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

Warning: Long post.*

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here’s some more stuff on related matters:

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

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

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

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

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

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

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

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

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

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

**An example:

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

Yeah, that sounds about right to me…

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

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


  1. “I may be judging scientific publications like this one way too harshly, when you compare them with most other books.”

    If I understand you correctly, my reply would be that I don’t think it makes sense to judge scientific publications by comparing them to non-scientific literature, just as it makes little or no sense to judge a film like ‘The Shawshank Redemption’ based on how it compares to ‘Spider-Man’. Even within scientific literature itself, it makes little sense to judge a textbook by comparing it to a pop science work.

    Comment by Manfred Bühler | May 24, 2014 | Reply

    • I don’t disagree (I’ve written about these things before) that there are problems associated with doing comparisons like these – and I’ve explicitly pointed out that there are some ‘double standards’ (well, different standards) at work when I rate books – see the link. But on the other hand it feels wrong to me to give a pop science book which took an author perhaps six months to write four or five stars, and then give 2 stars to the science publication describing the work of, say, 40 researchers and what they’ve found out about this particular topic over the decades. It’s simply not fair. I don’t disagree that you need different scales, but where I was going was that perhaps I have been a bit too fast to judge the latter type of book harshly just because of a few printing errors and stuff like that (there were almost no spelling errors or anything like that in this book, but the point is more general).

      A big problem in general in this context is that I’m very disinclined to think that a book is ‘amazing’ (5 stars) if it contains stuff like this: “Prolyl hydroxylase (PHD) is a tetrameric enzyme containing two hydroxylase units and two protein disulphide isomerase subunits…”, whereas I’m somewhat less reluctant to think so if the book is easier to read. This again does not seem fair to me, because undoubtedly part of the reason I find some of the really good books hard to read is that I’m an ignorant fool; and a big part of the reason why these books are written in the first place is presumably precisely that the authors want to attempt to combat such ignorance and foolishness. I have deliberately punished science book authors for writing stuff which is hard to understand in the past and I’ll keep doing it because sometimes I do feel this is a justified complaint, but I don’t like doing it in general because it tends to feel like I’m just the loud moron who blames others for me being stupid. But a related point is that even when no ‘deliberate punishment’ is going on, I may still be somewhat disinclined to give the highest ratings to books where I don’t really understand all of the details, and I’m thinking now that perhaps I should try to adjust a bit along that dimension.

      Comment by US | May 24, 2014 | Reply

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