Econstudentlog

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, Neurology, Papers, Pharmacology, Psychology | Leave a comment

Health econ stuff

In a post I published a few weeks ago I mentioned that I had decided against including some comments and observations I had written about health economics in that post because the post was growing unwieldy, but that I might post that stuff later on in a separate post. This post will include those observations, as well as some additional details I added to the post later. This sort of post is the sort of post that usually does not get past the ‘draft’ stage (in wordpress you can save posts you intend to publish later on as drafts), and as is usually the case for posts like these I already regret having written it, for multiple reasons. I should warn you from the start that this post is very long and will probably take you some time to read.

Anyway, the starting point for this post was some comments related to health insurance and health economics which I left on SSC in the past. A lot more people read those comments on SSC than will read this post so the motivation for posting it here was not to ‘increase awareness’ of the ideas and observations included in some kind of general sense; my primary motivation for adding this stuff here is rather that it’s a lot easier for me personally to find stuff I’ve written when it’s located here on this blog rather than elsewhere on the internet, and I figure that some of the things I wrote back then are topics which might well come up again later, and it would be convenient for me in that case to have a link at hand. Relatedly I have added many additional comments and observations in this post not included in the primary exchange, which it is no longer possible for me to do on SSC as my comments are no longer editable on that site.

Although the starting point for the post was as mentioned a comment exchange, I decided early on against just ‘quoting myself’ in this post, and I have thus made some changes in wording and structure in order to increase the precision of the statements included and in order to add a bit of context making the observations below easier to read and understand (and harder to misread). Major topics to which the observations included in this post relate are preventable diseases, the level of complexity that is present in the health care sector, and various topics which relate to health care cost growth. Included in the post are some perhaps not sufficiently well known complications which may arise in the context of the discussion of how different financing schemes may relate to various outcomes, and to cost growth. Much of the stuff included will probably be review to people who’ve read my previous posts on health economics, but that’s to be expected considering the nature of this post.

Although ‘normative stuff’ is not what interests me most – I generally tend to prefer discussions where the aim is to identify what happens if you do X, and I’ll often be happy to leave the discussion of whether outcome X or Y is ‘best’ to others – I do want to start out with stating a policy preference, as this preference was the starting point for the aforementioned debate that lead to the origination of this post. At the outset I should thus make clear that I would in general be in favour of changes to the financial structure of health care systems where people who take avoidable risks which systematically and demonstrably increase their expected health care expenditures at the population level pay a larger proportion of the cost than do people who did not take such avoidable risks.

Most developed societies have health care systems which are designed in a way that implicitly to some extent subsidize unhealthy behaviours. An important note in this context is incidentally that one way of looking at these things is that if you are not explicitly demanding people who behave in risky ways which tend to increase their expected costs to pay more for their health care (/insurance), then you are in fact by virtue of not doing this implicitly subsidizing those unhealthy individuals/behaviours. I mention this because some people might not like the idea of ‘subsidizing healthy behaviours’ (‘health fascism’) – which from a certain point of view is what you do if you charge people who behave in unhealthy ways more. Maybe some people would take issue with words like ‘subsidy’ and ‘implicit’, but regardless of what you call these things the major point that is important to have in mind here is that if one group of people (e.g. ‘unhealthy people’) cost more to treat (/are ill more often, get illnesses related to their behaviours, etc., etc.) than another group of people (‘healthy people’), then if you need to finance this shortfall – which you do, as you face a budget constraint – there are only two basic ways to do this; you can either charge the high-cost group (‘unhealthy people’) more, or you can require the other group (‘healthy people’) to make up the difference. Any scheme which deals with such a case of unequal net contribution rates are equivalent either to one of those schemes or a mix of the two, regardless of what you call things and how it’s done, and regardless of which groups we are talking about (old people also have higher health care expenditures than do young people, and most health care systems implicitly redistribute income from the young to the old). If you’re worried about ‘health fascism’ and the implications of subsidizing healthy behaviours (/’punishing’ unhealthy behaviours) you should at least keep in mind that if the health care costs of people who live healthy lives and people who do not are dissimilar then any system that deals with this issue – which all systems must – can either choose to ‘subsidize’ healthy behaviours or unhealthy behaviours; there’s no feasible way to design a ‘neutral system’ if the costs of the groups are dissimilar.

Having said all this, the very important next point is then that it is much more difficult to make simple schemes that would accomplish an outcome in which people who engage in unhealthy behaviours are required to pay more without at the same time introducing a significant number of new problems than people who are not familiar with this field would probably think it is. And it’s almost certainly much harder to evaluate if the proposed change actually accomplished what you wanted to accomplish than you think it is. Even if we are clear about what we want to accomplish and can all agree that that is what we are aiming for – i.e. we are disregarding the political preferences of large groups of voters and whether the setup in question is at all feasible to accomplish – this stuff is really much harder than it looks, for many reasons.

Let’s start out by assuming that smoking increases the risk of disease X by 50%. Say you can’t say which of the cases of X are caused by smoking, all you know is that smoking increases the risk at the population level. Say you don’t cover disease X at all if someone smokes, that is, smokers are required to pay the full treatment cost out of pocket if they contract disease X. It’s probably not too controversial to state that this approach might by some people be perceived of as not completely ‘fair’ to the many smokers who would have got disease X even if they had not smoked (a majority in this particular case, though of course the proportion will vary with the conditions and the risk factors in question). Now, a lot of the excess health care costs related to smoking are of this kind, and it is actually a pretty standard pattern in general with risk factors – smoking, alcohol, physical inactivity, etc. You know that these behaviours increase risk, but you usually can’t say for certain which of the specific cases you observe in clinical practice are actually (‘perfectly’/’completely’/’partially’?) attributable to the behaviour. And quite often the risk increase associated with a specific behaviour is actually really somewhat modest, compared to the relevant base rates, meaning that many of the people who engage in behaviours which increase risk and who get sick might well have got sick even if they hadn’t engaged in those risky behaviours.

On top of this problem usually it’s also the case that risk factors interact with each other. Smoking increases the risk of cancer of the esophagus, but so does alcohol and obesity, and if a person both smokes and drinks the potential interaction effect may not be linear – so you most likely often can’t just identify individual risk factors in specific studies and then pool them later and add them all together to get a proper risk assessment. A further complication is that behaviours may both increase as well as decrease risk – to stick with the example, diets high in fruits and vegetables both lower the risk of cancer of the esophagus. Exercise probably does as well – we know that exercise has important and highly complex effects on immune system function (see e.g. this post). Usually a large number of potential risk factors is at play at the same time, there may be multiple variables which lower risk and are also important to include if you want a proper risk assessment, and even if you knew in theory which interaction terms were likely to be relevant, you might even so find yourself in a situation unable to estimate the interaction terms of interest – this might take high-powered studies with large numbers of patients, which may not be available or the results of such high-powered studies may not apply to your specific subgroup of patients. Cost-effectiveness is also an issue – it’s expensive to assess risk properly. One take-away is that you’ll still have a lot of unfairness in a modified contribution rate model, and even evaluating fairness aspects of the change may be difficult to impossible because to some extent this question is unknowable. You might find yourself in a situation where you charge the obese guy more because obesity means he’s high risk, but in reality he is actually lower risk than is the non-fat guy who is charged a lower rate, because he also exercises and eats a lot of fruits and vegetables, which the other guy doesn’t.

Of course the above paragraph took it for granted that it was even possible to quantify the excess costs attributable to a specific condition. That may not be easy at all to do, and there may be large uncertainties involved. The estimated excess cost will depend upon a variety of factors which may or may not be of interest to the party performing the analysis, for example it may be very important which time frame you’re looking at and which discounting methodology is applied (see e.g. the last paragraph in this post). The usual average vs marginal cost problem (see the third-last paragraph in the post to which I link in the previous sentence – this post also has more on this topic) also applies here and is related to ‘the fat guy who exercises and is low-risk’-problem; ideally you’d want to charge people with higher health care utilization levels more (again, in a setting where we assume the excess cost is associated with life-style variables which are modifiable – this was our starting point), but if there’s a large amount of variation in costs across individuals in the specific subgroups of interest and you only have access to average costs rather than individual-level costs, then a scheme only taking into account the differences in the averages may be very sub-optimal when you look at it from the viewpoint of the individual. Care needs to be taken to avoid problems like e.g. Simpson’s paradox.

Risk factors are not the only things that cluster; so do diseases. An example:

“An analysis of the Robert Koch-Institute (RKI) from 2012 shows that more than 50 % of German people over 65 years suffer from at least one chronic disease, approximately 50 % suffer from two to four chronic diseases, and over a quarter suffer from five or more diseases [3].” (link)

78.3 % of the type 2 diabetics also suffered from hypertension in that study. Does this fact make it easier or harder to figure out what is ‘the true cost contribution’ of ‘type 2 diabetes’ and ‘hypertension’ (and, what we’re ultimately interested in in this setting – the ‘true cost contribution’ of the unhealthy behaviours which lead some individuals to develop type 2 diabetics and hypertension who would not otherwise have developed diabetes and/or hypertension (…/as early as they did)? It should be noted that diabetes was estimated to account for 11 % of total global healthcare expenditure on adults in 2013 (link). That already large proportion is expected to rise substantially in the decades to come – if you’re interested in cost growth trajectories, this is a major variable to account for. Attributability is really tricky here, and perhaps even more tricky in the case of hypertension – but for what it’s worth, according to a CDC estimate hypertension cost the US $46 billion per year, or ~$150/per person per year.

Anyway, you look at the data and you make guesses, but the point is that doctor Smith won’t know for certain if Mr. Hanson would have had a stroke even if he hadn’t smoked or not. A proposal of not providing payment for a health care service or medical product in the case of an ‘obviously risky-behaviour-related-health-condition’ may sometimes appear to be an appealing proposition and you sometimes see people make this sort of proposal in discussions of this nature, but it tends to be very difficult when you look at the details to figure out just what those ‘obviously risky-behaviour-related-health-conditions’ are, and even harder to make even remotely actuarially fair adjustments to the premiums and coverage patterns to reflect the risk. Smoking and lung cancer is a common example of a relatively ‘clean’ case, but most cases are ‘less clean’ and even here there are complications; a substantial proportion of lung cancer cases are not caused by tobacco – occupational exposures also cause a substantial proportion of cases, and: “If considered in its own disease category […] lung cancer in never smokers would represent the seventh leading cause of cancer mortality globally, surpassing cancers of the cervix, pancreas, and prostate,5 and among the top 10 causes of death in the United States.” (link) Occupational exposures (e.g. asbestos) are not likely to fully account for all cases, and for example it has also been found that other variables, including previous pneumonia infections and tuberculosis, affect risk (here are a couple of relevant links to some previous coverage I wrote on these topics).

I think many people who have preferences of this nature (‘if it’s their own fault they’re sick, they should pay for it themselves’) underestimate how difficult it may be to make changes which could be known with a reasonable level of certainty to actually have the intended consequences, even assuming everybody agreed on the goal to be achieved. This is in part because there are many other aspects and complications which need to be addressed as well. Withholding payment in the case of costly preventative illness may for example in some contexts increase cost, rather than decrease them. The risk of complications of some diseases – an important cost driver in the context of diabetes – tends to be dependent on post-diagnosis behavioural patterns. The risk of developing diabetes complications will depend upon the level of glycemic control. If you say you won’t cover complications at all in the case of ‘self-inflicted disease X’, then you also to some extent tend to remove the option of designing insurance schemes which might lower cost and complication rates post-diagnosis by rewarding ‘good’ (risk-minimizing) behaviours post-diagnosis and punishing ‘bad’ (risk-increasing) behaviours. This is not desirable in the context of diseases where post-diagnosis behaviour is an important component of the cost function, as it certainly is in the diabetes context. There are multiple potential mechanisms here, some of which are disease specific (e.g. suboptimal diet in a diagnosed type 2 diabetic) and some of which may not be (a more general mechanism could e.g. be lowered compliance/adherence to treatment in the uncovered populations because they can’t afford the drugs which are required to treat their illness; though the cost-compliance link is admittedly not completely clear in the general case, there are certainly multiple diseases where lowered compliance to treatment would be expected to increase cost long-term).

And again, also in the context of complications fairness issues are not as simple to evaluate as people might like them to be; some people may have a much harder time controlling their disease than others, or they may be more susceptible to complications given the same behaviour. Some may already have developed complications by the time of diagnosis. Such issues make it difficult to design simple rules which would achieve what you want them to achieve without having unfortunate side-effects; for example a rule that a microvascular diabetes-related complication is automatically ‘your own fault’ (so we won’t pay for it), which might be motivated by the substantial amount of research linking glycemic control with complication risk, would punish some diabetics who have had the disease for a longer amount of time (many complications are not only strongly linked to Hba1c but also display a substantial degree of duration-dependence; for example in type 1 diabetics one study found that diabetic retinopathy was present in 13% of patients with a duration of disease less than 5 years, whereas the corresponding figure was 90% for individuals with a disease duration of 10–15 years (Sperling et al., p. 393). I also recall reading a study finding that Hba1c itself is increasing with diabetes duration, which may be partly accounted for by the higher risk of hypoglycemia related to hypoglycemia-unawareness-syndromes in individuals with long-standing disease), individuals with diseases which are relatively hard to control (perhaps due to genetics, or maybe again due to the fact that they have had the disease for a longer amount of time; the presence of hypoglycemia unawareness is as alluded to above to a substantial degree duration-dependent, and this problem increases the risk of hospitalizations, which are expensive), diabetics who developed complications before they knew they were sick (a substantial proportion of type 2 diabetics develop some degree of microvascular damage pre-diagnosis), and diabetics with genetic variants which confer an elevated risk of complications (“observations suggest that involvement of genetic factors is increasing the risk of complications” (Sperling et al., p. 226), and for example in the DCCT trial familial clustering of both neuropathy and retinopathy was found; clustering which persisted after controlling for Hba1c – for more on these topics, see e.g. Sperling et al.’s chapter 11).

Other decision rules would similarly lead to potentially problematic incentives and fairness issues; for example requiring individuals to meet a specific Hba1c goal might be more desirable than to just not cover complications, but that one also leads to potential problems; ideally such an Hba1c goal should be individualized, because of the above-mentioned complexities and others I have not mentioned here; to require a newly-diagnosed individual to meet the same goals as someone who has had diabetes for decades does not make sense, and neither does it make sense to require these two groups to meet exactly the same Hba1c goal as the middle-aged female diabetic who desires to become pregnant (diabetes greatly increases the risk of pregnancy complications, and strict glycemic control is extremely important in this patient group). It’s important to note that these issues don’t just relate to whether or not the setup is perceived of as fair, but it also relates to whether or not you would expect the intended goals to actually be met or not when you implement the rule. If you were to require that a long-standing diabetic with severe hypoglycemia unawareness had to meet the same Hba1c goal as the newly diagnosed individual, this might well lead to higher overall cost, because said individual might suffer a large number of hypoglycemia-related hospitalizations which would have been avoidable if a more lax requirement was imposed; when you decrease Hba1c you decrease the risk of long-term complications, but you increase the risk of hypoglycemia. A few numbers might make it easier to make sense of how expensive hospitalizations really are, and why I emphasize them here. In this diabetes-care publication they assign a cost for an inpatient day for a diabetes-related hospitalization at $2,359 and an emergency visit at ~$800. The same publication estimates the total average annual excess expenditures of diabetics below the age of 45 at $4,394. Going to the hospital is really expensive (43% of the total medical costs of diabetes are accounted for by hospital inpatient care in that publication).

A topic which was brought up in the SSC discussion was the question of the extent to which private providers have a greater incentive to ‘get things right’ in terms of assessing risk. I don’t take issue with this notion in general, but there are a lot of complicating factors in the health care context. One factor of interest is that it is costly to get things right. If you’re looking at this from an insurance perspective, larger insurance providers may be better at getting things right because they can afford to hire specialists who provide good cost estimates – getting good cost estimates is really hard, as I’ve noted above. Larger providers translate into fewer firms, which increases firm concentration and may thus increase collusion risk, which may again increase the prices of health care services. Interestingly if your aim is to minimize health care cost growth increased market power of private firms may actually be a desirable state of affairs/goal, because cost growth is a function of both unit prices and utilization levels, and higher premiums are likely to translate into lower utilization rates, which may lower overall costs and -cost growth. I decided to include this observation here also in order to illustrate that what is an optimal outcome depends on what your goal is, and in the setting of the health care sector you sometimes need to be very careful about thinking about what your actual goal is, and which other goals might be relevant.

When private insurance providers become active in a market that also includes a government entity providing a level of guaranteed coverage, total medical outlays may easily increase rather than decrease. The firms may meed an unmet need, but some of that unmet need may be induced demand (here’s a related link). Additionally, the bargaining power of various groups of medical personnel may change in such a setting, leading to changes in compensation schedules which may not be considered desirable/fair. An increase in total outlays may or may not be considered a desirable outcome, but this does illustrate once again the point that you need to be careful about what you are trying to achieve.

There’s a significant literature on how the level of health care integration, both at the vertical and horizontal level, both in terms of financial structure and e.g. in terms of service provision structure, may impact health care costs, and this is an active area of research where we in some contexts do not yet know the answers.

Even when cost minimization mechanisms are employed in the context of private firms and the firm in question is efficient, the firm may not internalize all relevant costs. This may paradoxically lead to higher overall cost, due to coverage decisions taken ‘upstream’ influencing costs ‘downstream’ in an adverse manner; I have talked about this topic on this blog before. A diabetic might be denied coverage of glucose testing materials by his private insurer, and that might mean that the diabetic instead gets hospitalized for a foreseeable and avoidable complication (hypoglycemic coma due to misdosing), but because it might not be the same people paying for the testing material and the subsequent hospitalization it might not matter to the people denying coverage of the testing materials, and/so they won’t take it into account when they’re making their coverage decisions. That sort of thing is quite common in the health care sector – different entities pay for and receive payments for different things, and this is once again a problem to keep in mind if you’re interested in health care evaluation; interventions which seem to lower cost may not do so in reality, because the intervention lead to higher health care utilization elsewhere in the system. If incentives are not well-aligned things may go badly wrong, and they are often not well-aligned in the health care sector. When both the private and public sectors are involved in either the financial arrangements and/or actual health service provision – which is the default health care system setup for developed societies – this usually leads to highly complex systems, where the scope for such problems to appear seems magnified, rather than the opposite. I would assume that in many cases it matters a lot more that incentives are well-aligned than which specific entity is providing insurance or health care in the specific context, in part a conclusion drawn from the coverage included in Simmons, Wenzel & Zgibor‘s book.

In terms of the incentive structures of the people involved in the health care sector, this stuff is of course also adding another layer of complexity. In all sectors of the economy you have people with different interests who interact with each other, and when incentives change outcomes change. Outcomes may be car batteries, or baseball bats, or lectures. Evaluating outcomes is easier in some settings than in others, and I have already touched upon some of the problems that might be present when you’re trying to evaluate outcomes in the health care context. How easy it is to evaluate outcomes will naturally vary across sub-sectors of the health care sector but a general problem which tends to surface here is the existence of various forms of asymmetrical information. There are multiple layers, but a few examples are worth mentioning. To put it bluntly, the patient tends to know his symptoms and behavioural patterns – which may be disease-relevant, and this aspect is certainly important to include when discussing preventative illnesses caused at least in part by behaviours which increase the risk of said illnesses – better than his doctor, and the doctor will in general tend to know much more about the health condition and potential treatment options than will the patient. The patient wants to get better, but he also wants to look good in the eyes of the doctor, which means he might not be completely truthful when interacting with the doctor; he might downplay how much alcohol he drinks, misrepresent how often he exercises, or he may lie about smoking habits or about how much he weighs. These things make risk-assessments more difficult than they otherwise might have been. As for the GPs, usually we here have some level of regulation which restricts their behaviour to some extent, and part of the motivation for such regulation is to reduce the level of induced demand which might otherwise be the result of information asymmetry in the context of stuff like relevant treatment effects. If a patient is not sufficiently competent to evaluate the treatments he receives (‘did the drug the doctor ordered really work, or would I have gotten better without it?’), there’s a risk he might be talked into undergoing needless procedures or take medications for which he has no need, especially if the doctor who advises him has a financial interest in the treatment modality on offer.

General physicians have different incentives from nurses and specialists working in hospitals, and all of these groups may experience conflicts of interests when they’re dealing with insurance providers and with each other. Patients as mentioned have their own set of incentives, which may not align perfectly with those of the health care providers. Different approaches to how to deal with such problems lead to different organizational setups, all of which influence both the quantity and quality of care, subject to various constraints. It’s an active area of research whether decreasing competition between stakeholders/service providers may decrease costs; one thing that is relatively clear from diabetes research with which I have familiarized myself is that when different types of care providers coordinate activities, this tends to lead to better outcomes (and sometimes, but not always, lower costs), because some of the externalized costs become internalized by virtue of the coordination. It seems very likely to me that conclusions to such questions will be different for different subsectors of the health care sector. A general point might be that more complex diseases should be expected to be more likely to generate cost savings from increased coordination than should relatively simple diseases (if you’re fuzzy about what the concept of disease complexity refers to, this post includes some relevant observations). This may be important, because complex diseases also should probably tend to be more expensive to treat in general, because the level of need in patients is higher.

It’s perhaps hardly surprising, considering the problems I’ve already discussed related to how difficult it may be to properly assess costs, that there’s a big discussion to be had about how to even estimate costs (and benefits) in specific contexts, and that people write books about these kinds of things. A lot of things have already been said on this topic and a lot more could be said, but one general point perhaps worth repeating is that it may in the health care sector be very difficult to figure out what things (‘truly’) cost (/’is worth’). If you only have a public sector entity dealing with a specific health problem and patients are not charged for receiving treatment, it may be very difficult to figure out what things ‘should’ cost because relevant prices are simply missing from the picture. You know what the government entity paid the doctors in wages and what it paid for the drugs, but the link between payment and value is sometimes a bit iffy here. There are ways to at least try to address some of these issues, but as already noted people write books about these kinds of things so I’m not going to provide all the highlights here – I refer to the previous posts I’ve written on these topics instead.

Another important related point is that medical expenditures and medical costs are not synonyms. There are many costs associated with illness which are not directly related to e.g. a payment to a doctor. People who are ill may be less productive while they are at work, they may have more sick-days, they may retire earlier, their spouse may cut down on work hours to take care of them instead of going to work, a family caretaker may become ill as a result of the demands imposed by the caretaker role (for example Alzheimer’s disease significantly increases the risk of depression in the spouse). Those costs are relevant, there are literatures on these things, and in some contexts such ‘indirect costs’ (e.g. lower productivity at work and early retirement) may make up a very substantial proportion of the total costs of a health condition. I have seen diabetes cost estimates which indicated that the indirect costs may account for as much as 50 % of the total costs.

If there’s a significant disconnect between total costs and medical expenditures then minimizing expenditures may not be desirable from an economic viewpoint. A reasonable assessment model will/should in the context of models of outlays include both a monetary cost parameter and a quality/quantity (ideally both) parameter; if you neglect to take account of the latter, in some sense you’re only dealing with what you pay out, not what you get for that payment (which is relevant). If you don’t take into account indirect costs you implicitly allow cost switching practices to potentially muddle the picture and make assessments more difficult; for example if you provide fewer long-term care facilities then the number of people involved in ‘informal care’ (e.g. family members having to take care of granny) will go up, and that is going to have secondary effects downstream which should also be assessed (you improve the budget in the context of the long-term care facilities, but you may at the same time increase demands on e.g. psychiatric institutions and marginally lower especially the female labour market participation rate. The net effect may still be positive, but the point is that an evaluation will/should include costs like these in the analysis, at least if you want anything remotely close to the full picture).

Let’s return to those smokers we talked about earlier. A general point not mentioned yet is that if you don’t cover smokers in the public sector because of cost considerations, many of them may also not be covered by private insurance either. This is because a group of individuals that is high risk and expensive to treat will be demanded high premiums (or the insurance providers would go out of business), and for the sake of this discussion we’re now assuming smokers are expensive. If that is so, many of them probably would not be able to afford the premiums demanded. Now, one of the health problems which are very common in smokers is chronic obstructive pulmonary disease (COPD). Admission rates for COPD patients differ as much as 10-fold between European countries, and one of the most important parameters regarding pharmacoeconomics is the hospitalization rate (both observations are from this text). What does this mean? It means that we know that admission rate from COPD is highly responsive to the treatment regime; populations well-treated have much fewer hospitalizations. 4% of all Polish hospitalizations are due to COPD. If you remove the public sector subsidies, the most likely scenario you get seems to me to be a poor-outcomes scenario with lots of hospitalizations. Paying for those is likely to be a lot more expensive than it is to treat the COPD pharmacologically in the community. And if smokers aren’t going to be paying for it, someone else will have to do that. If you both deny them health insurance and refuse them treatment if they cannot pay for it they may just die of course, but in most cost-assessment models that’s a high-cost outcome, not a low-cost outcome (e.g. due to lost work-life productivity etc. Half of people with COPD are of working age, see the text referred to above.). This is one example where the ‘more fair’ option might lead to higher costs, rather than lower costs. Some people might still consider such an outcome desirable, it depends on the maximand of interest, but such outcomes are worth considering when assessing the desirability of different systems.

A broadly similar dynamic, in the context of post-diagnosis behaviour and links to complications and costs, may be present in the context of type 2 diabetes. I know much more about diabetes than I do about respirology, but certainly in the case of diabetes this is a potentially really big problem. Diabetics who are poorly regulated tend to die a lot sooner than other people, they develop horrible complications, they stop being able to work, etc. etc. Some of those costs you can ignore if you’re willing to ‘let them die in the streets’ (as the expression goes), but a lot of those costs are indirect costs due to lower productivity, and those costs aren’t going anywhere, regardless of who may or may not be paying the medical bills of these people. Even if they have become sick due to a high-risk behaviour of their own choosing, their health care costs post-diagnosis will still be highly dependent upon their future medical care and future health insurance coverage. Denying them coverage for all diabetes-related costs post-diagnosis may, paradoxical though it may seem to some, not be the cost-minimizing option.

I already talked about information asymmetries. Another problematic aspect linked to information management also presents itself here in a model of this nature (‘deny all diabetes-related coverage to known diabetics’); people who suspect they might be having type 2 diabetes may choose not to disclose this fact to a health care provider because of the insurance aspect (denial of coverage problems). Insurance providers can of course (and will try to) counter this by things like mandatory screening protocols, but this is expensive, and even assuming they are successful you again not only potentially neglect to try to minimize the costs of the high-cost individuals in the population (the known diabetics, who might be cheaper long-term if they had some coverage), you also price a lot of non-diabetics out of the market (because premiums went up to pay for the screening). And some of those non-diabetics are diabetics to-be, who may get a delayed diagnosis as a result, with an associated higher risk of (expensive) complications. Again, as in the smoking context if the private insurer does not cover the high-cost outcomes someone else will have to do that, and the blind diabetic in a wheel-chair is not likely to be able to pay for his dialysis himself.

More information may in some situations lead to a breakdown in insurance markets. This is particularly relevant in the context of genetics and genetic tests. If you have full information, or close to it, the problem you have to some extent stops being an insurance problem and instead becomes a problem of whether or not to, and to which extent you want to-, explicitly compensate people for having been dealt a bad hand by nature. To put it in very general terms, insurance is a better framework for diseases which can in principle be cured than it is for chronic conditions where future outlays are known with a great level of certainty; the latter type of disease tends to be difficult to handle in an insurance context.

People who have one disease may develop other diseases as time progresses, and having disease X may increase or decrease the risk of disease Y. People study such disease variability patterns, and have done so for years, but there’s still a lot of stuff we don’t know – here’s a recent post on these topics. Such patterns are interesting for multiple reasons. One major motivation for studying these things is that ‘different’ diseases may have common mechanisms, and the identification of these mechanisms may lead to new treatment options. A completely different motivation for studying these things relate rather to the kind of stuff covered in this post, where you instead wonder about economic aspects; for example, if the smoker stops smoking he may gain weight and eventually develop type 2 diabetes instead of developing some smoking-related condition. Is this outcome better or worse than the other? It’s important to keep in mind when evaluating changes in compensation schedules/insurance structures that diseases are not independent, and this is a problem regardless of whether you’re interested in total costs or ‘just’ direct outlays. Say you’re ‘only’ worried about outlays and you are trying to figure out if it is a good idea to deny coverage to smokers, and you know that ex-smokers are likely to gain weight and have an increased risk of type 2 diabetes. Then the relevant change in cost is not the money you save on smoking-related illness, it’s the cost change you arrive at when after you account for those savings also account for the increased cost of treating type 2 diabetes. Disease interdependencies are probably as complex as risk factor interdependencies – the two phenomena are to some extent representing the same basic phenomenon – so this makes true cost evaluation even harder than it already was. Not all relevant costs at the societal level are of course medical costs; if people live longer, and they rely partly on a pension scheme to which they are no longer contributing, that cost is also relevant.

If a group of people who live longer cost more than a group of people who do not live as long, and you need to cover the associated shortfall, then – as we concluded in the beginning – there are really only two ways to handle this: Make them pay more than the people who do not live as long, or make the people who do not live as long pay more to cover the shortfall. Another way to look at this is that in this situation you can either tax people ‘for not living long enough’, or you can tax people for ‘not dying at the appropriate time’. On the other hand (?), if a group of people who die early turns out to be the higher-cost group in the relevant comparison (perhaps because they have shorter working lives and so pay into the system for a shorter amount of time), then you can deal with this problem by… either taxing them for ‘not living long enough’ or by punishing the people who live long lives for ‘not dying at the appropriate time’. No, of course it doesn’t matter which group is high cost, the solution mechanism is the same in both cases – make one of the groups pay more. And every time you tweak things you change the incentives of various people, and implicit effects like these hide somewhere in the background.

March 31, 2017 Posted by | Cancer/oncology, Diabetes, Economics, health care, rambling nonsense | 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, Diabetes, Economics, Epidemiology, History, Infectious disease, Mathematics, Medicine, Papers, Physics, Psychology, Random stuff, Wikipedia | 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, Neurology | Leave a comment

Diabetes and the Brain (III)

Some quotes from the book below.

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the Brain (II)

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the brain (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Integrated Diabetes Care (II)

Here’s my first post about the book. In this post I’ll provide some coverage of the last half of the text.

Some stuff from the chapters dealing with the UK:

“we now know that reducing the HbA1c too far and fast in some patients can be harmful [7]. This is a particularly important issue, where primary care is paid through the Quality Outcomes Framework (QoF), a general practice “pay for performance” programme [8]. A major item within QoF, is the proportion of patients below HbA1c criteria: such reporting is not linked to rates of hypoglycaemia, ambulance call outs or hospitalisation, i.e., a practice could receive a high payment through achieving the QoF target, but with a high hospitalisation/ambulance callout rate.”

“nationwide audit data for England 2009–2010 showed that […] targets for HbA1c (≤7.5%/58.5 mmol/mol), blood pressure (BP) (<140/80 mmHg) and total cholesterol (

One thing that is perhaps worth noting here before moving any further is that the fact that you have actual data on this stuff is in itself indicative of an at least reasonable standard of care, compared to many places; in a lot of countries you just don’t have data on this kind of stuff, and it seems highly unlikely to me that the default assumption should be that things are going great in places where you do not have data on this kind of thing. Denmark also, incidentally, has a similar audit system, the results of which I’ve discussed in some detail before here on the blog).

“Our local audit data shows that approximately 85–90 % of patients with diabetes are managed by GPs and practice nurses in Coventry and Warwickshire. Only a small proportion of newly diagnosed patients with T2D (typically around 5–10 %) who attend the DESMOND (Diabetes Education and Self-Management for Ongoing and Newly Diagnosed) education programme come into contact with some aspect of the specialist services [12]. […] Payment by results (PBR) has […] actively, albeit indirectly, disincentivised primary care to seek opinion from specialist services [13]. […] Large volumes of data are collected by various services ranging between primary care, local laboratory facilities, ambulance services, hospital clinics (of varying specialties), retinal screening services and several allied healthcare professionals. However, the majority of these systems are not unified and therefore result in duplication of data collection and lack of data utilisation beyond the purpose of collection. This can result in missed opportunities, delayed communication, inability to use electronic solutions (prompts, alerts, algorithms etc.), inefficient use of resources and patient fatigue (repeated testing but no apparent benefit). Thus, in the majority of the regions in England, the delivery of diabetes care is disjointed and lacks integration. Each service collects and utilises data for their own “narrow” purpose, which could be used in a holistic way […] Potential consequences of the introduction of multiple service providers are fragmentation of care, reductions in continuity of care and propagation of a reluctance to refer on to a more specialist service [9]. […] There are calls for more integration and less fragmentation in health-care [30], yet so far, the major integration projects in England have revealed negligible, if any, benefits [25, 32]. […] to provide high quality care and reduce the cost burden of diabetes, any integrated diabetes care models must prioritise prevention and early aggressive intervention over downstream interventions (secondary and tertiary prevention).”

“It is estimated that 99 % of diabetes care is self-management […] people with diabetes spend approximately only 3 h a year with healthcare professionals (versus 8757 h of self-management)” [this is a funny way of looking at things, which I’d never really considered before.]

“In a traditional model of diabetes care the rigid divide between primary and specialist care is exacerbated by the provision of funding. For example the tariff system used in England, to pay for activity in specialist care, can create incentives for one part of the system to “hold on” to patients who might be better treated elsewhere. This system was originally introduced to incentivise providers to increase elective activity and reduce waiting times. Whilst it has been effective for improving access to planned care, it is not so well suited to achieving the continuity of care needed to facilitate integrated care [37].”

“Currently in the UK there is a miss-match between what the healthcare policies require and what the workforce is actually being trained for. […]  For true integrated care in diabetes and the other long term condition specialties to work, the education and training needs for both general practitioners and hospital specialists need to be more closely aligned.”

The chapter on Germany (Baden-Württemberg):

“An analysis of the Robert Koch-Institute (RKI) from 2012 shows that more than 50 % of German people over 65 years suffer from at least one chronic disease, approximately 50 % suffer from two to four chronic diseases, and over a quarter suffer from five or more diseases [3]. […] Currently the public sector covers the majority (77 %) of health expenditures in Germany […] An estimated number of 56.3 million people are living with diabetes in Europe [16]. […]  The mean age of the T2DM-cohort [from Kinzigtal, Germany] in 2013 was 71.2 years and 53.5 % were women. In 2013 the top 5 co-morbidities of patients with T2DM were essential hypertension (78.3 %), dyslipidaemia (50.5 %), disorders of refraction and accommodation (38.2 %), back pain (33.8 %) and obesity (33.3 %). […] T2DM in Kinzigtal was associated with mean expenditure of 5,935.70 € per person in 2013 (not necessarily only for diabetes care ) including 40 % from inpatient stays, 24 % from drug prescriptions, 19 % from physician remuneration in ambulatory care and the rest from remedies and adjuvants (e.g., insulin pen systems, wheelchairs, physiotherapy, etc.), work incapacity or rehabilitation.”

-ll- Netherlands:

“Zhang et al. [10] […] reported that globally, 12 % of health expenditures […] per person were spent on diabetes in 2010. The expenditure varies by region, age group, gender, and country’s income level.”

“Over the years many approaches [have been] introduced to improve the quality and continuity of care for chronic diseases. […] the Dutch minister of health approved, in 2007, the introduction of bundled-care (known is the Netherlands as a ‘chain-of-care’) approach for integrated chronic care, with special attention to diabetes. […] With a bundled payment approach – or episode-based payment – multiple providers are reimbursed a single sum of money for all services related to an episode of care (e.g., hospitalisation, including a period of post-acute care). This is in contrast to a reimbursement for each individual service (fee-for-service), and it is expected that this will reduce the volume of services provided and consequently lead to a reduction in spending. Since in a fee-for-service system the reimbursement is directly related to the volume of services provided, there is little incentive to reduce unnecessary care. The bundled payment approach promotes [in theory… – US] a more efficient use of services [26] […] As far as efficiency […] is concerned, after 3 years of evaluation, several changes in care processes have been observed, including task substitution from GPs to practice nurses and increased coordination of care [31, 36], thus improving process costs. However, Elissen et al. [31] concluded that the evidence relating to changes in process and outcome indicators, remains open to doubt, and only modest improvements were shown in most indicators. […] Overall, while the Dutch approach to integrated care, using a bundled payment system with a mixed payer approach, has created a limited improvement in integration, there is no evidence that the approach has reduced morbidity and premature mortality: and it has come at an increased cost.”

-ll- Sweden:

“In 2013 Sweden spent the equivalent of 4,904 USD per capita on health [OECD average: 3,453 USD], with 84 % of the expenditure coming from public sources [OECD average: 73 %]. […] Similarly high proportions [of public spending] can be found in the Netherlands (88 %), Norway (85 %) and Denmark (84 %) [11]. […] Sweden’s quality registers, for tracking the quality of care that patients receive and the corresponding outcomes for several conditions, are among the most developed across the OECD [17]. Yet, the coordination of care for patients with complex needs is less good. Only one in six patients had contact with a physician or specialist nurse after discharge from hospital for stroke, again with substantial variation across counties. Fewer than half of patients with type 1 diabetes […] have their blood pressure adequately controlled, with a considerable variation (from 26 % to 68 %) across counties [17]. […] at 260 admissions per 100,000 people aged over 80, avoidable hospital admissions for uncontrolled diabetes in Sweden’s elderly population are the sixth highest in the OECD, and about 1.5 times higher than in Denmark.”

“Waiting times [in Sweden] have long been a cause of dissatisfaction [19]. In an OECD ranking of 2011, Sweden was rated second worst [20]. […] Sweden introduced a health-care guarantee in 2005 [guaranteeing fast access in some specific contexts]. […] Most patients who appeal under the health-care guarantee and [are] prioritised in the “queue” ha[ve] acute conditions rather than medical problems as a consequence of an underlying chronic disease. Patients waiting for a hip replacement or a cataract surgery are cured after surgery and no life-long follow-up is needed. When such patients are prioritised, the long-term care for patients with chronic diseases is “crowded out,” lowering their priority and risking worse outcomes. The health-care guarantee can therefore lead to longer intervals between checkups, with difficulties in accessing health care if their pre-existing condition has deteriorated.”

“Within each region / county council the care of patients with diabetes is divided. Patients with type 1 diabetes get their care at specialist clinics in hospitals and the majority of patients with type 2 diabetes in primary care . Patients with type 2 diabetes who have severe complications are referred to the Diabetes Clinics at the hospital. Approximately 10 % of all patients with type 2 continue their care at the hospital clinics. They are almost always on insulin in high doses often in combination with oral agents but despite massive medication many of these patients have difficulties to achieve metabolic balance. Patients with advanced complications such as foot ulcers, macroangiopathic manifestations and treatment with dialysis are also treated at the hospitals.”

Do keep in mind here that even if only 10% of type 2 patients are treated in a hospital setting, type 2 patients may still make up perhaps half or more of the diabetes patients treated in a hospital setting; type 2 prevalence is much, much higher than type 1 prevalence. Also, in view of such treatment- and referral patterns the default assumption when doing comparative subgroup analyses should always be that the outcomes of type 2 patients treated in a hospital setting should be expected to be much worse than the outcomes of type 2 patients treated in general practice; they’re in much poorer health than the diabetics treated in general practice, or they wouldn’t be treated in a hospital setting in the first place. A related point is that regardless of how great the hospitals are at treating the type 2 patients (maybe in some contexts there isn’t actually much of a difference in outcomes between these patients and type 2 patients treated in general practice, even though you’d expect there to be one?), that option will usually not be scalable. Also, it’s to be expected that these patients are more expensive than the default type 2 patient treated by his GP [and they definitely are: “Only if severe complications arise [in the context of a type 2 patient] is the care shifted to specialised clinics in hospitals. […] these patients have the most expensive care due to costly treatment of for example foot ulcers and renal insufficiency”]; again, they’re sicker and need more comprehensive care. They would need it even if they did not get it in a hospital setting, and there are costs associated with under-treatment as well.

“About 90 % of the children [with diabetes in Sweden] are classified as having Type 1 diabetes based on positive autoantibodies and a few percent receive a diagnosis of “Maturity Onset Diabetes of the Young” (MODY) [39]. Type 2 diabetes among children is very rare in Sweden.”

Lastly, some observations from the final chapter:

“The paradox that we are dealing with is that in spite of health professionals wanting the best for their patients on a patient by patient basis, the way that individuals and institutions are organised and paid, directly influences the clinical decisions that are made. […] Naturally, optimising personal care and the provider/purchaser-commissioner budget may be aligned, but this is where diabetes poses substantial problems from a health system point of view: The majority of adverse diabetes outcomes […] are many years in the future, so a system based on this year’s budget will often not prioritise the future […] Even for these adverse “diabetes” outcomes, other clinical factors contribute to the end result. […]  attribution to diabetes may not be so obvious to those seeking ways to minimise expenditure.”

[I incidentally tried to get this point across in a recent discussion on SSC, but I’m not actually sure the point was understood, presumably because I did not explain it sufficiently clearly or go into enough detail. It is my general impression, on a related note, that many people who would like to cut down on the sort of implicit public subsidization of unhealthy behaviours that most developed economies to some extent engage in these days do not understand well enough the sort of problems that e.g. the various attribution problems and how to optimize ‘post-diagnosis care’ (even if what you want to optimize is the cost minimization function…) cause in specific contexts. As I hope my comments indicate in that thread, I don’t think these sorts of issues can be ignored or dealt with in some very simple manner – and I’m tempted to say that if you think they can, you don’t know enough about these topics. I say that as one of those people who would like people who engage in risky behaviours to pay a larger (health) risk premium than they currently do].

[Continued from above, …problems from a health system point of view:]
“Payment for ambulatory diabetes care , which is essentially the preventative part of diabetes care, usually sits in a different budget to the inpatient budget where the big expenses are. […] good evidence for reducing hospitalisation through diabetes integrated care is limited […] There is ample evidence [11, 12] where clinicians own, and profit from, other services (e.g., laboratory, radiology), that referral rates are increased, often inappropriately […] Under the English NHS, the converse exists, where GPs, either holding health budgets, or receiving payments for maintaining health budgets [13], reduce their referrals to more specialist care. While this may be appropriate in many cases, it may result in delays and avoidance of referrals, even when specialist care is likely to be of benefit. [this would be the under-treatment I was talking about above…] […] There is a mantra that fragmentation of care and reductions in continuity of care are likely to harm the quality of care [14], but hard evidence is difficult to obtain.”

“The problems outlined above, suggest that any health system that fails to take account of the need to integrate the payment system from both an immediate and long term perspective, must be at greater risk of their diabetes integration attempts failing and/or being unsustainable. […] There are clearly a number of common factors and several that differ between successful and less successful models. […] Success in these models is usually described in terms of hospitalisation (including, e.g., DKA, amputation, cardiovascular disease events, hypoglycaemia, eye disease, renal disease, all cause), metabolic outcomes (e.g., HbA1c ), health costs and access to complex care. Some have described patient related outcomes, quality of life and other staff satisfaction, but the methodology and biases have often not been open to scrutiny. There are some methodological issues that suggest that many of those with positive results may be illusory and reflect the pre-existing landscape and/or wider changes, particular to that locality. […] The reported “success” of intermediate diabetes clinics run by English General Practitioners with a Special Interest led to extension of the model to other areas. This was finally tested in a randomised controlled trial […] and shown to be a more costly model with no real benefit for patients or the system. Similarly in East Cambs and Fenland, the 1 year results suggested major reductions in hospitalisation and costs in practices participating fully in the integrated care initiative, compared with those who “engaged” later [9]. However, once the trends in neighbouring areas and among those without diabetes were accounted for, it became clear that the benefits originally reported were actually due to wider hospitalisation reductions, not just in those with diabetes. Studies of hospitalisation /hospital costs that do not compare with rates in the non-diabetic population need to be interpreted with caution.”

“Kaiser Permanente is often described as a great diabetes success story in the USA due to its higher than peer levels of, e.g., HbA1c testing [23]. However, in the 2015 HEDIS data, levels of testing, metabolic control achieved and complication rates show quality metrics lower than the English NHS, in spite of the problems with the latter [23]. Furthermore, HbA1c rates above 9 % remain at approximately 20 %, in Southern California [24] or 19 % in Northern California [25], a level much higher than that in the UK […] Similarly, the Super Six model […] has been lauded as a success, as a result of reductions in patients with, e.g., amputations. However, these complications were in the bottom quartile of performance for these outcomes in England [26] and hence improvement would be expected with the additional diabetes resources invested into the area. Amputation rates remain higher than the national average […] Studies showing improvement from a low baseline do not necessarily provide a best practice model, but perhaps a change from a system that required improvement. […] Several projects report improvements in HbA1c […] improvements in HbA1c, without reports of hypoglycaemia rates and weight gain, may be associated with worse outcomes as suggested from the ACCORD trial [28].”

December 18, 2016 Posted by | Books, Diabetes, Economics, Epidemiology, Medicine | Leave a comment

The Ageing Immune System and Health (I)

as we age, we observe a greater heterogeneity of ability and health. The variation in, say, walking speed is far greater in a group of 70 year olds, than in a group on 20 year olds. This makes the study of ageing and the factors driving that heterogeneity of health and functional ability in old age vital. […] The study of the immune system across the lifespan has demonstrated that as we age the immune system undergoes a decline in function, termed immunosenescence. […] the decline in function is not universal across all aspects of the immune system, and neither is the magnitude of functional loss similar between individuals. The theory of inflammageing, which represents a chronic low grade inflammatory state in older people, has been described as a major consequence of immunosenescence, though lifestyle factors such as reduced physical activity and increased adiposity also play a major role […] In poor health, older people accumulate disease, described as multimorbidity. This in turn means traditional single system based health care becomes less valid as each system affected by disease impacts on other systems. This leads some older people to be at greater risk of adverse events such as disability and death. The syndrome of this increased vulnerability is described as frailty, and increasing fundamental evidence is emerging that suggests immunosenescence and inflammageing may underpin frailty […] Thus frailty is seen as one clinical manifestation of immunosenescence.”

The above quotes are from the book‘s preface. I gave it 3 stars on goodreads. I should probably, considering that this topic is mentioned in the preface, mention explicitly that the book doesn’t actually go into a lot of details about the downsides of ‘traditional single system based health care’; the book is mainly about immunology and related topics, and although it provides coverage of intervention studies etc., it doesn’t really provide detailed coverage about issues like the optimization of organizational structures/systems analysis etc.. The book I was currently reading while I started out writing this post – Integrated Diabetes Care – A Multidisciplinary Approach (blog coverage here) – is incidentally pretty much exclusively devoted to providing coverage of these sorts of topics (and it did a fine job).

If you have never read any sort of immunology text before the book will probably be unreadable to you – “It is aimed at fundamental scientists and clinicians with an interest in ageing or the immune system.” In my coverage below I have not made any efforts towards picking out quotes which would be particularly easy for the average reader to read and understand; this is another way of saying that the post is mainly written for my own benefit, perhaps even more so than is usually the case, not for the benefit of potential readers reading along here.

“Physiological ageing is associated with significant re-modelling of the immune system. Termed immunosenescence, age-related changes have been described in the composition, phenotype and function of both the innate and adaptive arms of the immune system. […] Neutrophils are the most abundant leukocyte in circulation […] The first step in neutrophil anti-microbial defence is their extravasation from the bloodstream and migration to the site of infection. Whilst age appears to have no effect upon the speed at which neutrophils migrate towards chemotactic signals in vitro [15], the directional accuracy of neutrophil migration to inflammatory agonists […] as well as bacterial peptides […] is significantly reduced [15]. […] neutrophils from older adults clearly exhibit defects in several key defensive mechanisms, namely chemotaxis […], phagocytosis of opsonised pathogens […] and NET formation […]. Given this near global impairment in neutrophil function, alterations to a generic signalling element rather than defects in molecules specific to each anti-microbial defence strategy is likely to explain the aberrations in neutrophil function that occur with age. In support of this idea, ageing in rodents is associated with a significant increase in neutrophil membrane fluidity, which coincides with a marked reduction in neutrophil function […] ageing results in a reduction in NK cell production and proliferation […] Numerous studies have examined the impact of age […], with the general consensus that at the single cell level, NK cell cytotoxicity (NKCC) is reduced with age […] retrospective and prospective studies have reported relationships between low NK cell activity in older adults and (1) a past history of severe infection, (2) an increased risk of future infection, (3) a reduced probability of surviving infectious episodes and (4) infectious morbidity [49–51]. Related to this increased risk of infection, reduced NKCC prior to and following influenza vaccination in older adults has been shown to be associated with reduced protective anti-hemagglutinin titres, worsened health status and an increased incidence of respiratory tract infection […] Whilst age has no effect upon the frequency or absolute number of monocytes [54, 55], the composition of the monocyte pool is markedly different in older adults, who present with an increased frequency of non-classical and intermediate monocytes, and fewer classical monocytes when compared to their younger counterparts”.

“Via their secretion of growth factors, pro-inflammatory cytokines, and proteases, senescent cells compromise tissue homeostasis and function, and their presence has been causally implicated in the development of such age-associated conditions as sarcopenia and cataracts [92]. Several studies have demonstrated a role for innate immune cells in the recognition and clearance of senescent cells […] ageing is associated with a low-grade systemic up-regulation of circulating inflammatory mediators […] Results from longitudinal-based studies suggest inflammageing is deleterious to human health with studies in older cohorts demonstrating that low-grade increases in the circulating levels of TNF-α [103], IL-6 […] and CRP [105] are associated with both all-cause […] and cause-specific […] mortality. Furthermore, inflammageing is a predictor of frailty [106] and is considered a major factor in the development of several age-related pathologies, such as atherosclerosis [107], Alzheimer’s disease [100] and sarcopenia [108].”

“Persistent viral infections, reduced vaccination responses, increased autoimmunity, and a rise in inflammatory syndromes all typify immune ageing. […] These changes can be in part attributed to the accumulation of highly differentiated senescent T cells, characterised by their decreased proliferative capacity and the activation of senescence signaling pathways, together with alterations in the functional competence of regulatory cells, allowing inflammation to go unchecked. […] Immune senescence results from defects in different leukocyte populations, however the dysfunction is most profound in T cells [6, 7]. The responses of T cells from aged individuals are typically slower and of a lower magnitude than those of young individuals […] while not all equally affected by age, the overall T cell number does decline dramatically as a result of thymic atrophy […] T cell differentiation is a highly complex process controlled not only by costimulation but also by the strength and duration of T cell receptor (TCR) signalling [34]. Nearly all TCR signalling pathways have been found altered during ageing […] two phenotypically distinct subsets of B cells […] have been demonstrated to exert immunosuppressive functions. The frequency and function of both these Breg subsets declines with age”.

“The immune impairments in patients with chronic hyperglycemia resemble those seen during ageing, namely poor control of infections and reduced vaccination response [99].” [This is hardly surprising. ‘Hyperglycemia -> accelerated ageing’ seems generally to be a good (over-)simplified model in many contexts. To give another illustrative example from Czernik & Fowlkes text, “approximately 4–6 years of diabetes exposure in some children may be sufficient to increase skin AGEs to levels that would naturally accumulate only after ~25 years of chronological aging”].

“The term “immunosenescence” is commonly taken to mean age-associated changes in immune parameters hypothesized to contribute to increased susceptibility and severity of the older adult to infectious disease, autoimmunity and cancer. In humans, it is characterized by lower numbers and frequencies of naïve T and B cells and higher numbers and frequencies of late-differentiated T cells, especially CD8+ T cells, in the peripheral blood. […] Low numbers of naïve cells render the aged highly susceptible to pathogens to which they have not been previously exposed, but are not otherwise associated with an “immune risk profile” predicting earlier mortality. […] many of the changes, or most often, differences, in immune parameters of the older adult relative to the young have not actually been shown to be detrimental. The realization that compensatory changes may be developing over time is gaining ground […] Several studies have now shown that lower percentages and absolute numbers of naïve CD8+ T cells are seen in all older subjects whereas the accumulation of very large numbers of CD8+ late-stage differentiated memory cells is seen in a majority but not in all older adults [2]. The major difference between this majority of subjects with such accumulations of memory cells and those without is that the former are infected with human herpesvirus 5 (Cytomegalovirus, CMV). Nevertheless, the question of whether CMV is associated with immunosenescence remains so far uncertain as no causal relationship has been unequivocally established [5]. Because changes are seen rapidly after primary infection in transplant patients [6] and infants [7], it is highly likely that CMV does drive the accumulation of CD8+ late-stage memory cells, but the relationship of this to senescence remains unclear. […] In CMV-seropositive people, especially older people, a remarkably high fraction of circulating CD8+ T lymphocytes is often found to be specific for CMV. However, although the proportion of naïve CD8+ T cells is lower in the old than the young whether or not they are CMV-infected, the gross accumulation of late-stage differentiated CD8+ T cells only occurs in CMV-seropositive individuals […] It is not clear whether this is adaptive or pathological […] The total CMV-specific T-cell response in seropositive subjects constitutes on average approximately 10 % of both the CD4+ and CD8+ memory compartments, and can be far greater in older people. […] there are some published data suggesting that that in young humans or young mice, CMV may improve immune responses to some antigens and to influenza virus, probably by way of increased pro-inflammatory responses […] observations suggest that the effect of CMV on the immune system may be highly dependent also on an individuals’ age and circumstances, and that what is viewed as ageing is in fact later collateral damage from immune reactivity that was beneficial in earlier life [47, 48]. This is saying nothing more than that the same immune pathology that always accompanies immune responses to acute viruses is also caused by CMV, but over a chronic time scale and usually subclinical. […] data suggest that the remodeling of the T-cell compartment in the presence of a latent infection with CMV represents a crucial adaptation of the immune system towards the chronic challenge of lifelong CMV.”

The authors take issue with using the term ‘senescence’ to describe some of the changes discussed above, because this term by definition should be employed only in the context of changes that are demonstrably deleterious to health. It should be kept in mind in this context that insufficient immunological protection against CMV in old age could easily be much worse than the secondary inflammatory effects, harmful though these may well be; CMV in the context of AIDS, organ transplantation (“CMV is the most common and single most important viral infection in solid organ transplant recipients” – medscape) and other disease states involving compromised immune systems can be really bad news (“Disease caused by human herpesviruses tends to be relatively mild and self-limited in immunocompetent persons, although severe and quite unusual disease can be seen with immunosuppression.” Holmes et al.)

“The role of CMV in the etiology of […] age-associated diseases is currently under intensive investigation […] in one powerful study, the impact of CMV infection on mortality was investigated in a cohort of 511 individuals aged at least 65 years at entry, who were then followed up for 18 years. Infection with CMV was associated with an increased mortality rate in healthy older individuals due to an excess of vascular deaths. It was estimated that those elderly who were CMV- seropositive at the beginning of the study had a near 4-year reduction in lifespan compared to those who were CMV-seronegative, a striking result with major implications for public health [59]. Other data, such as those from the large US NHANES-III survey, have shown that CMV seropositivity together with higher than median levels of the inflammatory marker CRP correlate with a significantly lower 10-year survival rate of individuals who were mostly middle-aged at the start of the study [63]. Further evidence comes from a recently published Newcastle 85+ study of the immune parameters of 751 octogenarians investigated for their power to predict survival during a 65-month follow-up. It was documented that CMV-seropositivity was associated with increased 6-year cardiovascular mortality or death from stroke and myocardial infarction. It was therefore concluded that CMV-seropositivity is linked to a higher incidence of coronary heart disease in octogenarians and that senescence in both the CD4+ and CD8+ T-cell compartments is a predictor of overall cardiovascular mortality”.

“The incidence and severity of many infections are increased in older adults. Influenza causes approximately 36,000 deaths and more than 100,000 hospitalizations in the USA every year […] Vaccine uptake differs tremendously between European countries with more than 70 % of the older population being vaccinated against influenza in The Netherlands and the United Kingdom, but below 10 % in Poland, Latvia and Estonia during the 2012–2013 season […] several systematic reviews and meta-analyses have estimated the clinical efficacy and/or effectiveness of a given influenza vaccine, taking into consideration not only randomized trials, but also cohort and case-control studies. It can be concluded that protection is lower in the old than in young adults […] [in one study including “[m]ore than 84,000 pneumococcal vaccine-naïve persons above 65 years of age”] the effect of age on vaccine efficacy was studied and the statistical model showed a decline of vaccine efficacy for vaccine-type CAP and IPD [Invasive Pneumococcal Disease] from 65 % (95 % CI 38–81) in 65-year old subjects, to 40 % (95 % CI 17–56) in 75-year old subjects […] The most effective measure to prevent infectious disease is vaccination. […] Over the last 20–30 years tremendous progress has been achieved in developing novel/improved vaccines for children, but a lot of work still needs to be done to optimize vaccines for the elderly.”

December 12, 2016 Posted by | Books, Cardiology, Diabetes, Epidemiology, Immunology, Infectious disease, Medicine, Microbiology | Leave a comment

Integrated Diabetes Care (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Role of Biomarkers in Medicine

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

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

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

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

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

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

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

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

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

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

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

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

Diabetic nephropathies

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Diabetes and the Metabolic Syndrome in Mental Health (II)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Random Stuff

i. On the youtube channel of the Institute for Advanced Studies there has been a lot of activity over the last week or two (far more than 100 new lectures have been uploaded, and it seems new uploads are still being added at this point), and I’ve been watching a few of the recently uploaded astrophysics lectures. They’re quite technical, but you can watch them and follow enough of the content to have an enjoyable time despite not understanding everything:


This is a good lecture, very interesting. One major point made early on: “the take-away message is that the most common planet in the galaxy, at least at shorter periods, are planets for which there is no analogue in the solar system. The most common kind of planet in the galaxy is a planet with a radius of two Earth radii.” Another big take-away message is that small planets seem to be quite common (as noted in the conclusions, “16% of Sun-like stars have an Earth-sized planet”).


Of the lectures included in this post this was the one I liked the least; there are too many (‘obstructive’) questions/interactions between lecturer and attendants along the way, and the interactions/questions are difficult to hear/understand. If you consider watching both this lecture and the lecture below, I would say that it would probably be wise to watch the lecture below this one before you watch this one; I concluded that in retrospect some of the observations made early on in the lecture below would have been useful to know about before watching this lecture. (The first half of the lecture below was incidentally to me somewhat easier to follow than was the second half, but especially the first half hour of it is really quite good, despite the bad start (which one can always blame on Microsoft…)).

ii. Words I’ve encountered recently (…or ‘recently’ – it’s been a while since I last posted one of these lists): Divagationsperiphrasis, reedy, architravesettpedipalp, tout, togs, edentulous, moue, tatty, tearaway, prorogue, piscine, fillip, sop, panniers, auxology, roister, prepossessing, cantle, catamite, couth, ordure, biddy, recrudescence, parvenu, scupper, husting, hackle, expatiate, affray, tatterdemalion, eructation, coppice, dekko, scull, fulmination, pollarding, grotty, secateurs, bumf (I must admit that I like this word – it seems fitting, somehow, to use that word for this concept…), durophagy, randy, (brief note to self: Advise people having children who ask me about suggestions for how to name them against using this name (or variants such as Randi), it does not seem like a great idea), effete, apricity, sororal, bint, coition, abaft, eaves, gadabout, lugubriously, retroussé, landlubber, deliquescence, antimacassar, inanition.

iii. “The point of rigour is not to destroy all intuition; instead, it should be used to destroy bad intuition while clarifying and elevating good intuition. It is only with a combination of both rigorous formalism and good intuition that one can tackle complex mathematical problems; one needs the former to correctly deal with the fine details, and the latter to correctly deal with the big picture. Without one or the other, you will spend a lot of time blundering around in the dark (which can be instructive, but is highly inefficient). So once you are fully comfortable with rigorous mathematical thinking, you should revisit your intuitions on the subject and use your new thinking skills to test and refine these intuitions rather than discard them. One way to do this is to ask yourself dumb questions; another is to relearn your field.” (Terry Tao, There’s more to mathematics than rigour and proofs)

iv. A century of trends in adult human height. A figure from the paper (Figure 3 – Change in adult height between the 1896 and 1996 birth cohorts):

elife-13410-fig3-v1

(Click to view full size. WordPress seems to have changed the way you add images to a blog post – if this one is even so annoyingly large, I apologize, I have tried to minimize it while still retaining detail, but the original file is huge). An observation from the paper:

“Men were taller than women in every country, on average by ~11 cm in the 1896 birth cohort and ~12 cm in the 1996 birth cohort […]. In the 1896 birth cohort, the male-female height gap in countries where average height was low was slightly larger than in taller nations. In other words, at the turn of the 20th century, men seem to have had a relative advantage over women in undernourished compared to better-nourished populations.”

I haven’t studied the paper in any detail but intend to do so at a later point in time.

v. I found this paper, on Exercise and Glucose Metabolism in Persons with Diabetes Mellitus, interesting in part because I’ve been very surprised a few times by offhand online statements made by diabetic athletes, who had observed that their blood glucose really didn’t drop all that fast during exercise. Rapid and annoyingly large drops in blood glucose during exercise have been a really consistent feature of my own life with diabetes during adulthood. It seems that there may be big inter-individual differences in terms of the effects of exercise on glucose in diabetics. From the paper:

“Typically, prolonged moderate-intensity aerobic exercise (i.e., 30–70% of one’s VO2max) causes a reduction in glucose concentrations because of a failure in circulating insulin levels to decrease at the onset of exercise.12 During this type of physical activity, glucose utilization may be as high as 1.5 g/min in adolescents with type 1 diabetes13 and exceed 2.0 g/min in adults with type 1 diabetes,14 an amount that quickly lowers circulating glucose levels. Persons with type 1 diabetes have large interindividual differences in blood glucose responses to exercise, although some intraindividual reproducibility exists.15 The wide ranging glycemic responses among individuals appears to be related to differences in pre-exercise blood glucose concentrations, the level of circulating counterregulatory hormones and the type/duration of the activity.2

August 13, 2016 Posted by | Astronomy, Demographics, Diabetes, language, Lectures, Mathematics, Physics, Random stuff | Leave a comment

Diabetes and the Metabolic Syndrome in Mental Health (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Eating disorders… (I)

“Dermatologists have an important role in the early diagnosis of eating disorders since skin signs are, at times, the only easily detectable symptoms of hidden anorexia and bulimia nervosa. Forty cutaneous signs have been recognized”

The full title of the book is Eating Disorders and the Skin, but there’s a lot of stuff about eating disorders in general in this book as well, and I figured I’d mostly focus on the ‘general stuff’ in this post. Here’s my goodreads review of the book, which I gave 3 stars.

Here are the DSM-IV-TR diagnostic criteria for anorexia nervosa:

“1. Refusal to maintain body weight at or above a minimally normal weight for age and height (e.g., weight loss leading to maintenance of body weight less than 85% of that expected, or failure to make expected weight gain during period of growth, leading to body weight less than 85% of that expected).

2. Intense fear of gaining weight or becoming fat even though underweight.

3. Disturbance in the way in which one’s body weight or shape is experienced, undue influence of body weight or shape on self-evaluation, or denial of the seriousness of the current low body weight.

4.4. In postmenarcheal females, amenorrhea, i.e., the absence of at least three consecutive menstrual cycles.”

Interestingly, aside from anorexia [-AN] and bulimia [-BN] (diagnostic criteria here), there’s also a big category called ED-NOS – Eating Disorder Not Otherwise Specified. That’s for cases that don’t really fit into the standard criteria for specific eating disorders; they note than an example of this type could be a female fitting all diagnostic criteria for AN except that she has regular menses. It is perhaps worth mentioning here that surprisingly enough (…to me), menstrual irregularities are not limited to cases of AN, thus: “In almost 50% of bulimic patients, menstrual irregularities, such as oligomenorrhea or amenorrhea, take place”. They note in the book that there’s been some concern about the validity of the ED-NOS category, which makes up almost 60% of patients with an eating disorder. Eating disorders are much more common in females than in males (“Males are generally reported to account for 5–10% of anorectics and 10–15% of bulimics identified in the general population”), and particular subgroups mentioned to be at high risk are athletes, models and dancers. It’s noted in the book that most epidemiological studies are conducted in high-risk settings, whereas epidemiological studies assessing risk in the general population are somewhat rarer. One problem complicating matters a little in terms of estimating risk is that an eating disorder cannot be diagnosed through a self-report questionnaire; you need a structured or semi-structured interview to make a diagnosis, which makes things more expensive. As in other contexts one way to get around this issue, at least to some extent, is to employ multi-step screening protocols – in this case a two-step procedure in which individuals at high risk are identified at the first step through inexpensive means, and these individuals are then later assessed more carefully in the second step, employing more accurate (and expensive) methods.

They note that in Western countries, point prevalence of AN in female adolescent (the highest risk sub-group) is estimated at 0.2-1% of the population, whereas the prevalence studies on bulimia nervosa indicates that this eating disorder is somewhat more common, with the majority of studies finding prevalences of 1.5-5%; do recall again that most studies as already mentioned look at high-risk subgroups, so total population prevalence is likely to be lower than this. They observe in the book that general-practice studies find that the incidence of anorexia nervosa is less than one in ten-thousand per year (8 per 100,000 per year); so full-blown AN certainly is likely quite rare in low-risk populations.

On lifetime risk, the book notes that:

“Most of the epidemiological studies on ED [eating disorders] have evaluated the prevalence of full syndromes of both AN [anorexia nervosa] and BN [bulimia nervosa]. The few studies that have evaluated partial or subclinical manifestations of EDs in young females, however, found lifetime prevalence rates of 5–12% for atypical AN and 1–4.8% for atypical BN and up to 14.6% in adolescent samples”.

A review of epidemiological studies concluded that there’s no evidence of either a secular increase in AN or BN over time; to the extent that the number of people with diagnosed BN has increased over time, changes in diagnostic and referral practices likely account for this. On a related topic it is noted in the book that “It is a common idea among clinicians that early-onset cases of anorexia nervosa (AN) are increasing, but few data in the literature are available to demonstrate this trend.”

AN most commonly present among females at the age of 15-19, whereas BN presents a little later, most commonly at the age of 20-24. But eating disorders are not limited to teenagers and young adults: “Even if anorexia nervosa and bulimia nervosa occur characteristically in females during adolescence and young adulthood, there have been case reports of illness beginning after the age of 25 and even after the menopause, and some authors suggest that the rates of eating disorders in older patients may be increasing [2]. Clinical impression suggests that the late-onset cases present with more depressive features than the adolescent counterpart. […] dieting is considered one of the most salient precipitating factors.”

Self-report metrics can only help you so much when you’re trying to assess risk; a major problem in this context is that denial of illness is a very common feature in these patient populations (so you certainly can’t just ask people if their relationship with food/exercise etc. might be unhealthy…): “typically, [the] course [of an eating disorder] is characterized by a high fluidity between the diagnostic classes; furthermore, the patient often denies even to himself the psychiatric nature of the disease” (recall also that “denial of the seriousness of the current low body weight” is included in the diagnostic criteria). The book covers a lot of symptoms which relate to low body weight – like cold intolerance, bradycardia (slow heart rate), acrocyanosis (bluish discoloration of the hands and feet, caused by slow circulation), systemic hypotension (low blood pressure), lots of skin signs (I haven’t decided yet how much detail I’ll go into, so let’s leave it at that now) – or e.g. to purging behaviours (throat and tooth pain due to vomiting and enamel erosion), but it would go much too far to discuss all these in detail here. One to me interesting aspect of the coverage was that whereas BMI is a useful sign, it’s not itself a diagnostic criterion; the authors note that a BMI below 18.5 is considered pathological, but when listing main signs of anorexia nervosa the most important diagnostic sign (or at least the first one listed) is a BMI below 17.5; I assume part of the discussion surrounding the validity of the ED-NOS category probably relate to individuals who’re in this ‘border area’; they likely have some symptoms due to low body mass (like e.g. cold intolerance), but they don’t have full-blown AN (there are a lot of things that can go wrong when you have low body mass – there are a lot of symptoms described in this book!). It’s also important to note that very different symptom patterns can be present at similar levels of BMI, as the severity of symptoms also relate to how fast body mass decreases – the body is actually capable of adjusting quite well to lower energy intake states (in the short run at least), and so “if weight loss is gradual, it is possible to maintain, even for a long time, an apparent metabolic equilibrium.”

Anorexics have high mortality rates: “From a meta-analysis of 119 studies involving 5,590 patients, Steinhausen reported a crude mortality rate of 5% which exceeded 9% in a followup of 10 years.” Remember when thinking about those estimates that most of the people in these studies were likely young women – these numbers are high, and the authors note that anorexia nervosa “represents the major cause of death of young women in the age between 12 and 25 years.”

Most deaths are due to ventricular arrhythmia; the book goes into some detail about how anorexia affects the cardiovascular system, but I won’t discuss this in detail. An important observation is that: “Cardiac findings tend to disappear with weight recovery.” I assume this comment relates mostly to findings like QTc prolongation, QTc dispersion, and mitral valve prolapse, all of which are found in anorexics, whereas I’d be surprised if cardiac abnormalities related to direct damage to the heart muscle resolve themselves after weight gain, but the book does not go into details on this topic, except in the sense that it is noted that heart failure is uncommon in anorexics. Among those who survive their illness, osteoporosis is a major irreversible long-term problem. People with higher body mass tend to have a higher bone mineral density and thus a lower risk of osteoporosis (unless they get type 2 diabetes, in which case the situation is, well, complicated), so perhaps it’s not really surprising that women with AN and very low body mass index tend to develop osteoporosis. They certainly do:

“Osteopenia and osteoporosis represent one of the most relevant and potentially not reversible complications of eating disorders. This complication is particularly severe when eating disorders have an early onset […] Bone loss is an early effect of the disease, already present after 6–12 months […] In untreated patients, bone loss ranges from 4% up to 10% per year […]. In case of recovery, the progressive loss of BMD [bone mineral density] stops, but in most cases, a normal bone mass is not restored [64].”

It’s noted that bone loss is due to both hormonal and metabolic factors; estrogen plays a role, and “BMD loss in AN is more rapid and severe than in other hypoestrogenic conditions”. Despite this observation weight gain is considered the primary treatment modality of osteoporosis in this context (i.e., not estrogen therapy), and research using estrogen therapy to try to boost bone mineral density in anorexics who did not also gain weight has not been successful.

A to me interesting aspect of the coverage which I could not help but discuss here is how eating disorders relate to diabetes; the book has a few remarks on this topic:

“The concurrence of an eating disorder with insulin-dependent diabetes has been outlined by several researchers: especially bulimia nervosa and disorder not otherwise specified (EDNOS) are reported to be significantly higher in females with type 1 diabetes […] In case of comorbidity, ED onset followed the diagnosis of IDDM in 70% of the patients [10]. Specific aspects of diabetes and its management could, in fact, potentially increase a particular susceptibility to the development of an eating disorder: weight gain, associated with initiation of insulin treatment and dietary restraint, might, in fact, trigger body dissatisfaction and the drive for thinness with consequent weight control behaviors ranging from healthy to very unhealthy behaviors […] insulin omission [is] a common weight loss behavior in girls with IDDM and eating disorder […] APA Guidelines 2006 suggest that insulin omission should be considered a specific type of purging behavior in the next DSM revision”.

I don’t know if this suggested change has been implemented at this point, but it would make a lot of sense. To people who don’t know what this ‘insulin omission’ they talk about is all about, the short version is that if you’re a type 1 diabetic in need of regular insulin injections, if you don’t take enough insulin you lose weight and you can eat pretty much whatever you like without gaining weight; which is of course an unfortunate though likely very attractive option for young women to have. The downside of engaging in systematic insulin omission behaviour of that kind is that you’ll likely go blind from your diabetes and/or die of kidney failure or DKA if you do that for an extended period of time.

January 2, 2016 Posted by | Books, Diabetes, Epidemiology, Medicine, Psychology | 4 Comments

Diabetic Bone Disease

This is an excellent book. I decided to include in this post the entire book description included on goodreads, even if it’s somewhat long, because I thought the description gave a good overview of the topics covered in this book:

“Providing the most up-to-date research and current clinical knowledge of diabetic bone disease and the challenges still facing the research and clinical care communities, this book unites insights from endocrinology and orthopedics to create a truly unique text. The first part covers clinical and pre-clinical applications and research. The first two chapters present the clinical and epidemiological data about diabetic bone disease, evaluated and reviewed for type 1 and type 2, respectively. This is followed by discussions of how the propensity to fracture in diabetic bone disease can impact fracture risk assessments and how it can be adjusted for using current clinically relevant fracture risk models. A comprehensive overview of orthopedic complications observed in diabetes is next, as well as a focus on the consequences of diabetes on periodontal disease. Other topics include the utility of skeletal biomarkers in assessing diabetic bone disease, how drugs used to treat diabetes may also have skeletal consequences, and the possibility that diabetes may fundamentally impact early progenitor cells of various bone lineages and thus globally impact bone. The second part covers biomechanics and bone quality in diabetes: how diabetes ultimately may impact the architecture, integrity, and quality of bone. Utilizing the expertise of top researcher and clinicians in diabetic bone disease in one comprehensive text, this volume will be a useful and thought-provoking resource for endocrinologists and orthopedic surgeons alike.”

I would note that the book is also a useful and thought-provoking resource if you’re just a random diabetic who happens to know enough about medicine and related topics to make sense of a book like this one – i.e. if you’re someone like me. A few related observations from the book’s preface:

“Historically, most attention has been focused on four major complications known to afflict many individuals with T1DM and T2DM: retinopathy, neuropathy, nephropathy, and cardiovascular disease. However, epidemiological data now show that other tissues and organs may be significantly impacted by the diabetic state—and the skeletal system is now emerging as a primary target of diabetes-mediated damage (i.e., diabetic bone disease).
Studies have demonstrated that osteopenia and osteoporosis may be frequent complications of T1D, both in children and adults, and that T1D is associated with decreased bone density and increased fracture risk. In contrast to T1D, T2D has typically not been associated with osteopenia or osteoporosis and, in fact, has been more often associated with increased BMD [bone mineral density]. However, newer data show that bone quality and bone microarchitecture may be compromised in both conditions, suggesting that underlying mechanisms related to increased risk to fracture may be contributory to both forms of diabetes.
In this volume, we provide the reader with up-to-date information about what is currently known about diabetic bone disease and what are the challenges still facing the research and clinical care communities.”

This was a topic about which I knew next to nothing, and one of emotional responses I had early on to some of the coverage in the book was to think along the lines of: ‘Ah, type 1 diabetes, the gift that keeps on giving…’ or perhaps: ‘How was I not told this???’ It reminded me a bit of how I felt back when I realized some years ago that my diabetes was probably also messing with my lungs, without me knowing about it and despite nobody having told me anything about that (for details on that topic, see e.g. this paper). As far as I can remember, bone health has never come up during conversations I have had over the years in the past with endocrinologists or diabetes nurses, nor has it ever been discussed in detail in publications I’ve read on diabetes-related topics; the closest I’ve got has probably been remarks about individuals developing diabetics during childhood being slightly shorter than non-diabetics on average, due to (non-specific) disease-related adverse effects on growth during childhood. Relevant mechanisms have not been discussed in any detail, and actually what I had read on the topic of diabetes and growth had basically lead me to believe that a slight growth disadvantage was really all there was to this topic, as a potential interaction between diabetes status and osteoporosis risk was never touched upon in these publications. To give a great illustrative example, Sperling et al.‘s comprehensive textbook (~600 pages) about type 1 diabetes includes exactly 3 hits for osteoporosis in the text, all of which relate to very specific subtopics and none of which even remotely relate to the highly increased risk of fractures which type 1 diabetes in particular confers – the authors of that text clearly had no idea that type 1 diabetes dramatically increases the risk of fractures and poor bone health; there are zero indications to the contrary. It’s probably not uncommon to see important information in textbooks which people forget about in clinical practice (perhaps because the people working in clinical practice read different textbooks, in which this information was not included…), but it’s certainly less common to see important information not included in textbooks because the textbook authors simply don’t know about them. It seems highly likely to me that a lot of health care providers involved in diabetes care currently do not know anything about the topics discussed in this publication; I hope this state of affairs will change in the future.

As also noted in the comments above, the relationship between diabetes and bone health is complicated and interacts with type; type 1 seems to be much worse for the bones than is type 2, and the relationship between in particular type 2 diabetes and bone health is not at all simple. Type 2 diabetics tend to have both some elevated non-diabetes-related risk factors for fractures (in one chapter the authors thus list in that category obesity, reduced muscle quality, poor balance, and falls – e.g. but not only hypoglycemia-related) and some diabetes-specific risk factors ((/very) poor glycemic control probably increases risk (but see also below), duration of disease increases risk, medications – e.g. the thiazolidinedione drug class used to treat type 2 diabetes), but these don’t fully account for the increased risk.

Most of the standard metrics used to assess fracture risk, such as FRAX, do not take diabetes status into account, which is a problem – “studies indicate that FRAX systematically underestimates fracture risk in patients with T2DM” (this problem is not just related to FRAX, thus elsewhere in the publication it is noted more generally that: “fracture prediction tools underestimate fracture risk in diabetes”). The only one of the widely used risk assessment tools which does take diabetes status into account is the QFracture tool, but this tool “has not been specifically evaluated with regard to calibration in individuals with diabetes”; so there is a lot of uncertainty here. This state of affairs is of course hardly ideal, especially not considering how the number of type 2 diabetics is projected to increase over time in the years to come. It is worth keeping in mind that the total population prevalence of type 2 can be deceiving people here into thinking this is less of a problem than it really is, as most people at increased risk of fractures are old people, and type 2 incidence/prevalence increases with age: “Type 2 diabetes affects over 25 % of older adults in the United States, including diagnosed and undiagnosed cases [11].” The hip fracture estimates included in Vestergaard’s meta-review discussed below indicate a relative risk of hip-fracture of ~1.4 in the type 2 diabetic sub-population, and if you multiply that number by the 25% prevalence among elderly people in the US, that’s more than a third of all fractures in older adults. That’s a lot of people, and a lot of risk not well accounted for.

A problem related to the above observations in the context of type 2 diabetes (and most of the research that has been done in this area has been done on type 2 diabetes, for reasons which should be obvious (“type 1 diabetes mellitus (T1D) accounts for dual-energy X-ray absorptiometry (DXA), a standard way to measure bone mineral density also used to diagnose osteoporosis, does not ‘pick up on’ the excess risk associated with T2DM; in type 2 individuals risk is elevated even when taking DXA measurements into account (this fact may actually be one argument why the QFracture tool may not be bad at all to apply to people in this patient subgroup; QFracture does not include DXA numbers, and if a substantial proportion of the risk is unrelated to the DXA estimates in type 2 anyway then maybe they’re not that important to include). The arguably poor performance of DXA in the context of fracture risk in type 2 diabetes have lead to the development of other tools which might be better at assessing risk in this patient population, and the authors of some of the later chapters of the book talk in some detail about these tools and the results derived from related studies using these tools. It should perhaps be noted in the context of DXA and bone mineral density numbers that one of the clear differences between type 1 and type 2 here is that bone mineral density tends to be decreased in type 1 diabetes, whereas it’s usually if anything increased in type 2 (but the increased, or at least not lowered, bone mineral density in type 2 does not translate into a lower risk of fracture; risk is still elevated, which is what is surprising and not easy to fully account for).

An interesting aspect of the coverage was that the relationship between glycemic control and bone health seems to not be completely clear; to me the coverage of this topic throughout the various chapters (many chapters cover closely related topics and there’s some coverage overlap, but I didn’t mind this at all) reminded much more of the typical coverage you see in publications discussing how the risk of macrovascular complications relate to glycemic control (…’it’s complicated’) than it reminded me of how the risk of microvascular complications relate to glycemic control (…’hyperglycemia increases risk and there’s a dose-response relationship between complication risk and the level of hyperglycemia’). One problem is that low Hba1c may increase the fall risk because of an increased risk of hypoglycemic episodes, increasing risk at the lower end of the spectrum.

The book has a lot of stuff about the specifics of what might be going on at the cellular level and so on, but I won’t talk much about that here even if I found it interesting (it would take a lot of time to go over the details here); one key point to take take away from that part of the coverage should however be mentioned here, and that is that that stuff thoroughly convinced me that there’s no way the increased fracture risks observed in the various epidemiological studies presented at the beginning of the publication are flukes. There are good reasons to think that diabetes may be bad for the bones, quite aside from the reason that they seem to break their bones more often than other people do.

I have included some data and key observations from the book below. As the post is rather long I decided to highlight/bold a few of the most important observations (they’re not bolded in the book).

In patients with T1D, an increased incidence of osteopenia and osteoporosis has been recognized for over three decades [10–14], occurring not only in adults, but in children as well [15–17]. Many more recent studies have since validated these early findings, demonstrating a reduced bone mineral density (BMD) in T1D [18–22]. Clinical factors associated with lower bone density include: male gender […]; longer duration of disease […]; younger age at diagnosis […]; lower endogenous insulin or C-peptide levels [27]; low body mass index (BMI) […]; and possibly the presence of chronic diabetes comorbidities or associated autoimmunity [29]. Some studies also suggest that greater longitudinal decrements in BMD occur over time in males [24]. […] In most studies, poor glycemic control does not seem to be strongly associated with a reduced BMD [18–20, 22, 23, 30, 31] […] T1D is […] associated with an increased risk for fracture, higher than the risk in type 2 diabetes (T2D)”

among risk factors for hip fracture in >33,000 middle-aged adults in Sweden (~25–60 years), the strongest risk factor for both women […] and men […] was diabetes [49], suggesting that the presence of diabetes was a major risk determinant for this age group. Similar findings had been reported years before in middle-aged Norwegian women and men […] Together, [studies conducted during the last 15 years on type 1 diabetics] demonstrate an unequivocally increased fracture risk at the hip [compared to non-diabetic controls], with most demonstrating a six to ninefold increase in relative risk. […] type I DM patients have hip fractures at a younger age on average, with a mean of 43 for women and 41 for men in one study. Almost 7 % of people with type I DM can be expected to have sustained a hip fracture by age 65 [7] […] Patients with DM and hip fracture are at a higher risk of mortality than patients without DM, with 1-year rates as high as 32 % vs. 13 % of nondiabetic patients […] Though only a very few studies have examined fracture risk at other skeletal sites [51, 54], an increased risk for vertebral fracture is also a consistent finding in studies that have quantified this. […] in one study, an approximate threefold increase in risk for all non-vertebral fractures was reported in men with T1D”.

“studies […] suggest that cumulative changes in bone architecture are beginning early in childhood, particularly in those diagnosed with T1D at very young ages [73]. Compared with nondiabetic children, reductions in BMD [68, 74–78] and bone size, specifically total cross-sectional area (CSA) [73, 79] and cortical area [15, 80], are relatively consistent findings. […] As a whole, […] studies suggest that systemic markers of bone formation in T1D are generally indicative of a condition in which bone formation is reduced. […] Taken together, it would appear that T1D is characterized best as a state of inappropriately lowered bone turnover which exists in conjunction with relative osteoblast dysfunction [90] and, hence, low bone formation […] serum AGE concentrations are clearly elevated in T1D during childhood [138], even during preschool and prepubertal years […] skin AGEs […] are increased in children with both T1D and T2D, to the extent that “approximately 4–6 years of diabetes exposure in some children may be sufficient to increase skin AGEs to levels that would naturally accumulate only after ~25 years of chronological aging””.

“diabetic bone has a greater propensity for fracture than is predicted by BMD […] A role for the skeletal accumulation of advanced glycation end products […], chronic hyperglycemia [30], oxidative stress [63], and microarchitectural bone defects [64] have all been proposed, and it is expected that the pathological mechanisms leading to bone fragility in T1D are multifactorial […] Beyond fragility fractures, other skeletal complications also occur disproportionately in persons with T1D, including fracture-healing complications (nonunion, malunion) [66], Charcot osteoarthropathy [67], osteomyelitis, and diabetic foot syndrome.”

“In orthopaedics, patients with diabetes have a number of associated disorders, and these present a challenge as many have an increased hospital stay, higher risk of infection, and higher risk of complications after orthopaedic treatment. The orthopaedic-related problems in diabetes are varied, and the true causal links between diabetes and the disorders are largely unknown. […] The incidence of trigger finger [/stenosing tenosynovitis] is 7–20 % of patients with diabetes comparing to only about 1–2 % in nondiabetic patients […] The prevalence of [carpal tunnel syndrome, CTS] in patients with diabetes has been estimated at 11–30 % [130, 133, 153, 156], 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. However the prevalence does not seem to be associated with glycemic control”

“Diabetes increases the severity and risk of periodontitis, the most common lytic disease of bone and a frequent complication of diabetes […] The risk of periodontitis is increased approximately 2–4 times in diabetic versus nondiabetic subjects [4, 47]. In one study, periodontitis was found in 60 % of T1DM patients compared to 15 % without diabetes [48]. Patients with diabetes are at higher risk of severe periodontitis compared with nondiabetic subjects […] There is a direct link between persistent hyperglycemia, an exaggerated inflammatory response to periodontal pathogens and periodontal bone loss”.

Because diabetic bone disease in type 1 diabetes represents a deficit in osteoblast function and bone formation, antiresorptive therapies for osteoporosis (e.g., bisphosphonates, denosumab) may be ineffective in this form of secondary osteoporosis […] Calcium and vitamin D supplementation […] is considered standard-of-care for osteoporosis treatment [162]. Nonetheless, 1 year of calcitriol supplementation in young adults with recent-onset T1D did not significantly change circulating markers of bone turnover […] very little information from comparative effectiveness studies is available on the treatment of osteoporosis in T1D.”

Type 2 does not increase risk nearly as much as does type 1:

“In 2007 Vestergaard published a meta-analysis of hip fracture results that included eight studies and reported an age-adjusted summary relative risk for hip fracture of 1.38 (1.25–1.53), comparing those with and without T2D [14]. This increase in fracture risk with T2D occurred in spite of higher bone density in those with T2D. […] Most [15–21], but not all [22, 23], subsequent studies have reported increased rates of hip fracture with T2D in age-adjusted models. […] Evidence that more frequent falls do not fully account for increased fracture risk with T2D […], combined with evidence from rodent models [55], has led to the conclusion that diabetic bone is more fragile for a given BMD. Understanding the aspects of bone that are affected by diabetes and that result in fragile bone has been an important focus of research on diabetes and skeletal health.”

The effect of glycemic control on fracture risk, BMD, and falls remains poorly understood and controversial.

“Diabetic patients with multiple complications appear to be at higher risk of fracture, but results are mixed for the association between specific complications and fracture.”

“Our current understanding of the pathogenesis of skeletal fragility in [type 2] diabetes suggests a working model […], whereby poor glucose control in patients with T2DM leads to increases in AGEs that have negative effects on osteoblasts, which in turn causes a reduction in bone formation. This defect in bone formation subsequently results in low bone turnover in T2DM patients, which prolongs the lifespan of type I collagen in bone, thereby leaving it particularly vulnerable to damage from increased AGEs. Ultimately, this creates a “vicious cycle” that may contribute to reduced bone quality and increased fracture risk in patients with T2DM.”

As for an overall assessment of the book, I gave the book five stars on goodreads, because it’s basically to a significant extent written the way I’d like Springer publications like this one to be written. The language in one chapter (out of 11) was slightly sub-optimal, but aside from that chapter every single chapter was in my opinion well written, some of them very well written. Frequent discussions of the results of meta-analyses were included in the book. The authors seemed in general to be aware of potential problems with specific interpretations and to me seemed cautious about drawing strong conclusions from the data they had at hand; in terms of the analytical level of the coverage the publication for example included comments about problems with confounding by indication in cross section analyses. There were a couple of places in one of the later chapters where it was slightly difficult for me to figure out ‘what was going on’, but the coverage included in the next chapter of the book clarified these issues; I was not willing to subtract a star because of that.

December 3, 2015 Posted by | Books, Diabetes, Epidemiology, Medicine | Leave a comment

Effects of Antidepressants

I gave the book two stars on goodreads. The contributors to this volume are from Brazil, Spain, Mexico, Japan, Turkey, Denmark, and the Czech Republic; the editor is from Taiwan. In most chapters you can tell that the first language of these authors is not English; the language is occasionally quite bad, although you can usually tell what the authors are trying to say.

The book is open access and you can read it here. I have included some quotes from the book below:

“It is estimated that men and women with depression are 20.9 and 27 times, respectively, more likely to commit suicide than those without depression (Briley & Lépine, 2011).” [Well, that’s one way to communicate risk… See also this comment].

“depression is on average twice as common in women as in men (Bromet et al., 2011). […] sex differences have been observed in the prevalence of mental disorders as well as in responses to treatment […] When this [sexual] dimorphism is present [in rats, a common animal model], the drug effect is generally stronger in males than in females.”

“Several reports indicate that follicular stimulating and luteinizing hormones and estradiol oscillations are correlated with the onset or worsening of depression symptoms during early perimenopause […], when major depressive disorder incidence is 3-5 times higher than the male matched population of the same [age] […]. Several longitudinal studies that followed women across the menopausal transition indicate that the risk for significant depressive symptoms increases during the menopausal transition and then decreases in […] early postmenopause […] the impact of hormone oscillations during perimenopause transition may affect the serotonergic system function and increase vulnerability to develop depression.”

“The use of antidepressant drugs for treating patients with depression began in the late 1950s. Since then, many drugs with potential antidepressants have been made available and significant advances have been made in understanding their possible mechanisms of action […]. Only two classes of antidepressants were known until the 80’s: tricyclic antidepressants and monoamine oxidase inhibitors. Both, although effective, were nonspecific and caused numerous side effects […]. Over the past 20 years, new classes of antidepressants have been discovered: selective serotonin reuptake inhibitors, selective serotonin/norepinephrine reuptake inhibitors, serotonin reuptake inhibitors and alpha-2 antagonists, serotonin reuptake stimulants, selective norepinephrine reuptake inhibitors, selective dopamine reuptake inhibitors and alpha-2 adrenoceptor antagonists […] Neither the biological basis of depression […] nor the precise mechanism of antidepressant efficacy are completely understood […]. Indeed, antidepressants are widely prescribed for anxiety and disorders other than depression.”

“Taken together the TCAs and the MAO-Is can be considered to be non-selective or multidimensional drugs, comparable to a more or less rational polypharmacy at the receptor level. This is even when used as monotherapy in the acute therapy of major depression. The new generation of selective antidepressants (the selective serotonin reuptake inhibitors (SSRIs)), or the selective noradrenaline and serotonin reuptake inhibitors (SNRIs) have a selective mechanism of action, thus avoiding polypharmacy. However, the new generation antidepressants such as the SSRIs or SNRIs are less effective than the TCAs. […] The most selective second generation antidepressants have not proved in monotherapy to be more effective on the core symptoms of depression than the first generation TCAs or MAOIs. It is by their safety profiles, either in overdose or in terms of long term side effects, that the second generation antidepressants have outperformed the first generation.”

“Suicide is a serious global public health problem. Nearly 1 million individuals commit suicide every year. […] Suicide […] ranks among the top 10 causes of death in every country, and is one of the three leading causes of death in 15 to 35-year olds.”

“Considering patients that commit suicide, about half of them, at some point, had contact with psychiatric services, yet only a quarter had current or recent contact (Andersen et al., 2000; Lee et al., 2008). A study conducted by Gunnell & Frankel (1994) revealed that 20-25% of those committing suicide had contact with a health care professional in the week before death and 40% had such contact one month before death” (I’m assuming ‘things have changed’ during the last couple of decades, but it would be interesting to know how much they’ve changed).

“In cases of suicide by drug overdose, TCAs have the highest fatal toxicity, followed by serotonin and noradrenalin reuptake inhibitors (SNRIs), specific serotonergic antidepressants (NaSSA) and SSRIs […] SSRIs are considered to be less toxic than TCAs and MAOIs because they have an extended therapeutic window. The ingestion of up to 30 times its recommended daily dose produces little or no symptoms. The intake of 50 to 70 times the recommended daily dose can cause vomiting, mild depression of the CNS or tremors. Death rarely occurs, even at very high doses […] When we talk about suicide and suicide attempt with antidepressants overdose, we are referring mainly to women in their twenties – thirties who are suicide repeaters.”

“Physical pain is one of the most common somatic symptoms in patients that suffer depression and conversely, patients suffering from chronic pain of diverse origins are often depressed. […] While […] data strongly suggest that depression is linked to altered pain perception, pain management has received little attention to date in the field of psychiatric research […] The monoaminergic system influences both mood and pain […], and since many antidepressants modify properties of monoamines, these compounds may be effective in managing chronic pain of diverse origins in non-depressed patients and to alleviate pain in depressed patients. There are abundant evidences in support of the analgesic properties of tricyclic antidepressants (TCAs), particularly amitriptyline, and another TCA, duloxetine, has been approved as an analgesic for diabetic neuropathic pain. By contrast, there is only limited data regarding the analgesic properties of selective serotonin reuptake inhibitors (SSRIs) […]. In general, compounds with noradrenergic and serotonergic modes of action are more effective analgesics […], although the underlying mechanisms of action remain poorly understood […] While the utility of many antidepressant drugs in pain treatment is well established, it remains unclear whether antidepressants alleviate pain by acting on mood (emotional pain) or nociceptive transmission (sensorial pain). Indeed, in many cases, no correlation exists between the level of pain experienced by the patient and the effect of antidepressants on mood. […] Currently, TCAs (amitriptyline, nortriptiline, imipramine and clomipramine) are the most common antidepressants used in the treatment of neuropathic pain processes associated with diabetes, cancer, viral infections and nerve compression. […] TCAs appear to provide effective pain relief at lower doses than those required for their antidepressant effects, while medium to high doses of SNRIs are necessary to produce analgesia”. Do keep in mind here that in a neuropathy setting one should not expect to get anywhere near complete pain relief with these drugs – see also this post.

“Prevalence of a more or less severe depression is approximately double in patients with diabetes compared to a general population [for more on related topics, see incidentally this previous post of mine]. […] Diabetes as a primary disease is typically superimposed by depression as a reactive state. Depression is usually a result of exposure to psycho-social factors that are related to hardship caused by chronic disease. […] Several studies concerning comorbidity of type 1 diabetes and depression identified risk factors of depression development; chronic somatic comorbidity and polypharmacy, female gender, higher age, solitary life, lower than secondary education, lower financial status, cigarette smoking, obesity, diabetes complications and a higher glycosylated hemoglobin [Engum, 2005; Bell, 2005; Hermanns, 2005; Katon, 2004]”

November 11, 2015 Posted by | Books, Diabetes, Epidemiology, Medicine, Pharmacology, Psychiatry, Psychology | Leave a comment

Oxford Handbook of Clinical Medicine (II)

Here’s my first post about the book. I’ve read roughly 75% of the book at this point (~650 pages). The chapters I’ve read so far have dealt with the topics of: ‘thinking about medicine’ (an introductory chapter), ‘history and examination’, cardiovascular medicine, chest medicine, endocrinology, gastroenterology, renal medicine, haematology, infectious diseases, neurology, oncology and palliative care, rheumatology, and surgery (this last one is a long chapter – ~100 pages – which I have not yet finished). In my first post I (…mostly? I can’t recall if I included one or two observations made later in the coverage as well…) talked about observations included in the first 140 pages of the book, which relate only to the first three topics mentioned above; the chapter about chest medicine starts at page 154. In this post I’ll move on and discuss stuff covered in the chapters about cardiovascular medicine, chest medicine, and endocrinology.

In the previous post I talked a little bit about heart failure, acute coronary syndromes and a few related topics, but there’s a lot more stuff in the chapter about cardiovascular medicine and I figured I should add a few more observations – so let’s talk about aortic stenosis. The most common cause is ‘senile calcification’. The authors state that one should think of aortic stenosis in any elderly person with problems of chest pain, shortness of breath during exercise (exertional dyspnoea), and fainting episodes (syncope). Symptomatic aortic stenosis tends to be bad news; “If symptomatic, prognosis is poor without surgery: 2–3yr survival if angina/syncope; 1–2yr if cardiac failure. If moderate-to-severe and treated medically, mortality can be as high as 50% at 2yrs”. Surgery can improve the prognosis quite substantially; they note elsewhere in the coverage that a xenograft (e.g. from a pig) aortic valve replacement can last (“may require replacement at…”) 8-10 years, whereas a mechanical valve lasts even longer than that. Though it should also be noted in that context that the latter type requires life-long anticoagulation, whereas the former only requires this if there is atrial fibrilation.

Next: Infective endocarditis. Half of all cases of endocarditis occur on normal heart valves; the presentation in that case is one of acute heart failure. So this is one of those cases where your heart can be fine one day, and not many days later it’s toast and you’ll die unless you get treatment (often you’ll die even if you do get treatment as mortality is quite high: “Mortality: 5–50% (related to age and embolic events)”; mortality relates to which organism we’re dealing with: “30% with staphs [S. Aureus]; 14% if bowel organisms; 6% if sensitive streptococci.”). Multiple risk factors are known, but some of those are not easily preventable (renal failure, dermatitis, organ transplantation…); don’t be an IV drug (ab)user, and try to avoid getting (type 2) diabetes.. The authors note that: “There is no proven association between having an interventional procedure (dental or non-dental) and the development of IE”, and: “Antibiotic prophylaxis solely to prevent IE is not recommended”.

Speaking of terrible things that can go wrong with your heart for no good reason, hypertrophic cardiomyopathy (-HCM) is the leading cause of sudden cardiac death in young people, with an estimated prevalence of 1 in 500. “Sudden death may be the first manifestation of HCM in many patients”. Yeah…

The next chapter in the book as mentioned covers chest medicine. At the beginning of the chapter there’s some stuff about what the lungs look like and some stuff about how to figure out whether they’re working or not, or why they’re not working – I won’t talk about that here, but I would note that lung problems can relate to stuff besides ‘just’ lack of oxygen; they can also for example be related to retention of carbon dioxide and associated acidosis. In general I won’t talk much about this chapter’s coverage as I’m aware that I have covered many of the topics included in the book before here on the blog in other posts. It should perhaps be noted that whereas the chapter has two pages about lung tumours and two pages about COPD, it has 6 pages about pneumonia; this is still a very important disease and a major killer. Approximately one in five (the number 21% is included in the book) patients with pneumonia in a hospital setting die. Though it should perhaps also be observed that maybe one reason why more stuff is not included about lung cancer in that chapter is that this disease is just depressing and doctors can’t really do all that much. Carcinoma of the bronchus make up ~19% of all cancers and 27% of cancer deaths in the UK. In terms of prognosis, non-small cell lung cancer has a 50% 2-year mortality in cases where the cancer was not spread at presentation and a 90% 2-year mortality in cases with spread. That’s ‘the one you would prefer’: Small cell lung cancer is worse as small cell tumours “are nearly always disseminated at presentation” – here the untreated median survival is 3 months, increasing to 1-1,5 years if treated. The authors note that only 5% (of all cases, including both types) are ‘cured’ (they presumably use those citation marks for a reason). Malignant mesothelioma, a cancer strongly linked to asbestos exposure most often developing in the pleura, incidentally also has a terrible prognosis (”

5-8% of people in the UK have asthma; I was surprised the number was that high. Most people who get it during childhood either grow out of it or suffer much less as adults, but on the other hand there are also many people who develop chronic asthma late in life. In 2009 approximately 1000 people in the UK died of asthma – unless this number is a big underestimate, it would seem to me that asthma at least in terms of mortality is a relatively mild disease (if 5% of the UK population has asthma, that’s 3 million people – and 1000 deaths among 3 million people is not a lot, especially not considering that half of those deaths were in people above the age of 65). COPD is incidentally another respiratory disease which is more common than I had thought; they note that the estimated prevalence in people above the age of 40 in the UK is 10-20%.

The endocrinology chapter has 10 pages about diabetes, and I won’t talk much about that coverage here as I’ve talked about many of these things before on the blog – however a few observations are worth including and discussing here. The authors note that 4% of all pregnancies are complicated by diabetes, with the large majority of cases (3.5%) being new-onset gestational diabetes. In a way the 0,5% could be considered ‘good news’ because they reflect the fact that outcomes have improved so much that a female diabetic can actually carry a child to term without risking her own life or running a major risk that the fetus dies (“As late as 1980, physicians were still counseling diabetic women to avoid pregnancy” – link). But the 3,5%? That’s not good: “All forms [of diabetes] carry an increased risk to mother and foetus: miscarriage, pre-term labour, pre-eclampsia, congenital malformations, macrosomia, and a worsening of diabetic complications”. I’m not fully convinced this statement is actually completely correct, but there’s no doubt that diabetes during pregnancy is not particularly desirable. As to which part of the statement I’m uncertain about, I think gestational diabetes ‘ought to’ have somewhat different effects than type 1 especially in the context of congenial malformations. Based on my understanding of these things, gestational diabetes should be less likely to cause congenital malformations than type 1 diabetes in the mother; diabetes-related congenital malformations tend to happen/develop very early in pregnancy (for details, see the link above) and gestational pregnancy is closely related to hormonal changes and changing metabolic demands which happen over time during pregnancy. Hormonal changes which occur during pregnancy play a key role in the pathogenesis of gestational diabetes, as the hormonal changes in general increase insulin resistance significantly, which is what causes some non-diabetic women to become diabetic during pregnancy; these same processes incidentally also causes the insulin demands of diabetic pregnant women to increase a lot during pregnancy. You’d expect the inherently diabetogenic hormonal and metabolic processes which happen in pregnancy to play a much smaller role in the beginning of the pregnancy than they do later on, especially as women who develop gestational diabetes during their pregnancy would be likely to be able to compensate early in pregnancy, where the increased metabolic demands are much less severe than they are later on. So I’d expect the risk contribution from ‘classic gestational diabetes’ to be larger in the case of macrosomia than in the case of neural tube defects, where type 1s should probably be expected to dominate – a sort of ‘gestational diabetics don’t develop diabetes early enough in pregnancy for the diabetes to be very likely to have much impact on organogenesis’-argument. This is admittedly not a literature I’m intimately familiar with and maybe I’m wrong, but from my reading of their diabetes-related coverage I sort of feel like the authors shouldn’t be expected to be intimately familiar with the literature either, and I’m definitely not taking their views on these sorts of topics to be correct ‘by default’ at this point. This NHS site/page incidentally seems to support my take on this, as it’s clear that the first occasion for even testing for gestational diabetes is at week 8-12, which is actually after a substantial proportion of diabetes-related organ damage would already be expected to have occurred in the type 1 diabetes context (“There is an increased prevalence of congenital anomalies and spontaneous abortions in diabetic women who are in poor glycemic control during the period of fetal organogenesis, which is nearly complete by 7 wk postconception.” – Sperling et al., see again the link provided above. Note that that entire textbook is almost exclusively about type 1 diabetes, so ‘diabetes’ in the context of that quote equals T1DM), and a glucose tolerance test/screen does not in this setting take place until weeks 24-28.

The two main modifiable risk factors in the context of gestational diabetes are weight and age of pregnancy; the risk of developing gestational diabetes  increases with weight and is higher in women above the age of 25. One other sex/gender-related observation to make in the context of diabetes is incidentally that female diabetics are at much higher risk of cardiovascular disease than are non-diabetic females: “DM [diabetes mellitus] removes the vascular advantage conferred by the female sex”. Relatedly, “MI is 4-fold commoner in DM and is more likely to be ‘silent’. Stroke is twice as common.” On a different topic in which I’ve been interested they provided an observation which did not help much: “The role of aspirin prophylaxis […] is uncertain in DM with hypertension.”

They argue in the section about thyroid function tests (p. 209) that people with diabetes mellitus should be screened for abnormalities in thyroid function on the annual review; I’m not actually sure this is done in Denmark and I think it’s not – the DDD annual reports I’ve read have not included this variable, and if it is done I know for a fact that doctors do not report the results to the patient. I’m almost certain they neglected to include a ‘type 1’ in that recommendation, because it makes close to zero sense to screen type 2 diabetics for comorbid autoimmune conditions, and I’d say I’m probably also a little skeptical, though much less skeptical, about annual screenings of all type 1s being potentially cost-effective. Given that autoimmune comorbidities (e.g. Graves’ disease and Hashimoto’s) are much more common in women than in men and that they often present in middle-aged individuals (and given that they’re more common in people who develop diabetes relatively late, unlike me – see Sperling) I would assume I’m relatively low risk and that it would probably not make sense to screen someone like me annually from a cost-benefit/cost-effectiveness perspective; but it might make sense to ask the endocrinologist at my next review about how this stuff is actually being done in Denmark, if only to satisfy my own curiosity. Annual screening of *female*, *type 1* diabetics *above (e.g.) the age of 30* might be a great idea and perhaps less restrictive criteria than that can also be justified relatively easily, but this is an altogether very different recommendation from the suggestion that you should screen all diabetics annually for thyroid problems, which is what they recommend in the book – I guess you can add this one to the list of problems I have with the authors’ coverage of diabetes-related topics (see also my comments in the previous post). The sex- and age-distinction is likely much less important than the ‘type’ restriction and maybe you can justify screening all type 1 diabetics (For example: “Hypothyroid or hyperthyroid AITD [autoimmune thyroid disease] has been observed in 10–24% of patients with type 1 diabetes” – Sperling. Base rates are important here: Type 1 diabetes is rare, and Graves’ disease is rare, but if the same HLA mutation causes both in many cases then the population prevalence is not informative about the risk an individual with diabetes and an HLA mutation has of developing Graves’) – but most diabetics are not type 1 diabetics, and it doesn’t make sense to screen a large number of people without autoimmune disease for autoimmune comorbidities they’re unlikely to have (autoimmunity in diabetes is complicated – see the last part of this comment for a few observations of interest on that topic – but it’s not that complicated; most type 2 diabetics are not sick because of autoimmunity-related disease processes, and type 2 diabetics make up the great majority of people with diabetes mellitus in all patient populations around the world). All this being said, it is worth keeping in mind that despite overt thyroid disease being relatively rare in general, subclinical hypothyroidism is common in middle-aged and elderly individuals (“~10% of those >55yrs”); and the authors recommend treating people in this category who also have DM because they are more likely to develop overt disease (…again it probably makes sense to add a ‘T1’ in front of that DM).

Smoking is sexy, right? (Or at least it used to be…). And alcohol makes other people look sexy, right? In a way I find it a little amusing that alcohol and smoking are nevertheless two of the three big organic causes of erectile dysfunction (the third is diabetes).

How much better does it feel to have sex, compared to how it feels to masturbate? No, they don’t ask that question in the book (leave that to me…) but they do provide part of the answer because actually there are ways to quantify this, sort of: “The prolactin increase ( and ) after coitus is ~400% greater than after masturbation; post-orgasmic prolactin is part of a feedback loop decreasing arousal by inhibiting central dopaminergic processes. The size of post-orgasmic prolactin increase is a neurohormonal index of sexual satisfaction.”

November 1, 2015 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Epidemiology, Immunology, Medicine | Leave a comment