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.”
“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.”
“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.”
“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 (1–7) 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 (13–16). 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.”
The links above are links to topics I looked up while reading the second half of the book. The first link is quite relevant to the book’s coverage as a comprehensive longitudinal Grade of Membership (-GoM) model is covered in chapter 17. Relatedly, chapter 18 covers linear latent structure (-LLS) models, and as observed in the book LLS is a generalization of GoM. As should be obvious from the nature of the links some of the stuff included in the second half of the text is highly technical, and I’ll readily admit I was not fully able to understand all the details included in the coverage of chapters 17 and 18 in particular. On account of the technical nature of the coverage in Part 2 I’m not sure I’ll cover the second half of the book in much detail, though I probably shall devote at least one more post to some of those topics, as they were quite interesting even if some of the details were difficult to follow.
I have almost finished the book at this point, and I have already decided to both give the book five stars and include it on my list of favorite books on goodreads; it’s really well written, and it provides consistently highly detailed coverage of very high quality. As I also noted in the first post about the book the authors have given readability aspects some thought, and I am sure most readers would learn quite a bit from this text even if they were to skip some of the more technical chapters. The main body of Part 2 of the book, the subtitle of which is ‘Statistical Modeling of Aging, Health, and Longevity’, is however probably in general not worth the effort of reading unless you have a solid background in statistics.
This post includes some observations and quotes from the last chapters of the book’s Part 1.
“The proportion of older adults in the U.S. population is growing. This raises important questions about the increasing prevalence of aging-related diseases, multimorbidity issues, and disability among the elderly population. […] In 2009, 46.3 million people were covered by Medicare: 38.7 million of them were aged 65 years and older, and 7.6 million were disabled […]. By 2031, when the baby-boomer generation will be completely enrolled, Medicare is expected to reach 77 million individuals […]. Because the Medicare program covers 95 % of the nation’s aged population […], the prediction of future Medicare costs based on these data can be an important source of health care planning.”
“Three essential components (which could be also referred as sub-models) need to be developed to construct a modern model of forecasting of population health and associated medical costs: (i) a model of medical cost projections conditional on each health state in the model, (ii) health state projections, and (iii) a description of the distribution of initial health states of a cohort to be projected […] In making medical cost projections, two major effects should be taken into account: the dynamics of the medical costs during the time periods comprising the date of onset of chronic diseases and the increase of medical costs during the last years of life. In this chapter, we investigate and model the first of these two effects. […] the approach developed in this chapter generalizes the approach known as “life tables with covariates” […], resulting in a new family of forecasting models with covariates such as comorbidity indexes or medical costs. In sum, this chapter develops a model of the relationships between individual cost trajectories following the onset of aging-related chronic diseases. […] The underlying methodological idea is to aggregate the health state information into a single (or several) covariate(s) that can be determinative in predicting the risk of a health event (e.g., disease incidence) and whose dynamics could be represented by the model assumptions. An advantage of such an approach is its substantial reduction of the degrees of freedom compared with existing forecasting models (e.g., the FEM model, Goldman and RAND Corporation 2004). […] We found that the time patterns of medical cost trajectories were similar for all diseases considered and can be described in terms of four components having the meanings of (i) the pre-diagnosis cost associated with initial comorbidity represented by medical expenditures, (ii) the cost peak associated with the onset of each disease, (iii) the decline/reduction in medical expenditures after the disease onset, and (iv) the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity. The description of the trajectories was formalized by a model which explicitly involves four parameters reflecting these four components.”
As I noted earlier in my coverage of the book, I don’t think the model above fully captures all relevant cost contributions of the diseases included, as the follow-up period was too short to capture all relevant costs to be included in the part iv model component. This is definitely a problem in the context of diabetes. But then again nothing in theory stops people from combining the model above with other models which are better at dealing with the excess costs associated with long-term complications of chronic diseases, and the model results were intriguing even if the model likely underperforms in a few specific disease contexts.
“Models of medical cost projections usually are based on regression models estimated with the majority of independent predictors describing demographic status of the individual, patient’s health state, and level of functional limitations, as well as their interactions […]. If the health states needs to be described by a number of simultaneously manifested diseases, then detailed stratification over the categorized variables or use of multivariate regression models allows for a better description of the health states. However, it can result in an abundance of model parameters to be estimated. One way to overcome these difficulties is to use an approach in which the model components are demographically-based aggregated characteristics that mimic the effects of specific states. The model developed in this chapter is an example of such an approach: the use of a comorbidity index rather than of a set of correlated categorical regressor variables to represent the health state allows for an essential reduction in the degrees of freedom of the problem.”
“Unlike mortality, the onset time of chronic disease is difficult to define with high precision due to the large variety of disease-specific criteria for onset/incident case identification […] there is always some arbitrariness in defining the date of chronic disease onset, and a unified definition of date of onset is necessary for population studies with a long-term follow-up.”
“Individual age trajectories of physiological indices are the product of a complicated interplay among genetic and non-genetic (environmental, behavioral, stochastic) factors that influence the human body during the course of aging. Accordingly, they may differ substantially among individuals in a cohort. Despite this fact, the average age trajectories for the same index follow remarkable regularities. […] some indices tend to change monotonically with age: the level of blood glucose (BG) increases almost monotonically; pulse pressure (PP) increases from age 40 until age 85, then levels off and shows a tendency to decline only at later ages. The age trajectories of other indices are non-monotonic: they tend to increase first and then decline. Body mass index (BMI) increases up to about age 70 and then declines, diastolic blood pressure (DBP) increases until age 55–60 and then declines, systolic blood pressure (SBP) increases until age 75 and then declines, serum cholesterol (SCH) increases until age 50 in males and age 70 in females and then declines, ventricular rate (VR) increases until age 55 in males and age 45 in females and then declines. With small variations, these general patterns are similar in males and females. The shapes of the age-trajectories of the physiological variables also appear to be similar for different genotypes. […] The effects of these physiological indices on mortality risk were studied in Yashin et al. (2006), who found that the effects are gender and age specific. They also found that the dynamic properties of the individual age trajectories of physiological indices may differ dramatically from one individual to the next.”
“An increase in the mortality rate with age is traditionally associated with the process of aging. This influence is mediated by aging-associated changes in thousands of biological and physiological variables, some of which have been measured in aging studies. The fact that the age trajectories of some of these variables differ among individuals with short and long life spans and healthy life spans indicates that dynamic properties of the indices affect life history traits. Our analyses of the FHS data clearly demonstrate that the values of physiological indices at age 40 are significant contributors both to life span and healthy life span […] suggesting that normalizing these variables around age 40 is important for preventing age-associated morbidity and mortality later in life. […] results [also] suggest that keeping physiological indices stable over the years of life could be as important as their normalizing around age 40.”
“The results […] indicate that, in the quest of identifying longevity genes, it may be important to look for candidate genes with pleiotropic effects on more than one dynamic characteristic of the age-trajectory of a physiological variable, such as genes that may influence both the initial value of a trait (intercept) and the rates of its changes over age (slopes). […] Our results indicate that the dynamic characteristics of age-related changes in physiological variables are important predictors of morbidity and mortality risks in aging individuals. […] We showed that the initial value (intercept), the rate of changes (slope), and the variability of a physiological index, in the age interval 40–60 years, significantly influenced both mortality risk and onset of unhealthy life at ages 60+ in our analyses of the Framingham Heart Study data. That is, these dynamic characteristics may serve as good predictors of late life morbidity and mortality risks. The results also suggest that physiological changes taking place in the organism in middle life may affect longevity through promoting or preventing diseases of old age. For non-monotonically changing indices, we found that having a later age at the peak value of the index […], a lower peak value […], a slower rate of decline in the index at older ages […], and less variability in the index over time, can be beneficial for longevity. Also, the dynamic characteristics of the physiological indices were, overall, associated with mortality risk more significantly than with onset of unhealthy life.”
“Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward manner […]. Recent genome-wide association studies (GWAS) have reached fundamentally the same conclusion by showing that the traits in late life likely are controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny effect […] the weak effect of genes on traits in late life can be not only because they confer small risks having small penetrance but because they confer large risks but in a complex fashion […] In this chapter, we consider several examples of complex modes of gene actions, including genetic tradeoffs, antagonistic genetic effects on the same traits at different ages, and variable genetic effects on lifespan. The analyses focus on the APOE common polymorphism. […] The analyses reported in this chapter suggest that the e4 allele can be protective against cancer with a more pronounced role in men. This protective effect is more characteristic of cancers at older ages and it holds in both the parental and offspring generations of the FHS participants. Unlike cancer, the effect of the e4 allele on risks of CVD is more pronounced in women. […] [The] results […] explicitly show that the same allele can change its role on risks of CVD in an antagonistic fashion from detrimental in women with onsets at younger ages to protective in women with onsets at older ages. […] e4 allele carriers have worse survival compared to non-e4 carriers in each cohort. […] Sex stratification shows sexual dimorphism in the effect of the e4 allele on survival […] with the e4 female carriers, particularly, being more exposed to worse survival. […] The results of these analyses provide two important insights into the role of genes in lifespan. First, they provide evidence on the key role of aging-related processes in genetic susceptibility to lifespan. For example, taking into account the specifics of aging-related processes gains 18 % in estimates of the RRs and five orders of magnitude in significance in the same sample of women […] without additional investments in increasing sample sizes and new genotyping. The second is that a detailed study of the role of aging-related processes in estimates of the effects of genes on lifespan (and healthspan) helps in detecting more homogeneous [high risk] sub-samples”.
“The aging of populations in developed countries requires effective strategies to extend healthspan. A promising solution could be to yield insights into the genetic predispositions for endophenotypes, diseases, well-being, and survival. It was thought that genome-wide association studies (GWAS) would be a major breakthrough in this endeavor. Various genetic association studies including GWAS assume that there should be a deterministic (unconditional) genetic component in such complex phenotypes. However, the idea of unconditional contributions of genes to these phenotypes faces serious difficulties which stem from the lack of direct evolutionary selection against or in favor of such phenotypes. In fact, evolutionary constraints imply that genes should be linked to age-related phenotypes in a complex manner through different mechanisms specific for given periods of life. Accordingly, the linkage between genes and these traits should be strongly modulated by age-related processes in a changing environment, i.e., by the individuals’ life course. The inherent sensitivity of genetic mechanisms of complex health traits to the life course will be a key concern as long as genetic discoveries continue to be aimed at improving human health.”
“Despite the common understanding that age is a risk factor of not just one but a large portion of human diseases in late life, each specific disease is typically considered as a stand-alone trait. Independence of diseases was a plausible hypothesis in the era of infectious diseases caused by different strains of microbes. Unlike those diseases, the exact etiology and precursors of diseases in late life are still elusive. It is clear, however, that the origin of these diseases differs from that of infectious diseases and that age-related diseases reflect a complicated interplay among ontogenetic changes, senescence processes, and damages from exposures to environmental hazards. Studies of the determinants of diseases in late life provide insights into a number of risk factors, apart from age, that are common for the development of many health pathologies. The presence of such common risk factors makes chronic diseases and hence risks of their occurrence interdependent. This means that the results of many calculations using the assumption of disease independence should be used with care. Chapter 4 argued that disregarding potential dependence among diseases may seriously bias estimates of potential gains in life expectancy attributable to the control or elimination of a specific disease and that the results of the process of coping with a specific disease will depend on the disease elimination strategy, which may affect mortality risks from other diseases.”
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.
“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.”
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.”
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.”
“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.”
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).
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.“).
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).
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”).
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.”).
In my first post about the book I included a few general remarks about the book and what it’s about. In this post I’ll continue my coverage of the book, starting with a few quotes from and observations related to the content in chapter 4 (‘Evidence for Dependence Among Diseases‘).
“To compare the effects of public health policies on a population’s characteristics, researchers commonly estimate potential gains in life expectancy that would result from eradication or reduction of selected causes of death. For example, Keyfitz (1977) estimated that eradication of cancer would result in 2.265 years of increase in male life expectancy at birth (or by 3 % compared to its 1964 level). Lemaire (2005) found that the potential gain in the U.S. life expectancy from cancer eradication would not exceed 3 years for both genders. Conti et al. (1999) calculated that the potential gain in life expectancy from cancer eradication in Italy would be 3.84 years for males and 2.77 years for females. […] All these calculations assumed independence between cancer and other causes of death. […] for today’s populations in developed countries, where deaths from chronic non-communicable diseases are in the lead, this assumption might no longer be valid. An important feature of such chronic diseases is that they often develop in clusters manifesting positive correlations with each other. The conventional view is that, in a case of such dependence, the effect of cancer eradication on life expectancy would be even smaller.”
I think the great majority of people you asked would have assumed that the beneficial effect of hypothetical cancer eradication in humans on human life expectancy would be much larger than this, but that’s just an impression. I’ve seen estimates like these before, so I was not surprised – but I think many people would be if they knew this. A very large number of people die as a result of developing cancer today, but the truth of the matter is that if they hadn’t died from cancer they’d have died anyway, and on average probably not really all that much later. I linked to Richard Alexander’s comments on this topic in my last post about the book, and again his observations apply so I thought I might as well add the relevant quote from the book here:
“In the course of working against senescence, selection will tend to remove, one by one, the most frequent sources of mortality as a result of senescence. Whenever a single cause of mortality, such as a particular malfunction of any vital organ, becomes the predominant cause of mortality, then selection will more effectively reduce the significance of that particular defect (meaning those who lack it will outreproduce) until some other achieves greater relative significance. […] the result will be that all organs and systems will tend to deteriorate together. […] The point is that as we age, and as senescence proceeds, large numbers of potential sources of mortality tend to lurk ever more malevolently just “below the surface,”so that, unfortunately, the odds are very high against any dramatic lengthening of the maximum human lifetime through technology.”
Remove one cause of death and there are plenty of others standing in line behind it. We already knew that; two hundred years ago one out of every four deaths in England was the result of tuberculosis, but developing treatments for tuberculosis and other infectious diseases did not mean that English people stopped dying; these days they just die from cardiovascular disease and cancer instead. Do note in the context of that quote that Alexander is talking about the maximum human lifetime, not average life expectancy; again, we know and have known for a long time that human technology can have a dramatic effect on the latter variable. Of course a shift in one distribution will be likely to have spill-over effects on the other (if more people are alive at the age of 70, the potential group of people also living on to reach e.g. 100 years is higher, even if the mortality rate for the 70-100 year old group did not change) the point is just that these effects are secondary effects and are likely to be marginal at best.
Anyway, some more stuff from the chapter. Just like the previous chapter in the book did, this one also includes analyses of very large data sets:
“The Multiple Cause of Death (MCD) data files contain information about underlying and secondary causes of death in the U.S. during 1968–2010. In total, they include more than 65 million individual death certificate records. […] we used data for the period 1979–2004.”
There’s some formal modelling stuff in the chapter which I won’t go into in detail here, this is the chapter in which I encountered the comment about ‘the multivariate lognormal frailty model’ I included in my first post about the book. One of the things the chapter looks at are the joint frequencies of deaths from cancer and other fatal diseases; it turns out that there are multiple diseases that are negatively related with cancer as a cause of death when you look at the population-level data mentioned above. The chapter goes into some of the biological mechanisms which may help explain why these associations look the way they do, and I’ll quote a little from that part of the coverage. A key idea here is (as always..?) that there are tradeoffs at play; some genetic variants may help protect you against e.g. cancer, but at the same time increase the risk of other diseases for the same reason that they protect you against cancer. In the context of the relationship between cancer deaths and deaths from other diseases they note in the conclusion that: “One potential biological mechanism underlying the negative correlation among cancer and other diseases could be related to the differential role of apoptosis in the development of these diseases.” The chapter covers that stuff in significantly more detail, and I decided to add some observations from the chapter on these topics below:
“Studying the role of the p53 gene in the connection between cancer and cellular aging, Campisi (2002, 2003) suggested that longevity may depend on a balance between tumor suppression and tissue renewal mechanisms. […] Although the mechanism by which p53 regulates lifespan remains to be determined, […] findings highlight the possibility that careful manipulation of p53 activity during adult life may result in beneficial effects on healthy lifespan. Other tumor suppressor genes are also involved in regulation of longevity. […] In humans, Dumont et al. (2003) demonstrated that a replacement of arginine (Arg) by proline (Pro) at position 72 of human p53 decreases its ability to initiate apoptosis, suggesting that these variants may differently affect longevity and vulnerability to cancer. Van Heemst et al. (2005) showed that individuals with the Pro/Pro genotype of p53 corresponding to reduced apoptosis in cells had significantly increased overall survival (by 41%) despite a more than twofold increased proportion of cancer deaths at ages 85+, together with a decreased proportion of deaths from senescence related causes such as COPD, fractures, renal failure, dementia, and senility. It was suggested that human p53 may protect against cancer but at a cost of longevity. […] Other biological factors may also play opposing roles in cancer and aging and thus contribute to respective trade-offs […]. E.g., higher levels of IGF-1 [have been] linked to both cancer and attenuation of phenotypes of physical senescence, such as frailty, sarcopenia, muscle atrophy, and heart failure, as well as to better muscle regeneration”.
“The connection between cancer and longevity may potentially be mediated by trade-offs between cancer and other diseases which do not necessarily involve any basic mechanism of aging per se. In humans, it could result, for example, from trade-offs between vulnerabilities to cancer and AD, or to cancer and CVD […] There may be several biological mechanisms underlying the negative correlation among cancer and these diseases. One can be related to the differential role of apoptosis in their development. For instance, in stroke, the number of dying neurons following brain ischemia (and thus probability of paralysis or death) may be less in the case of a downregulated apoptosis. As for cancer, the downregulated apoptosis may, conversely, mean a higher risk of the disease because more cells may survive damage associated with malignant transformation. […] Also, the role of the apoptosis may be different or even opposite in the development of cancer and Alzheimer’s disease (AD). Indeed, suppressed apoptosis is a hallmark of cancer, while increased apoptosis is a typical feature of AD […]. If so, then chronically upregulated apoptosis (e.g., due to a genetic polymorphism) may potentially be protective against cancer, but be deleterious in relation to AD. […] Increased longevity can be associated not only with increased but also with decreased chances of cancer. […] The most popular to-date “anti-aging” intervention, caloric restriction, often results in increased maximal life span along with reduced tumor incidence in laboratory rodents […] Because the rate of apoptosis was significantly and consistently higher in food restricted mice regardless of age, James et al. (1998) suggested that caloric restriction may have a cancer-protective effect primarily due to the upregulated apoptosis in these mice.”
Below I’ll discuss content covered in chapter 5, which deals with ‘Factors That May Increase Vulnerability to Cancer and Longevity in Modern Human Populations’. I’ll start out with a few quotes:
“Currently, the overall cancer incidence rate (age-adjusted) in the less developed world is roughly half that seen in the more developed world […] For countries with similar levels of economic development but different climate and ethnic characteristics […], the cancer rate patterns look much more similar than for the countries that share the same geographic location, climate, and ethnic distribution, but differ in the level of economic development […]. This suggests that different countries may share common factors linked to economic prosperity that could be primarily responsible for the modern increases in overall cancer risk. […] Population aging (increases in the proportion of older people) may […] partly explain the rise in the global cancer burden […]; however, it cannot explain increases in age-specific cancer incidence rates over time […]. Improved diagnostics and elevated exposures to carcinogens may explain increases in rates for selected cancer sites, but they cannot fully explain the increase in the overall cancer risk, nor incidence rate trends for most individual cancers (Jemal et al. 2008, 2013).”
“[W]e propose that the association between the overall cancer risk and the economic progress and spread of the Western lifestyle could in part be explained by the higher proportion of individuals more susceptible to cancer in the populations of developed countries, and discuss several mechanisms of such an increase in the proportion of the vulnerable. […] mechanisms include but are not limited to: (i) Improved survival of frail individuals. […] (ii) Avoiding or reducing traditional exposures. Excessive disinfection and hygiene typical of the developed world can diminish exposure to some factors that were abundant in the past […] Insufficiently or improperly trained immune systems may be less capable of resisting cancer. (iii) Burden of novel exposures. Some new medicines, cleaning agents, foods, etc., that are not carcinogenic themselves may still affect the natural ways of processing carcinogens in the body, and through this increase a person’s susceptibility to established carcinogens. [If this one sounds implausible to you, I’ll remind you that drug metabolism is complicated – US] […] (iv) Some of the factors linked to economic prosperity and the Western lifestyle (e.g., delayed childbirth and food enriched with growth factors) may antagonistically influence aging and cancer risk.”
They provide detailed coverage of all of these mechanisms in the chapter, below I have included a few select observations from that part of the coverage.
“There was a dramatic decline in infant and childhood mortality in developed countries during the last century. For example, the infant mortality rate in the United States was about 6 % of live births in 1935, 3 % in 1950, 1.3 % in 1980, and 0.6 % in 2010. That is, it declined tenfold over the course of 75 years […] Because almost all children (including those with immunity deficiencies) survive, the proportion of the children who are inherently more vulnerable could be higher in the more developed countries. This is consistent with a typically higher proportion of children with chronic inflammatory immune disorders such as asthma and allergy in the populations of developed countries compared to less developed ones […] Over-reduction of such traditional exposures may result in an insufficiently/improperly trained immune system early in life, which could make it less able to resist diseases, including cancer later in life […] There is accumulating evidence of the important role of these effects in cancer risk. […] A number of studies have connected excessive disinfection and lack of antigenic stimulation (especially in childhood) of the immune system in Westernized communities with increased risks of both chronic inflammatory diseases and cancer […] The IARC data on migrants to Israel […] allow for comparison of the age trajectories of cancer incidence rates between adult Jews who live in Israel but were born in other countries […] [These data] show that Jews born in less developed regions (Africa and Asia) have overall lower cancer risk than those born in the more developed regions (Europe and America). The discrepancy is unlikely to be due to differences in cancer diagnostics because at the moment of diagnosis all these people were citizens of the same country with the same standard of medical care. These results suggest that surviving childhood and growing up in a less developed country with diverse environmental exposures might help form resistance to cancer that lasts even after moving to a high risk country.”
I won’t go much into the ‘burden of novel exposures’ part, but I should note that exposures that may be relevant include factors like paracetamol use and antibiotics for treatment of H. pylori. Paracetamol is not considered carcinogenic by the IARC, but we know from animal studies that if you give rats paratamol and then expose them to an established carcinogen (with the straightforward name N-nitrosoethyl-N-hydroxyethylamine), the number of rats developing kidney cancer goes up. In the context of H. pylori, we know that these things may cause stomach cancer, but when you treat rats with metronidazol (which is used to treat H. pylori) and expose them to an established carcinogen, they’re more likely to develop colon cancer. The link between colon cancer and antibiotics use has been noted in other contexts as well; decreased microbial diversity after antibiotics use may lead to suppression of the bifidobacteria and promotion of E. coli in the colon, the metabolic products of which may lead to increased cancer risk. Over time an increase in colon cancer risk and a decrease in stomach cancer risk has been observed in developed societies, but aside from changes in diet another factor which may play a role is population-wide exposure to antibiotics. Colon and stomach cancers are incidentally not the only ones of interest in this particular context; it has also been found that exposure to chloramphenicol, a broad-spectrum antibiotic used since the 40es, increases the risk of lymphoma in mice when the mice are exposed to a known carcinogen, despite the drug itself again not being clearly carcinogenic on its own.
Many new exposures aside from antibiotics are of course relevant. Two other drug-related ones that might be worth mentioning are hormone replacement therapy and contraceptives. HRT is not as commonly used today as it was in the past, but to give some idea of the scope here, half of all women in the US aged 50-65 are estimated to have been on HRT at the peak of its use, around the turn of the millennium, and HRT is assumed to be partly responsible for the higher incidence of hormone-related cancers observed in female populations living in developed countries. It’s of some note that the use of HRT dropped dramatically shortly after this peak (from 61 million prescriptions in 2001 to 21 million in 2004), and that the incidence of estrogen-receptor positive cancers subsequently dropped. As for oral contraceptives, these have been in use since the 1960s, and combined hormonal contraceptives are known to increase the risk of liver- and breast cancer, while seemingly also having a protective effect against endometrial cancer and ovarian cancer. The authors speculate that some of the cancer incidence changes observed in the US during the latter half of the last century, with a decline in female endometrial and ovarian cancer combined with an increase in breast- and liver cancer, could in part be related to widespread use of these drugs. An estimated 10% of all women of reproductive age alive in the world, and 16% of those living in the US, are estimated to be using combined hormonal contraceptives. In the context of the protective effect of the drugs, it should perhaps be noted that endometrial cancer in particular is strongly linked to obesity so if you are not overweight you are relatively low-risk.
Many ‘exposures’ in a cancer context are not drug-related. For example women in Western societies tend to go into menopause at a higher age, and higher age of menopause has been associated with hormone-related cancers; but again the picture is not clear in terms of how the variable affects longevity, considering that later menopause has also been linked to increased longevity in several large studies. In the studies the women did have higher mortality from the hormone-related cancers, but on the other hand they were less likely to die from some of the other causes, such as pneumonia, influenza, and falls. Age of childbirth is also a variable where there are significant differences between developed countries and developing countries, and this variable may also be relevant to cancer incidence as it has been linked to breast cancer and melanoma; in one study women who first gave birth after the age of 35 had a 40% increased risk of breast cancer compared to mothers who gave birth before the age of 20 (good luck ‘controlling for everything’ in a context like that, but…), and in a meta-analysis the relative risk for melanoma was 1.47 for women in the oldest age group having given birth, compared to the youngest (again, good luck controlling for everything, but at least it’s not just one study). Lest you think this literature only deals with women, it’s also been found that parental age seems to be linked to cancers in the offspring (higher parental age -> higher cancer risk in the offspring), though the effect sizes are not mentioned in the coverage.
Here’s what they conclude at the end of the chapter:
“Some of the factors associated with economic prosperity and a Western lifestyle may influence both aging and vulnerability to cancer, sometimes oppositely. Current evidence supports a possibility of trade-offs between cancer and aging-related phenotypes […], which could be influenced by delayed reproduction and exposures to growth factors […]. The latter may be particularly beneficial at very old age. This is because the higher levels of growth factors may attenuate some phenotypes of physical senescence, such as decline in regenerative and healing ability, sarcopenia, frailty, elderly fractures and heart failure due to muscles athrophy. They may also increase the body’s vulnerability to cancer, e.g., through growth promoting and anti-apoptotic effects […]. The increase in vulnerability to cancer due to growth factors can be compatible with extreme longevity because cancer is a major contributor to mortality mainly before age 85, while senescence-related causes (such as physical frailty) become major contributors to mortality at oldest old ages (85+). In this situation, the impact of growth factors on vulnerability to death could be more deleterious in middle-to-old life (~before 85) and more beneficial at older ages (85+).
The complex relationships between aging, cancer, and longevity are challenging. This complexity warns against simplified approaches to extending longevity without taking into account the possible trade-offs between phenotypes of physical aging and various health disorders, as well as the differential impacts of such tradeoffs on mortality risks at different ages (e.g., Ukraintseva and Yashin 2003a; Yashin et al. 2009; Ukraintseva et al. 2010, 2016).”
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).”
“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.”
“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).
“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 Parkinson’s 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.”
Here’s the first post about the book. I finished it a while ago but I recently realized I had not completed my intended coverage of the book here on the blog back then, and as some of the book’s material sort-of-kind-of relates to material encountered in a book I’m currently reading (Biodemography of Aging) I decided I might as well finish my coverage of the book now in order to review some things I might have forgot in the meantime, by providing coverage here of some of the material covered in the second half of the book. It’s a nice book with some interesting observations, but as I also pointed out in my first post it is definitely not an easy read. Below I have included some observations from the book’s second half.
“The aged lung is characterised by airspace enlargement similar to, but not identical with acquired emphysema . Such tissue damage is detected even in non-smokers above 50 years of age as the septa of the lung alveoli are destroyed and the enlarged alveolar structures result in a decreased surface for gas exchange […] Additional problems are that surfactant production decreases with age  increasing the effort needed to expand the lungs during inhalation in the already reduced thoracic cavity volume where the weakened muscles are unable to thoroughly ventilate. […] As ageing is associated with respiratory muscle strength reduction, coughing becomes difficult making it progressively challenging to eliminate inhaled particles, pollens, microbes, etc. Additionally, ciliary beat frequency (CBF) slows down with age impairing the lungs’ first line of defence: mucociliary clearance  as the cilia can no longer repel invading microorganisms and particles. Consequently e.g. bacteria can more easily colonise the airways leading to infections that are frequent in the pulmonary tract of the older adult.”
“With age there are dramatic changes in neutrophil function, including reduced chemotaxis, phagocytosis and bactericidal mechanisms […] reduced bactericidal function will predispose to infection but the reduced chemotaxis also has consequences for lung tissue as this results in increased tissue bystander damage from neutrophil elastases released during migration […] It is currently accepted that alterations in pulmonary PPAR profile, more precisely loss of PPARγ activity, can lead to inflammation, allergy, asthma, COPD, emphysema, fibrosis, and cancer […]. Since it has been reported that PPARγ activity decreases with age, this provides a possible explanation for the increasing incidence of these lung diseases and conditions in older individuals .”
“Age is an important risk factor for cancer and subjects aged over 60 also have a higher risk of comorbidities. Approximately 50 % of neoplasms occur in patients older than 70 years […] a major concern for poor prognosis is with cancer patients over 70–75 years. These patients have a lower functional reserve, a higher risk of toxicity after chemotherapy, and an increased risk of infection and renal complications that lead to a poor quality of life. […] [Whereas] there is a difference in organs with higher cancer incidence in developed versus developing countries [,] incidence increases with ageing almost irrespective of country […] The findings from Surveillance, Epidemiology and End Results Program [SEER – incidentally I likely shall at some point discuss this one in much more detail, as the aforementioned biodemography textbook covers this data in a lot of detail.. – US]  show that almost a third of all cancer are diagnosed after the age of 75 years and 70 % of cancer-related deaths occur after the age of 65 years. […] The traditional clinical trial focus is on younger and healthier patient, i.e. with few or no co-morbidities. These restrictions have resulted in a lack of data about the optimal treatment for older patients  and a poor evidence base for therapeutic decisions. […] In the older patient, neutropenia, anemia, mucositis, cardiomyopathy and neuropathy — the toxic effects of chemotherapy — are more pronounced […] The correction of comorbidities and malnutrition can lead to greater safety in the prescription of chemotherapy […] Immunosenescence is a general classification for changes occurring in the immune system during the ageing process, as the distribution and function of cells involved in innate and adaptive immunity are impaired or remodelled […] Immunosenescence is considered a major contributor to cancer development in aged individuals“.
“Dementia and age-related vision loss are major causes of disability in our ageing population and it is estimated that a third of people aged over 75 are affected. […] age is the largest risk factor for the development of neurodegenerative diseases […] older patients with comorbidities such as atherosclerosis, type II diabetes or those suffering from repeated or chronic systemic bacterial and viral infections show earlier onset and progression of clinical symptoms […] analysis of post-mortem brain tissue from healthy older individuals has provided evidence that the presence of misfolded proteins alone does not correlate with cognitive decline and dementia, implying that additional factors are critical for neural dysfunction. We now know that innate immune genes and life-style contribute to the onset and progression of age-related neuronal dysfunction, suggesting that chronic activation of the immune system plays a key role in the underlying mechanisms that lead to irreversible tissue damage in the CNS. […] Collectively these studies provide evidence for a critical role of inflammation in the pathogenesis of a range of neurodegenerative diseases, but the factors that drive or initiate inflammation remain largely elusive.”
“The effect of infection, mimicked experimentally by administration of bacterial lipopolysaccharide (LPS) has revealed that immune to brain communication is a critical component of a host organism’s response to infection and a collection of behavioural and metabolic adaptations are initiated over the course of the infection with the purpose of restricting the spread of a pathogen, optimising conditions for a successful immune response and preventing the spread of infection to other organisms . These behaviours are mediated by an innate immune response and have been termed ‘sickness behaviours’ and include depression, reduced appetite, anhedonia, social withdrawal, reduced locomotor activity, hyperalgesia, reduced motivation, cognitive impairment and reduced memory encoding and recall […]. Metabolic adaptation to infection include fever, altered dietary intake and reduction in the bioavailability of nutrients that may facilitate the growth of a pathogen such as iron and zinc . These behavioural and metabolic adaptions are evolutionary highly conserved and also occur in humans”.
“Sickness behaviour and transient microglial activation are beneficial for individuals with a normal, healthy CNS, but in the ageing or diseased brain the response to peripheral infection can be detrimental and increases the rate of cognitive decline. Aged rodents exhibit exaggerated sickness and prolonged neuroinflammation in response to systemic infection […] Older people who contract a bacterial or viral infection or experience trauma postoperatively, also show exaggerated neuroinflammatory responses and are prone to develop delirium, a condition which results in a severe short term cognitive decline and a long term decline in brain function […] Collectively these studies demonstrate that peripheral inflammation can increase the accumulation of two neuropathological hallmarks of AD, further strengthening the hypothesis that inflammation i[s] involved in the underlying pathology. […] Studies from our own laboratory have shown that AD patients with mild cognitive impairment show a fivefold increased rate of cognitive decline when contracting a systemic urinary tract or respiratory tract infection […] Apart from bacterial infection, chronic viral infections have also been linked to increased incidence of neurodegeneration, including cytomegalovirus (CMV). This virus is ubiquitously distributed in the human population, and along with other age-related diseases such as cardiovascular disease and cancer, has been associated with increased risk of developing vascular dementia and AD [66, 67].”
“Frailty is associated with changes to the immune system, importantly the presence of a pro-inflammatory environment and changes to both the innate and adaptive immune system. Some of these changes have been demonstrated to be present before the clinical features of frailty are apparent suggesting the presence of potentially modifiable mechanistic pathways. To date, exercise programme interventions have shown promise in the reversal of frailty and related physical characteristics, but there is no current evidence for successful pharmacological intervention in frailty. […] In practice, acute illness in a frail person results in a disproportionate change in a frail person’s functional ability when faced with a relatively minor physiological stressor, associated with a prolonged recovery time […] Specialist hospital services such as surgery , hip fractures  and oncology  have now begun to recognise frailty as an important predictor of mortality and morbidity.”
I should probably mention here that this is another area where there’s an overlap between this book and the biodemography text I’m currently reading; chapter 7 of the latter text is about ‘Indices of Cumulative Deficits’ and covers this kind of stuff in a lot more detail than does this one, including e.g. detailed coverage of relevant statistical properties of one such index. Anyway, back to the coverage:
“Population based studies have demonstrated that the incidence of infection and subsequent mortality is higher in populations of frail people. […] The prevalence of pneumonia in a nursing home population is 30 times higher than the general population [39, 40]. […] The limited data available demonstrates that frailty is associated with a state of chronic inflammation. There is also evidence that inflammageing predates a diagnosis of frailty suggesting a causative role. […] A small number of studies have demonstrated a dysregulation of the innate immune system in frailty. Frail adults have raised white cell and neutrophil count. […] High white cell count can predict frailty at a ten year follow up . […] A recent meta-analysis and four individual systematic reviews have found beneficial evidence of exercise programmes on selected physical and functional ability […] exercise interventions may have no positive effect in operationally defined frail individuals. […] To date there is no clear evidence that pharmacological interventions improve or ameliorate frailty.”
“[A]s we get older the time and intensity at which we exercise is severely reduced. Physical inactivity now accounts for a considerable proportion of age-related disease and mortality. […] Regular exercise has been shown to improve neutrophil microbicidal functions which reduce the risk of infectious disease. Exercise participation is also associated with increased immune cell telomere length, and may be related to improved vaccine responses. The anti-inflammatory effect of regular exercise and negative energy balance is evident by reduced inflammatory immune cell signatures and lower inflammatory cytokine concentrations. […] Reduced physical activity is associated with a positive energy balance leading to increased adiposity and subsequently systemic inflammation . […] Elevated neutrophil counts accompany increased inflammation with age and the increased ratio of neutrophils to lymphocytes is associated with many age-related diseases including cancer . Compared to more active individuals, less active and overweight individuals have higher circulating neutrophil counts . […] little is known about the intensity, duration and type of exercise which can provide benefits to neutrophil function. […] it remains unclear whether exercise and physical activity can override the effects of NK cell dysfunction in the old. […] A considerable number of studies have assessed the effects of acute and chronic exercise on measures of T-cell immunesenescence including T cell subsets, phenotype, proliferation, cytokine production, chemotaxis, and co-stimulatory capacity. […] Taken together exercise appears to promote an anti-inflammatory response which is mediated by altered adipocyte function and improved energy metabolism leading to suppression of pro-inflammatory cytokine production in immune cells.”
“A recent study estimated that 234 million surgical procedures requiring anaesthesia are performed worldwide annually. Anaesthesia is the largest hospital specialty in the UK, with over 12,000 practising anaesthetists […] In this book, I give a short account of the historical background of anaesthetic practice, a review of anaesthetic equipment, techniques, and medications, and a discussion of how they work. The risks and side effects of anaesthetics will be covered, and some of the subspecialties of anaesthetic practice will be explored.”
I liked the book, and I gave it three stars on goodreads; I was closer to four stars than two. Below I have added a few sample observations from the book, as well as what turned out in the end to be actually a quite considerable number of links (more than 60 it turned out, from a brief count) to topics/people/etc. discussed or mentioned in the text. I decided to spend a bit more time finding relevant links than I’ve previously done when writing link-heavy posts, so in this post I have not limited myself to wikipedia articles and I e.g. also link directly to primary literature discussed in the coverage. The links provided are, as usual, meant to be indicators of which kind of stuff is covered in the book, rather than an alternative to the book; some of the wikipedia articles in particular I assume are not very good (the main point of a link to a wikipedia article of questionable quality should probably be taken to be an indication that I consider ‘awareness of the existence of concept X’ to be of interest/important also to people who have not read this book, even if no great resource on the topic was immediately at hand to me).
Sample observations from the book:
“[G]eneral anaesthesia is not sleep. In physiological terms, the two states are very dissimilar. The term general anaesthesia refers to the state of unconsciousness which is deliberately produced by the action of drugs on the patient. Local anaesthesia (and its related terms) refers to the numbness produced in a part of the body by deliberate interruption of nerve function; this is typically achieved without affecting consciousness. […] The purpose of inhaling ether vapour [in the past] was so that surgery would be painless, not so that unconsciousness would necessarily be produced. However, unconsciousness and immobility soon came to be considered desirable attributes […] For almost a century, lying still was the only reliable sign of adequate anaesthesia.”
“The experience of pain triggers powerful emotional consequences, including fear, anger, and anxiety. A reasonable word for the emotional response to pain is ‘suffering’. Pain also triggers the formation of memories which remind us to avoid potentially painful experiences in the future. The intensity of pain perception and suffering also depends on the mental state of the subject at the time, and the relationship between pain, memory, and emotion is subtle and complex. […] The effects of adrenaline are responsible for the appearance of someone in pain: pale, sweating, trembling, with a rapid heart rate and breathing. Additionally, a hormonal storm is activated, readying the body to respond to damage and fight infection. This is known as the stress response. […] Those responses may be abolished by an analgesic such as morphine, which will counteract all those changes. For this reason, it is routine to use analgesic drugs in addition to anaesthetic ones. […] Typical anaesthetic agents are poor at suppressing the stress response, but analgesics like morphine are very effective. […] The hormonal stress response can be shown to be harmful, especially to those who are already ill. For example, the increase in blood coagulability which evolved to reduce blood loss as a result of injury makes the patient more likely to suffer a deep venous thrombosis in the leg veins.”
“If we monitor the EEG of someone under general anaesthesia, certain identifiable changes to the signal occur. In general, the frequency spectrum of the signal slows. […] Next, the overall power of the signal diminishes. In very deep general anaesthesia, short periods of electrical silence, known as burst suppression, can be observed. Finally, the overall randomness of the signal, its entropy, decreases. In short, the EEG of someone who is anaesthetized looks completely different from someone who is awake. […] Depth of anaesthesia is no longer considered to be a linear concept […] since it is clear that anaesthesia is not a single process. It is now believed that the two most important components of anaesthesia are unconsciousness and suppression of the stress response. These can be represented on a three-dimensional diagram called a response surface. [Here’s incidentally a recent review paper on related topics, US]”
“Before the widespread advent of anaesthesia, there were very few painkilling options available. […] Alcohol was commonly given as a means of enhancing the patient’s courage prior to surgery, but alcohol has almost no effect on pain perception. […] For many centuries, opium was the only effective pain-relieving substance known. […] For general anaesthesia to be discovered, certain prerequisites were required. On the one hand, the idea that surgery without pain was achievable had to be accepted as possible. Despite tantalizing clues from history, this idea took a long time to catch on. The few workers who pursued this idea were often openly ridiculed. On the other, an agent had to be discovered that was potent enough to render a patient suitably unconscious to tolerate surgery, but not so potent that overdose (hence accidental death) was too likely. This agent also needed to be easy to produce, tolerable for the patient, and easy enough for untrained people to administer. The herbal candidates (opium, mandrake) were too unreliable or dangerous. The next reasonable candidate, and every agent since, was provided by the proliferating science of chemistry.”
“Inducing anaesthesia by intravenous injection is substantially quicker than the inhalational method. Inhalational induction may take several minutes, while intravenous induction happens in the time it takes for the blood to travel from the needle to the brain (30 to 60 seconds). The main benefit of this is not convenience or comfort but patient safety. […] It was soon discovered that the ideal balance is to induce anaesthesia intravenously, but switch to an inhalational agent […] to keep the patient anaesthetized during the operation. The template of an intravenous induction followed by maintenance with an inhalational agent is still widely used today. […] Most of the drawbacks of volatile agents disappear when the patient is already anaesthetized [and] volatile agents have several advantages for maintenance. First, they are predictable in their effects. Second, they can be conveniently administered in known quantities. Third, the concentration delivered or exhaled by the patient can be easily and reliably measured. Finally, at steady state, the concentration of volatile agent in the patient’s expired air is a close reflection of its concentration in the patient’s brain. This gives the anaesthetist a reliable way of ensuring that enough anaesthetic is present to ensure the patient remains anaesthetized.”
“All current volatile agents are colourless liquids that evaporate into a vapour which produces general anaesthesia when inhaled. All are chemically stable, which means they are non-flammable, and not likely to break down or be metabolized to poisonous products. What distinguishes them from each other are their specific properties: potency, speed of onset, and smell. Potency of an inhalational agent is expressed as MAC, the minimum alveolar concentration required to keep 50% of adults unmoving in response to a standard surgical skin incision. MAC as a concept was introduced […] in 1963, and has proven to be a very useful way of comparing potencies of different anaesthetic agents. […] MAC correlates with observed depth of anaesthesia. It has been known for over a century that potency correlates very highly with lipid solubility; that is, the more soluble an agent is in lipid […], the more potent an anaesthetic it is. This is known as the Meyer-Overton correlation […] Speed of onset is inversely proportional to water solubility. The less soluble in water, the more rapidly an agent will take effect. […] Where immobility is produced at around 1.0 MAC, amnesia is produced at a much lower dose, typically 0.25 MAC, and unconsciousness at around 0.5 MAC. Therefore, a patient may move in response to a surgical stimulus without either being conscious of the stimulus, or remembering it afterwards.”
“The most useful way to estimate the body’s physiological reserve is to assess the patient’s tolerance for exercise. Exercise is a good model of the surgical stress response. The greater the patient’s tolerance for exercise, the better the perioperative outcome is likely to be […] For a smoker who is unable to quit, stopping for even a couple of days before the operation improves outcome. […] Dying ‘on the table’ during surgery is very unusual. Patients who die following surgery usually do so during convalescence, their weakened state making them susceptible to complications such as wound breakdown, chest infections, deep venous thrombosis, and pressure sores.”
“Mechanical ventilation is based on the principle of intermittent positive pressure ventilation (IPPV), gas being ‘blown’ into the patient’s lungs from the machine. […] Inflating a patient’s lungs is a delicate process. Healthy lung tissue is fragile, and can easily be damaged by overdistension (barotrauma). While healthy lung tissue is light and spongy, and easily inflated, diseased lung tissue may be heavy and waterlogged and difficult to inflate, and therefore may collapse, allowing blood to pass through it without exchanging any gases (this is known as shunt). Simply applying higher pressures may not be the answer: this may just overdistend adjacent areas of healthier lung. The ventilator must therefore provide a series of breaths whose volume and pressure are very closely controlled. Every aspect of a mechanical breath may now be adjusted by the anaesthetist: the volume, the pressure, the frequency, and the ratio of inspiratory time to expiratory time are only the basic factors.”
“All anaesthetic drugs are poisons. Remember that in achieving a state of anaesthesia you intend to poison someone, but not kill them – so give as little as possible. [Introductory quote to a chapter, from an Anaesthetics textbook – US] […] Other cells besides neurons use action potentials as the basis of cellular signalling. For example, the synchronized contraction of heart muscle is performed using action potentials, and action potentials are transmitted from nerves to skeletal muscle at the neuromuscular junction to initiate movement. Local anaesthetic drugs are therefore toxic to the heart and brain. In the heart, local anaesthetic drugs interfere with normal contraction, eventually stopping the heart. In the brain, toxicity causes seizures and coma. To avoid toxicity, the total dose is carefully limited”.
Links of interest:
Arthur Ernest Guedel.
Henry Hill Hickman.
William Thomas Green Morton.
James Young Simpson.
Joseph Thomas Clover.
Principles of Total Intravenous Anaesthesia (TIVA).
Laryngeal mask airway.
Gate control theory of pain.
Hartmann’s solution (…what this is called seems to be depending on whom you ask, but it’s called Hartmann’s solution in the book…).
Epidural nerve block.
Intensive care medicine.
Bjørn Aage Ibsen.
Pearse et al. (results of paper briefly discussed in the book).
Awareness under anaesthesia (skip the first page).
Pollard et al. (2007).
Postoperative nausea and vomiting.
Postoperative cognitive dysfunction.
Monk et al. (2008).
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.”
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.”
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).
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 . This is a particularly important issue, where primary care is paid through the Quality Outcomes Framework (QoF), a general practice “pay for performance” programme . 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 . […] Payment by results (PBR) has […] actively, albeit indirectly, disincentivised primary care to seek opinion from specialist services . […] 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 . […] There are calls for more integration and less fragmentation in health-care , 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 .”
“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 . […] 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 . […] 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.”
“Zhang et al.  […] 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  […] 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.  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.”
“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 %) . […] 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 . 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 . […] 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 . In an OECD ranking of 2011, Sweden was rated second worst . […] 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) . 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 , 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 , 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 . 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 . 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 . Furthermore, HbA1c rates above 9 % remain at approximately 20 %, in Southern California  or 19 % in Northern California , 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  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 .”
“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 , the directional accuracy of neutrophil migration to inflammatory agonists […] as well as bacterial peptides […] is significantly reduced . […] 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 . 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 , IL-6 […] and CRP  are associated with both all-cause […] and cause-specific […] mortality. Furthermore, inflammageing is a predictor of frailty  and is considered a major factor in the development of several age-related pathologies, such as atherosclerosis , Alzheimer’s disease  and sarcopenia .”
“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 . 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 .” [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 . 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 . Because changes are seen rapidly after primary infection in transplant patients  and infants , 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 . 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 . 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.”
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 . 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 . […] 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 . 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 , or segregation (absence of any cooperation) to full integration , 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 . 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 .”
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 , stroke , coronary heart disease , heart failure , and mortality . 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 .”
“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 . 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 . 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 . 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  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.  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.  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.  found that less than 20 % of studies that measured economic outcomes found a significant positive result. Similarly, de Bruin et al.  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 . 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.”
“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 . 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  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 . […] 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 . […] Metabolic syndrome including obesity, hypertension, insulin resistance, diabetes, and dyslipidemia increase the risk of developing multiple malignancies, particularly EmCa . Younger women diagnosed with EmCa are usually obese, and their carcinomas show a well-differentiated histology .”
“Normally, tumor suppressor genes act to inhibit or arrest cell proliferation and tumor development . 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 . Roughly 10–20% of endometrial carcinomas exhibit p53 mutations . […] 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 . […] 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] . More than 75% of patients who have had diabetes mellitus for more than 20 years will develop diabetic retinopathy (DR) . […] 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 . 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” .”
“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.  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].”
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 . 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 . In another study, this was reported to be 43 % and 12 %, respectively, in a Japanese population . 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) . 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 . 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 . 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 . 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 . 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 . 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 . The incident dialysis rate might even be higher after adjusting for patients dying from cardiovascular disease before reaching ESRD stage . 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 . 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 . 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  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 .”
“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 . 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 . The gain in projected years of life with transplantation amounted to 11 years in patients with DKD in comparison to patients without transplantation.”
“Data from animal studies in one country are usually comparable with that of another, provided the animal species and strain are the same. This provides a consistent picture of the basic pharmacological and toxicological actions of a candidate drug in a living organism […] it has been obvious since animal testing began that there would be large differences in the way a drug might perform in man compared with animal species […]. Unfortunately, there is no experimental model yet designed that can not only consider human biochemistry and physiology, but also the effects of age, smoking, legal and illegal drug usage, gender, diet, environment, disease and finally genetic variation. Indeed, many clinical studies have revealed enormous differences in drug clearance and pharmacological effect even in age, sex and ethnically matched individuals. In effect, this means that the first year or so of a drug’s clinical life is a vast, but monitored experiment, involving hundreds of thousands of patients and there is no guarantee of success.”
“Most biotransformational polymorphisms that might potentially cause a problem clinically are due to an inability of those with defective enzymes to remove the drug from the system. Drug failure can occur if the agent is administered as a pro-drug and requires some metabolic conversion to an active metabolite. Drug accumulation can lead to unpleasant side effects and loss of patient tolerance for the agent. […] Overall, there are a large number of factors that can influence drug metabolism, either by increasing clearance to cause drug failure, or by preventing clearance to lead to toxicity. In the real world, it is often impossible to delineate the different conflicting factors which result in net changes in drug clearance which cause a drug to fall out of, or climb above, the therapeutic window. It may only be possible clinically in many cases to try to change what appears to be the major cause to bring about a resolution of the situation to restore curative and non-toxic drug levels.”
“Most population studies of human polymorphisms list the allelic frequency, that is, how many of an ethnic group contain the alleles in question. […] The actual haplotypes in the population, that is, which individuals express which combinations of alleles, are not the same as the population allelic frequency. […] If an SNP or a combination of SNPs is a fairly mild defect in the enzyme when it is homozygously expressed, then the heterozygotes will show little impairment and the polymorphism may be clinically irrelevant. With other SNPs, the enzyme produced may be completely non-functional. Homozygotes will be virtually unable to clear the drug and heterozygotes will show impairment also. There are also smaller populations of UMs, or ultra rapid metabolizers which may have a feature of their enzyme which either makes it super efficient or expressed in abnormally high amounts. […] Phenotyping will group patients in very broad EMs [extensive metabolizers], IMs [intermediate metabolizers] or PM [poor metabolizers] categories, but will be unable to distinguish between heterozygous and homozygous EMs. Although genotyping may be very helpful in dosage estimation in the initiation of therapy, there is no substitute for the normal process of therapeutic monitoring, which is effectively phenotyping the individual in the real world in terms of maximizing response and minimizing toxicity.”
“it is clear that there is a vast amount of genetic variation across humanity in terms of biotransformational capability and so the idea that in therapeutics, ‘one size fits all’ is not only outdated, but fabulously naïve. […] Unfortunately, detecting and responding successfully to human biotransformational polymorphisms has proved to be extremely problematic. In terms of polymorphism detection, this area is a classic illustration of how the exploration of the human genome with powerful molecular biological tools may unearth many apparently marked polymorphic defects that may not necessarily translate into a measurable clinical impact in terms of efficacy and toxicity. In reality, many more scientists have the opportunity to discover and publish such polymorphisms in vitro, than there are clinical scientists, resources and indeed cooperative volunteers or patients in sufficient quantity to determine practical clinical relevance.”
“the CYP3A group (chromosome 7) metabolize around half of all drugs […] variation in the metabolism of CYP3A substrates […] can be up to ten-fold in terms of drug clearances and up to 90-fold in liver protein expression. […] It is likely that the full extent of the variation in CYP3A4 is still to be discovered […] While it is thought that CYP3A4 is not subject to an obvious major polymorphism, CYP3A5 definitely is. […] *3/*3 individuals form no serviceable CYP3A5. Functional CYP3A5 is found in around 20 per cent of Caucasians, half of Chinese/Japanese, 70 per cent of Hispanics and more than 80 per cent of African Americans.”
“A particularly dangerous polymorphism clinically was identified in the 1980s for one of the methyltransferases. The endogenous role of S-methylating thiopurine S-methyltransferase (TPMT) is not that clear, but […] [t]hese drugs are […] effective in some childhood leukaemias […] TPMT highlights the genotyping/phenotyping issue mentioned earlier in the management of patients with polymorphisms. Genotyping will reveal the level of TPMT expression that should be expected in the otherwise healthy patient. However, there are many factors which impact day-to-day TPMT expression during thiopurine therapy. […] Hence, what might be predicted from a genotype test may bear little resemblance to how the enzyme is performing on a particular day in a treatment cycle. So clinically, it is preferred to test actual TPMT activity.”
“Understanding of sulphonation and its roles in endogenous as well as xenobiotic metabolism is not as advanced compared with that of CYPs; however, the role of SULTs in the activation of carcinogens is becoming more apparent. One of the major influences on SULT activity is their polymorphic nature; in the case of one of the most important toxicologically relevant SULTs, SULT1A1, this isoform exists as three variants, SULT1A1*1 (wild-type), SULT1A1*2 and SULT1A1*3. The *1 variant allele is found in the majority of Caucasians (around 65 per cent), whilst the *2 variant differs only in the exchange of one amino acid for another. This single amino acid change has profound effects on the stability and catalytic activity of the isoform. The *2 variant is found in approximately 32 per cent of Caucasians and catalytically faulty […] About 9 in 10 Chinese people have the *1 allele and about 8 per cent have allele *2. About half of African-Americans have *1 and a third have *2. Interestingly, there is a *3 which is rare in most races but accounts for more than 22 per cent of African Americans. There is also considerable variation in SULT2A1 and SULT2B1, which are the major hydroxysteroid sulphators in the body, which may have implications for sex steroid and cholesterol handling. […] from the cancer-risk viewpoint, a highly active SULT1A1 *1 is usually an advantage in that it usually removes reactive species rapidly as stable sulphates. With some agents it is problematic as certain carcinogens such as acetylfluorene are indirectly activated to reactive species by SULTs. In addition, protective dietary flavonoids […] are also rapidly cleared by SULT1A1 *1, so there is a combination of production of toxins and loss of protective dietary agents. In terms of carcinogenesis risk, SULT1A1*2 could be a liability as potentially damaging substrates such as electrophilic toxins cannot be cleared rapidly. However, in some circumstances the *2 allele can be rather protective as […] it also allows protective agents [to] remain in tissues for longer periods. The combinations are endless and so it is often extremely difficult to predict risks of carcinogenicity for individuals and toxin exposures.”
“GSTs are polymorphic and much research has been directed at linking increased predisposition to cytotoxicity and carcinogenicity with defective GST phenotypes. Active wild-type GSTMu-1 is found in around 60 per cent of Caucasians, but a non-functional version of the isoform is found in the remainder. […] GST-M1 null (non-functional alleles) can predispose to risks of prostate abnormalities and GST Pi is subject to several SNPs and many attempts have been made to link these SNPs with the consequences of failure to detoxify reactive species, such as the risk of lung cancer. […] Carcinogenesis may be due to a complex mix of factors, where different enzyme expression and activities may combine with particular reactive species from specific parent xenobiotics that lead to DNA damage only in certain individuals. Resolving specific risk factors may be extremely difficult in such circumstances. […] in cancer chemotherapy, there is evidence that the presence of GST-M1 and GST-T1 null (non-functional) alleles predisposes children to a six-fold higher level of adverse events usually seen with antineoplastic drugs, such as bone marrow damage, nephrotoxicity and neurotoxicity.”
“The effects of age on drug clearance and metabolism have been known since the 1950s, but they have been extensively investigated in the last 20 or so years. It is now generally accepted that at the extremes of life, neonatal and geriatric, drug clearance can be significantly different from the rest of humanity. In general, neonates, i.e. those less than four weeks old, cannot clear certain agents due to immaturity of drug metabolizing systems. Those over retirement age cannot clear the drugs due to loss of efficiency in their metabolizing systems. Either way, the net result can be toxicity due to drug accumulation. […] It seems that the inability of older people to clear drugs is not necessarily related to the efficacy of their CYP-mediated oxidations, which are often not much different from that of younger individuals. Studies with the major CYPs in vitro have revealed that CYP2D6 is unaffected by age, as are most other CYPs, with the exception of CYP1A2, which does decline in activity in the elderly. […] In general, there is little significant change in the inducibility in most CYPs, or in the capability of conjugation systems in vitro. […] there are significant changes in the liver itself, as it decreases in mass and its blood flow is reduced as we age. This occurs at the rate of around 0.5–1.5 per cent per year, so by the time we hit 60–70, we may have up to a 40 per cent decline in liver blood flow compared with a 30-year-old. Other factors include gradual decline in renal function, increased fat deposits and reduction in gut blood flow, which affects absorption. […] The problem arises that the drug’s bioavailability increases due to lack of first-pass clearance; this means that from a standard dose, blood levels can be considerably higher than would be expected in a 40-year-old. This can be a serious problem in drugs with a narrow TI, such as antiarrhythmics. In addition, average doses of warfarin required to provide therapeutic anticoagulation in the elderly are less than half those required for younger people. The person’s lifelong smoking and drinking habits, as well as older individuals ’ sometimes erratic diet also complicate this situation. Among the drugs cleared more slowly in older people are antipsychotics, paracetamol, antidepressants, benzodiazepines, warfarin, beta-blockers and indomethicin.”
“Thousands of polyphenols are found in plants, vegetables, fruit, as well as tea, coffee, wine and fruit juices. […] Flavonoids such as quercetin and fisetin are excellent substrates for COMT, so competitively inhibiting the metabolism of endogenous catecholamine and catechol oestrogens. Quercetin and other polyphenols are found in various foods such as soy (genestein) and they are potent inhibitors of SULT1A1 which sulphate endogenous oestrogens, so potentiating the effects of oestrogens in the body. Many of these flavonoids and isoflavonoids are manufactured and sold as cancer preventative agents; however, it is more likely that their elevation of oestrogen levels may have the opposite effect in the long term. It is also likely that various polyphenols influence other endogenous substrates of sulphotransferases, such as thyroid hormones and various catecholamines. It is gradually becoming apparent that polyphenols can induce UGTs, indeed; it would be surprising if they did not. […] Overall, it is likely that there are a large number of polyphenols that are potent modulators of CYPs and conjugative enzymes. […] It is clear that diet can substantially modulate biotransformation […] As to the effects on prescription drugs, […] abrupt changes in a person’s diet may significantly alter the clearance of drugs and lead to loss of efficacy or toxicity.”
“In general, experimental or ‘probe’ drugs […] which are used to study the activities of a number of CYPs, are metabolized more quickly by women than men. This is allowing for differences in weight, fat distribution (body mass index) and volume of distribution […] It appears that CYP expression is linked to growth hormone (GH) and about the same amount is secreted over 24 hours in both sexes. In animals the pattern of release of the hormone is crucial to the effects on the CYPs; in females, GH is secreted in small but more or less continuous pulses, while males secrete large pulses, then periods of no secretion. The system is thought to be similar in humans. […] Little is known of the effects of the menopause and hormone replacement, where steroid metabolism changes dramatically. It is highly likely that these events could have profound effects on female drug clearance. […] females in general are more susceptible to drug adverse reactions than males, especially hepatotoxic effects.”
“For those chronically dependent on ethanol their CYP2E1 levels can be ten-fold higher than non-drinkers and they would clear CYP2E1 substrates extremely quickly if they chose to be sober for a period of time. This may lead to the accumulation of metabolites of the substrates. It is apparent that alcoholics who are sober can suffer paracetamol (acetaminophen)-induced liver toxicity at overdoses of around half that of non-drinkers, which is due to CYP2E1 induction. […] the vast variation in ADH [alcohol dehydrogenase] catalytic activity across the human race is mainly due to just a few SNPs that profoundly change the efficiency of the isoforms. ADH1B/*1 is the most effective variant and is the ADH wild-type […] Part of a ‘successful’ career as an alcoholic depends possessing the ADH1B/*1 isoform. The other defective isoforms are found in low frequencies in alcoholics and cirrhotics. […] in the vast majority of individuals, whatever their variant of ADH, they are able to process acetaldehyde to acetate and water, as the consequences of failing to do this are severe. With ALDH, the wild-type and gold standard is ALDH2*1/*1, which has the highest activity of all these isoforms and is the second essential component for an alcoholic career. […] the variant ALDH2*1/*2 has less than a quarter of the wild-type’s capacity and is found predominantly in Eastern races. The variant ALDH2*2/*2 is completely useless and renders the individuals very sensitive to acetaldehyde poisoning, although the toxin is removed eventually by ALDH1A1 which does not seem to be affected by polymorphisms. In a survey of 1300 Japanese alcoholics, there was nobody at all with the ALDH2*2/*2 variant. […] Women are much more vulnerable to ethanol damage and on average die in half the time it generally takes for a male alcoholic to drink himself to death. Women drink much less than men also – one study indicated that a group of women consumed about 14,000 drinks to induce cirrhosis, whilst men required more than 44,000 to achieve the same effect. Ethanol distributes in total body water only, so in women their greater fat content means that blood ethanol levels are higher than men of similar weight and age.“
“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.”
I was debating whether to blog this book at all, as it’s neither very long nor very good, but I decided it was worth adding a few observations from the book here. You can read my goodreads review of the publication here. Whenever quotes look a bit funny in the coverage below (i.e. when you see things like words in brackets or strangely located ‘[…]’, assume that the reason for this is that I tried to improve upon the occasionally frankly horrible language of some of the contributors to the publication. If you want to know exactly what they wrote, rather than what they presumably meant to write (basic grammar errors due to the authors having trouble with the English language are everywhere in this publication, and although I did choose to do so here I do feel a bit uncomfortable quoting a publication like this one verbatim on my blog), read the book.
I went off on a tangent towards the end of the post and I ended up adding some general remarks about medical cost, insurance and various other topics. So the post may have something of interest even to people who may not be highly interested in any of the stuff covered in the book itself.
“Despite intensive recommendations, [the] influenza vaccination rate in medical staff in Poland ranges from about 20 % in physicians to 10 % in nurses. […] It has been demonstrated that vaccination of health care workers against influenza significantly decreases mortality of elderly people remaining under [long-term care]. […] Vaccinating health care workers also substantially reduces sickness absenteeism, especially in emergency units […] Concerning physicians, vaccination avoidance stemmed from the lack of knowledge of protective value of vaccine (33 %), lack of time to get vaccinated (29 %), and Laziness (24 %). In nurses, these figures amounted to 55 %, 12 %, and 5 %, respectively (Zielonka et al. 2009).”
I just loved the fact that ‘laziness’ was included here as an explanatory variable, but on the other hand the fact that one-third of doctors cited lack of knowledge about the protective value of vaccination as a reason for not getting vaccinated is … well, let’s use the word ‘interesting’. But it gets even better:
“The questions asked and opinions expressed by physicians or nurses on vaccinations showed that their knowledge in this area was far from the current evidence-based medicine recommendations. Nurses, in particular, commonly presented opinions similar to those which can be found in anti-vaccination movements and forums […] The attitude of physicians toward influenza vaccination vary greatly. In many a ward, a majority of physicians were vaccinated (70–80 %). However, in the neurology and intensive care units the proportion of vaccinated physicians amounted only to 20 %. The reason for such a small yield […] was a critical opinion about the effectiveness and safety of vaccination. Similar differences, depending on medical specialty, were observed in Germany (4–71% of vaccines) (Roggendorf et al. 2011) […] It is difficult to explain the fear of influenza vaccination among the staff of intensive care units, since these are exactly the units where many patients with most severe cases of influenza are admitted and often die (Ayscue et al. 2014). In this group of health care workers, high efficiency of influenza vaccination has been clearly demonstrated […] In the present study a strong difference between the proportion of vaccinated physicians (55 %) and nurses (21 %) was demonstrated, which is in line with some data coming from other countries. In the US, 69 % of physicians and 46 % of nurses get a vaccine shot […] and in Germany the respective percentages are 39 % and 17 % […] In China, 21 % of nurses and only 13 % of physicians are vaccinated against influenza (Seale et al. 2010a), and in [South] Korea, 91 % and 68 % respectively (Lee et al. 2008).”
“[A] survey was conducted among Polish (243) and foreign (80) medical students at the Pomeranian Medical University in Szczecin, Poland. […] The survey results reveal that about 40 % of students were regular or occasional smoker[s]. […] 60 % of students declared themselves to be non-smokers, 20 % were occasional smokers, and 20 % were regular smokers”
40 % of medical students in a rather large sample turned out to be smokers. Wow. Yeah, I hadn’t seen that one coming. I’d probably expect a few alcoholics and I would probably not have been surprised about a hypothetical higher-than-average alcohol consumption in a sample like that (they don’t talk about alcohol so I don’t have data on this, I’m just saying I wouldn’t be surprised – after all I do know that doctors are high-risk for suicide), but such a large proportion smoking? That’s unexpected. It probably shouldn’t have been, considering that this is very much in line with the coverage included in Thirlaway & Upton’s book. I include some remarks about their coverage about smoking in my third post about the book here. The important observation of note from that part of the book’s coverage is probably that most smokers want to quit and yet very few manage to actually do it. “Although the majority of smokers want to stop smoking and predict that they will have stopped in twelve months, only 2–3 per cent actually stops permanently a year (Taylor et al. 2006).” If those future Polish doctors know that smoking is bad for them, but they assume that they can just ‘stop in time’ when ‘the time’ comes – well, some of those people are probably in for a nasty surprise (and they should have studied some more, so that they’d known this?).
“A prospective study of middle-aged British men […] revealed that the self-assessment of health status was strongly associated with mortality. Men who reported poor health had an eight-fold increase in total mortality compared with those reporting excellent health. Those who assessed their health as poor were manual workers, cigarette smokers, and often heavy drinkers. Half of those with poor health suffered from chest pain on exertion and other chronic diseases. Thus, self-assessment of health status appears to be a good measure of current physical health and risk of death“.
“It is estimated that globally 3.1 million people die each year due to chronic obstructive pulmonary disease (COPD). According to the World Health Organization (WHO 2014), the disease was the third leading cause of death worldwide in 2012. [In the next chapter of the book they state that: “COPD is currently the fourth leading cause of death among adult patients globally, and it is projected that it will be the third most common cause of death by 2020.” Whether it’s the third or fourth most common cause of death, it definitely kills a lot of people…] […] Approximately 40–50 % of lifelong smokers will go on to develop COPD […] the number of patients with a primary diagnosis of COPD […] constitutes […] 1.33 % of the total population of Poland. This result is consistent with that obtained during the Polish Spirometry Day in 2011 (Dabrowiecki et al. 2013) when 1.1 % of respondents declared having had a diagnosed COPD, while pulmonary function tests showed objectively the presence of obstruction in 12.3 % of patients.”
Based on numbers like these I feel tempted to conclude that the lungs may be yet another organ in which a substantial proportion of people of advanced age experience low-level organ dysfunction arguably not severe enough to lead to medical intervention. The kidneys are similar, as I also noted when I covered Longmore et al.‘s text.
“Generally, the costs of treatment of patients with COPD are highly variable […] estimates suggest […] that the costs of treatment of moderate stages of COPD may be 3–4-fold higher in comparison with the mild form of the disease, and in the severe form they reach up to 6–10 times the basic cost […] every second person with COPD is of working age […] Admission rates for COPD patients differ as much as 10-fold between European countries (European Lung White Book 2013).”
“In the EU, the costs of respiratory diseases are estimated at 6 % of the budget allocated to health care. Of this amount, 56 % is allocated for the treatment of COPD patients. […] Studies show that one per ten Poles over 30 year of age have COPD symptoms. Each year, around 4 % of all hospitalizations are due to COPD. […] One of the most important parameters regarding pharmacoeconomics is the hospitalization rate […] a high number of hospitalizations due to COPD exacerbations in Poland dramatically increase direct medical costs.”
I bolded the quote above because I knew this but had never seen it stated quite as clearly as it’s stated here, and I may be tempted to quote that one later on. Hospitalizations are often really expensive compared to drugs people who are not hospitalized take for their various health conditions, for example you can probably buy a year’s worth of anti-diabetic drugs, or more, for the costs of just one hospital admission due to drug mis-dosing. Before you get the idea that this might have ‘obvious implications’ for how ‘one’ should structure medical insurance arrangements in terms of copay structures etc., do however keep in mind that the picture here is really confusing:
Here’s the link, with more details – the key observation is that: “There is no consistency […] in the direction of change in costs resulting from changes in compliance”. That’s not diabetes, that’s ‘stuff in general’.
It would be neat if you could e.g. tell a story about how high costs of a drug always lead to non-compliance, which lead to increased hospitalization rates, which lead to higher costs than if the drugs had been subsidized. That would be a very strong case for subsidization. Or it would be neat if you could say that it doesn’t matter whether you subsidize a drug or not, because the costs of drugs are irrelevant in terms of usage patterns – people are told to take one pill every day by their doctor, and by golly that’s what they’re doing, regardless of what those pills cost. I know someone personally who wrote a PhD thesis about a drug where that clearly wasn’t the case, and the price elasticity was supposed to be ‘theoretically low’ in that case, so that one’s obviously out ‘in general’, but the point is that people have looked at this stuff, a lot. I’m assuming you might be able to spot a dynamic like this in some situations, and different dynamics in the case of other drugs. It gets even better when you include complicating phenomena like cost-switching; perhaps the guy/organization responsible for potentially subsidizing the drug is not the same guy(/-…) as the guy who’s supposed to pay for the medical admissions (this depends on the insurance structure/setup). But that’s not always the case, and the decision as to who pays for what is not necessarily a given; it may depend e.g. on health care provider preferences, and those preferences may themselves depend upon a lot of things unrelated to patient preferences or -incentives. A big question even in the relatively simple situation where the financial structure is – for these purposes at least – simple, is also the extent to which relevant costs are even measured, and/or how they’re measured (if a guy dies due to a binding budget constraint resulting in no treatment for a health condition that would have been treatable with a drug, is that outcome supposed to be ‘very cheap’ (he didn’t pay anything for drugs, so there were no medical outlays) or very expensive (he could have worked for another two decades if he’d been treated, and those productivity losses need to be included in the calculation somehow; to focus solely on medical outlays is thus to miss the point)? An important analytical point here is that if you don’t explicitly make those deaths/productivity losses expensive, they are going to look very cheap, because the default option will always be to have them go unrecorded and untallied.
A problem not discussed in the coverage was incidentally the extent to which survey results pertaining to the cost of vaccination are worth much. You ask doctors why they didn’t get vaccinated, and they tell you it’s because it’s too expensive. Well, how many of them would you have expected to tell you they did not get vaccinated because the vaccines were too cheap? This is more about providing people with a perceived socially acceptable out than it is about finding stuff out about their actual reasons for behaving the way they do. If the price of vaccination does not vary across communities it’s difficult to estimate the price elasticity, true (if it does, you probably got an elasticity estimate right there), but using survey information to implicitly assess the extent to which the price is too high? Allow the vaccination price to vary next year/change it/etc. (or even simpler/cheaper, if those data exist; look at price variation which happened in the past and observe how the demand varied), and see if/how the doctors and nurses respond. That’s how you do this, you don’t ask people. Asking people is also actually sort of risky; I’m pretty sure a smart doctor could make an argument that if you want doctors to get vaccinated you should pay them for getting the shot – after all, getting vaccinated is unpleasant, and as mentioned there are positive externalities here in terms of improved patient outcomes, which might translate into specific patients not dying, which is probably a big deal, for those patients at least. The smart doctor wouldn’t necessarily be wrong; if the price of vaccination was ‘sufficiently low’, i.e. a ‘large’ negative number (‘if you get vaccinated, we give you $10.000’), I’m pretty sure coverage rates would go up a lot. That doesn’t make it a good idea. (Or a bad idea per se, for that matter – it depends upon the shape of the implicit social welfare function we’re playing around with. Though I must add – so that any smart doctors potentially reading along here don’t get any ideas – that a ‘large’ negative price of vaccination for health care workers is a bad idea if a cheaper option which achieves the same outcome is potentially available to the decision makers in question, which seems highly likely to me. For example vaccination rates of medical staff would also go up a lot if regular vaccinations were made an explicit condition of their employment, the refusal of which would lead to termination of their employment… There would be implicit costs of such a scheme, in terms of staff selection effects, but if you’re comparing solely those options and you’re the guy who makes the financial decisions..?)
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.”