Econstudentlog

Hypoglycemia in Diabetes – Pathophysiology, Prevalence, and Prevention

I liked this book and I gave it 3 stars on goodreads. Much of it was a review of stuff also covered in Sperling et al. (or elsewhere, see also this blog-post which actually includes some of the same data included in the coverage below), but there was some new stuff as well. I’ve added some relevant observations from the book below – I incidentally do not think most of the stuff included in this post should be at all hard to read for people who do not have diabetes.

“Hypoglycemia is a fact of life for most people with type 1 diabetes […] The average patient suffers untold numbers of asymptomatic episodes, two episodes of symptomatic hypoglycemia per week (thousands of such episodes over a lifetime of diabetes), and one episode of severe, temporarily disabling hypoglycemia, often with seizure or coma, per year.

Given increased recognition of the magnitude of the problem of iatrogenic hypoglycemia in type 1 diabetes, and practical improvements in the glycemic management of diabetes, over the nearly two decades since the Diabetes Control and Complications Trial (DCCT) was reported in 1993 (DCCT 1993), one might anticipate that hypoglycemia would have become less of a problem. Unfortunately, there is no evidence of that in population-based studies. For example, in their study reported in 2007, the U.K. Hypoglycaemia Study Group (UK Hypo Group 2007) found the incidence of severe hypoglycemia in patients with type 1 diabetes treated with insulin for <5 years to be comparable to that in the Stockholm Diabetes Intervention Study (Reichard and Pihl 1994) (both 110 per 100 patient-years) reported in 1994 and higher than that in the DCCT”

“the U.K. Hypoglycaemia Study Group (UK Hypo Group 2007) found the incidence of severe hypoglycemia in patients with type 1 diabetes treated with insulin for >15 years (320 episodes per 100 patient-years) to be threefold higher than in individuals treated for <5 years […] Hypoglycemia is particularly common during the night […] A consistent observation since the DCCT (1991, 1993, 1997) is that more than half of the episodes of hypoglycemia, including severe hypoglycemia, occur during the night (Chico et al. 2003; Guillod et al. 2007). […] Antidiabetic drugs, mostly insulin, [have been] found to be second only to anticoagulants as a cause of emergency hospitalization for adverse drug events in people >65 years of age, and those visits [are] almost entirely because of hypoglycemia (Budnitz et al. 2011). […] Overall, hypoglycemia is less frequent in type 2 diabetes than in type 1 diabetes […] the risk of hypoglycemia is relatively low in the first few years of insulin treatment of type 2 diabetes […], [however] the risk increases substantially, approaching that in type 1 diabetes, later in the course of type 2 diabetes […] The prospective, population-based study of Donnelly and colleagues […] indicates that the overall incidence of hypoglycemia in insulin-treated type 2 diabetes is approximately one-third of that in type 1 diabetes […] Because the prevalence of type 2 diabetes is ~20-fold greater than that of type 1 diabetes […] most episodes of iatrogenic hypoglycemia, including severe iatrogenic hypoglycemia, occur in people with type 2 diabetes.”

“The physical morbidity of an episode of hypoglycemia ranges from unpleasant symptoms, such as palpitations, tremulousness, anxiety, sweating, hunger, and paresthesias (Towler et al. 1993), and cognitive impairments with behavioral changes, to seizure, coma, or, rarely, death (Cryer 2007). […] Hypoglycemia causes functional brain failure that is corrected in the vast majority of instances after the plasma glucose concentration is raised […] Prolonged, profound hypoglycemia can cause brain death, but that is very rare and most fatal episodes are the result of other mechanisms, presumably cardiac arrhythmias […] One cardiac mechanism is impaired ventricular repolarization, reflected in a prolonged corrected QT (QTc) interval in the electrocardiogram, which is known to be associated with lethal ventricular arrhythmias. […] Older estimates were that 2 to 4% of people with type 1 diabetes died from hypoglycemia (Deckert et al. 1978; Tunbridge 1981; Laing et al. 1999). More recent reports in type 1 diabetes include hypoglycemic mortality rates of 4% (Patterson et al. 2007), 6% (DCCT/EDIC 2007), 7% (Feltbower et al. 2008), and 10% (Skrivarhaug et al. 2006).”

“The first defense against falling plasma glucose concentrations is a decrease in pancreatic β-cell insulin secretion. The second defense is an increase in pancreatic α-cell glucagon secretion. The third defense, which becomes critical when glucagon is deficient, is an increase in adrenomedullary epinephrine secretion. If these three physiological defenses fail to abort the episode, lower plasma glucose levels trigger a more intense sympathoadrenal (sympathetic neural as well as adrenomedullary) response that causes symptoms and thus awareness of hypoglycemia that prompts the behavioral defense [which is ingestion of carbohydrates]. […] All of these defenses are typically compromised in type 1 diabetes and advanced type 2 diabetes […] compromised glucose counterregulation is the key feature of the pathogenesis of iatrogenic hypoglycemia in type 1 diabetes and advanced type 2 diabetes. Hypoglycemia in diabetes is typically the result of the interplay of relative or absolute therapeutic insulin excess and compromised physiological and behavioral defenses against falling plasma glucose concentrations […] In fully developed (i.e., C-peptide–negative) type 1 diabetes, circulating insulin levels do not decrease as plasma glucose concentrations decline through or below the physiological range. […] Furthermore, circulating glucagon levels do not increase as plasma glucose concentrations fall below the physiological range […] Thus, both the first defense against hypoglycemia — a decrease in insulin levels — and the second defense against hypoglycemia — an increase in glucagon levels — are lost in type 1 diabetes. Therefore, patients with type 1 diabetes are critically dependent on the third defense against hypoglycemia, an increase in epinephrine levels. However, the epinephrine secretory response to hypoglycemia is typically attenuated in type 1 diabetes […] Through mechanisms yet to be clearly defined but often thought to reside in the brain […], the glycemic threshold for sympathoadrenal — both adrenomedullary and sympathetic neural — activation is shifted to lower plasma glucose concentrations by recent antecedent hypoglycemia […], as well as by prior exercise […] and by sleep […] The reduced responses to a given level of hypoglycemia cause the clinical syndromes of defective glucose counterregulation and hypoglycemia unawareness [which is] impairment or even complete loss of the warning, largely neurogenic symptoms that previously prompted the behavioral defense, the ingestion of carbohydrates. Hypoglycemia unawareness—or more precisely impaired awareness of hypoglycemia—is common in type 1 diabetes […] Compared with patients with type 1 diabetes who have absent insulin and glucagon responses but have normal epinephrine responses, patients with absent insulin and glucagon responses and reduced epinephrine responses have been shown to be at 25-fold […] or greater […] increased risk for severe iatrogenic hypoglycemia during aggressive glycemic therapy […] At least in part because of the clinical importance of hypoglycemia in people with diabetes, studies of the molecular and cellular physiology and pathophysiology of the CNS [central nervous system]-mediated neuroendocrine, including sympathoadrenal, responses to falling plasma glucose concentrations are an increasingly active area of fundamental neuroscience research.”

“The risk factors for hypoglycemia in people with diabetes […] follow directly from the pathophysiology of glucose counterregulation in diabetes […]. The principle is that iatrogenic hypoglycemia in type 1 diabetes and advanced type 2 diabetes is typically the result of the interplay of relative or absolute therapeutic insulin excess and compromised physiological and behavioral defenses against falling plasma glucose concentrations, i.e., hypoglycemia-associated autonomic failure (HAAF) in diabetes.

People with diabetes are not immune to hypoglycemia caused by mechanisms other than the treatment of their diabetes […]. Those include 1) an array of drugs […] including alcohol, 2) critical illnesses such as renal, hepatic or cardiac failure, sepsis, or inanition, 3) hormone deficiency states such as adrenocortical failure, 4) nonislet tumor hypoglycemia, 5) endogenous hyperinsulinism, and 6) accidental, surreptitious, or even malicious hypoglycemia. However, aside from drug effects, those mechanisms are very uncommon. […] if all other factors are the same, patients treated to lower, compared with higher, A1C levels are at higher risk for hypoglycemia. Stated differently, studies with a control group treated to a higher A1C level consistently report higher rates of hypoglycemia in the group treated to a lower A1C level in type 1 diabetes […] and type 2 diabetes […] lower mean plasma glucose concentrations and greater plasma glucose variability are also associated with a higher risk of hypoglycemia […] Improved glycemic control before and during pregnancy is particularly important in the short term because it improves pregnancy outcomes in women with type 1 diabetes. But, it increases the frequency of hypoglycemia substantially […] In one series, 45% of 108 women with type 1 diabetes suffered severe hypoglycemia during their pregnancies; compared with a prepregnancy rate of 110 per 100 patient-years, the incidence was the equivalent of 530, 240, and 50 episodes per 100 patient-years in the first, second, and third trimesters, respectively (Neilsen et al. 2008).”

“Based on a systematic review and meta-analysis of randomized controlled trials published up to 2012, Yeh et al. (2012) concluded that CSII [Continuous subcutaneous insulin infusion] (compared with MDI [multiple daily injection]), real-time CGM [continuous glucose monitoring] (compared with SMPG [self-monitored plasma glucose]), and sensor-augmented CSII (compared with MDI and SMPG) had not been shown to reduce the incidence of severe hypoglycemia in type 1 or type 2 diabetes. […] these technologies may, or may not, be shown to reduce the frequency of hypoglycemia in the future.”

July 26, 2014 Posted by | books, diabetes, medicine | Leave a comment

Managing Cardiovascular Complications in Diabetes (2)

My first post about the book, which includes a few general remarks and observations, can be read here. In this post I’ll cover some stuff from the last 150 pages. I’ve bolded relevant key points here the same way I did in the first post about the book.

“Atherosclerosis-related disease, coronary heart disease (CHD), peripheral vascular disease (PVD), and thrombotic stroke are major complications in people with type 2 diabetes mellitus [1]. A recent meta-analysis of 102 prospective studies demonstrated a hazard ratio of 2 for coronary death and non-fatal myocardial infarction (MI) and 2.5 for ischemic stroke [2]. In the United Kingdom Prospective Diabetes Study (UKPDS), for each 1% increase in HbA1c there was a 28% increase in PVD [3]. […] In the National Health and Nutrition Examination (NHANES III) performed in the USA, the prevalence of metabolic syndrome in diabetes was 86%. The prevalence of CHD in this group was 19.2%. In those with diabetes and no evidence of metabolic syndrome, CHD prevalence was 7.5%, which is comparable to those without diabetes or metabolic syndrome [10]. Many studies in different populations have confirmed that dyslipidemia is a common finding in type 2 diabetes. […] A basic abnormality is the overproduction of large VLDL from the liver […] LDL-cholesterol concentrations are generally similar to those of the background population. However, LDL-cholesterol remains a major risk factor […] Qualitative changes in LDL particles increase their atherogenicity. The particles are smaller and denser with less lipid core. […] Statins are first-line pharmacotherapy for diabetic dyslipidemia. Their use is based on a wealth of data from robust, randomized trials for both primary and secondary prevention of CVD events. […] A large number of diabetic patients (n=2,912) was included in HPS. Simvastatin, which reduced LDL-cholesterol by 0.9 mmol/l, was associated with a 33% relative risk reduction in major CVD events (p = 0.0003). This benefit was independent of baseline lipids, diabetes duration, glycemic control, and age. The authors [of the HPS] calculated that simvastatin therapy over five years should prevent a first major cardiovascular event in about 45 people per 1,000 treated […] It is clear that patients with diabetes and CHD respond in a similar way to the nondiabetic population. However, a substantial residual vascular risk persists […] A contributory factor to the failure to achieve therapeutic goals is statin intolerance […] in practice there is a significant minority of patients who cannot tolerate statins at all, or can only tolerate a small dose, insufficient to achieve the LDL goal.”

Subjects with both type 1 and type 2 diabetes are at increased risk of developing cardiovascular disease, with approximately three-quarters of patients with diabetes ultimately dying from vascular causes.” [In the first post I included this quote from a previous chapter: “Mortality from CVD accounts for more than 60% of deaths in patients with type 2 diabetes mellitus”. Estimates vary (and these estimates need technically not be in conflict with each other as 75% is more than 60%…), but regardless of the differences this is ‘the big one’.]

“Overall, the available data indicate that diabetes is associated with a range of metabolic abnormalities that adversely influence platelet function [I should note that they go into a lot of detail about these ‘metabolic abnormalities’, and this is stuff I deliberately excluded from the coverage because it’s very technical stuff]. Management of the platelet aspect of this prothrombotic state should involve normalization of the metabolic changes seen in diabetes and the appropriate use of antiplatelet therapy […] aspirin is used for secondary cardiovascular protection in diabetes [38, 39], a practice supported by two large meta-analyses [40, 41]. […] data indicate that aspirin may be less effective in secondary cardiovascular protection in diabetes […] there is no convincing evidence for the use of aspirin monotherapy for primary cardiovascular protection in diabetes, although some guidelines recommend its use in high-risk subjects. […] There is evidence to suggest that the type of hypoglycemic agent used may modulate predisposition to future ischemic events. Metformin is normally used as first-line therapy in subjects with type 2 diabetes. The UK Prospective Diabetes Study (UKPDS) has demonstrated reduced ischemic heart disease (IHD) risk in overweight patients using metformin compared with subjects not on this therapy […] Insulin is mainly used in type 2 diabetes after the failure of other hypoglycemic agents. Insulin-treated type 2 diabetes subjects are at a greater risk of cardiovascular events compared with noninsulin-treated subjects, which may simply be a reflection of longer disease duration, with a consequent increase in the risk of complications [91]. In healthy individuals, insulin has antithrombotic effects, but it has the opposite effects in the presence of insulin resistance […] There are no clear guidelines for the treatment of diabetes with ACS and there is a great variability between countries and even centers in the same country, which is largely dependent on local resources and data interpretation of different trials. […] Antithrombotic therapy following ACS has been through major changes over the past decade. […] Despite major advances in therapy, atherothrombotic complications remain the main cause of morbidity and mortality in individuals with diabetes. […] Considered together, current evidence indicates that diabetes subjects have a differential response to antiplatelet and anticoagulant drug therapy compared to subjects with normal glucose metabolism. Further studies are still needed to clarify the optimal antithrombotic strategy in this high-risk population.”

“It is difficult, if not impossible, to assess directly the efficacy of individual dietary components on CVD risk because of the challenges, both practical and financial, in modifying the diets of a large group of people for long periods of time, as well as the difficulty that arises in studying individual dietary components within the context of habitual dietary patterns. Therefore, most dietary factors with the intent of reducing CVD risk are evaluated on the basis of short-term interventions (weeks or months) using biomarkers […] rather than hard endpoints. By combining data from different types of studies, dietary patterns have emerged that are associated with a lower risk of CVD […] Moderate fat intake (25% to 35% of energy) is associated with lower triglyceride concentrations than a low-fat diet. […] Current recommendations are to consume a diet containing 25%E [read: 25 percent of daily energy intake] to 35%E as total fat [3, 4]. For individuals with diabetes, the recommendation is to consume diets toward the higher end of this range [5, 6]. […] Low-fat diets are associated with elevated triglyceride concentrations and depressed high-density lipoprotein (HDL)-cholesterol concentrations resulting from what is commonly referred to as carbohydrate-induced hypertriglyceridemia […] Carbohydrate-induced hypertriglyceridemia, resulting in elevated triglyceride concentrations, is caused by an enhanced rate of hepatic fatty acid synthesis and is precipitated by an excess flow of glucose from the gut to the liver [14, 15] and subsequent production of hepatic triglyceride-rich particles, termed very low-density lipoprotein (VLDL) […]. In some cases delayed triglyceride clearance associated with low-fat diets has also been observed, contributing to the elevated triglyceride concentrations […] Within the context of a stable body weight, replacement of dietary fat with carbohydrate results in higher triglyceride and VLDL-cholesterol concentrations, lower HDL-cholesterol concentrations, and a higher (less favorable) total cholesterol to HDL-cholesterol ratio [20, 21, 22, 23, 24, 25].” [Eckel et al. pointed this out as well and I included coverage of this in my post about that book as well; but this is an important piece of information that I do not mind repeating here. Note that not all carbohydrates are the same, and that dietary fiber seems to have a protective effect. The chapter from which the above quote, and the paragraph below, was taken covered many of the same things covered in Barasi].

“a series of randomized controlled intervention trials […] have failed to demonstrate a benefit of supplemental vitamin E, beta-carotene, vitamin C, or folate on CVD risk reduction [156, 157]. Recently, interest has been focused on the potential effect of supplemental vitamin D in CVD risk reduction. In contrast to the prior vitamins, the relationship between vitamin D and CVD risk is focused on nutrient insufficiency rather than supplemental amounts [156, 158]. Until the results of randomized controlled trials with vitamin D become available, it is premature to make any recommendations.”

“Diabetics are more likely than nondiabetics to experience ACS, and diabetes is an independent predictor for mortality in ACS. Diabetics are also more likely to develop complications of ACS and its management such as heart failure and bleeding. With a few exceptions, the management of ACS is similar in patients with and without diabetes. In patients with diabetes, management does not differ between patients who are insulin dependent and patients who do not require insulin. […] The management of ACS begins with determining the appropriate timing for coronary artery reperfusion. Patients with STEMI [ST-elevation myocardial infarction – see this] or an equivalent should receive emergent reperfusion, preferably with PCI. Patients with UA/NSTEMI [see the link in the brackets above] can be risk stratified to determine the appropriate timing for coronary angiography. In these patients angiography is used to decide if medical therapy, PCI, or CABG [Coronary Artery Bypass Grafting] is the preferred treatment strategy. All patients with ACS should be treated with antiplatelet and antithrombin therapy, as well as adjuvant therapy with a statin, ACEI, and beta-blocker.” [there’s an entire chapter about these things where they go into quite a bit of detail, but I decided against covering this stuff here as most of it is once again highly technical stuff which is not easy to blog].

Amputation of the lower limb is one of the most feared adverse health outcomes among patients with diabetes. […] PAD [Peripheral Artery Disease], referring to atherosclerotic occlusive disease of the lower limb arteries is a common, debilitating complication that correlates with cardiovascular disease mortality [2]. Diabetes is a significant independent risk factor for PAD (odds ratio of 2–3) [3], together with hypertension, cardiovascular disease, hyperlipidemia, smoking, and obesity [3, 4]. The prevalence of PAD in patients with type 2 diabetes has been estimated at 23.5% in a UK population [5], and is strongly dependent on the duration of diabetes [6, 7]. Compared with men without diabetes, the adjusted relative risk of PAD among men with diabetes increased from 1.39 with diabetes duration of 1–5 years’ to 4.53 for diabetes of >25 years’ duration [7]. […] a very high prevalence (71%) of PAD was recently reported in 1,462 elderly patients with diabetes (>70 years) in Spain as evaluated by a pathological ABI (ankle-brachial index) [8]. […] A recent meta-analysis [7] including 94,640 participants and 1,227 LEA [Lower-Extremity Amputation] cases reported in 14 studies demonstrated a substantial increase in the risk of LEA associated with glycemia in individuals with diabetes. The overall risk reduction (RR) for LEA was 1.26 (95% CI 1.16–1.36) for each percentage point increase in HbA1c.”

“A Scottish study showed that after LEA diabetic subjects had a 55% greater risk of death than those without diabetes [10]. […] Median time to death […] was 27.2 months with diabetes versus 46.7 months without diabetes (p<0.01) and survival rate 10 years after amputation was 22.9% in nondiabetic patients but only 8.4% in diabetic patients (p=0.0007). [I’ve read about these things before, and I should note that I do not believe these estimates are unique or aberrant. It’s not just that losing a leg sucks – when you’re so far along in the disease process that they have to start cutting off parts of you to keep you alive, you’re really quite likely not to live for a very long time. The prognosis of a diabetic who just had a LEA is much worse than that of the average breast cancer patient.] […] The clinical stage of symptomatic PAD can be classified using the Fontaine staging system [21]. Fontaine stage I represents those who have PAD but are asymptomatic; stages IIa and IIb include patients with mild and moderate-to-severe intermittent claudication, respectively; those with ischemic rest pain are classified in Fontaine stage III; and patients with distal ulceration and gangrene represent Fontaine stage IV. Diagnosing PAD in patients with diabetes is of clinical importance for two reasons. The first is to identify a patient who has a high risk of subsequent MI or stroke regardless of whether symptoms of PAD are present. Indeed, patients with diabetes and PAD have a fivefold increased risk [of MI/stroke] compared to the presence of either disease alone [22, 23, 24, 25]. An observational study less then ten years ago demonstrated that patients with diabetes and PAD stage IV (=ulcer) have a 100% mortality within six years [26]. The second reason is to elicit and treat symptoms of PAD, which may be associated with functional disability and limb loss.”

“PAD is often more subtle in its presentation in patients with diabetes than in those without diabetes […] Importantly, PAD in individuals with diabetes is usually accompanied by peripheral neuropathy with impaired sensory feedback […] The majority of patients with early PAD are either asymptomatic or have atypical leg symptoms, with “classical” claudication in only 10–35%, therefore detection is elusive unless actively sought. Given shared risk factors, it is axiomatic that there exists a high coprevalence of atherosclerosis in other vascular beds, including the coronary arteries in PAD patients [74]. […] patients with PAD are at a high risk of cardiovascular events and therefore benefit from aggressive secondary prevention […] Many studies have documented that secondary prevention is underused in patients with PAD […] Antiplatelet drugs that have been shown to reduce the incidence of vascular death, nonfatal myocardial infarction, and nonfatal stroke in patients with PAD are aspirin, ticlopidine, and clopidogrel [101]. Aspirin plus dipyridamole has not been proven to be more efficacious than aspirin alone in the treatment of patients with PAD [101].”

“Compared to patients with intermittent claudication (IC; stage II of PAD), patients with critical limb ischemia (CLI; stages III and IV after Fontaine) are in a more difficult situation: while amputation is rather infrequently necessary in patients with IC [108], amputation rates of 23% at 12 months were reported in patients with CLI [109]. In patients with CLI, the incidence of diabetes mellitus and chronic renal insufficiency is 70.4% and 27.8%, respectively [109]. Thus, patients with CLI are in the majority among patients with diabetes […] The prevalence of gangrene is about 20 to 30 times higher in patients with diabetes mellitus [110].” [In terms of the treatment options, they put it frankly in their recommendations in that chapter: “Primary amputations only in a leg-for-life situation”].

June 30, 2014 Posted by | books, diabetes, medicine | Leave a comment

Managing Cardiovascular Complications in Diabetes (1)

I finished the book today. I wrote a brief review of the book on goodreads and gave it three stars. Many things covered in this book I’ve read about in detail elsewhere, e.g. in Sperling et al., Edwards et al., or, say, Eckel et al, but there was some new stuff in here as well. I really liked the first chapter, about ‘The Vascular Endothelium in Diabetes’; it covered some stuff which I’d never really gotten to the bottom of before (but due to the technical nature of that chapter I decided against covering it here). There are still a lot of details which I will not claim to fully understand, but I understand some of the main principles/mechanisms much better than I did. The book was occasionally difficult for me to read because it required knowledge about areas about which I didn’t know a great deal (e.g. haematology), and you should certainly not read this book if you don’t read more or less fluent medical textbook (“The focus of this book is to assist the physician or surgeon in preventing and managing CVD and CVD risk in diabetic patients”). As I pointed out in my goodreads review, the book was difficult for me to read for another reason as well. Authors of academic books should not use acronyms which they do not explain to the reader. Authors of such books should not explain unexplained acronyms five pages after they have used them for the first time. If they do, people might get angry at them.

I’m sure some people don’t care about such things, but this is the sort of stuff that can really piss me off, and it’s part of the reason why this book got three stars. Combining behaviour like that with some formatting errors and a few sentences which don’t make any sense because nobody seems to have proofread the damn thing, and you can end up with an academic publication which looks amateurish, even if it’s most certainly nothing of the sort. In terms of the formatting errors I will note that this is not the first Wiley-Blackwell publication like this I’ve seen – as I point out in my review of that book, the Edwards et al publication to which I link above had similar problems. It’s much rarer, I think, to see stuff like that in Springer publications.

I have added some observations from the book below. I plan to write another post about the book later on as I don’t think it’s fair to only give this book one post, considering how much stuff is in there. When I started out writing this post I was thinking that I’d make the quotes easier to read by adding relevant links where they might help. I realized quite fast that adding enough links to actually make a huge difference would most certainly not be worth it, though I have added a link here and there anyway in order to make the post more readable. I have also added a few bold sections below – I don’t like writing long posts and then have people not reading them because they’re long, so if you don’t particularly care about the topic covered below you might want to read the bolded parts in order to at least learn something from the post. There’s a lot more stuff about type 2 diabetes than about type 1 in this book, so when reading ‘diabetes’ below you should probably just think ‘type 2’.

I remember recently reading an article somewhere stating that there are many errors in medicine-related wikipedia articles and how that’s a problem, and I actually encountered an example of this while reading the book, though I can’t now remember which article it was. You should take it for granted that wiki articles to which I link in posts like these may have errors and inaccuracies (they may actually contain statements which are contradicted by the material covered in the book…), and I usually only link to them in posts like these to ‘translate’ the terms used without having to add a lot of additional text to the post in question. I’ll often not have read the articles to which I link when I link to as many as I do in this post, and a link to an article does not mean that I think all the stuff included in the article is correct. Okay, on to the book coverage:

“There is no doubt that diabetes is a significant contributor to the global burden of chronic non-communicable disease which accounts for over 36 million (63%) of deaths worldwide. Importantly, 80% of these deaths occur in low and middle income countries. [here’s a link to the source, the data above is from page 16. Note that “17.3 million (30%) [of all 57 million deaths worldwide] were due to CVDs.”] […] In an important contribution from the Global Burden of Metabolic Risk Factor of Chronic Disease Collaborating Group [4] national, regional and global trends in fasting plasma glucose and diabetes prevalence since 1980 were studied in a systematic analysis of health examination surveys involving over two and a half million participants and 370 country-years observations. They estimated that the number of people with diabetes increased from 153 (95% uncertainty interval 127–182) million in 1980 to 347 (314382) million in 2008 [4]. [I included the quote partly because those numbers are interesting, partly because this quote from the introduction contains a good example of the kind of sloppiness I mention in the goodreads review; that last parenthesis was surely meant to say 314-382. But it doesn’t. And those kinds of small errors are all over the place.] […] In addition to increased risk of CVD patients with diabetes and established vascular disease have a poorer outcome than those without diabetes [7, 8]. Peripheral arterial disease is increased 2-4 fold in the diabetic population and lower limb amputations are at least 10 fold more common such that half of non-traumatic amputations are performed in diabetic patients [3, 7, 8].”

a mean duration of diabetes of about a decade appears to confer an equivalent risk of CVD to a prior history of MI. In addition, recent work has shown that a history of DM results in six years of life years lost, mostly from CVD [3]. […] 20% of all vascular events occur in patients without any traditional risk factors, necessitating the need for more precise clinical tools that aid clinicians in identifying those at highest risk [4]. To help achieve this goal, there is growing interest in the development and exploitation of new biomarkers. […] A biomarker was defined by a National Institutes of Health (NIH) working group as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [5]. […] A biomarker should meet several criteria to be deemed clinically useful. This is structured around three fundamental questions [6]: 1 Is the biomarker measurable? 2 Does the biomarker add new information? 3 Will the biomarker help the clinician to manage patients? Additional criteria include cost-effectiveness, safety, and replication of the biomarker in clinical scenarios. […] [Reclassification] is a relatively new concept, but potentially the most clinically relevant [of four criteria covered] as it assesses the ability of a test to reclassify individuals correctly into a different risk category; for example, an intermediate-risk subject into a high-risk subject, or a low-risk subject into an intermediate-risk subject […] The ability of the new test to achieve reclassification can be statistically examined by net reclassification improvement (NRI) or integrated discrimination improvement (IDI). The NRI method, which is determined by the proportion of individuals whose risk is correctly escalated or de-escalated, is more useful in primary prevention, where well-accepted categories of risk exist. The IDI estimates the change in predicted probability of an outcome between those with and without the outcome after the biomarker is added to the prediction model. The larger the value of the NRI or the IDI, the better the biomarker.”

Quite a few biomarkers are covered in the chapter, but I’d rather not talk too much about that stuff. There are various types of circulating biomarkers, imaging biomarkers and genetic biomarkers. A few have been included in national guidelines and the only class which does not seem to be useful in this context is the genetic one [“The AHA has given genomic testing in risk assessment in asymptomatic adults a Class III recommendation (no benefit)”]. Naturally reasons besides those related to assessing cardiovascular risk exist for doing genetic testing on diabetics, but if such tests are not useful in that respect then of course that limits their potential somewhat. Incidentally many biomarkers they talk about seem to measure similar things, meaning that adding them together don’t add a lot of information:

“It is logical to assume that if one biomarker measure gives a small incremental gain in risk prediction, multiple biomarkers would result in a larger one. However, trials of multiple biomarkers have disappointingly only shown at best a moderate improvement in usefulness when compared to standard risk factors [72].”

The biomarkers are assumed to hold most promise in the context of primary prevention, but “there is scant data on cost-effectiveness or differential benefit from specific treatments”. Okay, on to other stuff:

“Diabetic kidney disease […] is a clinical diagnosis and is defined by the presence of albuminuria, often with associated abnormal kidney function (an increase in creatinine or a decrease in creatinine clearance or estimated glomerular filtration rate [eGFR]) […] Diabetic nephropathy is a histological diagnosis, characterized by typical histopathological features including mesangial expansion, glomerular basement membrane thickening, and glomerulosclerosis with Kimmelstiel–Wilson lesions. Diabetic kidney disease is most commonly caused by diabetic nephropathy, but other kidney pathologies may be present […] Diabetic kidney disease is a chronic complication of diabetes and affects approximately one third of all diabetic patients [1, 2]. It is the most common cause of kidney failure requiring renal replacement therapy in Western countries [3] and can occur in both type 1 and type 2 diabetes with equivalent risks [4]. The natural history and prognosis of diabetic kidney disease differ somewhat based on the type of diabetes and whether microalbuminuria is present […] In people with type 1 diabetes who have microalbuminuria, if left untreated, approximately 80% will develop macroalbuminuria (also called overt nephropathy) within 6–14 years [6, 7]. Subsequently, half of these will develop end-stage kidney disease (ESKD) over 10 years if there is still a lack of specific intervention. In contrast, approximately 20–40% of people with type 2 diabetes and microalbuminuria develop macroalbuminuria without intervention, and ESKD has been reported to develop in 20% of patients with overt nephropathy within 20 years [8]. Some of these differences may relate to the older age and greater burden of comorbidity experienced by people with type 2 diabetes for a given duration of diabetes, meaning that more of them will die of cardiovascular and other complications before developing kidney disease.”

“Diabetic kidney disease has a heterogeneous presentation. Early stages are often asymptomatic and only detected by abnormal laboratory tests (albuminuria and changes in GFR). Albuminuria is one of the earliest detectable features of diabetic kidney disease […] As diabetes manifests as a systemic disease, patients with type 1 DM almost always have other signs of diabetic microvascular complications, such as retinopathy and neuropathy. Diabetic retinopathy usually precedes the onset of overt nephropathy, while the relationship between diabetic kidney disease and retinopathy is less predictable in type 2 diabetes. […] For people with type 1 diabetes, approximately 20–30% will have microalbuminuria after a mean duration of diabetes of 15 years [37, 38]. Similarly, 25% of individuals with type 2 diabetes have microalbuminuria after 10 years […] Proteinuria and abnormal kidney function are independent risk factors for renal outcomes in diabetes [28]. […] As with treatment strategies for end-stage kidney disease secondary to other causes, dialysis and renal transplantation are both options for treatment for ESKD caused by diabetes. Lower survival rates have been observed for people with ESKD caused by diabetic kidney disease, with five years’ survival of 30%, according to USRDS data.”

Cardiovascular disease (CVD) including coronary heart disease (CHD) is the major cause of mortality in patients with diabetes […] no more than 25% of the excess CHD risk in diabetes can be accounted for by established risk factors […] Hyperglycemia as a risk factor for CVD has been established for many years. Mortality from CVD accounts for more than 60% of deaths in patients with type 2 diabetes mellitus and clearly accounts for this ultimate complication of diabetes [3, 8]. The association between differing degrees of hyperglycemia and CVD risk has been an area of debate. The United Kingdom Prospective Diabetes Study (UKPDS) demonstrated that the incidence of myocardial infarction rose by 14% per 1% rise in HBA1c [9]. This is in line with other studies showing that glucose is a continuous risk factor in people with both type 1 and type 2 diabetes. […] There is also evidence that glucose fluctuations (the highs and lows) are associated with increased oxidative stress […] Increased oxidative stress results from an imbalance between oxidant production and antioxidant defenses […] Diabetes mellitus, obesity, micro- and macrovascular complications have been consistently associated with increased oxidative stress [37, 38, 39] and several studies have demonstrated that hyperglycemia per se is associated with increased oxidative stress [39, 40]. […] Hypoglycemia is also associated with increased cardiovascular mortality [58, 59], although the mechanisms behind this remain unclear. […] As well as being associated with increased oxidative stress [62], hypoglycemia also has pro-inflammatory effects on the vasculature. […] These changes contribute to a hypercoagulable state associated with increased platelet aggregation and plasma concentrations of coagulation factors […] Acute hypoglycemia has also been associated with long QT syndrome, which is associated with an increased risk of sudden cardiac death [65].”

“The majority of people with type 2 DM [diabetes mellitus] are hypertensives […] There is no question about the need to treat hypertension in either the primary prevention or secondary prevention settings for cerebrovascular disease, irrespective of the presence of diabetes. A systematic review of the effects of different BP-lowering drug regimens in people with hypertension, diabetes, or vascular disease found that the relative risks of stroke and other major vascular outcomes were proportional to the BP reduction achieved [62]. […] there is a general consensus that ACE inhibitors or ARB are the first-line drugs of choice in both diabetes and metabolic syndrome. In primary prevention, the only question is the level of BP above which treatment is indicated. […] The recommended threshold for treatment in primary prevention is currently under discussion in both diabetics and nondiabetics. […] there is increasing uncertainty about the use of absolute thresholds of BP to determine the need for treatment […] Although “lower should be better,” the results of recent clinical trials examining the benefits of normalizing risk-factor levels have been counter-intuitive and, sometimes, disconcerting, and have called into question this belief […] Many hypertensive patients in clinical practice receive more than one antihypertensive drug, and the use of combination therapy is widely recommended in hypertension guidelines. Combinations may be especially important for patients with diabetes, for whom recommended BP targets are challenging.”

June 26, 2014 Posted by | books, diabetes, medicine | Leave a comment

100 Cases in Acute Medicine

100 Cases in Acute Medicine presents 100 acute conditions commonly seen by medical students and junior doctors in the emergency department, or on the ward, or in the community setting. A succinct summary of the patient’s history, examination, and initial investigations, including photographs where relevant, is followed by questions on the diagnosis and management of each case. The answer includes a detailed discussion of each topic, with further illustration where appropriate, providing an essential revision aid as well as a practical guide for students and junior doctors.

Making clinical decisions and choosing the best course of action is one of the most challenging and difficult parts of training to become a doctor. These cases will teach students and junior doctors to recognize important clinical symptoms and signs, and to develop their diagnostic and management skills.”

(link)

The book is quite simple. There are 100 medical cases. Each case has a brief description of symptoms and what we know about the patient, plus a couple of questions. On the next page of the book there are then answers to the questions posed with (semi-?)detailed explanations. In many cases one of, or perhaps the only question, is: ‘what’s wrong with this person?’, but sometimes the management aspect is considered to be the key variable (‘obese hypertensive and hyperlipidemic type 2 diabetic with previous MI has just been admitted with cardiac symptoms. Here are the results of his blood-work and an ECG. How do you proceed?’ – not a quote, but close enough…), and in such cases there are e.g. questions about which particular aspects of this presentation you should be most concerned about, or perhaps an open question related to aspects such as how to optimize the follow-up process. I’ll never diagnose anyone with anything or set up a medical management plan, as that is for doctors to do, but I thought it looked like an interesting book, so I figured I’d give it a shot. Reading a book like this is a little bit like watching House, except that the medicine in here is actually trustworthy and you avoid all the drama (I know that I have remarked upon how reading medical textbooks will change your viewing experience of medical dramas before, but in the context of this book that particular aspect seems perhaps even more relevant than usual – all the patients in this book have presented to the ER because they are sick and we are told about their symptoms and perhaps some of the test results which have come back from the lab; this setting, I believe, is pretty much the default setting for medical dramas…).

The blog currently has 118 posts related to the topic of medicine so I have read some stuff and watched some lectures on these topics; I figured it’d be interesting to see if I could figure out some of the cases, and I felt reasonably sure I’d learn from both the ones I could figure out and the ones I couldn’t (as I considered them likely to add details I didn’t know, e.g. about differential diagnoses, in the anwers). I also thought more generally that it’d be nice to have a book with some ‘common/standard’ health complaint cases presented. Diagnostics is often more difficult than you’d think from reading about specific diseases, because people in many cases don’t present with all the textbook symptoms, and because certain symptoms present in a lot of very different situations. A confused old person with altered mental status might for example ‘just’ be dehydrated with nothing else going on (severe dehydration can be quite dangerous, thus the ‘just’) – but it could also be a brain tumour, or a subarachnoid hemorrhage, or a urinary tract infection (“Elderly people, particularly females, are more prone to urinary tract infections and often present with confusion”), or… Severe abdominal pain and vomiting in a young person isn’t always appendicitis; this book had a young woman with familial Mediterranean fever present that way.

There were more than a few cases where I ‘got it right’, including some quite obscure ones like a case of Stevens-Johnson syndrome (-SJS – this one is really rare, something like 1 in 200.000 rare – I only guessed it because I read the wiki on that one a while back and it stuck) and a patient with an insulinoma (this one also has a very low incidence, “estimated at 1 to 4 new cases per million persons per year” – my knowledge of diabetes helped here, as did my recall of the coverage of this condition in McPhee et al (at least I think that was where I read about it). There were quite a few more common ones I got right, for example cases of pre-eclampsia (Hall covered that one in quite a bit of detail, so I had no problems figuring out what was going on there), mumps, diabetic ketoacidosis, hyperosmolar hyperglycaemic state (I found it interesting that they included both a DKA case and a HHS case, and/but I had of course no problem recognizing either of these), malaria, alcohol withdrawal syndrome (obvious from the patient history, but not if you don’t know about the risk of seizures and -progression to DT associated with alcohol withdrawal – which the patient obviously didn’t…), Lyme disease, trypanosomiasis (well, I couldn’t remember that that’s what it was called, but I did guess ‘sleeping sickness’, which is good enough, I think – though of course I’d have no idea how to treat someone with that disease…), anorexia nervosa, and pulmonary oedema. There were a lot of them I didn’t get right or didn’t know the answer to, which is in a way to be expected (the insulinoma and SJS cases were not the only quite rare ones – who’s ever heard about Goodpasture syndrome anyway?). In more than a few cases you need, in order to get the diagnosis right, to be able to read and understand the results of an electrocardiogram, a CT scan, an MRI or a chest X-ray; I’ve seen these before in textbooks, but I’ve never received formal training in interpreting them – however at least in the case of the pulmonary oedema the X-ray results were obvious. ‘He’s having a heart attack’ was a sort of a diagnosis in a couple of cases, but not what they were going for – if they thought his heart was fine they probably wouldn’t have asked the lab for troponin levels or ordered an ECG..

I have added some observations from the book below, most of them from the ‘answer sections’. As I didn’t assume anyone reading along here would be likely to read the book later on I have not tried very hard to avoid ‘spoilers’:

“[Neurocysticercosis] is the most common parasitic infection of the central nervous system and the leading cause of adult-onset seizures in the developing world.”

“Mumps is the most common cause of unilateral acquired sensorineural hearing loss in children and young adults worldwide […] Suspect mumps in a patient who presents with parotitis and fever.” (I did. The included vaccination history helped.).

“A 19-year-old woman has presented to the emergency department complaining of fevers and malaise after returning from a holiday in South Africa two weeks earlier. Over the preceding 3–4 days she noticed a rash and sore throat and is now feeling generally tired and unwell. She has no significant medical history and does not take any regular medications or recreational drugs. She does not smoke, nor drink alcohol. She admits to several episodes of unprotected sexual intercourse with a man she met in South Africa.”

My first thought when reading the case history above: Immediate psychiatric consult and an IQ test. If you’re having unprotected sex with a South African whom you don’t know well on multiple occasions you’re either insane or a moron. More seriously, this one was one of several really depressing presentations. There were ways to make the patient history even worse (‘when she came back to receive the results of her (positive) HIV test she mentioned during the followup that she’d been gaining a bit of weight lately and that she had been feeling nauseous occasionally, especially during the morning hours…’), but this was quite bad enough. Do note however that there could be other explanations for her illness than just HIV, and that these should be considered as well: “This woman is likely to have a viral illness, considering her history of fevers, rash and sore throat. Infectious mononucleosis (glandular fever) secondary to Epstein–Barr virus is a common illness in young adults, presenting with fever, rash and lymphadenopathy following on from a sore throat.”

“Urinary tract infections can often present with non-specific symptoms, such as confusion and general malaise, particularly in elderly patients. […] Early treatment according to the Surviving Sepsis protocol is key to ensuring patients have the best chance of surviving a serious infection.”

I include this one at least in part because people reading my comments above about confusion perhaps being the result of a urinary tract infection may have thought that ‘okay, so not all of these cases are all that severe’, as a urinary tract infection is probably perceived of as belonging on ‘the opposite side of the scale’ as brain cancer. In the specific case that would be an incorrect way to think about the situation: “The patient is haemodynamically unstable […] The patient’s daughter should be informed that her mother is very unwell and may not survive.” Yes, this was another one of the depressing ones. Here’s a related quote from another case: “Most women will experience a urinary tract infection (UTI) at some time in their life, so education towards UTI prevention is important (e.g. wipe from front to back after a bowel movement or after urinating, and try to empty the bladder before and after sexual intercourse).”

“Tuberculosis should be suspected in anyone presenting with shortness of breath, fever, haemoptysis and weight loss. […] An important differential diagnosis to consider is lung malignancy.”

“Alcohol misuse increases the risk of intracerebral bleeds, because head injury is more likely to be sustained or as a result of deranged liver function. Sustained alcohol misuse can lead to deranged liver function and therefore reduced production of vitamin K, which is essential for normal blood clotting properties. […] Seizures are a common way for patients with alcohol withdrawal to present.”

“In patients who are vomiting and develop signs of a chest infection, an aspiration pneumonia should be considered.”

“Angiodysplasia is a condition where the small vessels in the bowel are dilated, very fragile and prone to bleeding. […] Angiodysplasia of the colon is the second most common cause of GI bleeding in patients over the age of 60 years (diverticular disease being the most common in that age group). The most common presentation is intermittent bleeding without pain.”

“There are common steps in the management of acute intoxication and poisoning. As with most medical emergencies, the airway, breathing and circulation (ABC) should be assessed and managed appropriately in the first instance. Neurological examinations should be carried out to look for lateralizing and/or cerebellar signs. It is also important to examine for abnormal ocular movements and papillary changes as it helps to give clues to the common drugs/toxins involved. […] Often a ‘drug screen’ is requested but this is rarely necessary. A typical drug screen is expensive and difficult to interpret. The results may take 1–2 weeks to become available and it is not possible to screen for all possible toxins. Therefore it does not alter immediate patient management in most instances. Neuroimaging, such as CT of brain, is only necessary when patients are suspected to have a structural brain lesion or significant head injury. A provoked seizure from poisoning or substance abuse does not necessitate neuroimaging in most circumstances. […] In most cases the treatment of poisonings requires supportive therapy only as specific antidotes are often not available.” (ABC arguably isn’t enough – in a different answer they add on D and E as well:) “The approach to any critically ill person should start with ABCDE (airway, breathing, circulation, disability, exposure). Each step should consist of an assessment and appropriate management before moving on to subsequent stages. This approach is a logical way of thinking through and dealing with an acutely ill person.”

“[Anorexia nervosa] is a psychiatric diagnosis characterized by a refusal to maintain normal weight for age and height, a fear of gaining weight, body image distortion and amenorrhoea. There are other subtypes, which include ‘restricting’ calorie intake, or ‘binge eating/purging’ behaviours which can include laxative, diuretic or enema use. She has evidence of a low bodyweight (formal diagnosis relies on an ideal body weight <85 per cent, body mass index <17.5 kg/m2). Her body image perception is altered. […] Most people with anorexia nervosa are female, with the onset highest during late adolescence.”

“IgA [Immunoglobulin A] nephropathy is the most common glomerular disease worldwide. It occurs most commonly in those of Asian or Caucasian origin and is more common in males (2:1). Most cases occur between the ages of 20 and 30. Most cases are sporadic and the cause is not identified, but it tends to occur following an upper respiratory tract infection or gastrointestinal infection. […] Cases can present in several ways. About half of all cases present as in this case with frank haematuria and flank pain after an upper respiratory tract infection. A third of patients can present with asymptomatic microscopic haematuria. Ten per cent of patients can present with a more severe process characterized by either the nephrotic syndrome or an acute rapidly progressive glomerulonephritis (oedema, hypertension, haematuria and renal failure).”

“Atrial fibrillation becomes more common with increasing age such that more than 10 per cent of those aged over 80 years have AF. The most common causes of AF are hypertension, heart failure, ischaemic heart disease and valvular disease. Hyperthyroidism is another cause and may not have obvious clinical signs in the elderly. […] Stroke risk can be estimated from a score (CHA2DS2VASc: Congestive heart failure, Hypertension, Age ≥75 (doubled), Diabetes, Stroke (doubled), Vascular disease, Age 65–74, and Sex category (female) […] A score of 2 predicts a 2.2 per cent per year adjusted stroke risk […] This is generally accepted to be the cut-off to starting treatment with an oral anticoagulant provided there are no contraindications. […] The main concern with anticoagulants is the risk of bleeding and an assessment of this risk should be made prior to starting treatment. A bleeding risk score such as HAS-BLED can be used to assess risk […] Warfarin is still the anticoagulant of choice.”

“The incidence of stroke after thrombolysis is around 1–1.5 per cent and most strokes occur within five days of the MI, with most cases of haemorrhage within 24 hours of MI and thrombolysis.”

This is a risk it makes sense to be aware of – lots of people die from MIs and understanding the details of the risks involved when treating these may in some cases be helpful; if a person dies from a hemorrhagic stroke shortly after receiving treatment for an MI, this should not be considered a major indication that the doctors screwed up. Medical science has advanced a lot over the years, but ‘the anticoagulant of choice’ they talk about above is rat poison so do be careful not to overestimate how much doctors can really do for you if you get sick.

“In the setting of a positive family history of early death due to chest disease and a history of deranged liver function tests, one should […] consider α1-antitrypsin deficiency. α1-Antitrypsin deficiency (A1AD) is a disease which has various phenotypes […] It is one of the most commonly inherited genetic disorders. […] The severity of lung disease differs even in siblings with the same allele. This is partially explained by environmental factors such as smoking and dust exposure; therefore it is paramount to educate patients with α1-antitrypsin deficiency not to smoke.” (yep, you guessed it – the patient was a smoker. Despite having been diagnosed with COPD 3 years earlier. Again, depressing.)

“CURB 65 is one of the most commonly used tools for assessment of community-acquired pneumonia severity. It is a useful adjunct but should not replace thorough clinical assessment. CURB 65 stands for: C = confusion; U = Urea ≥7 mmol/L; R = Respiratory rate >30/min; B = Blood pressure systolic <90 or diastolic <60 mmHg; 65 = age ≥65 years. Mortality approaches 83 per cent if all four CURB components are present. […] Most if not all atypical pneumonias present with classical pneumonic symptoms (fever, productive cough and shortness of breath), so it is hard to differentiate clinically. Atypical pneumonia is a term used to describe pneumonia caused by (i) Mycoplasma pneumoniae, (ii) Chlamydophila pneumoniae, (iii) Chlamydophila psittaci, (iv) Coxiella burnetii, (v) Legionella spp, or (vi) Francisella tularensi [I talked about this last one before, in a completely different context…]. The term ‘atypical pneumonia’ remains useful to describe these pathogens as their treatment and sometimes duration of antibiotic therapy is different from typical pathogens.”

“Subdural haematomas are bleeds that occur between the dura mater and the arachnoid mater, enveloping the brain. They usually develop following traumatic injury […] Older people are particularly prone to such injuries as the brain naturally atrophies and shrinks with age. Blood collects in the space and draws in water due to osmotic pressures. The area of bleeding increases in size, causing compression of the cerebral tissue. […] Cushing’s triad of systolic hypertension with a wide pulse pressure, bradycardia and irregular or rapid respiratory rate is a major sign of raised intracranial pressure. These features occur due to insufficient blood flow to the brain and compression of arterioles. Subacute and chronic subdural haematomas classically present days to weeks after the insult. Any patient who presents with neurological signs several days after a head injury should be investigated for a subdural bleed.”

“Fever, jaundice and right upper quadrant abdominal pain make up the Charcot’s triad which are the main signs and symptoms of acute cholangitis. If a patient presents with Charcot’s triad and altered mental status and shock, it is called Reynold’s pentad. […] The most common cause of acute cholangitis is gallstone disease. […] Acute cholangitis carries a high mortality.”

I liked the book and gave it three stars on goodreads.

June 6, 2014 Posted by | books, diabetes, medicine | Leave a comment

Impact of Sleep and Sleep Disturbances on Obesity and Cancer (2)

Warning: Long post.*

Okay, I’ve finished the book. I gave it five stars on goodreads – it’s come to my attention that I may be judging scientific publications like this one way too harshly, when you compare them with most other books. But then again I’d probably have given it four or five stars anyway; this book is an excellent source of information about the stuff it covers, and it covers a lot of stuff. In a way it’s hard to evaluate a book like this, because on the one hand you have a pretty good idea whether it’s enjoyable to read it or not, but on the other there are small chunks of it (or huge portions of it, or entire chapters, in the case of some readers…) which you are really not at all qualified to evaluate in the first place because you’re not actually sure precisely what they’re talking about**. Oh well.

As mentioned this book has a lot of stuff, and I can’t cover it all here. I’m annoyed about this, because it’s a great book. Some of this stuff is quite technical and there were parts of a few of the chapters I will not pretend to have really understood, but most of the stuff is okay in terms of the difficulty level – the book isn’t any harder to deal with than are most of Springer’s medical textbooks – and it’s interesting. In the first post I talked a little about sleeping patterns and a bit about cancer. The book has a lot of other stuff, and it has a lot of additional stuff about those things as well. Writing posts where I go into the details of books like these takes a lot of time and it’s not always something I have a great desire to do because it’s really hard to know where to stop. Let’s say for example that I were to decide to cover this book in great detail, and that I were to start out in chapter two, dealing with ‘Effects of Sleep Deficiency on Hormones, Cytokines, and Metabolism’. In that case I might decide to start out with these observations:

“Laboratory studies of both chronic and acute partial sleep restriction indicate that insufficient sleep can lead to increased hunger and caloric intake.”

“Many studies […] report that sleep independently relates to diabetes risk, even after controlling for the confounding effects of obesity and overweight. […] Cappuccio et al. [29] analyzed ten prospective studies with a pool of over 100,000 adults to ascertain the association of type 2 diabetes with sleep duration and quality. After controlling for BMI, age, and other confounding factors, they found [that] sleeping less than 6 h per night conferred an RR of 1.28 in predicting the incidence of type 2 diabetes, and prolonged duration (>8–9 h) had a higher RR of 1.48. As for sleep quality, Cappuccio et al. found that difficulty falling and staying asleep were highly correlated with type 2 diabetes risk with RRs of 1.48 and 1.84, respectively. […] a 3-year prospective study show[ed] that of workers with prediabetic indices, such as elevated fasting glucose, night-shift workers [were] at fivefold risk for developing overt diabetes compared to day workers [79].”

And I’d move on from there. So already here we’ve established not only that sleep problems may lead to changes in appetite which may lead to weight gain; that sleep problems and type 2 diabetes may be related, and perhaps not only because of the weight gain; that different aspects of sleep may play different roles (difficulty falling asleep doesn’t seem to have the same effect as does difficulty staying asleep); and that the time course from pre-diabetes to overt diabetes may be drastically accelerated in people who work night shifts. This is a lot of information, and we’re still only scratching the surface of that chapter (there are 11 chapters in the book). If I were to go into details about the diabetes thing I might be tempted to talk about how in another chapter they describe a study where three out of eight completely healthy young men were basically (temporarily) converted into prediabetics just by messing around a bit with their circadian clock in order to cause it to get out of sync with their sleep-wake cycle (a common phenomenon in people suffering from jetlag, and actually also a common problem, it seems, in blind people, as they’re generally not capable of using light to adjust melatonin release patterns and keep the circadian clock ‘up to date’, so to speak), but I really wouldn’t need to look to other chapters to talk more about that kind of stuff as the chapter also has some coverage of studies on hormonal pathways such as those involving leptin [a ‘satiety hormone’] and ghrelin [a ‘hunger hormone’]. The role of cortisol is also discussed in the chapter (and elaborated upon in a later chapter). I might decide to go into a bit more detail about these things and explain that the leptin-ghrelin connection isn’t perfectly clear here, as some studies find that sleep deprivation reduces leptin production and stimulates ghrelin release whereas other studies do not, but perhaps I’d also feel tempted to add that although this is the case, most studies do after all seem to find the effects you’d expect in light of the results from the weight gain studies I talked about in the first post (sleep deprivation -> less leptin, more ghrelin). But maybe then I’d feel the need to also talk about how it seems that these effects may depend on gender and may change over time (/with age). And I’d add that most of the lab studies are quite small studies with limited power, so it’s all a bit uncertain what all this ‘really means’. Perhaps I’d add the observation from the last chapter, where they talk more about this stuff, that the literature on these two hormones are not equally convincing: “Conflicting results have been presented for leptin […], although increases in ghrelin, an appetite-stimulating hormone, may be more uniformly observed.” Perhaps when discussion these things I’d opt for including a few remarks about the role of other hormones and circulating peptides as well, for example the “hypothalamic factors (e.g., neuropeptide Y and agouti-related peptide), gut hormones [such as] glucagon-like peptide-1 [GLP-1], peptide YY [PYY], and cholecystokinin), and adiposity signals (e.g., leptin and adiponectin)”, all of which are briefly covered in chapter 11 and all of which “have been demonstrated to play a role in the regulation of hunger, appetite, satiety, and food intake.”

As for the increased hunger and caloric intake observation, I might decide to talk about how there’s an ‘if you’re awake, you have more time to eat’-effect that may play a role (aside perhaps from the rare somnambulist, few people eat while they’re sleeping – and I’m not sure about the somnambulists either…) – but on the other hand staying awake requires more calories than does sleeping (“Contrary to the common belief that insufficient sleep reduces energy expenditure, sleep loss increases total daily energy expenditure by approximately ~5 % (~111 kcal/day).”). Those are sort of behavioural approaches to the problem, but of course there are many others and multiple mechanisms have been explored in order to better understand what happens when people are deprived of sleep – hormonal pathways is one way to go, I’ve talked a little about them already, and of course they’re revisited later in the chapter when dealing with type 2 diabetes. As an aside, in terms of hormonal pathways there’s incidentally an entire chapter on melatonin and the various roles it may play, as well as some stuff on insulin sensitivity and related matters, but that’s not chapter 2, the one we were talking about – however if I were to cover chapter 2 in detail I’d probably feel tempted to add a few remarks about that as well. But of course chapter 2 doesn’t limit coverage to just behavioural stuff and the exploration of hormonal pathways, as it seems that sleep deprivation also has potentially important neurological effects, in that it affects how the brain responds to food – and so in the chapter they talk about a couple of fMRI studies which have suggested this and perhaps indicated how those things might work, and they talk about a related study the results of which suggest that sleep deprivation may also induce impairments in self-control.

If I we’re to talk about the weight gain stuff in the chapter, I might as well also talk a bit about how sleep patterns may affect people when they’re trying to lose weight, as they talk a little bit about that as well. Those results are interesting – for example one study on weight loss that followed individuals for two weeks found that the individuals who were assigned to the sleep-deprivation condition (5.5 hours, vs 8,5 hours in the control group) had higher respiratory rates than those who did not. The higher respiratory rate the authors of the study argued was an indicator that the sleep-deprived individuals relied more on carbohydrates and less on fat than the well-rested controls, which is important if you’re dealing with weight loss regimes; however the authors in the book do not seem convinced that this was a plausible inference… Before going any further I would probably also interpose that how sleep affects breathing – and how breathing affects sleep – is really important for many other reasons as well besides weight loss stuff, so it makes a lot of sense to look at these things; stuff like intermittent hypoxia during the sleeping state, sleep disordered breathing and sleep apnea are topics important enough to have their own chapters in the book. Perhaps I’d feel tempted to mention in this context that there’s some evidence that people with sleep apnea who get cancer have a poorer prognosis than people without such sleep problems, and that we have some idea why this is the case. I actually decided to quote a bit from that part of the book below… But anyway, back to the weight loss study, an important observation from that study I might decide to include in my coverage is that: “shorter sleep duration reduced weight loss by 55 % in sleep-restricted subjects”. This is not good news, at least not for people who don’t get enough sleep and are trying to lose weight; certainly not when combined with the observation that sleep-deprived individuals in that study disproportionately lost muscle tissue, whereas individuals in the well-rested group were far more likely to lose fat. One tentative conclusion to draw is that if you’re sleep deprived while dieting your diet may be less likely to work, and if it does work the weight loss you achieve may not be nearly as healthy as you perhaps would be tempted to think it is. Another conclusion is that researchers looking at these things may miss important metabolic effects if they limit their analyses to body mass measures without taking into account e.g. tissue composition responses as well.

Actually if I were to talk about the stuff covered in chapter 2 I wouldn’t really be finished talking the type 2 diabetes and sleep problems even though I talked a little bit about that above, and so I’d probably feel tempted to say a bit more about that stuff. Knowing that sleep disorders may lead to a higher type 2 diabetes risk doesn’t tell you much if you don’t know why. So you could perhaps talk a bit about whether this excess risk only relates to insulin sensitivity? Or maybe beta cell function is implicated as well? We probably shouldn’t limit the analysis to insulin either – cortisol is important in glucose homeostasis, and perhaps that one plays a role? – yep, they’ve looked at that stuff as well. And so on and so forth … for example what role does the sympathetic nervous system and the catecholamines play in the diabetes-sleep link? The one you’d expect, or at least what you’d expect if you knew a bit of stuff about these things. A few conclusions from the chapter:

“Overall, studies suggest a strong relationship between insufficient sleep and impaired glucose homeostasis and cortisol regulation. These proximal outcomes may explain observed associations between sleep and the diabetes epidemic.” […] “The relationship suggested between sleep loss and sympathetic nervous system dysfunction [‘increased catecholamine levels’, US] proposes another likely mediator of several of the negative metabolic effects of sleep loss and sleep disorders, including insulin resistance, decreased glucose tolerance, and reduced leptin signaling”).

I’d still leave out a bit of stuff from chapter two if I were to cover it in the amount of detail ‘outlined’ above, but I hope you sort of get the picture. There are a lot of connections to be made here all over the place, a lot of observations which you can sort of try to add together to get something resembling a full picture of what’s going on, and it gets really hard to limit your coverage to ‘the salient points’ of a specific topic without excluding many important links to other parts of the picture and overlooking a lot of crucial details. There’s way too much stuff in books like these for me to really provide a detailed coverage of all of it – most of the time I don’t even try, though I sort of did in this post, in a way. I encourage you to ask questions if there’s something specific you’d like to know about these things which might be covered in the book; if you do, I’ll try to answer. Of course it’s rather easy for me to say that you can just ask questions about stuff like this which you’d like to know more about, as part of the reason why people read books like these in the first place is so that they can get at least some idea which questions it makes sense to ask. On the other hand people who don’t know very much about science occasionally manage to ask some rather interesting questions with interesting answers on the askscience-subreddit, so…

I’ve added some additional observations from the book below, as well as some further observations and comments.

“Over the past few decades, the drastic increase in the prevalence of obesity has been reflected by substantial decreases in the amount of sleep being obtained. For example, whereas in 1960 modal sleep duration was observed to be 8–8.9 h/night, by 2004 more than 30 % of adults aged 30–64 years reported sleeping <6 h/night [3]. More recently, the results of a large, cross-sectional population-based study of adults in the United States showed that 7.8 % report sleeping <5 h/night, 28.3 % report sleeping ≤6 h/night, and 59.1 % of those surveyed report sleeping ≤7 h/night [4].”

Regardless of the extent to which you think these two variables are related (and how they’re related), this development is interesting to me. I’m pretty sure some of the authors of the book consider the (causal part of the?) link to be stronger than I do. I had no idea things had changed that much. Okay, let’s move on…

“For many years, it has been known that the timing of onset of severe adverse cardiovascular events, such as myocardial infarction, sudden cardiac death, cardiac arrest, angina, stroke, and arrhythmias, exhibits a diurnal rhythm with peak levels occurring between 6 am and noon […] It is clear that many variables and parameters within the cardiovascular system are under substantial regulation by the circadian clock, highlighting the relevance of circadian organization for cardiovascular disease. Shift work has consistently been associated with increased cardiovascular disease risk [68–71].”

“Molecular oxygen (O2) is essential for the survival of mammalian cells because of its critical role in generating ATP via oxidative phosphorylation [the link is to a featured article on the topic, US]. Hypoxia, i.e., low levels of O2, is a hallmark phenotype of tumors. As early as 1955, it was reported that tumors exhibit regions of severe hypoxia [16]. Oxygen diffuses to a distance of 100–150 μm from blood vessels. Cancer cells located more than 150 μm exhibit necrosis. The uncontrolled cell proliferation causes tumors to outgrow their blood supply, limiting O2 diffusion resulting in chronic hypoxia. In addition, structural abnormalities in tumor blood vessels result in changes in blood flow leading to cyclic hypoxia [17,18]. Measurement of blood flow fluctuations in murine [rats and mice, US – a lot of our knowledge about some of these things come from animal studies, and they’re covered in some detail in some of the chapters in the book] and human tumors by different methods have shown that the fluctuations in oxygen levels in tumors vary from several minutes to more than 1 h in duration. Hypoxia in tumors was shown to be associated with increased metastasis and poor survival in patients suffering from squamous tumors of head and neck, cervical, or breast cancers [19,20]. Tumor hypoxia is associated with resistance to radiation therapy and chemotherapy and poor outcome regardless of treatment modality. Cancer cells have adapted a variety of signaling pathways that regulate proliferation, angiogenesis, and death allowing tumors to grow under hypoxic conditions. Cancer cells shift their metabolism from aerobic to anaerobic glycolysis under hypoxia [21] and produce growth factors that induce angiogenesis [22,23]. […] It is increasingly recognized that hypoxia in cancer cells initiates a transcription program that promotes aggressive tumor phenotype. Hypoxia-inducible factor-1 (HIF-1) is a major activator of transcriptional responses to hypoxia [24]. […] It is now well recognized that HIF-1 activation is a key element in tumor growth and progression.”

“the existing epidemiologic evidence linking OSA [Obstructive Sleep Apnea] and cancer progression fits some of the key classic causality criteria [40]: the association is biologically plausible (in view of the existing pathophysiologic knowledge and in vitro evidence); the existing longitudinal evidence supports the existence of temporality in the cause-effect association; the effects are strong; there is evidence of a dose-response relationship; and it is consistent with animal experimental models and other evidence. Lacking is evidence regarding another important criterion: that treatment of OSA will result in a decrease in cancer mortality. Future studies in this area are critical.
If verified in future studies, the implications of the evidence presented here are profound. OSA might be one of the mechanisms by which obesity is a detrimental factor in cancer etiology and natural history. From a clinical standpoint, assessing the presence of OSA (particularly in overweight or obese patients) and treating it if present might have to become a routine part of the clinical management of cancer patients.”

It’s perhaps worth mentioning here that this is but one of presumably a number of areas of oncology where sleep research has shown promise in terms of potential treatment protocol optimization. It’s observed in the book that the effectiveness of- and side effect profile of chemotherapies may depend upon which time during the day (/night?) the treatment is given, which also seems like something oncologists may want to look into (unfortunately it does not however seem like they’ve made a lot of progress over the years):

“Arguably, a field in which little progress has been made in linking circadian rhythms to pathology, disease pathogenesis, and/or clinical medicine at the molecular and genetic levels is cancer. This is unfortunate given that a diurnal rhythm in efficacy and sensitivity to chemotherapeutic agents was reported in mice over 40 years ago [92]. More recently, screening studies in rodents have demonstrated clear circadian rhythmicity in the antitumor activity and side effect profile of many anticancer agents, although at present, it is not possible to predict a priori at which time of day a given drug will be maximally effective (i.e., although rhythms are clearly present, little is known of their mechanistic underpinnings) [93]. Results such as these have given rise to the concept of “chronotherapeutics,” in which the time of drug administration is taken into consideration in the treatment plan in order to maximize efficacy and minimize toxicity […] Although some progress has been made, by and large, this approach has not made significant inroads into clinical oncology”

The stuff above is probably closely related to discoveries made by other contributors, described elsewhere in the book:

“Our laboratory used actigraphy to measure circadian activity rhythms, fatigue, and sleep/wake patterns in breast cancer patients. We found that circadian rhythms were robust at baseline, but became desynchronized during chemotherapy […] desynchronization was correlated with fatigue, low daytime light exposure, and decreased quality of life [21,32].”

Here’s some more stuff on related matters:

“A diagnosis of cancer and the subsequent cancer treatments are often associated with sleep disturbances. […] Prevalence rates for sleep disturbance among oncology patients range from 30% to 55% [in another chapter it’s 30% to 75% – either way these numbers are high, US] […]  These sleep disturbances can last for years after the end of the cancer treatment. In cancer patients and survivors, sleep disturbances are associated with anxiety, depression, cognitive impairment, increased sensitivity to physical pain, impaired immune system functioning, lowered quality of life, and increased mortality. Given these associations and the high prevalence of sleep disturbance in cancer patients, it is paramount that clinicians assess sleep disturbances and treat sleep disorders in cancer patients and survivors. […] The effects of chemotherapy and anxiety on sleep quality in [cancer] patients have been well studied, and interventions to improve sleep quality and/or duration among cancer patients have shown widespread improvements in cancer mortality and outcomes, as well as mental health, and overall quality of life” [for more on quality of life aspects related to cancer, see incidentally Goerling et al.]

“We have previously demonstrated an inverse association of self-reported typical hours of sleep per night with likelihood of incident colorectal adenomas in a prospective screening colonoscopy-based study of colorectal adenomas [10]. Compared to individuals reporting at least 7 h of sleep per night, those individuals reporting fewer than 6 h of sleep per night had an estimated 50 % increase risk in colorectal adenomas […] A recent study as part of the Women’s Health Initiative (WHI) has shown similar results with regard to risk of colorectal cancer [48].”

Remember here that colorectal cancer is one of the most common types of cancer in industrialized countries – “[t]he lifetime risk of being diagnosed with cancer of the colon or rectum is about 5% for both men and women in the US” – some more neat numbers here. The more people are affected by the disease, in some sense the ‘bigger’ these ’50 % increases’ get.

“Probably, the cancer for which sleep duration has been studied most with regard to risk is breast cancer. There are also a number of epidemiological studies that have investigated the association of sleep duration and risk of breast cancer. In these studies, the association of short sleep duration and incidence of breast cancer has been mixed […] In a large, prospective cohort of over 20,000 men, Kakizaki et al. found that sleeping 6 or fewer hours was associated with an approximately 38 % increased risk of prostate cancer, compared with those reporting 7–8 h of sleep […] New evidence is also emerging on the role of sleep duration in cancer phenotype […] Breast cancer patients who reported less than 6 h of sleep per night prior to diagnosis were about twice as likely to fall into the “high-risk” recurrence category compared to women who reported at least 7 h of sleep per night before diagnosis. This suggests that short sleep may lead to a more aggressive breast cancer phenotype.”

“Pain in cancer patients is most often treated with opioids, and sedation is a common side effect of opioids. However, the relationship between opioid use and sleep has not been well studied. Limited PSG data show that opioids decrease REM sleep and slow-wave sleep [31], suggesting that rather than improving sleep by being sedated, opioids may actually contribute to the sleep disturbances in cancer patients with chronic pain. In addition, the most serious adverse effect of opioids is respiratory depression which may exacerbate the hypoxemia in those individuals with SDB [Sleep Disordered Breathing] and thus lead to more interrupted sleep […it may also promote tumor growth and/or lead to poorer treatment outcomes – see above. On the other hand not treating pain in cancer patients is also … problematic (yet probably still widespread, at least judging from the data in Clark & Treisman’s book)]. […] Although pharmacotherapy is the most prescribed therapy for cancer patients with sleep disturbances [10,35], there is a paucity of studies related to pharmacologic interventions in cancer patients. A recent review concluded that evidence is not sufficient to recommend specific pharmacologic interventions for sleep disturbances in cancer patients [6]. […] As several studies have now confirmed the beneficial effects of cognitive behavioral therapy for insomnia (CBT-I) in cancer patients (mostly breast cancer) and survivors, CBT-I needs to be considered as the first-line treatment. Hypnotics are commonly prescribed to cancer patients. Despite this common use, little to nothing is known about the safety of these drugs in cancer patients. Given the possible interaction effects of the hypnotic/sedatives with cancer treatment agents, the side effects, and potential tolerance and addiction issues, the common use of these drugs in cancer patients is concerning.”

The book is not only about sleep, and this part I found interesting:

“Emerging evidence supports the hypothesis […] that shared mechanisms exist for the co-occurrence of common [cancer] symptoms […] an increased understanding of the mechanisms that underlie the co-occurrence of multiple symptoms may prove crucial to the development of successful interventions […] The study of multiple co-occurring symptoms in cancer patients has led to the emergence of “symptom cluster” research. […] Although awareness of the co-occurrence of symptoms has existed for over two decades […], the study of symptom clusters is considerably more recent [118]. An enduring challenge in the study of symptom clusters remains the lack of consistency in the methods used to cluster symptoms [119]. Currently, the analytic methods used to cluster co-occurring symptoms include correlation, regression modeling [120,121], factor analysis [122], principal component analysis [121,123], cluster analysis [104,111], and latent variable modeling [109]. […] the decisions that dictate the use of a specific approach are beyond the scope of this chapter […] Symptom cluster research can be grouped into two categories: de novo identification of symptom clusters (i.e., clustering symptoms) and the identification of subgroups of patients based on a specific symptom cluster (i.e., clustering patients ) […] De novo identification of symptom clusters is the most common type of symptom cluster research that occurs with oncology patients.”

A lot of stuff didn’t make it into this post, but I’ll stop here. Or should I also mention that aside from what you eat, it may also matter a lot when you eat (“a study in mice showing that animals fed a high-fat diet during their inactive phase gained more weight than mice fed during their habitual active phase”)? Or should I mention that “individuals with later sleep schedules tended” … in one study … “to have higher energy intakes throughout the day than those whose midpoint of sleep was earlier?” No, probably not. I wouldn’t know where to stop…

[This is a big part of the reason why I often limit my coverage of books to mostly just quotes. Posts like these have a tendency to blow up in my face, and if they don’t I often still find myself having spent a lot of time on them.]

*Or maybe it isn’t actually all that long, perhaps it’s just slightly longer than average? Anyway now that you’ve scrolled down from the top of the post to the buttom in order to figure out what that asterisk meant (if you didn’t scroll down and are now only reading this after you’ve read the entire post above, that’s your fault, not mine…), you’ll know whether you think it’s long. The warning seemed to carry more weight this way. That a warning like this should carry some weight seems quite important to me, considering that I’m blogging a book about obesity. A book about obesity which covers dietary aspects in some detail, yet is occasionally itself a bit hard to digest. [Permission to groan: Granted.]

**An example:

“Prolyl hydroxylase (PHD) is a tetrameric enzyme containing two hydroxylase units and two protein disulphide isomerase subunits, which requires O2, ferrous iron, and 2-oxoglutarate for PHD enzyme activity. In the presence of O2, PHD covalently modifies the HIFα subunit to a hydroxylated form, which by interacting with Von Hippel-Lindau (VHL) protein, a tumor suppressor, is subjected to ubiquitylation and targeted to proteasome, where it gets degraded [25]. Hypoxia inhibits PHD activity resulting in accumulation of HIF-1α subunit, which dimerizes with HIF-1β subunit.”

Yeah, that sounds about right to me…

There isn’t much of this kind of stuff in the book; if there had been I would not have given it five stars, because in that case I would not have found it at all interesting/enjoyable to read.

May 23, 2014 Posted by | books, cancer, diabetes, health, medicine | 2 Comments

Impact of Sleep and Sleep Disturbances on Obesity and Cancer (1)

“Sleep has recently been recognized as a critical determinant of energy balance, regulating restoration and repair of many of the physiological and psychological processes involved in modulating energy intake and utilization. Emerging data indicate that sleep can now be added to caloric intake and physical activity as major determinants of energy balance with quantitative and qualitative imbalances leading to under- or overnutrition and associated comorbidities. Considerable research is now focused on disorders of sleep and circadian rhythm and their contribution to the worldwide obesity pandemic and the associated comorbidities of diabetes, cardiovascular disease, and cancer. In addition to having an impact on obesity, sleep and circadian rhythm abnormalities have been shown to have significant effects on obesity-associated comorbidities, including metabolic syndrome, premalignant lesions, and cancer. In addition to the observation that sleep disturbances are associated with increased risk for developing cancer, it has now become apparent that sleep disturbances may be associated with worse cancer prognosis and increased mortality. […] circadian misalignment, such as that experienced by “shift workers,” has been shown to be associated with an increased incidence of several malignancies, including breast, colorectal, and prostate cancer, consistent with the increasing recognition of the role of clock genes in metabolic processes […] This volume […] review[s] current state-of-the-art studies on sleep, obesity, and cancer, with chapters focusing on molecular and physiologic mechanisms by which sleep disruption contributes to normal and abnormal physiology, related clinical consequences, and future research needs for laboratory, clinical, and translational investigation.”

I’m currently reading this book. I probably shouldn’t be reading it; I realized a couple of weeks ago that if I continue at the present rate I’ll get to something like 100 books this year, and despite some of these books being rather short and/or fiction books I don’t think this is a healthy amount of reading. It’s probably worth noting in this context that despite the fact that the number of ‘books read’ is now much higher than it used to be, I incidentally am far from sure if I actually read any more stuff now than I did in the past; it may just be that these things have become easier to keep track of as I now read a lot more books and a lot less ‘unstructured online stuff’. It’s not a new problem, but it’s getting rather obvious.

But anyway I’m reading the book, and although it may not be a good way for me to spend my time I am at least learning some stuff I did not know. The book is a standard Springer publication, with 11 chapters each of which deals with a specific topic of interest (a few examples: ‘Effects of Sleep Deficiency on Hormones, Cytokines, and Metabolism’, ‘Biomedical Effects of Circadian Rhythm Disturbances’, and ‘Shift Work, Obesity, and Cancer’). I’ve added some observations from the book below as well as some comments – I’ll probably post another post about the book later on once I’ve finished reading it. The very short version is that insufficient sleep may be quite bad for you.

“Insomnia, identified by complaints of problems initiating and/or maintaining sleep, is common, especially among women. Insomnia is often associated with a state of hyperarousal and has been linked to increased risk of depression, myocardial infarction, and cardiovascular mortality [15]. Relative risks for cardiovascular disease for insomnia have been estimated to vary from 1.5 to 3.9; a dose-dependent association between frequency of insomnia symptoms and acute myocardial infarction has been demonstrated [16]. Insomnia may be particularly problematic at certain times in the lifespan, especially in the perimenopause period and in association with acute life stresses, such as loss of a loved one. The occurrence of insomnia during critical periods, such as menopause, may contribute to increased cardiometabolic risk factors at those times. Short sleep duration may occur secondary to a primary sleep disorder or secondary to behavioral/social issues. Regardless of etiology, short sleep duration has been associated with increased risk of obesity, weight gain, diabetes, cardiovascular disease, and premature mortality [17,18].”

“Sleep is characterized not only by its presence or absence (and timing) but by its quality. Sleep is composed of distinct neurophysiological stages […] associated with differences in arousal threshold, autonomic and metabolic activity, chemosensitivity, and hormone secretion [2] […] Each sleep stage is characterized by specific patterns of EEG activity, described by EEG amplitude (partly reflecting the synchronization of electrical activity across the brain) and EEG frequency. Lighter sleep (stages N1, N2) displays relatively low-amplitude and high-frequency EEG activity, while deeper sleep (slow-wave sleep, N3) is of higher amplitude and lower frequency. Stages N1, N2, and N3 comprise non-rapid eye movement (REM) sleep (NREM). In contrast, rapid eye movement (REM) sleep is a variable frequency, low-amplitude stage, in which rapid eye movements occur and muscle tone is low. […] In adults, over the course of the night, NREM and REM sleep cycles recur approximately every 90 min, although their composition differs across the night: early cycles typically have large amounts of N3, while later cycles have large amounts of REM. The absolute and percentage times in given sleep stages, as well as the pattern and timing of progression from one stage to another, provide information on overall sleep architecture and are used to quantify the degree of sleep fragmentation. Sleep characterized by frequent awakenings, arousals, and little N3 is considered to be lighter or non-restorative and contributes to daytime sleepiness and impaired daytime function. Higher levels of N3 are thought to be “restorative.””

“The circadian rhythm changes with age and one important change is a general shift to early sleep times (advanced sleep phase) with advancing age. While teenagers and college students have a tendency due to both intrinsic rhythm and external pressures to have later bedtimes, this starts to wane in young adulthood. This phase advance to an earlier sleep time has been referred to as “an end to adolescence” and happens at a younger age for women than for men [60]. […] During the transition from adolescence to adult, several changes occur to the sleep architecture. Most notably is the significant reduction in stage N3 sleep by approximately 40 % as the child progresses through the teenage years […] This means that other stages of NREM (N1 and N2) take up more of the sleep time. Functionally this translates to the child having lighter sleep during the night and therefore is easier to arouse and awaken. […] The sleep architecture of young adults is […] in a 90-min cycle with all sleep stages represented. The amount of stage N3 sleep continues to reduce at this time, at a rate of approximately 2 % per decade up to age 60 years. There is also a smaller reduction in REM sleep during early and mid-adulthood. Once through puberty and into the 20s, most adults sleep approximately 7–8 h per night. This remains relatively constant through mid-adulthood. Young adults may still sleep a bit longer, 8–9 h for a few years. The need for sleep does not change as people progress to mid-adulthood, but the ability to maintain sleep may be affected by medical conditions and environmental influences. […] although average sleep duration does not change over adulthood, there is a large degree of inter- and intraindividual variability in sleep duration. Individuals who are consistently short sleepers (e.g., <6 h per night) and long sleepers (>9 h per night) and who demonstrate high between-day variability in sleep duration are at increased risk for weight gain, diabetes, and other metabolic dysfunction and chronic disease.”

“Nine retrospective studies have indicated that shift work might be associated with a higher risk of breast cancer, including three studies in Denmark, three studies in Norway, two studies in France, and one study in the United States. […] Three of four prospective studies have provided evidence in favor of an association between shift work and breast cancer. […] evidence for a relation between shift work and prostate cancer is very limited, both by the small number of studies and by major limitations involved in those studies that have been conducted”

The increased risk of breast cancer may well be quite significant not only in the statistical sense of the word, but also in the normal, non-statistical, sense of the word; for example the estimated breast cancer odds ratio of Norwegian nurses who’d worked 30+ years of nightwork, compared to those who hadn’t done any nightwork, was 2.21 (1.10-4.45) – and that study involved more than 40.000 nurses. Another study dealing with the same cohort found that the nurses who’d worked more than five years with schedules involving more than 5 consecutive night shifts also had an elevated risk of breast cancer (odds ratio: 1.6 (1.0-2.4)). It’s noteworthy that many of the studies on this topic according to the authors suffer from identification problems which if anything are likely to bias the estimates towards zero. As you should be able to tell from the reported CIs above, the numbers are somewhat uncertain, but that doesn’t exactly make them irrelevant or useless; roughly 1 in 8 women at baseline can expect to get breast cancer during their lifetime (link), so an odds ratio of, say, 2 is actually a really big deal – and even if we don’t know precisely what the correct number is, the risk certainly seems to be high enough to warrant some attention. One mechanism proposed in the shift work chapter is that the altered sleep patterns of shift workers lead to weight gain, and that weight gain is then part of the explanation for the increased cancer risk. I’ve read about and written about the obesity-cancer link before so this is stuff I know a bit about, and that idea seems far from far-fetched to me. And actually it turns out that the link between shift work and weight gain seems significantly stronger than does the link between shift work and cancer – which is precisely what you’d expect if it’s not the altered sleep patterns per se which increase cancer risk, but rather the excess adipose tissue which so often follows in its wake:

“Numerous epidemiologic studies have examined the association between shift work and obesity in various different countries. Most of these studies have utilized existing data from employment records in particular companies, which provide convenient but typically limited information on shift work and health-related variables because this information was not originally collected for research purposes. As a result, many of these studies have methodological issues that potentially limit the interpretation of their results. Still, 22 of 23 currently published studies found some evidence that obesity is significantly more common among individuals with shift work experience compared to those without such experience [36–57]; only one study did not identify a possible link [58]. […] many analyses of shift work and obesity lack adjustment for potentially important confounding variables (e.g., other health and lifestyle factors), and therefore prospective studies with more extensive information on these variables have provided critical insight. Four such prospective studies have been conducted, all of which indicate that individuals who perform shift work tend to experience significant weight gain over time — including two studies in Japan, one study in Australia, and one study in the United States. […] in the largest and most detailed analysis to date, each 5-year increase in rotating shift work experience was associated with a gain of 0.17 kg/m2 in body mass index (95 % CI = 0.14–0.19) or 0.45 kg in weight (95 % CI = 0.38–0.53), among 107,663 women who were followed over 18 years in the US Nurses’ Health Study 2 [57]. Statistical models were adjusted extensively for age, baseline body mass index, alcohol intake, smoking, physical activity, and other health and lifestyle indicators.”

A major problem with the ‘shift work -> obesity -> cancer’ -story is however that the identified weight gain effect sizes seem really small (one pound over five years is not very much, and despite how dangerous excess adipose tissue may be, those kinds of weight differences certainly aren’t big enough to explain e.g. the breast cancer odds ratio of 1.6 mentioned above) – the authors don’t spell this out explicitly, but it’s obvious from the data. It may be slightly misleading to consider only the average effects, as some women may be more sensitive than others to these effects and outliers may be important, but not that misleading; I don’t think it’s plausible to argue that this is all about body mass. In the few studies where they have actually looked at obesity as a potential effect modifier, the results have not been convincing:

“Although it is possible that obesity predicts both shift work and cancer risk — as would be required for obesity to be a potential confounding factor of this relation — it is probably more likely that shift work predicts obesity, in addition to obesity being a risk factor for many types of cancer. This scenario is suggested by the prospective studies of shift work and obesity described above; that is, obesity is a stronger candidate for effect modification than confounding of the association between shift work and cancer, as shift work appears to influence the risk of obesity over time. Yet, only three prior studies have conducted stratified analyses based on obesity status to evaluate the possibility of effect modification. Two of these studies focused on shift work and breast cancer, but they found no evidence of effect modification by obesity [24,26]; a third study of shift work and endometrial cancer did identify obesity as an effect modifier [32]. […] Clearly, additional studies need to carefully consider the role of body mass index—a possible confounding factor, but more likely effect modifying factor—in the association between shift work and obesity.”

I should make clear that although it makes sense to assume that obesity is a potentially major variable in the sleep-cancer risk relation, there are a lot of other variables that likely play a role as well, and that the book actually talks about these things as well even though I haven’t covered them here:

“Although the exact mechanisms by which various sleep disorders may affect the initiation and progression of cancer are largely unknown, disruption of circadian rhythm, pervasive in individuals with sleep disorders, is thought to be the underlying denominator linking sleep disorders, as well as shift work and sleep deprivation, to cancer. The circadian system synchronizes the host’s daily cyclical physiology from gene expression to behavior [55]. Disruption of circadian rhythm may influence tumorigenesis through a number of mechanisms, including disturbed homeostasis and metabolism (details provided in Chap. 2), suppression of melatonin secretion (details provided in Chap. 3), intermittent hypoxia and oxidative stress (details provided in Chap. 5), reduced capacity in DNA repair, and energy imbalance.”

The obesity link relates to a few of these, but there’s a lot of other stuff going on as well. I may talk about some of those things later – I thought chapter 7 was quite interesting, so I’ve ended up talking quite a bit about that chapter in this post, and neglected to cover some of the earlier stuff covered in the book.

May 21, 2014 Posted by | books, cancer, diabetes, health | Leave a comment

Pathophysiology of disease – an introduction to clinical medicine (VI)

As I’ve now finished the book this will be the last post in the series.

The way I read this book has been different from the way I usually read books; most books I read I’ll read in one go over a relatively brief amount of time. As for this one, I certainly didn’t read it in one go and I had breaks from it lasting a quite significant amount of time. I’m not really sure why I read it that way, but one obvious factor which certainly contributed is that this book is hard to read and takes a lot of mental firepower to handle.

I gave the book five stars on goodreads and added it to my list of favourites. Here’s the review I wrote on that site:

“This review got to be rather longer than usual, but I guess I don’t have a hard time justifying that on account of the nature of the book.

To get this over with from the beginning: If you have never read a medical textbook before, don’t bother with this one. You’ll learn nothing and you’ll never finish it. Unless you speak more or less fluent medical textbook you’ll have to either look up a lot of new words, or you’ll read a lot of words you’ll not understand. The fact that the book is somewhat inaccessible was the most important factor pulling me towards 4 stars. I decided to let it have 5 stars anyway in the end – given how many hours I was willing to spend on this stuff I really couldn’t justify giving it any other rating, although there are also a few other small problems which I might have punished in other contexts.

If you know enough to benefit from reading this book it’s a great book, even though I’d prefer if future doctors – which would presumably make up most of the potential readers who ‘know enough to benefit from reading it’ – read a newer version of it. But in order to read it and get something out of it, you need some basic knowledge about stuff like microbiology, histology, immunology, endocrinology, oncology, (/bio-)chemistry, genetics, pharmacology, etc. And I don’t mean basic knowledge like what you’d get from a couple of wikipedia articles – having read textbooks and/or watched medical lectures on some of these topics is a must.

On top of relevant background knowledge you need to be willing to commit at the very least something like 50 hours of spare time to reading this thing. I spent significantly more time than that, and most people probably need to do that as well if they want to actually understand most of this stuff – you certainly do if you want some of it to actually stick.

There probably exist quite a few similar medical textbooks which are more up to date and which may provide slightly better coverage. But I’m not going to read those books. I read this one. And I’m glad I did. Don’t interpret the 5 stars to mean that this is the best book on this topic – I have no way of knowing whether or not it is, though I assume it isn’t. But it is a highly informative and well-written book which covers a lot of ground and from which I learned a lot.”

The ‘covers a lot of ground’ thing can’t be overemphasized – this book has 23 chapters mainly organized in terms of organ systems. It gives you an overview of how things work in general and some of the ‘classical’ ways which they may go wrong. It does this very well, and despite being the kind of book where one chapter will cover heart disease and another chapter will cover pulmonary disease they’re very good at ‘connecting the dots’ – that disorders are often interrelated and e.g. that a failing heart will cause problems with your lungs is not something they’re neglecting to deal with. Indeed the ‘big-picture view’ the book provides made me aware of multiple connections between ‘human subsystems’ which I’d been completely unaware of, and learning about these kinds of relationships was quite fascinating.

Another fascinating aspect was how much stuff there is to know about these things. It’s quite common for me to read books where the coverage overlap to some extent with what I’ve read in other books – I’ll often prefer to read such books (though I also take steps to avoid limiting my exposure to new stuff I don’t know about too much) because the information they cover will be easier to relate to and connect to other stuff up there in my head. One chapter (or a few pages) in one book may cover material which another book spent hundreds of pages dealing with. While reading this book I very often realized that I’d covered a specific topic somewhere else, which gave me a different perspective; ‘this topic is covered in more detail in Hall‘, ‘see Sperling for much more on this topic’, ‘see also Kolonin et al.’, ‘see also Eckel‘, ‘see Holmes et al.‘, and so on and so forth – I’ve added a lot of those kinds of comments along the way. While reading this book you sort of read the big-picture version, and at various points you’re likely to come across places where you can sort of ‘zoom in’, on account of knowing a lot about that topic. What was most amazing to me in this context was how many places I couldn’t zoom in. There’s such a lot of stuff to know and learn.

I won’t cover the last chapters in much detail. The chapters I’ve read over the last few days covered disorders of the hypothalamus and pituitary gland (chapter 19), thyroid disease (chapter 20), disorders of the adrenal cortex (chapter 21), and disorders of the female (chapter 22) and male (chapter 23) reproductive tracts. A few of these chapters I think I probably paid a bit more attention to than I would have done if I had not read Sperling (see link above) in one of my ‘breaks’ from this book. One reason for this is that Sperling, or rather ‘Tuomi and Perheentupa’ as they were the ones who wrote that specific chapter in the book, spent some time and effort in the book dealing with various forms of combinations of autoimmune conditions involving type 1 diabetes as one of the components, which suddenly makes in particular the chapter on thyroid disease more relevant than it otherwise would have been. Tuomi and Perheentupa covered this stuff because: “Two fundamentally different autoimmune polyendocrine syndromes (APSs) are generally recognized, and type 1 diabetes mellitus is common in both.” The risk of me developing another autoimmune condition on top of my diabetes one should think would be low, and it sort of is (it would incidentally most likely be significantly higher if I were a female); but a key observation here is that other autoimmune conditions usually show up later in life than does the diabetes, so the higher risk I face of developing e.g. Graves’ disease and Hashimoto’s disease (both are covered in chapter 20 of the Pathophysiology text) is not yet really accounted for, and the fact that I haven’t developed any of them yet is not very relevant to my risk of developing these conditions later in life (what is relevant is that I developed diabetes very early in my life – this actually makes it less likely that other organ systems will get hit as well, though it does not make the risk go away). I’ll include a quote from the relevant chapter from Sperling below as I’m aware this was some of the stuff I did not cover when I read that book and so people may be completely in the dark about what I’m talking about:

“All combinations of adrenocortical insufficiency, thyroid disease (Graves’ disease, goitrous or atrophic thyroiditis), type 1 diabetes, celiac disease, hypogonadism, pernicious anemia (vitamin B12 malabsorption), vitiligo, alopecia, myasthenia gravis, and the collagen vascular diseases, which include at least one of the said endocrine diseases but exclude hypoparathyroidism and mucocutaneous candidiasis, are collectively called APS type 2. The co-occurrence of these diseases is presumably the result of a common genetic background. No exact incidence or prevalence figures are available, and they would probably vary with the population concerned. APS-2 is more common than APS- 1, with a general prevalence of at least 1 per 10,000. Females are affected two to four times more often than men. The highest incidence of the components is in the third to the fifth decade of life, but a substantial number of patients develop the first component disease, usually type 1 diabetes, already in the first and second decade”

Note that the uncertain, yet seemingly low, prevalence estimate is easy to misunderstand. I haven’t looked at these numbers recently and I’m not going to go look for them now, but say type 1 diabetes (-T1DM) affects 1 out of 300 people. Now combine the ‘at least 1 in 10.000′ estimate with that one and observe that roughly 2 out of 3 patients with APS-2 have T1DM and the risk a type 1 diabetic will develop another autoimmune condition is already measured in percent. These numbers incidentally downplay the actual risk – I decided to include a few examples from Sperling to illustrate. It makes sense to start with Graves’ disease as I already mentioned that one: “Graves’ disease has been reported in 9.3% of patients with type 1 diabetes (76).” Also, “Hypothyroid or hyperthyroid AITD [AutoImmune Thyroid Disease] has been observed in 10–24% of patients with type 1 diabetes”  – uncertain figures with big error bars, but not exactly low risks of no import. Especially not when considering that: “In addition, between 5% and 25% of type 1 diabetic patients without clinical thyroid disease have antibodies to thyroid microsomal antigens (TMAb) or thyroid peroxidase (TPOAb)”. Although combination forms with multiple autoimmune disorders are quite rare, they’re not actually that rare (‘not rare enough…’) when you take into account that T1DM is also, well, rare.

The stuff above was mostly just an aside explaining why I perhaps cared a bit more about the stuff covered in these last chapters than I otherwise would have, but hopefully it was an informative aside. I should note that the ‘more interesting’ stuff was not all of it more interesting on account of dealing with some elevated risk of ugly things happening to me; other parts of the last chapters were ‘particularly relevant’ because of other stuff, like the role cortisol plays in circadian variation in insulin resistance and the role ACTH-excretion plays in hypoglycemia. But I think it would take too much time and effort to go into the details of these things in this post so I’ll cut it short here.

March 22, 2014 Posted by | books, diabetes, medicine | Leave a comment

Random stuff

i. Effects of Academic Acceleration on the Social-Emotional Status of Gifted Students.

I’ve never really thought about myself as ‘gifted’, but during a conversation with a friend not too long ago I was reminded that my parents discussed with my teachers at one point early on if it would be better for me to skip a grade or not. This was probably in the third grade or so. I was asked, and I seem to remember not wanting to – during my conversation with the friend I brought up some reasons I had (…may have had?) for not wanting to, but I’m not sure if I remember the context correctly and so perhaps it’s better to just say that I can’t recall precisely why I was against this idea, but that I was. Neither of my parents were all that keen on the idea anyway. Incidentally the question of grade-skipping was asked in a Mensa survey answered by a sizeable proportion of all Danish members last year; I’m not allowed to cover that data here (or I would have already), but I don’t think I’ll get in trouble by saying that grade-skipping was quite rare even in this group of people – this surprised me a bit.

Anyway, a snippet from the article:

“There are widespread myths about the psychological vulnerability of gifted students and therefore fears that acceleration will lead to an increase in disturbances such as anxiety, depression, delinquent behavior, and lowered self-esteem. In fact, a comprehensive survey of the research on this topic finds no evidence that gifted students are any more psychologically vulnerable than other students, although boredom, underachievement, perfectionism, and succumbing to the effects of peer pressure are predictable when needs for academic advancement and compatible peers are unmet (Neihart, Reis, Robinson, & Moon, 2002). Questions remain, however, as to whether acceleration may place some students more at risk than others.”

Note incidentally that relative age effects (how is the grade/other academic outcomes of individual i impacted by the age difference between individual i and his/her classmates) vary across countries, but are usually not insignificant; most places you look the older students in the classroom do better than their younger classmates, all else equal. It’s worth having both such effects as well as the cross-country heterogeneities (and the mechanisms behind them) in mind when considering the potential impact of acceleration on academic performance – given differences across countries there’s no good reason why ‘acceleration effects’ should be homogenous across countries either. Relative age effects are sizeable in most countries – see e.g. this. I read a very nice study a while back investigating the impact of relative age on tracking options of German students and later life outcomes (the effects were quite large), but I’m too lazy to go look for it now – I may add it to this post later (but I probably won’t).

ii. Publishers withdraw more than 120 gibberish papers. (…still a lot of papers to go – do remember that at this point it’s only a small minority of all published gibberish papers which are computer-generated…)

iii. Parental Binge Alcohol Abuse Alters F1 Generation Hypothalamic Gene Expression in the Absence of Direct Fetal Alcohol Exposure.

Nope, this is not another article about how drinking during pregnancy is bad for the fetus (for stuff on that, see instead e.g. this post – link i.); this one is about how alcohol exposure before conception may harm the child:

“It has been well documented that maternal alcohol exposure during fetal development can have devastating neurological consequences. However, less is known about the consequences of maternal and/or paternal alcohol exposure outside of the gestational time frame. Here, we exposed adolescent male and female rats to a repeated binge EtOH exposure paradigm and then mated them in adulthood. Hypothalamic samples were taken from the offspring of these animals at postnatal day (PND) 7 and subjected to a genome-wide microarray analysis followed by qRT-PCR for selected genes. Importantly, the parents were not intoxicated at the time of mating and were not exposed to EtOH at any time during gestation therefore the offspring were never directly exposed to EtOH. Our results showed that the offspring of alcohol-exposed parents had significant differences compared to offspring from alcohol-naïve parents. Specifically, major differences were observed in the expression of genes that mediate neurogenesis and synaptic plasticity during neurodevelopment, genes important for directing chromatin remodeling, posttranslational modifications or transcription regulation, as well as genes involved in regulation of obesity and reproductive function. These data demonstrate that repeated binge alcohol exposure during pubertal development can potentially have detrimental effects on future offspring even in the absence of direct fetal alcohol exposure.”

I haven’t read all of it but I thought I should post it anyway. It is a study on rats who partied a lot early on in their lives and then mated later on after they’d been sober for a while, so I have no idea about the external validity (…I’m sure some people will say the study design is unrealistic – on account of the rats not also being drunk while having sex…) – but good luck setting up a similar prospective study on humans. I think it’ll be hard to do much more than just gather survey data (with a whole host of potential problems) and perhaps combine this kind of stuff with studies comparing outcomes (which?) across different geographical areas using things like legal drinking age reforms or something like that as early alcohol exposure instruments. I’d say that even if such effects are there they’ll be very hard to measure/identify and they’ll probably get lost in the noise.

iv. The relationship between obesity and type 2 diabetes is complicated. I’ve seen it reported elsewhere that this study ‘proved’ that there’s no link between obesity and diabetes or something like that – apparently you need headlines like that to sell ads. Such headlines make me very, tired.

v. Scientific Freud. On a related note I have been considering reading the Handbook of Cognitive Behavioral Therapy, but I haven’t gotten around to that yet.

vi. If people from the future write an encyclopedic article about your head, does that mean you did well in life? How you answer that question may depend on what they focus on when writing about the head in question. Interestingly this guy didn’t get an article like that.

March 1, 2014 Posted by | alcohol, diabetes, genetics, personal, Psychology, studies, wikipedia | 2 Comments

A Practical Manual of Diabetic Retinopathy Management

I’ve read some articles etc. about this stuff before, but I’ve never read ‘the textbook’. I have now. Well, I’ve read a textbook anyway. I am not super impressed by the book, and I decided to give it two stars on goodreads. Maybe it deserves three, it’s in that neighbourhood.

So what’s the book about? Here’s what they write in the introduction:

“In this book the fundamental approach is to describe the classification of diabetes, risk factors for diabetic retinopathy and lesions of diabetic retinopathy, and explain the significance of these lesions in terms of progression of the disease, recommended treatment and consequences for vision. Methods of screening for diabetic retinopathy and other retinal conditions that are more frequent in diabetes or have similar appearances to diabetic retinopathy are also discussed.”

They deal with main concepts and they provide a lot of examples and case histories along the way. As is always the case in books like these many of the case histories are really quite depressing – I was considering skipping them altogether at one point after a particularly ‘bad one’, but I decided to read those parts anyway; they make up a substantial part of the book.

As you might have inferred from the remarks above, diabetic retinopathy is diabetes-related eye disease. How many diabetics are impacted by this? A rather large number, it turns out (well, I already knew that and I’ve talked about it before, but…):

“Diabetic retinopathy is a leading cause of adult blindness in the US, reported by Fong[1] et al. in 2004 to result in blindness for over 10,000 people with diabetes per year. Moss[2] reported the 10-year incidence of blindness in the Wisconsin Epidemiological study of Diabetic Retinopathy to be 1.8%, 4.0% and 4.8% in the younger-onset, older-onset taking insulin, and older-onset not taking insulin groups, respectively. Respective 10-year rates of visual impairment were 9.4%, 37.2% and 23.9%. […] In the Wisconsin study,[44] proliferative retinopathy occurred in 67% of people with type 1 diabetes for 35 or more years. One would therefore expect that two-thirds of people with type 1 diabetes would need laser treatment for proliferative diabetic retinopathy during their lifetime. […] In patients with type 2 diabetes, the rate of proliferative diabetic retinopathy is not as high but it is estimated that 1 in 3 patients with type 2 diabetes will develop sight-threatening diabetic retinopathy requiring laser during their lifetime. […] Despite major advances in treatment and early detection of diabetic eye disease, the ageing demographic and increased incidence of diabetes is resulting in greater numbers of diabetic visually impaired people in the population.” [my emphasis. Numbers differ across countries and there are a lot more numbers in the book, but these estimates provide some context; this is a complication that affects a huge number of diabetics.]

In the book they talk a lot about how you can use tiny (with sizes measured in microns!) and very short-lasting laser pulses to treat the damaged blood vessels in the eyes, and that stuff’s quite interesting. Equally interesting is the fact that people seem to be treating without really knowing exactly why the treatment works:

“The effectiveness of focal laser treatment may be due, in part, to the closure of leaky microaneurysms, but the specific mechanisms by which focal photocoagulation reduces macular oedema is not known. Studies have shown histopathological changes[18] and biochemical changes,[19,20] which have been suggested as mechanisms for improvement in macular oedema although some investigators have suggested alternative mechanisms for clearance of the oedema such as the application of Starling’s law and improved oxygenation.[21] […] the mechanism by which laser treatment improves the prognosis of sight-threatening diabetic retinopathy is ill-understood.”

A lot has happened when it comes to treatment over the last decades, as patients in the pre-laser era would often simply lose their vision because no good treatment options existed. A lot of people still do lose their vision to diabetes as mentioned above, but with the advent of laser treatments the prognosis has improved a lot. There are some adverse effects associated with these treatments, e.g. in the form of laser scars or scotomas and (paradoxical?) development of macular oedema afterwards (“McDonald[6] showed that 43% of the treated eyes in his study developed increased macular oedema 6–10 weeks following laser treatment.”), and it doesn’t always work (“if there is ischaemia that involves the central fovea, laser treatment in isolation is unlikely to improve the vision.” “It is not uncommon to successfully treat one area of leakage and subsequently find leakage appearing in a completely different area around the fovea of the same eye.”). But it’s still a big step in the right direction. Laser therapy is however surgical management of tissue damage, and some people are of course hoping to develop pharmacological treatment options as well. In that context I should note that in a way it was fun to read a medical textbook written by people who know less about some aspects of the stuff I’m reading about than do people I’ve met personally (people like Toke Bek). Latanoprost is being evaluated in a clinical trial right now as a drug which might be used to slow the progression of diabetic retinopathy in diabetics, but they don’t talk about that at all in the ‘Future advances in the management of diabetic retinopathy’-chapter (however on the other hand you can’t really blame them for not including this stuff, as that idea postdates the book..).

It should be noted – and they do this repeatedly throughout the book – that the damage to the small blood vessels in the eyes and the subsequent retinal ischaemia/bleeding etc. leading to vision loss in diabetics is strongly linked to factors such as glycemic control and (systemic) blood pressure. This means that improvements in glycemic control and blood pressure management will, if they can be achieved, also translate into better outcomes along these dimensions over time. A factor pulling in the other direction (‘more blind people’) is the high number of current and future undiagnosed type two diabetics who’ll incur extensive tissue damage without knowing it before getting their diagnoses:

“In the UKPDS study it was observed that up to 50% of [type 2] patients had some detectable form of tissue damage at diagnosis, the majority of this being background diabetic retinopathy[2]. […] Retinopathy is the commonest finding, with about 30% of all subjects newly diagnosed having detectable retinal lesions.”

This patient population poses some problems also because these people will by definition not be included in national screening programs. A related point they do not touch upon in the book is of course that non-compliant patients, the ones most likely to benefit from participation, would also be expected to be less likely than other patient groups to participate in screening programs; so even in places where you have national screening programs and so on you’ll likely still have some ‘theoretically preventable’/’excess’ diabetes-related blindness in the future. Perhaps I talk about screening programs as if I think they’re a good idea, but if that’s the case it’s because some forms of them are almost certainly pretty much a no-brainer – see e.g. this post. The book also spends a chapter on that stuff, unsurprisingly coming to the conclusion that screening is probably a good idea (there’s also consensus about which method of screening is best: “There is widespread agreement that digital photography is the best method of screening for sight-threatening DR.”). It’s worth noting in the context of the complication rates that it’s easier to spot eye damage than other types of tissue damage, and that this may provide part of the explanation for why this complication is so often found at diagnosis compared to other types of complications – here’s a relevant passage from the book:

“Retinopathy is often the easiest complication to detect because the smallest of lesions (microaneurysms) can be visualized long before any change to the subjective function of the eye would be apparent. Retinopathy tracks closely with nephropathy, and so careful screening of renal function needs to be carried out in those who have retinopathy and vice versa.”

The book has a lot more stuff, but I know that most readers probably aren’t too interested in this topic so I figured a rather limited coverage of the book would be preferable to most readers. One of multiple reasons why I did not give it a higher rating is that they repeat themselves quite a few times, covering the same stuff in multiple chapters. Unless you’re a diabetic there’s also no good reason why you should read the book as it is quite technical. Most diabetics will probably find it hard to read.

February 25, 2014 Posted by | books, data, diabetes, medicine | Leave a comment

Screening for Depression and Other Psychological Problems in Diabetes – A Practical Guide

Parts of this book hit relatively close to home and I should probably have read something along these lines some years ago, rather than now. Anyway.

Some critical remarks first. The book is not super great and parts of it are just beyond horrible, so I don’t recommend it. I gave it two stars, but this one was closer to one star than three. I wasn’t that impressed with Juth and Munthe (see also this post), but that book handles the screening stuff much better than does this one. Most of the authors of this book seem convinced that implementing some form of screening mechanism for depression in diabetics may be a good idea, but I’m far from convinced it can actually be justified. Cost aspects are somewhat neglected in the coverage, and cost-effectiveness is a key parameter in the justification process of screening initiatives; and despite what one author would like to have us believe, there’s almost zero chance such a scheme will save money in the long run – preventative medicine almost never does (Glied & Smith included a somewhat comprehensive review of these things in their coverage) and assuming otherwise is borderline arguing in bad faith. Especially problematic in terms of those things is the fact that many authors seem to agree that a screening procedure on its own, without follow-up mechanisms in place to deal with the patients after the identitification phase, probably is not justified, whereas a scheme with such mechanisms in place may be (as they put it in the introduction: “Screening for emotional problems without a comprehensive management plan has not proven to be efficacious in reducing depression and emotional problems in people with diabetes”), they don’t really talk a great deal about how this requirement of implementing proper follow-up etc. impacts the cost-effectiveness variable. Another problem is that the literature seem to find that psychiatric interventions impact quality of life metrics a lot more than they do Hba1c (in this context you can think of the latter as a variable determining to a significant extent the likelihood of developing expensive diabetes complications in the future); some authors mention this, but they are not completely clear on how this affects the cost-benefit side of the equation. The basic idea here is that if depression leads to poorer self-care behaviours among diabetics (this is not really an assumption, it’s clear that this is the case), part of this depression-mediated behavioural change may relate to lower adherence to the treatment regimen, and if so then one might think that psychiatric interventions might improve both quality of life measures and medical adherence/glycemic control measures. As mentioned it’s not clear that there’s much of an effect on glycemic control – some studies have found statistically significant effects, but their clinical relevance are questionable. Quality of life improvements are nice, don’t get me wrong, but without associated improvements in glycemic control it gets harder to justify screening – you save a lot more money by preventing a person from going blind than you do by making the guy feel better.

Some more personal comments of a less critical nature are probably in order as well. I should note that one of the most important observations made in this book – and part of why I actually didn’t really like giving it such a low rating, because it’s a very neat insight – is that it made me aware of how I may have been thinking the wrong way about depression, depressiveness and related stuff. In the past, I’ve mostly thought about depression as a dichotomous variable; either you are suffering from (major) depression or you’re not – if you do, there are specific symptom complexes which should be expected/observed (long term sleep disturbances, -changes in appetite, and so on and so forth), and if you don’t, whatever is wrong, if anything, probably isn’t a big deal. I have been thinking this way about this stuff because that’s how the DSM-IV (and V, if I’m not mistaken) approach the topic – focused on symptoms, with specific and well-defined cut-offs. The conclusion drawn on my part was that I don’t suffer from depression, because it seemed I did not meet the criteria.

If you let go of the dichotomy and start thinking about depressiveness as a continuous variable, things change. For one thing they probably get somewhat iffier in terms of empirical stuff. Mood states can change a lot over short amounts of time, and ‘objective criteria’ like weight gain may be better than unobservable self-report measures – this is presumably all part of why current criteria are the way they are. However a potential problem is that you may miss out on a lot of relevant variation by upholding a strict dichotomy, because mood states are not distributed that way in the real world (they can take on more than two values). In some patient subpopulations upholding a strict demarcation may be a lot more problematic than in others, on account of different distributions of realized mood states within subpopulations. Diabetics are probably one of the groups where it makes a lot of sense to at least think a little about how to approach people who don’t quite make the formal cut-offs (given observations made in the psycho-oncology textbook I’m currently reading, cancer patients would be another relevant patient group – and no, these two diseases are not actually that different in terms of some of the associated emotional responses to the disease; when measuring fear of progression scores based on the Fear of Progression Questionnaire, Berg et al. (2011) e.g. found quite similar scores for diabetics and cancer patients (see Goerling, page 14)). Here are some relevant remarks from the book on this topic:

“Subclinical depression is a term used when an individual presents with depressive symptoms but does not meet the criteria for a diagnosis of clinical depression. Recent reports note that approximately one-third of people with type 1 diabetes and 37–43% of people with type 2 diabetes report symptoms of depression [56, 57]. These rates were far higher than the proportion of people who had been given an actual diagnosis of clinical depression [45] . Rather than receiving treatment for depression, however, such individuals often have to cope with their symptoms alone. The impact on family, social life, and overall quality of life remains unknown to a large extent and is an area where further research is clearly needed. […] The natural course of depression is to worsen [58]”

The group of individuals with subclinical depression is likely highly heterogenous and there are some complications when dealing with this group which matter when it comes to how to approach screening mechanisms. One problem is whether the psychological distress is directly diabetes-related or not (there are measures one can use to separate non-directly-diabetes-related psychological distress from other forms of psychological distress) – this matters because different intervention types are optimal for different patient subpopulations. Another problem is that poorly regulated diabetes may actually cause physiological symptoms which mimic symptoms of depression, and that not all available screening tools which might be applied to the patient group take this into account.

With all that out of the way, a few observations from the book:

“In recent years, most research studying emotional problems in people with diabetes has focused on depression or elevated depressive symptoms. This has meant that depression in diabetes is the best understood emotional problem in people with diabetes. Depression rates in people with diabetes are roughly doubled compared to the general population. A meta-analysis of 42 studies demonstrated that clinical or major depression […] occurred in 11.4% of people with diabetes, whereas the prevalence in nondiabetic people was 5% [2]. People with diabetes also reported more intense depressive symptoms, without fulfilling the criteria for clinical or major depression. Elevated depressive symptoms were reported by 31% of diabetic patients, whereas only 14% of nondiabetic subjects reported elevated depressive symptoms. The doubling of depression rates in people with diabetes compared to nondiabetic people has been confirmed by a more recent meta-analysis [35].”

“The negative impact of the comorbidity of diabetes and depression on quality of life is greater than the sum of diabetes and depression alone, indicating an exponential detrimental effect of depression on quality of life in people with diabetes. Although depression is a rather common condition in chronic diseases [47], a WHO World Health Survey on quality of life in different chronic diseases (arthritis, asthma, angina, and diabetes) showed that quality of life was most impaired in diabetic patients with depression [48].”

“In a prospective study with 7-year follow-up, Black and colleagues demonstrated that the risk for macrovascular complications was more than three times higher if depressive symptoms were present in diabetic patients at the start of the study [11]. The risk of developing microvascular complications or functional disabilities in diabetic patients with minor depression is increased by a factor of 8.6 or 6.9, respectively. Interestingly, the risk difference for late complications between those with mild and more severe depression was rather small. Thus, it seems that even milder forms of depression have to be taken seriously. […] the experience of depressive symptoms that would not meet the diagnostic threshold for MDD is a risk factor for negative health outcomes in patients living with diabetes […] data clearly demonstrate an incremental relationship between symptoms of depression and negative health outcomes in diabetes, a relationship observed even at subclinical levels of depression severity. [This] challenge[s] the model of MDD in diabetes, which conceptualizes the problem of depression as a categorical construct that is either present or not.”

“Until recently, there has been a paucity of evidence about the treatment of depression in people with diabetes, and consequently there has been uncertainty about the most effective and safe way to do so […] The effectiveness of psychological interventions in people with diabetes has [however now] been demonstrated in a systematic review of 25 randomized controlled trials of psychological therapies, mostly CBT. Both psychological distress and glycemic control were improved in people receiving active psychological interventions [60]. A further systemic review of 29 trials and meta-analysis of 21 trials by the same group showed that psychological interventions improved glycated hemoglobin by approximately 0.5% (5 mmol/mol) in children but not in adults [61]. […] recent reviews by David-Ferdon and Kaslow [94] and prior work by Kazdin and Weisz [95] highlight the following components as primary targets of CBT: (1) increase participation in pleasant activities (that enhance mood), (2) increase and improve social interactions, (3) improve conflict resolution and social problem-solving skills, (4) reduce physiological tension or excessive affective arousal, and (5) identify and modify depressive thoughts and attributions.”

“Diabetes management in older patients presents unique challenges. Clinical (e.g., comorbidity, complications) and functional (e.g., impairment, disability) heterogeneity in the older population require special attention. Most diabetes patients have at least one comorbid condition [1] and as many as 40% have three or more distinct conditions [2].”

“Diagnosis and treatment of comorbid depression in older patients is a considerable challenge in routine diabetes care. Depression is frequently under-recognized and under-treated [51–54], with less than 25% of diabetes patients’ depression successfully identified and treated in clinical practice [55].”

“The risk of incident foot ulcers has been found to be increased twofold in individuals with comorbid depression compared to diabetic patients who are not depressed [9]. Depressed patients with diabetic neuropathy are more prone to developing first foot ulcers than nondepressed individuals, independently of biological risk factors and foot care [10]. […] There is also strong evidence of an inverse association between diabetes complications and depression. Patients burdened by diabetes complications are more likely to develop depression than are those without complications, especially in the case of nephropathy and neuropathy [13]. […] Depression is common in patients with erectile dysfunction, which reflects a continuous interplay between diabetes-related and psychological factors. […] There is substantial heterogeneity between type 1 and type 2 diabetes comorbidity with depression, which is partly explained by their different etiologies [8].”

“Overall, findings derived from reviews and individual studies suggest that more research-based evidence is needed to support the case for the widespread introduction of screening for depression in people with diabetes in primary care, or indeed in other settings. A recurrent message is that screening alone is unlikely to have a strong impact on patient outcomes unless case-finding is linked to other aspects of patient management. […] it remains to be shown that formal pro-active screening has benefits over improved methods of incorporating recognition and management of depression into routine models of care of people with diabetes.”

February 20, 2014 Posted by | books, diabetes, medicine, Psychology | Leave a comment

Metabolic Risk for Cardiovascular Disease

Here’s what I wrote on goodreads:

“The book is well sourced and actually does a good job of covering much of the material. But the editor has done a poor job, and as a result the book seems very sloppy compared to similar scientific publications. There are multiple spelling errors and typos along the way, and it frankly seems as if the book was ‘published too fast’, before all the errors could be corrected. At first I punished this severely when I rated it by only giving the book 2 stars, but I realized this was too harsh. There’s a lot of interesting stuff included in the book.”

Here’s the kind of thing I’m talking about:

“Numerous cardiovascular abnormalities may be encountered in obese subjects (Table 6.4) it is not written properly in the PDF files that I have but this version seems correct. Health service usage and medical costs associated with obesity …”

That comment was one of a kind (fortunately), but there are a lot of errors and typos. At one point they talk about a marginally insignificant finding with an associated P-value of 0.52. This kind of stuff makes you look sloppy. The book is a Wiley-Blackwell publication and you kind of expect a bit more from books like these.

I’ve dealt with many of the topics covered in the book before (e.g. here, here and here, Khan Academy, etc.). I got the book in part to have a book in which I knew I could easily find a reference if/when I needed one, so that I wouldn’t have to look around a lot, and I think it’ll serve that purpose reasonably well. I gave the book 3 stars on goodreads. The book deals with many of the things you’d expect a book like this to cover; lipid and lipoprotein metabolism, insulin resistance and its role in cardiovascular disease, the obesity epidemic, hypertension, type 2 diabetes and the metabolic syndrome, tobacco use and cardiovascular disease and the role of physical exercise and nutrition, among other things. There was some interesting stuff in the book, but not a lot which was all that surprising. I really liked parts of chapter 11 on diabetes management and cardiovascular risk reduction; the chapter went over some reviews and a few major studies well known to people who’re interested in these things (ACCORD, ADVANCE), and the interpretation of the data by the author was somewhat different from interpretations I’ve seen in the past. One main point in the chapter is that lowering of Hba1c may be more effective in preventing cardiovascular events/disease progression among patients without overt cardiovascular disease; the argument being that lowering of blood glucose may protect vessels from getting damaged, but once they’re damaged lowing of Hba1c may not do much difference because it’s basically too late (in part because glycemic control may play a greater relative role in the early course of the disease process, compared to other factors, than it does in the later stages, where other mechanisms may conceivably take over to a greater extent – he doesn’t spell this out explicitly but I’d be surprised if he has not been thinking along those lines). In terms of previous trials looking at the link between glycemic control and cardiovascular disease (CVD), researchers have usually looked disproportionately at diabetics with manifest CVD; this is understandable as these patients are high risk. But such applied selection mechanisms in the past may mean (among other things) that these studies may have been underpowered to find the effects they were looking for. This is an interesting line of argument I have not seen before. If you’re wondering why this is important, it’s important because whereas the link between small-vessel disease and glycemic control is incontrovertible and has been for a long time, the link between macrovascular complications (CVD, etc.) and glycemic control has long been questionable, with a lot of mixed findings. Study selection designs and similar mechanisms may help partially explain why previous studies have not been able to establish a clear relationship. There are of course other complicating factors as well. As I think I’ve said before, until it’s perfectly clear to me that glycemic control and macrovascular disease are unrelated (or at least until we know in more detail how they are related), I’ll pretend that better glycemic control may have a protective effect on both small and large blood vessels. Note that the reason why this is important is also that diabetics make up a huge proportion of all heart disease patients; in Denmark the Danish Endocrine Society noted in a report published a few years ago (I can no longer find it online, unfortunately) that roughly half of all Danish patients with chronic ischaemic heart disease, AMI or heart failure have diabetes (of course a lot of them didn’t know that they did, but that’s a different discussion).

I’ve added some observations from the book below as well as a few comments:

a general rule is that CVD risk approximately doubles for each 20mmHg increment of systolic BP and 10mmHg increment of diastolic BP above 115/75mmHg […] a substantial excess risk of stroke death among those who are overweight or obese may be largely accounted for by a higher blood pressure [31].”

“Despite the fact that obesity has been shown to be an independent risk factor for CVD, many studies have reported that obese patients with established CVD have a better prognosis than do patients with ideal bodyweight; the socalled “obesity paradox.” […] The improved survival of obese individuals is paradoxical principally because of the assumption that excessive weight is always and invariably injurious. As a matter of fact, among patients with congestive heart failure, subjects with higher BMI are at decreased risk for death and hospitalization compared with patients with a “healthy” BMI [2]. Further, obesity was associated, in a prospective cohort study, with lower all-cause and cardiovascular mortality after unstable angina/non-ST-segment elevation myocardial infarction treated with early revascularization [89]. The obesity paradox may reflect the lack of discriminatory power of BMI to adequately reflect body fat distribution [20,87,90]. Since BMI measures total body mass, i.e. both fat and lean mass, it may better represent the protective effect of lean body mass on mortality. This negative confounding may have been under-appreciated in prior studies that did not adjust for measures of abdominal obesity. It is possible that the favorable prognosis implications associated with mildly elevated BMI might actually reflect intrinsic limitations of BMI to differentiate adipose tissue from lean mass. The lack of specificity of BMI could dilute the adverse effects of excess fat with the beneficial effects of preserved or increased lean mass [91]. […] Another issue to consider is that normal-weight patients may have a significantly higher percentage of high-risk coronary anatomy compared with obese patients [97]. […] Another limitation in most studies reporting an obesity paradox in patients with CVD is that non-intentional weight loss, which would be associated with a poor prognosis, is not assessed as BMI is measured only at the beginning of the study. Patients who have decompensated heart failure may lose weight because of extensive caloric demands associated with the increased work of breathing […] the excess health risk associated with a higher BMI declines with increasing age. An explanation for the lack of a positive association between BMI and mortality at older ages is that, in older persons, higher BMI is a poor measure of body fat and may simply represent a measure of increased physical activity with preserved lean mass. Sarcopenic obesity, which is defined as excess fat with loss of lean body mass, is a highly prevalent problem in the older individual. […] in view of the importance of body fat distribution, one could argue that, instead of targeting bodyweight per se, one should pay more attention to the WC [waist circumference] and conservation of lean mass as a critical goal in intervention programs [23].”

“Self-reported diabetes mellitus is often used in studies, but that approach underestimates the true prevalence of diabetes mellitus, and may misclassify a sizable fraction of the participants. […] it has been estimated that the lifetime risk of T2DM for persons born in the USA in 2000 is approximately 33% for men and 39% for women [9].”

“Summary analyses have reported that about 65% of deaths among diabetic patients are from vascular or heart disease, 13% are from diabetes itself, 13% are from neoplasms, and the rest are from other causes [14]. Most data concerning diabetes and death in adults are concerned with T2DM, and the limited data on mortality associated with type 1 diabetes mellitus have suggested that approximately one-third are from diabetes itself, one-third are from kidney disease, and one-third are from cardiovascular disease [15,16].” [I should note that some of these numbers sound wrong to me, but for now I’ll just report the numbers. I may have a closer look at the studies later. Note that ‘deaths from diabetes’ is a variable which is incredibly hard to get right in general; everybody dies, but diabetics die faster – deaths incontrovertibly ‘directly attributable’ to diabetes like DKA or hypoglycemic coma don’t make up all the ‘excess deaths’.] Researchers have investigated the effect of diabetes on life expectancy. An Iowa study showed that estimated life expectancy was 59.7 years at birth for diabetic men and 69.8 years in diabetic women, and it was estimated that diabetes reduced the lifespan by 9.1 years in diabetic men and 6.7 years in diabetic women [17]. From US national survey data it has been estimated that men known to have diabetes at age 40 years will lose 11.6 life-years and similarly affected women will lose 14.3 life-years [9].” [Again, for now I’ll just report the numbers…]

“The Centers for Disease Control reported that there were 8 million diabetic American adults with CVD in 1997 and the number increased to more than 11 million in 2007 […] reports suggest that diabetic patients continue to experience CVD at a high rate and are surviving, which has resulted in an increased prevalence of diabetic patients with CVD [21]. […] Fewer diabetes complications such as mortality, renal failure, and neuropathy have been observed for adult T1DM patients in the Pittsburgh Epidemiology of Diabetes Complications Study over recent years. On the other hand, risk of proliferative retinopathy, overt nephropathy, and clinical CAD have not declined over the long-term follow-up interval of 30 years [97]. […] Overall 1-, 2-, and 5-year survival after myocardial infarction in a population-based Swedish cohort was 94%, 92%, and 82%, respectively, in non-diabetic patients and 82%, 78%, and 58%, respectively, in diabetic patients.” [I.e., the proportion of diabetics who can expect to survive one year after an MI corresponds to the proportion of non-diabetics who can expect to survive five years.]

“In the mid-1990s there was considerable interest in the potential benefit of antioxidant nutrients and CVD risk reduction [100–103]. Since that time a series of randomized controlled intervention trials have failed to demonstrate a benefit of vitamin E or other antioxidant vitamin supplementation on CVD risk [104, 105]. The most recent work focusing on vitamins C and E confirm these earlier trials [106]. At this time the data do not support a recommendation to use antioxidant vitamins for the prevention or management of CVD. […] The three major dietary omega-3 polyunsaturated fatty acids (PUFAs) are alphalinolenic acid (ALA, 18:3n-3), eicosapentaenoic acid (EPA, 20:5n-3), and docosahexaenoic acid (DHA,22:6n-3). The later two fatty acids are sometimes referred to as very-long-chain n-3 fatty acids. […] a number of studies have reported an inverse association between dietary n-3 fatty acids, CVD and stroke risk [41]. Intervention data have demonstrated that EPA and DHA, but not ALA, benefit cardiovascular outcomes in primarily and secondary prevention studies [42] […] Of note, the relationship between arrhythmea and EPA and DHA has recently been questioned [45]. The major source of ALA in the diet is soybean and canola oils, whereas the major source of EPA and DHA is marine oils found in fish.”

“The lipoproteins are defined by their density, for example, very low density (VLDL), low-density (LDL), and high-density (HDL). In this instance, “density” is mostly related to the triglyceride and cholesterol content; the more lipids in a lipoprotein the lower its density, as measured by how readily it floats toward the top of a tube during ultracentrifugation. TG-rich lipoproteins transport an energy source, triglyceride, to muscle and adipose tissue for use and storage. TG-rich lipoproteins also contain cholesterol, and can deliver the cholesterol to peripheral tissues and the arterial wall. LDL is a transporter of primarily cholesterol from the liver to peripheral tissues. HDL also functions to transport cholesterol but in the reverse direction as VLDL and LDL, from peripheral tissues to the liver. Lipoproteins also are required to transport fat-soluble vitamins.”

Relatively consistent evidence indicates that increasing the carbohydrate content of the diet at the expense of fat results in dyslipidemia [7–9]. The majority of the evidence suggests that carbohydrate-induced hypertriglyceridemia results from an increased rate of hepatic fatty acid synthesis [10,11] and subsequent production of hepatic triglyceride-rich particles, very-low-density lipoprotein (VLDL) […] Within the context of a stable bodyweight, replacement of dietary fat with carbohydrate results in higher triglyceride and VLDL cholesterol concentrations, lower HDL cholesterol concentrations and a higher (less favorable) total cholesterol to HDL cholesterol ratio [16–21]. […] Sedentary individuals characterized by visceral adiposity are at particularly high risk for carbohydrate-induced hypertrygliceridemia [9]. […] Studies performed in the mid 1960s demonstrated that changes in dietary fatty acid profiles altered plasma total cholesterol concentrations in most individuals […] Many studies have since confirmed these early observations using a variety of different experimental designs [29]. When carbohydrate is displaced by saturated fatty acids, LDL cholesterol concentrations increase, whereas when carbohydrate is displaced by unsaturated fatty acids LDL cholesterol concentrations decrease, with the effect of polyunsaturated fatty acids greater than monounsaturated fatty acids […] When carbohydrate is displaced by saturated, monounsaturated or polyunsaturated fatty acids, HDL cholesterol concentrations are increased, with saturated fatty acids having the greatest effect and polyunsaturated fatty acids having the least effect.”

“Some agents affect HDL and TG in the same direction. Drinking alcoholic beverages and postmenopausal estrogen treatment raise HDL and TG. Testosterone lowers HDL and TG. Since we do not have a way as yet to evaluate the function of HDL in reverse cholesterol transport [one of the chapters spends a significant amount of time on that one – there’s a lot more to be said about that stuff than what’s in the wiki article], we cannot be confident that these or any changes in HDL concentration affect atherosclerosis in the direction expected from the relation of HDL concentrations and CHD risk [59,65]. There is also no clear relation between genetic variants in enzymes or transporters in HDL metabolism that cause either very low or high HDL cholesterol concentrations and CHD [74].” [HDL is usually termed ‘good cholesterol’, but in reality it’s much more complicated than that. We are very sure by now that high ‘anything which is not HDL’ is bad for you, though – in fact:]The combination of VLDL cholesterol and LDL cholesterol, named “non-HDL cholesterol” [2], or perhaps better “atherogenic cholesterol,” is a measurement that generally predicts CVD better than LDL-C [LDL-Cholesterol].”

February 16, 2014 Posted by | books, diabetes, health, medicine | Leave a comment

Open Thread

Exam’s getting close – expect no further updates until Monday or Tuesday. Some random stuff of interest from the bookmarks:

i. First a very neat link: The Cost-Effectiveness Analysis Registry. It’s exactly what it says on the tin; a registry with information about cost-effectiveness stuff.

I really like the utility weight feature. And of course I was curious about my own disease so I looked up T1DM. According to the search I did, a utility weight estimate for ‘Diabetes with no complications’ is reported to be 0.757. One way to think about this is to say that that person’s life is about three-quarters as good as a healthy person’s life. Another way to think about it is that if person X gets type 1 diabetes during, say, the first year of life (pretty close to my situation), the lifetime utility loss that individual will incur from that diagnosis corresponds to losing two decades of his/her life (i.e. ‘die at the age of 56 instead of at the age of 75’, assuming ‘equivalent’ age-related (and other) utility variation in the two populations). With complications the utility weights of course drop further; diabetes + retinopathy yields a weight of 0.61, and nephropathy + heart disease equals 0.516 (‘his life is only half as good as that of a healthy person’). Of course one should have in mind that the utility contribution from complications impact fewer years of life because people with heart disease or kidney failure have a tendency to die at faster rates than people who do not suffer from these complications (certainly part of why the utility weights are lower…), and some people live many years without complications.

I’d say that if one wants a brief overview of how ‘severe’ a disease is thought to be the utility weight estimates provided at the site are actually really nice tools, but do have in mind that a lot of assumptions go into making such estimates, and there are lots of differences in treatment regimes and/or differences in disease impacts e.g. when you make cross-country comparisons (most estimates are not ‘globally valid’, it’s safe to say). ‘Proper’ utility weights are/ought to be highly heterogenous across subgroups, and will in many cases (not just when it comes to diabetes) be time-dependent, among other things. Individual variation is huge. In a way this is all a bit ‘quick and dirty’, but it’s better than nothing; either way it’s probably a good idea to check out the actual studies if you want more than just a quick estimate. Of course the site has as already mentioned stuff other than utility weight estimates – if you want to know if a given health intervention is likely to be cost-effective this also seems like a great place to start. (And on a related note, if you know nothing about cost-effectiveness analysis a good place to start would be to read this book, or at least the first half of it.)

ii. Being right or being happy: pilot study; a ‘study’ from the Christmas edition of the British Medical Journal. I’m sure some of you have already read this, but others may not have. Here’s the introduction (I should note that it’s not a very long ‘article’):

“Three of the authors are general practitioners who see many patients and couples who lead unnecessarily stressful lives by wanting to be right rather than happy. Mathieu encourages her psychotherapy clients “to try to live in the gray. There are a million shades of gray” (although a recent erotic novel suggests there are only 50) “on the spectrum of white to black, and each provides a much richer telling of a story that is hardly ever as clear as this or that. So, when we looked a bit more closely, we saw that ‘right versus happy’ was not so much about getting crowned the winner or loser, a genius or fool; it was more about flawed thinking and a desire to want to feel being in control.”1 This might be the first study to systematically assess whether it is better to be right than happy; a Medline search in May 2013 found no similar articles. Our null hypothesis was that it is better to be right than happy.”

I’m skeptical about the results…

iii.  Who did whom? A field guide to Pleistocene hookups, by John Hawks.

iv. At this point I’m roughly one-third of the way towards reaching the level of ‘walking dictionary’ on vocabulary.com (give it another month or two…). Many of the roughly 1700 words I’ve supposedly mastered on the site I already knew – considering how little I’ve focused on this stuff over the years, I’m actually quite surprised now how many words I ‘sort of know, but didn’t know that I knew’. On the other hand there have also been quite a few words I’ve never seen before, and some words I didn’t know as well as I thought I did. A funny thing about language, which I haven’t really thought about, is that like in the case of other areas of knowledge you’ll often not ever actually be made aware of the fact that your vocabulary (/knowledge) is limited unless you make an effort of actively seeking out words (facts) you don’t know; if you don’t know that there’s a word for X, you’ll often never be made aware that you didn’t know – especially if other people don’t know that word either. The ‘hey, I’m familiar with this concept but I didn’t know it actually had a name…’-experience a site like this will occasionally provide is really nice. Anyway, below a few words I’ve picked up along the way:

Eleemosynary (of, relating to, or supported by charity; charitable).
Martinet (a person who is very strict and demands obedience from others; a strict disciplinarian; a person who stresses a rigid adherence to the details of forms and methods).
Ratiocination (the process of exact thinking: reasoning; a reasoned train of thought).
Sagaciousness (the ability to understand inner qualities or relationships; having or showing acute mental discernment and keen practical sense; shrewd).
Sententious (having or expessing strong opinions about what people should and should not do; given to or abounding in aphoristic expression/excessive moralizing; terse, aphoristic, or moralistic in expression).
Solecism (an ungrammatical combination of words in a sentence; something deviating from the proper, normal, or accepted order; a breach of etiquette or decorum).
Echolalia ((psychiatry) mechanical and meaningless repetition of the words of another person; an infant’s repetition of sounds uttered by others).
Ingenuous (lacking in sophistication or worldliness; innocent and unsuspecting).
Ineluctable (not to be avoided, changed, or resisted; inevitable).
Supererogatory (more than is needed, desired, or required; superfluous).

Note that even if you’re an incorrigible reprobate who hates other people and don’t really want to learn new stuff, a larger vocabulary will be something you can make good use of; a larger vocabulary makes it a lot easier to surreptiously insult people. Rather than calling the overweight woman in front of you fat, you can just call her embonpoint. And instead of calling the moron next to you in the bar an alcoholic, you can just say that he’s bibulous…

v.

This is awesome! (And actually that hypothesis probably sounds more plausible than at least some of the ‘evolutionary theories’ I’ve seen presented (in earnest) in the past…)

Your turn – what have you been doing? Comments to the stuff above? Any new readers out there who’d like to tell us a bit about themselves? Any good books or links I should read (after my exam)?

January 11, 2014 Posted by | anthropology, diabetes, medicine, Open Thread | Leave a comment

Type 1 Diabetes – Etiology and treatment (2)

Here’s my first post about the book. I’ve now read roughly two-thirds of it (400 pages) and I like this book.

Not all chapters give me a lot of new insights – for example I know a lot more about the topic covered in the chapter about the Relationship Between Metabolic Control and Complications in Diabetes than what is covered in the book, and the ten-page chapter on The Diabetic Foot which I’ll soon read will not match the detailed coverage in Edmonds et al. – but anything else would be very surprising, and most chapters contain some stuff which I did not know. I understand the mechanisms driving microvascular complications better now than I did, but I’m still fuzzy on some of the details; like some of the genetics stuff in the first chapters that part of the book is very technical, and so I decided against covering that stuff in detail here. If you’re curious about that stuff, here’s a relevant link covering some of what the book has on that topic, in what seems from a brief skim to be a roughly similar amount of detail. To people who know nothing about this stuff (i.e., people who haven’t read my posts on related topics in the past…), diabetes in the long term causes damage to small and large blood vessels and may cause various forms of nerve damage (neuropathies) – here’s a brief and non-technical overview article. The connection between hyperglycemia – too high blood glucose – and small vessel disease is better established (and very well established at this point) than is the connection between hyperglycemia and large vessel disease, and although it may not sound too bad that small blood vessels are damaged, the consequences can be dire; long-term diabetes may among other things cause blindness and kidney failure. How precisely the blood vessels are damaged in diabetics was not very well understood for a very long time, but significant progress seems to have been made over the last couple of decades, and a ‘unifying theory’ of sorts – which brings together four separate mechanisms – seems to have been developed at this point. As mentioned you can have a look at ‘the relevant link’ above if you want to know more about the details.

Age is an important factor in treatment, as different age groups will respond in dissimilar manners to treatment and will face different problems (biological factors, behavioural factors), so the book has separate chapters on diabetes management in very young children, adolescents, etc. Though the level remains high throughout the book, I’d incidentally note that I don’t believe these chapters on special management issues in specific patient subgroups are that technical, and I think many diabetics would be able to benefit from reading those chapters. To a diabetic, much of the stuff covered in the treatment part will be well known although there’ll also be some new stuff. I was continually bothered throughout some of those chapters by the fact that when comparing treatment outcomes of patients on intensive treatment regimes with subcutaneous insulin injections and patients on insulin pumps, the obvious problems with selection into treatment in the latter group were not commented upon when comparing outcomes (though it must be said that one of the authors do comment on this aspect in a later chapter).

Below I’ve selected out some stuff from the middle 200 pages or so of the book. I’ve not completely ignored passages which may be a bit hard to understand for people without any knowledge of this disease – this is also a post written in order to make it easier for myself to remember what was covered in some of those chapters – however as mentioned above I’ve left out the really technical stuff. I have also bolded some key concepts and a few observations for the ‘lazy’ readers who can’t be bothered to read all of it, in order to make the post easier to navigate.

“Since its introduction, insulin has been life sustaining for patients with type 1 diabetes […] Although it is relativly inexpensive in the developed world, in many developing countries with limited health care resources, it is not routinely available (9). Indeed, children with type 1 diabetes in sub-Saharan Africa often do not live longer than 1 yr (10).” (I was wondering if this was an observation based on very old data (data access is a notorious problem when dealing with developing countries), but that seems not to be the case: “A child diagnosed with type 1 diabetes in sub-Saharan Africa has a life expectancy that varies between 7 months and 7 years, depending on the country” – link, original source is this article which I haven’t found an ungated copy of).

[A] major risk of insulin therapy is weight gain. Insulin promotes fat storage in adipocytes and protein synthesis in muscles. […] [In the Diabetes Control and Complications Trial (DCCT)] the body mass index (BMI) increased approx 2 more units with intensive than with conventional treatment in both genders. In the whole DCCT cohort, the risk of becoming overweight was almost twofold greater with IT [intensive treatment – US] […] on average, adult subjects achieving a mean HbA1c of 7.2% gained 4.8 kg more during a 6-yr follow-up than their conventionally controlled counterparts” [my HbA1c is below 7.2%US.]

“Exposure to a mean HbA1c of 11% for less than 3 yr yields the same rate of retinopathy as exposure to a HbA1c of 8% for 9 yr. The message is clear: The less time we allow a patient to be exposed to high levels of blood glucose, the better […] The adverse hyperglycemic effects on the eyes and kidneys exhibit a carryover effect manifested by a kind of “metabolic memory” displayed by these target organs. […] there is a momentum factor in retinopathy and nephropathy contributed to by the combination of glycemic level and time. The process of tissue damage builds up slowly, but in an accelerated fashion at higher HbA1c levels […], it decelerates slowly at lower HbA1c levels […], but also resumes its progression slowly after a period of time at lower HbA1c levels”

“It has long been recognized that treating and controlling diabetes is difficult. Diabetes is not an illness where a pill, an injection, or a particular diet is a cure. At best, there is hope to control it well. Optimal treatment demands dedication, motivation, energy, and knowledge. […] Dealing with these issues on a daily basis can be a psychological burden […] Thus, it is common for those with diabetes and/or close members of their families to have guilt, sorrow, and depression […] Although depression is not a complication of diabetes, it frequently is a consequence of the illness. The prevalence of depression in adults varies. Levels of diagnosable depression among those with diabetes are approximately three times the estimated prevalence in the population at large (8). Depression also might be more severe in people with diabetes and has especially adverse effects. Difficulty evolves in treatment when clinical depression contributes to poor self-care, worsened glycemia, and deepened depression (9).”

“Hyperglycemia before eating slows gastric emptying and results in a more prolonged glycemic response (8), whereas hypoglycemia speeds emptying and results in a faster, higher, and earlier peak response (9).” [I was not aware of this!]

“Persons with type 1 diabetes may attempt to substitute protein for carbohydrates to attenuate postprandial glucose response. A large cross-sectional study in type 1 diabetes found that protein intakes greater than 20% of total energy intake were associated with higher albumin excretions than <20% dietary protein (43). Concern over the role protein intake plays in renal function suggests that consuming more than 20% protein in the diet is unwise.” [As I’ve pointed out before (the second paper in the post), salt intake seems like a more obvious place to intervene – but protein intake is not irrelevant].

“Diabetes is less frequent in preschool children than in older ages. In a large survey in Europe, age-specific incidence was compared among 3 age groups in more than 3000 cases during 1989–1990 (1). Eighteen percent of the cases were observed in children younger than 4 yr, 34% between 5 and 9 yr, and 48% in children aged 10–14 yr. Similar results have been obtained in North America (2). [I got diagnosed at the age of 2US] […] A major characteristic of metabolic control in type 1 preschool children is the unstable glycemic control with its accompanying risk of severe hypoglycemia […] In young children, severe and recurrent hypoglycemias are of major concern because they may impair normal brain development. When tested during adolescence, patients who presented with early-onset diabetes and/or a history of severe hypoglycemia showed global or selective neuropsychological dysfunction such as impairment of visual–spatial skills, psychomotor efficiency, attention, or memory (28–32). As early as 2 yr after disease onset, evidence exists for mild neuropsychological dysfunction (33). Onset of diabetes early in life (before 5 yr of age) predicted negative changes in neuropsychological performances over the first 2 yr of the disease (34).” [I’ve talked about this aspect of the disease before. Below’s a bit more on this stuff:]

“The long-term risk of recurrent severe episodes of hypoglycemia, involving coma or convulsions, on the development of permanent cognitive impairment remains controversial. […] There continue to be concerns about young children with type 1 diabetes, particularly those diagnosed less than 5 yr of age in whom defects in tests of cognitive function have consistently been found (126–131). […] It is likely that the developing brain is more susceptible to damage during episodes of metabolic derangement. Deficiencies have been found in a number of cognitive domains but especially those that are more likely to be those originating in the frontal lobe. Not all of these studies have found a link with prior episodes of severe hypoglycemia, although more recent investigations have shown links between hypoglycemia and cognitive impairment.”

“The pubertal growth spurt is induced by sex hormones in both boys and girls, leading to increased amplitude of growth hormone (GH) pulses, and a rise in circulating insulinlike growth factor-1 (IGF-1) (26). Both the sex hormones and GH contribute to insulin resistance (27) and worsening glycemic control (28) […] Insulin also plays an important anabolic role during puberty. Failure to adequately increase insulin doses during this period has adverse effects on diabetic control, leading to the impairment of growth and pubertal development […] The GH/IGF axis, which plays a central role in the growth acceleration of puberty, can be significantly disordered in the diabetic adolescent with poor diabetic control, contributing to both growth impairment and greater insulin resistance (30).” [Incidentally both my brothers are higher than I am, though I can’t be absolutely certain this has anything to do with my diabetes… – US] […]

“In a retrospective, longitudinal study of 118 adolescent 18-yr-olds with type 1 diabetes, studied at three-monthly intervals between 8 and 18 yr, we found a significant deterioration in metabolic control throughout the period of adolescence (52). […] Quality of life may also deteriorate during this time (53) […] Adolescents with diabetes, unlike younger children, were reported by their parents as having poorer emotional and behavioral outcomes and poorer self-esteem outcomes than the nondiabetic adolescents.”

“Few diabetic women lived to childbearing age before the advent of insulin in 1922. Until then, less than 100 pregnancies were reported in diabetic women and most likely these women had type 2 and not type 1 diabetes. Even with this assumption, these cases of diabetes and pregnancy were associated with a greater than 90% infant mortality rate and a 30% maternal mortality rate (1,2). As late as 1980, physicians were still counseling diabetic women to avoid pregnancy (3). […] There is an increased prevalence of congenital anomalies and spontaneous abortions in diabetic women who are in poor glycemic control during the period of fetal organogenesis, which is nearly complete by 7 wk postconception. A woman may not even know she is pregnant at this time. It is for this reason that prepregnancy counseling and planning is essential in diabetic women of childbearing age. Because organogenesis is complete so early on, if a woman presents to her health care team and announces that she has missed her period by only a few days, there is still a chance to prevent cardiac anomalies by swiftly normalizing the glucose levels. However, potential neural tube defects are probably already established by the time the menstrual period is missed. […] HbA1c values early in pregnancy are correlated with the rates of spontaneous abortion and major congenital malformations […] normalizing blood glucose concentrations before and early in pregnancy can reduce the risks of spontaneous abortion and congenital malformations nearly to that of the general population (6–12).”

The life expectancy for patients with diabetic end-stage renal failure is only 3 or 4 yr.” [I was wondering if perhaps this statement was based on old data (you never know), so I had a look around. It doesn’t seem to be – this is really how ‘well’ people do today. See e.g. the figure on page 6 of this study published earlier this year – half of the diabetics with end-stage renal failure were dead after 3 years, and only about a third survived 5 years. Yes, sometimes people get lucky – they ‘get a transplant and live for decades’. But most diabetics don’t; they just die, quite fast.]

“Although all cells in a person with diabetes are exposed to elevated levels of plasma glucose, hyperglycemic damage is limited to those cell types, such as endothelial cells, that develop intracellular hyperglycemia. Endothelial cells develop intracellular hyperglycemia because, unlike most other cells, they are unable to downregulate glucose transport when exposed to extracellular hyperglycemia […] vascular smooth muscle cells, which are not damaged by hyperglycemia, show an inverse relationship between extracellular glucose concentration and subsequent rate of glucose transport […] In contrast, vascular endothelial cells show no significant change in subsequent rate of glucose transport after exposure to elevated glucose concentrations”

Diabetic ketoacidosis (DKA) is a potentially life-threatening medical emergency that reflects a state of metabolic decompensation in patients with insulin-dependent diabetes mellitus (IDDM) […] At least 25% of patients with new-onset diabetes mellitus type 1, especially children, will present in ketoacidosis (1–6). […] The cardinal hormonal alteration that triggers the metabolic decompensation of DKA is insulin deficiency accompanied by an excess of glucagon and the stress hormones epinephrine, norepinephrine, cortisol, and growth hormone (2,3,6). Insulin stimulates anabolic processes in liver, muscle, and adipose tissues and thereby permits glucose utilization and storage of the energy as glycogen, protein, and fat […] Concurrent with these anabolic actions, insulin inhibits catabolic processes such as glycogenolysis, gluconeogenesis, proteolysis, lipolysis, and ketogenesis. Insulin deficiency curtails glucose utilization by insulin-sensitive tissues, disinhibits lipolysis in adipose tissue, and enhances protein breakdown in muscle. Glucagon acting unopposed by insulin causes increased glycogenolysis, gluconeogenesis, and ketogenesis. Although insulin and glucagon may be considered as the primary hormones responsible for the development of DKA, increased levels of the stress hormones epinephrine, norepinephrine, cortisol, and growth hormone play critical auxiliary roles. Epinephrine and norepinephrine activate glycogenolysis, gluconeogenesis, and lipolysis and inhibit insulin release by the pancreas. Cortisol elevates blood glucose concentration by decreasing glucose utilization in muscle and by stimulating gluconeogenesis. Growth hormone increases lipolysis and impairs insulin’s action on muscle. The catabolic and metabolic effects of each of these counterregulatory hormones are accentuated during insulin deficiency […] the effects are synergistic and not merely additive. Even in normal persons, high concentrations of these counterregulatory hormones can induce hyperglycemia and ketonemia” (see also this and this – US)

“The classical patient with DKA is characterized by dehydration, acidosis with hyperventilation, with varying degrees of cerebral obtundation, and peripheral circulatory compromise […] the most common precipitating factors following initial presentation are omission of insulin, infection, and, in adults, typical or atypical myocardial infarction (1,7). […] In children, the major complication of concern during treatment for DKA is cerebral edema and related intracerebral complications […] [children are] at a disproportionately higher risk for developing clinical cerebral edema as compared to adults with DKA. Clinically relevant cerebral edema is estimated to occur in 0.7–1.0% of episodes of diabetic ketoacidosis in children (26–28). […] Once clinically obvious, cerebral edema is associated with a mortality of about 70% and only 7–14% of these patients escape permanent impairment of neurological function (31).”

December 12, 2013 Posted by | books, diabetes, medicine | Leave a comment

Type 1 Diabetes – Etiology and treatment

Although it’s not like I haven’t read some stuff about my disease over the years, the amount of textbook reading on the topic I’ve done has so far been limited to just a couple of books (and none of these have really been ‘textbooks on type 1 diabetes’); most of the stuff I know I’ve learned from the scientific literature, e.g. Diabetes Care articles, Cochrane reviews and similar, and in general the books which have dealt with diabetes which I’ve read have not been all that concerned about the various distinctions one might choose to make between the somewhat heterogenous disorders all going under the common name of ‘diabetes’. In ‘random books’ I think it’s fair to say that ‘diabetes’ usually is best translated ‘type 2 diabetes’, and the specific aspects of that disease most interesting to many book authors on health and related stuff are precisely the aspects which are completely irrelevant to type 1’s (e.g. lifestyle stuff related to prevention and disease progression in type 2’s).

So I decided to read this book to get a more solid background. Which kind of book is it? Here’s a quote from the introduction:

“The aim of Type 1 Diabetes: Etiology and Treatment is to fuse […] contemporary investigational and practical issues and make them available to those involved in the research and practice of type 1 diabetes. This volume is not intended to be a comprehensive or exhaustive treatise on the subject of diabetes. As in many such endeavors, the pace of discovery often exceeds the ability to incorporate the latest knowledge into printed text. Nevertheless, we believe that this volume presents contemporary information on contemporary issues by recognized authorities in the field. We hope it stimulates thought and action in the research and care of patients with type 1 diabetes mellitus.”

In case you were wondering, “make them available to those involved in the research and practice of type 1 diabetes” = this is not a book for patients and it’s not an undergraduate textbook; it’s mainly a book for PhD students and endocrinologists. I’d say that even if you skip the introduction you probably don’t need to read 10 pages to realize that. This is the kind of book where I’ll read all the words and then see how much of it I actually understand, occasionally looking up stuff which I’m particularly interested in; but I’ll not put in the work to actually understand all the details of what’s covered in all the chapters of this book. I don’t care enough about stuff like this to be willing to spend the time and effort it takes to understand all the details. I’ve tried to be very careful about getting at least some ‘take-away’ message out of all chapters covered so that e.g. even though I’ll not understand all the various processes which get you to the finish line, at least I know what’s at the starting line and where you end up on the other side. You may think that I’m lazy and that I’m just (mentally) skipping the hard stuff, but although this is certainly true to some extent I should add that I consider it justified to say that even though I’m mentally skipping a few steps occasionally while reading this book I’m still engaged in ‘learning in depth’ – most of the stuff covered in this book is knowledge at a level way beyond what the average patient knows about genetics, immunology, metabolic pathways etc. I actually feel reasonably sure at this point that I’d not have continued reading past the first chapter of this book if I had not read McPhee et al. first (I haven’t read that entire book yet, but I’ve read a lot of stuff relevant to the coverage here).

It would be wrong of me to only talk about the downsides to the coverage in this post, i.e. that it’s a hard book for most people to read; the flip side of course is that there are a lot of interesting details here. The book is full of stuff I didn’t know I didn’t know. Fortunately enough for my coverage of the book here, despite the fact that the book in general is somewhat inaccessible not all chapters are equally ‘bad’, and so there is also occasionally some stuff in there which I believe to be reasonably accessible even to people who don’t know a whole lot about type 1 diabetes (though I may be making assumptions about people’s background knowledge here which are not warranted). Anyway I’ve tried to pick out some of those passages in my coverage below, and on the other hand I’ve tried very hard to stay clear of stuff most readers could not possibly be expected to understand. Do ask questions if some of the stuff is unclear to you. I’ve read roughly the first 180 pages. Note that not all the stuff below is from the book; I decided to add some comments of my own towards the end of the post. I decided to bold some of the stuff below so that even people who only skim the post may get something out of it.

“By 1990, two international groups [the EURODIAB Project and the DiaMond Project] working on the epidemiology of type 1 diabetes had been developed. […] Because of these two important projects, the descriptive epidemiology of type 1 diabetes has been mapped for most of the world, and we now know more about the international variation in the incidence of type 1 diabetes than practically any other chronic disease. Within a short 15-yr time period, the epidemiology of type 1 diabetes rose from a “black hole” of ignorance to one of the best characterized chronic diseases worldwide” […]

The variation in the incidence of type 1 diabetes worldwide is greater than that observed for any other chronic disease in children. […] the global variation in risk is enormous. A child in Helsinki, Finland is almost 400 times more likely to develop diabetes than a child in Sichuan, China (8). To put this in perspective, consider the following example. If children in the United States had the same risk of developing type 1 diabetes as children in China, then instead of 13,000 newly diagnosed children each year, there would be only 56. In other words, over 99% of the annual new cases of type 1 diabetes in the United States would be avoided. […] Interestingly, the other epidemiologic features of type 1 diabetes are remarkably similar across populations, despite the enormous variation in disease risk (9). Incidence rates among males and females do not differ significantly, and the peak age at onset for both sexes occurs near the time of puberty. Thus, compared to all other risk factors, including human leukocyte antigen (HLA) haplotypes, viral infections, or the presence of autoantibodies, the place where a child lives is the most potent determinant of type 1 diabetes risk, excluding genetic/racial differences. If we knew what was causing the geographic patterns of type 1 diabetes, we would be well on our way to preventing the disease.” […]

“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. […] the incidence of type 1 diabetes is rising [and] these findings indicate that something in our environment is changing to trigger a disease response. […] The data […] clearly indicate that environmental factors are involved in the etiology of type 1 diabetes. 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 is also, in large part, genetically determined” [here’s a relevant link, I won’t go into the details here although they spend a lot of pages talking about that stuff in the book]

“Evidence that type 1 diabetes is an autoimmune disorder is based on the presence of lymphocytic infiltrates of the pancreas at the onset of the diseases (37), as well as the occurrence of autoantibodies to islet cell antigens (ICAs), tyrosine phosphatase IA-2 (IA-2), glutamic acid decarboxylase (GAD), and insulin autoantibodies (IAA) (38,39). The presence of these autoantibodies indicates that tissue damage has likely been initiated by other etiologic agents. Thus, they represent important preclinical markers rather than risk factors for the disease. […] most type 1 diabetes cases have β-cell autoantibodies at disease onset, [however] not all autoantibody positive individuals develop the disease. […]  first-degree relatives who are positive for multiple autoantibodies appear to be at very high risk for developing type 1 diabetes. […] about 90% of individuals who develop type 1 diabetes have a negative family history of the disease.

“The autoimmune response in type 1 diabetes is […] similar to most other organ-specific autoimmune disorders in that both T-cells and autoantibody-producing B-cells are involved in the immune abnormalities associated with, as well as predicting, the disease (24). The molecular biology of β-cell destruction is therefore both diverse and complicated and the detailed mechanisms are yet poorly understood. […] At the time of clinical diagnosis of type 1 diabetes, about 80% of the β-cells have been specifically destroyed.”

“We currently know that for individuals with two HLA-DQ susceptibility haplotypes, the cumulative risk of type 1 diabetes in the general Caucasian population is approximately 5% (25). However, it may range from 0.1% to >90%, depending on one’s risk factor profile, which includes age, ethnic, familial, genetic, environmental, and autoimmune determinants.” […] Diabetogenic alleles are not fully penetrant” […] There is no simple “rule” for diabetes risk […] the position of provisional loci found in T1DM colocalize or overlap with loci found in different autoimmune/inflammatory diseases […] This is consistent with the hypothesis that, like the MHC, some of these provisional loci may involve common susceptibility genes or biochemical pathways that are central to normal immune function.”

“At present, the prediction of type 1 diabetes is not a major clinical issue outside of trials for diabetes prevention. Patients, especially children, usually present acutely with diabetes with a dramatic history of polyuria, polydipsia, and weight loss. Despite what in retrospect is almost always a clear-cut clinical history of diabetes, a significant number of children have a delay in diagnosis, which increases the risk of severe metabolic decompensation with diabetic ketoacidosis (DKA), cerebral edema, and death. […] Overall in the United States, DKA occurs in 25–50% of children with new-onset diabetes, and symptomatic cerebral edema occurs in approx 1% of DKA episodes. Of those patients with clinically apparent cerebral edema, between 40% and 90% die (1). […] In the United States […] it is rare to find individuals presenting with diabetes with normal HbA1c [an indicator of average blood glucose over the last 3 months or so – US] and it is likely that the great majority have had hyperglycemia for months prior to diagnosis.”

Diabetes mellitus is classified based on clinical criteria into type 1 and type 2 diabetes (98). Recently, a growing number of monogenic diabetes disorders have been identified (98). Type 1 diabetes develops acutely. Ketoacidosis and coma develop unless insulin is administered. Type 2 diabetes develops mostly as a result of insulin resistance associated with obesity and β-cell dysfunction and occurs insidiously, and most patients are successfully controlled by diet, exercise, or oral hypoglycemic agents. […] the overall autoantibody frequency in type 2 patients varies between 6% and 10% (105). However, the positive predictive value that a GAD65Ab positive type 2 diabetes patient [that is, a type 2 diabetic with a specific genotype] will be treated with insulin within 5 yr is 100% […] Diabetes will appear as a function of loss of β-cell mass and loss of β-cell function. Different clinical phenotypes may develop, dependent on the combination of loss of β-cell mass and loss of function. […] A different severity of inflammation may lead to variable degree of β-cell inhibition and resulting hyperglycemia […] the degree of insulin resistance is also critical (99). Some subjects may encounter a severe loss of β-cells but, despite this, may not develop diabetes because of their high insulin sensitivity […] Other subjects may develop diabetes at modest β-cell loss because they are highly insulin resistant. Therefore, it is not surprising that type 1 diabetes or autoimmune diabetes is associated with a large number of different phenotypes […] To complicate the heterogeneity of autoimmune diabetes even further, it has also been found that patients with diabetes may develop GAD65 autoantibodies after the clinical diagnosis […] In contrast to […] patients masquerading as type 2 diabetic patients [because of slow onset of disease], an acute onset of type 1 diabetes is also reported (113). These patients have lower glycosylated hemoglobin values, diminished urinary excretion of C peptide, a more severe metabolic disorder with ketoacidosis, as well as higher serum pancreatic enzyme concentrations, compared to type 1 patients with a less dramatic onset […]

All vertebrates use insulin-producing pancreatic β-cells to achieve fuel homeostasis (1). These cells are able to measure the nutrient levels of the blood on a moment-to-moment basis and secrete insulin at rates that are exactly appropriate for the maintenance of optimal fuel levels. Therefore, the levels of circulating nutrients such as glucose, fatty acids, and amino acids are precisely controlled in mammals during fasting and feeding alike. The role of the pancreatic β-cells in fuel homeostasis is thus analogous to that of the thermostat in heating and cooling systems (2,3).” [This sounds simple enough. However it gets ‘not simple’ very fast.]

Hypoglycemia is the limiting factor in the glycemic management of diabetes because it generally precludes maintenance of euglycemia [normal blood glucose levels, US]. […] Were it not for the potentially devastating effects of hypoglycemia, particularly on the brain, glycemic control would be rather easy to achieve. Administration of enough insulin (or any effective medication) to lower plasma glucose concentrations to or below the nondiabetic range would eliminate the symptoms of hyperglycemia, prevent diabetic ketoacidosis and the nonketotic hyperosmolar syndrome, almost assuredly prevent retinopathy, nephropathy, and neuropathy, and likely reduce atherosclerotic risk. However, the devastating effects of hypoglycemia are real and the glycemic management of diabetes is therefore complex.” [much of chapter 7, from which the above and the following quotes originate, covers stuff I’ve covered before e.g. in this post, but there was some new stuff in that chapter as well and I actually think of this as the best of the chapters I’ve read so far]

Iatrogenic hypoglycemia is the result of the interplay of therapeutic insulin excess and compromised physiological and behavioral defenses against falling plasma glucose concentrations in T1DM […] Glucose is an obligate metabolic fuel for the brain under physiological conditions (4). (The brain can utilize other circulating substrates, including ketones such as β- hydroxybutyrate, but the blood levels of these seldom rise high enough for them to enter the brain in quantity and thus partially replace glucose, except during prolonged fasting.) Because of its unique dependence on glucose oxidation as an energy source and because it cannot synthesize glucose or store more than a few minute’s supply as glycogen, the brain requires a continuous supply of glucose from the circulation. At normal plasma glucose concentrations the rate of glucose transporter (GLUT-1) mediated blood-to-brain glucose transport down a concentration gradient exceeds that of brain glucose metabolism. However, when arterial glucose concentrations fall below the physiological range blood-to-brain glucose transport falls and ultimately becomes limiting to brain glucose metabolism and thus its functions and even its survival. Given the immediate survival value of maintenance of the plasma glucose concentration, it is not surprising that physiological mechanisms that very effectively prevent or rapidly correct hypoglycemia have evolved.”

I was considering covering these mechanisms in detail as well, but I reconsidered and decided to cut it short. However a few remarks should be included on this topic. One key point here is that one of the important reasons why diabetics are prone to hypoglycemia is that most of the normal physiological defence mechanisms against hypoglycemia are basically destroyed in diabetics. The first step in the body’s correction of low blood glucose is reduction of insulin production. This takes place way before symptoms ever occur in normal people. Type 1 diabetics who’ve taken insulin for a while don’t produce insulin on their own, so their body can’t regulate/lower insulin production – it’s already at zero. So step one in the process is deactivated. The second step in the natural process to reverse hypoglycemia involves increased glucagon secretion; glucagon is a hormone which tells the liver to convert its stores of glucogen (a type of sugar) into glucose and release them into the bloodstream. The authors note that although glucagon responses to other stimuli remain mostly intact in diabetics, the response to hypoglycemia is destroyed, for reasons not well known. So the first two defence mechanisms against hypoglycemia are completely out of the window in diabetics. The main one left is increased epinephrine secretion. Normally this one only sets in after the first two other responses have failed, but a very important point is that the set point for when this mechanism sets in depends on how often the diabetic is hypoglycemic; if hypoglycemia is common, the body will start tolerating lower blood glucose levels without initiating the remaining counterregulatory mechanism (there are a few other mechanisms at play, but they basically only apply to long-term hypoglycemia and will not play any significant role in a diabetic with acute hypoglycemia). The epinephrine response will still be initiated eventually, but the blood glucose level needed to initiate the process will be downregulated over time if hypoglycemic episodes occur often, which is problematic for reasons explained below. An important observation from the book regarding this counterregulatory mechanism:

“The development of an attenuated epinephrine response to falling glucose levels — loss of the third defense against hypoglycemia — is a critical pathophysiological event. Patients with T1DM who have combined deficiencies of their glucagon and epinephrine responses have been shown in prospective studies to suffer severe hypoglycemia at rates 25-fold (45) or more (46) higher than those with absent glucagon but intact epinephrine responses during aggressive glycemic therapy.”

The main reason things tend to go bad in these cases is presumably that if the epinephrine response is lost, the first manifestation of hypoglycemia is neuroglycopenia; the diabetic learns that she has a low blood glucose only when her brain stops working properly. This makes engaging in the correct behavioural responses (ingestion of glucose) problematic.

The concepts of hypoglycemia unawareness and what’s termed hypoglycemia-associated autonomic failure are closely related and important concepts to be familiar with:

“The concept of hypoglycemia-associated autonomic failure in T1DM […] posits that (1) periods of relative or absolute therapeutic insulin excess in the setting of absent glucagon responses lead to episodes of hypoglycemia, (2) these episodes, in turn, cause reduced autonomic (including adrenomedullary epinephrine) responses to falling glucose concentrations on subsequent occasions, and (3) these reduced autonomic responses result in both reduced symptoms of, and therefore the behavioral response to, developing hypoglycemia (i.e., hypoglycemia unawareness) and — because epinephrine responses are reduced in the setting of absent glucagon responses — impaired physiological defenses against developing hypoglycemia (i.e., defective glucose counterregulation). Thus, a vicious cycle of recurrent hypoglycemia is created and perpetuated.”

A few more concluding remarks from the chapter:

“hypoglycemia risk reduction requires consideration of both the conventional risk factors that lead to episodes of absolute or relative insulin excess — insulin (or other drug) dose, timing, and type, patterns of food ingestion and of exercise, interactions with alcohol or other drugs, and altered sensitivity to or clearance of insulin — and the risk factors for compromised glucose counterregulation that impair physiological and behavioral defenses against developing hypoglycemia […] The underlying principle is that iatrogenic hypoglycemia is the result of the interplay of insulin excess and compromised glucose counterregulation rather than insulin excess alone.”

I was well aware that diabetics can’t regulate insulin production (of course) and that this is a problem in terms of counter-regulation which makes hypoglycemia more likely, but I had no idea that ‘normal people’ had other natural counter-regulatory mechanisms which are also impacted by diabetes (to be clear, I was familiar with the concept of hypoglycemia unawareness but I’d never read about it in detail and the glycagon-response deactivation in diabetics I was not aware of. I knew that injections of glucagon is a treatment option in case of severe hypoglycemia – I’ve had such injections a few times, though fortunately not within the last decade – but I didn’t know that ‘normal people’ naturally secrete this stuff on their own if/when their blood glucose drops). In case you were wondering how to break the cycle:

“In a patient with hypoglycemia unawareness, a 2- to 3-wk period of scrupulous avoidance of iatrogenic hypoglycemia is advisable”.

Basically the idea is to avoid hypoglycemias for a while in order to change the threshold where the epinephrine response kicks in. Of course one shouldn’t change it too much in the other direction; poorly regulated diabetics tend to have thresholds higher than normal, so that they get symptoms of hypoglycemia even when their blood glucose is within the normal range. Something like that of course makes it harder for those individuals to achieve the therapeutic goals of reasonably low Hba-1c’s. I was wondering if I should mention this or not because it might get confusing but I decided to anyway; it should be noted that hypoglycemia-associated autonomic failure is a different form of nervous system dysregulation in diabetics than the one that takes place in long-term diabetics who develop diabetic autonomic neuropathy (DAN). Hypoglycemia-associated autonomic failure is reversible, whereas DAN most of the time isn’t, and DAN may have a lot of unpleasant effects aside from ‘just’ hypoglycemia unawareness – while covering the relevant chapter in McPhee not too long ago I noted that DAN may affect the enteric nervous system and cause problems with peristalsis, but this is but one of many problems caused by autonomic dysregulation; see the link above for more on this stuff. It should be noted that the authors in McPhee do not seem to be aware of the fact (at least they do not make it clear…) that not all hypoglycemia-unawareness in diabetics is related to DAN. On a different if related note it might be added that the autonomous nervous system is not the only part of the nervous system that is potentially affected by diabetes in the long run; for example sensorimotor polyneuropathy affecting the extremities is a far from uncommon complication.

December 3, 2013 Posted by | books, diabetes, medicine | Leave a comment

Pathophysiology of disease (IV)

“Stimulation of α1-adrenergic receptors results in an increase in intracellular calcium concentrations. Several mechanisms are involved. First, there is activation of phospholipase C by the guanine nucleotide binding (G) protein, [G8]. Phospholipase C hydrolyzes the membrane-bound phospholipid, phosphatidylinositol-4,5-biphosphate, to generate two second messengers: diacylglycerol and inositol 1,4,5-trisphosphate. Diaglycerol in turn activates protein kinase C, which phosphorylates various cellular substrates, and inositol-1,4,5-trisphosphate stimulates release of intracellular calcium, which then initiates various cellular responses. Activation of α2-adrenergic receptors results in a decrease in intracellular cAMP. The mechanism involves receptor interaction with an inhibitory G protein, Gi , leading to inhibition of adenylyl cyclase. The fall in cAMP level leads to a decrease in activity of the cAMP-dependent protein kinase A […]

Hypercalcemia may occur, related to excessive production of PTHrP in cases of malignancy or PTH itself in cases associated with hyperparathyroidism. Occasionally, ectopic production of ACTH by the pheochromocytoma may lead to a severe hypokalemic metabolic acidosis.”

The quote above is from chapter 12 of the book, which is about Disorders of the Adrenal Medulla. I was reminded of this. As I pointed out in my latest post about the book: “It’s a great book – it’s well written, I’m learning a lot. But on the flip-side the book is, and was, hard work to read”. I’m now getting to stuff which I don’t know much about, and there are places now where I’m basically completely lost; not many, but they’re there. I added some remarks about chapters 7-9 in the last post and back then I considered covering chapter 10 later; I’ve now finished chapter 11 and also read chapters 12-14. These chapters cover heart disease, vascular disease, disorders of the adrenal medulla (as mentioned), gastrointestinal disease and liver disease. I think I’ve talked a lot about cardiovascular disorders here on the blog before – at least I’ve read a lot – and I’ve seen quite a few lectures about the topic before, so I won’t talk much about that stuff here. There was some new and interesting stuff in those chapters, but I’d need to cover a lot of ground before I could get to that stuff if I wanted readers unfamiliar with this topic to also get something out of my coverage, and I can’t justify putting in that much work given how many people are likely to even read this post. I should perhaps note before moving on that the reason why I’m lost is because I’m lazy, not because the book is badly written. I’m sure I could look up all the relevant terms and mechanisms and make sense of most of this stuff; I’m just not going to do it because it would be too much work. Less will have to do.

Anyway, chapter 12 is a short yet interesting chapter which covers a topic I’d sort of been annoyed about not understanding very well in some of the previous chapters of the book. Not all of it is equally easy to understand (…), but I now believe that I understand the role catecholamines (norepinephrine, epinephrine, dopamine) play better than I did before. The pathophysiology part of that chapter only deals with pheochromocytoma in any amount of detail. Go have a look at those two links (and perhaps some of the links in those articles) if you want to know what kind of stuff is covered in this chapter. I should point out that even if few details are added about other stuff besides pheochromocytoma in that latter part of the chapter, one of the unrelated observations actually relates to a mechanism with which I was familiar, but had never really understood in detail (..I still don’t, really, but that’s not the point – see below..). Here’s the relevant quote:

Sympathetic neural failure, caused by deficient production of norepinephrine, presents clinically as orthostatic (postural) hypotension. Adrenomedullary failure, caused by deficient production of epinephrine, usually causes little or no disability. However, in some patients with insulin-dependent diabetes mellitus, adrenomedullary failure is associated with glucagon deficiency […] This combined deficiency was originally described in patients with advanced diabetic autonomic neuropathy. More recently, the combined deficiency has been found in a subset of patients who fail to develop hypoglycemia-related symptoms. In such patients, the central nervous system fails to recognize hypoglycemia and to mount defenses against it. The failure of the central nervous system to recognize hypoglycemia results both from strict antecedent glucose control and from one or more recent hypoglycemic episodes (hypoglycemia-associated autonomic failure). Obviously, this failure predisposes to recurrent episodes of hypoglycemia in a vicious circle.”

Now, a diabetic gets sick because of problems with the beta-cells in the pancreas. Yet here’s a chapter about how the adrenal medulla works, and diabetes just sort of pops up. It also pops up in the chapter after this, the one about gastrointestinal disease, where diabetic autonomic neuropathy can cause various problems e.g. related to peristalsis. Naturally there are a lot of other places it pops up, the main point is just that biological systems are actually really complicated and subsystems you might have assumed – for one reason or another – to be relatively independent of each other may sometimes surprise you and turn out to be in fact quite closely interconnected. I’m having quite a few realizations of this kind along the way while reading this book (most of them incidentally not in any way related to diabetes).

Chapter 13 deals with gastrointestinal disease. It starts out by giving a brief ‘big-picture’ overview of what the gastrointestinal system looks like, which functions it has to perform (motility, secretion, digestion, absorption), how it’s controlled. After that it goes into more details about each subsystem (oropharynx and esophagus, stomach, gallbladder, small intestine, colon), and covers briefly an overview of how things might go wrong on a functional level (disorders of motility, disorders of secretion, disorders of digestion and absorption). After this, it covers some specific disorders in more detail in the pathophysiology part of the chapter; these disorders include esophageal achalasia, reflex esophagitis (heartburn), various forms of acid-peptic disease (e.g. peptic ulcer), gastroparesis (here diabetes as mentioned pops up again!), gallstones, Crohn’s disease and ulcerative colitis, and diverticulosis.

A few remarks related to that chapter. The first remark relates to neural control of this biological system. I’ll quote from the book:

“Gastrointestinal tract functions are controlled by both the central nervous system, working through autonomic components of the peripheral nervous system, and by the enteric nervous system. The size and complexity of the enteric nervous system is remarkable: It contains more neurons than the spinal cord and receives sensory input from neurons specialized to detect chemical, osmotic, thermal and other changes in the lumen, or mechanical activity involving the gut wall. This information is integrated with and modified by input from the central nervous system via the sympathetic and parasympathetic neurons, which synapse with intramural neurons and provide the program for motor neurons. In this way, otherwise random and uncontrolled phasic motor and secretory activity of the gut becomes purposeful and coordinated, as manifested by characteristic gut programs such as peristalsis and sphincter control.”

As the wikipedia article also points out: “More than 90% of the body’s serotonin lies in the gut, as well as about 50% of the body’s dopamine”. Most people probably don’t think much about how their guts work when they think about how their neural wiring is set up, but this stuff’s a really important part of the big picture.

The second thing I’ll comment on from this chapter is probably a somewhat more familiar topic; I assume that today most people have heard about Helicobacter pylori (-Hp) and know that it plays a role in peptic-ulcer disease. An interesting thing that the book points out which I did not know is that roughly half the world’s population is infected with Hp, with higher rates in poor countries. “As many as 90% of infected individuals show signs of inflammation (gastritis or duodenitis) on endoscopy, though typically many of these individuals are clinically asymptomatic.” Given that far less than half the world’s population actually develop ulcers, there’s a lot of other stuff besides Hp playing a role in disease progression; “only about 15% of infected individuals ever develop a clinically significant ulcer.” However Hp is still an important pathogen with clinical relevance, because “of patients who do develop acid-peptic disease, almost all have H pylori infection. Furthermore, treatment that does not eradicate H pylori is associated with rapid recurrence of acid-peptic disease in most patients.”

Chapter 14 is about liver disease. I won’t have time to write about that stuff today, but I figured I really ought to post something today (sorry for the infrequent updates!) and this was as far as I got – posts like this one take quite a bit of time to write. I’ll probably get back to that chapter later, it’s quite interesting (for instance, did you know that the liver receives roughly 25% of the total cardiac output, corresponding to roughly 1,5 litres of blood each minute?)…

November 4, 2013 Posted by | books, diabetes, medicine | Leave a comment

A few papers

i. The Living Dead: Bacterial Community Structure of a Cadaver at the Onset and End of the Bloat Stage of Decomposition. There are a lot of questions one might ask about how the world works. Incidentally I should note that when I die I really wouldn’t mind contributing to a study like this. Here’s the abstract, with a couple of links added to ease understanding:

“Human decomposition is a mosaic system with an intimate association between biotic and abiotic factors. Despite the integral role of bacteria in the decomposition process, few studies have catalogued bacterial biodiversity for terrestrial scenarios. To explore the microbiome of decomposition, two cadavers were placed at the Southeast Texas Applied Forensic Science facility and allowed to decompose under natural conditions. The bloat stage of decomposition, a stage easily identified in taphonomy and readily attributed to microbial physiology, was targeted. Each cadaver was sampled at two time points, at the onset and end of the bloat stage, from various body sites including internal locations. Bacterial samples were analyzed by pyrosequencing of the 16S rRNA gene. Our data show a shift from aerobic bacteria to anaerobic bacteria in all body sites sampled and demonstrate variation in community structure between bodies, between sample sites within a body, and between initial and end points of the bloat stage within a sample site. These data are best not viewed as points of comparison but rather additive data sets. While some species recovered are the same as those observed in culture-based studies, many are novel. Our results are preliminary and add to a larger emerging data set; a more comprehensive study is needed to further dissect the role of bacteria in human decomposition.”

The introduction contains a good description of how decomposition in humans proceed:

“A cadaver is far from dead when viewed as an ecosystem for a suite of bacteria, insects, and fungi, many of which are obligate and documented only in such a context. Decomposition is a mosaic system with an intimate association between biotic factors (i.e., the individuality of the cadaver, intrinsic and extrinsic bacteria and other microbes, and insects) and abiotic factors (i.e., weather, climate, and humidity) and therefore a function of a specific ecological scenario. Slight alteration of the ecosystem, such as exclusion of insects or burial, may lead to a unique trajectory for decomposition and potentially anomalous results; therefore, it is critical to forensics that the interplay of these factors be understood. Bacteria are often credited as a major driving force for the process of decomposition but few studies cataloging the microbiome of decomposition have been published […]

A body passes through several stages as decomposition progresses driven by dehydration and discernible by characteristic gross taphonomic changes. The early stages of decomposition are wet and marked by discoloration of the flesh and the onset and cessation of bacterially-induced bloat. During early decay, intrinsic bacteria begin to digest the intestines from the inside out, eventually digesting away the surrounding tissues [3]. Enzymes from within the dead cells of the cadaver also begin to break down tissues (autolysis). During putrefaction, bacteria undergo anaerobic respiration and produce gases as by-products such as hydrogen sulfide, methane, cadaverine, and putrescine [5]. The buildup of resulting gas creates pressure, inflating the cadaver, and eventually forcing fluids out [3]. This purging event marks the shift from early decomposition to late decomposition and may not be uniform; the head may purge before the trunk, for example. Purge may also last for some period of time in some parts of the body even as other parts of the body enter the most advanced stages of decomposition. In the trunk, purge is associated with an opening of the abdominal cavity to the environment [3]. At this point, the rate of decay is reported by several authors to greatly increase as larval flies remove large portions of tissues; however, mummification may also occur, thus serving to preserve tissues [6][9]. The final stages of decomposition last through to skeletonization and are the driest stages [7], [10][13].”

It’s really quite an interesting paper, but you probably don’t want to read this while you’re having dinner. A few other interesting observations and conclusions:

“Many factors can influence the bacteria detected in and on a cadaver, including the individual’s “starting” microbiome, differences in the decomposition environments of the two cadavers, and differences in the sites sampled at end-bloat. The integrity of organs at end-bloat varied between cadavers (as decomposition varied between cadavers) and did not allow for consistent sampling of sites across cadavers. Specifically, STAFS 2011-016 no longer had a sigmoidal colon at the end-bloat sample time.” […]

“With the exception of the fecal sample from STAFS 2011-006, which was the least rich sample in the study with only 26 unique OTUs [operational taxonomic units – US] detected, fecal samples were the richest of all body sites sampled, with an average of nearly 400 OTUs detected. The stomach sample was the second least rich sample, with small intestine and mouth samples slightly richer. The body cavity, transverse colon, and sigmoidal colon samples were much richer. Overall, these data show that as one moves from the upper gastrointestinal tract (mouth, stomach, and small intestine) to the lower gastrointestinal tract (colon and rectal/fecal), microbiome richness increases.” […]

“It is important to note that while difference in abundance seen in particular species between this study and the others noted above could be due to the discussed constraints of culturing bacteria, differences could also be due to a variety of factors such as individual variability between the cadaver microbiomes, seasonality, climate, and species of colonizing insects. Finally, abundance does not necessarily indicate metabolic significance for decomposition, a point of importance that our study cannot address.” […]

“Our data represent initial insights into the bacteria populating decomposing human cadavers and an early start to discovering successive changes through time. While our data support the findings of previous culture studies, they also demonstrate that bacteria not detected by culture-based methods comprise a large portion of the community. No definitive conclusion regarding a shift in community structure through time can be made with this data set.”

ii. Protein restriction for diabetic renal disease.

Background

Diabetic renal disease (diabetic nephropathy) is a leading cause of end-stage renal failure. Once the process has started, it cannot be reversed by glycaemic control, but progression might be slowed by control of blood pressure and protein restriction.

Objectives

To assess the effects of dietary protein restriction on the pro gression of diabetic nephropathy in patients with diabetes .

Search methods

We searched The Cochrane Library , MEDLINE, EMBASE, ISI Proceedings, Science Citation Index Expanded and bibliographies of included studies.

Selection criteria

Randomised controlled trials (RCTs) and before and after studies of the effects of a modified or restricted protein diet on diabetic renal function in people with type 1 or type 2 diabetes following diet for at least four months were considered.

Data collection and analysis

Two reviewers performed data extraction and evaluation of quality independently. Pooling of results was done by means of random- effects model.

Main results

Twelve studies were included, nine RCTs and three before and after studies. Only one study explored all-cause mortality and end-stage renal disease (ESRD) as endpoints. The relative risk (RR) of ESRD or death was 0.23 (95% confidence interval (CI) 0.07 to 0.72) for patients assigned to a low protein diet (LPD). Pooling of the seven RCTs in patients with type 1 diabetes resulted in a non-significant reduction in the decline of glomerular filtration rate (GFR) of 0.1 ml/min/month (95% CI -0.1 to 0.3) in the LPD group. For type 2 diabetes, one trial showed a small insignificant improvement in the rate of decline of GFR in the protein-restricted group and a second found a similar decline in both the intervention and control groups. Actual protein intake in the intervention groups ranged from 0.7 to 1.1 g/kg/day. One study noted malnutrition in the LPD group. We found no data on the effects of LPDs on health-related quality of life and costs.

Authors’ conclusions

The results show that reducing protein intake appears to slightly slow progression to renal failure but not statistically significantly so. However, questions concerning the level of protein intake and compliance remain. Further longer-term research on large representative groups of patients with both type 1 and type 2 diabetes mellitus is necessary.”

The paper has a lot more. Do note that due to the link between kidney disease and dietary protein intake, at least one diabetic I know has actually considered the question of whether to adjust protein intake at an even earlier point in the disease process than the one comtemplated in these studies, i.e. before the lab tests show that the kidneys have started to fail – this is hardly an outrageous idea given evidence in related fields. I do think however that the evidence is much too inconclusive in the case of diabetic nephropathy for anything like this to make much sense at this point. Lowering salt intake seems to be far more likely to have positive effects. I’d be curious to know if the (very tentative..) finding that the type of dietary protein (‘chicken and fish vs red meat’) may matter for outcomes, and not just the amount of protein, holds; this seems very unclear at this point, but it’s potentially important as it also relates to the compliance/adherence problem.


iii. Direct evidence of 1,900 years of indigenous silver production in the Lake Titicaca Basin of Southern Peru:

“Archaeological excavations at a U-shaped pyramid in the northern Lake Titicaca Basin of Peru have documented a continuous 5-m-deep stratigraphic sequence of metalworking remains. The sequence begins in the first millennium AD and ends in the Spanish Colonial period ca. AD 1600. The earliest dates associated with silver production are 1960 ± 40 BP (2-sigma cal. 40 BC to AD 120) and 1870 ± 40 BP (2-sigma cal. AD 60 to 240) representing the oldest known silver smelting in South America. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis of production debris indicate a complex, multistage, high temperature technology for producing silver throughout the archaeological sequence. These data hold significant theoretical implications including the following: (i) silver production occurred before the development of the first southern Andean state of Tiwanaku, (ii) the location and process of silverworking remained consistent for 1,500 years even though political control of the area cycled between expansionist states and smaller chiefly polities, and (iii) that U-shaped structures were the location of ceremonial, residential, and industrial activities.”

A little more from the paper:

“Our data establish an initial date for silverworking that is at least three centuries earlier than previous studies had indicated. […] Three independent lines of evidence establish the chronological integrity of the deposit: 1) a ceramic sequence in uninterrupted stratigraphic layers, 2) absolute radiocarbon dates, and 3) absolute ceramic thermoluminescence (TL) dates (1). […] the two absolute dating methods are internally consistent, and […] these match the relative sequence derived from analyzing the diagnostic pottery or ceramics. The unit excavated at Huajje represents a rare instance of an intact, well-demarcated stratigraphic deposit that allows us to precisely define the material changes through time in silver production. […] The steps required for silver extraction include mining, beneficiation (i.e., crushing of the ore and sorting of metal-bearing mineral), optional roasting to remove sulfur via oxidation, followed by smelting, and cupellation […] Archaeological or ethnographic evidence for most of these steps is extremely scarce, making this a very significant assemblage for our understanding of early silver production. A total of 3,457 (7,215.84 g) smelting-related artifacts were collected.”

November 1, 2013 Posted by | archaeology, biology, diabetes, medicine, papers | Leave a comment

Adipose tissue and cancer (3)

I’ve finished the book.

It’s a Springer book, so those of you who’ve encountered these books before will know that this is not an easy-to-read popular-science book. The general level is high and occasionally I felt almost completely lost; chapter 8 for example was very technical. As I’ve pointed out before, I don’t like to fault authors for not taking into account the possibility that their books may also be picked up by ignorant fools who don’t know anything, but if you have a hard time understanding what the author is getting at it will affect your reading experience in a negative manner. It should be noted, though, that although it’s not an easy book to read you’ll learn a lot of stuff if you put in some effort (…and/but if you don’t put in some effort you’ll never finish it, and you’ll get nothing out of it at all).

Some of the chapters deal with similar stuff, and I got the impression a couple of times that the authors of a specific chapter had not read the other chapters. On the other hand it’s very clear in other chapters/contexts that they most certainly did, but even so there are a few things which are repeated a few times along the way which perhaps did not need to be repeated. On the third hand the book is structured in such a way that each chapter is pretty much self-contained (which is presumably part of the explanation for the occasional repetitions), and the fact that you probably don’t necessarily need to read it cover to cover from chapter one to chapter 9 the way I did to get a lot out of the book would presumably be appealing to some people.

I gave it four stars on goodreads, because of the high quality of the material included.

Some stuff from the last chapters, with some hopefully helpful links added to make the passages easier to understand (perhaps needless to say no such links are included in the book, so if you find the links helpful you’ll probably need to look up some stuff along the way if you decide to read it yourself…) as well as some comments here and there:

“Emerging studies clearly indicate that a bidirectional crosstalk is established between all cellular components of AT [adipose tissue, US] and cancer cells and that the tumor-surrounding AT contributes to inflammation, extracellular matrix remodeling as well as energy supply within the tumors. In this chapter, we present evidences showing how AT locally affects tumor progression in given types of tumors and how these results might be attractive to explain the link between obesity and the poor prognosis of some cancers. This will be preceded by the overall description of AT composition and function with special emphasis on the specificity of adipose depots, key aspects that need to be taken in account when paracrine effects of AT on tumor progression is considered. […]

The past two decades have provided substantial evidence for the major role of the tissue local environment for tumor progression. Cancer is now considered as a tissue-based disease in which malignant cells interact dynamically with the surrounding supportive tissue, the tumor stroma, composed by multiple normal cell types such as fibroblasts, infiltrating immune cells, and endothelial cells within the context of extracellular matrix [1]. This stroma/tumor cell interaction involves constant bidirectional crosstalk between normal and malignant cells. Cancer cells usually generate a supportive microenvironment by activating the wound-healing response of the host [2]. Conversely, the stromal cells, such as for example, cancerassociated fibroblasts (CAFs) or tumor-associated macrophages (TAMs), promote tumor progression through different mechanisms including enhancement of tumor survival, growth, and spread, by secreting growth factors, chemokines, extracellular matrix (ECM) components, and ECM-modifying enzymes [3,4]. Constituents of the tumor microenvironment can arise from two major sources: recruitment from nearby local tissue or systemic recruitment from distant tissues via circulation. Among the different cell types frequently found at close proximity of evolving tumors, little attention has been given to cells that compose the adipose tissue (AT) although a growing interest can be noted in recent years. Throughout the body, AT is mainly described as subcutaneous (i.e., superficial and deep hypodermic location) and visceral depots. Visceral adipose tissue (VAT) surrounds the inner organs and can be divided into omental, mesenteric, retroperitoneal (surrounding the kidney), gonadal, perivascular, and pericardial depots [5]. Of note, AT is also present in the breast (mammary adipose tissue or MAT) and in the bone marrow (BM). All these specific regional depots exhibit differences in structure, function, composition, and secretion profiles [6]. […] The cellular heterogeneity of AT adds an additional degree of complexity when AT/cancer cells crosstalk is considered. […] All the cells from adipose tissue (including mature adipocytes) produces a large number of secretory bioactive substances, such as hormones, growth factors, chemokines, proangiogenic or proinflammatory molecules [8], which could directly affect adjacent tumors. AT is therefore an excellent candidate to influence tumor behavior through heterotypic paracrine signaling processes and might prove to be critical for tumor survival, growth, local, and distant invasion. […] Fat depots from different region of the body have different incidence in pathology because they display distinct functional and structural properties in terms of energy metabolism and bioactive molecule (adipokines) release as well. Regional heterogeneity plays a central role in mammalian AT homeostasis.” (I talked about these aspects in the last post, but I figured I should give at least part of the ‘medical textbook version’ here..) […]

“Ovarian cancer is a highly fatal disease, with only about 40 % of women with ovarian cancer still alive more than 5 years postdiagnosis. This poor survival is largely attributable to the fact that a majority of ovarian cancer in developed countries is diagnosed with metastatic spread. The omentum, a peritoneal organ rich in AT and immune cells, has been shown to be a preferred site of metastatic dissemination in ovarian cancer patients. Omental dissemination, which is often accompanied by ascites, facilitates the further spread of the tumors [66].” […]

“Prostate cancer is the most common malignancy in males in Western countries, representing the second leading cause of cancer death. Prostate is surrounded by AT and tumor admixed with periprostatic fat is the most easily recognized manifestation of extraprostatic extension, a well-established adverse prognostic factor for prostate cancer [79,80]. Periprostatic AT (PPAT) is considered as VAT, but the specificities of this depot in terms of metabolism and adipokines secretion remain largely unknown. At laboratory levels, the contribution of this tissue to cancer progression has been first suggested by the report of Finley et al. that analyzed the PPAT features in patients undergoing prostatectomy for cancer [51]. In this study, the authors found that the level of IL-6 secreted by PPAT-conditioned medium (CM) was almost 375 times greater than the circulating levels of the cytokine in the same patient. Both IL-6 levels in PPAT and activation of IL-6 related signaling pathways were correlated to tumor aggressiveness [51]. Therefore, this study strongly suggests that PPAT represents an important source of IL-6 that favors tumor progression. Interestingly, several studies already reported that increased serum IL-6 and soluble interleukin-6 receptor levels are associated with aggressiveness of the disease and with a poor prognosis in prostate cancer patients, underlying the importance of this pathway in PC progression (for review see [81]). […] Recent studies suggest that, like in breast cancer, a bidirectional crosstalk exists between PC cells and surrounding AT.” […]

“During the last decade, pancreatic cancer has become the fourth leading cause of cancer-related death in the USA and the sixth leading cause in Europe. Despite major advances in surgical techniques and adjuvant therapies, overall 5-year survival remains under 5 %. While very few, if any, laboratory studies have been performed to date on the crosstalk between pancreatic cancers and AT, several clinical data have suggested that an adipose-rich environment leads to a deleterious outcome on this disease. […] it has been demonstrated that peripancreatic fat invasion is correlated to a poorer survival for pancreatic cancers [107]. Recent epidemiologic studies also suggest that obesity doubles the relative risk of pancreatic cancer [98]. In addition, central adiposity has been shown to be an independent risk factor in development of pancreatic cancer as well as to contribute to a poorer survival [108]. Interestingly, it has been demonstrated that increased pancreatic fat (pancreatic steatosis) promotes dissemination and lethality of pancreatic cancer [109].” […]

“The relationship between AT and cancer is complex and involves both paracrine and endocrine effects whose relative contribution to tumor progression remains to be determined. Regarding paracrine effects, we have underlined in this chapter the need to consider the appropriate neighboring AT for each cancer subtypes in experimental studies. […] there is clear variations between the different AT in terms of secretion and sensitivity to lipolysis […] Nevertheless, regarding AT/cancer crosstalk, there are common features found in several cancer subtypes. […] it is very important to underline that adipose cells are not inert to their surrounding and that their phenotype are profoundly modified by cancer cell secretions.” […]

“Present data suggest caution about the clinical use of lipotransfer-derived WAT cells for breast reconstruction in patients with breast cancer [15,16].”

I thought I should make a brief stop here to cover the observation above in a little more detail, because I think it’s a good illustration of why the finer details of how these things work actually matter. Now, one might well be tempted to say that if we know that fat people get cancer more often and have worse prognoses (this is, incidentally, a gross oversimplification – as should be clear from the posts), well – do we really need to know all that much more about how it all works out at the microscopical level and so on? Why not just tell people to lose weight and just leave it at that? Findings like the ones in [15,16] above indicate that it matters what goes on in these tissues. What did the studies tell us? Well, it has been observed that female breast cancer survivors who have undergone a specific type of reconstructive surgery (‘lipotransfer procedure for esthetical purposes’) had higher cancer recurrence risk than did females who had not undergone such a procedure; this is important information with clinical relevance. One basic idea behind what may be happening is that the adipose tissue that is transplanted into the reconstructed breast(/s) may work as a fuel source for any remaining cancerous cells still hiding in the tissues (/and it may spark new tumor development through the crosstalk and paracrine signalling mechanisms already mentioned). Note that this information may not yet be well known – see e.g. this webpage about reconstructive breast surgery from the website of Johns Hopkins University, which is hardly an institution to be found at the bottom of the barrel: “we try to give women the look and feel of an actual breast, using creative techniques such as fat grafting, also known as lipofilling or fat transfer. Fat can be taken from another part of your body, possibly the abdomen or somewhere on your buttocks, through liposuction. The fat will be purified and carefully layered within the new breast to create the desired shape. Our surgeons are experienced at these techniques.” They may want to reconsider at the very least the extent to which they are using these techniques. Anyway, back to the book:

“surgical options for treatment of the severely obese population have increased in popularity over the last few decades, with an estimated 344,000 cases performed globally in 2008 [40,41]. As previously noted, lifestyle therapy for weight loss intervention is generally insufficient for extremely obese patients and effective long-term weight loss using pharmacological therapy has been limited, leaving bariatric surgery as the only medical intervention providing substantial, long-term weight loss for most severely obese patients. […] Because post-bariatric surgical patients generally experience significant and sustained weight loss [2,47], they represent a unique population to study the relationship between voluntary weight loss and cancer risk. […] Generally, 80 % of patients who seek bariatric surgery are female.” […]

“Since 2009, there have been five reviews exploring the potential relationship between bariatric surgery and subsequent cancer risk [22,40,59–61], and two additional reviews of cancer risk associated with either weight loss from bariatric surgery or nonsurgical weight loss therapies [11,20]. […] [the following are some results from these studies:] Reported cancers subsequent to bariatric surgery were 117 cancers in the surgical group compared to 169 cancers among the control groups, representing an HR of 0.67 (95 % CI 0.53–0.85; p=0.0009). For female participants only, the surgical group had a reported 79 cancers compared to 130 cancers in the control females, giving an HR value of 0.58 (95 % CI 0.44–0.77; p = 0.0001). […] After a maximum of 5-year followup, the reported number of visits to the physician/hospital that led to a cancer-related diagnosis for the weight loss surgical group was 21 visits (2.0 %) compared with 487 visits (8.5 %) among the control group. This difference was reported to have a relative risk of 0.22 (95% CI 0.14–0.35; p=0.001) [55]. […] the relative risk for breast cancer was 0.17 (95% CI 0.01–0.31; p=0.001). […] For cancer deaths, the bariatric surgical group was 60 % lower when compared the control group ( p = 0.001; 31 deaths among surgical group compared to 73 deaths in control groups). […] For all cancers combined, there was a 24 % reduction in cancer incidence among the surgical group compared to controls (HR 0.76, 95% CI, 0.65–0.89; p=0.0006). […] Based upon these analyses, it was estimated that about 71 gastric bypass surgeries would be necessary to prevent one incident cancer [52]. […] [And so they conclude:] there are now studies that demonstrate a reduction in cancer mortality among postbariatric patients compared to severely obese, nonoperated controls. In addition, one prospective study (SOS study) and a few observational studies have also demonstrated a reduction in cancer incidence following metabolic surgery. To date, the reduced cancer risk benefits have been limited to females and there appears to be a stronger correlation of benefit associated with cancers that are “likely” to be obesity related. Given these limitations, the general consensus is that intentional weight loss does lead to a reduction in cancer incidence [11].” […]

“Multiple reviews have been published on the effect of metabolic surgery on diabetes, including a meta-analysis by Buchwald et al., which reported a 78.1 % remission of diabetes and an 86.6 % improvement or remission in diabetes following bariatric surgery [81]. The intriguing element related to diabetes remission is that a significant number of bariatric surgical patients (i.e., gastric bypass patients) have discarded their antidiabetic medication and returned to a normal blood glucose by the time they are discharged from the hospital following their metabolic surgery (i.e., 2–3 days after surgery) and long before significant weight loss has occurred [82]. Again, mechanisms accounting for this remarkable remittance or improvement of diabetes following surgery are multiple. In an analogous way, the reduced risk of cancer following metabolic surgery is also likely to be linked with several biological mechanisms, which may or may not be directly associated with weight loss.”

October 5, 2013 Posted by | biology, books, cancer, diabetes, medicine | Leave a comment

Adipose tissue and cancer

I’ve read the first third of this book, and it’s been a quite interesting read so far. Some parts have been easier to read than others and occasionally it gets a bit technical, but overall it’s a quite readable book for someone with my background and I’m certainly learning some new stuff by reading this.

Some observations from the book:

“obesity and metabolic syndrome are linked to various chronic diseases [6,7] including cardiovascular disease, type II diabetes, and the focus of this chapter, cancer. Importantly, not all obese individuals develop the metabolic dysregulation usually associated with obesity and metabolic syndrome, and these “metabolically healthy obese” individuals do not have elevated cancer risk. An estimated 30 % of obese individuals in the USA are metabolically healthy [8]. Conversely, some nonobese individuals can develop the metabolic perturbations usually associated with obesity, and these individuals appear to be more prone to chronic diseases including cancer [9]. Thus, an emerging hypothesis is that the obesity-related metabolic perturbations, and not specific dietary components or increased adiposity, are at the crux of the obesity–cancer connection.” […]

“Evidence-based guidelines for cancer prevention urge maintenance of a lean phenotype [10]. Overall, an estimated 15–20 % of all cancer deaths in the USA are attributable to overweight and obese body types [11]. Obesity is associated with increased mortality from cancer of the prostate and stomach in men; breast (postmenopausal), endometrium, cervix, uterus, and ovaries in women; and kidney (renal cell), colon, esophagus (adenocarcinoma), pancreas, gallbladder, and liver in both genders [11]. While the relationships between metabolic syndrome and specific cancers are less well established, first reports from the Metabolic Syndrome and Cancer Project, a European cohort study of ~580,000 adults, confirm associations between obesity (or BMI) in metabolic syndrome and risks of colorectal, thyroid, and cervical cancer [12].”

“During obesity, adipose tissue responds to the excess energy by increasing adipocyte size (hypertrophy) and enhancing adipocyte proliferation (hyperplasia) [14]. Adipocyte size strongly correlates with insulin resistance and secretion of proinflammatory cytokines [3]. Moreover, location of the adipose tissue also determines risk for metabolic diseases. […] Healthy adipose tissue must be able to rapidly respond to excess energy intake by inducing adipocyte hypertrophy and hyperplasia, remodeling of the extracellular matrix, and enhanced neovascularization to nourish the adipose tissue. In pathological states such as insulin resistance associated with obesity, rapid adipocyte hypertrophy occurs with restricted angiogenesis resulting in cellular hypoxia, and thereby resulting in local inflammation [15]. Macrophages surrounding necrotic adipocytes phagocytize fatty acids, which are released from the adipocyte. This produces bloated, lipid overburdened macrophages, which is characteristic of chronic inflammation and often observed in obese individuals [14]. […] inflammation is a recognized hallmark of cancer, and growing evidence continues to indicate that chronic inflammation is associated with increased cancer risk [75–77]. Several tissue-specific inflammatory lesions are established neoplastic precursors for invasive cancer, including gastritis for gastric cancer, inflammatory bowel disease for colon cancer, and pancreatitis for pancreatic cancer [78,79].”

“When lipid storage capacity in adipose tissue is exceeded, surplus lipids often accumulate within muscle, liver, and pancreatic tissue [16]. As a consequence, hepatic and pancreatic steatosis can develop; both have been positively associated with insulin resistance and ultimately lead to impairment of lipid processing and clearance within these tissues [16]. […] The term nonalcoholic fatty liver disease (NAFLD) refers to a disease spectrum that includes variable degrees of simple steatosis, nonalcoholic steatohepatitis (NASH), and cirrhosis [19,20]. Simple steatosis is benign, whereas NASH is defined by the presence of hepatocyte injury, inflammation, and/or fibrosis, which can lead to cirrhosis, liver failure, and hepatocellular carcinoma. […] NASH occurs in 20 % of cases of NAFLD and ~5–20 % of NASH cases progress to cirrhosis; 80 % of cryptogenic cirrhosis cases present with NASH [22]. Of this group, ~0.5 % will eventually progress to hepatocellular carcinoma […] In Western populations, overnutrition/obesity is the most common cause of NAFLD” […] NAFLD has evolved in parallel to the obesity pandemic as the most prevalent liver disease worldwide. Whereas the fact that chronic liver inflammation as observed in nonalcoholic steatohepatitis (NASH) finally leads to the development of hepatocellular carcinoma is well accepted [123], its association with increased formation of adenomatous polyps and CRC has just recently been established [124,125].”

“Hyperglycemia, a hallmark of metabolic syndrome, is associated with insulin resistance, aberrant glucose metabolism, chronic inflammation, and the production of other metabolic hormones such as IGF-1, leptin, and adiponectin [37]. […] In metabolic syndrome, the amount of bioavailable IGF-1 increases […] Elevated circulating IGF-1 is an established risk factor for many cancer types [38,39].”

VEGF [Vascular Endothelial Growth Factor], a heparin-binding glycoprotein produced by adipocytes and tumor cells, has angiogenic, mitogenic, and vascular permeability-enhancing activities specific for endothelial cells [83]. Circulating levels of VEGF are increased in obese, relative to lean, humans and animals, and increased tumoral expression of VEGF is associated with poor prognosis in several obesity-related cancers [84]. The need for nutrients and oxygen triggers tumor cells to produce VEGF, which leads to the formation of new blood vessels to nourish the rapidly growing tumor and may facilitate the metastatic spread of tumors cells [83].”

“Epidemiological studies indicate that obesity represents a significant risk factor for the development of various cancers such as prostate and breast cancer, leading cancers in the Western world. An impressive body of evidence, however, also indicates that the risk of colorectal adenoma, and cancer (CRC) is increased in subjects with obesity and related metabolic syndrome [2,3]. […] Colorectal cancer is the second leading cancer death in the Western world and its death rate correlates with body mass index [5]. […] Recent CRC screening studies suggest that obesity and an increased body mass index are a significant additional risk factor for the development of colonic polyps with evidence that advanced adenomas arise in men almost a decade earlier than in women [7]. […] menopausal status appears to modify the relationship between BMI and colon cancer with a strong association between BMI and colon cancer risk seen in premenopausal but not postmenopausal women [21]. […] being obese prior to being diagnosed with colon cancer increases your risk of dying from the disease [29–32]. […] more and more studies are now demonstrating the location of body fat tissue is the best predictor of all-cause and colorectal cancer mortality […] colon cancer survival may be less likely for patients who are […] too thin at diagnosis [34].”

“In a meta-analysis of 52 studies (24 case–control and 28 cohort studies) examining the link between physical activity and colon cancer, a significant 24 % reduced risk of colon cancer in people who were most active compared with the least was found [48]. This supports other reviews of the association between physical activity and colon cancer in the Asian and European populations [49,50]. […] Physical activity also appears to affect disease outcome and recurrence after diagnosis and treatment with the greatest effect on colon cancer incidence [53]. […] new well-controlled clinical trials on obesity prevention and obesity treatment are necessary before therapeutic implications of WAT [White Adipose Tissue] reduction on cancer predisposition are completely understood. One of the possibly important considerations is the number of adipocytes and the accompanying stromal/vascular cells in WAT increasing in obesity and remaining increased even upon subsequent weight loss, which occurs via adipocyte size reduction. The pool of ASC [Adipose Stem Cells] is likely to remain intact and could contribute to cancer onset or progression despite calorie restriction and reduced adiposity.”

“There is general agreement that obesity is associated with an increased incidence of breast cancer in postmenopausal women (reviewed in [14–17]). […] The European Prospective Investigation into Cancer and Nutrition (EPIC) study [18], which had 57,923 postmenopausal participants, is of particular interest because of its large size, its prospective design, and the observations made concerning exogenous estrogens as a confounder. The results showed that a long-term weight gain was related to an increase in risk, but only in those who were not taking hormone replacement medication: compared with women with a stable body weight the relative risk for women who gained 15–20 kg was 1.5 with a confidence interval of 1.60–2.13. As reported by others, adiposity ceased to be a risk factor in current replacement therapy users, who were already at a high risk for breast cancer compared with nonusers. […] Preexisting obesity and postoperative weight gain are associated with poor prognosis in both premenopausal and postmenopausal breast cancer patients. […] A pivotal review of the literature by Chlebowski et al. [25] found that in 26 out of 34 studies individual studies, totaling 29,460 women, obesity was related to an increased risk of recurrence or reduced survival.”

“Daling et al. [29] have provided a major contribution to our understanding in the relationships between body fat mass and tumor biomarkers of progression in young breast cancer patients. In their study, not only was a combination of obesity and an absence of ER expression in premenopausal breast cancer patients aged younger than 45 years associated with an increased risk of dying from the disease, but those with BMI values in the highest quartile were more likely to have larger tumors of high histologic grade. This observation is particularly significant because it implies that large tumors in overweight/obese women grow at a faster rate than tumors of similar size from leaner women, rather than simply arising from delayed diagnosis due to palpation difficulty in obese women.”

“Wolf et al. [36] and Schott et al. [37] suggested that up to 16 % of breast cancer patients have diabetes, and that T2D may be associated with a 10–20 % excessive risk of breast cancer. […] There is ample epidemiological evidence that diabetes contributes to breast cancer risk [17,36–40]. […] Overall survival in cancer patients, with or without preexisting diabetes, has shown diabetes to be associated with an increased all-cause mortality risk. […] The Danish Breast Cancer Cooperative Group, with 18,762 newly diagnosed T2D cases, found that the recurrence with metastases was 46 % higher in obese women with a BMI of 30 kg/m^2 or greater beyond the first 5 years.”

The relationship between obesity and prostate cancer is a complicated one. […] The explanation for this confusion may rest, at least in part, in the reports that obesity as a positive risk factor for prostate cancer relates specifically with the aggressive phenotype [56–60] […] a meta-analysis by Discacciati et al. [61] of the results from 25 studies that examined disease stage and BMI showed not only a positive relationship between obesity and advanced prostate cancer but also a decrease in the risk for localized disease. The association between obesity and an aggressive prostate cancer phenotype is reflected in the relationship between the BMI and prostate cancer mortality rate. For example, in one large retrospective cohort study by Andersson et al. [62] […] there was a significantly larger prostate cancer mortality rate in the higher BMI categories”

Two studies have been reported in which meta-analysis was used to examine previously published investigations into the relationship between diabetes mellitus and prostate cancer risk [66,67]. […] [The first] meta-analysis showed that there was an inverse relationship between diabetes and prostate cancer risk, which translated to a 9 % reduction in risk. […] The overall conclusion […in the second meta-analysis] was the same: diabetic men have a significantly decreased risk of developing prostate cancer (RR = 0.84; 95% CI, 0.76–0.93). […] Gong et al. [69] reported a large prospective study of diabetes and prostate cancer from the USA after the two meta-analyses described above had been published that also took account of potential confounding by obesity. Men with diabetes had a 34 % lower risk of prostate cancer compared with men without diabetes that was not affected by adjustment for the BMI […] In contrast to these results, recently published studies have found that the presence of diabetes is positively associated with prostate cancers of high-grade [71–73] and late-stage tumors [72] ], a reversal in the observed relationship that needs to be considered in the context of the duration of the presence of T2D and the detection of prostate cancer by prostatic-specific antigen screening.”

October 1, 2013 Posted by | books, cancer, diabetes, health, health care, medicine | Leave a comment

Stuff

i. I’ve played some good chess over the last few weeks. I’m currently participating in an unrated chess tournament –  the format is two games per evening (one with the white pieces and one with the black), with 45 minutes per person per game. The time control means that although the games aren’t rated, they’re at least long enough to be what I’d consider ‘semi-serious’.

Here’s a recent game I played, from that tournament – I was white. It wasn’t without flaws on my part but it was ‘good enough’ as he was basically lost out of the opening. I wasn’t actually sure if 7.Qd4 could be played (this should tell you all you need to know about how much I know about the Pirc…) but I was told after the game that it was playable – my opponent had seen it in a book before, but he’d forgotten how the theory went and so he made a blunder. It was the second game that evening, played shortly after I’d held my opponent, a ca. 2000 FIDE rated player, to a draw in the first game. I mention the first game also because I think it’s quite likely that the outcome of that game played a role in the mistake he made in the second game. The average rating of my opponents so far has been 1908 (I’ve also drawn a 2173 FIDE guy along the way, though the chess in that case was not that great), and I’m at +1 after six games. I’ve beaten FMs before in bullet and blitz, but as mentioned these games are a tad more serious than, say, random 3 minute games online, and this is one of the first times I’ve encountered opponents as strong as this in a ‘semi-serious’ setting. And I’m doing quite well. It probably can’t go on, but I’m enjoying it while it lasts.

ii. An interesting medical lecture about vaccines:

iii. Estimating Gender Disparities in Federal Criminal Cases.

“This paper assesses gender disparities in federal criminal cases. It finds large gender gaps favoring women throughout the sentence length distribution (averaging over 60%), conditional on arrest offense, criminal history, and other pre-charge observables. Female arrestees are also significantly likelier to avoid charges and convictions entirely, and twice as likely to avoid incarceration if convicted. Prior studies have reported much smaller sentence gaps because they have ignored the role of charging, plea-bargaining, and sentencing fact-finding in producing sentences. Most studies control for endogenous severity measures that result from these earlier discretionary processes and use samples that have been winnowed by them. I avoid these problems by using a linked dataset tracing cases from arrest through sentencing. Using decomposition methods, I show that most sentence disparity arises from decisions at the earlier stages, and use the rich data to investigate causal theories for these gender gaps.”

Here’s what she’s trying to figure out: “In short, I ask: do otherwise-similar men and women who are arrested for the same crimes end up with the same punishments, and if not, at what points do their fates diverge?”

Some stuff from the paper:

“The estimated gender disparities are strikingly large, conditional on observables. Most notably, treatment as male is associated with a 63% average increase in sentence length, with substantial unexplained gaps throughout the sentence distribution. These gaps are much larger than those estimated by previous research. This is because, as the sequential decomposition demonstrates, the gender gap in sentences is mostly driven by decisions earlier in the justice process—most importantly sentencing fact-finding, a prosecutor-driven process that other literature has ignored.

But why do these disparities exist? Despite the rich set of covariates, unobservable gender differences are still possible, so I cannot definitively answer the causal question. However, several plausible theories have testable implications, and I take advantage of the unusually rich dataset to explore them. I find substantial support for some theories (particularly accommodation of childcare responsibilities and perceived role differences in group crimes), but that these appear only to partially explain the observed disparities.” […]

“Columns 11-12 of Table 5 show that the gender gap is substantially larger among black than non-black defendants (74% versus 51%). The race-gender interaction adds to our understanding of racial disparity: racial disparities among men significantly favor whites,29 but among women, the race gap in this sample is insignificant (and reversed in sign). The interaction also offers another theory for the gender gap: it might partly reflect a “black male effect”—a special harshness toward black men, who are by far the most incarcerated group in the U.S. […] This theory only goes so far, however — the gender gap even among non-blacks is over 50%, far larger than the race gap among men.”

iv. Low glycaemic index, or low glycaemic load, diets for diabetes mellitus?

“Nutritional factors affect blood glucose levels, however there is currently no universal approach to the optimal dietary strategy for diabetes. Different carbohydrate foods have different effects on blood glucose and can be ranked by the overall effect on the blood glucose levels using the so-called glycaemic index. By contributing a gradual supply of glucose to the bloodstream and hence stimulating lower insulin release, low glycaemic index foods, such as lentils, beans and oats, may contribute to improved glycaemic control, compared to high glycaemic index foods, such as white bread. The so-called glycaemic load represents the overall glycaemic effect of the diet and is calculated by multiplying the glycaemic index by the grammes of carbohydrates.

We identified eleven relevant randomised controlled trials, lasting 1 to 12 months, involving 402 participants. Metabolic control (measured by glycated haemoglobin A1c (HbA1c), a long-term measure of blood glucose levels) decreased by 0.5% HbA1c with low glycaemic index diet, which is both statistically and clinically significant. Hypoglycaemic episodes significantly decreased with low glycaemic index diet compared to high glycaemic index diet. No study reported on mortality, morbidity or costs.”

v. I started reading Dinosaurs Past and Present a few days ago. It’s actually a quite short and neat book, but I haven’t gotten very far as other things have gotten in the way. I just noticed that a recently published PlosOne study deals with some of the same topics covered in the book – I haven’t read it yet but if you’re curious you can read the article on Forearm Posture and Mobility in Quadrupedal Dinosaurs here.

September 25, 2013 Posted by | Chess, data, diabetes, Lectures, medicine, Paleontology, personal, studies | Leave a comment

Stuff (and a few personal remarks)

i. Yesterday I had a very bad and prolonged hypoglycemic episode which lasted hours. I was in a semi-conscious state for a long time before realizing there was a problem, and the situation did not improve much even after intake of significant amounts of dextrose. This is by far the closest I’ve been to a hospital admission for more than a year – I had both severe neurological symptoms and GI-tract involvement. I don’t think I’ve ever been admitted without GI-tract involvement, and this tends to worsen outcomes significantly – it’s hard to reverse a disease process the main treatment of which is putting stuff into your stomach and keeping it there if you have severe nausea and vomit up the stuff you eat.

I really hope that if something like this happens again I’ll be smart enough to actually call an ambulance, or at the very least involve other people so that they can help me if things go really bad. I like to tell myself that I am a very self-reliant and independent person in general – the sort of person who don’t like to ask other people for help and so rarely do. And nobody likes to be seen and judged by others when they’re at their weakest. Combine these facts with the inherent difficulty of assessing when a situation such as this one is sufficiently severe to merit involving other people while you’re having neurological symptoms impacting your thought processes and impairing judgment, and you have the perfect recipe for a situation where you end up making bad decisions and running a major risk of things going very wrong by not getting help. I should really become better at reminding myself (to the extent that it’s possible; as mentioned impaired judgment is a symptom here, so this stuff is not completely under my control) that when I’m in a state like this I’m just a very sick person who very well may need other people’s help simply to survive. Type 1 diabetics die from such hypoglycemic episodes all the time.

Here’s a related post from the past.

ii. (Yet) A(nother) medical lecture:

iii. An event that changed the world:

Here’s more.

iv. Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials.

Abstract

Objectives To determine whether parachutes are effective in preventing major trauma related to gravitational challenge.

Design Systematic review of randomised controlled trials.

Data sources: Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists.

Study selection: Studies showing the effects of using a parachute during free fall.

Main outcome measure Death or major trauma, defined as an injury severity score > 15.

Results We were unable to identify any randomised controlled trials of parachute intervention.

Conclusions As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.”

v. On Being Sane in Insane Places, by David L. Rosenhan.

“At its heart, the question of whether the sane can be distinguished from the insane (and whether degrees of insanity can be distinguished from each other) is a simple matter: Do the salient characteristics that lead to diagnoses reside in the patients themselves or in the environments and contexts in which observers find them? From Bleuler, through Kretchmer, through the formulators of the recently revised Diagnostic and Statistical Manual of the American Psychiatric Association, the belief has been strong that patients present symptoms, that those symptoms can be categorized, and, implicitly, that the sane are distinguishable from the insane. More recently, however, this belief has been questioned. Based in part on theoretical and anthropological considerations, but also on philosophical, legal, and therapeutic ones, the view has grown that psychological categorization of mental illness is useless at best and downright harmful, misleading, and pejorative at worst. Psychiatric diagnoses, in this view, are in the minds of observers and are not valid summaries of characteristics displayed by the observed. [3-5]

Gains can be made in deciding which of these is more nearly accurate by getting normal people (that is, people who do not have, and have never suffered, symptoms of serious psychiatric disorders) admitted to psychiatric hospitals and then determining whether they were discovered to be sane and, if so, how. If the sanity of such pseudopatients were always detected, there would be prima facie evidence that a sane individual can be distinguished from the insane context in which he is found. Normality (and presumably abnormality) is distinct enough that it can be recognized wherever it occurs, for it is carried within the person. If, on the other hand, the sanity of the pseudopatients were never discovered, serious difficulties would arise for those who support traditional modes of psychiatric diagnosis. Given that the hospital staff was not incompetent, that the pseudopatient had been behaving as sanely as he had been out of the hospital, and that it had never been previously suggested that he belonged in a psychiatric hospital, such an unlikely outcome would support the view that psychiatric diagnosis betrays little about the patient but much about the environment in which an observer finds him.

This article describes such an experiment.”

Here’s the wikipedia article about the experiment. Below some more stuff from the paper:

“Eight sane people gained secret admission to 12 different hospitals [6]. […] the pseudopatients were never detected. Admitted, except in one case, with a diagnosis of schizophrenia [10], each was discharged with a diagnosis of schizophrenia “in remission.” The label “in remission” should in no way be dismissed as a formality, for at no time during any hospitalization had any question been raised about any pseudopatient’s simulation. Nor are there any indications in the hospital records that the pseudopatient’s status was suspect. Rather, the evidence is strong that, once labeled schizophrenic, the pseudopatient was stuck with that label. If the pseudopatient was to be discharged, he must naturally be “in remission”; but he was not sane, nor, in the institution’s view, had he ever been sane. […] Length of hospitalization ranged from 7 to 52 days, with an average of 19 days.” […]

“Failure to detect sanity during the course of hospitalization may be due to the fact that physicians operate with a strong bias toward what statisticians call the Type 2 error [5]. This is to say that physicians are more inclined to call a healthy person sick (a false positive, Type 2) than a sick person healthy (a false negative, Type 1). The reasons for this are not hard to find: it is clearly more dangerous to misdiagnose illness than health. Better to err on the side of caution, to suspect illness even among the healthy.” […]

“The following experiment was arranged at a research and teaching hospital whose staff had heard these findings but doubted that such an error could occur in their hospital. The staff was informed that at some time during the following three months, one or more pseudopatients would attempt to be admitted into the psychiatric hospital. Each staff member was asked to rate each patient who presented himself at admissions or on the ward according to the likelihood that the patient was a pseudopatient. A 10-point scale was used, with a 1 and 2 reflecting high confidence that the patient was a pseudopatient.

Judgments were obtained on 193 patients who were admitted for psychiatric treatment. All staff who had had sustained contact with or primary responsibility for the patient — attendants, nurses, psychiatrists, physicians, and psychologists — were asked to make judgments. Forty-one patients were alleged, with high confidence, to be pseudopatients by at least one member of the staff. Twenty-three were considered suspect by at least one psychiatrist. Nineteen were suspected by one psychiatrist and one other staff member. Actually, no genuine pseudopatient (at least from my group) presented himself during this period.

The experiment is instructive. It indicates that the tendency to designate sane people as insane can be reversed when the stakes (in this case, prestige and diagnostic acumen) are high. But what can be said of the 19 people who were suspected of being “sane” by one psychiatrist and another staff member? Were these people truly “sane” or was it rather the case that in the course of avoiding the Type 2 error the staff tended to make more errors of the first sort — calling the crazy “sane”? There is no way of knowing. But one thing is certain: any diagnostic process that lends itself too readily to massive errors of this sort cannot be a very reliable one. […]

It is clear that we cannot distinguish the sane from the insane in psychiatric hospitals. The hospital itself imposes a special environment in which the meaning of behavior can easily be misunderstood. The consequences to patients hospitalized in such an environment — the powerlessness, depersonalization, segregation, mortification, and self-labeling — seem undoubtedly counter-therapeutic.”

September 21, 2013 Posted by | diabetes, Lectures, medicine, papers, personal, Psychology | 2 Comments

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