering that later menopause has also been linked to increased longevity in several large studies. In the studies the women did have higher mortality from the hormone-related cancers, but on the other hand they were less likely to die from some of the other causes, such as pneumonia, influenza, and falls. Age of childbirth is also a variable where there are significant differences between developed countries and developing countries, and this variable may also be relevant to cancer incidence as it has been linked to breast cancer and melanoma; in one study women who first gave birth after the age of 35 had a 40% increased risk of breast cancer compared to mothers who gave birth before the age of 20 (good luck ‘controlling for everything’ in a context like that, but…), and in a meta-analysis the relative risk for melanoma was 1.47 for women in the oldest age group having given birth, compared to the youngest (again, good luck controlling for everything, but at least it’s not just one study). Lest you think this literature only deals with women, it’s also been found that parental age seems to be linked to cancers in the offspring (higher parental age -> higher cancer risk in the offspring), though the effect sizes are not mentioned in the coverage.

Here’s what they conclude at the end of the chapter:

“Some of the factors associated with economic prosperity and a Western lifestyle may influence both aging and vulnerability to cancer, sometimes oppositely. Current evidence supports a possibility of trade-offs between cancer and aging-related phenotypes […], which could be influenced by delayed reproduction and exposures to growth factors […]. The latter may be particularly beneficial at very old age. This is because the higher levels of growth factors may attenuate some phenotypes of physical senescence, such as decline in regenerative and healing ability, sarcopenia, frailty, elderly fractures and heart failure due to muscles athrophy. They may also increase the body’s vulnerability to cancer, e.g., through growth promoting and anti-apoptotic effects […]. The increase in vulnerability to cancer due to growth factors can be compatible with extreme longevity because cancer is a major contributor to mortality mainly before age 85, while senescence-related causes (such as physical frailty) become major contributors to mortality at oldest old ages (85+). In this situation, the impact of growth factors on vulnerability to death could be more deleterious in middle-to-old life (~before 85) and more beneficial at older ages (85+).

The complex relationships between aging, cancer, and longevity are challenging. This complexity warns against simplified approaches to extending longevity without taking into account the possible trade-offs between phenotypes of physical aging and various health disorders, as well as the differential impacts of such tradeoffs on mortality risks at different ages (e.g., Ukraintseva and Yashin 2003a; Yashin et al. 2009; Ukraintseva et al. 2010, 2016).”

March 7, 2017 Posted by | Books, Cancer/oncology, Epidemiology, Genetics, Immunology, Medicine, Pharmacology | 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

Anesthesia

“A recent study estimated that 234 million surgical procedures requiring anaesthesia are performed worldwide annually. Anaesthesia is the largest hospital specialty in the UK, with over 12,000 practising anaesthetists […] In this book, I give a short account of the historical background of anaesthetic practice, a review of anaesthetic equipment, techniques, and medications, and a discussion of how they work. The risks and side effects of anaesthetics will be covered, and some of the subspecialties of anaesthetic practice will be explored.”

I liked the book, and I gave it three stars on goodreads; I was closer to four stars than two. Below I have added a few sample observations from the book, as well as what turned out in the end to be actually a quite considerable number of links (more than 60 it turned out, from a brief count) to topics/people/etc. discussed or mentioned in the text. I decided to spend a bit more time finding relevant links than I’ve previously done when writing link-heavy posts, so in this post I have not limited myself to wikipedia articles and I e.g. also link directly to primary literature discussed in the coverage. The links provided are, as usual, meant to be indicators of which kind of stuff is covered in the book, rather than an alternative to the book; some of the wikipedia articles in particular I assume are not very good (the main point of a link to a wikipedia article of questionable quality should probably be taken to be an indication that I consider ‘awareness of the existence of concept X’ to be of interest/important also to people who have not read this book, even if no great resource on the topic was immediately at hand to me).

Sample observations from the book:

“[G]eneral anaesthesia is not sleep. In physiological terms, the two states are very dissimilar. The term general anaesthesia refers to the state of unconsciousness which is deliberately produced by the action of drugs on the patient. Local anaesthesia (and its related terms) refers to the numbness produced in a part of the body by deliberate interruption of nerve function; this is typically achieved without affecting consciousness. […] The purpose of inhaling ether vapour [in the past] was so that surgery would be painless, not so that unconsciousness would necessarily be produced. However, unconsciousness and immobility soon came to be considered desirable attributes […] For almost a century, lying still was the only reliable sign of adequate anaesthesia.”

“The experience of pain triggers powerful emotional consequences, including fear, anger, and anxiety. A reasonable word for the emotional response to pain is ‘suffering’. Pain also triggers the formation of memories which remind us to avoid potentially painful experiences in the future. The intensity of pain perception and suffering also depends on the mental state of the subject at the time, and the relationship between pain, memory, and emotion is subtle and complex. […] The effects of adrenaline are responsible for the appearance of someone in pain: pale, sweating, trembling, with a rapid heart rate and breathing. Additionally, a hormonal storm is activated, readying the body to respond to damage and fight infection. This is known as the stress response. […] Those responses may be abolished by an analgesic such as morphine, which will counteract all those changes. For this reason, it is routine to use analgesic drugs in addition to anaesthetic ones. […] Typical anaesthetic agents are poor at suppressing the stress response, but analgesics like morphine are very effective. […] The hormonal stress response can be shown to be harmful, especially to those who are already ill. For example, the increase in blood coagulability which evolved to reduce blood loss as a result of injury makes the patient more likely to suffer a deep venous thrombosis in the leg veins.”

“If we monitor the EEG of someone under general anaesthesia, certain identifiable changes to the signal occur. In general, the frequency spectrum of the signal slows. […] Next, the overall power of the signal diminishes. In very deep general anaesthesia, short periods of electrical silence, known as burst suppression, can be observed. Finally, the overall randomness of the signal, its entropy, decreases. In short, the EEG of someone who is anaesthetized looks completely different from someone who is awake. […] Depth of anaesthesia is no longer considered to be a linear concept […] since it is clear that anaesthesia is not a single process. It is now believed that the two most important components of anaesthesia are unconsciousness and suppression of the stress response. These can be represented on a three-dimensional diagram called a response surface. [Here’s incidentally a recent review paper on related topics, US]”

“Before the widespread advent of anaesthesia, there were very few painkilling options available. […] Alcohol was commonly given as a means of enhancing the patient’s courage prior to surgery, but alcohol has almost no effect on pain perception. […] For many centuries, opium was the only effective pain-relieving substance known. […] For general anaesthesia to be discovered, certain prerequisites were required. On the one hand, the idea that surgery without pain was achievable had to be accepted as possible. Despite tantalizing clues from history, this idea took a long time to catch on. The few workers who pursued this idea were often openly ridiculed. On the other, an agent had to be discovered that was potent enough to render a patient suitably unconscious to tolerate surgery, but not so potent that overdose (hence accidental death) was too likely. This agent also needed to be easy to produce, tolerable for the patient, and easy enough for untrained people to administer. The herbal candidates (opium, mandrake) were too unreliable or dangerous. The next reasonable candidate, and every agent since, was provided by the proliferating science of chemistry.”

“Inducing anaesthesia by intravenous injection is substantially quicker than the inhalational method. Inhalational induction may take several minutes, while intravenous induction happens in the time it takes for the blood to travel from the needle to the brain (30 to 60 seconds). The main benefit of this is not convenience or comfort but patient safety. […] It was soon discovered that the ideal balance is to induce anaesthesia intravenously, but switch to an inhalational agent […] to keep the patient anaesthetized during the operation. The template of an intravenous induction followed by maintenance with an inhalational agent is still widely used today. […] Most of the drawbacks of volatile agents disappear when the patient is already anaesthetized [and] volatile agents have several advantages for maintenance. First, they are predictable in their effects. Second, they can be conveniently administered in known quantities. Third, the concentration delivered or exhaled by the patient can be easily and reliably measured. Finally, at steady state, the concentration of volatile agent in the patient’s expired air is a close reflection of its concentration in the patient’s brain. This gives the anaesthetist a reliable way of ensuring that enough anaesthetic is present to ensure the patient remains anaesthetized.”

“All current volatile agents are colourless liquids that evaporate into a vapour which produces general anaesthesia when inhaled. All are chemically stable, which means they are non-flammable, and not likely to break down or be metabolized to poisonous products. What distinguishes them from each other are their specific properties: potency, speed of onset, and smell. Potency of an inhalational agent is expressed as MAC, the minimum alveolar concentration required to keep 50% of adults unmoving in response to a standard surgical skin incision. MAC as a concept was introduced […] in 1963, and has proven to be a very useful way of comparing potencies of different anaesthetic agents. […] MAC correlates with observed depth of anaesthesia. It has been known for over a century that potency correlates very highly with lipid solubility; that is, the more soluble an agent is in lipid […], the more potent an anaesthetic it is. This is known as the Meyer-Overton correlation […] Speed of onset is inversely proportional to water solubility. The less soluble in water, the more rapidly an agent will take effect. […] Where immobility is produced at around 1.0 MAC, amnesia is produced at a much lower dose, typically 0.25 MAC, and unconsciousness at around 0.5 MAC. Therefore, a patient may move in response to a surgical stimulus without either being conscious of the stimulus, or remembering it afterwards.”

“The most useful way to estimate the body’s physiological reserve is to assess the patient’s tolerance for exercise. Exercise is a good model of the surgical stress response. The greater the patient’s tolerance for exercise, the better the perioperative outcome is likely to be […] For a smoker who is unable to quit, stopping for even a couple of days before the operation improves outcome. […] Dying ‘on the table’ during surgery is very unusual. Patients who die following surgery usually do so during convalescence, their weakened state making them susceptible to complications such as wound breakdown, chest infections, deep venous thrombosis, and pressure sores.”

Mechanical ventilation is based on the principle of intermittent positive pressure ventilation (IPPV), gas being ‘blown’ into the patient’s lungs from the machine. […] Inflating a patient’s lungs is a delicate process. Healthy lung tissue is fragile, and can easily be damaged by overdistension (barotrauma). While healthy lung tissue is light and spongy, and easily inflated, diseased lung tissue may be heavy and waterlogged and difficult to inflate, and therefore may collapse, allowing blood to pass through it without exchanging any gases (this is known as shunt). Simply applying higher pressures may not be the answer: this may just overdistend adjacent areas of healthier lung. The ventilator must therefore provide a series of breaths whose volume and pressure are very closely controlled. Every aspect of a mechanical breath may now be adjusted by the anaesthetist: the volume, the pressure, the frequency, and the ratio of inspiratory time to expiratory time are only the basic factors.”

“All anaesthetic drugs are poisons. Remember that in achieving a state of anaesthesia you intend to poison someone, but not kill them – so give as little as possible. [Introductory quote to a chapter, from an Anaesthetics textbook – US] […] Other cells besides neurons use action potentials as the basis of cellular signalling. For example, the synchronized contraction of heart muscle is performed using action potentials, and action potentials are transmitted from nerves to skeletal muscle at the neuromuscular junction to initiate movement. Local anaesthetic drugs are therefore toxic to the heart and brain. In the heart, local anaesthetic drugs interfere with normal contraction, eventually stopping the heart. In the brain, toxicity causes seizures and coma. To avoid toxicity, the total dose is carefully limited”.

Links of interest:

Anaesthesia.
General anaesthesia.
Muscle relaxant.
Nociception.
Arthur Ernest Guedel.
Guedel’s classification.
Beta rhythm.
Frances Burney.
Laudanum.
Dwale.
Henry Hill Hickman.
Horace Wells.
William Thomas Green Morton.
Diethyl ether.
Chloroform.
James Young Simpson.
Joseph Thomas Clover.
Barbiturates.
Inhalational anaesthetic.
Antisialagogue.
Pulmonary aspiration.
Principles of Total Intravenous Anaesthesia (TIVA).
Propofol.
Patient-controlled analgesia.
Airway management.
Oropharyngeal airway.
Tracheal intubation.
Laryngoscopy.
Laryngeal mask airway.
Anaesthetic machine.
Soda lime.
Sodium thiopental.
Etomidate.
Ketamine.
Neuromuscular-blocking drug.
Neostigmine.
Sugammadex.
Gate control theory of pain.
Multimodal analgesia.
Hartmann’s solution (…what this is called seems to be depending on whom you ask, but it’s called Hartmann’s solution in the book…).
Local anesthetic.
Karl Koller.
Amylocaine.
Procaine.
Lidocaine.
Regional anesthesia.
Spinal anaesthesia.
Epidural nerve block.
Intensive care medicine.
Bjørn Aage Ibsen.
Chronic pain.
Pain wind-up.
John Bonica.
Twilight sleep.
Veterinary anesthesia.
Pearse et al. (results of paper briefly discussed in the book).
Awareness under anaesthesia (skip the first page).
Pollard et al. (2007).
Postoperative nausea and vomiting.
Postoperative cognitive dysfunction.
Monk et al. (2008).
Malignant hyperthermia.
Suxamethonium apnoea.

February 13, 2017 Posted by | Books, Chemistry, Medicine, Papers, Pharmacology | Leave a comment

Integrated Diabetes Care (I)

I’ll start out by quoting from my goodreads review of the book:

The book provides a good overview of studies and clinical trials which have attempted to improve the coordination of diabetes treatment in specific areas. The book covers research from all over the world – the UK, the US, Hong Kong, South Africa, Germany, Netherlands, Sweden, Australia. The language of the publication is quite good, considering the number of non-native English speaking contributors. An at least basic understanding of medical statistics is probably required for one to properly read and understand this book in full.

The book is quite good if you want to understand how people have tried to improve (mainly type 2) diabetes treatment ‘from an organizational point of view’ (the main focus here is not on new treatment options, but on how to optimize care delivery and make the various care providers involved work better together, in a way that improves outcomes for patients (at an acceptable cost?), which is to a large extent an organizational problem), but it’s actually also probably quite a nice book if you simply want to know more about how diabetes treatment systems differ across countries; the contributors don’t assume that the readers know how e.g. the Swedish approach to diabetes care differs from that of e.g. Pennsylvania, so many chapters contain interesting details on how specific countries/health care providers handle specific aspects of e.g. care delivery or finance.

What people mean by ‘integrated care’ varies a bit depending on whom you ask (patients and service providers may emphasize different dimensions when thinking about these topics), as should also be clear from the quotes below; however I assumed it might be a good idea to start out the post with the quote above, so that people who might have no idea what ‘integrated diabetes care’ is did not start out reading the post completely in the dark. In short, a big problem in health service delivery contexts is that care provision is often fragmented and uncoordinated, for many reasons. Ideally you might like doctors working in general practice to collaborate smoothly and efficiently with hospital staff and various other specialists involved in diabetes care (…and perhaps also with social services and mental health care providers…), but that kind of coordination often doesn’t happen, leading to what may well be sub-optimal care provision. Collaboration and a ‘desirable’ (whatever that might mean) level of coordination between service providers doesn’t happen automatically; it takes money, effort and a lot of other things (that the book covers in some detail…) to make it happen – and so often it doesn’t happen, at least there’s a lot of room for improvement even in places where things work comparatively well. Some quotes from the book on these topics:

“it is clear that in general, wherever you are in the world, service delivery is now fragmented [2]. Such fragmentation is a manifestation of organisational and financial barriers, which divide providers at the boundaries of primary and secondary care, physical and mental health care, and between health and social care. Diverse specific organisational and professional cultures, and differences in terms of governance and accountability also contribute to this fragmentation [2]. […] Many of these deficiencies are caused by organisational problems (barriers, silo thinking, accountability for budgets) and are often to the detriment of all of those involved: patients, providers and funders – in extreme cases – leading to lose-lose-lose-situations […] There is some evidence that integrated care does improve the quality of patient care and leads to improved health or patient satisfaction [10, 11], but evidence of economic benefits remain an issue for further research [10]. Failure to improve integration and coordination of services along a “care continuum” can result in suboptimal outcomes (health and cost), such as potentially preventable hospitalisation, avoidable death, medication errors and adverse drug events [3, 12, 13].”

Integrated care is often described as a continuum [10, 24], actually depicting the degree of integration. This degree can range from linkage, to coordination and integration [10], or segregation (absence of any cooperation) to full integration [25], in which the integrated organisation is responsible for the full continuum of care responsible for the full continuum of care […] this classification of integration degree can be expanded by introducing a second dimension, i.e., the user needs. User need should be defined by criteria, like stability and severity of condition, duration of illness (chronic condition), service needed and capacity for self-direction (autonomy). Accordingly, a low level of need will not require a fully integrated system, then [10, 24] […] Kaiser Permanente is a good example of what has been described as a “fully integrated system. […] A key element of Kaiser Permanente’s approach to chronic care is the categorisation of their chronically ill patients into three groups based on their degree of need“.

It may be a useful simplification to think along the lines of: ‘Higher degree of need = a higher level of integration becomes desirable/necessary. Disease complexity is closely related to degree of need.’ Some related observations from the book:

“Diabetes is a condition in which longstanding hyperglycaemia damages arteries (causing macrovascular, e.g., ischaemic heart, peripheral and cerebrovascular disease, and microvascular disease, e.g., retinopathy, nephropathy), peripheral nerves (causing neuropathy), and other structures such as skin (causing cheiroarthropathy) and the lens (causing cataracts). Different degrees of macrovascular, neuropathic and cutaneous complications lead to the “diabetic foot.” A proportion of patients, particularly with type 2 diabetes have metabolic syndrome including central adiposity, dyslipidaemia, hypertension and non alcoholic fatty liver disease. Glucose management can have severe side effects, particularly hypoglycaemia and weight gain. Under-treatment is not only associated with long term complications but infections, vascular events and increased hospitalisation. Absence of treatment in type 1 diabetes can rapidly lead to diabetic keto-acidosis and death. Diabetes doubles the risk for depression, and on the other hand, depression may increase the risk for hyperglycaemia and finally for complications of diabetes [41]. Essentially, diabetes affects every part of the body once complications set in, and the crux of diabetes management is to normalise (as much as possible) the blood glucose and manage any associated risk factors, thereby preventing complications and maintaining the highest quality of life. […] glucose management requires minute by minute, day by day management addressing the complexity of diabetes, including clinical and behavioural issues. While other conditions also have the patient as therapist, diabetes requires a fully empowered patient with all of the skills, knowledge and motivation every hour of the waking day. A patient that is fully engaged in self-management, and has support systems, is empowered to manage their diabetes and will likely experience better outcomes compared with those who do not have access to this support. […] in diabetes, the boundaries between primary care and secondary care are blurred. Diabetes specialist services, although secondary care, can provide primary care, and there are GPs, diabetes educators, and other ancillary providers who can provide a level of specialist care.”

In short, diabetes is a complex disease – it’s one of those diseases where a significant degree of care integration is likely to be necessary in order to achieve even close to optimal outcomes. A little more on these topics:

“The unique challenge to providers is to satisfy two specific demands in diabetes care. The first is to anticipate and recognize the onset of complications through comprehensive diabetes care, which demands meticulous attention to a large number of process-of-care measures at each visit. The second, arguably greater challenge for providers is to forestall the development of complications through effective diabetes care, which demands mastery over many different skills in a variety of distinct fields in order to achieve performance goals covering multiple facets of management. Individually and collectively, these dual challenges constitute a virtually unsustainable burden for providers. That is because (a) completing all the mandated process measures for comprehensive care requires far more time than is traditionally available in a single patient visit; and (b) most providers do not themselves possess skills in all the ancillary disciplines essential for effective care […] Diabetes presents patients with similarly unique dual challenges in mastering diabetes self-management with self-awareness, self-empowerment and self-confidence. Comprehensive Diabetes Self-Management demands the acquisition of a variety of skills in order to fulfil a multitude of tasks in many different areas of daily life. Effective Diabetes Self-Management, on the other hand, demands constant vigilance, consistent discipline and persistent attention over a lifetime, without respite, to nutritional self-discipline, monitoring blood glucose levels, and adherence to anti-diabetic medication use. Together, they constitute a burden that most patients find difficult to sustain even with expert assistance, and all-but-impossible without it.”

“Care coordination achieves critical importance for diabetes, in particular, because of the need for management at many different levels and locations. At the most basic level, the symptomatic management of acute hypo- and hyperglycaemia often devolves to the PCP [primary care provider], even when a specialist oversees more advanced strategies for glycaemic management. At another level, the wide variety of chronic complications requires input from many different specialists, whereas hospitalizations for acute emergencies often fall to hospitalists and critical care specialists. Thus, diabetes care is fraught with the potential for sometimes conflicting, even contradictory management strategies, making care coordination mandatory for success.”

“Many of the problems surrounding the provision of adequate person-centred care for those with diabetes revolve around the pressures of clinical practice and a lack of time. Good diabetes management requires attention to a number of clinical parameters
1. (Near) Normalization of blood glucose
2. Control of co-morbidities and risk factors
3. Attainment of normal growth and development
4. Prevention of Acute Complications
5. Screening for Chronic Complications
To fit all this and a holistic, patient-centred collaborative approach into a busy general practice, the servicing doctor and other team members must understand that diabetes cannot be “dealt with” coincidently during a patient consultation for an acute condition.”

“Implementation of the team model requires sharing of tasks and responsibilities that have traditionally been the purview of the physician. The term “team care” has traditionally been used to indicate a group of health-care professionals such as physicians, nurses, pharmacists, or social workers, who work together in caring for a group of patients. In a 2006 systematic review of 66 trials testing 11 strategies for improving glycaemic control for patients with diabetes, only team care and case management showed a significant impact on reducing HbA1c levels [18].”

Moving on, I found the chapter about Hong Kong interesting, for several reasons. The quality of Scandinavian health registries are probably widely known in the epidemiological community, but I was not aware of Hong Kong’s quality of diabetes data, and data management strategies, which seems to be high. Nor was I aware of some of the things they’ve discovered while analyzing those data. A few quotes from that part of the coverage:

“Given the volume of patients in the clinics, the team’s earliest work from the HKDR [Hong Kong Diabetes Registry, US] prioritized the development of prediction models, to allow for more efficient, data-driven risk stratification of patients. After accruing data for a decade on over 7000 patients, the team established 5-year probabilities for major diabetes-related complications as defined by the International Code for Diseases retrieved from the CMS [Clinical Management System, US]. These included end stage renal disease [7], stroke [8], coronary heart disease [9], heart failure [10], and mortality [11]. These risk equations have a 70–90 % sensitivity and specificity of predicting outcomes based on the parameters collected in the registry.”

“The lifelong commitments to medication adherence and lifestyle modification make diabetes self-management both physically and emotionally taxing. The psychological burdens result from insulin injection, self-monitoring of blood glucose, dietary restriction, as well as fear of complications, which may significantly increase negative emotions in patients with diabetes. Depression, anxiety, and distress are prevalent mental afflictions found in patients with diabetes […] the prevalence of depression was 18.3 % in Hong Kong Chinese patients with type 2 diabetes. Furthermore, depression was associated with poor glycaemic control and self-reported hypoglycaemia, in part due to poor adherence […] a prospective study involving 7835 patients with type 2 diabetes without cardiovascular disease (CVD) at baseline […] found that [a]fter adjusting for conventional risk factors, depression was independently associated with a two to threefold increase in the risk of incident CVD [22].”

“Diabetes has been associated with increased cancer risk, but the underlying mechanism is poorly understood. The linkage between the longitudinal clinical data within the HKDR and the cancer outcome data in the CMS has provided important observational findings to help elucidate these connections. Detailed pharmacoepidemiological analyses revealed attenuated cancer risk in patients treated with insulin and oral anti-diabetic drugs compared with non-users of these drugs”

“Among the many challenges of patient self-management, lack of education and empowerment are the two most cited barriers [59]. Sufficient knowledge is unquestionably important in self-care, especially in people with low health literacy and limited access to diabetes education. Several systematic reviews [have] showed that self-management education with comprehensive lifestyle interventions improved glycaemic and cardiovascular risk factor control [60–62].”

“Clinical trials are expensive because of the detail and depth of data required on each patient, which often require separate databases to be developed outside of the usual-care electronic medical records or paper-based chart systems. These databases must be built, managed, and maintained from scratch every time, often requiring double-entry of data by research staff. The JADE [Joint Asia Diabetes Evaluation] programme provides a more efficient means of collecting the key clinical variables in its comprehensive assessments, and allows researchers to add new fields as necessary for research purposes. This obviates the need for redundant entry into non-clinical systems, as the JADE programme is simultaneously a clinical care tool and prospective database. […] A large number of trials fail because of inadequate recruitment [67]. The JADE programme has allowed for ready identification of eligible clinical trial participants because of its detailed clinical database. […] One of the greatest challenges in clinical trials is maintaining the contact between researchers and patients over many years. […] JADE facilitates long-term contact with the patient, as part of routine periodic follow-up. This also allows researchers to evaluate longer term outcomes than many previous trials, given the great expense in maintaining databases for the tracking of longitudinal outcomes.”

Lastly, some stuff on cost and related matters from the book:

“Diabetes imposes a massive economic burden on all healthcare systems, accounting for 11 % of total global healthcare expenditure on adults in 2013.”

“Often, designated service providers institute managed care programmes to standardize and control care rendered in a safe and cost-effective manner. However, many of these programmes concentrate on cost-savings rather than patient service utilization and improved clinical outcomes. [this part of the coverage is from South Africa, but these kinds of approaches are definitely not limited to SA – US] […] While these approaches may save some costs in the short-term, Managed Care Programmes which do not address patient outcomes nor reduce long term complications, ignore the fact that that the majority of the costs for treating diabetes, even in the medium term, are due to the treatment of acute and chronic complications and for inpatient hospital care [14]. Additionally, it is well established that poor long-term clinical outcomes increase the cost burden of managing the patient with diabetes by up to 250 %. […] overall, the costs of medication, including insulin, accounts for just 7 % of all healthcare costs related to diabetes [this number varies across countries, I’ve seen estimates of 15% in the past – and as does the out-pocket share of that cost – but the costs of medications constitute a relatively small proportion of the total costs of diabetes everywhere you look, regardless of health care system and prevalence. If you include indirect costs as well, which you should, this becomes even more obvious – US]”

“[A] study of the Economic Costs of Diabetes in the U.S. in 2012 [25] showed that for people with diabetes, hospital inpatient care accounted for 43 % of the total medical cost of diabetes.”

“There is some evidence of a positive impact of integrated care programmes on the quality of patient care [10, 34]. There is also a cautious appraisal that warns that “Even in well-performing care groups, it is likely to take years before cost savings become visible” […]. Based on a literature review from 1996 to 2004 Ouwens et al. [11] found out that integrated care programmes seemed to have positive effects on the quality of care. […] because of the variation in definitions of integrated care programmes and the components used cover a broad spectrum, the results should be interpreted with caution. […] In their systematic review of the effectiveness of integrated care Ouwens et al. [11] could report on only seven (about 54 %) reviews which had included an economic analysis. Four of them showed financial advantages. In their study Powell Davies et al. [34] found that less than 20 % of studies that measured economic outcomes found a significant positive result. Similarly, de Bruin et al. [37] evaluated the impact of disease management programmes on health-care expenditures for patients with diabetes, depression, heart failure or chronic obstructive pulmonary disease (COPD). Thirteen studies of 21 showed cost savings, but the results were not statistically significant, or not actually tested for significance. […] well-designed economic evaluation studies of integrated care approaches are needed, in particular in order to support decision-making on the long-term financing of these programmes [30, 39]. Savings from integrated care are only a “hope” as long as there is no carefully designed economic analysis with a kind of full-cost accounting.”

“The cost-effectiveness of integrated care for patients with diabetes depends on the model of integrated care used, the system in which it is used, and the time-horizon chosen [123]. Models of cost benefit for using health coaching interventions for patients with poorly controlled diabetes have generally found a benefit in reducing HbA1c levels, but at the cost of paying for the added cost of health coaching which is not offset in the short term by savings from emergency department visits and hospitalizations […] An important question in assessing the cost of integrated care is whether it needs to be cost-saving or cost-neutral to be adopted, or is it enough to increase quality-adjusted life years (QALYs) at a “reasonable” cost (usually pegged at between $30,000 and $60,000 per QALY saved). Most integrated care programmes for patients with diabetes that have been evaluated for cost-effectiveness would meet this more liberal criterion […] In practice, integrated care programmes for patients with diabetes are often part of generalized programmes of care for patients with other chronic medical conditions, making the allocation of costs and savings with respect to integrated care for diabetes difficult to estimate. At this point, integrated care for patients with diabetes appears to be a widely accepted goal. The question becomes: which model of integrated care is most effective at reasonable cost? Answering this question depends both on what costs are included and what outcomes are measured; the answers may vary among different patient populations and different care systems.”

December 6, 2016 Posted by | Books, Diabetes, Economics, Health Economics, Medicine, Pharmacology | 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, Ophthalmology, Pharmacology | Leave a comment

Diabetic nephropathies

Bakris et al.‘s text on this topic is the first book I’ve read specifically devoted to the topic of DN. As I pointed out on goodreads, “this is a well-written and interesting work which despite the low page count cover quite a bit of ground. A well-sourced and to-the-point primer on these topics.” Below I have added a few observations from the book.

“Diabetic nephropathy (DN), also known as diabetic kidney disease (DKD), is one of the most important long-term complications of diabetes and the most common cause of endstage renal disease (ESRD) worldwide. DKD […] is defined as structural and functional renal damage manifested as clinically detected albuminuria in the presence of normal or abnormal glomerular filtration rate (GFR). […] Patients with DKD […] account for one-third of patients demanding renal transplantation. […] in the United States, Medicare expenditure on treating ESRD is approximately US $33 billion (as of 2010), which accounts for 8–9 % of the total annual health-care budget […] According to the United States Renal Data System […], the incidence of ESRD requiring RRT [in 2012] was 114,813 patients, with 44 % due to DKD [9]. A registry report from Japan revealed a nearly identical relative incidence, with 44.2 % of the patients with ESRD caused by diabetes”

Be careful not to confuse incidence and prevalence here; the proportion of diabetics diagnosed with ESDR in any given year is almost certainly higher than the proportion of people with ESDR who have diabetes, because diabetics with kidney failure die at a higher rate than do other people with kidney failure. This problem/fact tends to make some questions hard to answer; to give an example, how large a share of the total costs that diabetics contribute to the whole kidney disease component of medical costs seems to me to be far from an easy question to answer, because you in some sense are not really making an apples-to-apples comparison, and a lot might well depend on the chosen discount rate and how to address the excess mortality in the diabetes sample; and even ‘simply’ adding up medical outlays for the diabetes- and non-diabetes samples would require a lot of data (which may not be available) and work. You definitely cannot just combine the estimates provided above, and assume that the 44% incidence translates into 44% of people with ESDR having diabetes; it’s not clear in the text where the ‘one-third of patients’ number above comes from, but if that’s also US data then it should be obvious from the difference between these numbers that there’s a lot of excess mortality here in the diabetes sample (I have included specific data from the publication on these topics below). The book also talks about the fact that the type of dialysis used in a case of kidney failure will to some extent depend on the health status of the patient, and that diabetes is a significant variable in that context; this means that the available/tolerable treatment options for the kidney disease component may not be the same in the case of a diabetic and a case of a patient with, say, lupus nephritis, and it also means that the patient groups most likely are not ‘equally sick’, so basing cost estimates on cost averages might lead to misleading results if severity of disease and (true) treatment costs are related, as they usually are.

“A recent analysis revealed an estimated diabetes prevalence of 12–14 % among adults in the United States […] In the age group ≥65 years, this amounts to more than 20 %”.

It should be emphasized in the context of the above numbers that the prevalence of DKD is highly variable across countries/populations – the authors also include in the book the observation that: “Over a period of 20 years, 32 studies from 16 countries revealed a prevalence ranging from 11 to 83 % of patients with diabetes”. Some more prevalence data:

“DKD affects about 30 % of patients with type 1 diabetes and 25–40 % of the patients with type 2 diabetes. […] The global prevalence of micro- and macroalbuminuria is estimated at 39 % and 10 %, respectively […] (NHANES III) […] reported a prevalence of 35 % (microalbuminuria) and 6 % (macroalbuminuria) in patients with T2DM aged ≥40 years [24]. In another study, this was reported to be 43 % and 12 %, respectively, in a Japanese population [23]. According to the European Diabetes (EURODIAB) Prospective Complications Study Group, in patients with T1DM, the incidence of microalbuminuria was 12.6 % (over 7.3 years) [25]. This prevalence was further estimated at 33 % in an 18-year follow-up study in Denmark […] In the United Kingdom Prospective Diabetes Study (UKPDS), proteinuria [had] a peak incidence after around 15–20 years after diabetes diagnosis.”

I won’t cover the pathophysiology parts in too much detail here, but a few new things I learned does need to be mentioned:

“A natural history of DKD was first described in the 1970s by Danish physicians [32]. It was characterized by a long silent period without overt clinical signs and symptoms of nephropathy and progression through various stages, starting from hyperfiltration, microalbuminuria, macroalbuminuria, and overt renal failure to ESRD. Microalbuminuria (30–300 mg/day of albumin in urine) is a sign of early DKD, whereas macroalbuminuria (>300 mg/day) represents DKD progression. [I knew this stuff. The stuff that follows below was however something I did not know:]
However, this ‘classical’ natural evolution of urinary albumin excretion and change in GFR is not present in many patients with diabetes, especially those with type 2 diabetes [34]. These patients can have reduction or disappearance of proteinuria over time or can develop even overt renal disease in the absence of proteinuria [30, 35]. […] In the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) of patients with T2DM, 45.2 % of participants developed albuminuria, and 29 % developed renal impairment over a 15-year follow-up period [37]. Of those patients who developed renal impairment, 61 % did not have albuminuria beforehand, and 39 % never developed albuminuria during the study. Of the patients that developed albuminuria, only 24 % subsequently developed renal impairment during the study. A significant degree of discordance between development of albuminuria and renal impairment is apparent [37]. These data, thus, do not support the classical paradigm of albuminuria always preceding renal impairment in the progression of DKD. […] renal hyperfiltration and rapid GFR decline are considered stronger predictors of nephropathy progression in type 1 diabetes than presence of albuminuria [67]. The annual eGFR loss in patients with DKD is >3 mL/min/1.73 m2 or 3.3 % per year.”

As for the last part about renal hyperfiltration, they however also note later in the coverage in a different chapter that “recent long-term prospective surveys cast doubt on the validity of glomerular hyperfiltration being predictive of renal outcome in patients with type 1 diabetes”. Various factors mentioned in the coverage – some of which are very hard to avoid and some of which are actually diabetes-specific – contribute to measurement error, which may be part of the explanation for the sub-optimal performance of the prognostic markers employed.

An important observation I think I have mentioned before here on the blog is that diabetic nephropathy is not just bad because people who develop this complication may ultimately develop kidney failure, but is also bad because diabetics may die before they even do that; diabetics with even moderate stages of nephropathy have high mortality from cardiovascular disease, so if you only consider diabetics who actually develop kidney failure you may miss some of the significant adverse health effects of this complication; it might be argued that doing this would be a bit like analyzing the health outcomes of smokers while only tallying the cancer cases, and ignoring e.g. the smoking-associated excess deaths from cardiovascular disease. Some observations from the book on this topic:

“Comorbid DM and DKD are associated with high cardiovascular morbidity and mortality. The risk of cardiovascular disease is disproportionately higher in patients with DKD than patients with DM who do not have kidney disease [76]. The incident dialysis rate might even be higher after adjusting for patients dying from cardiovascular disease before reaching ESRD stage [19]. The United States Renal Data System (USRDS) data shows that elderly patients with a triad of DM, chronic kidney disease (CKD), and heart failure have a fivefold higher chance of death than progression to CKD and ESRD [36]. The 5-year survival rate for diabetic patients with ESRD is estimated at 20 % […] This is higher than the mortality rate for many solid cancers (including prostate, breast, or renal cell cancer). […] CVD accounts for more than half of deaths of patients undergoing dialysis […] the 5-year survival rate is much lower in diabetic versus nondiabetic patients undergoing hemodialysis […] Adler et al. tested whether HbA1c levels were associated with death in adults with diabetes starting HD or peritoneal dialysis [38]. Of 3157 patients observed for a median time of 2.7 years, 1688 died. [this example provided, I thought, a neat indication of what sort of data you end up with when you look at samples with a 20% 5-year survival rate] […] Despite modern therapies […] most patients continue to show progressive renal damage. This outcome suggests that the key pathogenic mechanisms involved in the induction and progression of DN remain, at least in part, active and unmodified by the presently available therapies.” (my emphasis)

The link between blood glucose (Hba1c) and risk of microvascular complications such as DN is strong and well-documented, but Hba1c does not explain everything:

“Only a subset of individuals living with diabetes […] develop DN, and studies have shown that this is not just due to poor blood glucose control [50–54]. DN appears to cluster in families […] Several consortia have investigated genetic risk factors […] Genetic risk factors for DN appear to differ between patients with type 1 and type 2 diabetes […] The pathogenesis of DN is complex and has not yet been completely elucidated […] [It] is multifactorial, including both genetic and environmental factors […]. Hyperglycemia affects patients carrying candidate genes associated with susceptibility to DN and results in metabolic and hemodynamic alterations. Hyperglycemia alters vasoactive regulators of glomerular arteriolar tone and causes glomerular hyperfiltration. Production of AGEs and oxidative stress interacts with various cytokines such as TGF-β and angiotensin II to cause kidney damage. Additionally, oxidative stress can cause endothelial dysfunction and systemic hypertension. Inflammatory pathways are also activated and interact with the other pathways to cause kidney damage.”

“An early clinical sign of DN is moderately increased urinary albumin excretion, referred to as microalbuminuria […] microalbuminuria has been shown to be closely associated with an increased risk of cardiovascular morbidity and mortality [and] is [thus] not only a biomarker for the early diagnosis of DN but also an important therapeutic target […] Moderately increased urinary albumin excretion that progresses to severely increased albuminuria is referred to as macroalbuminuria […] Severely increased albuminuria is defined as an ACR≥300 mg/g Cr; it leads to a decline in renal function, which is defined in terms of the GFR [8] and generally progresses to ESRD 6–8 years after the onset of overt proteinuria […] patients with type 1 diabetes are markedly younger than type 2 patients. The latter usually develop ESRD in their mid-fifties to mid-sixties. According to a small but carefully conducted study, both type 1 and type 2 patients take an average of 77–81 months from the stage of producing macroproteinuria with near-normal renal function to developing ESRD [17].”

“Patients with diabetes and kidney disease are at increased risk of hypoglycemia due to decreased clearance of some of the medications used to treat diabetes such as insulin, as well as impairment of renal gluconeogenesis from having a lower kidney mass. As the kidney is responsible for about 30–80 % of insulin removal, reduced kidney function is associated with a prolonged insulin half-life and a decrease in insulin requirements as estimated glomerular filtration rate (eGFR) decline […] Metformin [a first-line drug for treating type 2 diabetes, US] should be avoided in patients with an eGFR < 30 mL/min /1.73 m2. It is recommended that metformin is stopped in the presence of situations that are associated with hypoxia or an acute decline in kidney function such as sepsis/shock, hypotension, acute myocardial infarction, and use of radiographic contrast or other nephrotoxic agents […] The ideal medication regimen is based on the specific needs of the patient and physician experience and should be individualized, especially as renal function changes. […] Lower HbA1c levels are associated with higher risks of hypoglycemia so the HbA1c target should be individualized […] Whereas patients with mild renal insufficiency can receive most antihyperglycemic treatments without any concern, patients with CKD stage 3a and, in particular, with CKD stages 3b, 4, and 5 often require treatment adjustments according to the degree of renal insufficiency […] Higher HbA1c targets should be considered for those with shortened life expectancies, a known history of severe hypoglycemia or hypoglycemia unawareness, CKD, and children.”

“In cases where avoidance of development of DKD has failed, the second approach is slowing disease progression. The most important therapeutic issues at this stage are control of hypertension and hyperglycemia. […] Hypertension is present in up to 85 % of patients with DN/ DKD, depending on the duration and stage (e.g., higher in more progressive cases). […] In a recent meta-analysis, the efficacy and safety of blood pressure-lowering agents in adults with diabetes and kidney disease was analyzed […] In total, 157 studies comprising 43,256 participants, mostly with type 2 diabetes and CKD, were included in the network meta-analysis. No drug regimen was found to be more effective than placebo for reducing all-cause mortality. […] DKD is accompanied by abnormalities in lipid metabolism related to decline in kidney function. The association between higher low-density lipoprotein cholesterol (LDL-C) and risk of myocardial infarction is weaker for people with lower baseline eGFR, despite higher absolute risk of myocardial infarction [53]. Thus, increased LDL-C seems to be less useful as a marker of coronary risk among people with CKD than in the general population.”

“An analysis of the USRDS data revealed an RR of 0.27 (95 % CI, 0.24–0.30) 18 months after transplantation in patients with diabetes in comparison to patients on dialysis on a transplant waiting list [76]. The gain in projected years of life with transplantation amounted to 11 years in patients with DKD in comparison to patients without transplantation.”

October 27, 2016 Posted by | Books, Cardiology, Diabetes, Epidemiology, Health Economics, Medicine, Nephrology, Pharmacology | Leave a comment

Human Drug Metabolism (III)

This is my third post about this book. You can read my previous posts here and here. In this post I have covered material from chapter 7, dealing with ‘factors affecting drug metabolism’.

“Data from animal studies in one country are usually comparable with that of another, provided the animal species and strain are the same. This provides a consistent picture of the basic pharmacological and toxicological actions of a candidate drug in a living organism […] it has been obvious since animal testing began that there would be large differences in the way a drug might perform in man compared with animal species […]. Unfortunately, there is no experimental model yet designed that can not only consider human biochemistry and physiology, but also the effects of age, smoking, legal and illegal drug usage, gender, diet, environment, disease and finally genetic variation. Indeed, many clinical studies have revealed enormous differences in drug clearance and pharmacological effect even in age, sex and ethnically matched individuals. In effect, this means that the first year or so of a drug’s clinical life is a vast, but monitored experiment, involving hundreds of thousands of patients and there is no guarantee of success.”

“Most biotransformational polymorphisms that might potentially cause a problem clinically are due to an inability of those with defective enzymes to remove the drug from the system. Drug failure can occur if the agent is administered as a pro-drug and requires some metabolic conversion to an active metabolite. Drug accumulation can lead to unpleasant side effects and loss of patient tolerance for the agent. […] Overall, there are a large number of factors that can influence drug metabolism, either by increasing clearance to cause drug failure, or by preventing clearance to lead to toxicity. In the real world, it is often impossible to delineate the different conflicting factors which result in net changes in drug clearance which cause a drug to fall out of, or climb above, the therapeutic window. It may only be possible clinically in many cases to try to change what appears to be the major cause to bring about a resolution of the situation to restore curative and non-toxic drug levels.”

“Most population studies of human polymorphisms list the allelic frequency, that is, how many of an ethnic group contain the alleles in question. […] The actual haplotypes in the population, that is, which individuals express which combinations of alleles, are not the same as the population allelic frequency. […] If an SNP or a combination of SNPs is a fairly mild defect in the enzyme when it is homozygously expressed, then the heterozygotes will show little impairment and the polymorphism may be clinically irrelevant. With other SNPs, the enzyme produced may be completely non-functional.  Homozygotes will be virtually unable to clear the drug and heterozygotes will show impairment also. There are also smaller populations of UMs, or ultra rapid metabolizers which may have a feature of their enzyme which either makes it super efficient or expressed in abnormally high amounts. […] Phenotyping will group patients in very broad EMs [extensive metabolizers], IMs [intermediate metabolizers] or PM [poor metabolizers] categories, but will be unable to distinguish between heterozygous and homozygous EMs. Although genotyping may be very helpful in dosage estimation in the initiation of therapy, there is no substitute for the normal process of therapeutic monitoring, which is effectively phenotyping the individual in the real world in terms of maximizing response and minimizing toxicity.”

“it is clear that there is a vast amount of genetic variation across humanity in terms of biotransformational capability and so the idea that in therapeutics, ‘one size fits all’ is not only outdated, but fabulously naïve. […] Unfortunately, detecting and responding successfully to human biotransformational polymorphisms has proved to be extremely problematic. In terms of polymorphism detection, this area is a classic illustration of how the exploration of the human genome with powerful molecular biological tools may unearth many apparently marked polymorphic defects that may not necessarily translate into a measurable clinical impact in terms of efficacy and toxicity. In reality, many more scientists have the opportunity to discover and publish such polymorphisms in vitro, than there are clinical scientists, resources and indeed cooperative volunteers or patients in sufficient quantity to determine practical clinical relevance.”

the CYP3A group (chromosome 7) metabolize around half of all drugs […] variation in the metabolism of CYP3A substrates […] can be up to ten-fold in terms of drug clearances and up to 90-fold in liver protein expression. […] It is likely that the full extent of the variation in CYP3A4 is still to be discovered […] While it is thought that CYP3A4 is not subject to an obvious major polymorphism, CYP3A5 definitely is. […] *3/*3 individuals form no serviceable CYP3A5. Functional CYP3A5 is found in around 20 per cent of Caucasians, half of Chinese/Japanese, 70 per cent of Hispanics and more than 80 per cent of African Americans.”

“A particularly dangerous polymorphism clinically was identified in the 1980s for one of the methyltransferases. The endogenous role of S-methylating thiopurine S-methyltransferase (TPMT) is not that clear, but […] [t]hese drugs are […] effective in some childhood leukaemias […] TPMT highlights the genotyping/phenotyping issue mentioned earlier in the management of patients with polymorphisms. Genotyping will reveal the level of TPMT expression that should be expected in the otherwise healthy patient. However, there are many factors which impact day-to-day TPMT expression during thiopurine therapy. […] Hence, what might be predicted from a genotype test may bear little resemblance to how the enzyme is performing on a particular day in a treatment cycle. So clinically, it is preferred to test actual TPMT activity.”

“Understanding of sulphonation and its roles in endogenous as well as xenobiotic metabolism is not as advanced compared with that of CYPs; however, the role of SULTs in the activation of carcinogens is becoming more apparent. One of the major influences on SULT activity is their polymorphic nature; in the case of one of the most important toxicologically relevant SULTs, SULT1A1, this isoform exists as three variants, SULT1A1*1 (wild-type), SULT1A1*2 and SULT1A1*3. The *1 variant allele is found in the majority of Caucasians (around 65 per cent), whilst the *2 variant differs only in the exchange of one amino acid for another. This single amino acid change has profound effects on the stability and catalytic activity of the isoform. The *2 variant is found in approximately 32 per cent of Caucasians and catalytically faulty […] About 9 in 10 Chinese people have the *1 allele and about 8 per cent have allele *2. About half of African-Americans have *1 and a third have *2. Interestingly, there is a *3 which is rare in most races but accounts for more than 22 per cent of African Americans. There is also considerable variation in SULT2A1 and SULT2B1, which are the major hydroxysteroid sulphators in the body, which may have implications for sex steroid and cholesterol handling. […] from the cancer-risk viewpoint, a highly active SULT1A1 *1 is usually an advantage in that it usually removes reactive species rapidly as stable sulphates. With some agents it is problematic as certain carcinogens such as acetylfluorene are indirectly activated to reactive species by SULTs. In addition, protective dietary flavonoids […] are also rapidly cleared by SULT1A1 *1, so there is a combination of production of toxins and loss of protective dietary agents. In terms of carcinogenesis risk, SULT1A1*2 could be a liability as potentially damaging substrates such as electrophilic toxins cannot be cleared rapidly. However, in some circumstances the *2 allele can be rather protective as […] it also allows protective agents [to] remain in tissues for longer periods. The combinations are endless and so it is often extremely difficult to predict risks of carcinogenicity for individuals and toxin exposures.”

GSTs are polymorphic and much research has been directed at linking increased predisposition to cytotoxicity and carcinogenicity with defective GST phenotypes. Active wild-type GSTMu-1 is found in around 60 per cent of Caucasians, but a non-functional version of the isoform is found in the remainder. […] GST-M1 null (non-functional alleles) can predispose to risks of prostate abnormalities and GST Pi is subject to several SNPs and many attempts have been made to link these SNPs with the consequences of failure to detoxify reactive species, such as the risk of lung cancer. […] Carcinogenesis may be due to a complex mix of factors, where different enzyme expression and activities may combine with particular reactive species from specific parent xenobiotics that lead to DNA damage only in certain individuals. Resolving specific risk factors may be extremely difficult in such circumstances. […] in cancer chemotherapy, there is evidence that the presence of GST-M1 and GST-T1 null (non-functional) alleles predisposes children to a six-fold higher level of adverse events usually seen with antineoplastic drugs, such as bone marrow damage, nephrotoxicity and neurotoxicity.”

“The effects of age on drug clearance and metabolism have been known since the 1950s, but they have been extensively investigated in the last 20 or so years. It is now generally accepted that at the extremes of life, neonatal and geriatric, drug clearance can be significantly different from the rest of humanity. In general, neonates, i.e. those less than four weeks old, cannot clear certain agents due to immaturity of drug metabolizing systems. Those over retirement age cannot clear the drugs due to loss of efficiency in their metabolizing systems. Either way, the net result can be toxicity due to drug accumulation. […] It seems that the inability of older people to clear drugs is not necessarily related to the efficacy of their CYP-mediated oxidations, which are often not much different from that of younger individuals. Studies with the major CYPs in vitro have revealed that CYP2D6 is unaffected by age, as are most other CYPs, with the exception of CYP1A2, which does decline in activity in the elderly. […] In general, there is little significant change in the inducibility in most CYPs, or in the capability of conjugation systems in vitro. […] there are significant changes in the liver itself, as it decreases in mass and its blood flow is reduced as we age. This occurs at the rate of around 0.5–1.5 per cent per year, so by the time we hit 60–70, we may have up to a 40 per cent decline in liver blood flow compared with a 30-year-old. Other factors include gradual decline in renal function, increased fat deposits and reduction in gut blood flow, which affects absorption. […] The problem arises that the drug’s bioavailability increases due to lack of first-pass clearance; this means that from a standard dose, blood levels can be considerably higher than would be expected in a 40-year-old. This can be a serious problem in drugs with a narrow TI, such as antiarrhythmics. In addition, average doses of warfarin required to provide therapeutic anticoagulation in the elderly are less than half those required for younger people. The person’s lifelong smoking and drinking habits, as well as older individuals ’ sometimes erratic diet also complicate this situation. Among the drugs cleared more slowly in older people are antipsychotics, paracetamol, antidepressants, benzodiazepines, warfarin, beta-blockers and indomethicin.”

“Thousands of polyphenols are found in plants, vegetables, fruit, as well as tea, coffee, wine and fruit juices. […] Flavonoids such as quercetin and fisetin are excellent substrates for COMT, so competitively inhibiting the metabolism of endogenous catecholamine and catechol oestrogens. Quercetin and other polyphenols are found in various foods such as soy (genestein) and they are potent inhibitors of SULT1A1 which sulphate endogenous oestrogens, so potentiating the effects of oestrogens in the body. Many of these flavonoids and isoflavonoids are manufactured and sold as cancer preventative agents; however, it is more likely that their elevation of oestrogen levels may have the opposite effect in the long term. It is also likely that various polyphenols influence other endogenous substrates of sulphotransferases, such as thyroid hormones and various catecholamines. It is gradually becoming apparent that polyphenols can induce UGTs, indeed; it would be surprising if they did not. […] Overall, it is likely that there are a large number of polyphenols that are potent modulators of CYPs and conjugative enzymes. […] It is clear that diet can substantially modulate biotransformation […] As to the effects on prescription drugs, […] abrupt changes in a person’s diet may significantly alter the clearance of drugs and lead to loss of efficacy or toxicity.”

In general, experimental or ‘probe’ drugs […] which are used to study the activities of a number of CYPs, are metabolized more quickly by women than men. This is allowing for differences in weight, fat distribution (body mass index) and volume of distribution […] It appears that CYP expression is linked to growth hormone (GH) and about the same amount is secreted over 24 hours in both sexes. In animals the pattern of release of the hormone is crucial to the effects on the CYPs; in females, GH is secreted in small but more or less continuous pulses, while males secrete large pulses, then periods of no secretion. The system is thought to be similar in humans. […] Little is known of the effects of the menopause and hormone replacement, where steroid metabolism changes dramatically. It is highly likely that these events could have profound effects on female drug clearance. […] females in general are more susceptible to drug adverse reactions than males, especially hepatotoxic effects.”

“For those chronically dependent on ethanol their CYP2E1 levels can be ten-fold higher than non-drinkers and they would clear CYP2E1 substrates extremely quickly if they chose to be sober for a period of time. This may lead to the accumulation of metabolites of the substrates. It is apparent that alcoholics who are sober can suffer paracetamol (acetaminophen)-induced liver toxicity at overdoses of around half that of non-drinkers, which is due to CYP2E1 induction. […]  the vast variation in ADH [alcohol dehydrogenase] catalytic activity across the human race is mainly due to just a few SNPs that profoundly change the efficiency of the isoforms. ADH1B/*1 is the most effective variant and is the ADH wild-type […] Part of a ‘successful’ career as an alcoholic depends possessing the ADH1B/*1 isoform. The other defective isoforms are found in low frequencies in alcoholics and cirrhotics. […] in the vast majority of individuals, whatever their variant of ADH, they are able to process acetaldehyde to acetate and water, as the consequences of failing to do this are severe. With ALDH, the wild-type and gold standard is ALDH2*1/*1, which has the highest activity of all these isoforms and is the second essential component for an alcoholic career. […] the variant ALDH2*1/*2 has less than a quarter of the wild-type’s capacity and is found predominantly in Eastern races. The variant ALDH2*2/*2 is completely useless and renders the individuals very sensitive to acetaldehyde poisoning, although the toxin is removed eventually by ALDH1A1 which does not seem to be affected by polymorphisms. In a survey of 1300 Japanese alcoholics, there was nobody at all with the ALDH2*2/*2 variant. […] Women are much more vulnerable to ethanol damage and on average die in half the time it generally takes for a male alcoholic to drink himself to death. Women drink much less than men also – one study indicated that a group of women consumed about 14,000 drinks to induce cirrhosis, whilst men required more than 44,000 to achieve the same effect. Ethanol distributes in total body water only, so in women their greater fat content means that blood ethanol levels are higher than men of similar weight and age.

September 15, 2016 Posted by | Books, Cancer/oncology, Genetics, Medicine, Pharmacology | Leave a comment

Diabetes and the Metabolic Syndrome in Mental Health (II)

Here’s my first post about the book. This will be my last post about the book. In the coverage below I’ll include some quotes from the second half of the publication, as well as some comments.

“To date, no prospective study has directly compared the efficacy and tolerability of selective serotonin reuptake inhibitors (SSRIs), serotonin/ norepinephrine reuptake inhibitors (SNRIs), or other second-generation antidepressants in patients with diabetes versus patients without diabetes.”

“Weight is a common and well-known adverse effect of short-term and long-term treatment with TCAs, primarily as a result of excessive appetite. […] weight gain is the most common cause for premature discontinuation of all TCAs. […] TCAs are […] likely to impair diabetes control, because they increase serum glucose levels by up to 150%, increase appetite (particularly carbohydrate craving), and reduce the metabolic rate. […] SSRIs have been associated with both weight gain and weight loss. […] Weight gain is less likely with SSRIs when they are used short term — for 6 months or less. Contradictory evidence exists about whether an increase in body weight occurs in patients using SSRIs for 1 year or longer. […] The mean incidence of weight gain across comparative randomized controlled trials ranges from 4.1% for fluoxetine, 7.6% for sertraline, and 9.6% for paroxetine. […] SSRIs may reduce serum glucose by up to 30% and cause appetite suppression, resulting in weight loss. Fluoxetine should be used cautiously in patients with diabetes, because of its increased potential for hypoglycemia […]. Its side effects of tremor, nausea, sweating, and anxiety may also be misinterpreted as due to hypoglycemia.”

“Prior to the development of the second-generation antipsychotics (SGAs), or atypical antipsychotics, phenothiazines were the dominant therapy for schizophrenia. Numerous studies at this time began documenting that the use of phenothiazines led to aggravation of preexisting diabetes and the development of new-onset type 2 diabetes. […] high-potency neuroleptics […] appeared to be less implicated in the development of diabetes. These drugs eventually became the predominant form of therapy for schizophrenia […] Unfortunately, the high-potency neuroleptics are also associated with a high rate of occurrence of extrapyramidal symptoms, tardive dyskinesia, and subsequent noncompliance […]  In the late 1980s, a new class of antipsychotics, the thiobenzodiazepines or “atypical antipsychotics,” was introduced. […] One major advantage of these agents was a marked reduction in the occurrence of extrapyramidal symptoms. […] However, the atypical antipsychotics have also proven to carry their own unique side-effect profile. Side effects include substantial weight gain […] lipid abnormalities […] Hyperglycemia and diabetes are strongly associated with some of the newer atypical antipsychotics […] Thus, many psychiatrists are finding themselves in the difficult position of trading efficacy in the treatment of schizophrenia for an array of adverse metabolic side effects.”

“Weight gain is one of the more noticeable effects of all of the psychotropics. Although the SGAs appear to be a major culprit, TCAs, lithium, and mood stabilizers such as valproic acid or divalproex sodium and carbamazepine are also associated with weight gain. […] A range of evidence suggests that treatment with certain antipsychotic medications is associated with an increased risk of insulin resistance, hyperglycemia, and type 2 diabetes, compared with no treatment or treatment with alternative antipsychotics. […] A growing body of evidence supports the key observation that treatments producing the greatest increases in body weight and adiposity are also associated with a consistent pattern of clinically significant adverse effects on insulin resistance and changes in blood glucose and lipid levels. However, there are a growing number of cases of antipsychotic-associated hyperglycemia that involve patients without substantial weight gain, and reports that involve patients who improve when the offending agent is discontinued or who experience deterioration of glycemic control when re-challenged with the drug. […] Antipsychotics may lead to diabetes in susceptible individuals by causing decreased insulin secretion, increased insulin resistance, or a combination of both. Data suggest, however, that insulin resistance is primarily the responsible mechanism. […] The mechanism through which antipsychotics lead to insulin resistance is not clear.

“Many drugs may influence glucose insulin homeostasis. Commonly prescribed drugs that may have adverse effects on carbohydrate metabolism, especially in patients with diabetes mellitus or those at risk of developing glucose intolerance, include diuretics, beta-blockers, sympathomimetics, corticosteroids, and sex hormones”.

The book’s Table 4.11 include a really nice list of drugs, or drug classes, that can increase blood glucose levels, which includes quite a few commonly used drugs. A couple of to me surprising culprits on that list were marijuana and oral contraceptives; the oral contraceptives one certainly makes a lot of sense in retrospect (I don’t really know much about the metabolism of marijuana/cannabis, all I’ve ever learned about that stuff includes what was covered in the appendix of Coleman’s excellent textbook – and I have no personal experience…), I just hadn’t thought about the fact that very commonly used drugs like these may also have side effects of this nature).

“Patients with depression or bipolar depression may lack interest in their well-being and suffer from difficulty maintaining focus. Furthermore, many depressed patients suffer from decreased energy, psychomotor retardation, and changes in appetite, which may further promote weight gain. All of these make it very challenging to successfully implement a weight loss program in depressed patients. […] In addition, many patients with mental illnesses such as depression […] often state that eating is one of the few highlights of their day.” (So it’s probably a good idea to avoid giving these people drugs which will cause them to gain a substantial amount of weight/increase appetite/increase carbohydrate cravings, to the extent that this is possible…)

“Diabetes is considered a coronary artery disease equivalent by the National Cholesterol Education Panel (NCEP) […] Aspirin therapy is considered a routine part of secondary prevention in people with diabetes and a history of cardiovascular disease, and it is also recommended as part of primary prevention for cardiovascular disease in all patients with diabetes older than 40 years of age; additionally treatment with 75 to 325 mg/day of aspirin should be considered in patients 30 to 40 years of age with one additional cardiovascular risk factor.1,13 […] for all people older than 40 years of age with diabetes, statin therapy is recommended to lower the LDL by 30% to 40%, regardless of baseline levels.14 […] Lowering triglycerides to levels less than 150 mg/dL also confers cardiovascular benefit.1,14 However, hyperglycemia and hypertriglyceridemia are intricately linked, likely through elevations of free fatty acids. Free fatty acids are potent inhibitors of insulin action and transport, and act to disrupt glucose transport into skeletal muscle. Thus, triglyceride goals are often difficult to attain in uncontrolled diabetes.”

In some weird way some aspects of the last part of the book’s coverage was quite funny. So you have a diabetic whose disease has caused extensive damage to the nervous system leading to painful neuropathy. How do you treat the (in general difficult to treat) symptoms of neuropathy? Why, you give him tricyclic antidepressants (which will of course make his diabetes harder to treat, and cause him to gain weight). No, I’m not making this up:

“The most widely used medical treatments for symptoms of diabetic neuropathy include gabapentin and tricyclic antidepressants.”

Or how about this one – you have a type 2 diabetic who’s most likely overweight and who could probably benefit quite a bit from losing weight; why, let’s treat his diabetes with a drug that causes him to gain weight! People actually do this: “Thiazolidinediones (rosiglitazone, pioglitazone) act as agonists of the peroxisome proliferator-activator receptor gamma and improve insulin sensitivity at the tissue level. These agents are contraindicated in patients with heart failure and can worsen peripheral edema. Unfortunately, a common side effect of the glitazone class of agents is weight gain.” They’re not first-line agents, but they are used in diabetics. Just to make things even better, these drugs also seem to increase the risk of osteoporosis, a risk which is already somewhat elevated in type 2 diabetics: “Additionally, these drugs [thiazolidinediones] appear to decrease appendicular bone mass with associated increased risk of fractures.34

…or perhaps now some people might start thinking here: ‘Is stuff like this actually part of the explanation for Vestergaard’s findings described in the link above?’ I should add to these people that this is unlikely to be the case, especially considering the big difference between the (really quite substantial) type 1- and (significantly lower) type 2 fracture risk elevation; thiazolidinediones are not used in the treatment of type 1, and it’s not even a first-line treatment of type 2 – other explanations, such as those covered in Czernik & Fowlkes’s text, seem much more likely to matter (though in the context of a few individuals these drugs may still be of relevance).

“In addition to glycemic goals, nonglycemic treatment goals of blood pressure control, lipid management, and initiation of aspirin therapy are often necessary. For many patients, the diagnosis of diabetes results in multidrug therapy. For patients with mental illness who are likely to already be on multiple medications, the addition of several new agents can be difficult. Several studies have suggested that medication adherence in patients with psychiatric illness is poor at baseline,38 and may worsen when an increasing number of medications are prescribed.”

It’s also worth remembering here that “asymptomatic and chronic diseases needing long-term treatment […] result in poorer compliance”, although on the other hand “patient-controlled non-compliance [is] lower in treatment for diseases in which the relationship between non-compliance and recurrence is very clear, such as diabetes, compared to treatment for diseases in which this relationship is less clear” (Kermani and Davies). Combine psychiatric disease with chronic illnesses of a different kind and potential polypharmacy and non-compliance certainly becomes an issue worth taking into account when considering what might be the optimal treatment regime. It’s also worth keeping in mind that even in people without psychiatric problems adherence tends to be low in the case of antihypertensives and lipid-lowering drugs – again I refer to Kermani and Davies’ text:

“Chapman et al. (2005) recently examined compliance with concomitant antihypertensive and lipid-lowering drug therapy in 8406 enrollees in a US-managed care plan […] Less than half of patients (44.7 per cent) were adherent with both therapies three months after medication initiation, a figure that decreased to 35.8 per cent at 12 months.”

September 7, 2016 Posted by | Books, Cardiology, Diabetes, Medicine, Pharmacology | Leave a comment

Diabetes and the Metabolic Syndrome in Mental Health (I)

As I stated in my goodreads review, ‘If you’re a schizophrenic and/or you have a strong interest in e.g. the metabolic effects of various anti-psychotics, the book is a must-read’. If that’s not true, it’s a different matter. One reason why I didn’t give the book a higher rating is that many of the numbers in there are quite dated, which is a bit annoying because it means you might feel somewhat uncertain about how valid the estimates included still are at this point.

As pointed out in my coverage of the human drug metabolism text there are a lot of things that can influence the way that drugs are metabolized, and this text includes some details about a specific topic which may help to illustrate what I meant by stating in that post that people ‘self-experimenting’ may be taking on risks they may not be aware of. Now, diabetics who need insulin injections are taking a drug with a narrow therapeutic index, meaning that even small deviations from the optimal dose may have serious repercussions. A lot of things influence what is actually the optimal dose in a specific setting; food (“food is like a drug to a person with diabetes”, as pointed out in Matthew Neal’s endocrinology text, which is yet another text I, alas, have yet to cover here), sleep patterns, exercise (sometimes there may be an impact even days after you’ve exercised), stress, etc. all play a role, and even well-educated diabetics may not know all the details.

A lot of drugs also affect glucose metabolism and insulin sensitivity, one of the best known drug types of this nature probably being the corticosteroids because of their widespread use in a variety of disorders, including autoimmune disorders which tend to be more common in autoimmune forms of diabetes (mainly type 1). However many other types of drugs can also influence blood glucose, and on the topic of antidepressants and antipsychotics we actually know some stuff about these things and about how various medications influence glucose levels; it’s not a big coincidence that people have looked at this, they’ve done that because it has become clear that “[m]any medications, in particular psychotropics, including antidepressants, antipsychotics, and mood stabilizers, are associated with elevations in blood pressure, weight gain, dyslipidemias, and/or impaired glucose homeostasis.” (p. 49). Which may translate into an increased risk of type 2 diabetes, and impaired glucose control in diabetics. Incidentally the authors of this text observes in the text that: “Our research group was among the first in the field to identify a possible link between the development of obesity, diabetes, and other metabolic derangements (e.g., lipid abnormalities) and the use of newer, second-generation antipsychotic medications.” Did the people who took these drugs before this research was done/completed know that their medications might increase their risk of developing diabetes? No, because the people prescribing it didn’t know, nor did the people who developed the drugs. Some probably still don’t know, including some of the medical people prescribing these medications. But the knowledge is out there now, and the effect size is in the case of some drugs argued to be large enough to be clinically relevant. In the context of a ‘self-experimentation’-angle the example is also interesting because the negative effect in question here is significantly delayed; type 2 diabetes takes time to develop, and this is an undesirable outcome which you’re not going to spot the way you might link a headache the next day to a specific drug you just started out with (another example of a delayed adverse event is incidentally cancer). You’re not going to spot dyslipidemia unless you keep track of your lipid levels on your own or e.g. develop xanthomas as a consequence of it, leading you to consult a physician. It helps a lot if you have proper research protocols and large n studies with sufficient power when you want to discover things like this, and when you want to determine whether an association like this is ‘just an association’ or if the link is actually causal (and then clarifying what we actually mean by that, and whether the causal link is also clinically relevant and/or for whom it might be clinically relevant). Presumably many people taking all kinds of medical drugs these days are taking on risks which might in a similar manner be ‘hidden from view’ as was the risk of diabetes in people taking second-generation antipsychotics in the near-past; over time epidemiological studies may pick up on some of these risks, but many will probably remain hidden from view on account of the amount of complexity involved. Even if a drug ‘works’ as intended in the context of the target variable in question, you can get into a lot of trouble if you only focus on the target variable (“if a drug has no side effects, then it is unlikely to work“). People working in drug development know this.

The book has a lot of blog-worthy stuff so I decided to include some quotes in the coverage below. The quotes are from the first half of the book, and this part of the coverage actually doesn’t talk much about the effects of drugs; it mainly deals with epidemiology and cost estimates. I thus decided to save the ‘drug coverage’ to a later post. It should perhaps be noted that some of the things I’d hoped to learn from Ru-Band Lu et al.’s book (blog coverage here) was actually included in this one, which was nice.

“Those with mental illness are at higher risk and are more likely to suffer the severe consequences of comorbid medical illness. Adherence to treatment is often more difficult, and other factors such as psychoneuroendocrine interactions may complicate already problematic treatments. Additionally, psychiatric medications themselves often have severe side effects and can interact with other medications, rendering treatment of the mental illness more complicated. Diabetes is one example of a comorbid medical illness that is seen at a higher rate in people with mental illness.”

“Depression rates have been studied and are increased in type 1 and type 2 diabetes. In a meta-analysis, Barnard et al. reviewed 14 trials in which patients with type 1 diabetes were surveyed for rates of depression.16 […] subjects with type 1 diabetes had a 12.0% rate of depression compared with a rate of 3.4% in those without diabetes. In noncontrolled trials, they found an even higher rate of depression in patients with type 1 diabetes (13.4%). However, despite these overall findings, in trials that were considered of an adequate design, and with a substantially rigorous depression screening method (i.e., use of structured clinical interview rather than patient reported surveys), the rates were not statistically significantly increased (odds ratio [OR] 2.36, 95% confidence interval [CI] 0.69–5.4) but had such substantial variation that it was not sufficient to draw a conclusion regarding type 1 diabetes. […] When it comes to rates of depression, type 2 diabetes has been studied more extensively than type 1 diabetes. Anderson et al. compiled a large metaanalysis, looking at 42 studies involving more than 21,000 subjects to assess rates of depression among patients with type 1 versus type 2 diabetes mellitus.18 Regardless of how depression was measured, type 1 diabetes was associated with lower rates of depression than type 2 diabetes. […] Depression was significantly increased in both type 1 and type 2 diabetes, with increased ORs for subjects with type 1 (OR = 2.9, 95% CI 1.6 –5.5, […] p=0.0003) and type 2 disease (OR = 2.9, 95% CI 2.3–3.7, […] p = 0.0001) compared with controls. Overall, with multiple factors controlled for, the risk of depression in people with diabetes was approximately twofold. In another large meta-analysis, Ali et al. looked at more than 51,000 subjects in ten different studies to assess rates of depression in type 2 diabetes mellitus. […] the OR for comorbid depression among the diabetic patients studied was higher for men than for women, indicating that although women with diabetes have an overall increased prevalence of depression (23.8 vs. 12.8%, p = 0.0001), men with diabetes have an increased risk of developing depression (men: OR = 1.9, 95% CI = 1.7–2.1 vs. women: OR = 1.3, 95% CI = 1.2–1.4). […] Research has shown that youths 12–17 years of age with type 1 diabetes had double the risk of depression compared with a teenage population without diabetes.21 This amounted to nearly 15% of children meeting the criteria for depression.

As many as two-thirds of patients with diabetes and major depression have been ill with depression for more than 2 years.44 […] Depression has been linked to decreased adherence to self-care regimens (exercise, diet, and cessation of smoking) in patients with diabetes, as well as to the use of diabetes control medications […] Patients with diabetes and depression are twice as likely to have three or more cardiac risk factors such as smoking, obesity, sedentary lifestyle, or A1c > 8.0% compared with patients with diabetes alone.47 […] The costs for individuals with both major depression and diabetes are 4.5 times greater than for those with diabetes alone.53

“A 2004 cross-sectional and longitudinal study of data from the Health and Retirement Study demonstrated that the cumulative risk of incident disability over an 8-year period was 21.3% for individuals with diabetes versus 9.3% for those without diabetes. This study examined a cohort of adults ranging in age from 51 to 61 years from 1992 through 2000.”

Although people with diabetes comprise just slightly more than 4% of the U.S. population,3 19% of every dollar spent on health care (including hospitalizations, outpatient and physician visits, ambulance services, nursing home care, home health care, hospice, and medication/glucose control agents) is incurred by individuals with diabetes” (As I noted in the margin, these are old numbers, and prevalence in particular is definitely higher today than it was when that chapter was written, so diabetics’ proportion of the total cost is likely even higher today than it was when that chapter was written. As observed multiple times previously on this blog, most of these costs are unrelated to the costs of insulin treatment and oral anti-diabetics like metformin, and indirect costs make out a quite substantial proportion of the total costs).

In 1997, only 8% of the population with a medical claim of diabetes was treated for diabetes alone. Other conditions influenced health care spending, with 13.8% of the population with one other condition, 11.2% with two comorbidities, and 67% with three or more related conditions.6 Patients with diabetes who suffer from comorbid conditions related to diabetes have a greater impact on health services compared with those patients who do not have comorbid conditions. […] Overall, comorbid conditions and complications are responsible for 75% of total medical expenditures for diabetes.” (Again, these are old numbers)

“Heart disease and stroke are the largest contributors to mortality for individuals with diabetes; these two conditions are responsible for 65% of deaths. Death rates from heart disease in adults with diabetes are two to four times higher than in adults without diabetes. […] Adults with diabetes are more than twice as likely to have multiple diagnoses related to macrovascular disease compared to patients without diabetes […] Although the prevalence of cardiovascular disease increases with age for both diabetics and nondiabetics, adults with diabetes have a significantly higher rate of disease. […] The management of macrovascular disease, such as heart attacks and strokes, represents the largest factor driving medical service use and related costs, accounting for 52% of costs to treat diabetes over a lifetime. The average costs of treating macrovascular disease are $24,330 of a total of $47,240 per person (in year 2000 dollars) over the course of a lifetime.17 Moreover, macrovascular disease is an important determinant of cost at an earlier time than other complications, accounting for 85% of the cumulative costs during the first 5 years following diagnosis and 77% over the initial decade. [Be careful here: This is completely driven by type 2 diabetics; a 10-year old newly diagnosed type 1 diabetic does not develop heart disease in the first decade of disease – type 1s are also at high risk of cardiovascular disease, but the time profile here is completely different] […] Cardiovascular disease in the presence of diabetes affects not only cost but also the allocation of health care resources. Average annual individual costs attributed to the treatment of diabetes with cardiovascular disease were $10,172. Almost 51% of costs were for inpatient hospitalizations, 28% were for outpatient care, and 21% were for pharmaceuticals and related supplies. In comparison, the average annual costs for adults with diabetes and without cardiovascular disease were $4,402 for management and treatment of diabetes. Only 31.2% of costs were for inpatient hospitalizations, 40.3% were for outpatient care, and 28.6% were for pharmaceuticals.16

Of individuals with diabetes, 2% to 3% develop a foot ulcer during any given year. The lifetime incidence rate of lower extremity ulcers is 15% in the diabetic population.20 […] The rate of amputation in individuals with diabetes is ten times higher than in those without diabetes.5 Diabetic lower-extremity ulcers are responsible for 92,000 amputations each year,21 accounting for more than 60% of all nontraumatic amputations.5 The 10-year cumulative incidence of lower-extremity amputation is 7% in adults older than 30 years of age who are diagnosed with diabetes.22 […] Following amputation, the 5-year survival rate is 27%.23 […] The majority of annual costs associated with treating diabetic peripheral neuropathy are associated with treatment of ulcers […] Overall, inpatient hospitalization is a major driver of cost, accounting for 77% of expenditures associated with individual episodes of lower-extremity ulcers.24

By 2003, diabetes accounted for 37% of individuals being treated for renal disease in the United States. […] Diabetes is the leading cause of kidney failure, accounting for 44% of all newly diagnosed cases. […] The amount of direct medical costs for ESRD attributed to diabetes is substantial. The total adjusted costs in a 24-month period were 76% higher among ESRD patients with diabetes compared with those without diabetes. […] Nearly one half of the costs of ESRD are due to diabetes.27” [How much did these numbers change since the book was written? I’m not sure, but these estimates do provide some sort of a starting point, which is why I decided to include the numbers even though I assume some of them may have changed since the publication of the book]

Every percentage point decrease in A1c levels reduces the risk of microvascular complications such as retinopathy, neuropathy, and nephropathy by 40%.5 However, the trend is for A1c to drift upward at an average of 0.15% per year, increasing the risk of complications and costs.17 […] A1c levels also affect the cost of specific complications associated with diabetes. Increasing levels affect overall cost and escalate more dramatically when comorbidities are present. A1c along with cardiovascular disease, hypertension, and depression are significant independent predictors of health care
costs in adults with diabetes.”

August 10, 2016 Posted by | Books, Cardiology, Diabetes, Economics, Epidemiology, Health Economics, Medicine, Nephrology, Pharmacology, Psychiatry | Leave a comment