“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 . 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 . 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  […] 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 . […] 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 . […] 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 . 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 . […] 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 . 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.
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.
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 motives 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…)
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.
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.
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 et al. in 2004 to result in blindness for over 10,000 people with diabetes per year. Moss 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, 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 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. […] 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 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. […] 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.
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  . 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 ”
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% . 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 .”
“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 , 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 .”
“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 . 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 . 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 . […] recent reviews by David-Ferdon and Kaslow  and prior work by Kazdin and Weisz  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  and as many as 40% have three or more distinct conditions .”
“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 .”
“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 . Depressed patients with diabetic neuropathy are more prone to developing first foot ulcers than nondepressed individuals, independently of biological risk factors and foot care . […] 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 . […] 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 .”
“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.”
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 .”
“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 . 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 . 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 . […] Another issue to consider is that normal-weight patients may have a significantly higher percentage of high-risk coronary anatomy compared with obese patients . […] 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 .”
“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 .”
“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 . 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 . 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 .” [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 . […] 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 . […] 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 . 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 . Intervention data have demonstrated that EPA and DHA, but not ALA, benefit cardiovascular outcomes in primarily and secondary prevention studies  […] Of note, the relationship between arrhythmea and EPA and DHA has recently been questioned . 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 . […] 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 . 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 .” [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” , or perhaps better “atherogenic cholesterol,” is a measurement that generally predicts CVD better than LDL-C [LDL-Cholesterol].”
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…
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)?
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 2 – US] […] 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).”
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.
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 . 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 . The buildup of resulting gas creates pressure, inflating the cadaver, and eventually forcing fluids out . 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 . 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 –. The final stages of decomposition last through to skeletonization and are the driest stages , –.”
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.”
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.
To assess the effects of dietary protein restriction on the pro gression of diabetic nephropathy in patients with diabetes .
We searched The Cochrane Library , MEDLINE, EMBASE, ISI Proceedings, Science Citation Index Expanded and bibliographies of included studies.
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.
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.
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.
“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.”
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 . 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 . 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 . 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 . […] 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 , 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 .” […]
“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 . 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 . 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 ). […] 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 . Recent epidemiologic studies also suggest that obesity doubles the relative risk of pancreatic cancer . 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 . Interestingly, it has been demonstrated that increased pancreatic fat (pancreatic steatosis) promotes dissemination and lethality of pancreatic cancer .” […]
“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) . […] 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 . […] [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 .” […]
“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 . 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 . 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.”
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 . 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 . 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 . Overall, an estimated 15–20 % of all cancer deaths in the USA are attributable to overweight and obese body types . 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 . 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 .”
“During obesity, adipose tissue responds to the excess energy by increasing adipocyte size (hypertrophy) and enhancing adipocyte proliferation (hyperplasia) . Adipocyte size strongly correlates with insulin resistance and secretion of proinflammatory cytokines . 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 . 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 . […] 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 . 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 . […] 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 . 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 , 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 . […] 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 . 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 . 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 .”
“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 . […] 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 . […] 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 . […] 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 .”
“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 . 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 . […] 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 , 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.  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.  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.  and Schott et al.  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.  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.  […] 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.  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  ], 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.”
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:
“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.”
“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.
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:
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 . […] the pseudopatients were never detected. Admitted, except in one case, with a diagnosis of schizophrenia , 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 . 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.”
“The share of one-person households in the U.S. maintained by men ages 15 to 64 rose to 34% in 2012, up from 23% in 1970, according to a Census report on the status of families released Tuesday. For women of the same age, this figure actually dropped slightly, to 30% in 2012 from 31% in 1970.
The findings may reflect, in part, the sharp increase in divorce rates in the U.S. throughout the 1970s, Census said. The dominant living arrangement for children following their parents’ divorce is custody by mothers.”
I would have preferred to read the actual Census report and I did go have a look for it; but when I click the pdf link to the report in question at the census site all I get is an error message (link) – they seem to have put up a corrupt link. Annoying. Here are some related Danish numbers which I blogged a while ago. Although the 2012 report doesn’t seem to be available, there’s a lot of 2009-2011 data on related matters here. I messed around a little with that data – below some stuff from that source:
Naturally there’s a big gender disparity; at the age range of 24-29, 89.1% of males have never married whereas only 80,7% of the females have never married. For people in the 25-29 year age range 64% of males and 50,1% of females have never married. You’d expect the numbers to converge somewhat ‘over time’ (/as people get older) and they do, but not until we reach the age group of 55-64 year olds do the proportion of females who have never married surpass the proportion of males who have not (and these numbers are quite small – less than 9% have never married at that age, both when looking at males and females).
Higher earnings seem to confer an advantage when it comes to minimizing the risk of never getting married, which is of course a big surprise. For example, of the 45-49 year old people with a reported income of $25,000 to $39,999 17,6% of them have never married, whereas the corresponding number for people with an income of $40,000-75,000 is 11,5%. For people with incomes in the $75,000-$100,000 range the number is 5.5%, and incidentally the number of 45-49 year olds with incomes above $100k who’ve never married is also 5,5%. The relationship is not perfectly linear, but it’s clear that people with higher earnings have a higher likelihood of getting married. Incidentally almost a third of people in that age range who reported annual earnings less than $5000 have never married (29.2%).
The numbers above are from the first third of the first document. There’s a lot of data available here if you’re curious.
ii. Global Reality of Type 1 Diabetes Care in 2013. Not much to see here – here’s why I bookmarked it:
“from a global perspective, the most common cause of death for a child with type 1 diabetes is lack of access to insulin (2). Yet, this is not just a problem for low-income countries, with one recent study in the U.S. noting that discontinuation of insulin therapy represents the leading precipitating cause of diabetic ketoacidosis (3). Indeed, lack of insulin explained 68% of such episodes in people living in an inner-city setting, with approximately one-third of people reporting a lack of financial resources to buy insulin and eking out their insulin supplies.”
We’re talking about the United States of America, a very rich country – and in fact the country in the world with the highest health care expenditures. And still you have type 1 diabetics who go into ketoacidosis because they can’t afford their drugs. That’s messed up. Note that low medical subsidies to type 1s may not necessarily be cost saving at a systemic level as hospital admissions are very expensive; based on the average estimates at the link and these length of stay estimates, a back of the envelope estimate of the average cost of a DKA-related hospital admission would be $5.500. This estimate is probably too low as this study (which I may blog in more detail later) estimated non-compliance-related DKA-admissions to cost on average roughly $7.500 (and the non-compliance admissions were actually significantly cheaper than the other admissions on a per-case basis). To put this estimate into perspective, the mean annual cost of intensive diabetes care per diabetic patient in the U.S. is $4,000 (same link).
iii. Related to i., but I figured it deserved to be linked to separately: A theory of marriage, by Gary Becker.
iv. Some maps illustrating racial segregation patterns in the US. Don’t miss the sixth map of Detroit. The one of Saint Louis is also…
v. Vocabulary.com. I haven’t used it much yet, so I don’t really know if it’s any good – but it looks interesting and I’ve missed such a resource. I sometimes feel a bit guilty about not working harder on improving my vocabulary, especially on account of the fact that I’ve basically ended up only speaking two languages – I used to speak French reasonably well, but that’s many years ago and at this point I’d rather spend time improving my English than spend a lot of effort on a third language which most likely will only be of very limited use to me.
“Robots offer new possibilities for investigating animal social behaviour. This method enhances controllability and reproducibility of experimental techniques, and it allows also the experimental separation of the effects of bodily appearance (embodiment) and behaviour. In the present study we examined dogs’ interactive behaviour in a problem solving task (in which the dog has no access to the food) with three different social partners, two of which were robots and the third a human behaving in a robot-like manner. The Mechanical UMO (Unidentified Moving Object) and the Mechanical Human differed only in their embodiment, but showed similar behaviour toward the dog. In contrast, the Social UMO was interactive, showed contingent responsiveness and goal-directed behaviour and moved along varied routes. The dogs showed shorter looking and touching duration, but increased gaze alternation toward the Mechanical Human than to the Mechanical UMO. This suggests that dogs’ interactive behaviour may have been affected by previous experience with typical humans. We found that dogs also looked longer and showed more gaze alternations between the food and the Social UMO compared to the Mechanical UMO. These results suggest that dogs form expectations about an unfamiliar moving object within a short period of time and they recognise some social aspects of UMOs’ behaviour. This is the first evidence that interactive behaviour of a robot is important for evoking dogs’ social responsiveness.”
From the discussion:
“The aim of this study was to investigate whether dogs are able to differentiate agents on the basis of their behaviour and show social behaviours toward an UMO (Unidentified Moving Object) if the agent behaves appropriately in an interactive situation. In order to observe such interaction we modelled an experimental situation in which the dog is faced with inaccessible food. Miklósi et al  showed that in this case dogs increase their looking time at a human helper and show gaze alternation between the inaccessible food and the human. These observations have been replicated by Gaunet  and Horn et al , and the authors implicated that the dogs’ behaviour reflects communicative intentions. The present experiment showed that these behaviour features also emerge in the dogs while they are interacting with an UMO, moreover the onset of these behaviours is facilitated by the social features of the UMO: Dogs look longer and show more gaze alternation if the UMO carries eyes, shows variations in its path of movement, displays goal-directed behaviour and contingent reactivity (reacts to the looking action of the dog by retrieving the inaccessible food item).”
If you’re curious about how they actually did this stuff, don’t miss the neat video towards the end.
First, a link. I hadn’t heard about Gresham College until yesterday, so I’m assuming that some readers are at this point unaware of the existence of this resource. With that out of the way – some lectures!
I don’t want to talk a lot about the stuff covered here, but I probably should mention that I’m pretty sure I read an article not long ago showing that biennial eye screenings are more cost-effective than annual screenings, and that expected outcomes in the two cases are pretty similar. I’m too lazy to look up the article though, this is just to say that if you’re a diabetic getting your eyes screened regularly, you probably shouldn’t lose sleep about the fact that they only look at your eyes every second year.
I think I may have mentioned this before, but in my childhood I caught myself wondering if our cat could actually tell the difference between me and some other person – that question related to the bigger question of how the cat perceived the world; if I looked different enough for it to tell that I wasn’t somebody else. Well, if I’d had a pet sheep instead there’d have been no doubt:
“So this is sheep, showing that they can discriminate between faces by pressing panels with their nose […] they’re extremely good at doing this, they can remember or discriminate up to about 50 different sheep faces … it’s probably more than that, this is as far as we went – and at least 10 different human faces, and they can remember them for several years.” (from the video, roughly 40 minutes in)
i. I’ve read The Murder of Roger Ackroyd. I’ll say very little about the book here because I don’t want to spoil it in any way – but I do want to say that the book is awesome. I read it in one sitting, and I gave it 5 stars on goodreads (av.: 4,09); I think it’s safe to say it’s one of the best crime novels I’ve ever read (and I’ll remind you again that even though I haven’t read that much crime fiction, I have read some – e.g. every Sherlock Holmes story ever published and every inspector Morse novel written by Colin Dexter). The cleverness of the plot reminded me of a few Asimov novels I read a long time ago. A short while after I’d finished the book I was in the laundry room about to start the washing machine and a big smile spread on my face, I was actually close to laughing – because damn, the book is just so clever, so brilliant!
I highly recommend the book.
ii. I have been watching a few of the videos in the Introduction to Higher Mathematics youtube-series by Bill Shillito, here are a couple of examples:
I’m not super impressed by these videos at this point, but I figured I might as well link to them anyway. There are 19 videos in the playlist.
iii. Mind the Gap: Disparity Between Research Funding and Costs of Care for Diabetic Foot Ulcers. A brief comment from this month’s issue of Diabetes Care. The main point:
“Diabetic foot ulceration (DFU) is a serious and prevalent complication of diabetes, ultimately affecting some 25% of those living with the disease (1). DFUs have a consistently negative impact on quality of life and productivity […] Patients with DFUs also have morbidity and mortality rates equivalent to aggressive forms of cancer (2). These ulcers remain an important risk factor for lower-extremity amputation as up to 85% of amputations are preceded by foot ulcers (6). It should therefore come as no surprise that some 33% of the $116 billion in direct costs generated by the treatment of diabetes and its complications was linked to the treatment of foot ulcers (7). Another study has suggested that 25–50% of the costs related to inpatient diabetes care may be directly related to DFUs (2). […] The cost of care of people with diabetic foot ulcers is 5.4 times higher in the year after the first ulcer episode than the cost of care of people with diabetes without foot ulcers (10). […]
We identified 22,531 NIH-funded projects in diabetes between 2002–2011. Remarkably, of these, only 33 (0.15%) were specific to DFUs. Likewise, these 22,531 NIH-funded projects yielded $7,161,363,871 in overall diabetes funding, and of this, only $11,851,468 (0.17%) was specific to DFUs. Thus, a 604-fold difference exists between overall diabetes funding and that allocated to DFUs. […] As DFUs are prevalent and have a negative impact on the quality of life of patients with diabetes, it would stand to reason that U.S. federal funding specifically for DFUs would be proportionate with this burden. Unfortunately, this yawning gap in funding (and commensurate development of a culture of sub-specialty research) stands in stark contrast to the outsized influence of DFUs on resource utilization within diabetes care. This disparity does not appear to be isolated to [the US].”
I’ve read about diabetic foot care before, but I had no idea about this stuff. Of the roughly 175.000 peer-reviewed publications about diabetes published in the period of 2000-2009, only 1200 of them – 0.69% – were about the diabetic foot. You can quibble over the cost estimates and argue that perhaps they’ve overstated because these guys want more money, but I think that it’s highly unlikely that the uncertainties related to the cost estimates are so big as to somehow make the current (research) ressource allocation scheme appear cost efficient in a CBA with reasonable assumptions – there simply has to be some low-hanging fruit here.
A slightly related (if you stretch the definition of ‘related’ a little) article which I also found interesting here.
iv. “How quickly would the ocean’s drain if a circular portal 10 meters in radius leading into space was created at the bottom of Challenger Deep, the deepest spot in the ocean? How would the Earth change as the water is being drained?”
And, “Supposing you did Drain the Oceans, and dumped the water on top of the Curiosity rover, how would Mars change as the water accumulated?”
v. Take news of cancer ‘breakthrough’ with a big grain of salt. I’d have added the word ‘any’ and probably an ‘s’ to the word breakthrough as well if I’d authored the headline, in order to make a more general point – but be that as it may… The main thrust:
“scientific breakthroughs should not be announced at press conferences using the vocabulary of public relations professionals.
The language of science and medicine should be cautious and humble because diseases like cancer are relentless and humbling. […]
The reality is that biomedical research is a slow process that yields small incremental results. If there is a lesson to retain from the tale of CFI-400945, it’s that finding new treatments takes a lot of time and a lot of money. It is a venture worthy of support, but unworthy of exaggerated expectations and casual overstatement.
Hype only serves to create false hope.”
People who’re not familiar with how science actually works (and how related processes such as drug development work) often have weird ideas about how fast things tend to proceed and how (/un?)likely a ‘promising’ result in the lab might be to be translated into, say, a new treatment option available to the general patient population. And yeah, that set of ‘people who’re not familiar with how science works’ would include almost everybody.
It should be noted, as I’m sure Picard knows, that it’s a lot easier to get funding for your project if you’re exaggerating benefits and downplaying costs; if you’re too optimistic; if you’re saying nice things about the guy writing the checks even though you think he’s an asshole; etc. Some types of dishonesty are probably best perceived of as nothing more than ‘good salesmanship’ whereas other types might have different interpretations; but either way it’d be silly to pretend that stuff like false hope does not sell a lot of tickets (and newspapers, and diluted soap water, and…). Given that, it’s hardly likely that things will change much anytime soon – the demand for information here is much higher than is the demand for accurate information. But it’s nice to read an article like this one every now and then anyway.
“The finding of abnormal lung function in some diabetic subjects suggests that the lung should be considered a “target organ” in diabetes mellitus; however, the clinical implications of these findings in terms of respiratory disease are at present unknown.”
Malcolm Sandler wrote this almost 25 years ago. What’s happened since then? Well, I should perhaps point out that you still today have a situation where highly educated individuals who’ve had diabetes for decades may not even be aware that their disease may affect the lung tissue – I should know, because until a few years ago I didn’t know this. You care about the kidneys, you care about the feet, the eyes, the heart, sometimes the autonomous nervous system – but your lungs aren’t very likely to be brought up in a discussion with the endocrinonologist unless you happen to be a smoker, and in that case the concern is cancer risk and cardiovascular risk.
One main explanation is likely that the effects of the disease are minor, and so do not have much influence on the quality of life of the patient:
“Clear decrements in lung function have been reported in patients with diabetes over the past 2 decades, and many reports have suggested plausible pathophysiological mechanisms. However, at the present time, there are no reports of functional limitations of activities of daily living ascribable to pulmonary disease in patients with diabetes. Accordingly, this review is directed toward a description of the nature of reported lung dysfunction in diabetes, with an emphasis on the emerging potential clinical implications of such dysfunction.” (my emphasis, quote from this review)
I am interested in this matter because, well, at least partly because I’m just the kind of person who takes an interest in such matters. But recently I’ve also started to become a bit curious about whether the disease may have already have had an impact on my own lung function, ‘compared to baseline’. It’s far from certain – most studies find that microvascular complications are correlated (say if your eyes start to display signs of damage, it’s more likely that one may also observe damage to the kidneys) and that the link between those complications and metabolic control is strong; and my metabolic control is close to optimal, and my eyes and kidneys look fine.
I’m a long-distance runner. I run ~35 km/week now (and increasing with ~3 km/week), so of course I should not have breathing difficulties walking up and down stairs, and I don’t. And as the quote above makes clear even for patients who may be impacted, the damage is not likely to be all that major. So the fact that I don’t have any overt lung problems isn’t relevant – we wouldn’t expect such to present anyway. But it is worth asking whether I perform as well as I would do without my disease when I run. The obvious answer would be ‘of course not’ – for reasons unrelated to my lungs (taking blood samples take time, loading up on carbohydrates during a run after the blood sample is taken takes time – and I can’t do these things while running). But is there an impact from the lungs as well? I don’t know. Maybe. You can’t observe the counterfactual.
Which is why I thought this recent-ish meta-analysis was interesting:
“Background: Research into the association between diabetes and pulmonary function has resulted in inconsistent outcomes among studies. We performed a metaanalysis to clarify this association.
Methods: From a systematic search of the literature, we included 40 studies describing pulmonary function data of 3,182 patients with diabetes and 27,080 control subjects. Associations were summarized pooling the mean difference (MD) (standard error) between patients with diabetes and control subjects of all studies for key lung function parameters.
Results: For all studies, the pooled MD for FEV 1 , FVC, and diffusion of the lungs for carbon monoxide were -5.1 (95% CI, -6.4 to -3.7; P<.001), -6.3 (95% CI, -8.0 to -4.7; P<.001), and -7.2 (95% CI, -10.0 to -4.4; P<.001) % predicted, respectively, and for FEV 1 /FVC 0.1% (95% CI, -0.8 to 1.0; P = .78). Metaregression analyses showed that between-study heterogeneity was not explained by BMI, smoking, diabetes duration, or glycated hemoglobin (all P<.05).
Conclusions: Diabetes is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. […]
Our metaanalysis shows that diabetes, in the absence of overt pulmonary disease, is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. The results were irrespective of BMI, smoking, diabetes duration, and HbA1c levels. In subanalyses, the association seemed to be more pronounced in type 2 diabetes than in type 1 diabetes. Our study adds evidence for yet another organ system to be involved in bothtype 1 and type 2 diabetes. As a consequence of exclusion criteria, the levels of functional impairment fell within values that are generally considered to be normal. However, to place this in perspective, the magnitude of impairment found in our study closely resembles that of smoking per se.57 Similarly, given the relatively high prevalence of diabetes in COPD,58 it is tempting to speculate that (uncontrolled) diabetes may accelerate progressive lung function decline. However, from our metaanalysis summarizing crosssectional studies, it is difficult to draw conclusions on causality and progression into overt pulmonary diseases.” (my emphasis)
Whether you smoke or not is certainly not a trivial effect when you’re considering the fitness level of a long-distance runner! I know the effects are smaller for T1’s, but this is most certainly an effect to have in mind. Back when I ran my marathon three years ago both me and my brother were surprised that he did so much better than I did (he came in more than half an hour before I did, despite the fact that we both assumed beforehand that I was the one who was in better shape).
I consider some of the findings quite weird, and it’s hard to make heads or tails of some of this stuff:
“One would expect that a longer exposure to diabetes would proportionally increase the chance of connective tissue being nonenzymatically glycated. However, our study suggests that a longer duration is not necessarily associated with additional loss of pulmonary reserves. This is in line with previous longitudinal studies on this topic.59,60 […]
It is intriguing to observe that the pulmonary system remains relatively spared in diabetes when compared with other organs with wide microvascular beds. It is speculated that the large pulmonary reserves protect against severe pulmonary dysfunction.
Because neither the duration of diabetes nor glycemic state appeared to influence the association in our study, one might question whether there is a causal relationship between diabetes and impaired pulmonary function.”
I’ll try to keep my eyes open for updates on this stuff – although the estimated effects may not be big enough for people to seek out medical advice, they’re huge if you’re a long-distance runner considering whether it’s even worth it to participate in future official runs solely for the sake of improving your performance in such competitions.
On a sidenote I should point out that I don’t (/no longer) run in order to obtain a faster time in an official run – I run because I like to run, and I no longer have much desire to participate in official runs – but I’d be lying if I said I didn’t care at all about that stuff some years back when I started out participating in such runs. Imagine what happens with your desire to participate in such official runs if you don’t seem to be able to improve your time much even with strict adherence to running schedules, especially considering the fact that other people who in other respects are similar to you can out-perform you without doing a lot of work. I was above 70 km/week and had several 30+ kilometer runs behind me before my marathon; my brother never even crossed the 40 km/week threshold. And he beat me by more than half an hour. Go figure. I had a bad run for diabetes-related reasons so during the day this was not a surprising outcome, but it was a profoundly annoying outcome. And no, I was not ‘overtraining’; I was rather at the point where a 25+ km run was the ‘standard running distance’ – you know, that distance you managed without thinking much about it every Tuesday, and Saturday, with a short 20 km run in between – and I decreased the kilometer count up to the run as advised by the plan I was following (more or less stringently, but compared to the people whom I entered the goal line with the word ‘more’ is by far the more accurate one). And no, it’s not like I hadn’t heard about interval training, and it’s not like this stuff is hard to implement in a hilly place like Aarhus.
I did make progress from I started running to the point where I decided not to really consider ‘official runs’ to be be worth it anymore – the first half-marathon took me more than 2 hours, the best one I did in an hour and 47 minutes (this performance was achieved at a point in time where I ran 65 km/week and at least cared somewhat about speed and time taken – so, yeah… Compare this again with my brother, whose next goal is 1.35, without ever having been near 50 km/week). Right now my ‘standard running distance’ is 12-15 km – I like to run, but I have a very limited desire to participate in official runs in the future. It’s not worth it – if I go back to very-high intensity training I may improve my official performances, but that could just as easily be due to factors completely unrelated to my actual shape, like whether I was lucky about the starting blood glucose (fewer tests during the run, less time wasted on that), or whether I’d slept well. Who cares? And it’s not like I need to participate in these runs to motivate myself to get out there – I find running enjoyable as it is, especially in the summer when the weather is nice.
But in case you’d forgotten because of all the personal stuff in the end – to just reiterate the main points that made me start out writing this post:
“Diabetes is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. […] the magnitude of impairment found in our study closely resembles that of smoking”.
This is perhaps also a good illustration of how dangerous diabetes is; the fact that the disease may impact the performance of the lungs in a manner not too dissimilar from smoking is not even considered clinically relevant; the patients have much bigger problems to worry about as it is.
i. I had a doctor’s appointment today and got the results of my bloodwork back. My Hba1c was 48, or 6.5%. This is the lowest it’s been for as long as I can remember. I have had some trouble with hypoglycemic episodes now and then, but not significantly more than usual and I’ve had no major episodes. I believe the lowered Hba1c is probably mostly a result of lowered nocturnal blood glucose values. These have however at some points been uncomfortably low, so I’m not sure 6,5 is a realistic long-term goal and because of those uncomfortably low values I have made adjustments along the way which probably means that the Hba1c may be a bit higher next time if other things stay pretty much the same (which I know they won’t; for instance I’m planning on significantly increasing my running over the next four months). But even so I was very happy about this result, as I choose to believe that it means I’ll actually be able to obtain <7.0% results in the future without major adverse events if I’m careful and vigilant.
This recent post goes into more detail about the hypoglycemia risk and what it’s about. This Danish post has some data on the distribution of Hba1c results among Danish diabetics – the relevant figure is this one (with 6.5%, I’m in the 10% fractile).
ii. I’m now ‘officially’ a researcher. I have just become a member of Statistics Denmark’s research programme (-forskerordning), which means that I’ve obtained access to a specific data set which I’ll do work on during the next year. Danish registers contain a lot of good information compared to the registers of most other countries, so I may actually be able to look at stuff that a lot of researchers elsewhere are simply not able to analyze due to data issues – which is exciting. Unfortunately I’ll not be comfortable blogging anything about this stuff, as there are a huge number of restrictions on data access/sharing etc. – but I believe it’ll be interesting to work with this stuff and I’m looking forward to it.
iii. A couple of Khan Academy videos:
Abstract: “We analyzed one decade of data collected by the Programme for International Student Assessment (PISA), including the mathematics and reading performance of nearly 1.5 million 15 year olds in 75 countries. Across nations, boys scored higher than girls in mathematics, but lower than girls in reading. The sex difference in reading was three times as large as in mathematics. There was considerable variation in the extent of the sex differences between nations. There are countries without a sex difference in mathematics performance, and in some countries girls scored higher than boys. Boys scored lower in reading in all nations in all four PISA assessments (2000, 2003, 2006, 2009). Contrary to several previous studies, we found no evidence that the sex differences were related to nations’ gender equality indicators. Further, paradoxically, sex differences in mathematics were consistently and strongly inversely correlated with sex differences in reading: Countries with a smaller sex difference in mathematics had a larger sex difference in reading and vice versa. We demonstrate that this was not merely a between-nation, but also a within-nation effect. This effect is related to relative changes in these sex differences across the performance continuum: We did not find a sex difference in mathematics among the lowest performing students, but this is where the sex difference in reading was largest. In contrast, the sex difference in mathematics was largest among the higher performing students, and this is where the sex difference in reading was smallest. The implication is that if policy makers decide that changes in these sex differences are desired, different approaches will be needed to achieve this for reading and mathematics. Interventions that focus on high-achieving girls in mathematics and on low achieving boys in reading are likely to yield the strongest educational benefits.”
Abstract: “A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are non-existent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g.,R^2 between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.”
vi. Married men at the age of 40 can expect to live on average 7.1 years longer than unmarried men at the age of 40, and 6.6 years longer than divorced men at the age of 40. For women the life expectancy difference between the married and unmarried group is 4.8 years, and the difference between married women and divorced women is 4.3 years. The excess mortality for unmarried men in their forties (compared with married males) is around 250%, and for men in their fifties it’s still above 200%.
The data reported above is from a new publication by Statistics Denmark which you can read here. Here’s a related publication. Here is a recent publication on the education levels of Danish emigrants. All three publications are unfortunately in Danish.
vii. Nasa – The Tyranny of the Rocket Equation. This part was surprising to me, because I’d never really thought about this:
“If the radius of our planet were larger, there could be a point at which an Earth escaping rocket could not be built. Let us assume that building a rocket at 96% propellant (4% rocket), currently the limit for just the Shuttle External Tank, is the practical limit for launch vehicle engineering. Let us also choose hydrogen-oxygen, the most energetic chemical propellant known and currently capable of use in a human rated rocket engine. By plugging these numbers into the rocket equation, we can transform the calculated escape velocity into its equivalent planetary radius. That radius would be about 9680 kilometers (Earth is 6670 km). If our planet was 50% larger in diameter, we would not be able to venture into space, at least using rockets for transport.”
Here’s the link to the article. I’d missed this one, even if it’s a few years old (from December 2008). It is a great article and it covers a lot of stuff – I’ve decided to quote extensively from it below:
“Glycemic control, a worthwhile goal for people with diabetes, is limited by the barrier of iatrogenic hypoglycemia (1). Iatrogenic hypoglycemia 1) causes recurrent morbidity in most people with type 1 diabetes and many with advanced type 2 diabetes and is sometimes fatal, 2) compromises physiological and behavioral defenses against subsequent falling plasma glucose concentrations and thus causes a vicious cycle of recurrent hypoglycemia, and 3) precludes maintenance of euglycemia over a lifetime of diabetes and therefore full realization of the vascular benefits of glycemic control. […] Unfortunately, maintenance of euglycemia over a lifetime of diabetes cannot be accomplished safely with currently available treatment methods because of the barrier of hypoglycemia (1). […]
Hypoglycemia is a fact of life for most people with type 1 diabetes (1). The average patient has untold numbers of episodes of asymptomatic hypoglycemia and suffers two episodes of symptomatic hypoglycemia per week (thousands of such episodes over a lifetime of diabetes). He or she suffers one or more episodes of severe, temporarily disabling hypoglycemia, often with seizure or coma, per year. There is no evidence that this problem has abated over the decade and a half since it was highlighted by the report of the DCCT (2) in 1993. For example, in 2007 the U.K. Hypoglycemia Study Group (9) reported an incidence of severe hypoglycemia of 110 episodes per 100 patient-years (nearly twice that in the DCCT) in patients with type 1 diabetes, who were necessarily treated with insulin, for <5 years and an incidence of 320 episodes per 100 patient-years in those with type 1 diabetes for >15 years. […]
Although they represent only a small fraction of the total hypoglycemia experience, estimates of the frequency of severe hypoglycemia, particularly if determined in prospective, population-based studies, are the most reliable because they are dramatic events that are more likely to be reported (by the patient or an associate) (1). The prospective, population-based data of Donnelly et al. (10) indicate that the overall incidence of hypoglycemia in insulin-treated type 2 diabetes is approximately one-third of that in type 1 diabetes. The incidence of any and of severe hypoglycemia was ∼4,300 and 115 episodes per 100 patient-years, respectively, in type 1 diabetes and ∼1,600 and 35 episodes per 100 patient-years, respectively, in insulin-treated type 2 diabetes. In addition, in population-based studies the incidence of severe hypoglycemia requiring emergency treatment in insulin-treated type 2 diabetes was ∼40% (11) and ∼100% (12) of that in type 1 diabetes. Since the prevalence of type 2 diabetes is ∼20-fold greater than that of type 1 diabetes, and most people with type 2 diabetes ultimately require treatment with insulin, these data suggest that most episodes of iatrogenic hypoglycemia, including severe hypoglycemia, occur in people with type 2 diabetes. […]
Iatrogenic hypoglycemia causes recurrent physical and psychological morbidity and some mortality, impairs defenses against subsequent hypoglycemia, and precludes maintenance of euglycemia over a lifetime of diabetes (1). Hypoglycemia causes brain fuel deprivation that, if unchecked, results in functional brain failure that is typically corrected after the plasma glucose concentration is raised (13). Rarely, it causes sudden, presumably cardiac arrhythmic death or, if it is profound and prolonged, brain death (13). To the extent that there is a macrovascular benefit of glycemic control (6), the barrier of hypoglycemia also contributes to cardiovascular morbidity and mortality.
The physical morbidity of an episode of hypoglycemia ranges from unpleasant symptoms to seizure and coma (1). Hypoglycemia can impair judgment, behavior, and performance of physical tasks. Permanent neurological damage is rare. While there is concern that recurrent hypoglycemia might cause chronic cognitive impairment, long-term follow-up of the DCCT patients is largely reassuring in that regard (14). […]
Three early reports indicated that 2–4% of people with diabetes die from hypoglycemia (1). More recent reports indicated that 6% (14), 7% (15), and 10% (16) of deaths of people with type 1 diabetes were the result of hypoglycemia. Up to 10% of episodes of severe sulfonylurea-induced hypoglycemia in type 2 diabetes may be fatal (17). [my emphasis, US]
In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study, 10,251 patients with type 2 diabetes at high cardiovascular risk (but with no history of frequent or recent serious hypoglycemic events) were randomized to either intensive glycemic therapy with an A1C goal of <6.0% or to standard glycemic therapy (7). After a median follow-up of 3.4 years, with stable median A1C levels of 6.4 and 7.5%, respectively, intensive glycemic therapy was discontinued because 5.0% of the patients in the intensive therapy group, compared with 4.0% of those in the standard therapy group, had died. […] the most plausible cause of excess mortality during intensive therapy in the ACCORD study is iatrogenic hypoglycemia […]
Glucose is an obligate oxidative fuel for the brain under physiological conditions (1). The brain accounts for >50% of whole-body glucose utilization. The brain can oxidize alternative fuels, such as ketones, if their circulating levels rise high enough to enter the brain in quantity, but that is seldom the case. Because it cannot synthesize glucose, utilize physiological levels of circulating nonglucose fuels effectively, or store more than a few minutes supply of glucose as glycogen, the brain requires a virtually continuous supply of glucose from circulation. Since facilitated blood-to-brain glucose transport is a direct function of the arterial plasma glucose concentration, that supply requires maintenance of plasma glucose concentration. At some level of hypoglycemia (perhaps ∼50–55 mg/dl [2.8–3.1 mmol/l] since symptoms normally occur at that level [19–21]), blood-to-brain glucose transport becomes limiting to brain glucose metabolism and, therefore, function. […]
Early in the course of type 2 diabetes, by far the most common type of diabetes, hyperglycemia may respond to lifestyle changes, specifically weight loss, or to plasma glucose–lowering drugs that should not, and probably do not, cause hypoglycemia. In theory, when such drugs are effective in the absence of side effects, there is no reason not to accelerate their dosing until euglycemia is achieved. Over time, however, as people with type 2 diabetes become progressively more insulin deficient, these drugs, even in combination, fail to maintain glycemic control. Insulin secretagogues are also effective early in type 2 diabetes, but they can cause hyperinsulinemia and therefore introduce the risk of hypoglycemia. Euglycemia is not an appropriate goal during therapy with an insulin secretagogue or with insulin in people with type 2 diabetes. Nonetheless, as discussed earlier, the frequency of hypoglycemia is relatively low (at least with current glycemic goals that are above the euglycemic range) during treatment with an insulin secretagogue, or even with insulin, early in type 2 diabetes (9) when defenses against hypoglycemia are intact. Thus, over much of the course of the most common type of diabetes it is possible to achieve a meaningful degree of glycemic control with no risk or relatively low risk of hypoglycemia. The challenge is greater in people with advanced type 2 diabetes or type 1 diabetes because of compromised defenses against hypoglycemia. In such patients, therapy with insulin is demonstrably effective, but it is not demonstrably safe. Nonetheless, concerns about hypoglycemia should not be used as an excuse for poor glycemic control by patients or their caregivers. Both should strive to achieve and maintain the greatest degree of glycemic control that can be accomplished safely in a given person with diabetes at a given stage of the progression of his or her diabetes.”
For diabetics, especially type 1 diabetics, there’s a very real risk that the (life-saving and non-optional) treatment may kill the patient. All this stuff above may sound very theoretical, but it’s not. It’s quite simple, really: For me personally, pretty much every time I eat a meal I have a decision to make. I have to take some insulin to enable my body to process the carbohydrates in the meal, and I need to estimate how big of a dosis is optimal. ‘The barrier of hypoglycemia’ is the reason why even though ‘my problem’ is that I don’t produce insulin I can’t just take a ‘big enough’ dosis of insulin when that stuff is needed and solve the problem that way without having to worry – the point is that if the dosis I take is ‘too big’, I’ll get hypoglycemia. And if it’s not ‘big enough’ I may not necessarily get symptoms, but I’ll still harm my body and increase the risk of complications later on (and if I consistently take too little on a day to day basis, the long-term risk will go up a lot). It’s simply impossible to ‘get it just right every time’, it’s very easy to get it wrong among other things because the insulin’s therapeutic index is quite low, and the consequences of getting it wrong may be very severe – and the tradeoff is always there, every day, every meal. I know I’ve written about it before but a lot of people, even relatively well-informed people, I’ve talked to about my disease don’t know this, and this tradeoff really is at the very heart of what living with diabetes is all about.