Econstudentlog

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

Harnessing phenotypic heterogeneity to design better therapies

Unlike many of the IAS lectures I’ve recently blogged this one is a new lecture – it was uploaded earlier this week. I have to say that I was very surprised – and disappointed – that the treatment strategy discussed in the lecture had not already been analyzed in a lot of detail and been implemented in clinical practice for some time. Why would you not expect the composition of cancer cell subtypes in the tumour microenvironment to change when you start treatment – in any setting where a subgroup of cancer cells has a different level of responsiveness to treatment than ‘the average’, that would to me seem to be the expected outcome. And concepts such as drug holidays and dose adjustments as treatment responses to evolving drug resistance/treatment failure seem like such obvious approaches to try out here (…the immunologists dealing with HIV infection have been studying such things for decades). I guess ‘better late than never’.

A few papers mentioned/discussed in the lecture:

Impact of Metabolic Heterogeneity on Tumor Growth, Invasion, and Treatment Outcomes.
Adaptive vs continuous cancer therapy: Exploiting space and trade-offs in drug scheduling.
Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer.

June 11, 2017 Posted by | Cancer/oncology, Genetics, Immunology, Lectures, Mathematics, Medicine, Studies | Leave a comment

A few papers

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

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

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

ii. Exercise for depression (Cochrane review).

Background

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

Objectives

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

Selection criteria 

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

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

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

Authors’ conclusions

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

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

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

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

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

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

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

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

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

v. Immigrant Labor Market Integration across Admission Classes.

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

Some quotes from the paper:

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

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

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

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

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

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

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

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

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

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

vii. Gaze direction detection in autism spectrum disorder.

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

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

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

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some more stuff from the paper:

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

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

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

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

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

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

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

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

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

iv. The Mental Health Comorbidities of Diabetes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

“Summary

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

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

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

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

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

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

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

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

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

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

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

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

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

May 17, 2017 Posted by | Diabetes, Immunology, Medicine, Nephrology, Statistics, Studies | 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, Immunology, Lectures, Medicine, Pharmacology | Leave a comment

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Standing on the Shoulders of Mice: Aging T-cells

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

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

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some additional observations from the paper:

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

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

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

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

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

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

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

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

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

Biodemography of aging (III)

Latent class representation of the Grade of Membership model.
Singular value decomposition.
Affine space.
Lebesgue measure.
General linear position.

The links above are links to topics I looked up while reading the second half of the book. The first link is quite relevant to the book’s coverage as a comprehensive longitudinal Grade of Membership (-GoM) model is covered in chapter 17. Relatedly, chapter 18 covers linear latent structure (-LLS) models, and as observed in the book LLS is a generalization of GoM. As should be obvious from the nature of the links some of the stuff included in the second half of the text is highly technical, and I’ll readily admit I was not fully able to understand all the details included in the coverage of chapters 17 and 18 in particular. On account of the technical nature of the coverage in Part 2 I’m not sure I’ll cover the second half of the book in much detail, though I probably shall devote at least one more post to some of those topics, as they were quite interesting even if some of the details were difficult to follow.

I have almost finished the book at this point, and I have already decided to both give the book five stars and include it on my list of favorite books on goodreads; it’s really well written, and it provides consistently highly detailed coverage of very high quality. As I also noted in the first post about the book the authors have given readability aspects some thought, and I am sure most readers would learn quite a bit from this text even if they were to skip some of the more technical chapters. The main body of Part 2 of the book, the subtitle of which is ‘Statistical Modeling of Aging, Health, and Longevity’, is however probably in general not worth the effort of reading unless you have a solid background in statistics.

This post includes some observations and quotes from the last chapters of the book’s Part 1.

“The proportion of older adults in the U.S. population is growing. This raises important questions about the increasing prevalence of aging-related diseases, multimorbidity issues, and disability among the elderly population. […] In 2009, 46.3 million people were covered by Medicare: 38.7 million of them were aged 65 years and older, and 7.6 million were disabled […]. By 2031, when the baby-boomer generation will be completely enrolled, Medicare is expected to reach 77 million individuals […]. Because the Medicare program covers 95 % of the nation’s aged population […], the prediction of future Medicare costs based on these data can be an important source of health care planning.”

“Three essential components (which could be also referred as sub-models) need to be developed to construct a modern model of forecasting of population health and associated medical costs: (i) a model of medical cost projections conditional on each health state in the model, (ii) health state projections, and (iii) a description of the distribution of initial health states of a cohort to be projected […] In making medical cost projections, two major effects should be taken into account: the dynamics of the medical costs during the time periods comprising the date of onset of chronic diseases and the increase of medical costs during the last years of life. In this chapter, we investigate and model the first of these two effects. […] the approach developed in this chapter generalizes the approach known as “life tables with covariates” […], resulting in a new family of forecasting models with covariates such as comorbidity indexes or medical costs. In sum, this chapter develops a model of the relationships between individual cost trajectories following the onset of aging-related chronic diseases. […] The underlying methodological idea is to aggregate the health state information into a single (or several) covariate(s) that can be determinative in predicting the risk of a health event (e.g., disease incidence) and whose dynamics could be represented by the model assumptions. An advantage of such an approach is its substantial reduction of the degrees of freedom compared with existing forecasting models  (e.g., the FEM model, Goldman and RAND Corporation 2004). […] We found that the time patterns of medical cost trajectories were similar for all diseases considered and can be described in terms of four components having the meanings of (i) the pre-diagnosis cost associated with initial comorbidity represented by medical expenditures, (ii) the cost peak associated with the onset of each disease, (iii) the decline/reduction in medical expenditures after the disease onset, and (iv) the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity. The description of the trajectories was formalized by a model which explicitly involves four parameters reflecting these four components.”

As I noted earlier in my coverage of the book, I don’t think the model above fully captures all relevant cost contributions of the diseases included, as the follow-up period was too short to capture all relevant costs to be included in the part iv model component. This is definitely a problem in the context of diabetes. But then again nothing in theory stops people from combining the model above with other models which are better at dealing with the excess costs associated with long-term complications of chronic diseases, and the model results were intriguing even if the model likely underperforms in a few specific disease contexts.

Moving on…

“Models of medical cost projections usually are based on regression models estimated with the majority of independent predictors describing demographic status of the individual, patient’s health state, and level of functional limitations, as well as their interactions […]. If the health states needs to be described by a number of simultaneously manifested diseases, then detailed stratification over the categorized variables or use of multivariate regression models allows for a better description of the health states. However, it can result in an abundance of model parameters to be estimated. One way to overcome these difficulties is to use an approach in which the model components are demographically-based aggregated characteristics that mimic the effects of specific states. The model developed in this chapter is an example of such an approach: the use of a comorbidity index rather than of a set of correlated categorical regressor variables to represent the health state allows for an essential reduction in the degrees of freedom of the problem.”

“Unlike mortality, the onset time of chronic disease is difficult to define with high precision due to the large variety of disease-specific criteria for onset/incident case identification […] there is always some arbitrariness in defining the date of chronic disease onset, and a unified definition of date of onset is necessary for population studies with a long-term follow-up.”

“Individual age trajectories of physiological indices are the product of a complicated interplay among genetic and non-genetic (environmental, behavioral, stochastic) factors that influence the human body during the course of aging. Accordingly, they may differ substantially among individuals in a cohort. Despite this fact, the average age trajectories for the same index follow remarkable regularities. […] some indices tend to change monotonically with age: the level of blood glucose (BG) increases almost monotonically; pulse pressure (PP) increases from age 40 until age 85, then levels off and shows a tendency to decline only at later ages. The age trajectories of other indices are non-monotonic: they tend to increase first and then decline. Body mass index (BMI) increases up to about age 70 and then declines, diastolic blood pressure (DBP) increases until age 55–60 and then declines, systolic blood pressure (SBP) increases until age 75 and then declines, serum cholesterol (SCH) increases until age 50 in males and age 70 in females and then declines, ventricular rate (VR) increases until age 55 in males and age 45 in females and then declines. With small variations, these general patterns are similar in males and females. The shapes of the age-trajectories of the physiological variables also appear to be similar for different genotypes. […] The effects of these physiological indices on mortality risk were studied in Yashin et al. (2006), who found that the effects are gender and age specific. They also found that the dynamic properties of the individual age trajectories of physiological indices may differ dramatically from one individual to the next.”

“An increase in the mortality rate with age is traditionally associated with the process of aging. This influence is mediated by aging-associated changes in thousands of biological and physiological variables, some of which have been measured in aging studies. The fact that the age trajectories of some of these variables differ among individuals with short and long life spans and healthy life spans indicates that dynamic properties of the indices affect life history traits. Our analyses of the FHS data clearly demonstrate that the values of physiological indices at age 40 are significant contributors both to life span and healthy life span […] suggesting that normalizing these variables around age 40 is important for preventing age-associated morbidity and mortality later in life. […] results [also] suggest that keeping physiological indices stable over the years of life could be as important as their normalizing around age 40.”

“The results […] indicate that, in the quest of identifying longevity genes, it may be important to look for candidate genes with pleiotropic effects on more than one dynamic characteristic of the age-trajectory of a physiological variable, such as genes that may influence both the initial value of a trait (intercept) and the rates of its changes over age (slopes). […] Our results indicate that the dynamic characteristics of age-related changes in physiological variables are important predictors of morbidity and mortality risks in aging individuals. […] We showed that the initial value (intercept), the rate of changes (slope), and the variability of a physiological index, in the age interval 40–60 years, significantly influenced both mortality risk and onset of unhealthy life at ages 60+ in our analyses of the Framingham Heart Study data. That is, these dynamic characteristics may serve as good predictors of late life morbidity and mortality risks. The results also suggest that physiological changes taking place in the organism in middle life may affect longevity through promoting or preventing diseases of old age. For non-monotonically changing indices, we found that having a later age at the peak value of the index […], a lower peak value […], a slower rate of decline in the index at older ages […], and less variability in the index over time, can be beneficial for longevity. Also, the dynamic characteristics of the physiological indices were, overall, associated with mortality risk more significantly than with onset of unhealthy life.”

“Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward manner […]. Recent genome-wide association studies (GWAS) have reached fundamentally the same conclusion by showing that the traits in late life likely are controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny effect […] the weak effect of genes on traits in late life can be not only because they confer small risks having small penetrance but because they confer large risks but in a complex fashion […] In this chapter, we consider several examples of complex modes of gene actions, including genetic tradeoffs, antagonistic genetic effects on the same traits at different ages, and variable genetic effects on lifespan. The analyses focus on the APOE common polymorphism. […] The analyses reported in this chapter suggest that the e4 allele can be protective against cancer with a more pronounced role in men. This protective effect is more characteristic of cancers at older ages and it holds in both the parental and offspring generations of the FHS participants. Unlike cancer, the effect of the e4 allele on risks of CVD is more pronounced in women. […] [The] results […] explicitly show that the same allele can change its role on risks of CVD in an antagonistic fashion from detrimental in women with onsets at younger ages to protective in women with onsets at older ages. […] e4 allele carriers have worse survival compared to non-e4 carriers in each cohort. […] Sex stratification shows sexual dimorphism in the effect of the e4 allele on survival […] with the e4 female carriers, particularly, being more exposed to worse survival. […] The results of these analyses provide two important insights into the role of genes in lifespan. First, they provide evidence on the key role of aging-related processes in genetic susceptibility to lifespan. For example, taking into account the specifics of aging-related processes gains 18 % in estimates of the RRs and five orders of magnitude in significance in the same sample of women […] without additional investments in increasing sample sizes and new genotyping. The second is that a detailed study of the role of aging-related processes in estimates of the effects of genes on lifespan (and healthspan) helps in detecting more homogeneous [high risk] sub-samples”.

“The aging of populations in developed countries requires effective strategies to extend healthspan. A promising solution could be to yield insights into the genetic predispositions for endophenotypes, diseases, well-being, and survival. It was thought that genome-wide association studies (GWAS) would be a major breakthrough in this endeavor. Various genetic association studies including GWAS assume that there should be a deterministic (unconditional) genetic component in such complex phenotypes. However, the idea of unconditional contributions of genes to these phenotypes faces serious difficulties which stem from the lack of direct evolutionary selection against or in favor of such phenotypes. In fact, evolutionary constraints imply that genes should be linked to age-related phenotypes in a complex manner through different mechanisms specific for given periods of life. Accordingly, the linkage between genes and these traits should be strongly modulated by age-related processes in a changing environment, i.e., by the individuals’ life course. The inherent sensitivity of genetic mechanisms of complex health traits to the life course will be a key concern as long as genetic discoveries continue to be aimed at improving human health.”

“Despite the common understanding that age is a risk factor of not just one but a large portion of human diseases in late life, each specific disease is typically considered as a stand-alone trait. Independence of diseases was a plausible hypothesis in the era of infectious diseases caused by different strains of microbes. Unlike those diseases, the exact etiology and precursors of diseases in late life are still elusive. It is clear, however, that the origin of these diseases differs from that of infectious diseases and that age-related diseases reflect a complicated interplay among ontogenetic changes, senescence processes, and damages from exposures to environmental hazards. Studies of the determinants of diseases in late life provide insights into a number of risk factors, apart from age, that are common for the development of many health pathologies. The presence of such common risk factors makes chronic diseases and hence risks of their occurrence interdependent. This means that the results of many calculations using the assumption of disease independence should be used with care. Chapter 4 argued that disregarding potential dependence among diseases may seriously bias estimates of potential gains in life expectancy attributable to the control or elimination of a specific disease and that the results of the process of coping with a specific disease will depend on the disease elimination strategy, which may affect mortality risks from other diseases.”

April 17, 2017 Posted by | Biology, Books, Cancer/oncology, Demographics, Economics, Epidemiology, Genetics, Medicine, Statistics | Leave a comment

Random stuff

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

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

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

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

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

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

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

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

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

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

Some other related links below:

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

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

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

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

Rheumatic Manifestations of Diabetes Mellitus.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ix. Some wikipedia links:

Heroic Age of Antarctic Exploration (featured).

Wien’s displacement law.

Kuiper belt (featured).

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

Lymphatic filariasis.

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

Australian gold rushes.

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

Quark–gluon plasma.

Simo Häyhä.

Chernobyl liquidators.

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

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

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

W Ursae Majoris variable.

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

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

Stimming.

Irish Civil War.

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

Biodemography of aging (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Ageing Immune System and Health (II)

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

Lungs:

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

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

Cancer:

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

Neurodegenerative diseases:

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

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

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

Frailty:

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

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

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

Exercise:

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

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

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

Diabetes and the Brain (III)

Some quotes from the book below.

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the Brain (II)

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the brain (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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