This one was mostly review for me, but there was also some new stuff and it was a ‘sort of okay’ lecture even if I was highly skeptical about a few points covered. I was debating whether to even post the lecture on account of those points of contention, but I figured that by adding a few remarks below I could justify doing it. So below a few skeptical comments relating to content covered in the lecture:
a) 28-29 minutes in he mentions that the cutoff for hypertension in diabetics is a systolic pressure above 130. Here opinions definitely differ, and opinions about treatment cutoffs differ; in the annual report from the Danish Diabetes Database they follow up on whether hospitals and other medical decision-making units are following guidelines (I’ve talked about the data on the blog, e.g. here), and the BP goal of involved decision-making units evaluated is currently whether diabetics with systolic BP above 140 receive antihypertensive treatment. This recent Cochrane review concluded that: “At the present time, evidence from randomized trials does not support blood pressure targets lower than the standard targets in people with elevated blood pressure and diabetes” and noted that: “The effect of SBP targets on mortality was compatible with both a reduction and increase in risk […] Trying to achieve the ‘lower’ SBP target was associated with a significant increase in the number of other serious adverse events”.
b) Whether retinopathy screenings should be conducted yearly or biennially is also contested, and opinions differ – this is not mentioned in the lecture, but I sort of figure maybe it should have been. There’s some evidence that annual screening is better (see e.g. this recent review), but the evidence base is not great and clinical outcomes do not seem to differ much in general; as noted in the review, “Observational and economic modelling studies in low-risk patients show little difference in clinical outcomes between screening intervals of 1 year or 2 years”. To stratify based on risk seems desirable from a cost-effectiveness standpoint, but how to stratify optimally seems to not be completely clear at the present point in time.
c) The Somogyi phenomenon is highly contested, and I was very surprised about his coverage of this topic – ‘he’s a doctor lecturing on this topic, he should know better’. As the wiki notes: “Although this theory is well known among clinicians and individuals with diabetes, there is little scientific evidence to support it.” I’m highly skeptical, and I seriously question the advice of lowering insulin in the context of morning hyperglycemia. As observed in Cryer’s text: “there is now considerable evidence against the Somogyi hypothesis (Guillod et al. 2007); morning hyperglycemia is the result of insulin lack, not post-hypoglycemic insulin resistance (Havlin and Cryer 1987; Tordjman et al. 1987; Hirsch et al. 1990). There is a dawn phenomenon—a growth hormone–mediated increase in the nighttime to morning plasma glucose concentration (Campbell et al. 1985)—but its magnitude is small (Periello et al. 1991).”
I decided not to embed this lecture in the post mainly because the resolution is unsatisfactorily low so that a substantial proportion of the visual content is frankly unintelligible; I figured this would bother others more than it did me and that a semi-satisfactory compromise solution in terms of coverage would be to link to the lecture, but not embed it here. You can hear what the lecturer is saying, which was enough for me, but you can’t make out stuff like effect differences, p-values, or many of the details in the graphic illustrations included. Despite the title of the lecture on youtube, the lecture actually mainly consists of a brief overview of pharmacological treatment options for diabetes.
If you want to skip the introduction, the first talk/lecture starts around 5 minutes and 30 seconds into the video. Note that despite the long running time of this video the lectures themselves only take about 50 minutes in total; the rest of it is post-lecture Q&A and discussion.
Like in the first post I cannot promise I have not already covered the topics I’m about to cover in this post before on the blog. In this post I’ll include and discuss material from two chapters of the book: the chapters on how to measure, value, and analyze health outcomes, and the chapter on how to define, measure, and value costs. In the last part of the post I’ll also talk a little bit about some research related to the coverage which I’ve recently looked at in a different context.
In terms of how to measure health outcomes the first thing to note is that there are lots and lots of different measures (‘thousands’) that are used to measure aspects of health. The symptoms causing problems for an elderly man with an enlarged prostate are not the same symptoms as the ones which are bothering a young child with asthma, and so it can be very difficult to ‘standardize’ across measures (more on this below).
A general distinction in this area is that between non-preference-based measures and preference-based measures. Many researchers working with health data are mostly interested in measuring symptoms, and metrics which do (‘only’) this would be examples of non-preference-based measures. Non-preference based measures can again be subdivided into disease- and symptom-specific measures, and non-disease-specific/generic measures; an example of the latter would be the SF-36, ‘the most widely used and best-known example of a generic or non-disease-specific measure of general health’.
Economists will often want to put a value on symptoms or quality-of-life states, and in order to do this you need to work with preference-based measures – there are a lot of limitations one confronts when dealing with non-preference-based measures. Non-preference based measures tend for example to be very different in design and purpose (because asthma is not the same thing as, say, bulimia), which means that there is often a lack of comparability across measures. It is also difficult to know how to properly trade off various dimensions included when using such metrics (for example pain relief can be the result of a drug which also increases nausea, and it’s not perfectly clear when you use such measures whether such a change is to be considered desirable or not); similar problems occur when taking the time dimension into account, where problems with aggregation over time and how to deal with this pop up. Various problems related to weighting are recurring problems; for example a question can be asked when using such measures which symptoms/dimensions included are more important? Are they all equally important? This goes for both the weighting of various different domains included in the metric, and for how to weigh individual questions within a given domain. Many non-preference-based measures contain an implicit equal-interval assumption, so that a move from (e.g.) level one to level two on the metric (e.g. from ‘no pain at all’ to ‘a little’) is considered the same as a move from (e.g.) level three to level four (e.g. ‘quite a bit’ to ‘very much’), and it’s not actually clear that the people who supply the information that goes into these metrics would consider such an approach to be a correct reflection of how they perceive these things. Conceptually related to the aggregation problem mentioned above is the problem that people may have different attitudes toward short-term and long-term health effects/outcomes, but non-preference-based measures usually give equal weight to a health state regardless of the timing of the health state. The issue of some patients dying is not addressed at all when using these measures, as they do not contain information about mortality; which may be an important variable. For all these reasons the authors argue in the text that:
“In summary, non-preference-based health status measures, whether disease specific or generic, are not suitable as outcome measures in economic evaluation. Instead, economists require a measure that combines quality and quantity of life, and that also incorporates the valuations that individuals place on particular states of health.
The outcome metric that is currently favoured as meeting these requirements and facilitating the widest possible comparison between alternative uses of health resources is the quality-adjusted life year“.
Non-preference-based tools may be useful, but you will usually need to go ‘further’ than those to be able to handle the problems economists will tend to care the most about. Some more observations from the chapter below:
“the most important challenge [when valuing health states] is to find a reliable way of quantifying the quality of life associated with any particular health state. There are two elements to this: describing the health state, which […] could be either a disease-specific description or a generic description intended to cover many different diseases, and placing a valuation on the health state. […] these weights or valuations are related to utility theory and are frequently referred to as utilities or utility values.
Obtaining utility values almost invariably involves some process by which individuals are given descriptions of a number of health states and then directly or indirectly express their preferences for these states. It is relatively simple to measure ordinal preferences by asking respondents to rank-order different health states. However, these give no information on strength of preference and a simple ranking suffers from the equal interval assumption […]; as a result they are not suitable for economic evaluation. Instead, analysts make use of cardinal preference measurement. Three main methods have been used to obtain cardinal measures of health state preferences: the rating scale, the time trade-off, and the standard gamble. […] The large differences typically observed between RS [rating scale] and TTO [time trade-off] or SG [standard gamble] valuations, and the fact that the TTO and SG methods are choice based and therefore have stronger foundations in decision theory, have led most standard texts and guidelines for technology appraisal to recommend choice-based valuation methods [The methods are briefly described here, where the ‘VAS’ corresponds to the rating scale method mentioned – the book covers the methods in much more detail, but I won’t go into those details here].”
“Controversies over health state valuation are not confined to the valuation method; there are also several strands of opinion concerning who should provide valuations. In principle, valuations could be provided by patients who have had first-hand experience of the health state in question, or by experts such as clinicians with relevant scientific or clinical expertise, or by members of the public. […] there is good evidence that the valuations made by population samples and patients frequently vary quite substantially [and] the direction of difference is not always consistent. […] current practice has moved towards the use of valuations obtained from the general public […], an approach endorsed by recent guidelines in the UK and USA explicitly recommend that population valuations are used”.
Given the very large number of studies which have been based on non-preference based instruments, it would be desirable for economists working in this field to somehow ‘translate’ the information contained in those studies so that this information can also be used for cost-effectiveness evaluations. As a result of this an increasing number of so-called ‘mapping studies’ have been conducted over the years, the desired goal of which is to translate the non-preference based measures into health state utilities, allowing outcomes and effects derived from the studies to be expressed in terms of QALYs. There’s more than one way to try to get from a non-preference based metric to a preference-based metric and the authors describe three approaches in some detail, though I’ll not discuss those approaches or details here. They make this concluding assessment of mapping studies in the text:
“Mapping studies are continuing to proliferate, and the literature on new mapping algorithms and methods, and comparisons between approaches, is expanding rapidly. In general, mapping methods seem to have reasonable ability to predict group mean utility scores and to differentiate between groups with or without known existing illness. However, they all seem to predict increasingly poorly as health states become more serious. […] all forms of mapping are ‘second best’, and the existence of a range of techniques should not be taken as an argument for relying on mapping instead of obtaining direct preference-based measurements in prospectively designed studies.”
I won’t talk too much about the chapter on how to define, measure and value costs, but I felt that a few observations from the chapter should be included in the coverage:
“When asking patients to complete resource/time questionnaires (or answer interview questions), a particularly important issue is deciding on the optimum recall period. Two types of recall error can be distinguished: simply forgetting an entire episode, or incorrectly recalling when it occurred. […] there is a trade-off between recall bias and complete sampling information. […] the longer the period of recall the greater is the likelihood of recall error, but the shorter the recall period the greater is the problem of missing information.”
“The range of patient-related costs included in economic valuations can vary considerably. Some studies include only the costs incurred by patients in travelling to a hospital or clinic for treatment; others may include a wider range of costs including over-the-counter purchases of medications or equipment. However, in some studies a much broader approach is taken, in which attempts are made to capture both the costs associated with treatments and the consequences of illness in terms of absence from or cessation of work.”
An important note here which I thought I should add is that whereas many people unfamiliar with this field may translate ‘medical costs of illness’ with ‘the money that is paid to the doctor(s)’, direct medical costs will in many cases drastically underestimate the ‘true costs’ of disease. To give an example, Ferber et al. (2006) when looking at the costs of diabetes included two indirect cost components in their analysis – inability to work, and early retirement – and concluded that these two cost components made up approximately half of the total costs of diabetes. I think there are reasons to be skeptical of the specific estimate on account of the way it is made (for example if diabetics are less productive/earn less than the population in general, which seems likely if the disease is severe enough to cause many people to withdraw prematurely from the labour market, the cost estimate may be argued to be an overestimate), but on the other hand there are multiple other potentially important indirect cost components they do not include in the calculation, such as e.g. disease-related lower productivity while at work (for details on this, see e.g. this paper – that cost component may also be substantial in some contexts) and things like spousal employment spill-over effects (it is known from related research – for an example, see this PhD dissertation – that disease may impact on the retirement decisions of the spouse of the individual who is sick, not just the individual itself, but effects here are likely to be highly context-dependent and to vary across countries). Another potentially important variable in an indirect cost context is informal care provision. Here’s what they authors say about that one:
“Informal care is often provided by family members, friends, and volunteers. Devoting time and resources to collecting this information may not be worthwhile for interventions where informal care costs are likely to form a very small part of the total costs. However, in other studies non-health-service costs could represent a substantial part of the total costs. For instance, dementia is a disease where the burden of care is likely to fall upon other care agencies and family members rather than entirely on the health and social care services, in which case considering such costs would be important.
To date [however], most economic evaluations have not considered informal care costs.”
It’s been a while since I posted anything here so I figured I should at least post something…
i. A few Khan Academy videos I watched a while back:
(Bookmark remark: (‘Not completely devoid of slight inaccuracies as usual – e.g. in meningitis, neck stiffness is not as much as symptom as it is a clinical sign (see Chamberlain’s symptoms and signs…))’
(Bookmark remark: ‘Very simplified, but not terrible’)
ii. I previously read the wiki on strategic bombing during WW2, but the article did not really satisfy my curiosity and it turns out that the wiki also has a great (featured) article about Air raids on Japan (a topic not covered in a great amount of detail in the aforementioned wiki article). A few random observations from the article:
“Overall, the attacks in May destroyed 94 square miles (240 km2) of buildings, which was equivalent to one seventh of Japan’s total urban area.”
“In Tokyo, Osaka, Nagoya, Yokohama, Kobe, and Kawasaki, “over 126,762 people were killed … and a million and a half dwellings and over 105 square miles (270 km2) of urban space were destroyed.” In Tokyo, Osaka and Nagoya, “the areas leveled (almost 100 square miles (260 km2)) exceeded the areas destroyed in all German cities by both the American and English air forces (approximately 79 square miles (200 km2)).””
“In financial terms, the Allied air campaign and attacks on merchant ships destroyed between one third and a quarter of Japan’s wealth.”
“Approximately 40 percent of the urban area of the 66 cities subjected to area attacks were destroyed. This included the loss of about 2.5 million housing units, which rendered 8.5 million people homeless.”
iii. A few longer lectures I’ve watched recently but did not think were particularly good: The Fortress (GM Akobian, Chess), Safety in the Nuclear Industry (Philip Thomas, Gresham College), War, Health and Medicine: The medical lessons of World War I (Mark Harrison, Gresham College – topic had potential, somehow did not like ‘the delivery’; others may find it worth watching).
iv. I play a lot of (too much) chess these days, so I guess it makes sense to post a little on this topic as well. Here’s a list of some of my recent opponents on the ICC: GM Zurab Azmaiparashvili, IM Jerzy Slaby, IM Petar Gojkovic, GM Goran Kosanovic, IM Jeroen Bosch, WGM Alla Grinfeld. I recall encountering a few titled players when I started out on the ICC and my rating was still adjusting and stabilizing, but now I’ve sort of fixed at a level around 1700-1800 in both the 1, 3 and 5 minute pools – sometimes a bit higher, sometimes a bit lower (and I’ve played relatively few 5 minute games so far)). This is a level where at least in bullet some of the semi-regular opponents I’ll meet in the rating pool are guys like these. I was quite dissatisfied with my play when I started out on the ICC because I hadn’t realized how tough it is to maintain a high rating there; having a closer look at which sort of opponents I was actually facing gradually made me realize I was probably doing quite well, all things considered. Lately I’ve been thinking that I have probably even been doing quite a bit better than I’d thought I had. See also this and this link. I’ve gradually concluded that I’m probably never ‘going back’ now that I’ve familiarized myself with the ICC server.
And yes, I do occasionally win against opposition like that, also on position – below an example from a recent game against a player not on the list above (there are quite a few anonymous title-holders as well on the server):
Click to view full size – the list to the lower left is a list of other players online on the server at that point in time, ordered by rating; as should be clear, lots of title-holders have relatively low ratings (I’m not completely sure which rating pool was displayed in the sidebar at that time, but the defaults on display for me are 5- or 3-minutes, so for example the international master ‘softrain’ thus had either a 3 or 5 minute rating of 1799 at that time. Do note that ICC requires proof for titles to display on the server; random non-titled players do not display as titleholders on the ICC (actually the formally approved titled accounts obviously do not account for all accounts held by title-holders as some titled players on the server use accounts which do not give away the fact that they have a title).
Here’s another very nice illustration of how tough the X-minute pools are (/how strong the players playing on the ICC are):
Again, click to view in full size. This is Chinese Grandmaster Wang Hao‘s ICC account. Wang Hao is currently #39 on the FIDE list of active chess players in the world, with a FIDE rating above 2700. Even his 5-minute rating on the ICC, based on more than a thousand games, is below 2300, and his current 3 minute rating is barely above 2000. With numbers like those, I currently feel quite satisfied with my 1700-1800 ratings (although I know I should be spending less time on chess than I currently do).
vi. A few other wiki links: Fritz Haber, Great Stink (featured), Edward Low (a really nice guy, it seems – “A story describes Low burning a French cook alive, saying he was a “greasy fellow who would fry well”, and another tells he once killed 53 Spanish captives with his cutlass.“), 1940 Soviet ultimatum to Lithuania (‘good article’).
vii. A really cute paper from the 2013 Christmas edition of the British Medical Journal: Were James Bond’s drinks shaken because of alcohol induced tremor? Here’s the abstract:
“Objective To quantify James Bond’s consumption of alcohol as detailed in the series of novels by Ian Fleming.
Design Retrospective literature review.
Setting The study authors’ homes, in a comfy chair.
Participants Commander James Bond, 007; Mr Ian Lancaster Fleming.
Main outcome measures Weekly alcohol consumption by Commander Bond.
Methods All 14 James Bond books were read by two of the authors. Contemporaneous notes were taken detailing every alcoholic drink taken. Predefined alcohol unit levels were used to calculate consumption. Days when Bond was unable to consume alcohol (such as through incarceration) were noted.
Results After exclusion of days when Bond was unable to drink, his weekly alcohol consumption was 92 units a week, over four times the recommended amount. His maximum daily consumption was 49.8 units. He had only 12.5 alcohol free days out of 87.5 days on which he was able to drink.
Conclusions James Bond’s level of alcohol intake puts him at high risk of multiple alcohol related diseases and an early death. The level of functioning as displayed in the books is inconsistent with the physical, mental, and indeed sexual functioning expected from someone drinking this much alcohol. We advise an immediate referral for further assessment and treatment, a reduction in alcohol consumption to safe levels, and suspect that the famous catchphrase “shaken, not stirred” could be because of alcohol induced tremor affecting his hands.”
viii. A couple of other non-serious links which I found hilarious:
1) The Prof(essor) or Hobo quiz (via SSC).
2) Today’s SMBC. I’ll try to remember the words in the votey in the highly unlikely case I’ll ever have use for them – in my opinion it would be a real tragedy if one were to miss an opportunity to make a statement like that, given that it was at all suitable to the situation at hand..
Sorry for the infrequent updates. I realized blogging Wodehouse books takes more time than I’d imagined, so posting this sort of stuff is probably a better idea.
“On the first day of the evacuation, only 7,669 men were evacuated, but by the end of the eighth day, a total of 338,226 soldiers had been rescued by a hastily assembled fleet of over 800 boats. Many of the troops were able to embark from the harbour’s protective mole onto 39 British destroyers and other large ships, while others had to wade out from the beaches, waiting for hours in the shoulder-deep water. Some were ferried from the beaches to the larger ships by the famous little ships of Dunkirk, a flotilla of hundreds of merchant marine boats, fishing boats, pleasure craft, and lifeboats called into service for the emergency. The BEF lost 68,000 soldiers during the French campaign and had to abandon nearly all of their tanks, vehicles, and other equipment.”
One way to make sense of the scale of the operations here is to compare them with the naval activities on D-day four years later. The British evacuated more people from France during three consecutive days in 1940 (30th and 31st of May, and 1st of June) than the Allies (Americans and British combined) landed on D-day four years later, and the British evacuated roughly as many people on the 31st of May (68,014) as they landed by sea on D-day (75,215). Here’s a part of the story I did not know:
“Three British divisions and a host of logistic and labour troops were cut off to the south of the Somme by the German “race to the sea”. At the end of May, a further two divisions began moving to France with the hope of establishing a Second BEF. The majority of the 51st (Highland) Division was forced to surrender on 12 June, but almost 192,000 Allied personnel, 144,000 of them British, were evacuated through various French ports from 15–25 June under the codename Operation Ariel. […] More than 100,000 evacuated French troops were quickly and efficiently shuttled to camps in various parts of southwestern England, where they were temporarily lodged before being repatriated. British ships ferried French troops to Brest, Cherbourg, and other ports in Normandy and Brittany, although only about half of the repatriated troops were deployed against the Germans before the surrender of France. For many French soldiers, the Dunkirk evacuation represented only a few weeks’ delay before being killed or captured by the German army after their return to France.”
ii. A pretty awesome display by the current world chess champion:
If you feel the same way I do about Maurice Ashley, you’ll probably want to skip the first few minutes of this video. Don’t miss the games, though – this is great stuff. Do keep in mind when watching this video that the clock is a really important part of this event; other players in the past have played a lot more people at the same time while blindfolded than Carlsen does here – “Although not a full-time chess professional [Najdorf] was one of the world’s leading chess players in the 1950s and 1960s and he excelled in playing blindfold chess: he broke the world record twice, by playing blindfold 40 games in Rosario, 1943, and 45 in São Paulo, 1947, becoming the world blindfold chess champion” (link) – but a game clock changes things a lot. A few comments and discussion here.
In very slightly related news, I recently got in my first win against a grandmaster in a bullet game on the ICC.
iii. Gastric-brooding frog.
“The genus was unique because it contained the only two known frog species that incubated the prejuvenile stages of their offspring in the stomach of the mother. […] What makes these frogs unique among all frog species is their form of parental care. Following external fertilization by the male, the female would take the eggs or embryos into her mouth and swallow them. […] Eggs found in females measured up to 5.1 mm in diameter and had large yolk supplies. These large supplies are common among species that live entirely off yolk during their development. Most female frogs had around 40 ripe eggs, almost double that of the number of juveniles ever found in the stomach (21–26). This means one of two things, that the female fails to swallow all the eggs or the first few eggs to be swallowed are digested. […] During the period that the offspring were present in the stomach the frog would not eat. […] The birth process was widely spaced and may have occurred over a period of as long as a week. However, if disturbed the female may regurgitate all the young frogs in a single act of propulsive vomiting.”
Fascinating creatures.. Unfortunately they’re no longer around (they’re classified as extinct).
iv. I’m sort of conflicted about what to think about this:
“Epidemiological studies show that patients with type-2-diabetes (T2DM) and individuals with a diabetes-independent elevation in blood glucose have an increased risk for developing dementia, specifically dementia due to Alzheimer’s disease (AD). These observations suggest that abnormal glucose metabolism likely plays a role in some aspects of AD pathogenesis, leading us to investigate the link between aberrant glucose metabolism, T2DM, and AD in murine models. […] Recent epidemiological studies demonstrate that individuals with type-2 diabetes (T2DM) are 2–4 times more likely to develop AD (3–5), individuals with elevated blood glucose levels are at an increased risk to develop dementia (5), and those with elevated blood glucose levels have a more rapid conversion from mild cognitive impairment (MCI) to AD (6), suggesting that disrupted glucose homeostasis could play a […] causal role in AD pathogenesis. Although several prominent features of T2DM, including increased insulin resistance and decreased insulin production, are at the forefront of AD research (7–10), questions regarding the effects of elevated blood glucose independent of insulin resistance on AD pathology remain largely unexplored. In order to investigate the potential role of glucose metabolism in AD, we combined glucose clamps and in vivo microdialysis as a method to measure changes in brain metabolites in awake, freely moving mice during a hyperglycemic challenge. Our findings suggest that acute hyperglycemia raises interstitial fluid (ISF) Aβ levels by altering neuronal activity, which increases Aβ production. […] Since extracellular Aβ, and subsequently tau, aggregate in a concentration-dependent manner during the preclinical period of AD while individuals are cognitively normal (27), our findings suggest that repeated episodes of transient hyperglycemia, such as those found in T2DM, could both initiate and accelerate plaque accumulation. Thus, the correlation between hyperglycemia and increased ISF Aβ provides one potential explanation for the increased risk of AD and dementia in T2DM patients or individuals with elevated blood glucose levels. In addition, our work suggests that KATP channels within the hippocampus act as metabolic sensors and couple alterations in glucose concentrations with changes in electrical activity and extracellular Aβ levels. Not only does this offer one mechanistic explanation for the epidemiological link between T2DM and AD, but it also provides a potential therapeutic target for AD. Given that FDA-approved drugs already exist for the modulation of KATP channels and previous work demonstrates the benefits of sulfonylureas for treating animal models of AD (26), the identification of these channels as a link between hyperglycemia and AD pathology creates an avenue for translational research in AD.”
Why am I conflicted? Well, on the one hand it’s nice to know that they’re making progress in terms of figuring out why people get Alzheimer’s and potential therapeutic targets are being identified. On the other hand this – “our findings suggest that repeated episodes of transient hyperglycemia […] could both initiate and accelerate plaque accumulation” – is bad news if you’re a type 1 diabetic (I’d much rather have them identify risk factors to which I’m not exposed).
v. I recently noticed that Khan Academy has put up some videos about diabetes. From the few ones I’ve had a look at they don’t seem to contain much stuff I don’t already know so I’m not sure I’ll explore this playlist in any more detail, but I figured I might as well share a few of the videos here; the first one is about the pathophysiology of type 1 diabetes and the second one’s about diabetic nephropathy (kidney disease):
vi. On Being the Right Size, by J. B. S. Haldane. A neat little text. A few quotes:
“To the mouse and any smaller animal [gravity] presents practically no dangers. You can drop a mouse down a thousand-yard mine shaft; and, on arriving at the bottom, it gets a slight shock and walks away, provided that the ground is fairly soft. A rat is killed, a man is broken, a horse splashes. For the resistance presented to movement by the air is proportional to the surface of the moving object. Divide an animal’s length, breadth, and height each by ten; its weight is reduced to a thousandth, but its surface only to a hundredth. So the resistance to falling in the case of the small animal is relatively ten times greater than the driving force.
An insect, therefore, is not afraid of gravity; it can fall without danger, and can cling to the ceiling with remarkably little trouble. It can go in for elegant and fantastic forms of support like that of the daddy-longlegs. But there is a force which is as formidable to an insect as gravitation to a mammal. This is surface tension. A man coming out of a bath carries with him a film of water of about one-fiftieth of an inch in thickness. This weighs roughly a pound. A wet mouse has to carry about its own weight of water. A wet fly has to lift many times its own weight and, as everyone knows, a fly once wetted by water or any other liquid is in a very serious position indeed. An insect going for a drink is in as great danger as a man leaning out over a precipice in search of food. If it once falls into the grip of the surface tension of the water—that is to say, gets wet—it is likely to remain so until it drowns. A few insects, such as water-beetles, contrive to be unwettable; the majority keep well away from their drink by means of a long proboscis. […]
It is an elementary principle of aeronautics that the minimum speed needed to keep an aeroplane of a given shape in the air varies as the square root of its length. If its linear dimensions are increased four times, it must fly twice as fast. Now the power needed for the minimum speed increases more rapidly than the weight of the machine. So the larger aeroplane, which weighs sixty-four times as much as the smaller, needs one hundred and twenty-eight times its horsepower to keep up. Applying the same principle to the birds, we find that the limit to their size is soon reached. An angel whose muscles developed no more power weight for weight than those of an eagle or a pigeon would require a breast projecting for about four feet to house the muscles engaged in working its wings, while to economize in weight, its legs would have to be reduced to mere stilts. Actually a large bird such as an eagle or kite does not keep in the air mainly by moving its wings. It is generally to be seen soaring, that is to say balanced on a rising column of air. And even soaring becomes more and more difficult with increasing size. Were this not the case eagles might be as large as tigers and as formidable to man as hostile aeroplanes.
But it is time that we pass to some of the advantages of size. One of the most obvious is that it enables one to keep warm. All warmblooded animals at rest lose the same amount of heat from a unit area of skin, for which purpose they need a food-supply proportional to their surface and not to their weight. Five thousand mice weigh as much as a man. Their combined surface and food or oxygen consumption are about seventeen times a man’s. In fact a mouse eats about one quarter its own weight of food every day, which is mainly used in keeping it warm. For the same reason small animals cannot live in cold countries. In the arctic regions there are no reptiles or amphibians, and no small mammals. The smallest mammal in Spitzbergen is the fox. The small birds fly away in winter, while the insects die, though their eggs can survive six months or more of frost. The most successful mammals are bears, seals, and walruses.” [I think he’s a bit too categorical in his statements here and this topic is more contested today than it probably was when he wrote his text – see wikipedia’s coverage of Bergmann’s rule].
[Warning: Long post].
I’ve blogged data related to the data covered in this post before here on the blog, but when I did that I only provided coverage in Danish. Part of my motivation for providing some coverage in English here (which is a slightly awkward and time consuming thing to do as all the source material is in Danish) is that this is the sort of data you probably won’t ever get to know about if you don’t understand Danish, and it seems like some of it might be worth knowing about also for people who do not live in Denmark. Another reason for posting stuff in English is of course that I dislike writing a blog post which I know beforehand that some of my regular readers will not understand. I should perhaps note that some of the data is at least peripherally related to my academic work at the moment.
The report which I’m covering in this post (here’s a link to it) deals primarily with various metrics collected in order to evaluate whether treatment goals which have been set centrally are being met by the Danish regions, one of the primary political responsibilities of which is to deal with health care service delivery. To take an example from the report, a goal has been set that at least 95 % of patients with known diabetes in the Danish regions should have their Hba1c (an important variable in the treatment context) measured at least once per year. The report of course doesn’t just contain a list of goals etc. – it also presents a lot of data which has been collected throughout the country in order to figure out to which extent the various goals have been met at the local levels. Hba1c is just an example; there are also goals set in relation to the variables hypertension, regular eye screenings, regular kidney function tests, regular foot examinations, and regular tests for hyperlipidemia, among others.
Testing is just one aspect of what’s being measured; other goals relate to treatment delivery. There’s for example a goal that the proportion of (known) type 2 diabetics with an Hba1c above 7.0% who are not receiving anti-diabetic treatment should be at most 5% within regions. A thought that occurred to me while reading the report was that it seemed to me that some interesting incentive problems might pop up here if these numbers were more important than I assume they are in the current decision-making context, because adding this specific variable without also adding a goal for ‘finding diabetics who do not know they are sick’ – and no such goal is included in the report, as far as I’ve been able to ascertain – might lead to problems; in theory a region that would do well in terms of identifying undiagnosed type 2 patients, of which there are many, might get punished for this if their higher patient population in treatment as a result of better identification might lead to binding capacity constraints at various treatment levels; capacity constraints which would not affect regions which are worse at identifying (non-)patients at risk because of the existence of a tradeoff between resources devoted to search/identification and resources devoted to treatment. Without a goal for identifying undiagnosed type 2 diabetics, it seems to me that to the extent that there’s a tradeoff between devoting resources to identifying new cases and devoting resources to the treatment of known cases, the current structure of evaluation, to the extent that it informs decision-making at the regional level, favours treatment over identification – which might or might not be problematic from a cost-benefit point of view. I find it somewhat puzzling that no goals relate to case-finding/diagnostics because a lot of the goals only really make sense if the people who are sick actually get diagnosed so that they can receive treatment in the first place; that, say, 95% of diabetics with a diagnosis receives treatment option X is much less impressive if, say, a third of all people with the disease do not have a diagnosis. Considering the relatively low amount of variation in some of the metrics included you’d expect a variable of this sort to be included here, at least I did.
The report has an appendix with some interesting information about the sex ratios, age distributions, how long people have had diabetes, whether they smoke, what their BMIs and blood pressures are like, how well they’re regulated (in terms of Hba1c), what they’re treated with (insulin, antihypertensive drugs, etc.), their cholesterol levels and triglyceride levels, etc. I’ll talk about these numbers towards the end of the post – if you want to get straight to this coverage and don’t care about the ‘main coverage’, you can just scroll down until you reach the ‘…’ point below.
The report has 182 pages with a lot of data, so I’m not going to talk about all of it. It is based on very large data sets which include more than 37.000 Danish diabetes patients from specialized diabetes units (diabetesambulatorier) (these are usually located in hospitals and provide ambulatory care only) as well as 34.000 diabetics treated by their local GPs – the aim is to eventually include all Danish diabetics in the database, and more are added each year, but even as it is a very big proportion of all patients are ‘accounted for’ in the data. Other sources also provide additional details, for example there’s a database on children and young diabetics collected separately. Most of the diabetics which are not included here are patients treated by their local GPs, and there’s still a substantial amount of uncertainty related to this group; approximately 90% of all patients connected to the diabetes units are assumed at this point to be included in the database, but the report also notes that approximately 80 % of diabetics are assumed to be treated in general practice. Coverage of this patient population is currently improving rapidly and it seems that most diabetics in Denmark will likely be included in the database within the next few years. They speculate in the report that the inclusion of more patients treated in general practice may be part of the explanation why goal achievement seems to have decreased slightly over time; this seems to me like a likely explanation considering the data they present as the diabetes units in general are better at achieving the goals set than are the GPs. The data is up to date – as some of you might have inferred from the presumably partly unintelligible words in the parenthesis in the title, the report deals with data from the time period 2013-2014. I decided early on not to copy tables into this post directly as it’s highly annoying to have to translate terms in such tables; instead I’ve tried to give you the highlights. I may or may not have succeeded in doing that, but you should be aware, especially if you understand Danish, that the report has a lot of details, e.g. in terms of intraregional variation etc., which are excluded from this coverage. Although I far from cover all the data, I do cover most of the main topics dealt with in the publication in at least a little bit of detail.
The report concludes in the introduction that for most treatment indicators no clinically significant differences in the quality of the treatment provided to diabetics are apparent when you compare the different Danish regions – so if you’re looking at the big picture, if you’re a Danish diabetic it doesn’t matter all that much if you live in Jutland or in Copenhagen. However some significant intra-regional differences do exist. In the following I’ll talk in a bit more detail about some of data included in the report.
When looking at the Hba1c goal (95% should be tested at least once per year), they evaluate the groups treated in the diabetes units and the groups treated in general practice separately; so you have one metric for patients treated in diabetes units living in the north of Jutland (North Denmark Region) and you have another group of patients treated in general practice living in the north of Jutland – this breakdown of the data makes it possible to not only compare people across regions but also to investigate whether there are important differences between the care provided by diabetes units and the care provided by general practitioners. When dealing with patients receiving ambulatory care from the diabetes units all regions meet the goal, but in Copenhagen (Capital Region of Denmark, (-CRD)) only 94% of patients treated in general practice had their Hba1c measured within the last year – this was the only region which did not meet the goal for the patient population treated in general practice. I would have thought beforehand that all diabetes units would have 100% coverage here, but that’s actually only the case in the region in which I live (Central Denmark Region) – on the other hand in most other regions, aside from Copenhagen again, the number is 99%, which seems reasonable as I’m assuming a substantial proportion of the remainder is explained by patient noncompliance, which is difficult to avoid completely. I speculate that patient compliance differences between patient populations treated at diabetes units and patient populations treated by their GP might also be part of the explanation for the lower goal achievement of the general practice population; as far as I’m aware diabetes units can deny care in the case of non-compliance whereas GPs cannot, so you’d sort of expect the most ‘difficult’ patients to end up in general practice; this is speculation to some extent and I’m not sure it’s a big effect, but it’s worth keeping in mind when analyzing this data that not all differences you observe necessarily relate to service delivery inputs (whether or not a doctor reminds a patient it’s time to get his eyes checked, for example); the two main groups analyzed are likely to also be different due to patient population compositions. Differences in patient population composition may of course also drive some of the intraregional variation observed. They mention in their discussion of the results for the Hba1c variable that they’re planning on changing the standard here to one which relate to the distributional results of the Hba1c, not just whether the test was done, which seems like a good idea. As it is, the great majority of Danish diabetics have their Hba1c measured at least annually, which is good news because of the importance of this variable in the treatment context.
In the context of hypertension, there’s a goal that at least 95% of diabetics should have their blood pressure measured at least once per year. In the context of patients treated in the diabetes units, all regions achieve the goal and the national average for this patient population is 97% (once again the region in which I live is the only one that achieved 100 % coverage), but in the context of patients treated in general practice only one region (North Denmark Region) managed to get to 95% and the national average is 90%. In most regions, one in ten diabetics treated in general practice do not have their blood pressure measured once per year, and again Copenhagen (CRD) is doing worst with a coverage of only 87%. As mentioned in the general comments above some of the intraregional variation is actually quite substantial, and this may be a good example because not all hospitals are doing great on this variable. Sygehus Sønderjylland, Aabenraa (in southern Jutland), one of the diabetes units, had a coverage of only 67%, and the percentage of patients treated at Hillerød Hospital in Copenhagen (CRD), another diabetes unit, was likewise quite low, with 83% of patients having had their blood pressure measured within the last year. These hospitals are however the exceptions to the rule. Evaluating whether it has been tested if patients do or do not have hypertension is different from evaluating whether hypertension is actually treated after it has been discovered, and here the numbers are less impressive; for the type 1 patients treated in the diabetes units, roughly one third (31%) of patients with a blood pressure higher than 140/90 are not receiving treatment for hypertension (the goal was at most 20%). The picture was much better for type 2 patients (11% at the national level) and patients treated in general practice (13%). They note that the picture has not improved over the last years for the type 1 patients and that this is not in their opinion a satisfactory state of affairs. A note of caution is that the variable only includes patients who have had a blood pressure measured within the last year which was higher than 140/90 and that you can’t use this variable as an indication of how many patients with high blood pressure are not being treated; some patients who are in treatment for high blood pressure have blood pressures lower than 140/90 (achieving this would in many cases be the point of treatment…). Such an estimate will however be added to later versions of the report. In terms of the public health consequences of undertreatment, the two patient populations are of course far from equally important. As noted later in the coverage, the proportion of type 2 patients on antihypertensive agents is much higher than the proportion of type 1 diabetics receiving treatment like this, and despite this difference the blood pressure distributions of the two patient populations are reasonably similar (more on this below).
Screening for albuminuria: The goal here is that at least 95 % of adult diabetics are screened within a two-year period (There are slightly different goals for children and young adults, but I won’t go into those). In the context of patients treated in the diabetes units, the northern Jutland Region and Copenhagen/RH failed to achieve the goal with a coverage slightly below 95% – the other regions achieved the goal, although not much more than that; the national average for this patient population is 96%. In the context of patients treated in general practice none of the regions achieve the goal and the national average for this patient population is 88%. Region Zealand was doing worst with 84%, whereas the region in which I live, Region Midtjylland, was doing best with a 92% coverage. Of the diabetes units, Rigshospitalet, “one of the largest hospitals in Denmark and the most highly specialised hospital in Copenhagen”, seems to also be the worst performing hospital in Denmark in this respect, with only 84 % of patients being screened – which to me seems exceptionally bad considering that for example not a single hospital in the region in which I live is below 95%. Nationally roughly 20% of patients with micro- or macroalbuminuria are not on ACE-inhibitors/Angiotensin II receptor antagonists.
Eye examination: The main process goal here is at least one eye examination every second year for at least 90% of the patients, and a requirement that the treating physician knows the result of the eye examination. This latter requirement is important in the context of the interpretation of the results (see below). For patients treated in diabetes units, four out of five regions achieved the goal, but there were also what to me seemed like large differences across regions. In Southern Denmark, the goal was not met and only 88 % had had an eye examination within the last two years, whereas the number was 98% in Region Zealand. Region Zealand was a clear outlier here and the national average for this patient population was 91%. For patients treated in general practice no regions achieved the goal, and this variable provides a completely different picture from the previous variables in terms of the differences between patients treated in diabetes units and patients treated in general practice: In most regions, the coverage here for patients in general practice is in the single digits and the national average for this patient population is just 5 %. They note in the report that this number has decreased over the years through which this variable has been analyzed, and they don’t know why (but they’re investigating it). It seems to be a big problem that doctors are not told about the results of these examinations, which presumably makes coordination of care difficult.
The report also has numbers on how many patients have had their eyes checked within the last 4 years, rather than within the last two, and this variable makes it clear that more infrequent screening is not explaining anything in terms of the differences between the patient populations; for patients treated in general practice the numbers are still here in the single digits. They mention that data security requirements imposed on health care providers are likely the reason why the numbers are low in general practice as it seems common that the GP is not informed of the results of screenings taking place, so that the only people who gets to know about the results are the ophthalmologists doing them. A new variable recently included in the report is whether newly-diagnosed type 2 diabetics are screened for eye-damage within 12 months of receiving their diagnosis – here they have received the numbers directly from the ophthalmologists so uncertainty about information sharing doesn’t enter the picture (well, it does, but the variable doesn’t care; it just measures whether an eye screen has been performed or not) – and although the standard set is 95% (at most one in twenty should not have their eyes checked within a year of diagnosis) at the national level only half of patients actually do get an eye screen within the first year (95% CI: 46-53%) – uncertainty about the date of diagnosis makes it slightly difficult to interpret some of the specific results, but the chosen standard is not achieved anywhere and this once again underlines how diabetic eye care is one of the areas where things are not going as well as the people setting the goals would like them to. The rationale for screening people within the first year of diagnosis is of course that many type 2 patients have complications at diagnosis – “30–50 per cent of patients with newly diagnosed T2DM will already have tissue complications at diagnosis due to the prolonged period of antecedent moderate and asymptomatic hyperglycaemia.” (link).
The report does include estimates of the number of diabetics who receive eye screenings regardless of whether the treating physician knows the results or not; at the national level, according to this estimate 65% of patients have their eyes screened at least once every second year, leaving more than a third of patients in a situation where they are not screened as often as is desirable. They mention that they have had difficulties with the transfer of data and many of the specific estimates are uncertain, including two of the regional estimates, but the general level – 65% or something like that – is based on close to 10.000 patients and is assumed to be representative. Approximately 1% of Danish diabetics are blind, according to the report.
Foot examinations: Just like most of the other variables: At least 95 % of patients, at least once every second year. For diabetics treated in diabetes units, the national average is here 96% and the goal was not achieved in Copenhagen (CRD) (94%) and northern Jutland (91%). There are again remarkable differences within regions; at Helsingør Hospital only 77% were screened (95% CI: 73-82%) (a drop from 94% the year before), and at Hillerød Hospital the number was even lower, 73% (95% CI: 70-75), again a drop from the previous year where the coverage was 87%. Both these numbers are worse than the regional averages for all patients treated in general practice, even though none of the regions meet the goal. Actually I thought the year-to-year changes in the context of these two hospitals were almost as interesting as the intraregional differences because I have a hard time explaining those; how do you even set up a screening programme such that a coverage drop of more than 10 % from one year to the next is possible? To those who don’t know, diabetic feet are very expensive and do not seem to get the research attention one might from a cost-benefit perspective assume they would (link, point iii). Going back to the patients in general practice on average 81 % of these patients have a foot examination at least once every second year. The regions here vary from 79% to 84%. The worst covered patients are patients treated in general practice in the Vordingborg sygehus catchment area in the Zealand Region, where only roughly two out of three (69%, 95% CI: 62-75%) patients have regularly foot examinations.
Aside from all the specific indicators they’ve collected and reported on, the authors have also constructed a combined indicator, an ‘all-or-none’ indicator, in which they measure the proportion of patients who have not failed to get their Hba1c measured, their feet checked, their blood pressure measured, kidney function tests, etc. … They do not include in this metric the eye screening variable because of the problems associated with this variable, but this is the only process variable not included, and the variable is sort of an indicator of how many of the patients are actually getting all of the care that they’re supposed to get. As patients treated in general practice are generally less well covered than patients treated in the diabetes units at the hospitals I was interested to know how much these differences ‘added up to’ in the end. For the diabetes units, 11 % of patients failed on at least one metric (i.e. did not have their feet checked/Hba1c measured/blood pressure measured/etc.), whereas this was the case for a third of patients in general practice (67%). Summed up like that it seems to me that if you’re a Danish diabetes patient and you want to avoid having some variable neglected in your care, it matters whether you’re treated by your local GP or by the local diabetes unit and that you’re probably going to be better off receiving care from the diabetes unit.
Some descriptive statistics from the appendix (p. 95 ->):
Sex ratio: In the case of this variable, they have multiple reports on the same variable based on data derived from different databases. In the first database, including 16.442 people, 56% are male and 44% are female. In the next database (n=20635), including only type 2 diabetics, the sex ratio is more skewed; 60% are males and 40% are females. In a database including only patients in general practice (n=34359), like in the first database 56% of the diabetics are males and 44% are females. For the patient population of children and young adults included (n=2624), the sex ratio is almost equal (51% males and 49% females). The last database, Diabase, based on evaluation of eye screening and including only adults (n=32842), have 55% males and 45% females. It seems to me based on these results that the sex ratio is slightly skewed in most patient populations, with slightly more males than females having diabetes – and it seems not improbable that this is to due to a higher male prevalence of type 2 diabetes (the children/young adult database and type 2 database seem to both point in this direction – the children/young adult group mainly consists of type 1 patients as 98% of this sample is type 1. The fact that the prevalence of autoimmune disorders is in general higher in females than in males also seems to support this interpretation; to the extent that the sex ratio is skewed in favour of males you’d expect lifestyle factors to be behind this.
Next, age distribution. In the first database (n=16.442), the average and the median age is 50, the standard deviation is 16, the youngest individual is 16 and the oldest is 95. It is worth remembering in this part of the reporting that the oldest individual in the sample is not a good estimate of ‘how long a diabetic can expect to live’ – for all we know the 95 year old in the database got diagnosed at the age of 80. You need diabetes duration before you can begin to speculate about that variable. Anyway, in the next database, of type 2 patients (n=20635), the average age is 64 (median=65), the standard deviation is 12 and the oldest individual is 98. In the context of both of the databases mentioned so far some regions do better than others in terms of the oldest individual, but it also seems to me that this may just be a function of the sample size and ‘random stuff’ (95+ year olds are rare events); Northern Jutland doesn’t have a lot of patients so the oldest patient in that group is not as old as the oldest patient from Copenhagen – this is probably but what you’d expect. In the general practice database (n=34359), the average age is 68 (median=69) and the standard deviation is 11; the oldest individual there is 102. In the Diabase database (n=32842), the average age is 62 (median=64), the standard deviation is 15 and the oldest individual is 98. It’s clear from these databases that most diabetics in Denmark are type 2 diabetics (this is no surprise) and that a substantial proportion of them are at or close to retirement age.
The appendix has a bit of data on diabetes type, but I think the main thing to take away from the tables that break this variable down is that type 1 is overrepresented in the databases compared to the true prevalence – in the Diabase database for example almost half of patients are type 1 (46%), despite the fact that type 1 diabetics are estimated to make up only 10% of the total in Denmark (see e.g. this (Danish source)). I’m sure this is to a significant extent due to lack of coverage of type 2 diabetics treated in general practice.
Diabetes duration: In the first data-set including 16.442 individuals the patients have a median diabetes duration of 21,2 years. The 10% cutoff is 5,4 years, the 25% cutoff is 11,3 years, the 75% cutoff is 33,5 years, and the 90% cutoff is 44,2 years. High diabetes durations are more likely to be observed in type 1 patients as they’re in general diagnosed earlier; in the next database involving only type 2 patients (n=20635), the median duration is 12.9 years and the corresponding cutoffs are 3,8 years (10%); 7,4 years (25%); 18,6 years (75%); and 24,7 years (90%). In the database involving patients treated in general practice, the median duration is 6,8 years and the cutoffs reported for the various percentiles are 2,5 years (10%), 4,0 (25%), 11,2 (75%) and 15,6 (90%). One note not directly related to the data but which I thought might be worth adding here is that of one were to try to use these data for the purposes of estimating the risk of complications as a function of diabetes duration, it would be important to have in mind that there’s probably often a substantial amount of uncertainty associated with the diabetes duration variable because many type 2 diabetics are diagnosed after a substantial amount of time with sub-optimal glycemic control; i.e. although diabetes duration is lower in type 2 populations than in type 1 populations, I’d assume that the type 2 estimates of duration are still biased downwards compared to type 1 estimates causing some potential issues in terms of how to interpret associations found here.
Next, smoking. In the first database (n=16.442), 22% of diabetics smoke daily and another 22% are ex-smokers who have not smoked within the last 6 months. According to the resource to which you’re directed when you’re looking for data on that kind of stuff on Statistics Denmark, the percentage of daily smokers was 17% in 2013 in the general population (based on n=158.870 – this is a direct link to the data), which seems to indicate that the trend (this is a graph of the percentage of Danes smoking daily as a function of time, going back to the 70es) I commented upon (Danish link) a few years back has not reversed or slowed down much. If we go back to the appendix and look at the next source, dealing with type 2 diabetics, 19% of them are smoking daily and 35% of them are ex-smokers (again, 6 months). In the general practice database (n=34.359) 17% of patients smoke daily and 37% are ex-smokers.
BMI. Here’s one variable where type 1 and type 2 look very different. The first source deals with type 1 diabetics (n=15.967) and here the median BMI is 25.0, which is comparable to the population median (if anything it’s probably lower than the population median) – see e.g. page 63 here. Relevant percentile cutoffs are 20,8 (10%), 22,7 (25%), 28,1 (75%), and 31,3 (90%). Numbers are quite similar across regions. For the type 2 data, the first source (n=20.035) has a median BMI of 30,7 (almost equal to the 1 in 10 cutoff for type 1 diabetics), with relevant cutoffs of 24,4 (10%), 27,2 (25%), 34,9 (75%), and 39,4 (90%). According to this source, one in four type 2 diabetics in Denmark are ‘severely obese‘ and more diabetics are obese than are not. It’s worth remembering that using these numbers to implicitly estimate the risk of type 2 diabetes associated with overweight is problematic as especially some of the people in the lower end of the distribution are quite likely to have experienced weight loss post-diagnosis. For type 2 patients treated in general practice (n=15.736), the median BMI is 29,3 and cutoffs are 23,7 (10%), 26,1 (25%), 33,1 (75%), and 37,4 (90%).
Distribution of Hba1c. The descriptive statistics included also have data on the distribution of Hba1c values among some of the patients who have had this variable measured. I won’t go into the details here except to note that the differences between type 1 and type 2 patients in terms of the Hba1c values achieved are smaller than I’d perhaps expected; the median Hba1c among type 1s was estimated at 62, based on 16.442 individuals, whereas the corresponding number for type 2s was 59, based on 20.635 individuals. Curiously, a second data source finds a median Hba1c of only 48 for type 2 patients treated in general practice; the difference between this one and the type 1 median is definitely high enough to matter in terms of the risk of complications (it’s more questionable how big the effect of a jump from 59 to 62 is, especially considering measurement error and the fact that the type 1 distribution seems denser than the type 2 distribution so that there aren’t that many more exceptionally high values in the type 1 dataset), but I wonder if this actually quite impressive level of metabolic control in general practice may not be due to biased reporting, with GPs doing well in terms of diabetes management being also more likely to report to the databases; it’s worth remembering that most patients treated in general practice are still not accounted for in these data-sets.
Oral antidiabetics and insulin. In one sample of 20.635 type 2 patients, 69% took oral antidiabetics, and in another sample of 34.359 type 2 patients treated in general practice the number was 75%. 3% of type 1 diabetics in a sample of 16.442 individuals also took oral antidiabetics, which surprised me. In the first-mentioned sample of type 2 patients 69% (but not the same amount of individuals – this was not a reporting error) also took insulin, so there seems to be a substantial number of patients on both treatments. In the general practice sample included the number of patients on insulin was much lower, as only 14% of type 2 patients were on insulin – again concerns about reporting bias may play a role here, but even taking this number at face value and extrapolating out of sample you reach the conclusion that the majority of patients on insulin are probably type 2 diabetics, as only roughly one patient in 10 is type 1.
Antihypertensive treatment and treatment for hyperlipidemia: Although there as mentioned above seems to be less focus on hypertension in type 1 patients than on hypertension in type 2 patients, it’s still the case that roughly half (48%) of all patients in the type 1 sample (n=16.442) was on antihypertensive treatment. In the first type 2 sample (n=20635), 82% of patients were receiving treatment against hypertension, and this number was similar in the general practice sample (81%). The proportions of patients in treatment for hyperlipidemia are roughly similar (46% of type 1, and 79% and 73% in the two type 2 samples, respectively).
Blood pressure. The median level of systolic blood pressure among type 1 diabetics (n=16442) was 130, with the 75% cutoff intersecting the hypertension level (140) and 10% of patients having a systolic blood pressure above 151. These numbers are almost identical to the sample of type 2 patients treated in general practice, however as earlier mentioned this blood pressure level is achieved with a lower proportion of patients in treatment for hypertension. In the second sample of type 2 patients (n=20635), the numbers were slightly higher (median: 133, 75% cutoff: 144, 90% cutoff: 158). The median diastolic blood pressure was 77 in the type 1 sample, with 75 and 90% cutoffs of 82 and 89; the data in the type 2 samples are almost identical.
Here’s a previous post in the series covering this book. There’s a lot of stuff in these chapters, so the stuff below’s just some of the things I thought were interesting and worth being aware of. I’ve covered three chapters in this post: One about skin, nails and hair, one about the eye, and one about infectious and tropical diseases. I may post one more post about the book later on, but I’m not sure if I’ll do that or not at this point so this may be the last post in the series.
Okay, on to the book – skin, nails and hair (my coverage mostly deals with the skin):
“The skin is a highly specialized organ that covers the entire external surface of the body. Its various roles include protecting the body from trauma, infection and ultraviolet radiation. It provides waterproofing and is important for fluid and temperature regulation. It is essential for the detection of some sensory stimuli. […] Skin problems are extremely common and are responsible for 10–15 per cent of all consultations in general practice. […] Given that there are around 2000 dermatological conditions described, only common and important conditions, including some that might be especially relevant in the examination setting, can be covered here.”
“Urticaria is characterized by the development of red dermal swellings known as weals […]. Scaling is not seen and the lesions are typically very itchy. The lesions result from the release of histamine from mast cells. An important clue to the diagnosis is that individual lesions come and go within 24 hours, although new lesions may be appearing at other sites. Another associated feature is dermographism: a firm scratch of the skin with an orange stick will produce a linear weal within a few minutes. Urticaria is common, estimated to affect up to 20 per cent of the population at some point in their lives.”
“Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are thought to be two ends of a spectrum of the same condition. They are usually attributable to drug hypersensitivity, though a precipitant is not always identified. The latent period following initiation of the drug tends to be longer than seen with a classical maculopapular drug eruption. The disease is termed:
*SJS when 10 per cent or less of the body surface area epidermis detaches
*TEN when greater than 30 per cent detachment occurs.
Anything in between is designated SJS/TEN overlap. Following a prodrome of fever, an erythematous eruption develops. Macules, papules, or plaques may be seen. Some or all of the affected areas become vesicular or bullous, followed by sloughing off of the dead epidermis. This leads to potentially widespread denudation of skin. […] The affected skin is typically painful rather than itchy. […] The risk of death relates to the extent of epidermal loss and can exceed 30 per cent. […] A widespread ‘drug rash’ that is very painful should ring alarm bells.”
“Various skin problems arise in patients with diabetes mellitus. Bacterial and fungal infections are more common, due to impaired immunity. Vascular disease and neuropathy lead to ulceration on the feet, which can sometimes be very deep and there may be underlying osteomyelitis. Granuloma annulare […] and necrobiosis lipoidica have also been associated with diabetes, though many cases are seen in non-diabetic patients. The former produces smooth papules in an annular configuration, often coalescing into a ring. The latter usually occurs over the shins giving rise to yellow-brown discoloration, with marked atrophy and prominent telangiectasia. There is often an annular appearance, with a red or brown border. Acanthosis nigricans, velvety thickening of the flexural skin […], is seen with insulin resistance, with or without frank diabetes. […] Diabetic bullae are also occasionally seen and diabetic dermopathy produces hyperpigmented, atrophic plaques on the legs. The aetiology of these is unknown.”
“Malignant melanoma is one of the commonest cancers in young adults [and it] is responsible for almost three-quarters of skin cancer deaths, despite only accounting for around 4 per cent of skin cancers. Malignant melanoma can arise de novo or from a pre-existing naevus. Most are pigmented, but some are amelanotic. The most important prognostic factor for melanoma is the depth of the tumour when it is excised – Breslow’s thickness. As most malignant melanomas undergo a relatively prolonged radial (horizontal) growth phase prior to invading vertically, there is a window of opportunity for early detection and management, while the prognosis remains favourable. […] ‘Red flag’ findings […] in pigmented lesions are increasing size, darkening colour, irregular pigmentation, multiple colours within the same lesion, and itching or bleeding for no reason. […] In general, be suspicious if a lesion is rapidly changing.”
“Most ocular surface diseases […] are bilateral, whereas most serious pathology (usually involving deeper structures) is unilateral […] Any significant reduction of vision suggests serious pathology [and] [s]udden visual loss always requires urgent investigation and referral to an ophthalmologist. […] Sudden loss of vision is commonly due to a vascular event. These may be vessel occlusions giving rise to ischaemia of vision-serving structures such as the retina, optic nerve or brain. Alternatively there may be vessel rupture and consequent bleeding which may either block transmission of light as in traumatic hyphaema (haemorrhage into the anterior chamber) and vitreous haemorrhage, or may distort the retina as in ‘wet’ age-related macular degeneration (AMD). […] Gradual loss of vision is commonly associated with degenerations or depositions. […] Transient loss of vision is commonly due to temporary or subcritical vascular insufficiency […] Persistent loss of vision suggests structural changes […] or irreversible damage”.
There are a lot of questions one might ask here, and I actually found it interesting to know how much can be learned simply by asking some questions which might help narrow things down – the above are just examples of variables to consider, and there are others as well, e.g. whether or not there is pain (“Painful blurring of vision is most commonly associated with diseases at the front of the eye”, whereas “Painless loss of vision usually arises from problems in the posterior part of the eye”), whether there’s discharge, just how the vision is affected (a blind spot, peripheral field loss, floaters, double vision, …), etc.
“Ptosis (i.e. drooping lid) and a dilated pupil suggest an ipsilateral cranial nerve III palsy. This is a neuro-ophthalmic emergency since it may represent an aneurysm of the posterior communicating artery. […] In such cases the palsy may be the only warning of impending aneurysmal rupture with subsequent subarachnoid haemorrhage. One helpful feature that warns that a cranial nerve III palsy may be compressive is pupil involvement (i.e. a dilated pupil).”
“Although some degree of cataract (loss of transparency of the lens) is almost universal in those >65 years of age, it is only a problem when it is restricting the patient’s activity. It is most commonly due to ageing, but it may be associated with ocular disease (e.g. uveitis), systemic disease (e.g. diabetes), drugs (e.g. systemic corticosteroids) or it may be inherited. It is the commonest cause of treatable blindness worldwide. […] Glaucoma describes a group of eye conditions characterized by a progressive optic neuropathy and visual field loss, in which the intraocular pressure is sufficiently raised to impair normal optic nerve function. Glaucoma may present insidiously or acutely. In the more common primary open angle glaucoma, there is an asymptomatic sustained elevation in intraocular pressure which may cause gradual unnoticed loss of visual field over years, and is a significant cause of blindness worldwide. […] Primary open angle glaucoma is asymptomatic until sufficiently advanced for field loss to be noticeable to the patient. […] Acute angle closure glaucoma is an ophthalmic emergency in which closure of the drainage angle causes a sudden symptomatic elevation of intraocular pressure which may rapidly damage the optic nerve.”
“Age-related macular degeneration is the commonest cause of blindness in the older population (>65 years) in the Western world. Since it is primarily the macula […] that is affected, patients retain their peripheral vision and with it a variable level of independence. There are two forms: ‘dry’ AMD accounts for 90 per cent of cases and the more dramatic ‘wet’ (also known as neovascular) AMD accounts for 10 per cent. […] Treatments for dry AMD do not alter the course of the disease but revolve around optimizing the patient’s remaining vision, such as using magnifiers. […] Treatments for wet AMD seek to reverse the neovascular process”.
“Diabetes is the commonest cause of blindness in the younger population (<65 years) in the Western world. Diabetic retinopathy is a microvascular disease of the retinal circulation. In both type 1 and type 2 diabetes glycaemic control and blood pressure should be optimized to reduce progression. Progression of retinopathy to the proliferative stage is most commonly seen in type 1 diabetes, whereas maculopathy is more commonly a feature of type 2 diabetes. […] Symptoms
*Bilateral. *Usually asymptomatic until either maculopathy or vitreous haemorrhage. [This is part of why screening programs for diabetic eye disease are so common – the first sign of eye disease may well be catastrophic and irreversible vision loss, despite the fact that the disease process may take years or decades to develop to that point] *Gradual loss of vision – suggests diabetic maculopathy (especially if distortion) or cataract. *Sudden loss of vision – most commonly vitreous haemorrhage secondary to proliferative diabetic retinopathy.”
Recap of some key points made in the chapter:
“*For uncomfortable/red eyes, grittiness, itchiness or a foreign body sensation usually indicate ocular surface problems such as conjunctivitis.
*Severe ‘aching’ eye pain suggests serious eye pathology such as acute angle closure glaucoma or scleritis. *Photophobia is most commonly seen with acute anterior uveitis or corneal disease (ulcers or trauma). [it’s also common in migraine]
*Sudden loss of vision is usually due to a vascular event (e.g. retinal vessel occlusions, anterior ischaemic optic neuropathy, ‘wet’ AMD).
*Gradual loss of vision is common in the ageing population. It is frequently due to cataract […], primary open angle glaucoma (peripheral field loss) or ‘dry’ AMD (central field loss).
*Recent-onset flashes and floaters should be presumed to be retinal tear/detachment.
*Double vision may be monocular (both images from the same eye) or binocular (different images from each eye). Binocular double vision is serious, commonly arising from a cranial nerve III, IV or VI palsy. […]
the following presentations are sufficiently serious to warrant urgent referral to an ophthalmologist: sudden loss of vision, severe ‘aching’ eye pain, new-onset flashes and floaters, [and] new-onset binocular diplopia.”
Infectious and tropical diseases:
“Patients with infection (and inflammatory conditions or, less commonly, malignancy) usually report fever […] Whatever the cause, body temperature generally rises in the evening and falls during the night […] Fever is often lower or absent in the morning […]. A sensation of ‘feeling hot’ or ‘feeling cold’ is unreliable – healthy individuals often feel these sensations, as may those with menopausal flushing, thyrotoxicosis, stress, panic, or migraine. The height and duration of fever are important. Rigors (chills or shivering, often uncontrollable and lasting for 20–30 minutes) are highly significant, and so is a documented temperature over 37.5 °C taken with a reliable oral thermometer. Drenching sweats are also highly significant. Rigors generally indicate serious bacterial infections […] or malaria. An oral temperature >39 °C has the same significance as rigors. Rigors generally do not occur in mild viral infections […] malignancy, connective tissue diseases, tuberculosis and other chronic infections. […] Anyone with fever lasting longer than a week should have lost weight – if a patient reports a prolonged fever but no weight loss, the ‘fever’ usually turns out to be of no consequence. […] untouched meals indicate ongoing illness; return of appetite is a reliable sign of recovery.”
“Bacterial infections are the most common cause of sepsis, but other serious infections (e.g. falciparum malaria) or inflammatory states (e.g. pancreatitis, pre-eclamptic toxaemia, burns) can cause the same features. Below are listed the indicators of sepsis – the more abnormal the result, the more severe is the patient’s condition.
*Check if it is above 38 °C or below 36 °C.
*Simple viral infections seldom exceed 39 °C.
*Temperatures (from any cause) are generally higher in the evening than in the early morning.
*As noted above, rigors (uncontrollable shivering) are important indicators of severe bacterial infection or malaria. […] A heart rate greater than 90 beats/min is abnormal, and in severe sepsis a pulse of 140/min is not unusual. […] Peripheries (fingers, toes, nose) are often markedly cooler than central skin (trunk, forehead) with prolonged capillary refill time […] Blood pressure (BP) is low in the supine position (systolic BP <90 mmHg) and falls further when the patient is repositioned upright. In septic shock sometimes the BP is unrecordable on standing, and the patient may faint when they are helped to stand up […] The first sign [of respiratory disturbance] is a respiratory rate greater than 20 breaths/min. This is often a combination of two abnormalities: hypoxia caused by intrapulmonary shunts, and lactic acidosis. […] in hypoxia, the respiratory pattern is normal but rapid. Acidotic breathing has a deep, sighing character (also known as Kussmaul’s respiration). […] Also called toxic encephalopathy or delirium, confusion or drowsiness is often present in sepsis. […] Sepsis is always severe when it is accompanied by organ dysfunction. Septic shock is defined as severe sepsis with hypotension despite adequate fluid replacement.”
“Involuntary neck stiffness (‘nuchal rigidity’) is a characteristic sign of meningitis […] Patients with meningitis or subarachnoid haemorrhage characteristically lie still and do not move the head voluntarily. Patients who complain about a stiff neck are often worried about meningitis; patients with meningitis generally complain of a sore head, not a sore neck – thus neck stiffness is a sign, not a symptom, of meningitis.”
“General practitioners are generally correct when they say an infection is ‘a virus’, but the doctor needs to make an accurate assessment to be sure of not missing a serious bacterial infection masquerading as ‘flu’. […]
*Influenza is highly infectious, so friends, family, or colleagues should also be affected at the same time – the incubation period is short (1–3 days). If there are no other cases, question the diagnosis.
*The onset of viraemic symptoms is abrupt and often quite severe, with chills, headache, and myalgia. There may be mild rigors on the first day, but these are not sustained.
*As the next few days pass, the fever improves each day, and by day 3 the fever is settling or absent. A fever that continues for more than 3 days is not uncomplicated ’flu, and nor is an illness with rigors after the first day.
*As the viraemia subsides, so the upper respiratory symptoms become prominent […] The patient experiences a combination of: rasping sore throat, dry cough, hoarseness, coryza, red eyes, congested sinuses. These persist for a long time (10 days is not unusual) and the patient feels ‘miserable’ but the fever is no longer prominent.”
“Several infections cause a similar picture to ‘glandular fever’. The commonest is EBV [Epstein–Barr Virus], with cytomegalovirus (CMV) a close second; HIV seroconversion may look clinically identical, and acute toxoplasmosis similar (except for the lack of sore throat). Glandular fever in the USA is called ‘infectious mononucleosis’ […] The illness starts with viraemic symptoms of fever (without marked rigors), myalgia, lassitude, and anorexia. A sore throat is characteristic, and the urine often darkens (indicating liver involvement). […] Be very alert for any sign of stridor, or if the tonsils meet in the middle or are threatening to obstruct (a clue is that the patient is unable to swallow their saliva and is drooling or spitting it out). If there are any of these signs of upper airway obstruction, give steroids, intravenous fluids, and call the ENT surgeons urgently – fatal obstruction occasionally occurs in the middle of the night. […] Be very alert for a painful or tender spleen, or any signs of peritonism. In glandular fever the spleen may rupture spontaneously; it is rare, but tragic. It usually begins as a subcapsular haematoma, with pain and tenderness in the left upper quadrant. A secondary rupture through the capsule then occurs at a later date, and this is often rapidly fatal.”
Yesterday I gave some of the reasons I had for disliking the book; in this post I’ll provide some of the reasons why I kept reading. The book had a lot of interesting data. I know I’ve covered some of these topics and numbers before (e.g. here), but I don’t mind repeating myself every now and then; some things are worth saying more than once, and as for the those that are not I must admit I don’t really care enough about not repeating myself here to spend time perusing the archives in order to make sure I don’t repeat myself here. Anyway, here are some number from the coverage:
“Twenty-two high-burden countries account for over 80 % of the world’s TB cases […] data referring to 2011 revealed 8.7 million new cases of TB [worldwide] (13 % coinfected with HIV) and 1.4 million people deaths due to such disease […] Around 80 % of TB cases among people living with HIV were located in Africa. In 2011, in the WHO European Region, 6 % of TB patients were coinfected with HIV […] In 2011, the global prevalence of HIV accounted for 34 million people; 69 % of them lived in Sub-Saharan Africa. Around five million people are living with HIV in South, South-East and East Asia combined. Other high-prevalence regions include the Caribbean, Eastern Europe and Central Asia . Worldwide, HIV incidence is in downturn. In 2011, 2.5 million people acquired HIV infection; this number was 20 % lower than in 2001. […] Sub-Saharan Africa still accounts for 70 % of all AIDS-related deaths […] Worldwide, an estimated 499 million new cases of curable STIs (as gonorrhoea, chlamydia and syphilis) occurred in 2008; these findings suggested no improvement compared to the 448 million cases occurring in 2005. However, wide variations in the incidence of STIs are reported among different regions; the burden of STIs mainly occurs in low-income countries”.
“It is estimated that in 2010 alone, malaria caused 216 million clinical episodes and 655,000 deaths. An estimated 91 % of deaths in 2010 were in the African Region […]. A total of 3.3 billion people (half the world’s population) live in areas at risk of malaria transmission in 106 countries and territories”.
“Diarrhoeal diseases amount to an estimated 4.1 % of the total disability-adjusted life years (DALY) global burden of disease, and are responsible for 1.8 million deaths every year. An estimated 88 % of that burden is attributable to unsafe supply of water, sanitation and hygiene […] It is estimated that diarrhoeal diseases account for one in nine child deaths worldwide, making diarrhoea the second leading cause of death among children under the age of 5 after pneumonia”
“NCDs [Non-Communicable Diseases] are the leading global cause of death worldwide, being responsible for more
deaths than all other causes combined. […] more than 60 % of all deaths worldwide currently stem from NCDs .
In 2008, the leading causes of all NCD deaths (36 million) were:
• CVD [cardiovascular disease] (17 million, or 48 % of NCD deaths) [nearly 30 % of all deaths];
• Cancer (7.6 million, or 21 % of NCD deaths) [about 13 % of all deaths]
• Respiratory diseases (4.2 million, or 12 % of NCD deaths) [7 % of all deaths]
• Diabetes (1.3 million, 4 % of NCD deaths) .” [Elsewhere in the publication they report that: “In 2010, diabetes was responsible for 3.4 million deaths globally and 3.6 % of DALYs” – obviously there’s a lot of uncertainty here. How to avoid ‘double-counting’ is one of the major issues, because we have a pretty good idea what they die of: “CVD is by far the most frequent cause of death in both men and women with diabetes, accounting for about 60 % of all mortality”].
“Behavioural risk factors such as physical inactivity, tobacco use and unhealthy diet explain nearly 80 % of the CVD burden”
“nearly 80 % of NCD deaths occur in low- and middle-income countries , up sharply from just under 40 % in 1990 […] Low- and lower-middle-income countries have the highest proportion of deaths from NCDs under 60 years. Premature deaths under 60 years for high-income countries were 13 and 25 % for upper-middle-income countries. […] In low-income countries, the proportion of premature NCD deaths under 60 years is 41 %, three times the proportion in high-income countries . […] Overall, NCDs account for more than 50 % of DALYs [disability-adjusted life years] in most counties. This percentage rises to over 80 % in Australia, Japan and the richest countries of Western Europe and North America […] In Europe, CVD causes over four million deaths per year (52 % of deaths in women and 42 % of deaths in men), and they are the main cause of death in women in all European countries.”
“Overall, age-adjusted CVD death rates are higher in most low- and middle-income countries than in developed countries […]. CHD [coronary heart disease] and stroke together are the first and third leading causes of death in developed and developing countries, respectively. […] excluding deaths from cancer, these two conditions were responsible for more deaths in 2008 than all remaining causes among the ten leading causes of death combined (including chronic diseases of the lungs, accidents, diabetes, influenza, and pneumonia)”.
“The global prevalence of diabetes was estimated to be 10 % in adults aged 25 + years […] more than half of all nontraumatic lower limb amputations are due to diabetes [and] diabetes is one of the leading causes of visual impairment and blindness in developed countries .”
“Almost six million people die from tobacco each year […] Smoking is estimated to cause nearly 10 % of CVD […] Approximately 2.3 million die each year from the harmful use of alcohol. […] Alcohol abuse is responsible for 3.8 % of all deaths (half of which are due to CVD, cancer, and liver cirrhosis) and 4.5 % of the global burden of disease […] Heavy alcohol consumption (i.e. ≥ 4 drinks/day) is significantly associated with an about fivefold increased risk of oral and pharyngeal cancer and oesophageal squamous cell carcinoma (SqCC), 2.5-fold for laryngeal cancer, 50 % for colorectal and breast cancers and 30 % for pancreatic cancer . These estimates are based on a large number of epidemiological studies, and are generally consistent across strata of several covariates. […] The global burden of cancer attributable to alcohol drinking has been estimated at 3.6 and 3.5 % of cancer deaths , although this figure is higher in high-income countries (e.g. the figure of 6 % has been proposed for UK  and 9 % in Central and Eastern Europe).”
“At least two million cancer cases per year (18 % of the global cancer burden) are attributable to chronic infections by human papillomavirus, hepatitis B virus, hepatitis C virus and Helicobacter pylori. These infections are largely preventable or treatable […] The estimate of the attributable fraction is higher in low- and middle-income countries than in high-income countries (22.9 % of total cancer vs. 7.4 %).”
“Information on the magnitude of CVD in high-income countries is available from three large longitudinal studies that collect multidisciplinary data from a representative sample of European and American individuals aged 50 and older […] according to the Health Retirement Survey (HRS) in the USA, almost one in three adults have one or more types of CVD [11, 12]. By contrast, the data of Survey of Health, Ageing and Retirement in Europe (SHARE), obtained from 11 European countries, and English Longitudinal Study of Aging (ELSA) show that disease rates (specifically heart disease, diabetes, and stroke) across these populations are lower (almost one in five)”
“In 1990, the major fraction of morbidity worldwide was due to communicable, maternal, neonatal, and nutritional disorders (47 %), while 43 % of disability adjusted life years (DALYs) lost were attributable to NCDs. Within two decades, these estimates had undergone a drastic change, shifting to 35 % and 54 %, respectively”
“Estimates of the direct health care and nonhealth care costs attributable to CVD in many countries, especially in low- and middle-income countries, are unclear and fragmentary. In high-income countries (e.g., USA and Europe), CVD is the most costly disease both in terms of economic costs and human costs. Over half (54 %) of the total cost is due to direct health care costs, while one fourth (24 %) is attributable to productivity losses and 22 % to the informal care of people with CVD. Overall, CVD is estimated to cost the EU economy, in terms of health care, almost €196 billion per year, i.e., 9 % of the total health care expenditure across the EU”
“In the WHO European Region, the Eastern Mediterranean Region, and the Region of the Americas, over 50 % of women are overweight. The highest prevalence of overweight among infants and young children is in upper-to-middle-income populations, while the fastest rise in overweight is in the lower-to-middle-income group . Globally, in 2008, 9.8 % of men and 13.8 % of women were obese compared to 4.8 % of men and 7.9 % of women in 1980 .”
“In low-income countries, around 25 % of adults have raised total cholesterol, while in high-income countries, over 50 % of adults have raised total cholesterol […]. Overall, one third of CHD disease is attributable to high cholesterol levels” (These numbers seem very high to me, but I’m reporting them anyway).
“interventions based on tobacco taxation have a proportionally greater effect on smokers of lower SES and younger smokers, who might otherwise be difficult to influence. Several studies suggest that the application of a 10 % rise in price could lead to as much as a 2.5–10 % decline in smoking [20, 45, 50, 56].”
“The decision to allocate resources for implementing a particular health intervention depends not only on the strength of the evidence (effectiveness of intervention) but also on the cost of achieving the expected health gain. Cost-effectiveness analysis is the primary tool for evaluating health interventions on the basis of the magnitude of their incremental net benefits in comparison with others, which allows the economic attractiveness of one program over another to be determined [More about this kind of stuff here]. If an intervention is both more effective and less costly than the existing one, there are compelling reasons to implement it. However, the majority of health interventions do not meet these criteria, being either more effective but more costly, or less costly but less effective, than the existing interventions [see also this]. Therefore, in most cases, there is no “best” or absolute level of cost-effectiveness, and this level varies mainly on the basis of health care system expenditure and needs .”
“The number of new cases of cancer worldwide in 2008 has been estimated at about 12,700,000 . Of these, 6,600,000 occurred in men and 6,000,000 in women. About 5,600,000 cases occurred in high-resource countries […] and 7,100,000 in low- and middle-income countries. Among men, lung, stomach, colorectal, prostate and liver cancers are the most common […], while breast, colorectal, cervical, lung and stomach are the most common neoplasms among women […]. The number of deaths from cancer was estimated at about 7,600,000 in 2008 […] No global estimates of survival from cancer are available: Data from selected cancer registries suggest wide disparities between high- and low-income countries for neoplasms with effective but expensive treatment, such as leukaemia, while the gap is narrow for neoplasms without an effective therapy, such as lung cancer […]. The overall 5-year survival of cases diagnosed during 1995– 1999 in 23 European countries was 49.6 % […] Tobacco smoking is the main single cause of human cancer worldwide […] In high-income countries, tobacco smoking causes approximately 30 % of all human cancers .”
“Systematic reviews have concluded that nutritional factors may be responsible for about one fourth of human cancers in high-income countries, although, because of the limitations of the current understanding of the precise role of diet in human cancer, the proportion of cancers known to be avoidable in practicable ways is much smaller . The only justified dietary recommendation for cancer prevention is to reduce the total caloric intake, which would contribute to a decrease in overweight and obesity, an established risk factor for human cancer. […] The magnitude of the excess risk [associated with obesity] is not very high (for most cancers, the relative risk (RR) ranges between 1.5 and 2 for body weight higher than 35 % above the ideal weight). Estimates of the proportion of cancers attributable to overweight and obesity in Europe range from 2 %  to 5 % . However, this figure is likely to be larger in North America, where the prevalence of overweight and obesity is higher.”
“Estimates of the global burden of cancer attributable to occupation in high-income countries result in the order of 1–5 % [9, 42]. In the past, almost 50 % of these were due to asbestos alone […] The available evidence suggests, in most populations, a small role of air, water and soil pollutants. Global estimates are in the order of 1 % or less of total cancers [9, 42]. This is in striking contrast with public perception, which often identifies pollution as a major cause of human cancer.”
“Avoidance of sun exposure, in particular during the middle of the day, is the primary preventive measure to reduce the incidence of skin cancer. There is no adequate evidence of a protective effect of sunscreens, possibly because use of sunscreens is associated with increased exposure to the sun. The possible benefit in reducing skin cancer risk by reduction of sun exposure, however, should be balanced against possible favourable effects of UV radiation in promoting vitamin D metabolism.”
Despite not actually having reading all that many books this year I’m way behind on blogging the books I’ve read, so I thought I might as well try to catch up a bit. You can find my previous coverage of the book here and here.
In this post I’ll cover the chapters about the musculoskeletal system, the endocrine system, and the breast.
“Disorders of the musculoskeletal system make up 20–25 per cent of a general practitioner’s workload and account for significant disability in the general population. […] The chief symptoms to identify in the musculoskeletal assessment are: *pain *stiffness *swelling *impaired function *constitutional [regarding constitutional symptoms, “Patients with arthritis may describe symptoms of fatigue, fever, sweating and weight loss”]. […] As a rule mechanical disorders (e.g. OA [Osteoarthritis], spondylosis, and tendinopathies) are worsened by activity and relieved by rest. In severe degenerative disease the pain may, however, be present at rest and disturb sleep. Inflammatory disorders tend to be painful both at rest and during activity and are associated with worsened stiffness after periods of prolonged rest. The patient may note that stiffness is relieved somewhat by movement. Both mechanical and inflammatory disorders may be worsened by excessive movement.”
“The lifetime incidence of lower back pain is about 60 per cent and the greatest prevalence is between ages 45 and 65 years. Over 90 per cent of low back pain is mechanical and self-limiting. […] Indicators of serious pathology in lumbar pain: ‘red flags’ of serious pathology that requires further investigation […] are: *presenting under age 20 and over age 55 years *prolonged stiffness (>6 weeks) *sudden onset of severe pain *pain that disturbs sleep (>6 weeks) *thoracic pain *nerve root symptoms – including spinal claudication (pain on walking resolved by rest), saddle numbness, and loss of bladder or bowel control *chronic persistent pain (>12 weeks) *weight loss *history of carcinoma.”
“Osteoarthritis is a chronic degenerative and mechanical disorder characterized by cartilage loss. It is the most common form of arthritis, estimated to affect 15 per cent of the population of the UK over the age of 55 years. It is second only to cardiovascular disease as a cause of disability. Weight-bearing joints are chiefly involved (e.g. facets in the spine, hip and knee). […] There is little evidence to link OA with repetitive injury from occupation, except perhaps knee bending in men. Dockers and miners have a higher incidence of knee OA.”
“Rheumatoid arthritis […] is the most common ARD [Autoimmune Rheumatic Diseases] and is characterized by the presence of a symmetrical destructive polyarthritis with a predisposition for the small joints of the hands, wrists and feet. It is more common in women than men and may present at any age though most often in the third to fourth decade. […] Onset is typically insidious and progressive pain, stiffness and symmetrical swelling of small joints occurs. Up to a third of patients may have a subacute onset with symptoms of fatigue, malaise, weight loss, myalgia, morning stiffness and joint pain without overt signs of swelling. A mono- or bilateral arthropathy of the shoulder or wrist may account for up to 30–40 per cent of initial presentations”
“[Osteoporosis] remains a significant cause of morbidity and mortality. Peak bone mass is usually achieved in the third decade and is determined by both genetic and environmental factors. After the age of 35 the amount of bone laid down is less than that reabsorbed during each remodelling cycle. The net effect is age-related loss of bone mass. Up to 15 per cent of bone mass can also be lost over the 5-year period immediately post menopause. Symptomless reduction in bone mass and strength results in an increased risk of fracture; it is the resulting fractures that lead to pain and morbidity. Major risk factors to be considered in osteoporosis are: *race (white or Asian > African Caribbean) *age *gender *family history of maternal hip fracture *previous low trauma fracture (low trauma defined as no greater than falling from standing height) *long-term use of corticosteroids *malabsorption disorders *endocrinopathies […] *inflammatory arthritis […] Other risk factors include: *low body mass index […] *late menarche and early menopause *nulliparity *reduced physical activity *low intake of calcium (below 240 mg daily) *excess alcohol intake *smoking *malignancy (multiple myeloma).”
“Infection may give rise to systemic inflammatory arthritis or vasculitis. The condition ‘reactive arthritis’ is also recognized. […] It is usually triggered by sexually transmitted infection such as with Chlamydia trachomatis. The acute inflammatory reaction is treated with NSAIDs and corticosteroids and often ‘burns out’ after 6–18 months [Had to read that one twice: 18 months…]. It may leave lasting joint damage. […] Septic arthritis constitutes an acute emergency. The presentation is usually one of a rapid onset of severe pain in a hot swollen joint, the pain so severe that the patient cannot bear for it to be touched or moved.”
“Focal pain, swelling, or a low trauma fracture in the spine or long bones should alert suspicion [of neoplasia]. Primary tumours of bone include the benign (but often very painful) osteoid-osteoma, chondromas, and malignant osteosarcoma. Metastatic carcinoma may be secondary to a primary lesion in the lung, breast, prostate, kidney or thyroid. Haematological malignancies including lymphomas and leukaemias may also lead to diffuse bone involvement.”
“Diabetes mellitus is becoming a major public health problem. This is particularly true for type 2 diabetes, the prevalence of which is increasing rapidly due to the association with obesity and physical inactivity. Much of the morbidity, and cost, of diabetes care is due to the associated complications, rather than directly to hyperglycaemia and its management. Thyroid disease and polycystic ovarian syndrome are also prevalent [endocrine] conditions. Most other endocrine disorders are uncommon”
“The classic triad of symptoms associated with diabetes mellitus consists of: *thirst *polyuria (often nocturia) *weight loss.
Many patients will also experience pruritus or balanitis, fatigue and blurred vision. Some people, particularly those with newly presenting type 1 diabetes diabetes mellitus (T1DM) or with marked hyperglycaemia in type 2 diabetes mellitus (T2DM), may have a ‘full house’ of symptoms, in which case it is generally not difficult to suspect the diagnosis. However, other patients, particularly those with only modestly elevated blood glucose concentrations in T2DM, will have fewer, milder symptoms, and some may be entirely asymptomatic. […] symptoms potentially suggestive of diabetes may have alternative causes, particularly in elderly people, for example, frequency and nocturia in an older man may be due to bladder outflow obstruction, and many medical disorders are associated with weight loss. The symptom complex of thirst, polydipsia and polyuria most commonly suggests a diagnosis of uncontrolled diabetes mellitus but can occur in other settings. Some patients taking diuretics will experience similar symptoms. A dry mouth, perhaps associated with drug usage (e.g. tricyclic antidepressants) or certain medical conditions (e.g. Sjögren’s syndrome), may lead to increased fluid intake in an attempt at symptom relief.”
“The blood glucose concentration at diagnosis is not useful as a guide to whether an individual patient has T1DM or T2DM. Patients with T1DM can be in severe ketoacidosis with a blood glucose less than 20 mmol/L, and even below 10 mmol/L on occasions, whereas T2DM can present with a hyperosmolar state with blood glucose levels over 50 mmol/L.”
“30–50 per cent of patients with newly diagnosed T2DM will already have tissue complications at diagnosis due to the prolonged period of antecedent moderate and asymptomatic hyperglycaemia. […] Diabetes mellitus is much more than a disorder of glucose metabolism. The complications of diabetes can affect many of the organ systems leading to associated cardiac, vascular, renal, retinal, neurological and other disorders.”
“Pain is one of the commonest presenting disorders in the female breast, occurring in both pre-and postmenopausal women. […] In most women, there is no obvious or serious underlying breast pathology present […] In males, pain is not uncommon in gynaecomastia (swelling of male breast). […] A discrete lump, nodularity or thickening is the next most common mode of presentation. Size may vary (frequently ‘pea-sized’), but can be large. Onset may be acute (several days) or longstanding (several months). Fluctuation with the menstrual cycle is common in young women. Pain and tenderness are features of cysts, less common with fibroadenomas (unless rapidly growing or phylloides tumours), uncommon with cancer, except with rapidly expanding, aggressive (grade 3) and inflammatory tumours. The commonest lump in women below 30 years is a fibroadenoma; in women 30–45 years, a cyst and those over 45 years, cancer. […] Careful assessment of a lump can indicate whether the breast lesion is benign or malignant: *if it is rounded, smooth, mobile, tense and tender it is most likely to be a cyst (30 to 45 years of age) *if it is rounded, smooth, mobile, firm and non-tender it is most likely to be a fibroadenoma (under 30 years of age) *malignant lumps are rare in women under 30 years and uncommon under 40 years (4 per cent of breast cancers). Cancers are usually irregular, firm or hard, with variable involvement of overlying skin or deeper structures.”
“Retraction (intermittent, partial or chronic) is often a concern to women. It can be idiopathic or associated with malignancy in the retroareolar region, but usually is seen in the postmenopausal breast and is secondary to glandular atrophy and replacement by fibrosis and major duct ectasia. Congenital absence is very rare, whereas accessory nipples are seen in 2 per cent of women.” [Again, I had to read that one twice. 2 %! Who knew! Also, this condition seems to be even more common in males (see the link above).]
“Five to 10 per cent of women will, at some stage, present with a macrocyst. Microcysts are more common but tend to be occult. Breast cysts are commonest between the ages of 35 and 50, but can occur outside this age range, particularly in women who have been taking HRT. […] Patients present with a palpable lump or nodularity. When acute and large, the lump can be tender and the patient complains of pain. Typically cysts are well-circumscribed, smooth, mobile and, on occasion, tender lumps.”
“Nipple discharge in premenopausal women is likely to be associated with, or be due to, benign disease. It is the predominant clinical feature in up to 10 per cent of women presenting with breast cancer. […] *Purulent and coloured discharges are usually indicative of benign disease (infection and fibrocystic disease, respectively). *Spontaneous bilateral milky discharge (multiple ducts) most commonly occurs in women of reproductive age and is called galactorrhoea. […] *Clear, serous or bloodstained discharges are not infrequently associated with neoplastic disease”
“Carcinoma of the breast is one of the most common cancers (23 per cent of all female malignancies in the developed world) […]. One in 10 women develops breast cancer during her lifetime. […] Breast cancer is very rare in women under the age of 25. About 4 per cent occur under the age of 40. There is a plateau in incidence between the ages of 45 and 55, and beyond 55 years it continues to increase steadily into the 80s. […] The most common (70 per cent) presentation is a palpable lump, nodularity or thickening in the breast, usually detected by the patient. Typically the lump is firm or hard, well defined, with an irregular surface. […] About 25 per cent of women in the UK present with large primary tumours […], or locally advanced breast cancers […]. In some cases, particularly elderly patients, the tumour may have been present for some time, but hidden by the patient from her relatives due to fear and anxiety […]. Occasionally patients may even deny the presence of a tumour as a psychological coping strategy. […] Breast cancer is the most common malignant condition occurring during pregnancy. The incidence is approximately 1 in 2500 pregnancies, and poses many medical and psychological problems, both for the woman and her relatives.”
“Improving lifestyles is thought to be one of the most effective means of reducing mortality and morbidity in the developed world. However, despite decades of health promotion, there has been no significant difference to lifestyles and instead there are rising levels of inactivity and obesity. The Psychology of Lifestyle addresses the role psychology can play in reversing the trend of deleterious lifestyle choices. It considers the common characteristics of lifestyle behaviours and reflects on how we can inform and improve interventions to promote healthy lifestyles. […] The chapters cover key lifestyle behaviours that impact on health – eating, physical activity, drinking, smoking, sex and drug use – as well as combinations of behaviours.”
I gave the book two stars on goodreads. There are multiple reasons why it did not get a higher rating despite containing quite a lot of material which I consider to be worth blogging. One reason is that the book is really UK-centric; it’s written by British authors for a British audience. Which is fine if you’re from Britain, but it does mean that some of the details included (such as drinking pattern breakdowns for England, Scotland, and Wales) may not be super interesting to the non-British readership. Another reason is that some of the numbers included in the publication are frankly not trustworthy, and the inclusion of those numbers without critical comments on part of the authors occasionally made me question their judgment. To give an example, it is at one point during the coverage noted that: “Women aged 16–19 were least likely to be using contraception despite almost two-thirds of teenagers having had intercourse by age 13 (CDC 2007b).” The problem I have with this quote is that they don’t comment anywhere in the publication upon the fact that this estimate is, if applied to the general population, frankly unbelievable, taking into account other estimates from the literature, including other estimates from US samples (see e.g. this previous post of mine). It’s clear that it’s an estimate derived from a specific sample, but it’s not made clear that the characteristics of the sample were probably very different from the characteristics of the population about which the reader is using the quote to make inferences. To illustrate just how difficult it is to believe that the estimate has much, if any, external validity, according to the estimates reported in fig. 6.2 in the link in the parenthesis above, you don’t get to the point where two-thirds have had sexual intercourse before the age of 19. The estimate they include in the book is not just weird and strange, it’s so weird and strange that anybody who knows anything about that literature would know the estimate is weird and strange, and would at least comment upon why it is perhaps not to be trusted (my guess would be that this estimate is derived from a sample displaying a substantial amount of selection bias due to opportunistic sampling from a very high-risk group). Yet they don’t comment on these things at all, apparently not only taking it to some extent at face value, but also asking the reader to do the same. This was almost an unforgivable error on part of the authors and I was strongly considering not reading on when I got to this point – I don’t really think you can not comment on this kind of thing if you decide to include numbers like those in your coverage in the first place.
Another problem is that there’s also occasionally some sloppy reporting which makes it hard to understand what the research they’re reporting on is actually saying; one example is that they note in the publication (p.185) that: “Young people aged over 15 accounted for 40 per cent of new HIV infections in 2006” – which immediately makes me start wondering whether e.g. a 25-year old would be considered ‘young’, according to this estimate? What about a 30-year old? The publication is silent on the issue of where the right-hand side cut-off is located, making the estimate much less useful than it otherwise would be.
A fourth(?) issue is that a lot of this stuff is correlational research, and there are a lot of cross-section studies and pretty much no longitudinal studies. At a few points do the authors caution against drawing strong conclusions from this kind of research and are frank about the problems which are present, but at other points in the coverage they then to me seem to later on just draw some of those semi-strong conclusions anyway, disregarding the methodological concerns (which are huge).
A fifth issue is that there are some hidden assumptions hidden in the coverage, assumptions which some people might categorize as ‘political’ or something along those lines; these didn’t much bother me because politics and that kind of thing isn’t something I care very much about, as mentioned many times before (though do also see my comments below..), but I’m sure some readers will take issue with what in some sense might be described as ‘the tone’ of the coverage. To be fair they do briefly touch upon e.g. the ethics of smoking bans, but you’re never in doubt where they stand on these issues (bans are fine, most interventions aimed at making the population healthier seem to be fine with the authors), and readers who find government interventions less desirable/justifiable than the authors do may take issue with specific recommendations and implicit assumptions in the coverage. The coverage in the last chapter is sort of a counter-weight to much of the rest of the coverage in the sense that ‘the case against bans and regulation’ gets reasonable coverage here, but I’d say the rest of the book is not really written in a manner which would lead most readers to believe it’s not a good idea to regulate *a lot*.
A sixth personal issue I have with the book is that the book is written in a manner I personally consider to be somewhat disagreeable. It’s a really classic textbook with stuff like a section in the beginning of the chapter outlining ‘what you’ll learn from this chapter’. These kinds of things perhaps wouldn’t be as much of an issue to me if I actually agreed with the authors about what you might be argued to be learning, or not learning, from the coverage in a given chapter. To take an example of what I’m talking about, at the beginning of chapter 7 you learn that: “At the end of this chapter you will: […] understand the nature of sexually transmitted diseases and their health consequences, along with their extent nationwide”. This is just one of 6 learning goals presented. Having read roughly the first third of Holmes et al., I can safely say that reading that book instead would be a lot more helpful than reading the chapter in this book in terms of achieving the learning goal presented, and I might add that if an author of a textbook thinks that you’ll ‘understand the nature of sexually transmitted diseases and their health consequences’ after having read a chapter in a textbook like this one, maybe that author shouldn’t be writing textbooks. This isn’t really fair because the chapter has a lot of useful stuff (and because I have a nagging suspicion that such silly learning goals may well be (politically?) mandated, and that this is probably part of the explanation for why they’re included in books like this one in the first place), but I hate interacting with clueless people with delusions of competence/knowledge, and if people are writing textbooks this way you’ll end up with a lot of people like that coming out the other end.
Despite the above-mentioned problems (and a few others) there’s also a lot of nice stuff in the book, and I’ll share some of that stuff below and in future posts about the book.
“One of the problems with attempting to arrive at a conclusion about what constitutes a lifestyle disease is the myriad of definitions under which diseases are categorised. […] Interestingly, few authors would include sexually transmitted diseases under the lifestyle umbrella, although they could be argued to be entirely under behavioural control, with none of the genetic component that plays a part in aetiology of the six major lifestyle diseases as identified by Doyle (2001). […] In between an ‘imprudent lifestyle’ (Doyle 2001) and the development of a chronic life-threatening or life-foreshortening condition lie a number of precursors of disease. High cholesterol, high blood pressure and obesity are risk factors for the development of a number of the aforementioned lifestyle diseases. The distinction between these precursors, the diseases they predict and the behaviours that are associated with them is often blurred. They are often presented as diseases per se”.
Even though there’s some disagreement about whether or not risk factors are actually Diseases or not, I would caution against the idea that they’re somehow ‘less severe’ than ‘an actual Disease’, unless they actually are; high blood pressure increases the risk of e.g. stroke substantially, so in some ways it’s actually quite a bit worse than some ‘agreed-upon Diseases’ which have less significant health impacts and may not actually kill anybody. I was reminded of this stuff (the blurring of diseases and risk factors) and some related problems very recently during a conversation with a friend, and I’ll allow myself to digress a bit to talk about this stuff in a little more detail here even though it’s only marginally related to the book coverage. Anyway, it seems to me that a lot of people who’d prefer a more ‘fair’ health care resource allocation (‘less money for people who caused their own health problems and more for the others’), a goal towards which I feel sympathetically inclined, are not really aware of how complicated these things are and how difficult it may be to make anything even resembling ‘fair’ distinctions between conditions which are/may be caused by behaviour and conditions which are not, to take but one of many issues. I can usually easily see the impetus for ‘changing things in the direction suggested’, but new problems pop up at every junction and it seems perfectly obvious to me that you’re not going to get rid of unfairness by not giving fat people any money to pay for their insulin. Some of the politically feasible solutions may conceivably make matters worse, e.g. because restricting access to (some types of) medical care may just shift expenditures and perhaps lead to higher expenditures on other treatments to which coverage is maintained (and you’d expect coverage to be maintained to some degree – alternatives are not politically viable). I’m aware that the role of preventative care is from a ‘pure cost standpoint’ probably somewhat overblown (usually preventative care does not save money in the long run, as they tend to cost more money than they save – see e.g. Glied and Smith’s coverage), but this stuff is complicated for many reasons. Some of the current disease treatment modalities in widespread use might well be conceived of as preventative medicine as well, and it’d probably make sense to think of them that way in the case of major changes to insurance coverage profiles. Let’s for example try to compare two models. In the first one insulin for type 2 diabetes is covered, and acute hospitalizations as a result of hypo- and hyperglycemia (DKA, HHS) are also covered. Assume now that the coverage for insulin is removed, but acute hospitalizations would still be covered. It would be quite easy for this change to result in an increase in the total costs incurred by the insurance provider, because hospitalizations are a lot more expensive than insulin, and it’s easy to see why excluding coverage of insulin might lead to more acute hospitalizations among type 2 diabetics (I’m too lazy to look up the numbers, but to people who have no idea about the magnitudes involved here one number which I seem to recall and which should illustrate the issues quite nicely is that in terms of the costs involved, one diabetes-related hospitalization corresponds to something like 8 months of treatment – not insulin, all treatment, including doctor’s visits, blood tests, etc., etc.). Evaluating efficiency in such a context would be really difficult because the conclusion drawn would also depend upon how a third factor, long-term complications, are managed. On the margin, a lot of patients face a tradeoff between the risk of hospitalization from hypoglycemia and the risk of developing chronic health complications such as kidney disease (many patients could decrease their risk of e.g. diabetic retinopathy, -neuropathy or -nephropathy by lowering their Hba1c, but this could easily lead to an increased risk of hypoglycemic episodes – which is part of why patients don’t), and if insurance companies are only expected to care about short-term complications/acute stuff then that may lead to some interesting dynamics, e.g. insurers offering cheaper contracts to diabetics with poor (and known to be sub-optimal, from a health standpoint) glycemic control. Another problem/complication is that even if preventative care-interventions tend to cost more money than they save by decreasing the need for other interventions long-term, they may easily cost less money (sometimes substantially less) per unit of health than a lot of other stuff we’re willing to have cost-sharing mechanisms, whether public or private, pay for – which means that if you’re very strongly in favour of ‘not subsidizing the unhealthy’, you may end up rejecting cost-sharing mechanisms promoting interventions which could potentially add a lot of health on the cheap and might be considered no-brainers in any other context. One could also talk about genes and how the impact of life-style is probably highly heterogeneous, so that some people have a lot more leeway in terms of living unhealthily than do others, making a ‘nobody gets insurance coverage if it might be their own fault’ perhaps just as unfair as the converse position where everybody gets covered. I don’t know, I haven’t added it all together and done the math, but I’m willing to bet that neither have the people who may suggest that sort of thing, and I’d be skeptical about assuming you even can ‘do the math’ given the amount of knowledge required to make sense of all the complications. I’m reasonably certain the system most people would evaluate as optimal through a Rawlsian veil of ignorance would not be at either end of the extremes of what might be termed ‘the responsibility axis’ (‘if there’s any chance it might be your own fault, you don’t get any money from us’ being at one end, and ‘it doesn’t matter how you’ve behaved during your life – of course we’ll cover all your treatment costs related to those five chronic, very expensive, and completely preventable diseases you seem to have contracted’-being at the other end), even assuming the proposed model would be the only one available (thus sidestepping the problem that both models would certainly be outcompeted by alternatives in an actual insurance market where different options might be available to health care consumers). Tradeoffs are everywhere, and they’re not going away. I could probably add another related rant here about how many of the issues private insurance market decision-makers have to deal with are identical to the ones confronting public sector decisions-makers, but I think I’ll stop here as the post is quite long enough as it is – back to the book coverage:
“The behaviours that are usually cited as being involved in the aetiology of lifestyle diseases are poor diet, lack of physical activity, cigarette smoking […] and, increasingly, excess drinking […] The taking of illegal drugs is also lifestyle behaviour with health consequences […] Sexual practices are also often described as health and/or lifestyle behaviours by public health professionals […] Major lifestyle diseases are coronary heart disease, stroke, lung cancer, colon cancer, diabetes and chronic obstructive pulmonary disease. […] health-related lifestyles can be defined as behavioural choices made by individuals about eating, physical activity, drinking alcohol, smoking tobacco, taking drugs and sexual practices. […] lifestyle behaviours are all chronic rather than acute behaviours. Usually individuals will practise regular patterns of these behaviours and their future behaviour will be best predicted by the choices they have made in the past. […] lifestyle behaviours have the majority of their positive consequences in the present and the majority of their negative outcomes in the future. Any lifestyle behavioural change intervention consequently requires individuals to be future orientated.”
“Measuring any type of behaviour creates a number of challenges for psychologists. Instruments need to be valid, reliable, practical, non-reactive (that is to say they should not alter the behaviour they seek to measure) and have the appropriate degree of specificity […]. Few methods of measurement meet all these requirements. For none of the lifestyle behaviours identified by this text is there a single accepted ‘gold standard’ measurement tool. Methods of behavioural assessment can be categorised as observational, self report or physiological. Observational and self-report methods are often not validated effectively, whereas physiological methods are often valid but impractical or unacceptable to the study population. […] The variation in methods available to measure lifestyle behaviours creates problems in interpreting research and survey data. First, researchers differ in what they choose to measure and second, even if they choose to measure the same aspect of behaviour, they can differ widely in the method they choose to collect their data and the way they choose to present their findings. Throughout the research literature on lifestyle behaviours, different methods of measurement confuse and hinder direct comparisons.”
“Since the late 1970s regular travel by foot or by bicycle has declined by 26 per cent (Department of Health, Physical Activity, Health Improvement and Prevention 2004).”
“emotional reactions to risky situations can often diverge from cognitive assessments of the same situation. If division occurs emotional reactions usually override cognitive reactions and drive behaviour. One reason for the domination of emotional responses over cognitive assessment is that emotional responses are rapid and rational analyses usually take time […] Many researchers investigating the role of emotion in risk perception conceptualise it as inferior to analytical responses. Indeed it is often dismissed as a source of lay error […] The emotion most usually associated with risk is anxiety (Joffe 2003). Dismissing anxiety as a biasing factor in ‘accurate’ risk perception is problematic. Anxiety is the intermediate goal of many risk communications, particularly public health communications. The primary goal is preventative behaviour but anxiety is considered an essential initiating motivation. Many health promotions are based on this fear drive hypothesis […]. The fear-drive model is generally considered outdated in academic health psychology […] but it is worth considering as it remains a central, if unacknowledged, tenet of many health promotion campaigns. […] The fear-drive model principally proposes that fear is an unpleasant emotion and people are motivated to try to reduce their state of fear. Health promotion has taken this notion and applied it to communication. If a communication evokes fear or anxiety then the fear drive model suggests that the recipient will be motivated to reduce this unpleasant emotive state. If the communication also contains behavioural advice, either implicitly or explicitly, then individuals may follow this advice […] Fear is intuitively appealing as a means of promoting behavioural change but the role it plays in initiating behavioural change is not clear cut or consistent […]. However, this has been effectively denied […] by health professionals for over half a century.”
“Self-efficacy is the belief that one can carry out specific behaviours in specified situations […]. Self-efficacy has been extensively studied [and] has been argued to be enhanced by personal accomplishment or mastery, vicarious experience or verbal persuasion […]. Self-efficacy is not unrealistic optimism as it is based on experience […]. Self-efficacy is similar to the broader construct of self-esteem but can be distinguished by three aspects: self-efficacy implies a personal attribution; it is prospective, referring to future behaviours and finally it is an operative construct in that the cognition is proximal to the behaviour […]. Self-efficacy is one of the best predictors of behavioural change whereas self-esteem has been found to be a poor predictor of behavioural change […]. Ajzen (1988, 1998) has consistently argued that behaviour-specific constructs fare better than generalised dispositions in predicting behaviour. The success of self-efficacy and the failure of self-esteem in predicting a range of behaviours adds considerable weight to this principle of compatibility [I remember an analogous argument being made in Leary et al.]. […] Perceived self-efficacy has been found to be the major instigating force in both intentions to change lifestyle behaviours and actual behavioural change […] Outcome expectancies, goals and perceived impediments have also been found to be predictive in some studies”
“Stage theories have become increasing popular in recent years […]. Many theorists have argued that different cognitions may be important at different stages in promoting health behaviour […] According to all stage theories a person can move through a series of stages in the process of behavioural change […] Different factors are important at different stages, although the theory allows for some overlap. […] interpreting whether the data supports a stage theory of behaviour is fraught with difficulties. […] Regardless of the method of analysis there appears [however] to be little empirical evidence for the existent of discrete stages that could not equally well be explained as categorisation of a continuum […].”
“There are differences in the level of obesity between the different UK countries. In Northern Ireland, some 64 per cent of men and 53 per cent of women are overweight or obese (NISRA 2006). Similarly, in Scotland 64 per cent of men and 57 per cent of women are so classified (Scottish Executive 2005) […] In England, 65.2 per cent of men and 57 per cent of women were reported as being at least overweight. The results from the Health Survey for England show that the proportion of adults with a desirable BMI decreased between 1993 and 2005, from 41.0 per cent to 32.2 per cent among men and from 49.5 per cent to 40.7 per cent among women. There was no significant change in the proportion of adults who were overweight. The proportion who were categorised as obese (BMI 30+) increased from 13.2 per cent of men in 1993 to 23.1 per cent in 2005 and from 16.4 per cent to 24.8 per cent of women (Information Centre 2006).”
“The National Diet and Nutrition Survey (DoH/FSA 2002) reported on a range of socio-demographic factors related to diet and obesity. For example, those in the low working-class group consumed more calories, considerably more fat, more salt and non-milk extrinsic sugars than those in the middle and upper classes. Furthermore those on low income eat a less varied diet compared to those in the upper classes. […] people living on state benefits and reduced income eat less fruit and vegetables, less fish and less high-fibre foods […] children of semi-skilled and unskilled manual workers are more likely to eat fatty food, less fruit and vegetables, and more sweets than those children of professionals and managers. […] research suggests that nearly 20 per cent of those aged between 4 and 18 years eat no fruit at all during a typical week […] Rayner and Scarborough (2005) estimated that food related ill-health is responsible for about 10 per cent of morbidity and mortality in the UK. […] They estimated that food accounts for costs of £6 billion a year (9 per cent of the NHS budget).”
“the amount of sedentary time spent watching TV by children in the UK has doubled since the 1960s (Reilly and Dorosty 1999)”
“When I retired from clinical practice in 1998, my intention was (and still is) to write a definitive, exhaustively referenced, history of diabetes, which would be of interest primarily to doctors. However, I jumped at the suggestion of the editors of this series at Oxford University Press that I should write a biography of diabetes that would be about a tenth of the length of a full history with a minimum of references, for a wide general readership.”
This book is the result. As I pointed out on goodreads, this book is really great. The book is not particularly technical compared to other books about diabetes which I’ve read in the past, however this semi-critical review does make the point that the coverage is occasionally implicitly ‘asking too much’ even from diabetic readers (“There were parts of all this that lost my interest or that I lacked the background to appreciate”). Whereas the reviewer was apparently to some extent getting lost in the details, so was I – but in a completely different way; I was simply amazed at the amount of small details and interesting observations included in the book that I did not know, and I loved every single chapter of the book. The author of the other review incidentally also states that: “I don’t recommend that anyone read this who is not already familiar with diabetes, either by having it or knowing someone with it.” I’d note that I’m not sure I agree with this recommendation, to the extent that it’s even ‘relevant’ – these days such people who don’t even know anyone with diabetes might well be a bit hard to find, on account of the fact that diabetes has become a quite common illness. Presumably a significant proportion of the people who assume they don’t know anyone with the disease might well do so anyway, because a very large number of people have type 2 diabetes without knowing it. I think a reader would get more out of the book if he or she has diabetes or knows someone with diabetes, but a lot of people who do not would also benefit from knowing the stuff in this book. Not only in a ‘and now you know how bad type 2 is and why you should get checked out if you think you’re at risk’-sense (there’s incidentally also a lot of stuff about type 1), but also in the ‘the history of diabetes is really quite fascinating’-sense. I do think it is.
Have a look at this image. The book included a similar picture (not exactly the same one, but it’s of the same patient and the ‘before’ picture is obviously taken at the same time this one was), which is of Billy Leroy, a type 1 diabetic, before and after he started insulin. He was one of the first patients treated with insulin (the first human treated with insulin was Leonard Thompson, in 1922). Billy Leroy’s weight in the first picture, where he was 3 years old, was 6.8 kg (the 5 % (underweight) CDC body weight cutoff at the age of 3 is 12 kg) – during the three months after he started on insulin, his weight doubled. The author argues in the beginning of the book that: “When people are asked to rank diseases in order of seriousness, diabetes is usually at the mild end of the spectrum.” This may or may not be true, but the picture to which I link above certainly adds a detail which is important to keep in mind but easy to forget when evaluating ‘the severity’ of the disease today – type 1 diabetes in particular is not much fun if you don’t have access to insulin, and until the early 1920s people with this disease simply died, most of them quite fast. (They all still do – like all other humans – but they live a lot longer before they die…)
The author knows his stuff and the book has a lot of content, making it hard to know what to pick out and mention in particular in a review like this – however below I have added a few quotes from the book and some observations made along the way. The content covering the late nineteenth century and the first couple of decades of the twentieth century, before it was discovered that insulin could save the lives of a lot of sick children, would in my opinion on its own be a strong reason for reading the book; but the chapters covering the periods that came after are wonderful as well. When insulin was discovered a religiously inclined mind might well be tempted to think of the effects on young type 1 diabetic children as almost miraculous; but gradually doctors treating diabetics came to realize (the patients never knew, because they were not told – it is pointed out in the book that the fact that it might make a lot of sense to give patients with a disease like diabetes some discretion in terms of how to treat their illness is a in a historical context very new idea; active patient involvement in medical decision-making is one of the cornerstones of current treatment regimes, for good reason, and I found it really surprising and frustrating to learn how this disease was treated in the past) that things might be more complicated than they had initially been assumed to be. Type 2 diabetics had suffered from late stage complications like blindness and kidney failure for centuries, but such complications had never before been observed in type type 1 diabetics before insulin, because diabetes presenting in children were pre-insulin universally fatal. It turned out that many of the children who were initially ‘saved’ by insulin in the early 1920s ended up suffering from severe complications just a couple of decades later, and many of them died early from these complications:
“After the Second World War it became clear that [diabetic] kidney disease could also affect the young, and there were increasingly frequent reports of diabetics who had been saved by insulin as children only to succumb to kidney failure in their 20s and 30s. Fifty of Joslin’s child patients who had started insulin before 1929 were followed up in 1949, when a third had died at an average age of 25, after having had diabetes for an average of 17.6 years. One half had died of kidney failure and the other half of tuberculosis and other infections. […] In the experience of the Joslin group, only 2 per cent of deaths of young diabetic patients before 1937 were due to kidney disease, but, of those who died between 1944 and 1950, more than half had advanced kidney disease. Results in Europe were equally bad. In 1955 all of eighty-seven Swiss children had signs of kidney disease after sixteen years of diabetes, and after twenty-one years all had died. Most young people with diabetic kidney disease also had severe retinopathy and many became blind—by the mid 1950s diabetes was the commonest cause of new blindness in people under the age of 50. […] Such devastating cases were being increasingly reported in the medical literature in the late 1940s and early 1950s, but they were not publicized in the lay press, presumably to avoid spreading despair and despondency and puncturing the myth that insulin had solved the problem of diabetes […] The British Diabetic Association (founded in 1935) produced a quarterly Diabetic Journal for its lay members, but no issue from 1940 to 1960 mentions complications”.
The book makes it clear that patients were for many years basically to some extent kept in the dark about the severity of their condition, but in all fairness for a long time the doctors treating them frankly didn’t know enough to give them good information on a lot of topics anyway. The book has some really interesting observations included about how medical men of the times thought about various aspects of the illness and treatment, and how many of the things we know today, some of which ‘seem obvious’, really were not to people at the time. Many attempts have been made over time to explain why people got diabetes, and especially type 1 was really quite hard to pin down – type 2 was somewhat easier because the lifestyle component was hard to miss; however it was natural to explain the disease in terms of the symptoms it caused, and some of those symptoms in type 2 diabetics were complications which are best considered secondary to the ‘true’ disease process. For example because many type 2 diabetics suffered from disorders of the nervous system, neuropathy, the nervous system was for a while assumed to be implicated in causing diabetes – but although disorders of the nervous system can and often do present in long-standing diabetes, they are not why type 2 diabetics get sick. Kidney problems were thought to be “part and parcel of diabetes in the 19th century.” Oskar Minkowsky made it clear in 1889 that removal of the pancreas caused severe (‘type 1-like’) diabetes in dogs – but despite this discovery it still took a long time for people to figure out how it worked. This wasn’t because people at the time were stupid. One problem faced at the time was that the pancreas actually looked quite normal in people who died from diabetes – the islet cells which are implicated in the disease weigh around 1-1.5 grams altogether, and make up only a very small proportion of the pancreas (1% or so). Many doctors found it hard to imagine that the islets cells could be reponsible for controlling carbohydrate metabolism (and other aspects of metabolism as well – “It is important to realize that diabetes is not just a glucose disease. There are also abnormalities of fat metabolism”). The pancreas wasn’t the only organ that looked normal – despite the excessive urination the kidneys did as well, and so did other organs, to the naked eye. All major features of diabetic retinopathy (diabetic eye disease) had been described by the year 1890 with the aid of the ophthalmoscope, so people knew the eyes of people with long-standing diabetes looked different; how to interpret these findings was however not clear at the time – some argued the eye damage found in diabetics was not different from eye damage caused by hypertension, and treatment options were non-existent anyway.
Many of the treatment options discussed among medical men before insulin were diets, and although dietary considerations are important in the treatment context today, it’s probably fair to say that not all of the supposed dietary remedies of the past were equally sensible: “One diet that had a short vogue in the 1850s was sugar feeding, brainchild of the well-known but eccentric French physician Pierre Piorry (1794–1879). He thought that diabetics lost weight and felt so weak because of the amount of sugar they lost in the urine and that replacing it should restore their strength”. (Aargh!). For the curious (or desperate) man, though, there were alternatives to diets: “A US government publication in 1894 listed no less than forty-two anti-diabetic remedies including bromides, uranium nitrate, and arsenic.” Relatedly, “in England until 1925, any drug could be advertised and marketed as a cure for any disease, even if it was completely ineffective”. Whether or not diets ‘worked’ depended in part on what those proposed diets included (see above..), whether people followed them, and whether people who presented were thin or fat. In the book Tattersall mentions that already from the middle of the nineteenth century many physicians thought that there were two different types of diabetes (there are more than two, but…). The thin young people presenting with symptoms were by many for decades considered hopeless cases (that they were hopeless cases was even noted in medical textbooks at the time), because they had this annoying habit of dying no matter what you did.
It should be noted that the book indirectly provides some insights into the general state of medical research and medical treatment options over time; for an example of the former it is mentioned that the first clinical trial (with really poor randomization/selection mechanisms, it seems from the description in the book) dealing with diabetes was undertaken in the 1960es: “the FDA demanded randomized controlled trials for the first time in 1962, and [the University Group Diabetes Program (UGDP)] was the first in diabetes. Before 1962 the evidence in support of therapeutic efficacy put to the FDA was often just ‘testimonials’ from physicians who casually tested experimental drugs on their patients and were paid for doing so.” See also this link. An example of the latter would be the observation made in the book that: “until the 1970s treatment for a heart attack was bed rest for five or six weeks, while nature took its course.” Diabetics were not the only sick people who had a tough time in the past.
One interesting question related to what people didn’t know in the beginning after the introduction of insulin was how the treatment might work long-term. The author notes that newspapers in the early years made people believe that insulin would be a cure; it was thought that insulin might nurse the islet cells back to health, so that they’d start producing insulin on their own again – which was actually not a completely stupid idea, as e.g. kidneys had the ability to recover after acute glomerulonephritis. The fact that diabetics often started on high doses which could then be lowered a month or two later even lent support to this idea; however it was discovered quite fast that regeneration was not taking place. Remarkably, insulin was explored as a treatment option for other diseases in the 1920s, and was actually used to stimulate appetite in tuberculosis patients and ‘in the insane refusing food’, an idea which came about because one of its most obvious effects was weight gain. This effect was also part of the reason why insulin was for a long time not considered an attractive option for type 2 diabetics, who instead were treated only with diet unless this treatment failed to reduce blood sugar levels sufficiently (these were the only two treatment options until the 1950s); most of them were already overweight and insulin caused weight gain, and besides insulin didn’t work nearly as well in them as it did in young and lean people with type 1 because of insulin resistance, which lead to the requirement of high doses of the drug.
Throughout much of the history of diabetes, diabetics did not measure their blood glucose regularly – what they did instead was measuring their urine, figuring out if it contained glucose or not (glucose in the urine indicates that the blood glucose is quite high). This meant that the only metric they had available to them to monitor their disease on a day to day basis was one which was unable to measure low blood glucose, and which could only (badly) distinguish between much too high blood glucose values and not-much-too-high values. Any type of treatment regime like the one I’m currently on would be completely impossible without regular blood tests on a daily basis, and I was very surprised about how late the idea of self-monitoring of blood glucose appeared; like the measurement of Hba1c, this innovation did not appear until the late 1970s. Few years after that, the first insulin pen revolutionized treatment regimes and made treatment regimes using multiple rejections each day much more common than they had been in the past, facilitating much better metabolic control.
The book has a lot of stuff about specific complications and the history of treatment advances – both the ones that worked and some of the ones that didn’t. If you’re a diabetic today, you tend to take a lot of stuff for granted – and reading a book like this will really make you appreciate how many ideas had to be explored, how many false starts there were, how much work by so many different people actually went into giving you the options you have today, keeping you alive, and perhaps even relatively well. One example of the type of treatment options which were considered in the past but turned out not to work was curative pancreas transplants, which were explored in the 60es and 70es: “Pancreas transplantation offered a potential cure of type 1 diabetes. The first was done in 1966 […] Worldwide in the next eleven years, fifty-seven transplants were done, but only two worked for more than a year”. Recent attempts to stop people at risk of developing type 1 diabetes from becoming sick are also discussed in the last part of the book, and in this context he makes a point I was familiar with: “[Repeated] failures [in this area] are particularly frustrating, because, in the best animal model of type 1 diabetes, the NOD mouse, over 100 different interventions can prevent diabetes.” This is one of the reasons why I tend to be skeptical about results from animal studies. Although he spends many pages on complications – which in a book like this makes a lot of sense given how common these complications were (and to some extent still are), and how important a role they have played in the lives of people suffering from diabetes throughout the ages – I have talked about many of these things before, just as I have talked about the results of various large-scale trials like the DCCT trial and the UKPDS (see e.g. this and this), so I will not discuss such topics in detail here. I do however want to briefly remind people of what kind of a disease badly managed type 2 diabetes (the by far most common of the two) is, especially if it is true as the author argues in the introduction that many people perceive of it as a relatively mild disease – so I’ll end the post with a few quotes from the book:
“I took over the diabetic clinic in Nottingham in 1975 and three years later met Lilian, an overweight 60-year-old woman who was on tablets for diabetes. She had had sugar in her urine during her last pregnancy in 1957 but was well until 1963, when genital itching (pruritus vulvae) led to a diagnosis of diabetes. She attended the clinic for two years but was then sent back to her GP with a letter that read: ‘I am discharging this lady with mild maturity onset diabetes back to your care.’ She continued to collect her tablets but had no other supervision. When I met her after she had had diabetes for eighteen years she was blind, had had a heart attack, and had had one leg amputated below the knee. The reason for the referral to me was an ulcer on her remaining foot, which would not heal. […] Someone whose course is not dissimilar to that of Lilian is Sue Townsend (b. 1946), author of the Adrian Mole books. She developed diabetes at the age of 38 and after only fifteen years was blind from retinopathy and wheelchair bound because of a Charcot foot, a condition in which the ankle disintegrates as a result of nerve damage. Neuropathy has also destroyed the nerve endings in her fingers, so that, like most other blind diabetics, she cannot read Braille. She blames her complications on the fact that she cavalierly disregarded the disease and kept her blood sugars high to avoid the inconvenience of hypoglycaemic (low-blood-sugar) attacks.”
I friend pointed me to a Danish article talking about this. I pointed out a few problems and reasons to be skeptical to my friend, and I figured I might as well share a few thoughts on these matters here as well. I do not have access to my library at the present point in time, so this post will be less well sourced than most posts I’ve written on related topics in the past.
i. I’ve had diabetes for over 25 years. A cure for type 1 diabetes has been just around the corner for decades. This is not a great argument for assuming that a cure will not be developed in a few years’ time, but you do at some point become a bit skeptical.
ii. The type of ‘mouse diabetes’ people use when they’re doing research on animal models such as e.g. NOD mice, from which many such ‘breakthroughs’ are derived, is different from ‘human diabetes’. As pointed out in the reddit thread, “Doug’s group alone has cured diabetes in mice nearly a dozen times”. This may or may not be true, but I’m pretty sure that at the present point in time my probability of being cured of diabetes would be significantly higher if I happened to be one of those lab mice.
iii. A major related point often overlooked in contexts like these is that type 1 diabetes is not one disease – it is a group of different disorders all sharing the feature that the disease process involved leads to destruction of the pancreatic beta-cells. At least this is not a bad way to think about it. This potentially important neglected heterogeneity is worth mentioning when we’re talking about cures. To talk about ‘type 1 diabetes’ as if it’s just one disease is a gross simplification, as multiple different, if similar, disease processes are at work in different patients; some people with ‘the disease’ get sick in days or weeks, in others it takes years to get to the point where symptoms develop. Multiple different gene complexes are involved. Prognosis – both regarding the risk of diabetes-related organ damage and the risk of developing ‘other’ autoimmune conditions (‘other’ because it may be the same disease process causing the ‘other’ diseases as well), such as Hashimoto’s thyroiditis – depends to some extent on the mutations involved. This stuff relates also to the question of what we mean by the word ‘cure’ – more on this below. You might argue that although diabetics are different from each other and vary in a lot of ways, the same thing could be said about the sufferers of all kinds of other diseases, such as, say, prostate cancer. So maybe heterogeneity within this particular patient population is not that important. But the point remains that we don’t treat all prostate cancer patients the same way, and that some are much easier to cure than others.
iv. The distinction between types (type 1, type 2) makes it easy to overlook the fact that there are significant within-group heterogeneities, as mentioned above. But the complexity of the processes involved are perhaps even better illustrated by pointing out that even between-group distinctions can also sometimes be quite complicated. The distinction between type 1 and type 2 diabetes is a case in point; usually people say only type 1 is auto-immune, but it was made clear in Sperling et al.’s textbook that that’s not really true; in a minority of type 2 diabetics autoimmune processes are also clearly involved – and this is actually highly relevant as these subgroups of patients have a much worse prognosis than the type 2 diabetics without autoantibody markers, as they’ll on average progress to insulin-dependent disease (uncontrollable by e.g. insulin-sensitizers) much faster than people without an auto-immune disease process. In my experience most people who talk about diabetes online, also well-informed people e.g. in reddit/askscience threads, are not (even?) aware of this. I mention it because it’s one obvious example of how within-group hidden heterogeneities can have huge relevance for which treatment modalities are desirable or useful. You’d expect type 2’s with auto-immune processes involved would need a different sort of ‘cure’ than ‘ordinary type 2’s’. For a little more on different ‘varieties’ of diabetes, see also this and this.
There are as already mentioned also big differences in outcomes between subgroups within the type 1 group; some people with type 1 diabetes will end up with three or four ‘different'(?) auto-immune diseases, whereas others will get lucky and ‘only’ ever get type 1 diabetes. Not only that, we also know that glycemic control differences between those groups do not account for all the variation in between-group differences in outcomes in terms of diabetes-related complications; type 1 diabetics hit by ‘other’ auto-immune processes (e.g. Graves’ disease) tend to be more likely to develop complications to their diabetes than the rest, regardless of glycemic control. Would successful beta-cell transplants, assuming these at some point become feasible, and achieved euglycemia in that patient population still prevent thyroid failure later on? Would the people more severely affected, e.g. people with multiple autoimmune conditions, still develop some of the diabetes-related complications, such as cardiovascular complications, even if they had functional beta cells and were to achieve euglycemia, because those problems may be caused by disease aspects like accelerated atherosclerosis to some extent perhaps unrelated to glycemic control? These are things we really don’t know. It’s very important in that context to note that most diabetics, both type 1 and type 2, die from cardiovascular disease, and that the link between glycemic control and cardiovascular outcomes is much weaker than the one between glycemic control and microvascular complications (e.g., eye disease, kidney disease). There may be reasons why we do not yet have a good picture of just how important euglycemia really is, e.g. because glucose variability and not just average glucose levels may be important in terms of outcomes (I recall seeing this emphasized recently in a paper, but I’m not going to look for a source) – and Hba1c only account for the latter. So maybe it does all come back to glycemic control, it’s just that we don’t have the full picture yet. Maybe. But to the extent that e.g. cardiovascular outcomes – or other complications in diabetics – are unrelated to glycemic control, beta-cell transplants may not improve cardiovascular outcomes at all. One potential cure might be one where diabetics get beta-cell transplants, achieve euglycemia and are able to drop the insulin injections – yet they still die too soon from heart disease because other aspects of the disease process has not been addressed by the ‘cure’. I don’t think at the current point in time that we really know enough about these diseases to really judge if a hypothetical diabetic with functional transplanted beta-cells may not still to some extent be ‘sick’.
v. If your cure requires active suppression of the immune system, not much will really be gained. A to some people perhaps surprising fact is that we already know how to do ‘curative’ pancreas transplants in diabetics, and these are sometimes done in diabetic patients with kidney failure (“In most cases, pancreas transplantation is performed on individuals with type 1 diabetes with end-stage renal disease, brittle diabetes [poor glycemic control, US] and hypoglycaemia unawareness. The majority of pancreas transplantation (>90%) are simultaneous pancreas-kidney transplantation.” – link) – these people would usually be dead without a kidney transplant and as they already have to suffer through all the negative transplant-related effects of immune suppression and so on, the idea is that you might as well switch both defective organs now you’re at it, if they’re both available. But immune suppression sucks and these patients do not have great prognoses so this is not a good way to deal with diabetes in a ‘healthy diabetic’; if rejection problems are not addressed in a much better manner than the ones currently available in whole-organ-transplant cases, the attractiveness of any such type of intervention/’cure’ goes down a lot. In the study they tried to engineer their way around this issue, but whether they’ve been successful in any meaningful way is subject to discussion – I share ‘SirT6”s skepticism at the original reddit link. I’d have to see something like this working in humans for some years before I get too optimistic.
vi. One final aspect is perhaps noting. Even a Complete and Ideal Cure involving beta-cell transplants in a setting where it turns out that everything that goes wrong with all diabetics is really blood-glucose related, is not going to repair the damage that’s already been done. Such aspects will of course matter much more to some people than to others.
This book is another publication from the 100 Cases … series which I’ve talked about before – I refer to these posts for some general comments about what this series is like and some talk about the other books in the series which I’ve read. The book is much like the others, though of course the specific topics covered are different in the various publications. I liked this book and gave it 3 stars on goodreads. The book has three sections: a section dealing with ‘chemical pathology, immunology and genetics’; a section dealing with ‘histopathology’; and a section dealing with ‘haematology’. As usual I knew a lot more about some of the topics covered than I did about some of the others. Some cases were quite easy, others were not. Some of the stuff covered in Greenstein & Wood’s endocrinology text came in handy along the way and enabled me for example to easily identify a case of Cushing’s syndrome and a case of Graves’ disease. I don’t think I’ll spoil anything by noting that two of the cases in this book involved these disorders, but if you plan on reading it later on you may want to skip the coverage below, as I have included some general comments from the answer sections of the book in this post.
As someone who’s not working in the medical field and who will almost certainly never need to know how to interpret a water deprivation test (also covered in detail in Greenstein and Wood, incidentally), there are some parts of books like this one which are not particularly ‘relevant’ to me; however I’d argue that far from all the stuff included in a book like this one is ‘stuff you don’t need to know’, as there are also for example a lot of neat observations included about how specific symptoms (and symptom complexes) are linked to specific disorders, some related ideas about which other medical conditions might cause similar health problems, and which risk factors are potentially important to have in mind in specific contexts. If you’ve had occasional fevers, night sweats and experienced weight loss over the last few months, you should probably have seen a doctor a while ago – knowledge included in books like this one may make the reader perhaps a bit less likely to overlook an important and potentially treatable health problem, and/or increase awareness of potential modifiable risk factors in specific contexts. A problem is however that the book will be hard to read if you have not read any medical textbooks before, and in that case I would probably advise you against reading it as it’s almost certainly not worth the effort.
I have added a few observations from the book below.
“After a bone marrow transplant (and any associated chemotherapy), the main risks are infection (from low white cell counts and the use of immunosuppressants, such as cyclosporin), bleeding (from low platelet counts) and graft versus host disease (GVHD). […] An erythematous rash that develops on the palms or soles of the feet of a patient 10–30 days after a bone marrow transplant is characteristic of GVHD. […] GVHD is a potentially life-threatening problem that can occur in up to 80% of successful allogeneic bone marrow transplants. […] Clinically, GVHD manifests like an autoimmune disease with a macular-papular rash, jaundice and hepatosplenomegaly and ultimately organ fibrosis. It classically involves the skin, gastrointestinal tract and the liver. […] Depending on severity, treatment of acute GVHD may involve topical and intravenous steroid therapy, immunosuppression (e.g. cyclosporine), or biologic therapies targeting TNF-α […], a key inflammatory cytokine. […] Prognosis is related to response to treatment. The mortality of patients who completely respond can still be around 20%, and the mortality in those who do not respond is as high as 75%.”
“The leading indication for a liver transplant is alcoholic cirrhosis in adults and biliary atresia in children. […] The overall one-year survival of a liver transplant is over 90%, with 10-year survival of around 70%. […] Transplant rejection can be classified by time course, which relates to the underlying immune mechanism: • Hyperacute organ rejection occurs within minutes of the graft perfusion in the operating theatre. […] The treatment for hyperacute rejection is immediate removal of the graft. • Acute organ rejection take place a number of weeks after the transplant […] The treatment for acute rejection includes high dose steroids. • Chronic organ rejection can take place months to years after the transplant. […] As it is irreversible, treatment for chronic rejection is difficult, and may include re-transplantation.”
“Chronic kidney disease (CKD) is characterized by a reduction in GFR over a period of 3 or more months (normal GFR is >90–120 mL/min). It arises from a progressive impairment of renal function with a decrease in the number of functioning nephrons; generally, patients remain asymptomatic until GFR reduces to below 15 mL/min (stage V CKD). Common causes of CKD are (1) diabetes mellitus, (2) hypertension, (3) glomerulonephritis, (4) renovascular disease, (5) chronic obstruction or interstitial nephritis, and (6) hereditary or cystic renal disease”
“The definition of an aneurysm is an abnormal permanent focal dilatation of all the layers of a blood vessel. An AAA [abdominal aortic aneurysm] is defined when the aortic diameter, as measured below the level of the renal arteries, is one and a half times normal. Women have smaller aortas, but for convenience, more than 3 cm qualifies as aneurysmal. The main risk factors for aneurysm formation are male gender, smoking, hypertension, Caucasian/European descent and atherosclerosis. Although atherosclerosis is a risk factor and both diseases share common predisposing factors, there are also differences. Atherosclerosis is primarily a disease of the intima, the innermost layer of the vessel wall, whereas in aneurysms there is degeneration of the media, the middle layer. […] The annual risk of rupture equals and begins to outstrip the risk of dying from surgery when the aneurysm exceeds 5.5 cm. This is the size above which surgical repair is recommended, comorbidities permitting. […] Catastrophic rupture, as in this case, presents with hypovolaemic shock and carries a dismal prognosis.” [The patient in the case history died soon after having arrived at the hospital]
“Stroke refers to an acquired focal neurological deficit caused by an acute vascular event. The neurological deficit persists beyond 24 hours, in contrast to a transient ischaemic attack (TIA) where symptoms resolve within 24 hours, although the distinction is now blurred with the advent of thrombolysis. […] Strokes are broadly categorized into ischaemic and haemorrhagic types, the majority being ischaemic. The pathophysiology in a haemorrhagic stroke is rupture of a blood vessel causing extravasation of blood into the brain substance with tissue damage and disruption of neuronal connections. The resulting haematoma also compresses surrounding normal tissue. In most ischaemic strokes, there is thromboembolic occlusion of vessels due to underlying atherosclerosis of the aortic arch and carotid arteries. In 15–20% of cases, there is atherosclerotic disease of smaller intrinsic blood vessels within the brain[…]. A further 15–20% are due to emboli from the heart. […] The territory and the extent of the infarct influences the prognosis; [for example] expressive dysphasia and right hemiparesis are attributable to infarcts in Broca’s area and the motor cortex, both frontal lobe territories supplied by the left middle cerebral artery.”
“The stereotypical profile of a gallstone patient is summed up by the 4Fs: female, fat, fertile and forty. However, while gallstones are twice as common in females, increasing age is a more important risk factor. Above the age of 60, 10–20% of the Western population have gallstones. […] Most people with cholelithiasis are asymptomatic, but there is a 1–4% annual risk of developing symptoms or complications. […] Complications depend on the size of the stones. Smaller stones may escape into the common bile duct, but may lodge at the narrowing of the hepatopancreatic sphincter (sphincter of Oddi), obstructing the common bile duct and pancreatic duct, leading to obstructive jaundice and pancreatitis respectively. […] In most series, alcohol and gallstones each account for 30–35% of cases [of acute pancreatitis]. […] Once symptomatic, the definitive treatment of gallstone disease is generally surgical via a cholecystectomy.”
“Breast cancer affects 1 in 8 women (lifetime risk) in the UK. […] Between 10 and 40% of women who are found to have a mass by mammography will have breast cancer. […] The presence of lymphovascular invasion indicates the likelihood of spread of tumour cells beyond the breast, thereby conferring a poorer outlook. Without lymph node involvement, the 10-year disease-free survival is close to 70–80% but falls progressively with the number of involved nodes.”
“Melanoma is a cancer of melanocytes, the pigmented cells in the skin, and is caused by injury to lightly pigmented skin by excessive exposure to ultraviolet (UV) radiation […] The change in colour of a pre-existing pigmented lesion with itching and bleeding and irregular margins on examination are indicators of transformation to melanoma. Melanomas progress through a radial growth phase to a vertical growth phase. In the radial growth phase, the lesion expands horizontally within the epidermis and superficial dermis often for a long period of time. Progression to the vertical phase is characterized by downward growth of the lesion into the deeper dermis and with absence of maturation of cells at the advancing front. During this phase, the lesion acquires the potential to metastasize through lymphovascular channels. The probability of this happening increases with increasing depth of invasion (Breslow thickness) by the melanoma cells. […] The ABCDE mnemonic aids in the diagnosis of melanoma: Asymmetry – melanomas are likely to be irregular or asymmetrical. Border – melanomas are more likely to have an irregular border with jagged edges. Colour – melanomas tend to be variegated in colour […]. Diameter – melanomas are usually more than 7 mm in diameter. Evolution – look for changes in the size, shape or colour of a mole.”
“CLL [chronic lymphocytic leukaemia] is the most common leukaemia in the Western world. Typically, it is picked up via an incidental lymphocytosis in an asymptomatic individual. […] The disease is staged according to the Binet classification. Typically, patients with Binet stage A disease require no immediate treatment. Symptomatic stage B and all stage C patients receive chemotherapy. […] cure is rare and the aim is to achieve periods of remission and symptom control. […] The median survival in CLL is between four and six years, though some patients survive a decade or more. […] There is […] a tendency of CLL to transform into a more aggressive leukaemia, typically a prolymphocytic transformation (in 15–30% of patients) or, less commonly (<10% of cases), transformation into a diffuse large B-cell lymphoma (a so-called Richter transformation). Appearance of transformative disease is an ominous sign, with few patients surviving for more than a year with such disease.”
“Pain, swelling, warmth, tenderness and immobility are the five cardinal signs of acute inflammation.”
“Osteomyelitis is an infection of bone that is characterized by progressive inflammatory destruction with the formation of sequestra (dead pieces of bone within living bone), which if not treated leads to new bone formation occurring on top of the dead and infected bone. It can affect any bone, although it occurs most commonly in long bones. […] Bone phagocytes engulf the bacteria and release osteolytic enzymes and toxic oxygen free radicals, which lyse the surrounding bone. Pus raises intraosseus pressure and impairs blood flow, resulting in thrombosis of the blood vessels. Ischaemia results in bone necrosis and devitalized segments of bone (known as sequestra). These sequestra are important in the pathogenesis of non-resolving infection, acting as an ongoing focus of infection if not removed. Osteomyelitis is one of the most difficult infections to treat. Treatment may require surgery in addition to antibiotics, especially in chronic osteomyelitis where sequestra are present. […] Poorly controlled diabetics are at increased risk of infections, and having an infection leads to poor control of diabetes via altered physiology occurring during infection. Diabetics are prone to developing foot ulcers, which in turn are prone to becoming infected, which then act as a source of bacteria for infecting the contiguous bones of the feet. This process is exacerbated in patients with peripheral neuropathy, poor diabetic control and peripheral vascular disease, as these all increase the risk of development of skin breakdown and subsequent osteomyelitis.” [The patient was of course a diabetic…]
“Recent onset fever and back pain suggest an upper UTI [urinary tract infection]. UTIs are classified by anatomy into lower and upper UTIs. Lower UTIs refer to infections at or below the level of the bladder, and include cystitis, urethritis, prostatitis, and epididymitis (the latter three being more often sexually transmitted). Upper UTIs refer to infection above the bladder, and include the ureters and kidneys. Infection of the urinary tract above the bladder is known as pyelonephritis [which] may be life threatening or lead to permanent kidney damage if not promptly treated. UTIs are also classified as complicated or uncomplicated. UTIs in men, the elderly, pregnant women, those who have an indwelling catheter, and anatomic or functional abnormality of the urinary tract are considered to be complicated. A complicated UTI will often receive longer courses of broader spectrum antibiotics. Importantly, the clinical history alone of dysuria and frequency (without vaginal discharge) is associated with more than 90% probability of a UTI in healthy women. […] In women, a UTI develops when urinary pathogens from the bowel or vagina colonize the urethral mucosa, and ascend via the urethra into the bladder. During an uncomplicated symptomatic UTI in women, it is rare for infection to ascend via the ureter into the kidney to cause pyelonephritis. […] Up to 40% of uncomplicated lower UTIs in women will resolve spontaneously without antimicrobial therapy. The use of antibiotics in this cohort is controversial when taking into account the side effects of antibiotics and their effect on normal flora. If prescribed, antibiotics for uncomplicated lower UTIs should be narrow-spectrum […] Most healthcare-associated UTIs are associated with the use of urinary catheters. Each day the catheter remains in situ, the risk of UTI rises by around 5%. Thus inserting catheters only when absolutely needed, and ensuring they are removed as soon as possible, can prevent these.”
(Smbc – click to view full size).
Compare with the book:
(There are other diagrams in the book which look quite a bit more like the SMBC one than does the table above, but none of them deal with 17α-hydroxylase, progesterone, androstenedione and their friends and acquaintances…).
No, I didn’t get all of the stuff covered in this book, but this would probably also be a bit much to expect. There were however more than a few principles presented here which I’d sort of come across elsewhere which I now have a much better understanding of than I used to do. Many of the things covered (but far from all of them) were things I’d read about before, e.g. in McPhee et al. and Sperling et al. But there was also some new stuff in there.
The book has ~118 pages, plus some pages with reading comprehension questions in the back, but the page count is a very deceptive metric if you want to estimate the amount of work required to get through the book; I may be wrong, but I think most people will find it very hard to read even close to 10 pages per hour, certainly ‘in the long run’ (after the first couple of hours) and certainly if they want to understand the stuff and do not have a medical background. You probably need to be willing to look stuff up every now and then to follow what’s going on. I basically read the book two to four pages at a time, with lots of short breaks. Actually I’m not sure this is a good book to read at all if you do not have some medical knowledge – as they state in the introduction: “The book is aimed at undergraduate medical students […] it should also be a useful source of information for clinical medical students and junior doctors”.
The book belongs in the same series as the nutrition text by Barasi I read a while back (another book covering topics which were also looked at in the book, e.g. stuff like energy homeostasis), and it suffers from the same main problem I had with that publication; there’s not a single source provided in the publication. Actually that’s not entirely true as I think one of the graphs were described in the text and so you were told where the numbers came from in that case; but almost all numbers provided are provided without any indication of where they come from, and you have no way to figure out what kind of research they are based on. “Diabetics are three times more likely to have a stroke and 15 times more likely to undergo lower limb amputation than non-diabetic subjects”, they write in the text, yet the latter number is likely to be an underestimate as: “The relative risk of an individual with diabetes undergoing a lower extremity amputation was 20.3 in 2004 and 21.2 in 2008, compared with that of individuals without diabetes.” (link). Given that some of the other numbers included in the book clearly have not been updated since the previous editions – there’s a particularly hilarious forecast of obesity in the UK which provides a linear forecast (using OLS) of what’s likely to happen (?) to the obesity rate from the year 1998 and forward, based on the numbers observed during the previous two decades… (the third edition of the book which I read was published in 2011) – I find it highly unlikely that the mismatch between the relative risk numbers is the result of outcome improvements observed since 2008. The authors are incidentally British, which is why I refer to the specific ~20 RR estimate – I’m assuming they’re relying on UK/British numbers when providing their estimates, even though even that is actually unclear, so relying on such numbers seemed the most fair way to evaluate the accuracy of the estimate. The discrepancy in question is not one of a kind – for example elsewhere they write that: “Macrovascular complications are the major cause of death in people with Type 2 diabetes, accounting for 50% of deaths in this group” – which again seems to be a significant underestimate: “Subjects with both type 1 and type 2 diabetes are at increased risk of developing cardiovascular disease, with approximately three-quarters of patients with diabetes ultimately dying from vascular causes” and: “Mortality from CVD accounts for more than 60% of deaths in patients with type 2 diabetes mellitus”. (Quotes from Betteridge and Nicholls’ Managing Cardiovascular Complications in Diabetes – blog link here. 50% is way too low).
I assume most of the numbers included in the book are reasonably in line with the evidence, and there aren’t many numbers to begin with as the book deals almost exclusively with key principles etc., but I do find it annoying and slightly troubling that the numbers seem to be a little bit off in areas where I actually know something about those things they talk about, and regardless of whether they’re wrong or not you should provide a damn source anyway. I don’t think it would be that hard to add a few sources without making drastic changes to the format of the book, as one could just add a number in the text and a source in the back. I should make clear that to the extent the estimates provided in the publication are ‘wrong’ I believe them to be wrong on account of being based on old data; I don’t think any inaccuracies in the book are due to the authors ‘not knowing what they’re talking about’.
As might be inferred from the screenshot above the book is very technical, and so it’s a bit difficult to blog. There are many chapters where most of the coverage consists of complicated diagrams as well as verbal coverage of the same stuff dealt with in those diagrams, and little else. I have tried in my coverage below to mostly cover stuff from the book which I thought might at least be reasonably easy to understand for people reading along here.
“Endocrinology is the study of endocrine hormones and of the organs involved in endocrine hormone release. Classically, hormones have been described as chemical messengers, released and having their actions at distant sites. It is now clear, however, that there is a close relationship between hormones and other factors such as neurotransmitters and growth factors acting in a paracrine or autocrine fashion. Hormones are essential for the maintenance of normal physiological function and hormonal disorders occur at all stages of human life. Clinical endocrinologists thus look after patients of all ages and with a very wide range of disorders”
“Hormones are chemical messengers. They may be classified several ways […]: 1 Autocrine: acting on the cells that synthesized them […] 2 Paracrine: acting on neighbouring cells. An example is insulin, secreted by pancreatic β cells and affecting secretion of glucagon by pancreatic α cells. 3 Endocrine: acting on cells or organs to which they are carried in the bloodstream or through another aqueous ducting system, such as lymph. Examples include insulin, estradiol and cortisol. 4 Neuroendocrine: this is really paracrine or endocrine, except that the hormones are synthesized in a nerve cell (neurone) which releases the hormone adjacent to the target cell (paracrine), or releases it into the bloodstream, which carries it to the target cell […] 5 Neural: this is neurotransmission, when a chemical is released by one neurone and acts on an adjacent neurone […]. These chemicals are termed neurotransmitters. […] 6 Pheromonal transmission is the release of volatile hormones, called pheromones, into the atmosphere, where they are transmitted to another individual and are recognized as an olfactory signal.”
“The movement of chemicals between cells and organs is usually tightly controlled. Diffusion is the movement of molecules in a fluid phase, in random thermal (Brownian) motion […] Facilitated transport is the transport of chemicals across membranes by carrier proteins. The process does not require energy and cannot, therefore, transport chemicals against a concentration gradient. […] Active transport uses energy in the form of adenosine triphosphate (ATP) or other metabolic fuels. Therefore chemicals can be transported across the membrane against a concentration gradient […] Ion channels mediate active transport, and consist of proteins containing charged amino acids that may form activation and inactivation ‘gates’. Ion channels may be activated by receptors, or by voltage changes through the cell membrane. Channels of the ion Ca2+ can be activated by these two methods. Osmosis is the passive movement of water through a semipermeable membrane, from a compartment of low solute concentration to one which has a greater concentration of the solute.”
“Hormones interact with target cells through a primary interaction with receptors which recognize the hormones selectively. There are several different receptor systems, which vary in mechanism and timing […] Receptor antagonism is an important aspect of endocrinology and drug use generally […] antagonists play a large part in the treatment of endocrine disease. The molecule which binds to the receptor and elicits the normal cellular response is termed the agonist. The ligand which binds, but elicits no response, is the antagonist. Antagonists act at the membrane in different ways. For example the β-receptor blocker propranolol competes with epinephrine at its binding site. The anticonvulsant phenytoin blocks ion channels.”
“Living systems possess their own internal environment, which has to survive within an external environment. […] Internal control is achieved through integration of the different systems: neural, biochemical and physical. In all cases, the fundamental components of these systems are: (i) signals; (ii) transducers; (iii) sensors; and (iv) responders. […] Integration of endocrine systems is achieved through a complex interplay of regulatory feedback mechanisms operated through both hormonal and neural communication networks. The most important mechanisms are those commonly called feedback, whereby systems limit each other’s activity around a preset oscillator. […] In endocrinology, the brain–pituitary–target gland axes provide examples of feedback mechanisms in action […]. For virtually every anterior pituitary hormone, a corresponding hypothalamic releasing hormone has been discovered, and in some cases a corresponding inhibitory hypothalamic hormone has been found […]. Feedback systems may involve more than two hormones […] Understanding basic feedback mechanisms is vital in clinical endocrinology where it forms the basis of diagnostic testing. […] Characteristically, endocrine disorders disrupt normal feedback mechanisms and this feature is exploited in the interpretation of a number of endocrine function tests. Furthermore, certain hormones rise in response to stressful stimuli and this too can be utilized for diagnostic purposes. […] Most hormones: • Are subject to diurnal or ultradian rhythms • Are secreted in a pulsatile fashion • Are controlled by feedback from target organs (usually negative) • Develop autonomous secretion in pathological states […] As a general rule: • If the clinical suspicion is of hormone excess then suppression tests are used • If the clinical suspicion is of hormone deficiency then stimulation tests are used”
“Many endocrine conditions have an autoimmune aetiology and patients frequently exhibit antibodies to multiple endocrine organs and have evidence of associated autoimmune disease […] Autoimmunity may be defined as an attack by the host’s immune system on the host’s own tissues. These attacks may be transient immune reactions to infection, for example, which resolve spontaneously. They may, however, become chronic, with pathological consequences. Endocrine autoimmunity often involves an immune attack on specific endocrine glands, for example Addison’s disease, Graves’ disease, Hashimoto’s thyroiditis and insulin-dependent diabetes mellitus, where the gland is damaged or destroyed altogether [‘type 1 diabetes mellitus’ is much better than ‘insulin-dependent diabetes mellitus’ [IDDM] in this context – I dislike the unfortunate naming convention applied, given that many type 2 diabetics, as mentioned before here on this blog, will require insulin-injections over time. I’ve previously seen an estimate which I reported here on the blog which indicated that half of type 2 diabetics will need insulin within 6 years of diagnosis, but the original source for that quote has been taken down and I didn’t write down who the authors were so I can’t find the original estimate. I don’t really trust wikipedia on these things, but the article on insulin includes the (likewise unsourced) observation that: “Over 40% of those with Type 2 diabetes require insulin as part of their diabetes management plan”. As something like ~85% of all diabetics are type 2 diabetics, type 2’s make up a substantial majority of all IDDM cases if that estimate can be trusted. Regardless of precisely how many type 2 diabetics are treated with insulin it’s a very substantial number of patients, and the relevant distinction here, when thinking about autoimmunity-mediated organ damage, is between type 1 and type 2, though that distinction is admittedly also not perfect. This post has more about specific subtypes of diabetes, as does this comment – as the remarks included in the latter link in particular illustrates, this stuff is complicated and none of the applied diagnostic conventions really distinguish 100 percent between auto-immune and not-autoimmune, though the type 1/2 distinction comes much closer than does the IDDM/NIDDM distinction, which is one of the reasons why the latter distinction is these years rarely used in lieu of the type 1/2 categorization convention]. These are examples of mainly organ-specific autoimmune diseases […]. In systemic autoimmune disease, on the other hand, the immune system attacks several tissues that may be anatomically distant from each other. Examples of systemic autoimmune disease include rheumatoid arthritis, scleroderma and systemic lupus erythematosus (SLE). There may be both organ-specific and systemic components in most, if not all, autoimmune diseases.”
I liked this book and I gave it 3 stars on goodreads. Much of it was a review of stuff also covered in Sperling et al. (or elsewhere, see also this blog-post which actually includes some of the same data included in the coverage below), but there was some new stuff as well. I’ve added some relevant observations from the book below – I incidentally do not think most of the stuff included in this post should be at all hard to read for people who do not have diabetes.
“Hypoglycemia is a fact of life for most people with type 1 diabetes […] The average patient suffers untold numbers of asymptomatic episodes, two episodes of symptomatic hypoglycemia per week (thousands of such episodes over a lifetime of diabetes), and one episode of severe, temporarily disabling hypoglycemia, often with seizure or coma, per year.
Given increased recognition of the magnitude of the problem of iatrogenic hypoglycemia in type 1 diabetes, and practical improvements in the glycemic management of diabetes, over the nearly two decades since the Diabetes Control and Complications Trial (DCCT) was reported in 1993 (DCCT 1993), one might anticipate that hypoglycemia would have become less of a problem. Unfortunately, there is no evidence of that in population-based studies. For example, in their study reported in 2007, the U.K. Hypoglycaemia Study Group (UK Hypo Group 2007) found the incidence of severe hypoglycemia in patients with type 1 diabetes treated with insulin for <5 years to be comparable to that in the Stockholm Diabetes Intervention Study (Reichard and Pihl 1994) (both 110 per 100 patient-years) reported in 1994 and higher than that in the DCCT”
“the U.K. Hypoglycaemia Study Group (UK Hypo Group 2007) found the incidence of severe hypoglycemia in patients with type 1 diabetes treated with insulin for >15 years (320 episodes per 100 patient-years) to be threefold higher than in individuals treated for <5 years […] Hypoglycemia is particularly common during the night […] A consistent observation since the DCCT (1991, 1993, 1997) is that more than half of the episodes of hypoglycemia, including severe hypoglycemia, occur during the night (Chico et al. 2003; Guillod et al. 2007). […] Antidiabetic drugs, mostly insulin, [have been] found to be second only to anticoagulants as a cause of emergency hospitalization for adverse drug events in people >65 years of age, and those visits [are] almost entirely because of hypoglycemia (Budnitz et al. 2011). […] Overall, hypoglycemia is less frequent in type 2 diabetes than in type 1 diabetes […] the risk of hypoglycemia is relatively low in the first few years of insulin treatment of type 2 diabetes […], [however] the risk increases substantially, approaching that in type 1 diabetes, later in the course of type 2 diabetes […] The prospective, population-based study of Donnelly and colleagues […] indicates that the overall incidence of hypoglycemia in insulin-treated type 2 diabetes is approximately one-third of that in type 1 diabetes […] Because the prevalence of type 2 diabetes is ~20-fold greater than that of type 1 diabetes […] most episodes of iatrogenic hypoglycemia, including severe iatrogenic hypoglycemia, occur in people with type 2 diabetes.”
“The physical morbidity of an episode of hypoglycemia ranges from unpleasant symptoms, such as palpitations, tremulousness, anxiety, sweating, hunger, and paresthesias (Towler et al. 1993), and cognitive impairments with behavioral changes, to seizure, coma, or, rarely, death (Cryer 2007). […] Hypoglycemia causes functional brain failure that is corrected in the vast majority of instances after the plasma glucose concentration is raised […] Prolonged, profound hypoglycemia can cause brain death, but that is very rare and most fatal episodes are the result of other mechanisms, presumably cardiac arrhythmias […] One cardiac mechanism is impaired ventricular repolarization, reflected in a prolonged corrected QT (QTc) interval in the electrocardiogram, which is known to be associated with lethal ventricular arrhythmias. […] Older estimates were that 2 to 4% of people with type 1 diabetes died from hypoglycemia (Deckert et al. 1978; Tunbridge 1981; Laing et al. 1999). More recent reports in type 1 diabetes include hypoglycemic mortality rates of 4% (Patterson et al. 2007), 6% (DCCT/EDIC 2007), 7% (Feltbower et al. 2008), and 10% (Skrivarhaug et al. 2006).”
“The first defense against falling plasma glucose concentrations is a decrease in pancreatic β-cell insulin secretion. The second defense is an increase in pancreatic α-cell glucagon secretion. The third defense, which becomes critical when glucagon is deficient, is an increase in adrenomedullary epinephrine secretion. If these three physiological defenses fail to abort the episode, lower plasma glucose levels trigger a more intense sympathoadrenal (sympathetic neural as well as adrenomedullary) response that causes symptoms and thus awareness of hypoglycemia that prompts the behavioral defense [which is ingestion of carbohydrates]. […] All of these defenses are typically compromised in type 1 diabetes and advanced type 2 diabetes […] compromised glucose counterregulation is the key feature of the pathogenesis of iatrogenic hypoglycemia in type 1 diabetes and advanced type 2 diabetes. Hypoglycemia in diabetes is typically the result of the interplay of relative or absolute therapeutic insulin excess and compromised physiological and behavioral defenses against falling plasma glucose concentrations […] In fully developed (i.e., C-peptide–negative) type 1 diabetes, circulating insulin levels do not decrease as plasma glucose concentrations decline through or below the physiological range. […] Furthermore, circulating glucagon levels do not increase as plasma glucose concentrations fall below the physiological range […] Thus, both the first defense against hypoglycemia — a decrease in insulin levels — and the second defense against hypoglycemia — an increase in glucagon levels — are lost in type 1 diabetes. Therefore, patients with type 1 diabetes are critically dependent on the third defense against hypoglycemia, an increase in epinephrine levels. However, the epinephrine secretory response to hypoglycemia is typically attenuated in type 1 diabetes […] Through mechanisms yet to be clearly defined but often thought to reside in the brain […], the glycemic threshold for sympathoadrenal — both adrenomedullary and sympathetic neural — activation is shifted to lower plasma glucose concentrations by recent antecedent hypoglycemia […], as well as by prior exercise […] and by sleep […] The reduced responses to a given level of hypoglycemia cause the clinical syndromes of defective glucose counterregulation and hypoglycemia unawareness [which is] impairment or even complete loss of the warning, largely neurogenic symptoms that previously prompted the behavioral defense, the ingestion of carbohydrates. Hypoglycemia unawareness—or more precisely impaired awareness of hypoglycemia—is common in type 1 diabetes […] Compared with patients with type 1 diabetes who have absent insulin and glucagon responses but have normal epinephrine responses, patients with absent insulin and glucagon responses and reduced epinephrine responses have been shown to be at 25-fold […] or greater […] increased risk for severe iatrogenic hypoglycemia during aggressive glycemic therapy […] At least in part because of the clinical importance of hypoglycemia in people with diabetes, studies of the molecular and cellular physiology and pathophysiology of the CNS [central nervous system]-mediated neuroendocrine, including sympathoadrenal, responses to falling plasma glucose concentrations are an increasingly active area of fundamental neuroscience research.”
“The risk factors for hypoglycemia in people with diabetes […] follow directly from the pathophysiology of glucose counterregulation in diabetes […]. The principle is that iatrogenic hypoglycemia in type 1 diabetes and advanced type 2 diabetes is typically the result of the interplay of relative or absolute therapeutic insulin excess and compromised physiological and behavioral defenses against falling plasma glucose concentrations, i.e., hypoglycemia-associated autonomic failure (HAAF) in diabetes.
People with diabetes are not immune to hypoglycemia caused by mechanisms other than the treatment of their diabetes […]. Those include 1) an array of drugs […] including alcohol, 2) critical illnesses such as renal, hepatic or cardiac failure, sepsis, or inanition, 3) hormone deficiency states such as adrenocortical failure, 4) nonislet tumor hypoglycemia, 5) endogenous hyperinsulinism, and 6) accidental, surreptitious, or even malicious hypoglycemia. However, aside from drug effects, those mechanisms are very uncommon. […] if all other factors are the same, patients treated to lower, compared with higher, A1C levels are at higher risk for hypoglycemia. Stated differently, studies with a control group treated to a higher A1C level consistently report higher rates of hypoglycemia in the group treated to a lower A1C level in type 1 diabetes […] and type 2 diabetes […] lower mean plasma glucose concentrations and greater plasma glucose variability are also associated with a higher risk of hypoglycemia […] Improved glycemic control before and during pregnancy is particularly important in the short term because it improves pregnancy outcomes in women with type 1 diabetes. But, it increases the frequency of hypoglycemia substantially […] In one series, 45% of 108 women with type 1 diabetes suffered severe hypoglycemia during their pregnancies; compared with a prepregnancy rate of 110 per 100 patient-years, the incidence was the equivalent of 530, 240, and 50 episodes per 100 patient-years in the first, second, and third trimesters, respectively (Neilsen et al. 2008).”
“Based on a systematic review and meta-analysis of randomized controlled trials published up to 2012, Yeh et al. (2012) concluded that CSII [Continuous subcutaneous insulin infusion] (compared with MDI [multiple daily injection]), real-time CGM [continuous glucose monitoring] (compared with SMPG [self-monitored plasma glucose]), and sensor-augmented CSII (compared with MDI and SMPG) had not been shown to reduce the incidence of severe hypoglycemia in type 1 or type 2 diabetes. […] these technologies may, or may not, be shown to reduce the frequency of hypoglycemia in the future.”
My first post about the book, which includes a few general remarks and observations, can be read here. In this post I’ll cover some stuff from the last 150 pages. I’ve bolded relevant key points here the same way I did in the first post about the book.
“Atherosclerosis-related disease, coronary heart disease (CHD), peripheral vascular disease (PVD), and thrombotic stroke are major complications in people with type 2 diabetes mellitus . A recent meta-analysis of 102 prospective studies demonstrated a hazard ratio of 2 for coronary death and non-fatal myocardial infarction (MI) and 2.5 for ischemic stroke . In the United Kingdom Prospective Diabetes Study (UKPDS), for each 1% increase in HbA1c there was a 28% increase in PVD . […] In the National Health and Nutrition Examination (NHANES III) performed in the USA, the prevalence of metabolic syndrome in diabetes was 86%. The prevalence of CHD in this group was 19.2%. In those with diabetes and no evidence of metabolic syndrome, CHD prevalence was 7.5%, which is comparable to those without diabetes or metabolic syndrome . Many studies in different populations have confirmed that dyslipidemia is a common finding in type 2 diabetes. […] A basic abnormality is the overproduction of large VLDL from the liver […] LDL-cholesterol concentrations are generally similar to those of the background population. However, LDL-cholesterol remains a major risk factor […] Qualitative changes in LDL particles increase their atherogenicity. The particles are smaller and denser with less lipid core. […] Statins are first-line pharmacotherapy for diabetic dyslipidemia. Their use is based on a wealth of data from robust, randomized trials for both primary and secondary prevention of CVD events. […] A large number of diabetic patients (n=2,912) was included in HPS. Simvastatin, which reduced LDL-cholesterol by 0.9 mmol/l, was associated with a 33% relative risk reduction in major CVD events (p = 0.0003). This benefit was independent of baseline lipids, diabetes duration, glycemic control, and age. The authors [of the HPS] calculated that simvastatin therapy over five years should prevent a first major cardiovascular event in about 45 people per 1,000 treated […] It is clear that patients with diabetes and CHD respond in a similar way to the nondiabetic population. However, a substantial residual vascular risk persists […] A contributory factor to the failure to achieve therapeutic goals is statin intolerance […] in practice there is a significant minority of patients who cannot tolerate statins at all, or can only tolerate a small dose, insufficient to achieve the LDL goal.”
“Subjects with both type 1 and type 2 diabetes are at increased risk of developing cardiovascular disease, with approximately three-quarters of patients with diabetes ultimately dying from vascular causes.” [In the first post I included this quote from a previous chapter: “Mortality from CVD accounts for more than 60% of deaths in patients with type 2 diabetes mellitus”. Estimates vary (and these estimates need technically not be in conflict with each other as 75% is more than 60%…), but regardless of the differences this is ‘the big one’.]
“Overall, the available data indicate that diabetes is associated with a range of metabolic abnormalities that adversely influence platelet function [I should note that they go into a lot of detail about these ‘metabolic abnormalities’, and this is stuff I deliberately excluded from the coverage because it’s very technical stuff]. Management of the platelet aspect of this prothrombotic state should involve normalization of the metabolic changes seen in diabetes and the appropriate use of antiplatelet therapy […] aspirin is used for secondary cardiovascular protection in diabetes [38, 39], a practice supported by two large meta-analyses [40, 41]. […] data indicate that aspirin may be less effective in secondary cardiovascular protection in diabetes […] there is no convincing evidence for the use of aspirin monotherapy for primary cardiovascular protection in diabetes, although some guidelines recommend its use in high-risk subjects. […] There is evidence to suggest that the type of hypoglycemic agent used may modulate predisposition to future ischemic events. Metformin is normally used as first-line therapy in subjects with type 2 diabetes. The UK Prospective Diabetes Study (UKPDS) has demonstrated reduced ischemic heart disease (IHD) risk in overweight patients using metformin compared with subjects not on this therapy […] Insulin is mainly used in type 2 diabetes after the failure of other hypoglycemic agents. Insulin-treated type 2 diabetes subjects are at a greater risk of cardiovascular events compared with noninsulin-treated subjects, which may simply be a reflection of longer disease duration, with a consequent increase in the risk of complications . In healthy individuals, insulin has antithrombotic effects, but it has the opposite effects in the presence of insulin resistance […] There are no clear guidelines for the treatment of diabetes with ACS and there is a great variability between countries and even centers in the same country, which is largely dependent on local resources and data interpretation of different trials. […] Antithrombotic therapy following ACS has been through major changes over the past decade. […] Despite major advances in therapy, atherothrombotic complications remain the main cause of morbidity and mortality in individuals with diabetes. […] Considered together, current evidence indicates that diabetes subjects have a differential response to antiplatelet and anticoagulant drug therapy compared to subjects with normal glucose metabolism. Further studies are still needed to clarify the optimal antithrombotic strategy in this high-risk population.”
“It is difficult, if not impossible, to assess directly the efficacy of individual dietary components on CVD risk because of the challenges, both practical and financial, in modifying the diets of a large group of people for long periods of time, as well as the difficulty that arises in studying individual dietary components within the context of habitual dietary patterns. Therefore, most dietary factors with the intent of reducing CVD risk are evaluated on the basis of short-term interventions (weeks or months) using biomarkers […] rather than hard endpoints. By combining data from different types of studies, dietary patterns have emerged that are associated with a lower risk of CVD […] Moderate fat intake (25% to 35% of energy) is associated with lower triglyceride concentrations than a low-fat diet. […] Current recommendations are to consume a diet containing 25%E [read: 25 percent of daily energy intake] to 35%E as total fat [3, 4]. For individuals with diabetes, the recommendation is to consume diets toward the higher end of this range [5, 6]. […] Low-fat diets are associated with elevated triglyceride concentrations and depressed high-density lipoprotein (HDL)-cholesterol concentrations resulting from what is commonly referred to as carbohydrate-induced hypertriglyceridemia […] Carbohydrate-induced hypertriglyceridemia, resulting in elevated triglyceride concentrations, is caused by an enhanced rate of hepatic fatty acid synthesis and is precipitated by an excess flow of glucose from the gut to the liver [14, 15] and subsequent production of hepatic triglyceride-rich particles, termed very low-density lipoprotein (VLDL) […]. In some cases delayed triglyceride clearance associated with low-fat diets has also been observed, contributing to the elevated triglyceride concentrations […] Within the context of a stable body weight, replacement of dietary fat with carbohydrate results in higher triglyceride and VLDL-cholesterol concentrations, lower HDL-cholesterol concentrations, and a higher (less favorable) total cholesterol to HDL-cholesterol ratio [20, 21, 22, 23, 24, 25].” [Eckel et al. pointed this out as well and I included coverage of this in my post about that book as well; but this is an important piece of information that I do not mind repeating here. Note that not all carbohydrates are the same, and that dietary fiber seems to have a protective effect. The chapter from which the above quote, and the paragraph below, was taken covered many of the same things covered in Barasi].
“a series of randomized controlled intervention trials […] have failed to demonstrate a benefit of supplemental vitamin E, beta-carotene, vitamin C, or folate on CVD risk reduction [156, 157]. Recently, interest has been focused on the potential effect of supplemental vitamin D in CVD risk reduction. In contrast to the prior vitamins, the relationship between vitamin D and CVD risk is focused on nutrient insufficiency rather than supplemental amounts [156, 158]. Until the results of randomized controlled trials with vitamin D become available, it is premature to make any recommendations.”
“Diabetics are more likely than nondiabetics to experience ACS, and diabetes is an independent predictor for mortality in ACS. Diabetics are also more likely to develop complications of ACS and its management such as heart failure and bleeding. With a few exceptions, the management of ACS is similar in patients with and without diabetes. In patients with diabetes, management does not differ between patients who are insulin dependent and patients who do not require insulin. […] The management of ACS begins with determining the appropriate timing for coronary artery reperfusion. Patients with STEMI [ST-elevation myocardial infarction – see this] or an equivalent should receive emergent reperfusion, preferably with PCI. Patients with UA/NSTEMI [see the link in the brackets above] can be risk stratified to determine the appropriate timing for coronary angiography. In these patients angiography is used to decide if medical therapy, PCI, or CABG [Coronary Artery Bypass Grafting] is the preferred treatment strategy. All patients with ACS should be treated with antiplatelet and antithrombin therapy, as well as adjuvant therapy with a statin, ACEI, and beta-blocker.” [there’s an entire chapter about these things where they go into quite a bit of detail, but I decided against covering this stuff here as most of it is once again highly technical stuff which is not easy to blog].
“Amputation of the lower limb is one of the most feared adverse health outcomes among patients with diabetes. […] PAD [Peripheral Artery Disease], referring to atherosclerotic occlusive disease of the lower limb arteries is a common, debilitating complication that correlates with cardiovascular disease mortality . Diabetes is a significant independent risk factor for PAD (odds ratio of 2–3) , together with hypertension, cardiovascular disease, hyperlipidemia, smoking, and obesity [3, 4]. The prevalence of PAD in patients with type 2 diabetes has been estimated at 23.5% in a UK population , and is strongly dependent on the duration of diabetes [6, 7]. Compared with men without diabetes, the adjusted relative risk of PAD among men with diabetes increased from 1.39 with diabetes duration of 1–5 years’ to 4.53 for diabetes of >25 years’ duration . […] a very high prevalence (71%) of PAD was recently reported in 1,462 elderly patients with diabetes (>70 years) in Spain as evaluated by a pathological ABI (ankle-brachial index) . […] A recent meta-analysis  including 94,640 participants and 1,227 LEA [Lower-Extremity Amputation] cases reported in 14 studies demonstrated a substantial increase in the risk of LEA associated with glycemia in individuals with diabetes. The overall risk reduction (RR) for LEA was 1.26 (95% CI 1.16–1.36) for each percentage point increase in HbA1c.”
“A Scottish study showed that after LEA diabetic subjects had a 55% greater risk of death than those without diabetes . […] Median time to death […] was 27.2 months with diabetes versus 46.7 months without diabetes (p<0.01) and survival rate 10 years after amputation was 22.9% in nondiabetic patients but only 8.4% in diabetic patients (p=0.0007). [I’ve read about these things before, and I should note that I do not believe these estimates are unique or aberrant. It’s not just that losing a leg sucks – when you’re so far along in the disease process that they have to start cutting off parts of you to keep you alive, you’re really quite likely not to live for a very long time. The prognosis of a diabetic who just had a LEA is much worse than that of the average breast cancer patient.] […] The clinical stage of symptomatic PAD can be classified using the Fontaine staging system . Fontaine stage I represents those who have PAD but are asymptomatic; stages IIa and IIb include patients with mild and moderate-to-severe intermittent claudication, respectively; those with ischemic rest pain are classified in Fontaine stage III; and patients with distal ulceration and gangrene represent Fontaine stage IV. Diagnosing PAD in patients with diabetes is of clinical importance for two reasons. The first is to identify a patient who has a high risk of subsequent MI or stroke regardless of whether symptoms of PAD are present. Indeed, patients with diabetes and PAD have a fivefold increased risk [of MI/stroke] compared to the presence of either disease alone [22, 23, 24, 25]. An observational study less then ten years ago demonstrated that patients with diabetes and PAD stage IV (=ulcer) have a 100% mortality within six years . The second reason is to elicit and treat symptoms of PAD, which may be associated with functional disability and limb loss.”
“PAD is often more subtle in its presentation in patients with diabetes than in those without diabetes […] Importantly, PAD in individuals with diabetes is usually accompanied by peripheral neuropathy with impaired sensory feedback […] The majority of patients with early PAD are either asymptomatic or have atypical leg symptoms, with “classical” claudication in only 10–35%, therefore detection is elusive unless actively sought. Given shared risk factors, it is axiomatic that there exists a high coprevalence of atherosclerosis in other vascular beds, including the coronary arteries in PAD patients . […] patients with PAD are at a high risk of cardiovascular events and therefore benefit from aggressive secondary prevention […] Many studies have documented that secondary prevention is underused in patients with PAD […] Antiplatelet drugs that have been shown to reduce the incidence of vascular death, nonfatal myocardial infarction, and nonfatal stroke in patients with PAD are aspirin, ticlopidine, and clopidogrel . Aspirin plus dipyridamole has not been proven to be more efficacious than aspirin alone in the treatment of patients with PAD .”
“Compared to patients with intermittent claudication (IC; stage II of PAD), patients with critical limb ischemia (CLI; stages III and IV after Fontaine) are in a more difficult situation: while amputation is rather infrequently necessary in patients with IC , amputation rates of 23% at 12 months were reported in patients with CLI . In patients with CLI, the incidence of diabetes mellitus and chronic renal insufficiency is 70.4% and 27.8%, respectively . Thus, patients with CLI are in the majority among patients with diabetes […] The prevalence of gangrene is about 20 to 30 times higher in patients with diabetes mellitus .” [In terms of the treatment options, they put it frankly in their recommendations in that chapter: “Primary amputations only in a leg-for-life situation”].
I finished the book today. I wrote a brief review of the book on goodreads and gave it three stars. Many things covered in this book I’ve read about in detail elsewhere, e.g. in Sperling et al., Edwards et al., or, say, Eckel et al, but there was some new stuff in here as well. I really liked the first chapter, about ‘The Vascular Endothelium in Diabetes’; it covered some stuff which I’d never really gotten to the bottom of before (but due to the technical nature of that chapter I decided against covering it here). There are still a lot of details which I will not claim to fully understand, but I understand some of the main principles/mechanisms much better than I did. The book was occasionally difficult for me to read because it required knowledge about areas about which I didn’t know a great deal (e.g. haematology), and you should certainly not read this book if you don’t read more or less fluent medical textbook (“The focus of this book is to assist the physician or surgeon in preventing and managing CVD and CVD risk in diabetic patients”). As I pointed out in my goodreads review, the book was difficult for me to read for another reason as well. Authors of academic books should not use acronyms which they do not explain to the reader. Authors of such books should not explain unexplained acronyms five pages after they have used them for the first time. If they do, people might get angry at them.
I’m sure some people don’t care about such things, but this is the sort of stuff that can really piss me off, and it’s part of the reason why this book got three stars. Combining behaviour like that with some formatting errors and a few sentences which don’t make any sense because nobody seems to have proofread the damn thing, and you can end up with an academic publication which looks amateurish, even if it’s most certainly nothing of the sort. In terms of the formatting errors I will note that this is not the first Wiley-Blackwell publication like this I’ve seen – as I point out in my review of that book, the Edwards et al publication to which I link above had similar problems. It’s much rarer, I think, to see stuff like that in Springer publications.
I have added some observations from the book below. I plan to write another post about the book later on as I don’t think it’s fair to only give this book one post, considering how much stuff is in there. When I started out writing this post I was thinking that I’d make the quotes easier to read by adding relevant links where they might help. I realized quite fast that adding enough links to actually make a huge difference would most certainly not be worth it, though I have added a link here and there anyway in order to make the post more readable. I have also added a few bold sections below – I don’t like writing long posts and then have people not reading them because they’re long, so if you don’t particularly care about the topic covered below you might want to read the bolded parts in order to at least learn something from the post. There’s a lot more stuff about type 2 diabetes than about type 1 in this book, so when reading ‘diabetes’ below you should probably just think ‘type 2’.
I remember recently reading an article somewhere stating that there are many errors in medicine-related wikipedia articles and how that’s a problem, and I actually encountered an example of this while reading the book, though I can’t now remember which article it was. You should take it for granted that wiki articles to which I link in posts like these may have errors and inaccuracies (they may actually contain statements which are contradicted by the material covered in the book…), and I usually only link to them in posts like these to ‘translate’ the terms used without having to add a lot of additional text to the post in question. I’ll often not have read the articles to which I link when I link to as many as I do in this post, and a link to an article does not mean that I think all the stuff included in the article is correct. Okay, on to the book coverage:
“There is no doubt that diabetes is a significant contributor to the global burden of chronic non-communicable disease which accounts for over 36 million (63%) of deaths worldwide. Importantly, 80% of these deaths occur in low and middle income countries. [here’s a link to the source, the data above is from page 16. Note that “17.3 million (30%) [of all 57 million deaths worldwide] were due to CVDs.”] […] In an important contribution from the Global Burden of Metabolic Risk Factor of Chronic Disease Collaborating Group  national, regional and global trends in fasting plasma glucose and diabetes prevalence since 1980 were studied in a systematic analysis of health examination surveys involving over two and a half million participants and 370 country-years observations. They estimated that the number of people with diabetes increased from 153 (95% uncertainty interval 127–182) million in 1980 to 347 (314382) million in 2008 . [I included the quote partly because those numbers are interesting, partly because this quote from the introduction contains a good example of the kind of sloppiness I mention in the goodreads review; that last parenthesis was surely meant to say 314-382. But it doesn’t. And those kinds of small errors are all over the place.] […] In addition to increased risk of CVD patients with diabetes and established vascular disease have a poorer outcome than those without diabetes [7, 8]. Peripheral arterial disease is increased 2-4 fold in the diabetic population and lower limb amputations are at least 10 fold more common such that half of non-traumatic amputations are performed in diabetic patients [3, 7, 8].”
“a mean duration of diabetes of about a decade appears to confer an equivalent risk of CVD to a prior history of MI. In addition, recent work has shown that a history of DM results in six years of life years lost, mostly from CVD . […] 20% of all vascular events occur in patients without any traditional risk factors, necessitating the need for more precise clinical tools that aid clinicians in identifying those at highest risk . To help achieve this goal, there is growing interest in the development and exploitation of new biomarkers. […] A biomarker was defined by a National Institutes of Health (NIH) working group as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” . […] A biomarker should meet several criteria to be deemed clinically useful. This is structured around three fundamental questions : 1 Is the biomarker measurable? 2 Does the biomarker add new information? 3 Will the biomarker help the clinician to manage patients? Additional criteria include cost-effectiveness, safety, and replication of the biomarker in clinical scenarios. […] [Reclassification] is a relatively new concept, but potentially the most clinically relevant [of four criteria covered] as it assesses the ability of a test to reclassify individuals correctly into a different risk category; for example, an intermediate-risk subject into a high-risk subject, or a low-risk subject into an intermediate-risk subject […] The ability of the new test to achieve reclassification can be statistically examined by net reclassification improvement (NRI) or integrated discrimination improvement (IDI). The NRI method, which is determined by the proportion of individuals whose risk is correctly escalated or de-escalated, is more useful in primary prevention, where well-accepted categories of risk exist. The IDI estimates the change in predicted probability of an outcome between those with and without the outcome after the biomarker is added to the prediction model. The larger the value of the NRI or the IDI, the better the biomarker.”
Quite a few biomarkers are covered in the chapter, but I’d rather not talk too much about that stuff. There are various types of circulating biomarkers, imaging biomarkers and genetic biomarkers. A few have been included in national guidelines and the only class which does not seem to be useful in this context is the genetic one [“The AHA has given genomic testing in risk assessment in asymptomatic adults a Class III recommendation (no benefit)”]. Naturally reasons besides those related to assessing cardiovascular risk exist for doing genetic testing on diabetics, but if such tests are not useful in that respect then of course that limits their potential somewhat. Incidentally many biomarkers they talk about seem to measure similar things, meaning that adding them together don’t add a lot of information:
“It is logical to assume that if one biomarker measure gives a small incremental gain in risk prediction, multiple biomarkers would result in a larger one. However, trials of multiple biomarkers have disappointingly only shown at best a moderate improvement in usefulness when compared to standard risk factors .”
The biomarkers are assumed to hold most promise in the context of primary prevention, but “there is scant data on cost-effectiveness or differential benefit from specific treatments”. Okay, on to other stuff:
“Diabetic kidney disease […] is a clinical diagnosis and is defined by the presence of albuminuria, often with associated abnormal kidney function (an increase in creatinine or a decrease in creatinine clearance or estimated glomerular filtration rate [eGFR]) […] Diabetic nephropathy is a histological diagnosis, characterized by typical histopathological features including mesangial expansion, glomerular basement membrane thickening, and glomerulosclerosis with Kimmelstiel–Wilson lesions. Diabetic kidney disease is most commonly caused by diabetic nephropathy, but other kidney pathologies may be present […] Diabetic kidney disease is a chronic complication of diabetes and affects approximately one third of all diabetic patients [1, 2]. It is the most common cause of kidney failure requiring renal replacement therapy in Western countries  and can occur in both type 1 and type 2 diabetes with equivalent risks . The natural history and prognosis of diabetic kidney disease differ somewhat based on the type of diabetes and whether microalbuminuria is present […] In people with type 1 diabetes who have microalbuminuria, if left untreated, approximately 80% will develop macroalbuminuria (also called overt nephropathy) within 6–14 years [6, 7]. Subsequently, half of these will develop end-stage kidney disease (ESKD) over 10 years if there is still a lack of specific intervention. In contrast, approximately 20–40% of people with type 2 diabetes and microalbuminuria develop macroalbuminuria without intervention, and ESKD has been reported to develop in 20% of patients with overt nephropathy within 20 years . Some of these differences may relate to the older age and greater burden of comorbidity experienced by people with type 2 diabetes for a given duration of diabetes, meaning that more of them will die of cardiovascular and other complications before developing kidney disease.”
“Diabetic kidney disease has a heterogeneous presentation. Early stages are often asymptomatic and only detected by abnormal laboratory tests (albuminuria and changes in GFR). Albuminuria is one of the earliest detectable features of diabetic kidney disease […] As diabetes manifests as a systemic disease, patients with type 1 DM almost always have other signs of diabetic microvascular complications, such as retinopathy and neuropathy. Diabetic retinopathy usually precedes the onset of overt nephropathy, while the relationship between diabetic kidney disease and retinopathy is less predictable in type 2 diabetes. […] For people with type 1 diabetes, approximately 20–30% will have microalbuminuria after a mean duration of diabetes of 15 years [37, 38]. Similarly, 25% of individuals with type 2 diabetes have microalbuminuria after 10 years […] Proteinuria and abnormal kidney function are independent risk factors for renal outcomes in diabetes . […] As with treatment strategies for end-stage kidney disease secondary to other causes, dialysis and renal transplantation are both options for treatment for ESKD caused by diabetes. Lower survival rates have been observed for people with ESKD caused by diabetic kidney disease, with five years’ survival of 30%, according to USRDS data.”
“Cardiovascular disease (CVD) including coronary heart disease (CHD) is the major cause of mortality in patients with diabetes […] no more than 25% of the excess CHD risk in diabetes can be accounted for by established risk factors […] Hyperglycemia as a risk factor for CVD has been established for many years. Mortality from CVD accounts for more than 60% of deaths in patients with type 2 diabetes mellitus and clearly accounts for this ultimate complication of diabetes [3, 8]. The association between differing degrees of hyperglycemia and CVD risk has been an area of debate. The United Kingdom Prospective Diabetes Study (UKPDS) demonstrated that the incidence of myocardial infarction rose by 14% per 1% rise in HBA1c . This is in line with other studies showing that glucose is a continuous risk factor in people with both type 1 and type 2 diabetes. […] There is also evidence that glucose fluctuations (the highs and lows) are associated with increased oxidative stress […] Increased oxidative stress results from an imbalance between oxidant production and antioxidant defenses […] Diabetes mellitus, obesity, micro- and macrovascular complications have been consistently associated with increased oxidative stress [37, 38, 39] and several studies have demonstrated that hyperglycemia per se is associated with increased oxidative stress [39, 40]. […] Hypoglycemia is also associated with increased cardiovascular mortality [58, 59], although the mechanisms behind this remain unclear. […] As well as being associated with increased oxidative stress , hypoglycemia also has pro-inflammatory effects on the vasculature. […] These changes contribute to a hypercoagulable state associated with increased platelet aggregation and plasma concentrations of coagulation factors […] Acute hypoglycemia has also been associated with long QT syndrome, which is associated with an increased risk of sudden cardiac death .”
“The majority of people with type 2 DM [diabetes mellitus] are hypertensives […] There is no question about the need to treat hypertension in either the primary prevention or secondary prevention settings for cerebrovascular disease, irrespective of the presence of diabetes. A systematic review of the effects of different BP-lowering drug regimens in people with hypertension, diabetes, or vascular disease found that the relative risks of stroke and other major vascular outcomes were proportional to the BP reduction achieved . […] there is a general consensus that ACE inhibitors or ARB are the first-line drugs of choice in both diabetes and metabolic syndrome. In primary prevention, the only question is the level of BP above which treatment is indicated. […] The recommended threshold for treatment in primary prevention is currently under discussion in both diabetics and nondiabetics. […] there is increasing uncertainty about the use of absolute thresholds of BP to determine the need for treatment […] Although “lower should be better,” the results of recent clinical trials examining the benefits of normalizing risk-factor levels have been counter-intuitive and, sometimes, disconcerting, and have called into question this belief […] Many hypertensive patients in clinical practice receive more than one antihypertensive drug, and the use of combination therapy is widely recommended in hypertension guidelines. Combinations may be especially important for patients with diabetes, for whom recommended BP targets are challenging.”
“100 Cases in Acute Medicine presents 100 acute conditions commonly seen by medical students and junior doctors in the emergency department, or on the ward, or in the community setting. A succinct summary of the patient’s history, examination, and initial investigations, including photographs where relevant, is followed by questions on the diagnosis and management of each case. The answer includes a detailed discussion of each topic, with further illustration where appropriate, providing an essential revision aid as well as a practical guide for students and junior doctors.
Making clinical decisions and choosing the best course of action is one of the most challenging and difficult parts of training to become a doctor. These cases will teach students and junior doctors to recognize important clinical symptoms and signs, and to develop their diagnostic and management skills.”
The book is quite simple. There are 100 medical cases. Each case has a brief description of symptoms and what we know about the patient, plus a couple of questions. On the next page of the book there are then answers to the questions posed with (semi-?)detailed explanations. In many cases one of, or perhaps the only question, is: ‘what’s wrong with this person?’, but sometimes the management aspect is considered to be the key variable (‘obese hypertensive and hyperlipidemic type 2 diabetic with previous MI has just been admitted with cardiac symptoms. Here are the results of his blood-work and an ECG. How do you proceed?’ – not a quote, but close enough…), and in such cases there are e.g. questions about which particular aspects of this presentation you should be most concerned about, or perhaps an open question related to aspects such as how to optimize the follow-up process. I’ll never diagnose anyone with anything or set up a medical management plan, as that is for doctors to do, but I thought it looked like an interesting book, so I figured I’d give it a shot. Reading a book like this is a little bit like watching House, except that the medicine in here is actually trustworthy and you avoid all the drama (I know that I have remarked upon how reading medical textbooks will change your viewing experience of medical dramas before, but in the context of this book that particular aspect seems perhaps even more relevant than usual – all the patients in this book have presented to the ER because they are sick and we are told about their symptoms and perhaps some of the test results which have come back from the lab; this setting, I believe, is pretty much the default setting for medical dramas…).
The blog currently has 118 posts related to the topic of medicine so I have read some stuff and watched some lectures on these topics; I figured it’d be interesting to see if I could figure out some of the cases, and I felt reasonably sure I’d learn from both the ones I could figure out and the ones I couldn’t (as I considered them likely to add details I didn’t know, e.g. about differential diagnoses, in the anwers). I also thought more generally that it’d be nice to have a book with some ‘common/standard’ health complaint cases presented. Diagnostics is often more difficult than you’d think from reading about specific diseases, because people in many cases don’t present with all the textbook symptoms, and because certain symptoms present in a lot of very different situations. A confused old person with altered mental status might for example ‘just’ be dehydrated with nothing else going on (severe dehydration can be quite dangerous, thus the ‘just’) – but it could also be a brain tumour, or a subarachnoid hemorrhage, or a urinary tract infection (“Elderly people, particularly females, are more prone to urinary tract infections and often present with confusion”), or… Severe abdominal pain and vomiting in a young person isn’t always appendicitis; this book had a young woman with familial Mediterranean fever present that way.
There were more than a few cases where I ‘got it right’, including some quite obscure ones like a case of Stevens-Johnson syndrome (-SJS – this one is really rare, something like 1 in 200.000 rare – I only guessed it because I read the wiki on that one a while back and it stuck) and a patient with an insulinoma (this one also has a very low incidence, “estimated at 1 to 4 new cases per million persons per year” – my knowledge of diabetes helped here, as did my recall of the coverage of this condition in McPhee et al (at least I think that was where I read about it). There were quite a few more common ones I got right, for example cases of pre-eclampsia (Hall covered that one in quite a bit of detail, so I had no problems figuring out what was going on there), mumps, diabetic ketoacidosis, hyperosmolar hyperglycaemic state (I found it interesting that they included both a DKA case and a HHS case, and/but I had of course no problem recognizing either of these), malaria, alcohol withdrawal syndrome (obvious from the patient history, but not if you don’t know about the risk of seizures and -progression to DT associated with alcohol withdrawal – which the patient obviously didn’t…), Lyme disease, trypanosomiasis (well, I couldn’t remember that that’s what it was called, but I did guess ‘sleeping sickness’, which is good enough, I think – though of course I’d have no idea how to treat someone with that disease…), anorexia nervosa, and pulmonary oedema. There were a lot of them I didn’t get right or didn’t know the answer to, which is in a way to be expected (the insulinoma and SJS cases were not the only quite rare ones – who’s ever heard about Goodpasture syndrome anyway?). In more than a few cases you need, in order to get the diagnosis right, to be able to read and understand the results of an electrocardiogram, a CT scan, an MRI or a chest X-ray; I’ve seen these before in textbooks, but I’ve never received formal training in interpreting them – however at least in the case of the pulmonary oedema the X-ray results were obvious. ‘He’s having a heart attack’ was a sort of a diagnosis in a couple of cases, but not what they were going for – if they thought his heart was fine they probably wouldn’t have asked the lab for troponin levels or ordered an ECG..
I have added some observations from the book below, most of them from the ‘answer sections’. As I didn’t assume anyone reading along here would be likely to read the book later on I have not tried very hard to avoid ‘spoilers’:
“[Neurocysticercosis] is the most common parasitic infection of the central nervous system and the leading cause of adult-onset seizures in the developing world.”
“Mumps is the most common cause of unilateral acquired sensorineural hearing loss in children and young adults worldwide […] Suspect mumps in a patient who presents with parotitis and fever.” (I did. The included vaccination history helped.).
“A 19-year-old woman has presented to the emergency department complaining of fevers and malaise after returning from a holiday in South Africa two weeks earlier. Over the preceding 3–4 days she noticed a rash and sore throat and is now feeling generally tired and unwell. She has no significant medical history and does not take any regular medications or recreational drugs. She does not smoke, nor drink alcohol. She admits to several episodes of unprotected sexual intercourse with a man she met in South Africa.”
My first thought when reading the case history above: Immediate psychiatric consult and an IQ test. If you’re having unprotected sex with a South African whom you don’t know well on multiple occasions you’re either insane or a moron. More seriously, this one was one of several really depressing presentations. There were ways to make the patient history even worse (‘when she came back to receive the results of her (positive) HIV test she mentioned during the followup that she’d been gaining a bit of weight lately and that she had been feeling nauseous occasionally, especially during the morning hours…’), but this was quite bad enough. Do note however that there could be other explanations for her illness than just HIV, and that these should be considered as well: “This woman is likely to have a viral illness, considering her history of fevers, rash and sore throat. Infectious mononucleosis (glandular fever) secondary to Epstein–Barr virus is a common illness in young adults, presenting with fever, rash and lymphadenopathy following on from a sore throat.”
“Urinary tract infections can often present with non-specific symptoms, such as confusion and general malaise, particularly in elderly patients. […] Early treatment according to the Surviving Sepsis protocol is key to ensuring patients have the best chance of surviving a serious infection.”
I include this one at least in part because people reading my comments above about confusion perhaps being the result of a urinary tract infection may have thought that ‘okay, so not all of these cases are all that severe’, as a urinary tract infection is probably perceived of as belonging on ‘the opposite side of the scale’ as brain cancer. In the specific case that would be an incorrect way to think about the situation: “The patient is haemodynamically unstable […] The patient’s daughter should be informed that her mother is very unwell and may not survive.” Yes, this was another one of the depressing ones. Here’s a related quote from another case: “Most women will experience a urinary tract infection (UTI) at some time in their life, so education towards UTI prevention is important (e.g. wipe from front to back after a bowel movement or after urinating, and try to empty the bladder before and after sexual intercourse).”
“Tuberculosis should be suspected in anyone presenting with shortness of breath, fever, haemoptysis and weight loss. […] An important differential diagnosis to consider is lung malignancy.”
“Alcohol misuse increases the risk of intracerebral bleeds, because head injury is more likely to be sustained or as a result of deranged liver function. Sustained alcohol misuse can lead to deranged liver function and therefore reduced production of vitamin K, which is essential for normal blood clotting properties. […] Seizures are a common way for patients with alcohol withdrawal to present.”
“In patients who are vomiting and develop signs of a chest infection, an aspiration pneumonia should be considered.”
“Angiodysplasia is a condition where the small vessels in the bowel are dilated, very fragile and prone to bleeding. […] Angiodysplasia of the colon is the second most common cause of GI bleeding in patients over the age of 60 years (diverticular disease being the most common in that age group). The most common presentation is intermittent bleeding without pain.”
“There are common steps in the management of acute intoxication and poisoning. As with most medical emergencies, the airway, breathing and circulation (ABC) should be assessed and managed appropriately in the first instance. Neurological examinations should be carried out to look for lateralizing and/or cerebellar signs. It is also important to examine for abnormal ocular movements and papillary changes as it helps to give clues to the common drugs/toxins involved. […] Often a ‘drug screen’ is requested but this is rarely necessary. A typical drug screen is expensive and difficult to interpret. The results may take 1–2 weeks to become available and it is not possible to screen for all possible toxins. Therefore it does not alter immediate patient management in most instances. Neuroimaging, such as CT of brain, is only necessary when patients are suspected to have a structural brain lesion or significant head injury. A provoked seizure from poisoning or substance abuse does not necessitate neuroimaging in most circumstances. […] In most cases the treatment of poisonings requires supportive therapy only as specific antidotes are often not available.” (ABC arguably isn’t enough – in a different answer they add on D and E as well:) “The approach to any critically ill person should start with ABCDE (airway, breathing, circulation, disability, exposure). Each step should consist of an assessment and appropriate management before moving on to subsequent stages. This approach is a logical way of thinking through and dealing with an acutely ill person.”
“[Anorexia nervosa] is a psychiatric diagnosis characterized by a refusal to maintain normal weight for age and height, a fear of gaining weight, body image distortion and amenorrhoea. There are other subtypes, which include ‘restricting’ calorie intake, or ‘binge eating/purging’ behaviours which can include laxative, diuretic or enema use. She has evidence of a low bodyweight (formal diagnosis relies on an ideal body weight <85 per cent, body mass index <17.5 kg/m2). Her body image perception is altered. […] Most people with anorexia nervosa are female, with the onset highest during late adolescence.”
“IgA [Immunoglobulin A] nephropathy is the most common glomerular disease worldwide. It occurs most commonly in those of Asian or Caucasian origin and is more common in males (2:1). Most cases occur between the ages of 20 and 30. Most cases are sporadic and the cause is not identified, but it tends to occur following an upper respiratory tract infection or gastrointestinal infection. […] Cases can present in several ways. About half of all cases present as in this case with frank haematuria and flank pain after an upper respiratory tract infection. A third of patients can present with asymptomatic microscopic haematuria. Ten per cent of patients can present with a more severe process characterized by either the nephrotic syndrome or an acute rapidly progressive glomerulonephritis (oedema, hypertension, haematuria and renal failure).”
“Atrial fibrillation becomes more common with increasing age such that more than 10 per cent of those aged over 80 years have AF. The most common causes of AF are hypertension, heart failure, ischaemic heart disease and valvular disease. Hyperthyroidism is another cause and may not have obvious clinical signs in the elderly. […] Stroke risk can be estimated from a score (CHA2DS2VASc: Congestive heart failure, Hypertension, Age ≥75 (doubled), Diabetes, Stroke (doubled), Vascular disease, Age 65–74, and Sex category (female) […] A score of 2 predicts a 2.2 per cent per year adjusted stroke risk […] This is generally accepted to be the cut-off to starting treatment with an oral anticoagulant provided there are no contraindications. […] The main concern with anticoagulants is the risk of bleeding and an assessment of this risk should be made prior to starting treatment. A bleeding risk score such as HAS-BLED can be used to assess risk […] Warfarin is still the anticoagulant of choice.”
“The incidence of stroke after thrombolysis is around 1–1.5 per cent and most strokes occur within five days of the MI, with most cases of haemorrhage within 24 hours of MI and thrombolysis.”
This is a risk it makes sense to be aware of – lots of people die from MIs and understanding the details of the risks involved when treating these may in some cases be helpful; if a person dies from a hemorrhagic stroke shortly after receiving treatment for an MI, this should not be considered a major indication that the doctors screwed up. Medical science has advanced a lot over the years, but ‘the anticoagulant of choice’ they talk about above is rat poison so do be careful not to overestimate how much doctors can really do for you if you get sick.
“In the setting of a positive family history of early death due to chest disease and a history of deranged liver function tests, one should […] consider α1-antitrypsin deficiency. α1-Antitrypsin deficiency (A1AD) is a disease which has various phenotypes […] It is one of the most commonly inherited genetic disorders. […] The severity of lung disease differs even in siblings with the same allele. This is partially explained by environmental factors such as smoking and dust exposure; therefore it is paramount to educate patients with α1-antitrypsin deficiency not to smoke.” (yep, you guessed it – the patient was a smoker. Despite having been diagnosed with COPD 3 years earlier. Again, depressing.)
“CURB 65 is one of the most commonly used tools for assessment of community-acquired pneumonia severity. It is a useful adjunct but should not replace thorough clinical assessment. CURB 65 stands for: C = confusion; U = Urea ≥7 mmol/L; R = Respiratory rate >30/min; B = Blood pressure systolic <90 or diastolic <60 mmHg; 65 = age ≥65 years. Mortality approaches 83 per cent if all four CURB components are present. […] Most if not all atypical pneumonias present with classical pneumonic symptoms (fever, productive cough and shortness of breath), so it is hard to differentiate clinically. Atypical pneumonia is a term used to describe pneumonia caused by (i) Mycoplasma pneumoniae, (ii) Chlamydophila pneumoniae, (iii) Chlamydophila psittaci, (iv) Coxiella burnetii, (v) Legionella spp, or (vi) Francisella tularensi [I talked about this last one before, in a completely different context…]. The term ‘atypical pneumonia’ remains useful to describe these pathogens as their treatment and sometimes duration of antibiotic therapy is different from typical pathogens.”
“Subdural haematomas are bleeds that occur between the dura mater and the arachnoid mater, enveloping the brain. They usually develop following traumatic injury […] Older people are particularly prone to such injuries as the brain naturally atrophies and shrinks with age. Blood collects in the space and draws in water due to osmotic pressures. The area of bleeding increases in size, causing compression of the cerebral tissue. […] Cushing’s triad of systolic hypertension with a wide pulse pressure, bradycardia and irregular or rapid respiratory rate is a major sign of raised intracranial pressure. These features occur due to insufficient blood flow to the brain and compression of arterioles. Subacute and chronic subdural haematomas classically present days to weeks after the insult. Any patient who presents with neurological signs several days after a head injury should be investigated for a subdural bleed.”
“Fever, jaundice and right upper quadrant abdominal pain make up the Charcot’s triad which are the main signs and symptoms of acute cholangitis. If a patient presents with Charcot’s triad and altered mental status and shock, it is called Reynold’s pentad. […] The most common cause of acute cholangitis is gallstone disease. […] Acute cholangitis carries a high mortality.”
I liked the book and gave it three stars on goodreads.
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.  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 .”
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 . 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 .”
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 . 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  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 . […] 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 : 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 . 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) . 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 . 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 .”
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 , 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 . […] 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 . An enduring challenge in the study of symptom clusters remains the lack of consistency in the methods used to cluster symptoms . Currently, the analytic methods used to cluster co-occurring symptoms include correlation, regression modeling [120,121], factor analysis , principal component analysis [121,123], cluster analysis [104,111], and latent variable modeling . […] 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.]
“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 . 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.
“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.