Econstudentlog

Type 1 Diabetes Mellitus and Cardiovascular Disease

“Despite the known higher risk of cardiovascular disease (CVD) in individuals with type 1 diabetes mellitus (T1DM), the pathophysiology underlying the relationship between cardiovascular events, CVD risk factors, and T1DM is not well understood. […] The present review will focus on the importance of CVD in patients with T1DM. We will summarize recent observations of potential differences in the pathophysiology of T1DM compared with T2DM, particularly with regard to atherosclerosis. We will explore the implications of these concepts for treatment of CVD risk factors in patients with T1DM. […] The statement will identify gaps in knowledge about T1DM and CVD and will conclude with a summary of areas in which research is needed.”

The above quote is from this paper: Type 1 Diabetes Mellitus and Cardiovascular Disease: A Scientific Statement From the American Heart Association and American Diabetes Association.

I originally intended to cover this one in one of my regular diabetes posts, but I decided in the end that there was simply too much stuff to cover here for it to make sense not to devote an entire post to it. I have quoted extensively from the paper/statement below and I also decided to bold a few of the observations I found particularly important/noteworthy(/worth pointing out to people reading along?).

“T1DM has strong human leukocyte antigen associations to the DQA, DQB, and DRB alleles (2). One or more autoantibodies, including islet cell, insulin, glutamic acid decarboxylase 65 (GAD65), zinc transporter 8 (3), and tyrosine phosphatase IA-2β and IA-2β antibodies, can be detected in 85–90% of individuals on presentation. The rate of β-cell destruction varies, generally occurring more rapidly at younger ages. However, T1DM can also present in adults, some of whom can have enough residual β-cell function to avoid dependence on insulin until many years later. When autoantibodies are present, this is referred to as latent autoimmune diabetes of adulthood. Infrequently, T1DM can present without evidence of autoimmunity but with intermittent episodes of ketoacidosis; between episodes, the need for insulin treatment can come and go. This type of DM, called idiopathic diabetes (1) or T1DM type B, occurs more often in those of African and Asian ancestry (4). Because of the increasing prevalence of obesity in the United States, there are also obese individuals with T1DM, particularly children. Evidence of insulin resistance (such as acanthosis nigricans); fasting insulin, glucose, and C-peptide levels; and the presence of islet cell, insulin, glutamic acid decarboxylase, and phosphatase autoantibodies can help differentiate between T1DM and T2DM, although both insulin resistance and insulin insufficiency can be present in the same patient (5), and rarely, T2DM can present at an advanced stage with low C-peptide levels and minimal islet cell function.”

Overall, CVD events are more common and occur earlier in patients with T1DM than in nondiabetic populations; women with T1DM are more likely to have a CVD event than are healthy women. CVD prevalence rates in T1DM vary substantially based on duration of DM, age of cohort, and sex, as well as possibly by race/ethnicity (8,11,12). The Pittsburgh Epidemiology of Diabetes Complications (EDC) study demonstrated that the incidence of major coronary artery disease (CAD) events in young adults (aged 28–38 years) with T1DM was 0.98% per year and surpassed 3% per year after age 55 years, which makes it the leading cause of death in that population (13). By contrast, incident first CVD in the nondiabetic population ranges from 0.1% in 35- to 44-year-olds to 7.4% in adults aged 85–94 years (14). An increased risk of CVD has been reported in other studies, with the age-adjusted relative risk (RR) for CVD in T1DM being ≈10 times that of the general population (1517). One of the most robust analyses of CVD risk in this disease derives from the large UK General Practice Research Database (GPRD), comprising data from >7,400 patients with T1DM with a mean ± SD age of 33 ± 14.5 years and a mean DM duration of 15 ± 12 years (8). CVD events in the UK GPRD study occurred on average 10 to 15 years earlier than in matched nondiabetic control subjects.”

“When types of CVD are reported separately, CHD [coronary heart disease] predominates […] The published cumulative incidence of CHD ranges between 2.1% (18) and 19% (19), with most studies reporting cumulative incidences of ≈15% over ≈15 years of follow-up (2022). […] Although stroke is less common than CHD in T1DM, it is another important CVD end point. Reported incidence rates vary but are relatively low. […] the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) reported an incidence rate of 5.9% over 20 years (≈0.3%) (21); and the European Diabetes (EURODIAB) Study reported a 0.74% incidence of cerebrovascular disease per year (18). These incidence rates are for the most part higher than those reported in the general population […] PAD [peripheral artery disease] is another important vascular complication of T1DM […] The rate of nontraumatic amputation in T1DM is high, occurring at 0.4–7.2% per year (28). By 65 years of age, the cumulative probability of lower-extremity amputation in a Swedish administrative database was 11% for women with T1DM and 20.7% for men (10). In this Swedish population, the rate of lower-extremity amputation among those with T1DM was nearly 86-fold that of the general population.

“Abnormal vascular findings associated with atherosclerosis are also seen in patients with T1DM. Coronary artery calcification (CAC) burden, an accepted noninvasive assessment of atherosclerosis and a predictor of CVD events in the general population, is greater in people with T1DM than in nondiabetic healthy control subjects […] With regard to subclinical carotid disease, both carotid intima-media thickness (cIMT) and plaque are increased in children, adolescents, and adults with T1DM […] compared with age- and sex-matched healthy control subjects […] Endothelial function is altered even at a very early stage of T1DM […] Taken together, these data suggest that preclinical CVD can be seen more frequently and to a greater extent in patients with T1DM, even at an early age. Some data suggest that its presence may portend CVD events; however, how these subclinical markers function as end points is not clear.”

“Neuropathy in T1DM can lead to abnormalities in the response of the coronary vasculature to sympathetic stimulation, which may manifest clinically as resting tachycardia or bradycardia, exercise intolerance, orthostatic hypotension, loss of the nocturnal decline in BP, or silent myocardial ischemia on cardiac testing. These abnormalities can lead to delayed presentation of CVD. An early indicator of cardiac autonomic neuropathy is reduced heart rate variability […] Estimates of the prevalence of cardiac autonomic neuropathy in T1DM vary widely […] Cardiac neuropathy may affect as many as ≈40% of individuals with T1DM (45).”

CVD events occur much earlier in patients with T1DM than in the general population, often after 2 decades of T1DM, which in some patients may be by age 30 years. Thus, in the EDC study, CVD was the leading cause of death in T1DM patients after 20 years of disease duration, at rates of >3% per year (13). Rates of CVD this high fall into the National Cholesterol Education Program’s high-risk category and merit intensive CVD prevention efforts (48). […] CVD events are not generally expected to occur during childhood, even in the setting of T1DM; however, the atherosclerotic process begins during childhood. Children and adolescents with T1DM have subclinical CVD abnormalities even within the first decade of DM diagnosis according to a number of different methodologies”.

Rates of CVD are lower in premenopausal women than in men […much lower: “Cardiovascular disease develops 7 to 10 years later in women than in men” – US]. In T1DM, these differences are erased. In the United Kingdom, CVD affects men and women with T1DM equally at <40 years of age (23), although after age 40 years, men are affected more than women (51). Similar findings on CVD mortality rates were reported in a large Norwegian T1DM cohort study (52) and in the Allegheny County (PA) T1DM Registry (13), which reported the relative impact of CVD compared with the general population was much higher for women than for men (standardized mortality ratio [SMR] 13.2 versus 5.0 for total mortality and 24.7 versus 8.8 for CVD mortality, women versus men). […] Overall, T1DM appears to eliminate most of the female sex protection seen in the nondiabetic population.”

“The data on atherosclerosis in T1DM are limited. A small angiographic study compared 32 individuals with T1DM to 31 nondiabetic patients matched for age and symptoms (71). That study found atherosclerosis in the setting of T1DM was characterized by more severe (tighter) stenoses, more extensive involvement (multiple vessels), and more distal coronary findings than in patients without DM. A quantitative coronary angiographic study in T1DM suggested more severe, distal disease and an overall increased burden compared with nondiabetic patients (up to fourfold higher) (72).”

“In the general population, inflammation is a central pathological process of atherosclerosis (79). Limited pathology data suggest that inflammation is more prominent in patients with DM than in nondiabetic control subjects (70), and those with T1DM in particular are affected. […] Knowledge of the clinical role of inflammatory markers in T1DM and CVD prediction and management is in its infancy, but early data suggest a relationship with preclinical atherosclerosis. […] Studies showed C-reactive protein is elevated within the first year of diagnosis of T1DM (80), and interleukin-6 and fibrinogen levels are high in individuals with an average disease duration of 2 years (81), independent of adiposity and glycemia (82). Other inflammatory markers such as soluble interleukin-2 receptor (83) and CD40 ligand (84,85) are higher in patients with T1DM than in nondiabetic subjects. Inflammation is evident in youth, even soon after the diagnosis of T1DM. […] The mechanisms by which inflammation operates in T1DM are likely multiple but may include hyperglycemia and hypoglycemia, excess adiposity or altered body fat distribution, thrombosis, and adipokines. Several recent studies have demonstrated a relationship between acute hypoglycemia and indexes of systemic inflammation […] These studies suggest that acute hypoglycemia in T1DM produces complex vascular effects involved in the activation of proinflammatory, prothrombotic, and proatherogenic mechanisms. […] Fibrinogen, a prothrombotic acute phase reactant, is increased in T1DM and is associated with premature CVD (109), and it may be important in vessel thrombosis at later stages of CVD.”

“Genetic polymorphisms appear to influence the progression and prognosis of CVD in T1DM […] Like fibrinogen, haptoglobin is an acute phase protein that inhibits hemoglobin-induced oxidative tissue damage by binding to free hemoglobin (110). […] In humans, there are 2 classes of alleles at the haptoglobin locus, giving rise to 3 possible genotypes: haptoglobin 1-1, haptoglobin 2-1, and haptoglobin 2-2. […] In T1DM, there is an independent twofold increased incidence of CAD in haptoglobin 2-2 carriers compared with those with the haptoglobin 1-1 genotype (117); the 2-1 genotype is associated with an intermediate effect of increased CVD risk. More recently, an independent association was reported in T1DM between the haptoglobin 2-2 genotype and early progression to end-stage renal disease (ESRD) (118). In the CACTI study group, the presence of the haptoglobin 2-2 genotype also doubled the risk of CAC [coronary artery calcification] in patients free from CAC at baseline, after adjustment for traditional CVD risk factors (119). […] At present, genetic testing for polymorphisms in T1DM [however] has no clear clinical utility in CVD prediction or management.”

“Dysglycemia is often conceived of as a vasculopathic process. Preclinical atherosclerosis and epidemiological studies generally support this relationship. Clinical trial data from the DCCT supplied definitive findings strongly in favor of beneficial effects of better glycemic control on CVD outcomes. Glycemia is associated with preclinical atherosclerosis in studies that include tests of endothelial function, arterial stiffness, cIMT, autonomic neuropathy, and left ventricular (LV) function in T1DM […] LV mass and function improve with better glycemic control (126,135,136). Epidemiological evidence generally supports the relationship between hyperglycemia and clinical CHD events in T1DM. […] A large Swedish database review recently reported a reasonably strong association between HbA1c and CAD in T1DM (HR, 1.3 per 1% HbA1c increase) (141). […] findings support the recommendation that early optimal glycemic control in T1DM will have long-term benefits for CVD reduction.”

“Obesity is a known independent risk factor for CVD in nondiabetic populations, but the impact of obesity in T1DM has not been fully established. Traditionally, T1DM was a condition of lean individuals, yet the prevalence of overweight and obesity in T1DM has increased significantly […] This is related to epidemiological shifts in the population overall, tighter glucose control leading to less glucosuria, more frequent/greater caloric intake to fend off real and perceived hypoglycemia, and the specific effects of intensive DM therapy, which has been shown to increase the prevalence of obesity (152). Indeed, several clinical trials, including the DCCT, demonstrate that intensive insulin therapy can lead to excessive weight gain in a subset of patients with T1DM (152). […] No systematic evaluation has been conducted to assess whether improving insulin sensitization lowers rates of CVD. Ironically, the better glycemic control associated with insulin therapy may lead to weight gain, with a superimposed insulin resistance, which may be approached by giving higher doses of insulin. However, some evidence from the EDC study suggests that weight gain in the presence of improved glycemic control is associated with an improved CVD risk profile (162). […] Although T1DM is characteristically a disease of absolute insulin deficiency (154), insulin resistance appears to contribute to CHD risk in patients with T1DM. For example, having a family history of T2DM, which suggests a genetic predisposition for insulin resistance, has been associated with an increased CVD risk in patients with T1DM (155).”

“In general, the lipid levels of adults with well-controlled T1DM are similar to those of individuals without DM […] Worse glycemic control, higher weight (164), and more insulin resistance as measured by euglycemic clamp (165) are associated with a more atherogenic cholesterol distribution in men and women with T1DM […] Studies in pediatric and young adult populations suggest higher lipid values than in youth without T1DM, with glycemic control being a significant contributor (148). […] Most studies show that as is true for the general population, dyslipidemia is a risk factor for CVD in T1DM. Qualitative differences in lipid and lipoprotein fractions are being investigated to determine whether abnormal lipid function may contribute to this. The HDL-C fraction has been of particular interest because the metabolism of HDL-C in T1DM may be altered because of abnormal lipoprotein lipase and hepatic lipase activities related to exogenously administered insulin […] Additionally, as noted earlier, the less efficient handling of heme by the haptoglobin 2-2 genotype in patients with T1DM leaves these complexes less capable of being removed by macrophages, which allows them to associate with HDL, which renders it less functional (116). […] Conventionally, pharmacotherapy is used more aggressively for patients with T1DM and lipid disorders than for nondiabetic patients; however, recommendations for treatment are mostly extrapolated from interventional trials in adults with T2DM, in which rates of CVD events are equivalent to those in secondary prevention populations. Whether this is appropriate for T1DM is not clear […] Awareness of CVD risk and screening for hypercholesterolemia in T1DM have increased over time, yet recent data indicate that control is suboptimal, particularly in younger patients who have not yet developed long-term complications and might therefore benefit from prevention efforts (173). Adults with T1DM who have abnormal lipids and additional risk factors for CVD (e.g., hypertension, obesity, or smoking) who have not developed CVD should be treated with statins. Adults with CVD and T1DM should also be treated with statins, regardless of whether they have additional risk factors.”

“Diabetic kidney disease (DKD) is a complication of T1DM that is strongly linked to CVD. DKD can present as microalbuminuria or macroalbuminuria, impaired GFR, or both. These represent separate but complementary manifestations of DKD and are often, but not necessarily, sequential in their presentation. […] the risk of all-cause mortality increased with the severity of DKD, from microalbuminuria to macroalbuminuria to ESRD. […] Microalbuminuria is likely an indicator of diffuse vascular injury. […] Microalbuminuria is highly correlated with CVD (49,180182). In the Steno Diabetes Center (Gentofte, Denmark) cohort, T1DM patients with isolated microalbuminuria had a 4.2-fold increased risk of CVD (49,180). In the EDC study, microalbuminuria was associated with mortality risk, with an SMR of 6.4. In the FinnDiane study, mortality risk was also increased with microalbuminuria (SMR, 2.8). […] A recent review summarized these data. In patients with T1DM and microalbuminuria, there was an RR of all-cause mortality of 1.8 (95% CI, 1.5–2.1) that was unaffected by adjustment for confounders (183). Similar RRs were found for mortality from CVD (1.9; 95% CI, 1.3–2.9), CHD (2.1; 95% CI, 1.2–3.5), and aggregate CVD mortality (2.0; 95% CI, 1.5–2.6).”

“Macroalbuminuria represents more substantial kidney damage and is also associated with CVD. Mechanisms may be more closely related to functional consequences of kidney disease, such as higher LDL-C and lower HDL-C. Prospective data from Finland indicate the RR for CVD is ≈10 times greater in patients with macroalbuminuria than in those without macroalbuminuria (184). Historically, in the [Danish] Steno cohort, patients with T1DM and macroalbuminuria had a 37-fold increased risk of CVD mortality compared with the general population (49,180); however, a more recent report from EURODIAB suggests a much lower RR (8.7; 95% CI, 4.03–19.0) (185). […] In general, impaired GFR is a risk factor for CVD, independent of albuminuria […] ESRD [end-stage renal disease, US], the extreme form of impaired GFR, is associated with the greatest risk of CVD of all varieties of DKD. In the EDC study, ESRD was associated with an SMR for total mortality of 29.8, whereas in the FinnDiane study, it was 18.3. It is now clear that GFR loss and the development of eGFR <60 mL · min−1 · 1.73 m−2 can occur without previous manifestation of microalbuminuria or macroalbuminuria (177,178). In T1DM, the precise incidence, pathological basis, and prognosis of this phenotype remain incompletely described.”

“Prevention of DKD remains challenging. Although microalbuminuria and macroalbuminuria are attractive therapeutic targets for CVD prevention, there are no specific interventions directed at the kidney that prevent DKD. Inhibition of the renin-angiotensin-aldosterone system is an attractive option but has not been demonstrated to prevent DKD before it is clinically apparent. […] In contrast to prevention efforts, treatment of DKD with agents that inhibit the renin-angiotensin-aldosterone system is effective. […] angiotensin-converting enzyme (ACE) inhibitors reduce the progression of DKD and death in T1DM (200). Thus, once DKD develops, treatment is recommended to prevent progression and to reduce or minimize other CVD risk factors, which has a positive effect on CVD risk. All patients with T1DM and hypertension or albuminuria should be treated with an ACE inhibitor. If an ACE inhibitor is not tolerated, an angiotensin II receptor blocker (ARB) is likely to have similar efficacy, although this has not been studied specifically in patients with T1DM. Optimal dosing for ACE inhibitors or ARBs in the setting of DKD is not well defined; titration may be guided by BP, albuminuria, serum potassium, and creatinine. Combination therapy of ACE and ARB blockade cannot be specifically recommended at this time.”

“Hypertension is more common in patients with T1DM and is a powerful risk factor for CVD, regardless of whether an individual has DKD. In the CACTI [Coronary Artery Calcification in Type 1 Diabetes] study, hypertension was much more common in patients with T1DM than in age- and sex-matched control subjects (43% versus 15%, P < 0.001); in fact, only 42% of all T1DM patients met the Joint National Commission 7 goal (BP <130/80 mmHg) (201). Hypertension also affects youth with T1DM. The SEARCH trial of youth aged 3–17 years with T1DM (n = 3,691) found the prevalence of elevated BP was 5.9% […] Abnormalities in BP can stem from DKD or obesity. Hyperglycemia may also contribute to hypertension over the long term. In the DCCT/EDIC cohort, higher HbA1c was strongly associated with increased risk of hypertension, and intensive DM therapy reduced the long-term risk of hypertension by 24% (203). […] There are few published trials about the ideal pharmacotherapeutic agent(s) for hypertension in T1DM.”

“Smoking is a major risk factor for CVD, particularly PAD (213); however, there is little information on the prevalence or effects of smoking in T1DM. […] The added CVD risk of smoking may be particularly important in patients with DM, who are already vulnerable. In patients with T1DM, cigarette smoking [has been shown to increase] the risk of DM nephropathy, retinopathy, and neuropathy (214,215) […] Smoking increases CVD risk factors in T1DM via deterioration in glucose metabolism, lipids, and endothelial function (216). Unfortunately, smoking cessation can result in weight gain, which may deter smokers with DM from quitting (217). […] Smoking cessation should be strongly recommended to all patients with T1DM as part of an overall strategy to lower CVD, in particular PAD.”

“CVD risk factors are more common in children with T1DM than in the general pediatric population (218). Population-based studies estimate that 14–45% of children with T1DM have ≥2 CVD risk factors (219221). As with nondiabetic children, the prevalence of CVD risk factors increases with age (221). […] The American Academy of Pediatrics, the American Heart Association, and the ADA recognize patients with DM, and particularly T1DM, as being in a higher-risk group who should receive more aggressive risk factor screening and treatment than nondiabetic children […] The available data suggest many children and adolescents with T1DM do not receive the recommended treatment for their dyslipidemia and hypertension (220,222).”

“There are no CVD risk-prediction algorithms for patients with T1DM in widespread use. […] Use of the Framingham Heart Study and UK Prospective Diabetes Study (UKPDS) algorithms in the EDC study population did not provide good predictive results, which suggests that neither general or T2DM risk algorithms are sufficient for risk prediction in T1DM (235). On the basis of these findings, a model has been developed with the use of EDC cohort data (236) that incorporates measures outside the Framingham construct (white blood cell count, albuminuria, DM duration). Although this algorithm was validated in the EURODIAB Study cohort (237), it has not been widely adopted, and diagnostic and therapeutic decisions are often based on global CVD risk-estimation methods (i.e., Framingham risk score or T2DM-specific UKPDS risk engine [http://www.dtu.ox.ac.uk/riskengine/index.php]). Other options for CVD risk prediction in patients with T1DM include the ADA risk-assessment tool (http://main.diabetes.org/dorg/mha/main_en_US.html?loc=dorg-mha) and the Atherosclerosis Risk in Communities (ARIC) risk predictor (http://www.aricnews.net/riskcalc/html/RC1.html), but again, accuracy for T1DM is not clear.”

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September 25, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Medicine, Nephrology, Neurology, Pharmacology, Studies | Leave a comment

The Biology of Moral Systems (III)

This will be my last post about the book. It’s an important work which deserves to be read by far more people than have already read it. I have added some quotes and observations from the last chapters of the book below.

“If egoism, as self-interest in the biologists’ sense, is the reason for the promotion of ethical behavior, then, paradoxically, it is expected that everyone will constantly promote the notion that egoism is not a suitable theory of action, and, a fortiori, that he himself is not an egoist. Most of all he must present this appearance to his closest associates because it is in his best interests to do so – except, perhaps, to his closest relatives, to whom his egoism may often be displayed in cooperative ventures from which some distant- or non-relative suffers. Indeed, it may be arguable that it will be in the egoist’s best interest not to know (consciously) or to admit to himself that he is an egoist because of the value to himself of being able to convince others he is not.”

“The function of [societal] punishments and rewards, I have suggested, is to manipulate the behavior of participating individuals, restricting individual efforts to serve their own interests at others’ expense so as to promote harmony and unity within the group. The function of harmony and unity […] is to allow the group to compete against hostile forces, especially other human groups. It is apparent that success of the group may serve the interests of all individuals in the group; but it is also apparent that group success can be achieved with different patterns of individual success differentials within the group. So […] it is in the interests of those who are differentially successful to promote both unity and the rules so that group success will occur without necessitating changes deleterious to them. Similarly, it may be in the interests of those individuals who are relatively unsuccessful to promote dissatisfaction with existing rules and the notion that group success would be more likely if the rules were altered to favor them. […] the rules of morality and law alike seem not to be designed explicitly to allow people to live in harmony within societies but to enable societies to be sufficiently united to deter their enemies. Within-society harmony is the means not the end. […] extreme within-group altruism seems to correlate with and be historically related to between-group strife.”

“There are often few or no legitimate or rational expectations of reciprocity or “fairness” between social groups (especially warring or competing groups such as tribes or nations). Perhaps partly as a consequence, lying, deceit, or otherwise nasty or even heinous acts committed against enemies may sometimes not be regarded as immoral by others withing the group of those who commit them. They may even be regarded as highly moral if they seem dramatically to serve the interests of the group whose members commit them.”

“Two major assumptions, made universally or most of the time by philosophers, […] are responsible for the confusion that prevents philosophers from making sense out of morality […]. These assumptions are the following: 1. That proximate and ultimate mechanisms or causes have the same kind of significance and can be considered together as if they were members of the same class of causes; this is a failure to understand that proximate causes are evolved because of ultimate causes, and therefore may be expected to serve them, while the reverse is not true. Thus, pleasure is a proximate mechanism that in the usual environments of history is expected to impel us toward behavior that will contribute to our reproductive success. Contrarily, acts leading to reproductive success are not proximate mechanisms that evolved because they served the ultimate function of bringing us pleasure. 2. That morality inevitably involves some self-sacrifice. This assumption involves at least three elements: a. Failure to consider altruism as benefits to the actor. […] b. Failure to comprehend all avenues of indirect reciprocity within groups. c. Failure to take into account both within-group and between-group benefits.”

“If morality means true sacrifice of one’s own interests, and those of his family, then it seems to me that we could not have evolved to be moral. If morality requires ethical consistency, whereby one does not do socially what he would not advocate and assist all others also to do, then, again, it seems to me that we could not have evolved to be moral. […] humans are not really moral at all, in the sense of “true sacrifice” given above, but […] the concept of morality is useful to them. […] If it is so, then we might imagine that, in the sense and to the extent that they are anthropomorphized, the concepts of saints and angels, as well as that of God, were also created because of their usefulness to us. […] I think there have been far fewer […] truly self-sacrificing individuals than might be supposed, and most cases that might be brought forward are likely instead to be illustrations of the complexity and indirectness of reciprocity, especially the social value of appearing more altruistic than one is. […] I think that […] the concept of God must be viewed as originally generated and maintained for the purpose – now seen by many as immoral – of furthering the interests of one group of humans at the expense of one or more other groups. […] Gods are inventions originally developed to extend the notion that some have greater rights than others to design and enforce rules, and that some are more destined to be leaders, others to be followers. This notion, in turn, arose out of prior asymmetries in both power and judgment […] It works when (because) leaders are (have been) valuable, especially in the context of intergroup competition.”

“We try to move moral issues in the direction of involving no conflict of interest, always, I suggest, by seeking universal agreement with our own point of view.”

“Moral and legal systems are commonly distinguished by those, like moral philosophers, who study them formally. I believe, however, that the distinction between them is usually poorly drawn, and based on a failure to realize that moral as well as legal behavior occurs as a result of probably and possible punishments and reward. […] we often internalize the rules of law as well as the rules of morality – and perhaps by the same process […] It would seem that the rules of law are simply a specialized, derived aspect of what in earlier societies would have been a part of moral rules. On the other hand, law covers only a fraction of the situations in which morality is involved […] Law […] seems to be little more than ethics written down.”

“Anyone who reads the literature on dispute settlement within different societies […] will quickly understand that genetic relatedness counts: it allows for one-way flows of benefits and alliances. Long-term association also counts; it allows for reliability and also correlates with genetic relatedness. […] The larger the social group, the more fluid its membership; and the more attenuated the social interactions of its membership, the more they are forced to rely on formal law”.

“[I]ndividuals have separate interests. They join forces (live in groups; become social) when they share certain interests that can be better realized for all by close proximity or some forms of cooperation. Typically, however, the overlaps of interests rarely are completely congruent with those of either other individuals or the rest of the group. This means that, even during those times when individual interests within a group are most broadly overlapping, we may expect individuals to temper their cooperation with efforts to realize their own interests, and we may also expect them to have evolved to be adept at using others, or at thwarting the interests of others, to serve themselves (and their relatives). […] When the interests of all are most nearly congruent, it is essentially always due to a threat shared equally. Such threats almost always have to be external (or else they are less likely to affect everyone equally […] External threats to societies are typically other societies. Maintenance of such threats can yield situations in which everyone benefits from rigid, hierarchical, quasi-military, despotic government. Liberties afforded leaders – even elaborate perquisites of dictators – may be tolerated because such threats are ever-present […] Extrinsic threats, and the governments they produce, can yield inflexibilities of political structures that can persist across even lengthy intervals during which the threats are absent. Some societies have been able to structure their defenses against external threats as separate units (armies) within society, and to keep them separate. These rigidly hierarchical, totalitarian, and dictatorial subunits rise and fall in size and influence according to the importance of the external threat. […] Discussion of liberty and equality in democracies closely parallels discussions of morality and moral systems. In either case, adding a perspective from evolutionary biology seems to me to have potential for clarification.”

“It is indeed common, if not universal, to regard moral behavior as a kind of altruism that necessarily yields the altruist less than he gives, and to see egoism as either the opposite of morality or the source of immorality; but […] this view is usually based on an incomplete understanding of nepotism, reciprocity, and the significance of within-group unity for between-group competition. […] My view of moral systems in the real world, however, is that they are systems in which costs and benefits of specific actions are manipulated so as to produce reasonably harmonious associations in which everyone nevertheless pursues his own (in evolutionary terms) self-interest. I do not expect that moral and ethical arguments can ever be finally resolved. Compromises and contracts, then, are (at least currently) the only real solutions to actual conflicts of interest. This is why moral and ethical decisions must arise out of decisions of the collective of affected individuals; there is no single source of right and wrong.

I would also argue against the notion that rationality can be easily employed to produce a world of humans that self-sacrifice in favor of other humans, not to say nonhuman animals, plants, and inanimate objects. Declarations of such intentions may themselves often be the acts of self-interested persons developing, consciously or not, a socially self-benefiting view of themselves as extreme altruists. In this connection it is not irrelevant that the more dissimilar a species or object is to one’s self the less likely it is to provide a competitive threat by seeking the same resources. Accordingly, we should not be surprised to find humans who are highly benevolent toward other species or inanimate objects (some of which may serve them uncomplainingly), yet relatively hostile and noncooperative with fellow humans. As Darwin (1871) noted with respect to dogs, we have selected our domestic animals to return our altruism with interest.”

“It is not easy to discover precisely what historical differences have shaped current male-female differences. If, however, humans are in a general way similar to other highly parental organisms that live in social groups […] then we can hypothesize as follows: for men much of sexual activity has had as a main (ultimate) significance the initiating of pregnancies. It would follow that when a man avoids copulation it is likely to be because (1) there is no likelihood of pregnancy or (2) the costs entailed (venereal disease, danger from competition with other males, lowered status if the event becomes public, or an undesirable commitment) are too great in comparison with the probability that pregnancy will be induced. The man himself may be judging costs against the benefits of immediate sensory pleasures, such as orgasms (i.e., rather than thinking about pregnancy he may say that he was simply uninterested), but I am assuming that selection has tuned such expectations in terms of their probability of leading to actual reproduction […]. For women, I hypothesize, sexual activity per se has been more concerned with the securing of resources (again, I am speaking of ultimate and not necessarily conscious concerns) […]. Ordinarily, when women avoid or resist copulation, I speculate further, the disinterest, aversion, or inhibition may be traceable eventually to one (or more) of three causes: (1) there is no promise of commitment (of resources), (2) there is a likelihood of undesirable commitment (e.g., to a man with inadequate resources), or (3) there is a risk of loss of interest by a man with greater resources, than the one involved […] A man behaving so as to avoid pregnancies, and who derives from an evolutionary background of avoiding pregnancies, should be expected to favor copulation with women who are for age or other reasons incapable of pregnancy. A man derived from an evolutionary process in which securing of pregnancies typically was favored, may be expected to be most interested sexually in women most likely to become pregnant and near the height of the reproductive probability curve […] This means that men should usually be expected to anticipate the greatest sexual pleasure with young, healthy, intelligent women who show promise of providing superior parental care. […] In sexual competition, the alternatives of a man without resources are to present himself as a resource (i.e., as a mimic of one with resources or as one able and likely to secure resources because of his personal attributes […]), to obtain sex by force (rape), or to secure resources through a woman (e.g., allow himself to be kept by a relatively undesired woman, perhaps as a vehicle to secure liaisons with other women). […] in nonhuman species of higher animals, control of the essential resources of parenthood by females correlates with lack of parental behavior by males, promiscuous polygyny, and absence of long-term pair bonds. There is some evidence of parallel trends within human societies (cf. Flinn, 1981).” [It’s of some note that quite a few good books have been written on these topics since Alexander first published his book, so there are many places to look for detailed coverage of topics like these if you’re curious to know more – I can recommend both Kappeler & van Schaik (a must-read book on sexual selection, in my opinion) & Bobby Low. I didn’t think too highly of Miller or Meston & Buss, but those are a few other books on these topics which I’ve read – US].

“The reason that evolutionary knowledge has no moral content is [that] morality is a matter of whose interests one should, by conscious and willful behavior, serve, and how much; evolutionary knowledge contains no messages on this issue. The most it can do is provide information about the reasons for current conditions and predict some consequences of alternative courses of action. […] If some biologists and nonbiologists make unfounded assertions into conclusions, or develop pernicious and fallible arguments, then those assertions and arguments should be exposed for what they are. The reason for doing this, however, is not […should not be..? – US] to prevent or discourage any and all analyses of human activities, but to enable us to get on with a proper sort of analysis. Those who malign without being specific; who attack people rather than ideas; who gratuitously translate hypotheses into conclusions and then refer to them as “explanations,” “stories,” or “just-so-stories”; who parade the worst examples of argument and investigation with the apparent purpose of making all efforts at human self-analysis seem silly and trivial, I see as dangerously close to being ideologues at least as worrisome as those they malign. I cannot avoid the impression that their purpose is not to enlighten, but to play upon the uneasiness of those for whom the approach of evolutionary biology is alien and disquieting, perhaps for political rather than scientific purposes. It is more than a little ironic that the argument of politics rather than science is their own chief accusation with respect to scientists seeking to analyze human behavior in evolutionary terms (e.g. Gould and Levontin, 1979 […]).”

“[C]urrent selective theory indicates that natural selection has never operated to prevent species extinction. Instead it operates by saving the genetic materials of those individuals or families that outreproduce others. Whether species become extinct or not (and most have) is an incidental or accidental effect of natural selection. An inference from this is that the members of no species are equipped, as a direct result of their evolutionary history, with traits designed explicitly to prevent extinction when that possibility looms. […] Humans are no exception: unless their comprehension of the likelihood of extinction is so clear and real that they perceive the threat to themselves as individuals, and to their loved ones, they cannot be expected to take the collective action that will be necessary to reduce the risk of extinction.”

“In examining ourselves […] we are forced to use the attributes we wish to analyze to carry out the analysis, while resisting certain aspects of the analysis. At the very same time, we pretend that we are not resisting at all but are instead giving perfectly legitimate objections; and we use our realization that others will resist the analysis, for reasons as arcane as our own, to enlist their support in our resistance. And they very likely will give it. […] If arguments such as those made here have any validity it follows that a problem faced by everyone, in respect to morality, is that of discovering how to subvert or reduce some aspects of individual selfishness that evidently derive from our history of genetic individuality.”

“Essentially everyone thinks of himself as well-meaning, but from my viewpoint a society of well-meaning people who understand themselves and their history very well is a better milieu than a society of well-meaning people who do not.”

September 22, 2017 Posted by | Anthropology, Biology, Books, Evolutionary biology, Genetics, Philosophy, Psychology, Religion | Leave a comment

Utility of Research Autopsies for Understanding the Dynamics of Cancer

A few links:
Pancreatic cancer.
Jaccard index.
Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer.
Epitope.
Tissue-specific mutation accumulation in human adult stem cells during life.
Epigenomic reprogramming during pancreatic cancer progression links anabolic glucose metabolism to distant metastasis.

August 25, 2017 Posted by | Cancer/oncology, Genetics, Immunology, Lectures, Medicine, Statistics | Leave a comment

Quantifying tumor evolution through spatial computational modeling

Two general remarks: 1. She talks very fast, in my opinion unpleasantly fast – the lecture would have been at least slightly easier to follow if she’d slowed down a little. 2. A few of the lectures uploaded in this lecture series (from the IAS Mathematical Methods in Cancer Evolution and Heterogeneity Workshop) seem to have some sound issues; in this lecture there are multiple 1-2 seconds long ‘chunks’ where the sound drops out and some words are lost. This is really annoying, and a similar problem (which was likely ‘the same problem’) previously lead me to quit another lecture in the series; however in this case I decided to give it a shot anyway, and I actually think it’s not a big deal; the sound-losses are very short in duration, and usually no more than one or two words are lost so you can usually figure out what was said. During this lecture there was incidentally also some issues with the monitor roughly 27 minutes in, but this isn’t a big deal as no information was lost and unlike the people who originally attended the lecture you can just skip ahead approximately one minute (that was how long it took to solve that problem).

A few relevant links to stuff she talks about in the lecture:

A Big Bang model of human colorectal tumor growth.
Approximate Bayesian computation.
Site frequency spectrum.
Identification of neutral tumor evolution across cancer types.
Using tumour phylogenetics to identify the roots of metastasis in humans.

August 22, 2017 Posted by | Cancer/oncology, Evolutionary biology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

Depression and Heart Disease (II)

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

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

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

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

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

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

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

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

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

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

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

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

Depression and Heart Disease (I)

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

Below I’ve added some quotes from the book.

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

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

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

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

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

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

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

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

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

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

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

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

 

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

The Personality Puzzle (III)

I have added some more quotes and observations from the book below.

“Across many, many traits, the average correlation across MZ twins is about .60, and across DZ twins it is about .40, when adjusted for age and gender […] This means that, according to twin studies, the average heritability of many traits is about .40, which is interpreted to mean that 40 percent of phenotypic (behavioral) variance is accounted for by genetic variance. The heritabilities of the Big Five traits are a bit higher; according to one comprehensive summary they range from .42, for agreeableness, to .57, for openness (Bouchard, 2004). […] behavioral genetic analyses and the statistics they produce refer to groups or populations, not individuals. […] when research concludes that a personality trait is, say, 50 percent heritable, this does not mean that half of the extent to which an individual expresses that trait is determined genetically. Instead, it means that 50 percent of the degree to which the trait varies across the population can be attributed to genetic variation. […] Because heritability is the proportion of variation due to genetic influences, if there is no variation, then the heritability must approach zero. […] Heritability statistics are not the nature-nurture ratio; a biologically determined trait can have a zero heritability.”

The environment can […] affect heritability […]. For example, when every child receives adequate nutrition, variance in height is genetically controlled. […] But in an environment where some are well fed while others go hungry, variance in height will fall more under the control of the environment. Well-fed children will grow near the maximum of their genetic potential while poorly fed children will grow closer to their genetic minimum, and the height of the parents will not matter so much; the heritability coeffcient for height will be much closer to 0. […] A trait that is adaptive in one situation may be harmful in another […] the same environments that promote good outcomes for some people can promote bad outcomes for others, and vice versa […] More generally, the same circumstances might be experienced as stressful, enjoyable, or boring, depending on the genetic predispositions of the individuals involved; these variations in experience can lead to very different behaviors and, over time, to the development of different personality traits.”

Mihalyi Csikszentmihalyi [argued] that the best way a person can spend time is in autotelic activities, those that are enjoyable for their own sake. The subjective experience of an autotelic activity — the enjoyment itself — is what Csikszentmihalyi calls flow.
Flow is not the same thing as joy, happiness, or other, more familiar terms for subjective well-being. Rather, the experience of flow is characterized by tremendous concentration, total lack of distractibility, and thoughts concerning only the activity at hand. […] Losing track of time is one sign of experiencing flow. According to Csikszentmihalyi, flow arises when the challenges an activity presents are well matched with your skills. If an activity is too diffcult or too confusing, you will experience anxiety, worry, and frustration. If the activity is too easy, you will experience boredom and (again) anxiety. But when skills and challenges are balanced, you experience flow. […] Csikszentmihalyi thinks that the secret for enhancing your quality of life is to spend as much time in flow as possible. Achieving flow entails becoming good at something you find worthwhile and enjoyable. […] Even in the best of circumstances [however], flow seems to describe a rather solitary kind of happiness. […] The drawback with flow is that somebody experiencing it can be difficult to interact with”. [I really did not like most of the stuff included in the part of the book from which this quote is taken, but I did find Csikszentmihalyi’s flow concept quite interesting.]

“About 80 percent of the participants in psychological research come from countries that are Western, Educated, Industrialized, Rich, and Democratic — ”WEIRD” in other words — although only 12 percent of the world’s population live there (Henrich et al., 2010).”

“If an animal or a person performs a behavior, and the behavior is followed by a good result — a reinforcement — the behavior becomes more likely. If the behavior is followed by a punishment, it becomes less likely. […] the results of operant conditioning are not necessarily logical. It can increase the frequency of any behavior, regardless of its real connection with the consequences that follow.”

“A punishment is an aversive consequence that follows an act in order to stop it and prevent its repetition. […] Many people believe the only way to stop or prevent somebody from doing something is punishment. […] You can [however] use reward for this purpose too. All you have to do is find a response that is incompatible with the one you are trying to get rid of, and reward that incompatible response instead. Reward a child for reading instead of punishing him for watching television. […] punishment works well when it is done right. The only problem is, it is almost never done right. […] One way to see how punishment works, or fails to work, is to examine the rules for applying it correctly. The classic behaviorist analysis says that five principles are most important […] 1. Availability of Alternatives: An alternative response to the behavior that is being punished must be available. This alternative response must not be punished and should be rewarded. […] 2. Behavioral and Situational Specificity: Be clear about exactly what behavior you are punishing and the circumstances under which it will and will not be punished. […] 3. Timing and Consistency: To be effective, a punishment needs to be applied immediately after the behavior you wish to prevent, every time that behavior occurs. Otherwise, the person (or animal) being punished may not understand which behavior is forbidden. […] 4. Conditioning Secondary Punishing Stimuli: One can lessen the actual use of punishment by conditioning secondary stimuli to it [such as e.g.  verbal warnings] […] 5. Avoiding Mixed Messages: […] Sometimes, after punishing a child, the parent feels so guilty that she picks the child up for a cuddle. This is a mistake. The child might start to misbehave just to get the cuddle that follows the punishment. Punish if you must punish, but do not mix your message. A variant on this problem occurs when the child learns to play one parent against the other. For example, after the father punishes the child, the child goes to the mother for sympathy, or vice versa. This can produce the same counterproductive result.”

Punishment will backfire unless all of the guidelines [above] are followed. Usually, they are not. A punisher has to be extremely careful, for several reasons. […] The first and perhaps most important danger of punishment is that it creates emotion. […] powerful emotions are not conducive to clear thinking. […] Punishment [also] tends to vary with the punisher’s mood, which is one reason it is rarely applied consistently. […] Punishment [furthermore] [m]otivates [c]oncealment: The prospective punishee has good reasons to conceal behavior that might be punished. […] Rewards have the reverse effect. When workers anticipate rewards for good work instead of punishment for bad work, they are naturally motivated to bring to the boss’s attention everything they are doing, in case it merits reward.”

Gordon Allport observed years ago [that] [“]For some the world is a hostile place where men are evil and dangerous; for others it is a stage for fun and frolic. It may appear as a place to do one’s duty grimly; or a pasture for cultivating friendship and love.[“] […] people with different traits see the world differently. This perception affects how they react to the events in their lives which, in turn, affects what they do. […] People [also] differ in the emotions they experience, the emotions they want to experience, how strongly they experience emotions, how frequently their emotions change, and how well they understand and control their emotions.”

July 9, 2017 Posted by | Books, Genetics, Psychology | Leave a comment

A few SSC comments

I recently left a few comments in an open thread on SSC, and I figured it might make sense to crosspost some of the comments made there here on the blog. I haven’t posted all my contributions to the debate here, rather I’ve just quoted some specific comments and observations which might be of interest. I’ve also added some additional remarks and comments which relate to the topics discussed. Here’s the main link (scroll down to get to my comments).

“One thing worth keeping in mind when evaluating pre-modern medicine characterizations of diabetes and the natural history of diabetes is incidentally that especially to the extent that one is interested in type 1 survivorship bias is a major problem lurking in the background. Prognostic estimates of untreated type 1 based on historical accounts of how long people could live with the disease before insulin are not in my opinion likely to be all that reliable, because the type of patients that would be recognized as (type 1) diabetics back then would tend to mainly be people who had the milder forms, because they were the only ones who lived long enough to reach a ‘doctor’; and the longer they lived, and the milder the sub-type, the more likely they were to be studied/’diagnosed’. I was a 2-year old boy who got unwell on a Tueday and was hospitalized three days later. Avicenna would have been unlikely to have encountered me, I’d have died before he saw me. (Similar lines of reasoning might lead to an argument that the incidence of diseases like type 1 diabetes may also today be underdiagnosed in developing countries with poorly developed health care systems.)”

Douglas Knight mentioned during our exchange that medical men of the far past might have been more likely to attend to patients with acute illnesses than patients with chronic conditions, making them more likely to attend to such cases than would otherwise be the case, a point I didn’t discuss in any detail during the exchange. I did however think it important to note here that information exchange was significantly slower, and transportation costs were much higher, in the past than they are today. This should make such a bias less relevant, all else equal. Avicenna and his colleagues couldn’t take a taxi, or learn by phone that X is sick. He might have preferentially attended to the acute cases he learned about, but given high transportation costs and inefficient communication channels he might often never arrive in time, or at all. A particular problem here is that there are no good data on the unobserved cases, because the only cases we know about today are the ones people like him have told us about.

Some more comments:

“One thing I was considering adding to my remarks about survivorship bias is that it is not in my opinion unlikely that what you might term the nature of the disease has changed over the centuries; indeed it might still be changing today. Globally the incidence of type 1 has been increasing for decades and nobody seems to know why, though there’s consensus about an environmental trigger playing a major role. Maybe incidence is not the only thing that’s changed, maybe e.g. the time course of the ‘average case’ has also changed? Maybe due to secondary factors; better nutritional status now equals slower progression of beta cell failure than was the case in the past? Or perhaps the other way around: Less exposure to bacterial agents the immune system throughout evolutionary time has been used to having to deal with today means that the autoimmune process is accelerated today, compared to in the far past where standards of hygiene were different. Who knows? […] Maybe survivorship bias wasn’t that big of a deal, but I think one should be very cautious about which assumptions one might implicitly be making along the way when addressing questions of this sort of nature. Some relevant questions will definitely be unknowable due to lack of good data which we will never be able to obtain.”

I should perhaps interpose here that even if survivorship bias ‘wasn’t that big of a deal’, it’s still sort of a big problem in the analytical setting because it seems perfectly plausible to me to be making the assumption that it might even so have been a big deal. These kinds of problems magnify our error bars and reduce confidence in our conclusions, regardless of the extent to which they actually played a role. When you know the exact sign and magnitude of a given moderating effect you can try to correct for it, but this is very difficult to do when a large range of moderator effect sizes might be considered plausible. It might also here be worth mentioning explicitly that biases such as the survivorship bias mentioned can of course impact a lot of things besides just the prognostic estimates; for example if a lot of cases never come to the attention of the medical people because these people were unavailable (due to distance, cost, lack of information, etc.) to the people who were sick, incidence and prevalence will also implicitly be underestimated. And so on. Back to the comments:

“Once you had me thinking that it might have been harder [for people in the past] to distinguish [between type 1 and type 2 diabetes] than […] it is today, I started wondering about this, and the comments below relate to this topic. An idea that came to mind in relation to the type 1/type 2 distinction and the ability of people in the past to make this distinction: I’ve worked on various identification problems present in the diabetes context before, and I know that people even today make misdiagnoses and e.g. categorize type 1 diabetics as type 2. I asked a diabetes nurse working in the local endocrinology unit about this at one point, and she told me they had actually had a patient not long before then who had been admitted a short while after having been diagnosed with type 2. Turned out he was type 1, so the treatment failed. Misdiagnoses happen for multiple reasons, one is that obese people also sometimes develop type 1, and if it’s an acute onset setting the weight loss is not likely to be very significant. Patient history should in such a case provide the doctor with the necessary clues, but if the guy making the diagnosis is a stressed out GP who’s currently treating a lot of obese patients for type 2, mistakes happen. ‘Pre-scientific method’ this sort of individual would have been inconvenient to encounter, because a ‘counter-example’ like that supposedly demonstrating that the obese/thin(/young/old, acute/protracted…) distinction was ‘invalid’ might have held a lot more weight than it hopefully would today in the age of statistical analysis. A similar problem would be some of the end-stage individuals: A type 1 pre-insulin would be unlikely to live long enough to develop long term complications of the disease, but would instead die of DKA. The problem is that some untreated type 2 patients also die of DKA, though the degree of ketosis varies in type 2 patients. DKA in type 2 could e.g. be triggered by a superimposed cardiovascular event or an infection, increasing metabolic demands to an extent that can no longer be met by the organism, and so might well present just as acutely as it would in a classic acute-onset type 1 case. Assume the opposite bias you mention is playing a role; the ‘doctor’ in the past is more likely to see the patients in such a life-threatening setting than in the earlier stages. He observes a 55 year old fat guy dying in a very similar manner to the way a 12 year old girl died a few months back – very characteristic symptoms, breath smells fruity, Kussmaul respiration, polyuria and polydipsia…). What does he conclude? Are these different diseases?”

Making the doctor’s decision problem even harder is of course the fact that type 2 diabetes even today often goes undiagnosed until complications arise. Some type 2 patients get their diagnosis only after they had their first heart attack as a result of their illness. So the hypothetical obese middle-aged guy presenting with DKA might not have been known by anyone to be ‘a potentially different kind of diabetic’.

‘The Nybbler’ asked this question in the thread: “Wouldn’t reduced selection pressure be a major reason for increase of Type I diabetes? Used to be if you had it, chance of surviving to reproduce was close to nil.”

I’ll mention here that I’ve encountered this kind of theorizing before, but that I’ve never really addressed it – especially the second part – explicitly, though I’ve sometimes felt like doing that. I figured this post might be a decent place to at least scratch the surface. The idea that there are more type 1 diabetics now than there used to be because type 1 diabetics used to die of their disease and now they don’t (…and so now they are able to transmit their faulty genes to subsequent generations, leading to more diabetic individuals over time) sounds sort of reasonable if you don’t know very much about diabetes, but it sounds less reasonable to people who do. Genes matter, and changed selection pressures have probably played a role, but I find it hard to believe this particular mechanism is a major factor. I have included both my of my replies to ‘Nybbler’ below:

First comment:

“I’m not a geneticist and this is sort-of-kind-of near the boundary area of where I feel comfortable providing answers (given that others may be more qualified to evaluate questions like this than I am). However a few observations which might be relevant are the following:

i) Although I’ll later go on to say that vertical transmission is low, I first have to point out that some people who developed type 1 diabetes in the past did in fact have offspring, though there’s no doubt about the condition being fitness-reducing to a very large degree. The median age of diagnosis of type 1 is somewhere in the teenage years (…today. Was it the same way 1000 years ago, or has the age profile changed over time? This again relates to questions asked elsewhere in this discussion…), but people above the age of 30 get type 1 too.

ii) Although type 1 display some level of familia[l] clustering, most cases of type 1 are not the result of diabetics having had children who then proceed to inherit their parents’ disease. To the extent that reduced selection is a driver of increased incidence, the main cause would be broad selection effects pertaining to immune system functioning in general in the total population at risk (i.e. children in general, including many children with what might be termed suboptimal immune system functioning, being more likely to survive and later develop type 1 diabetes), not effects derived from vertical transmission of the disease (from parent to child). Roughly 90% of newly diagnosed type 1 diabetics in population studies have a negative family history of the disease, and on average only 2% of the children of type 1 diabetic mothers, and 5% of the children of type 1 diabetic fathers, go on to develop type 1 diabetes themselves.

iii) Historically vertical transmission has even in modern times been low. On top of the quite low transmission rates mentioned above, until well into the 80es or 90es many type 1 diabetic females were explicitly advised by their medical care providers not to have children, not because of the genetic risk of disease transmission but because pregnancy outcomes were likely to be poor; and many of those who disregarded the advice gave birth to offspring who were at a severe fitness disadvantage from the start. Poorly controlled diabetes during pregnancy leads to a very high risk of birth defects and/or miscarriage, and may pose health risks to the mother as well through e.g. an increased risk of preeclampsia (relevant link). It is only very recently that we’ve developed the knowledge and medical technology required to make pregnancy a reasonably safe option for female diabetics. You still had some diabetic females who gave birth before developing diabetes, like in the far past, and the situation was different for males, but either way I feel reasonably confident claiming that if you look for genetic causes of increasing incidence, vertical transmission should not be the main factor to consider.

iv) You need to be careful when evaluating questions like these to keep a distinction between questions relating to drivers of incidence and questions relating to drivers of prevalence at the back of your mind. These two sets of questions are not equivalent.

v) If people are interested to know more about the potential causes of increased incidence of type 1 diabetes, here’s a relevant review paper.”

I followed up with a second comment a while later, because I figured a few points of interest might not have been sufficiently well addressed in my first comment:

“@Nybbler:

A few additional remarks.

i) “Temporal trends in chronic disease incidence rates are almost certainly environmentally induced. If one observes a 50% increase in the incidence of a disorder over 20 yr, it is most likely the result of changes in the environment because the gene pool cannot change that rapidly. Type 1 diabetes is a very dynamic disease. […] results clearly demonstrate that the incidence of type 1 diabetes is rising, bringing with it a large public health problem. Moreover, these findings indicate that something in our environment is changing to trigger a disease response. […] With the exception of a possible role for viruses and infant nutrition, the specific environmental determinants that initiate or precipitate the onset of type 1 diabetes remain unclear.” (Type 1 Diabetes, Etiology and Treatment. Just to make it perfectly clear that although genes matter, environmental factors are the most likely causes of the rising levels of incidence we’ve seen in recent times.)

ii. Just as you need to always keep incidence and prevalence in mind when analyzing these things (for example low prevalence does not mean incidence is necessarily low, or was low in the past; low prevalence could also be a result of a combination of high incidence and high case mortality. I know from experience that even diabetes researchers tend to sometimes overlook stuff like this), you also need to keep the distinction between genotype and phenotype in mind. Given the increased importance of one or more environmental triggers in modern times, penetrance is likely to have changed over time. This means for example that ‘a diabetic genotype’ may have been less fitness reducing in the past than it is today, even if the associated ‘diabetic phenotype’ may on the other hand have been much more fitness reducing than it is now; people who developed diabetes died, but many of the people who might in the current environment be considered high-risk cases may not have been high risk in the far past, because the environmental trigger causing disease was absent, or rarely encountered. Assessing genetic risk for diabetes is complicated, and there’s no general formula for calculating this risk either in the type 1 or type 2 case; monogenic forms of diabetes do exist, but they account for a very small proportion of cases (1-5% of diabetes in young individuals) – most cases are polygenic and display variable levels of penetrance. Note incidentally that a story of environmental factors becoming more important over time is actually implicitly also, to the extent that diabetes is/has been fitness-reducing, a story of selection pressures against diabetic genotypes potentially increasing over time, rather than the opposite (which seems to be the default assumption when only taking into account stuff like the increased survival rates of type 1 diabetics over time). This stuff is complicated.”

I wasn’t completely happy with my second comment (I wrote it relatively fast and didn’t have time to go over it in detail after I’d written it), so I figured it might make sense to add a few details here. One key idea here is of course that you need to distinguish between people who are ‘vulnerable’ to developing type 1 diabetes, and people who actually do develop the disease. If fewer people who today would be considered ‘vulnerable’ developed the disease in the past than is the case now, selection against the ‘vulnerable’ genotype would – all else equal – have been lower throughout evolutionary time than it is today.

All else is not equal because of insulin treatment. But a second key point is that when you’re interested in fitness effects, mortality is not the only variable of interest; many diabetic women who were alive because of insulin during the 20th century but who were also being discouraged from having children may well have left no offspring. Males who committed suicide or died from kidney failure in their twenties likely also didn’t leave many offspring. Another point related to the mortality variable is that although diabetes mortality might in the past have been approximated reasonably well by a simple binary outcome variable/process (no diabetes = alive, diabetes = dead), type 1 diabetes has had large effects on mortality rates also throughout the chunk of history during which insulin has been a treatment option; mortality rates 3 or 4 times higher than those of non-diabetics are common in population studies, and such mortality rates add up over time even if base rates are low, especially in a fitness context, as they for most type 1 diabetics are at play throughout the entire fertile period of the life history. Type 2 diabetes is diagnosed mainly in middle-aged individuals, many of whom have already completed their reproductive cycle, but type 1 diabetes is very different in that respect. Of course there are multiple indirect effects at play as well here, e.g. those of mate choice; which is the more attractive potential partner, the individual with diabetes or the one without? What if the diabetic also happens to be blind?

A few other quotes from the comments:

“The majority of patients on insulin in the US are type 2 diabetics, and it is simply wrong that type 2 diabetics are not responsive to insulin treatment. They were likely found to be unresponsive in early trials because of errors of dosage, as they require higher levels of the drug to obtain the same effect as will young patients diagnosed with type 1 (the primary group on insulin in the 30es). However, insulin treatment is not the first-line option in the type 2 context because the condition can usually be treated with insulin-sensitizing agents for a while, until they fail (those drugs will on average fail in something like ~50% of subjects within five years of diagnosis, which is the reason – combined with the much (order(/s, depending on where you are) of magnitude) higher prevalence of type 2 – why a majority of patients on insulin have type 2), and these tend to a) be more acceptable to the patients (a pill vs an injection) and b) have fewer/less severe side effects on average. One reason which also played a major role in delaying the necessary use of insulin to treat type 2 diabetes which could not be adequately controlled via other means was incidentally the fact that insulin ca[u]ses weight gain, and the obesity-type 2 link was well known.”

“Type 1 is autoimmune, and most cases of type 2 are not, but some forms of type 2 seem to have an autoimmune component as well (“the overall autoantibody frequency in type 2 patients varies between 6% and 10%” – source) (these patients, who can be identified through genetic markers, will on average proceed to insulin dependence because of treatment failure in the context of insulin sensitizing-agents much sooner than is usually the case in patients with type 2). In general type 1 is caused by autoimmune beta cell destruction and type 2 mainly by insulin resistance, but combinations of the two are also possible […], and patients with type 1 can develop insulin resistance just as patients with type 2 can lose beta cells via multiple pathways. The major point here being that the sharp diagnostic distinction between type 1 and type 2 is a major simplification of what’s really going on, and it’s hiding a lot of heterogeneity in both samples. Some patients with type 1 will develop diabetes acutely or subacutely, within days or hours, whereas others will have elevated blood glucose levels for months before medical attention is received and a diagnosis is made (you can tell whether or not blood glucose has been elevated pre-diagnosis by looking at one of the key diagnostic variables, Hba1c, which is a measure of the average blood glucose over the entire lifetime of a red blood cell (~3-4 months) – in some newly diagnosed type 1s, this variable is elevated, in others it is not. Some type 1 patients will develop other autoimmune conditions later on, whereas others will not, and some will be more likely to develop complications than others who have the same level of glycemic control.

Type 1 and type 2 diabetes are quite different conditions, but in terms of many aspects of the diseases there are significant degrees of overlap (for example they develop many of the same complications, for similar (pathophysiological) reasons), yet they are both called diabetes. You don’t want to treat a type 2 diabetic with insulin if he can be treated with metformin, and treating a type 1 with metformin will not help – so different treatments are required.”

“In terms of whether it’s ideal to have one autistic diagnostic group or two (…or three, or…) [this question was a starting point for the debate from which I quote, but I decided not to go much into this topic here], I maintain that to a significant extent the answer to that question relates to what the diagnosis is supposed to accomplish. If it makes sense for researchers to be able to distinguish, which it probably does, but it is not necessary for support organizers/providers to know the subtype in order to provide aid, then you might end up with one ‘official’ category and two (or more) ‘research categories’. I would be fine with that (but again I don’t find this discussion interesting). Again a parallel might be made to diabetes research: Endocrinologists are well aware that there’s a huge amount of variation in both the type 1 and type 2 samples, to the extent that it’s sort of silly to even categorize these illnesses using the same name, but they do it anyway for reasons which are sort of obvious. If you’re type 1 diabetic and you have an HLA mutation which made you vulnerable to diabetes and you developed diabetes at the age of 5, well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. If on the other hand you’re an adult guy who due to a very different genetic vulnerability developed type 1 diabetes at the age of 30 (and later on Graves’ disease at the age of 40, due to the same mutation), well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. The only thing type 1 diabetics have in common is the fact that their beta cells die due to some autoimmune processes. But it could easily be conceived of instead as literally hundreds of different diseases. Currently the distinctions between the different disease-relevant pathophysiological processes don’t matter very much in the treatment context, but they might do that at some point in the future, and if that happens the differences will start to become more important. People might at that point start to talk about type 1a diabetes, which might be the sort you can delay or stop with gene therapy, and type 1b which you can’t delay or stop (…yet). Lumping ‘different’ groups together into one diagnostic category is bad if it makes you overlook variation which is important, and this may be a problem in the autism context today, but regardless of the sizes of the diagnostic groups you’ll usually still end up with lots of residual (‘unexplained’) variation.”

I can’t recall to which extent I’ve discussed this last topic – the extent to which type 1 diabetes is best modeled as one illness or many – but it’s an important topic to keep at the back of your mind when you’re reading the diabetes literature. I’m assuming that in some contexts the subgroup heterogeneities, e.g. in terms of treatment response, will be much more important than in other contexts, so you probably need specific subject matter knowledge to make any sort of informed decision about to which extent potential unobserved heterogeneities may be important in a specific setting, but even if you don’t have that ‘a healthy skepticism’, derived from keeping the potential for these factors to play a role in mind, is likely to be more useful than the alternative. In that context I think the (poor, but understandable) standard practice of lumping together type 1 and type 2 diabetics in studies may lead many people familiar with the differences between the two conditions to think along the lines that as long as you know the type, you’re good to go – ‘at least this study only looked at type 1 individuals, not like those crappy studies which do not distinguish between type 1 and type 2, so I can definitely trust these results to apply to the subgroup of type 1 diabetics in which I’m interested’ – and I think this tendency, to the extent that it exists, is unfortunate.

July 8, 2017 Posted by | autism, Diabetes, Epidemiology, Genetics, Medicine, Psychology | Leave a comment

Melanoma therapeutic strategies that select against resistance

A short lecture, but interesting:

If you’re not an oncologist, these two links in particular might be helpful to have a look at before you start out: BRAF (gene) & Myc. A very substantial proportion of the talk is devoted to math and stats methodology (which some people will find interesting and others …will not).

July 3, 2017 Posted by | Biology, Cancer/oncology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

The Biology of Moral Systems (II)

There are multiple really great books I have read ‘recently’ and which I have either not blogged at all, or not blogged in anywhere near the amount of detail they deserve; Alexander’s book is one of those books. I hope to get rid of some of the backlog soon. You can read my first post about the book here, and it might be a good idea to do so as I won’t allude to material covered in the first post here. In this post I have added some quotes from and comments related to the book’s second chapter, ‘A Biological View of Morality’.

“Moral systems are systems of indirect reciprocity. They exist because confluences of interest within groups are used to deal with conflicts of interest between groups. Indirect reciprocity develops because interactions are repeated, or flow among a society’s members, and because information about subsequent interactions can be gleaned from observing the reciprocal interactions of others.
To establish moral rules is to impose rewards and punishments (typically assistance and ostracism, respectively) to control social acts that, respectively, help or hurt others. To be regarded as moral, a rule typically must represent widespread opinion, reflecting the fact that it must apply with a certain degree of indiscrimininateness.”

“Moral philosophers have not treated the beneficence of humans as a part, somehow, of their selfishness; yet, as Trivers (1971) suggested, the biologist’s view of lifetimes leads directly to this argument. In other words, the normally expressed beneficence, or altruism, of parenthood and nepotism and the temporary altruism (or social investment) of reciprocity are expected to result in greater returns than their alternatives.
If biologists are correct, all that philosophers refer to as altruistic or utilitarian behavior by individuals will actually represent either the temporary altruism (phenotypic beneficence or social investment) of indirect somatic effort [‘Direct somatic effort refers to self-help that involves no other persons. Indirect somatic effort involves reciprocity, which may be direct or indirect. Returns from direct and indirect reciprocity may be immediate or delayed’ – Alexander spends some pages classifying human effort in terms of such ‘atoms of sociality’, which are useful devices for analytical purposes, but I decided not to cover that stuff in detail here – US] or direct and indirect nepotism. The exceptions are what might be called evolutionary mistakes or accidents that result in unreciprocated or “genetic” altruism, deleterious to both the phenotype and genotype of the altruist; such mistakes can occur in all of the above categories” [I should point out that Boyd and Richerson’s book Not by Genes Alone – another great book which I hope to blog soon – is worth having a look at if after reading Alexander’s book you think that he does not cover the topic of how and why such mistakes might happen in the amount of detail it deserves; they also cover related topics in some detail, from a different angle – US]

“It is my impression that many moral philosophers do not approach the problem of morality and ethics as if it arose as an effort to resolve conflicts of interests. Their involvement in conflicts of interest seems to come about obliquely through discussions of individuals’ views with respect to moral behavior, or their proximate feelings about morality – almost as if questions about conflicts of interest arise only because we operate under moral systems, rather than vice versa.”

“The problem, in developing a theory of moral systems that is consistent with evolutionary theory from biology, is in accounting for the altruism of moral behavior in genetically selfish terms. I believe this can be done by interpreting moral systems as systems of indirect reciprocity.
I regard indirect reciprocity as a consequence of direct reciprocity occurring in the presence of interested audiences – groups of individuals who continually evaluate the members of their society as possible future interactants from whom they would like to gain more than they lose […] Even in directly reciprocal interactions […] net losses to self […] may be the actual aim of one or even both individuals, if they are being scrutinized by others who are likely to engage either individual subsequently in reciprocity of greater significance than that occurring in the scrutinized acts. […] Systems of indirect reciprocity, and therefore moral systems, are social systems structured around the importance of status. The concept of status implies that an individual’s privileges, or its access to resources, are controlled in part by how others collectively think of him (hence, treat him) as a result of past interactions (including observations of interactions with others). […] The consequences of indirect reciprocity […] include the concomitant spread of altruism (as social investment genetically valuable to the altruist), rules, and efforts to cheat […]. I would not contend that we always carry out cost-benefit analyses on these issues deliberately or consciously. I do, however, contend that such analyses occur, sometimes consciously, sometimes not, and that we are evolved to be exceedingly accurate and quick at making them […] [A] conscience [is what] I have interpreted (Alexander, 1979a) as the “still small voice that tells us how far we can go in serving our own interests without incurring intolerable risks.””

“The long-term existence of complex patterns of indirect reciprocity […] seems to favor the evolution of keen abilities to (1) make one’s self seem more beneficent than is the case; and (2) influence others to be beneficent in such fashions as to be deleterious to themselves and beneficial to the moralizer, e.g. to lead others to (a) invest too much, (b) invest wrongly in the moralizer or his relatives and friends, or (c) invest indiscriminately on a larger scale than would otherwise be the case. According to this view, individuals are expected to parade the idea of much beneficence, and even of indiscriminate altruism as beneficial, so as to encourage people in general to engage in increasing amounts of social investment whether or not it is beneficial to their interests. […] They may also be expected to depress the fitness of competitors by identifying them, deceptively or not, as reciprocity cheaters (in other words, to moralize and gossip); to internalize rules or evolve the ability to acquire a conscience, interpreted […] as the ability to use or own judgment to serve our own interests; and to self-deceive and display false sincerity as defenses against detection of cheating and attributions of deliberateness in cheating […] Everyone will with to appear more beneficent than he is. There are two reasons: (1) this appearance, if credible, is more likely to lead to direct social rewards than its alternatives; (2) it is also more likely to encourage others to be more beneficent.”

“Consciousness and related aspects of the human psyche (self-awareness, self-reflection, foresight, planning, purpose, conscience, free will, etc.) are here hypothesized to represent a system for competing with other humans for status, resources, and eventually reproductive success. More specifically, the collection of these attributes is viewed as a means of seeing ourselves and our life situations as others see us and our life situations – most particularly in ways that will cause (the most and the most important of) them to continue to interact with us in fashions that will benefit us and seem to benefit them.
Consciousness, then, is a game of life in which the participants are trying to comprehend what is in one another’s minds before, and more effectively than, it can be done in reverse.”

“Provided with a means of relegating our deceptions to the subconsciousness […] false sincerity becomes easier and detection more difficult. There are reasons for believing that one does not need to know his own personal interests consciously in order to serve them as much as he needs to know the interests of others to thwart them. […] I have suggested that consciousness is a way of making our social behavior so unpredictable as to allow us to outmaneuver others; and that we press into subconsciousness (as opposed to forgetting) those things that remain useful to us but would be detrimental to us if others knew about them, and on which we are continually tested and would have to lie deliberately if they remained in our conscious mind […] Conscious concealment of interests, or disavowal, is deliberate deception, considered more reprehensible than anything not conscious. Indeed, if one does not know consciously what his interests are, he cannot, in some sense, be accused of deception even though he may be using an evolved ability of self-deception to deceive others. So it is not always – maybe not usually – in our evolutionary or surrogate-evolutionary interests to make them conscious […] If people can be fooled […] then there will be continual selection for becoming better at fooling others […]. This may include causing them to think that it will be best for them to help you when it is not. This ploy works because of the thin line everybody must continually tread with respect to not showing selfishness. If some people are self-destructively beneficent (i.e., make altruistic mistakes), and if people often cannot tell if one is such a mistake-maker, it might be profitable even to try to convince others that one is such a mistake-maker so as to be accepted as a cooperator or so that the other will be beneficent in expectation of large returns (through “mistakes”) later. […] Reciprocity may work this way because it is grounded evolutionarily in nepotism, appropriate dispensing of nepotism (as well as reciprocity) depends upon learning, and the wrong things can be learned. [Boyd and Richerson talk about this particular aspect, the learning part, in much more detail in their books – US] Self-deception, then may not be a pathological or detrimental trait, at least in most people most of the time. Rather, it may have evolved as a way to deceive others.”

“The only time that utilitarianism (promoting the greatest good to the greatest number) is predicted by evolutionary theory is when the interests of the group (the “greatest number”) and the individual coincide, and in such cases utilitarianism is not really altruistic in either the biologists’ or the philosophers’ sense of the term. […] If Kohlberg means to imply that a significant proportion of the populace of the world either implicitly or explicitly favors a system in which everyone (including himself) behaves so as to bring the greatest good to the greatest number, then I simply believe that he is wrong. If he supposes that only a relatively few – particularly moral philosophers and some others like them – have achieved this “stage,” then I also doubt the hypothesis. I accept that many people are aware of this concept of utility, that a small minority may advocate it, and that an even smaller minority may actually believe that they behave according to it. I speculate, however, that with a few inadvertent or accidental exceptions, no one actually follows this precept. I see the concept as having its main utility as a goal towards which one may exhort others to aspire, and towards which one may behave as if (or talk as if) aspiring, which actually practicing complex forms of self-interest.”

“Generally speaking, the bigger the group, the more complex the social organization, and the greater the group’s unity of purpose the more limited is individual entrepreneurship.”

“The function or raison d’etre [sic] of moral systems is evidently to provide the unity required to enable the group to compete successfully with other human groups. […] the argument that human evolution has been guided to some large extent by intergroup competition and aggression […] is central to the theory of morality presented here”.

June 29, 2017 Posted by | Anthropology, Biology, Books, Evolutionary biology, Genetics, Philosophy | Leave a comment

Harnessing phenotypic heterogeneity to design better therapies

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

A few papers mentioned/discussed in the lecture:

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

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

Standing on the Shoulders of Mice: Aging T-cells

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

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

Biodemography of aging (IV)

My working assumption as I was reading part two of the book was that I would not be covering that part of the book in much detail here because it would simply be too much work to make such posts legible to the readership of this blog. However I then later, while writing this post, had the thought that given that almost nobody reads along here anyway (I’m not complaining, mind you – this is how I like it these days), the main beneficiary of my blog posts will always be myself, which lead to the related observation/notion that I should not be limiting my coverage of interesting stuff here simply because some hypothetical and probably nonexistent readership out there might not be able to follow the coverage. So when I started out writing this post I was working under the assumption that it would be my last post about the book, but I now feel sure that if I find the time I’ll add at least one more post about the book’s statistics coverage. On a related note I am explicitly making the observation here that this post was written for my benefit, not yours. You can read it if you like, or not, but it was not really written for you.

I have added bold a few places to emphasize key concepts and observations from the quoted paragraphs and in order to make the post easier for me to navigate later (all the italics below are on the other hand those of the authors of the book).

Biodemography is a multidisciplinary branch of science that unites under its umbrella various analytic approaches aimed at integrating biological knowledge and methods and traditional demographic analyses to shed more light on variability in mortality and health across populations and between individuals. Biodemography of aging is a special subfield of biodemography that focuses on understanding the impact of processes related to aging on health and longevity.”

“Mortality rates as a function of age are a cornerstone of many demographic analyses. The longitudinal age trajectories of biomarkers add a new dimension to the traditional demographic analyses: the mortality rate becomes a function of not only age but also of these biomarkers (with additional dependence on a set of sociodemographic variables). Such analyses should incorporate dynamic characteristics of trajectories of biomarkers to evaluate their impact on mortality or other outcomes of interest. Traditional analyses using baseline values of biomarkers (e.g., Cox proportional hazards or logistic regression models) do not take into account these dynamics. One approach to the evaluation of the impact of biomarkers on mortality rates is to use the Cox proportional hazards model with time-dependent covariates; this approach is used extensively in various applications and is available in all popular statistical packages. In such a model, the biomarker is considered a time-dependent covariate of the hazard rate and the corresponding regression parameter is estimated along with standard errors to make statistical inference on the direction and the significance of the effect of the biomarker on the outcome of interest (e.g., mortality). However, the choice of the analytic approach should not be governed exclusively by its simplicity or convenience of application. It is essential to consider whether the method gives meaningful and interpretable results relevant to the research agenda. In the particular case of biodemographic analyses, the Cox proportional hazards model with time-dependent covariates is not the best choice.

“Longitudinal studies of aging present special methodological challenges due to inherent characteristics of the data that need to be addressed in order to avoid biased inference. The challenges are related to the fact that the populations under study (aging individuals) experience substantial dropout rates related to death or poor health and often have co-morbid conditions related to the disease of interest. The standard assumption made in longitudinal analyses (although usually not explicitly mentioned in publications) is that dropout (e.g., death) is not associated with the outcome of interest. While this can be safely assumed in many general longitudinal studies (where, e.g., the main causes of dropout might be the administrative end of the study or moving out of the study area, which are presumably not related to the studied outcomes), the very nature of the longitudinal outcomes (e.g., measurements of some physiological biomarkers) analyzed in a longitudinal study of aging assumes that they are (at least hypothetically) related to the process of aging. Because the process of aging leads to the development of diseases and, eventually, death, in longitudinal studies of aging an assumption of non-association of the reason for dropout and the outcome of interest is, at best, risky, and usually is wrong. As an illustration, we found that the average trajectories of different physiological indices of individuals dying at earlier ages markedly deviate from those of long-lived individuals, both in the entire Framingham original cohort […] and also among carriers of specific alleles […] In such a situation, panel compositional changes due to attrition affect the averaging procedure and modify the averages in the total sample. Furthermore, biomarkers are subject to measurement error and random biological variability. They are usually collected intermittently at examination times which may be sparse and typically biomarkers are not observed at event times. It is well known in the statistical literature that ignoring measurement errors and biological variation in such variables and using their observed “raw” values as time-dependent covariates in a Cox regression model may lead to biased estimates and incorrect inferences […] Standard methods of survival analysis such as the Cox proportional hazards model (Cox 1972) with time-dependent covariates should be avoided in analyses of biomarkers measured with errors because they can lead to biased estimates.

“Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements have become known in the mainstream biostatistical literature as “joint models for longitudinal and time-to-event data” (“survival” or “failure time” are often used interchangeably with “time-to-event”) or simply “joint models.” This is an active and fruitful area of biostatistics with an explosive growth in recent years. […] The standard joint model consists of two parts, the first representing the dynamics of longitudinal data (which is referred to as the “longitudinal sub-model”) and the second one modeling survival or, generally, time-to-event data (which is referred to as the “survival sub-model”). […] Numerous extensions of this basic model have appeared in the joint modeling literature in recent decades, providing great flexibility in applications to a wide range of practical problems. […] The standard parameterization of the joint model (11.2) assumes that the risk of the event at age t depends on the current “true” value of the longitudinal biomarker at this age. While this is a reasonable assumption in general, it may be argued that additional dynamic characteristics of the longitudinal trajectory can also play a role in the risk of death or onset of a disease. For example, if two individuals at the same age have exactly the same level of some biomarker at this age, but the trajectory for the first individual increases faster with age than that of the second one, then the first individual can have worse survival chances for subsequent years. […] Therefore, extensions of the basic parameterization of joint models allowing for dependence of the risk of an event on such dynamic characteristics of the longitudinal trajectory can provide additional opportunities for comprehensive analyses of relationships between the risks and longitudinal trajectories. Several authors have considered such extended models. […] joint models are computationally intensive and are sometimes prone to convergence problems [however such] models provide more efficient estimates of the effect of a covariate […] on the time-to-event outcome in the case in which there is […] an effect of the covariate on the longitudinal trajectory of a biomarker. This means that analyses of longitudinal and time-to-event data in joint models may require smaller sample sizes to achieve comparable statistical power with analyses based on time-to-event data alone (Chen et al. 2011).”

“To be useful as a tool for biodemographers and gerontologists who seek biological explanations for observed processes, models of longitudinal data should be based on realistic assumptions and reflect relevant knowledge accumulated in the field. An example is the shape of the risk functions. Epidemiological studies show that the conditional hazards of health and survival events considered as functions of risk factors often have U- or J-shapes […], so a model of aging-related changes should incorporate this information. In addition, risk variables, and, what is very important, their effects on the risks of corresponding health and survival events, experience aging-related changes and these can differ among individuals. […] An important class of models for joint analyses of longitudinal and time-to-event data incorporating a stochastic process for description of longitudinal measurements uses an epidemiologically-justified assumption of a quadratic hazard (i.e., U-shaped in general and J-shaped for variables that can take values only on one side of the U-curve) considered as a function of physiological variables. Quadratic hazard models have been developed and intensively applied in studies of human longitudinal data”.

“Various approaches to statistical model building and data analysis that incorporate unobserved heterogeneity are ubiquitous in different scientific disciplines. Unobserved heterogeneity in models of health and survival outcomes can arise because there may be relevant risk factors affecting an outcome of interest that are either unknown or not measured in the data. Frailty models introduce the concept of unobserved heterogeneity in survival analysis for time-to-event data. […] Individual age trajectories of biomarkers can differ due to various observed as well as unobserved (and unknown) factors and such individual differences propagate to differences in risks of related time-to-event outcomes such as the onset of a disease or death. […] The joint analysis of longitudinal and time-to-event data is the realm of a special area of biostatistics named “joint models for longitudinal and time-to-event data” or simply “joint models” […] Approaches that incorporate heterogeneity in populations through random variables with continuous distributions (as in the standard joint models and their extensions […]) assume that the risks of events and longitudinal trajectories follow similar patterns for all individuals in a population (e.g., that biomarkers change linearly with age for all individuals). Although such homogeneity in patterns can be justifiable for some applications, generally this is a rather strict assumption […] A population under study may consist of subpopulations with distinct patterns of longitudinal trajectories of biomarkers that can also have different effects on the time-to-event outcome in each subpopulation. When such subpopulations can be defined on the base of observed covariate(s), one can perform stratified analyses applying different models for each subpopulation. However, observed covariates may not capture the entire heterogeneity in the population in which case it may be useful to conceive of the population as consisting of latent subpopulations defined by unobserved characteristics. Special methodological approaches are necessary to accommodate such hidden heterogeneity. Within the joint modeling framework, a special class of models, joint latent class models, was developed to account for such heterogeneity […] The joint latent class model has three components. First, it is assumed that a population consists of a fixed number of (latent) subpopulations. The latent class indicator represents the latent class membership and the probability of belonging to the latent class is specified by a multinomial logistic regression function of observed covariates. It is assumed that individuals from different latent classes have different patterns of longitudinal trajectories of biomarkers and different risks of event. The key assumption of the model is conditional independence of the biomarker and the time-to-events given the latent classes. Then the class-specific models for the longitudinal and time-to-event outcomes constitute the second and third component of the model thus completing its specification. […] the latent class stochastic process model […] provides a useful tool for dealing with unobserved heterogeneity in joint analyses of longitudinal and time-to-event outcomes and taking into account hidden components of aging in their joint influence on health and longevity. This approach is also helpful for sensitivity analyses in applications of the original stochastic process model. We recommend starting the analyses with the original stochastic process model and estimating the model ignoring possible hidden heterogeneity in the population. Then the latent class stochastic process model can be applied to test hypotheses about the presence of hidden heterogeneity in the data in order to appropriately adjust the conclusions if a latent structure is revealed.”

The longitudinal genetic-demographic model (or the genetic-demographic model for longitudinal data) […] combines three sources of information in the likelihood function: (1) follow-up data on survival (or, generally, on some time-to-event) for genotyped individuals; (2) (cross-sectional) information on ages at biospecimen collection for genotyped individuals; and (3) follow-up data on survival for non-genotyped individuals. […] Such joint analyses of genotyped and non-genotyped individuals can result in substantial improvements in statistical power and accuracy of estimates compared to analyses of the genotyped subsample alone if the proportion of non-genotyped participants is large. Situations in which genetic information cannot be collected for all participants of longitudinal studies are not uncommon. They can arise for several reasons: (1) the longitudinal study may have started some time before genotyping was added to the study design so that some initially participating individuals dropped out of the study (i.e., died or were lost to follow-up) by the time of genetic data collection; (2) budget constraints prohibit obtaining genetic information for the entire sample; (3) some participants refuse to provide samples for genetic analyses. Nevertheless, even when genotyped individuals constitute a majority of the sample or the entire sample, application of such an approach is still beneficial […] The genetic stochastic process model […] adds a new dimension to genetic biodemographic analyses, combining information on longitudinal measurements of biomarkers available for participants of a longitudinal study with follow-up data and genetic information. Such joint analyses of different sources of information collected in both genotyped and non-genotyped individuals allow for more efficient use of the research potential of longitudinal data which otherwise remains underused when only genotyped individuals or only subsets of available information (e.g., only follow-up data on genotyped individuals) are involved in analyses. Similar to the longitudinal genetic-demographic model […], the benefits of combining data on genotyped and non-genotyped individuals in the genetic SPM come from the presence of common parameters describing characteristics of the model for genotyped and non-genotyped subsamples of the data. This takes into account the knowledge that the non-genotyped subsample is a mixture of carriers and non-carriers of the same alleles or genotypes represented in the genotyped subsample and applies the ideas of heterogeneity analyses […] When the non-genotyped subsample is substantially larger than the genotyped subsample, these joint analyses can lead to a noticeable increase in the power of statistical estimates of genetic parameters compared to estimates based only on information from the genotyped subsample. This approach is applicable not only to genetic data but to any discrete time-independent variable that is observed only for a subsample of individuals in a longitudinal study.

“Despite an existing tradition of interpreting differences in the shapes or parameters of the mortality rates (survival functions) resulting from the effects of exposure to different conditions or other interventions in terms of characteristics of individual aging, this practice has to be used with care. This is because such characteristics are difficult to interpret in terms of properties of external and internal processes affecting the chances of death. An important question then is: What kind of mortality model has to be developed to obtain parameters that are biologically interpretable? The purpose of this chapter is to describe an approach to mortality modeling that represents mortality rates in terms of parameters of physiological changes and declining health status accompanying the process of aging in humans. […] A traditional (demographic) description of changes in individual health/survival status is performed using a continuous-time random Markov process with a finite number of states, and age-dependent transition intensity functions (transitions rates). Transitions to the absorbing state are associated with death, and the corresponding transition intensity is a mortality rate. Although such a description characterizes connections between health and mortality, it does not allow for studying factors and mechanisms involved in the aging-related health decline. Numerous epidemiological studies provide compelling evidence that health transition rates are influenced by a number of factors. Some of them are fixed at the time of birth […]. Others experience stochastic changes over the life course […] The presence of such randomly changing influential factors violates the Markov assumption, and makes the description of aging-related changes in health status more complicated. […] The age dynamics of influential factors (e.g., physiological variables) in connection with mortality risks has been described using a stochastic process model of human mortality and aging […]. Recent extensions of this model have been used in analyses of longitudinal data on aging, health, and longevity, collected in the Framingham Heart Study […] This model and its extensions are described in terms of a Markov stochastic process satisfying a diffusion-type stochastic differential equation. The stochastic process is stopped at random times associated with individuals’ deaths. […] When an individual’s health status is taken into account, the coefficients of the stochastic differential equations become dependent on values of the jumping process. This dependence violates the Markov assumption and renders the conditional Gaussian property invalid. So the description of this (continuously changing) component of aging-related changes in the body also becomes more complicated. Since studying age trajectories of physiological states in connection with changes in health status and mortality would provide more realistic scenarios for analyses of available longitudinal data, it would be a good idea to find an appropriate mathematical description of the joint evolution of these interdependent processes in aging organisms. For this purpose, we propose a comprehensive model of human aging, health, and mortality in which the Markov assumption is fulfilled by a two-component stochastic process consisting of jumping and continuously changing processes. The jumping component is used to describe relatively fast changes in health status occurring at random times, and the continuous component describes relatively slow stochastic age-related changes of individual physiological states. […] The use of stochastic differential equations for random continuously changing covariates has been studied intensively in the analysis of longitudinal data […] Such a description is convenient since it captures the feedback mechanism typical of biological systems reflecting regular aging-related changes and takes into account the presence of random noise affecting individual trajectories. It also captures the dynamic connections between aging-related changes in health and physiological states, which are important in many applications.”

April 23, 2017 Posted by | Biology, Books, Demographics, Genetics, Mathematics, Statistics | Leave a comment

Biodemography of aging (III)

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

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

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

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

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

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

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

Moving on…

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

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

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

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

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

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

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

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

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

Biodemography of aging (II)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Biodemography of aging (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Random stuff

i. Fire works a little differently than people imagine. A great ask-science comment. See also AugustusFink-nottle’s comment in the same thread.

ii.

iii. I was very conflicted about whether to link to this because I haven’t actually spent any time looking at it myself so I don’t know if it’s any good, but according to somebody (?) who linked to it on SSC the people behind this stuff have academic backgrounds in evolutionary biology, which is something at least (whether you think this is a good thing or not will probably depend greatly on your opinion of evolutionary biologists, but I’ve definitely learned a lot more about human mating patterns, partner interaction patterns, etc. from evolutionary biologists than I have from personal experience, so I’m probably in the ‘they-sometimes-have-interesting-ideas-about-these-topics-and-those-ideas-may-not-be-terrible’-camp). I figure these guys are much more application-oriented than were some of the previous sources I’ve read on related topics, such as e.g. Kappeler et al. I add the link mostly so that if I in five years time have a stroke that obliterates most of my decision-making skills, causing me to decide that entering the dating market might be a good idea, I’ll have some idea where it might make sense to start.

iv. Stereotype (In)Accuracy in Perceptions of Groups and Individuals.

“Are stereotypes accurate or inaccurate? We summarize evidence that stereotype accuracy is one of the largest and most replicable findings in social psychology. We address controversies in this literature, including the long-standing  and continuing but unjustified emphasis on stereotype inaccuracy, how to define and assess stereotype accuracy, and whether stereotypic (vs. individuating) information can be used rationally in person perception. We conclude with suggestions for building theory and for future directions of stereotype (in)accuracy research.”

A few quotes from the paper:

Demographic stereotypes are accurate. Research has consistently shown moderate to high levels of correspondence accuracy for demographic (e.g., race/ethnicity, gender) stereotypes […]. Nearly all accuracy correlations for consensual stereotypes about race/ethnicity and  gender exceed .50 (compared to only 5% of social psychological findings; Richard, Bond, & Stokes-Zoota, 2003).[…] Rather than being based in cultural myths, the shared component of stereotypes is often highly accurate. This pattern cannot be easily explained by motivational or social-constructionist theories of stereotypes and probably reflects a “wisdom of crowds” effect […] personal stereotypes are also quite accurate, with correspondence accuracy for roughly half exceeding r =.50.”

“We found 34 published studies of racial-, ethnic-, and gender-stereotype accuracy. Although not every study examined discrepancy scores, when they did, a plurality or majority of all consensual stereotype judgments were accurate. […] In these 34 studies, when stereotypes were inaccurate, there was more evidence of underestimating than overestimating actual demographic group differences […] Research assessing the accuracy of  miscellaneous other stereotypes (e.g., about occupations, college majors, sororities, etc.) has generally found accuracy levels comparable to those for demographic stereotypes”

“A common claim […] is that even though many stereotypes accurately capture group means, they are still not accurate because group means cannot describe every individual group member. […] If people were rational, they would use stereotypes to judge individual targets when they lack information about targets’ unique personal characteristics (i.e., individuating information), when the stereotype itself is highly diagnostic (i.e., highly informative regarding the judgment), and when available individuating information is ambiguous or incompletely useful. People’s judgments robustly conform to rational predictions. In the rare situations in which a stereotype is highly diagnostic, people rely on it (e.g., Crawford, Jussim, Madon, Cain, & Stevens, 2011). When highly diagnostic individuating information is available, people overwhelmingly rely on it (Kunda & Thagard, 1996; effect size averaging r = .70). Stereotype biases average no higher than r = .10 ( Jussim, 2012) but reach r = .25 in the absence of individuating information (Kunda & Thagard, 1996). The more diagnostic individuating information  people have, the less they stereotype (Crawford et al., 2011; Krueger & Rothbart, 1988). Thus, people do not indiscriminately apply their stereotypes to all individual  members of stereotyped groups.” (Funder incidentally talked about this stuff as well in his book Personality Judgment).

One thing worth mentioning in the context of stereotypes is that if you look at stuff like crime data – which sadly not many people do – and you stratify based on stuff like country of origin, then the sub-group differences you observe tend to be very large. Some of the differences you observe between subgroups are not in the order of something like 10%, which is probably the sort of difference which could easily be ignored without major consequences; some subgroup differences can easily be in the order of one or two orders of magnitude. The differences are in some contexts so large as to basically make it downright idiotic to assume there are no differences – it doesn’t make sense, it’s frankly a stupid thing to do. To give an example, in Germany the probability that a random person, about whom you know nothing, has been a suspect in a thievery case is 22% if that random person happens to be of Algerian extraction, whereas it’s only 0,27% if you’re dealing with an immigrant from China. Roughly one in 13 of those Algerians have also been involved in a case of ‘body (bodily?) harm’, which is the case for less than one in 400 of the Chinese immigrants.

v. Assessing Immigrant Integration in Sweden after the May 2013 Riots. Some data from the article:

“Today, about one-fifth of Sweden’s population has an immigrant background, defined as those who were either born abroad or born in Sweden to two immigrant parents. The foreign born comprised 15.4 percent of the Swedish population in 2012, up from 11.3 percent in 2000 and 9.2 percent in 1990 […] Of the estimated 331,975 asylum applicants registered in EU countries in 2012, 43,865 (or 13 percent) were in Sweden. […] More than half of these applications were from Syrians, Somalis, Afghanis, Serbians, and Eritreans. […] One town of about 80,000 people, Södertälje, since the mid-2000s has taken in more Iraqi refugees than the United States and Canada combined.”

“Coupled with […] macroeconomic changes, the largely humanitarian nature of immigrant arrivals since the 1970s has posed challenges of labor market integration for Sweden, as refugees often arrive with low levels of education and transferable skills […] high unemployment rates have disproportionately affected immigrant communities in Sweden. In 2009-10, Sweden had the highest gap between native and immigrant employment rates among OECD countries. Approximately 63 percent of immigrants were employed compared to 76 percent of the native-born population. This 13 percentage-point gap is significantly greater than the OECD average […] Explanations for the gap include less work experience and domestic formal qualifications such as language skills among immigrants […] Among recent immigrants, defined as those who have been in the country for less than five years, the employment rate differed from that of the native born by more than 27 percentage points. In 2011, the Swedish newspaper Dagens Nyheter reported that 35 percent of the unemployed registered at the Swedish Public Employment Service were foreign born, up from 22 percent in 2005.”

“As immigrant populations have grown, Sweden has experienced a persistent level of segregation — among the highest in Western Europe. In 2008, 60 percent of native Swedes lived in areas where the majority of the population was also Swedish, and 20 percent lived in areas that were virtually 100 percent Swedish. In contrast, 20 percent of Sweden’s foreign born lived in areas where more than 40 percent of the population was also foreign born.”

vi. Book recommendations. Or rather, author recommendations. A while back I asked ‘the people of SSC’ if they knew of any fiction authors I hadn’t read yet which were both funny and easy to read. I got a lot of good suggestions, and the roughly 20 Dick Francis novels I’ve read during the fall I’ve read as a consequence of that thread.

vii. On the genetic structure of Denmark.

viii. Religious Fundamentalism and Hostility against Out-groups: A Comparison of Muslims and Christians in Western Europe.

“On the basis of an original survey among native Christians and Muslims of Turkish and Moroccan origin in Germany, France, the Netherlands, Belgium, Austria and Sweden, this paper investigates four research questions comparing native Christians to Muslim immigrants: (1) the extent of religious fundamentalism; (2) its socio-economic determinants; (3) whether it can be distinguished from other indicators of religiosity; and (4) its relationship to hostility towards out-groups (homosexuals, Jews, the West, and Muslims). The results indicate that religious fundamentalist attitudes are much more widespread among Sunnite Muslims than among native Christians, even after controlling for the different demographic and socio-economic compositions of these groups. […] Fundamentalist believers […] show very high levels of out-group hostility, especially among Muslims.”

ix. Portal: Dinosaurs. It would have been so incredibly awesome to have had access to this kind of stuff back when I was a child. The portal includes links to articles with names like ‘Bone Wars‘ – what’s not to like? Again, awesome!

x. “you can’t determine if something is truly random from observations alone. You can only determine if something is not truly random.” (link) An important insight well expressed.

xi. Chessprogramming. If you’re interested in having a look at how chess programs work, this is a neat resource. The wiki contains lots of links with information on specific sub-topics of interest. Also chess-related: The World Championship match between Carlsen and Karjakin has started. To the extent that I’ll be following the live coverage, I’ll be following Svidler et al.’s coverage on chess24. Robin van Kampen and Eric Hansen – both 2600+ elo GMs – did quite well yesterday, in my opinion.

xii. Justified by More Than Logos Alone (Razib Khan).

“Very few are Roman Catholic because they have read Aquinas’ Five Ways. Rather, they are Roman Catholic, in order of necessity, because God aligns with their deep intuitions, basic cognitive needs in terms of cosmological coherency, and because the church serves as an avenue for socialization and repetitive ritual which binds individuals to the greater whole. People do not believe in Catholicism as often as they are born Catholics, and the Catholic religion is rather well fitted to a range of predispositions to the typical human.”

November 12, 2016 Posted by | Books, Chemistry, Chess, Data, dating, Demographics, Genetics, Geography, immigration, Paleontology, Papers, Physics, Psychology, Random stuff, Religion | Leave a comment

Role of Biomarkers in Medicine

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

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

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

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

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

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

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

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

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

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

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

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

The Biology of Moral Systems (I)

I have quoted from the book before, but I decided that this book deserves to be blogged in more detail. I’m close to finishing the book at this point (it’s definitely taken longer than it should have), and I’ll probably give it 5 stars on goodreads; I might also add it to my list of favourite books on the site. In this post I’ve added some quotes and ideas from the book, and a few comments. Before going any further I should note that it’s frankly impossible to cover anywhere near all the ideas covered in the book here on the blog, so if you’re even remotely interested in these kinds of things you really should pick up a copy of the book and read all of it.

“I believe that something crucial has been missing from all of the great debates of history, among philosophers, politicians, theologians, and thinkers from other and diverse backgrounds, on the issues of morality, ethics, justice, right and wrong. […] those who have tried to analyze morality have failed to treat the human traits that underlie moral behavior as outcomes of evolution […] for many conflicts of interest, compromises and enforceable contracts represent the only real solutions. Appeals to morality, I will argue, are simply the invoking of such compromises and contracts in particular ways. […] the process of natural selection that has given rise to all forms of life, including humans, operates such that success has always been relative. One consequence is that organisms resulting from the long-term cumulative effects of selection are expected to resist efforts to reveal their interests fully to others, and also efforts to place limits on their striving or to decide for them when their interests are being “fully” satisfied. These are all reasons why we should expect no “terminus” – ever – to debates on moral and ethical issues.” (these comments I also included in the quotes post to which I link at the beginning, but I thought it was worth including them in this post as well even so – US).

“I am convinced that biology can never offer […] easy or direct answers to the questions of what is right and wrong. I explicitly reject the attitude that whatever biology tells us is so is also what ought to be (David Hume’s so-called “naturalistic fallacy”) […] there are within biology no magic solutions to moral problems. […] Knowledge of the human background in organic evolution can [however] provide a deeper self-understanding by an increasing proportion of the world’s population; self-understanding that I believe can contribute to answering the serious questions of social living.”

“If there had been no recent discoveries in biology that provided new ways of looking at the concept of moral systems, then I would be optimistic indeed to believe that I could say much that is new. But there have been such discoveries. […] The central point in these writings [Hamilton, Williams, Trivers, Cavalli-Sforza, Feldman, Dawkins, Wilson, etc. – US] […] is that natural selection has apparently been maximizing the survival by reproduction of genes, as they have been defined by evolutionists, and that, with respect to the activities of individuals, this includes effects on copies of their genes, even copies located in other individuals. In other words, we are evidently evolved not only to aid the genetic materials in our own bodies, by creating and assisting descendants, but also to assist, by nepotism, copies of our genes that reside in collateral (nondescendant) relatives. […] ethics, morality, human conduct, and the human psyche are to be understood only if societies are seen as collections of individuals seeking their own self-interests […] In some respects these ideas run contrary to what people have believed and been taught about morality and human values: I suspect that nearly all humans believe it is a normal part of the functioning of every human individual now and then to assist someone else in the realization of that person’s own interests to the actual net expense of those of the altruist. What [the above-mentioned writings] tells us is that, despite our intuitions, there is not a shred of evidence to support this view of beneficence, and a great deal of convincing theory suggests that any such view will eventually be judged false. This implies that we will have to start all over again to describe and understand ourselves, in terms alien to our intuitions […] It is […] a goal of this book to contribute to this redescription and new understanding, and especially to discuss why our intuitions should have misinformed us.”

“Social behavior evolves as a succession of ploys and counterploys, and for humans these ploys are used, not only among individuals within social groups, but between and among small and large groups of up to hundreds of millions of individuals. The value of an evolutionary approach to human sociality is thus not to determine the limits of our actions so that we can abide by them. Rather, it is to examine our life strategies so that we can change them when we wish, as a result of understanding them. […] my use of the word biology in no way implies that moral systems have some kind of explicit genetic background, are genetically determined, or cannot be altered by adjusting the social environment. […] I mean simply to suggest that if we wish to understand those aspects of our behavior commonly regarded as involving morality or ethics, it will help to reconsider our behavior as a product of evolution by natural selection. The principal reason for this suggestion is that natural selection operates according to general principles which make its effects highly predictive, even with respect to traits and circumstances that have not yet been analyzed […] I am interested […] not in determining what is moral and immoral, in the sense of what people ought to be doing, but in elucidating the natural history of ethics and morality – in discovering how and why humans initiated and developed the ideas we have about right and wrong.”

I should perhaps mention here that sort-of-kind-of related stuff is covered in Aureli et al. (see e.g. this link), and that some parts of that book will probably make you understand Alexander’s ideas a lot better even if perhaps he didn’t read those specific authors – mainly because it gets a lot easier to imagine the sort of mechanisms which might be at play here if you’ve read this sort of literature. Here’s one relevant quote from the coverage of that book, which also deals with the question Alexander discusses above, and in a lot more detail throughout his book, namely ‘where our morality comes from?’

“we make two fundamental assertions regarding the evolution of morality: (1) there are specific types of behavior demonstrated by both human and nonhuman primates that hint at a shared evolutionary background to morality; and (2) there are theoretical and actual connections between morality and conflict resolution in both nonhuman primates and human development. […] the transition from nonmoral or premoral to moral is more gradual than commonly assumed. No magic point appears in either evolutionary history or human development at which morality suddenly comes into existence. In both early childhood and in animals closely related to us, we can recognize behaviors (and, in the case of children, judgments) that are essential building blocks of the morality of the human adult. […] the decision making and emotions underlying moral judgments are generated within the individual rather than being simply imposed by society. They are a product of evolution, an integrated part of the human genetic makeup, that makes the child construct a moral perspective through interactions with other members of its species. […] Much research has shown that children acquire morality through a social-cognitive process; children make connections between acts and consequences. Through a gradual process, children develop concepts of justice, fairness, and equality, and they apply these concepts to concrete everyday situations […] we assert that emotions such as empathy and sympathy provide an experiential basis by which children construct moral judgments. Emotional reactions from others, such as distress or crying, provide experiential information that children use to judge whether an act is right or wrong […] when a child hits another child, a crying response provides emotional information about the nature of the act, and this information enables the child, in part, to determine whether and why the transgression is wrong. Therefore, recognizing signs of distress in another person may be a basic requirement of the moral judgment process. The fact that responses to distress in another have been documented both in infancy and in the nonhuman primate literature provides initial support for the idea that these types of moral-like experiences are common to children and nonhuman primates.”

Alexander’s coverage is quite different from that found in Aureli et al.,, but some of the contributors to the latter work deal with similar questions to the ones in which he’s interested, using approaches not employed in Alexander’s book – so this is another place to look if you’re interested in these topics. Margalit’s The Emergence of Norms is also worth mentioning. Part of the reason why I mention these books here is incidentally that they’re not talked about in Alexander’s coverage (for very natural reasons, I should add, in the case of the former book at least; Natural Conflict Resolution was published more than a decade after Alexander wrote his book…).

“In the hierarchy of explanatory principles governing the traits of living organisms, evolutionary reductionism – the development of principles from the evolutionary process – tends to subsume all other kinds. Proximate-cause reductionism (or reduction by dissection) sometimes advances our understanding of the whole phenomena. […] When evolutionary reduction becomes trivial in the study of life it is for a reason different from incompleteness; rather, it is because the breadth of the generalization distances it too significantly from the particular problem that may be at hand. […] the greatest weakness of reduction by generalization is not that it is likely to be trivial but that errors are probable through unjustified leaps from hypothesis to conclusion […] Critics such as Gould and Lewontin […] do not discuss the facts that (a) all students of human behavior (not just those who take evolution into account) run the risk of leaping unwarrantedly from hypothesis to conclusion and (b) just-so stories were no less prevalent and hypothesis-testing no more prevalent in studies of human behavior before evolutionary biologists began to participate. […] I believe that failure by biologists and others to distinguish proximate- or partial-cause and evolutionary- or ultimate-cause reductionism […] is in some part responsible for the current chasm between the social and the biological sciences and the resistance to so-called biological approaches to understanding humans. […] Both approaches are essential to progress in biology and the social sciences, and it would be helpful if their relationship, and that of their respective practitioners, were not seen as adversarial.”

(Relatedly, love is motivationally prior to sugar. This one also seems relevant, though in a different way).

“Humans are not accustomed to dealing with their own strategies of life as if they had been tuned by natural selection. […] People are not generally aware of what their lifetimes have been evolved to accomplish, and, even if they are roughly aware of this, they do not easily accept that their everyday activities are in any sense means to that end. […] The theory of lifetimes most widely accepted among biologists is that individuals have evolved to maximize the likelihood of survival of not themselves, but their genes, and that they do this by reproducing and tending in various ways offspring and other carriers of their own genes […] In this theory, survival of the individual – and its growth, development, and learning – are proximate mechanisms of reproductive success, which is a proximate mechanism of genic survival. Only the genes have evolved to survive. […] To say that we are evolved to serve the interests of our genes in no way suggests that we are obliged to serve them. […] Evolution is surely most deterministic for those still unaware of it. If this argument is correct, it may be the first to carry us from is to ought, i.e., if we desire to be the conscious masters of our own fates, and if conscious effort in that direction is the most likely vehicle of survival and happiness, then we ought to study evolution.”

“People are sometimes comfortable with the notion that certain activities can be labeled as “purely cultural” because they also believe that there are behaviors that can be labeled “purely genetic.” Neither is true: the environment contributes to the expression of all behaviors, and culture is best described as part of the environment.”

“Happiness and its anticipation are […] proximate mechanisms that lead us to perform and repeat acts that in the environments of history, at least, would have led to greater reproductive success.”

“The remarkable difference between the patterns of senescence in semelparous (one-time breeding) and iteroparous (repeat-breeding) organisms is probably one of the best simple demonstrations of the central significance of reproduction in the individual’s lifetime. How, otherwise, could we explain the fact that those who reproduce but once, like salmon and soybeans, tend to die suddenly right afterward, while those like ourselves who have residual reproductive possibilities after the initial reproductive act decline or senesce gradually? […] once an organism has completed all possibilities of reproducing (through both offspring production and assistance, and helping other relatives), then selection can no longer affect its survival: any physiological or other breakdown that destroys it may persist and even spread if it is genetically linked to a trait that is expressed earlier and is reproductively beneficial. […] selection continually works against senescence, but is just never able to defeat it entirely. […] senescence leads to a generalized deterioration rather than one owing to a single effect or a few effects […] 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. […] natural selection maximizes the likelihood of genetic survival, which is incompatible with eliminating senescence. […] Senescence, and the finiteness of lifetimes, have evolved as incidental effects […] Organisms compete for genetic survival and the winners (in evolutionary terms) are those who sacrifice their phenotypes (selves) earlier when this results in greater reproduction.”

“altruism appears to diminish with decreasing degree of relatedness in sexual species whenever it is studied – in humans as well as nonhuman species”

October 5, 2016 Posted by | Anthropology, Biology, Books, Evolutionary biology, Genetics, Philosophy | Leave a comment

Human Drug Metabolism (III)

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

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

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

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

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

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

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

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

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

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

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

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

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

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