Harnessing phenotypic heterogeneity to design better therapies

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

A few papers mentioned/discussed in the lecture:

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

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

Today’s Landscape of Pharmaceutical Research in Cancer

It’s been a while since I watched this lecture so I don’t remember the details very well, but I usually add notes in my bookmarks when I watch lectures so that I know what details to include in my comments here on the blog, and I have added the details from the bookmark notes below.

It is a short lecture, the lecture itself lasts only roughly 30 minutes; it doesn’t really start until roughly the 9 minutes and 30 seconds mark, and it finishes around the 44 min mark (the rest is Q&A – I skipped some of the introduction, but watched the Q&A). The lecture is not very technical, I think the content is perfectly understandable also to people without a medical background. One data point from the lecture which I thought worth including in these comments is this: According to Sigal, “roughly 30 per cent of the biopharmaceutical industry’s portfolio … is focused on research in oncology.”

May 17, 2017 Posted by | Cancer/oncology, Immunology, Lectures, Medicine, Pharmacology | Leave a comment

Standing on the Shoulders of Mice: Aging T-cells

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

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

Biodemography of aging (III)

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

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

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

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

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

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

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

Moving on…

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

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

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

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

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

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

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

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

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

Health econ stuff

In a post I published a few weeks ago I mentioned that I had decided against including some comments and observations I had written about health economics in that post because the post was growing unwieldy, but that I might post that stuff later on in a separate post. This post will include those observations, as well as some additional details I added to the post later. This sort of post is the sort of post that usually does not get past the ‘draft’ stage (in wordpress you can save posts you intend to publish later on as drafts), and as is usually the case for posts like these I already regret having written it, for multiple reasons. I should warn you from the start that this post is very long and will probably take you some time to read.

Anyway, the starting point for this post was some comments related to health insurance and health economics which I left on SSC in the past. A lot more people read those comments on SSC than will read this post so the motivation for posting it here was not to ‘increase awareness’ of the ideas and observations included in some kind of general sense; my primary motivation for adding this stuff here is rather that it’s a lot easier for me personally to find stuff I’ve written when it’s located here on this blog rather than elsewhere on the internet, and I figure that some of the things I wrote back then are topics which might well come up again later, and it would be convenient for me in that case to have a link at hand. Relatedly I have added many additional comments and observations in this post not included in the primary exchange, which it is no longer possible for me to do on SSC as my comments are no longer editable on that site.

Although the starting point for the post was as mentioned a comment exchange, I decided early on against just ‘quoting myself’ in this post, and I have thus made some changes in wording and structure in order to increase the precision of the statements included and in order to add a bit of context making the observations below easier to read and understand (and harder to misread). Major topics to which the observations included in this post relate are preventable diseases, the level of complexity that is present in the health care sector, and various topics which relate to health care cost growth. Included in the post are some perhaps not sufficiently well known complications which may arise in the context of the discussion of how different financing schemes may relate to various outcomes, and to cost growth. Much of the stuff included will probably be review to people who’ve read my previous posts on health economics, but that’s to be expected considering the nature of this post.

Although ‘normative stuff’ is not what interests me most – I generally tend to prefer discussions where the aim is to identify what happens if you do X, and I’ll often be happy to leave the discussion of whether outcome X or Y is ‘best’ to others – I do want to start out with stating a policy preference, as this preference was the starting point for the aforementioned debate that lead to the origination of this post. At the outset I should thus make clear that I would in general be in favour of changes to the financial structure of health care systems where people who take avoidable risks which systematically and demonstrably increase their expected health care expenditures at the population level pay a larger proportion of the cost than do people who did not take such avoidable risks.

Most developed societies have health care systems which are designed in a way that implicitly to some extent subsidize unhealthy behaviours. An important note in this context is incidentally that one way of looking at these things is that if you are not explicitly demanding people who behave in risky ways which tend to increase their expected costs to pay more for their health care (/insurance), then you are in fact by virtue of not doing this implicitly subsidizing those unhealthy individuals/behaviours. I mention this because some people might not like the idea of ‘subsidizing healthy behaviours’ (‘health fascism’) – which from a certain point of view is what you do if you charge people who behave in unhealthy ways more. Maybe some people would take issue with words like ‘subsidy’ and ‘implicit’, but regardless of what you call these things the major point that is important to have in mind here is that if one group of people (e.g. ‘unhealthy people’) cost more to treat (/are ill more often, get illnesses related to their behaviours, etc., etc.) than another group of people (‘healthy people’), then if you need to finance this shortfall – which you do, as you face a budget constraint – there are only two basic ways to do this; you can either charge the high-cost group (‘unhealthy people’) more, or you can require the other group (‘healthy people’) to make up the difference. Any scheme which deals with such a case of unequal net contribution rates are equivalent either to one of those schemes or a mix of the two, regardless of what you call things and how it’s done, and regardless of which groups we are talking about (old people also have higher health care expenditures than do young people, and most health care systems implicitly redistribute income from the young to the old). If you’re worried about ‘health fascism’ and the implications of subsidizing healthy behaviours (/’punishing’ unhealthy behaviours) you should at least keep in mind that if the health care costs of people who live healthy lives and people who do not are dissimilar then any system that deals with this issue – which all systems must – can either choose to ‘subsidize’ healthy behaviours or unhealthy behaviours; there’s no feasible way to design a ‘neutral system’ if the costs of the groups are dissimilar.

Having said all this, the very important next point is then that it is much more difficult to make simple schemes that would accomplish an outcome in which people who engage in unhealthy behaviours are required to pay more without at the same time introducing a significant number of new problems than people who are not familiar with this field would probably think it is. And it’s almost certainly much harder to evaluate if the proposed change actually accomplished what you wanted to accomplish than you think it is. Even if we are clear about what we want to accomplish and can all agree that that is what we are aiming for – i.e. we are disregarding the political preferences of large groups of voters and whether the setup in question is at all feasible to accomplish – this stuff is really much harder than it looks, for many reasons.

Let’s start out by assuming that smoking increases the risk of disease X by 50%. Say you can’t say which of the cases of X are caused by smoking, all you know is that smoking increases the risk at the population level. Say you don’t cover disease X at all if someone smokes, that is, smokers are required to pay the full treatment cost out of pocket if they contract disease X. It’s probably not too controversial to state that this approach might by some people be perceived of as not completely ‘fair’ to the many smokers who would have got disease X even if they had not smoked (a majority in this particular case, though of course the proportion will vary with the conditions and the risk factors in question). Now, a lot of the excess health care costs related to smoking are of this kind, and it is actually a pretty standard pattern in general with risk factors – smoking, alcohol, physical inactivity, etc. You know that these behaviours increase risk, but you usually can’t say for certain which of the specific cases you observe in clinical practice are actually (‘perfectly’/’completely’/’partially’?) attributable to the behaviour. And quite often the risk increase associated with a specific behaviour is actually really somewhat modest, compared to the relevant base rates, meaning that many of the people who engage in behaviours which increase risk and who get sick might well have got sick even if they hadn’t engaged in those risky behaviours.

On top of this problem usually it’s also the case that risk factors interact with each other. Smoking increases the risk of cancer of the esophagus, but so does alcohol and obesity, and if a person both smokes and drinks the potential interaction effect may not be linear – so you most likely often can’t just identify individual risk factors in specific studies and then pool them later and add them all together to get a proper risk assessment. A further complication is that behaviours may both increase as well as decrease risk – to stick with the example, diets high in fruits and vegetables both lower the risk of cancer of the esophagus. Exercise probably does as well – we know that exercise has important and highly complex effects on immune system function (see e.g. this post). Usually a large number of potential risk factors is at play at the same time, there may be multiple variables which lower risk and are also important to include if you want a proper risk assessment, and even if you knew in theory which interaction terms were likely to be relevant, you might even so find yourself in a situation unable to estimate the interaction terms of interest – this might take high-powered studies with large numbers of patients, which may not be available or the results of such high-powered studies may not apply to your specific subgroup of patients. Cost-effectiveness is also an issue – it’s expensive to assess risk properly. One take-away is that you’ll still have a lot of unfairness in a modified contribution rate model, and even evaluating fairness aspects of the change may be difficult to impossible because to some extent this question is unknowable. You might find yourself in a situation where you charge the obese guy more because obesity means he’s high risk, but in reality he is actually lower risk than is the non-fat guy who is charged a lower rate, because he also exercises and eats a lot of fruits and vegetables, which the other guy doesn’t.

Of course the above paragraph took it for granted that it was even possible to quantify the excess costs attributable to a specific condition. That may not be easy at all to do, and there may be large uncertainties involved. The estimated excess cost will depend upon a variety of factors which may or may not be of interest to the party performing the analysis, for example it may be very important which time frame you’re looking at and which discounting methodology is applied (see e.g. the last paragraph in this post). The usual average vs marginal cost problem (see the third-last paragraph in the post to which I link in the previous sentence – this post also has more on this topic) also applies here and is related to ‘the fat guy who exercises and is low-risk’-problem; ideally you’d want to charge people with higher health care utilization levels more (again, in a setting where we assume the excess cost is associated with life-style variables which are modifiable – this was our starting point), but if there’s a large amount of variation in costs across individuals in the specific subgroups of interest and you only have access to average costs rather than individual-level costs, then a scheme only taking into account the differences in the averages may be very sub-optimal when you look at it from the viewpoint of the individual. Care needs to be taken to avoid problems like e.g. Simpson’s paradox.

Risk factors are not the only things that cluster; so do diseases. An example:

“An analysis of the Robert Koch-Institute (RKI) from 2012 shows that more than 50 % of German people over 65 years suffer from at least one chronic disease, approximately 50 % suffer from two to four chronic diseases, and over a quarter suffer from five or more diseases [3].” (link)

78.3 % of the type 2 diabetics also suffered from hypertension in that study. Does this fact make it easier or harder to figure out what is ‘the true cost contribution’ of ‘type 2 diabetes’ and ‘hypertension’ (and, what we’re ultimately interested in in this setting – the ‘true cost contribution’ of the unhealthy behaviours which lead some individuals to develop type 2 diabetics and hypertension who would not otherwise have developed diabetes and/or hypertension (…/as early as they did)? It should be noted that diabetes was estimated to account for 11 % of total global healthcare expenditure on adults in 2013 (link). That already large proportion is expected to rise substantially in the decades to come – if you’re interested in cost growth trajectories, this is a major variable to account for. Attributability is really tricky here, and perhaps even more tricky in the case of hypertension – but for what it’s worth, according to a CDC estimate hypertension cost the US $46 billion per year, or ~$150/per person per year.

Anyway, you look at the data and you make guesses, but the point is that doctor Smith won’t know for certain if Mr. Hanson would have had a stroke even if he hadn’t smoked or not. A proposal of not providing payment for a health care service or medical product in the case of an ‘obviously risky-behaviour-related-health-condition’ may sometimes appear to be an appealing proposition and you sometimes see people make this sort of proposal in discussions of this nature, but it tends to be very difficult when you look at the details to figure out just what those ‘obviously risky-behaviour-related-health-conditions’ are, and even harder to make even remotely actuarially fair adjustments to the premiums and coverage patterns to reflect the risk. Smoking and lung cancer is a common example of a relatively ‘clean’ case, but most cases are ‘less clean’ and even here there are complications; a substantial proportion of lung cancer cases are not caused by tobacco – occupational exposures also cause a substantial proportion of cases, and: “If considered in its own disease category […] lung cancer in never smokers would represent the seventh leading cause of cancer mortality globally, surpassing cancers of the cervix, pancreas, and prostate,5 and among the top 10 causes of death in the United States.” (link) Occupational exposures (e.g. asbestos) are not likely to fully account for all cases, and for example it has also been found that other variables, including previous pneumonia infections and tuberculosis, affect risk (here are a couple of relevant links to some previous coverage I wrote on these topics).

I think many people who have preferences of this nature (‘if it’s their own fault they’re sick, they should pay for it themselves’) underestimate how difficult it may be to make changes which could be known with a reasonable level of certainty to actually have the intended consequences, even assuming everybody agreed on the goal to be achieved. This is in part because there are many other aspects and complications which need to be addressed as well. Withholding payment in the case of costly preventative illness may for example in some contexts increase cost, rather than decrease them. The risk of complications of some diseases – an important cost driver in the context of diabetes – tends to be dependent on post-diagnosis behavioural patterns. The risk of developing diabetes complications will depend upon the level of glycemic control. If you say you won’t cover complications at all in the case of ‘self-inflicted disease X’, then you also to some extent tend to remove the option of designing insurance schemes which might lower cost and complication rates post-diagnosis by rewarding ‘good’ (risk-minimizing) behaviours post-diagnosis and punishing ‘bad’ (risk-increasing) behaviours. This is not desirable in the context of diseases where post-diagnosis behaviour is an important component of the cost function, as it certainly is in the diabetes context. There are multiple potential mechanisms here, some of which are disease specific (e.g. suboptimal diet in a diagnosed type 2 diabetic) and some of which may not be (a more general mechanism could e.g. be lowered compliance/adherence to treatment in the uncovered populations because they can’t afford the drugs which are required to treat their illness; though the cost-compliance link is admittedly not completely clear in the general case, there are certainly multiple diseases where lowered compliance to treatment would be expected to increase cost long-term).

And again, also in the context of complications fairness issues are not as simple to evaluate as people might like them to be; some people may have a much harder time controlling their disease than others, or they may be more susceptible to complications given the same behaviour. Some may already have developed complications by the time of diagnosis. Such issues make it difficult to design simple rules which would achieve what you want them to achieve without having unfortunate side-effects; for example a rule that a microvascular diabetes-related complication is automatically ‘your own fault’ (so we won’t pay for it), which might be motivated by the substantial amount of research linking glycemic control with complication risk, would punish some diabetics who have had the disease for a longer amount of time (many complications are not only strongly linked to Hba1c but also display a substantial degree of duration-dependence; for example in type 1 diabetics one study found that diabetic retinopathy was present in 13% of patients with a duration of disease less than 5 years, whereas the corresponding figure was 90% for individuals with a disease duration of 10–15 years (Sperling et al., p. 393). I also recall reading a study finding that Hba1c itself is increasing with diabetes duration, which may be partly accounted for by the higher risk of hypoglycemia related to hypoglycemia-unawareness-syndromes in individuals with long-standing disease), individuals with diseases which are relatively hard to control (perhaps due to genetics, or maybe again due to the fact that they have had the disease for a longer amount of time; the presence of hypoglycemia unawareness is as alluded to above to a substantial degree duration-dependent, and this problem increases the risk of hospitalizations, which are expensive), diabetics who developed complications before they knew they were sick (a substantial proportion of type 2 diabetics develop some degree of microvascular damage pre-diagnosis), and diabetics with genetic variants which confer an elevated risk of complications (“observations suggest that involvement of genetic factors is increasing the risk of complications” (Sperling et al., p. 226), and for example in the DCCT trial familial clustering of both neuropathy and retinopathy was found; clustering which persisted after controlling for Hba1c – for more on these topics, see e.g. Sperling et al.’s chapter 11).

Other decision rules would similarly lead to potentially problematic incentives and fairness issues; for example requiring individuals to meet a specific Hba1c goal might be more desirable than to just not cover complications, but that one also leads to potential problems; ideally such an Hba1c goal should be individualized, because of the above-mentioned complexities and others I have not mentioned here; to require a newly-diagnosed individual to meet the same goals as someone who has had diabetes for decades does not make sense, and neither does it make sense to require these two groups to meet exactly the same Hba1c goal as the middle-aged female diabetic who desires to become pregnant (diabetes greatly increases the risk of pregnancy complications, and strict glycemic control is extremely important in this patient group). It’s important to note that these issues don’t just relate to whether or not the setup is perceived of as fair, but it also relates to whether or not you would expect the intended goals to actually be met or not when you implement the rule. If you were to require that a long-standing diabetic with severe hypoglycemia unawareness had to meet the same Hba1c goal as the newly diagnosed individual, this might well lead to higher overall cost, because said individual might suffer a large number of hypoglycemia-related hospitalizations which would have been avoidable if a more lax requirement was imposed; when you decrease Hba1c you decrease the risk of long-term complications, but you increase the risk of hypoglycemia. A few numbers might make it easier to make sense of how expensive hospitalizations really are, and why I emphasize them here. In this diabetes-care publication they assign a cost for an inpatient day for a diabetes-related hospitalization at $2,359 and an emergency visit at ~$800. The same publication estimates the total average annual excess expenditures of diabetics below the age of 45 at $4,394. Going to the hospital is really expensive (43% of the total medical costs of diabetes are accounted for by hospital inpatient care in that publication).

A topic which was brought up in the SSC discussion was the question of the extent to which private providers have a greater incentive to ‘get things right’ in terms of assessing risk. I don’t take issue with this notion in general, but there are a lot of complicating factors in the health care context. One factor of interest is that it is costly to get things right. If you’re looking at this from an insurance perspective, larger insurance providers may be better at getting things right because they can afford to hire specialists who provide good cost estimates – getting good cost estimates is really hard, as I’ve noted above. Larger providers translate into fewer firms, which increases firm concentration and may thus increase collusion risk, which may again increase the prices of health care services. Interestingly if your aim is to minimize health care cost growth increased market power of private firms may actually be a desirable state of affairs/goal, because cost growth is a function of both unit prices and utilization levels, and higher premiums are likely to translate into lower utilization rates, which may lower overall costs and -cost growth. I decided to include this observation here also in order to illustrate that what is an optimal outcome depends on what your goal is, and in the setting of the health care sector you sometimes need to be very careful about thinking about what your actual goal is, and which other goals might be relevant.

When private insurance providers become active in a market that also includes a government entity providing a level of guaranteed coverage, total medical outlays may easily increase rather than decrease. The firms may meed an unmet need, but some of that unmet need may be induced demand (here’s a related link). Additionally, the bargaining power of various groups of medical personnel may change in such a setting, leading to changes in compensation schedules which may not be considered desirable/fair. An increase in total outlays may or may not be considered a desirable outcome, but this does illustrate once again the point that you need to be careful about what you are trying to achieve.

There’s a significant literature on how the level of health care integration, both at the vertical and horizontal level, both in terms of financial structure and e.g. in terms of service provision structure, may impact health care costs, and this is an active area of research where we in some contexts do not yet know the answers.

Even when cost minimization mechanisms are employed in the context of private firms and the firm in question is efficient, the firm may not internalize all relevant costs. This may paradoxically lead to higher overall cost, due to coverage decisions taken ‘upstream’ influencing costs ‘downstream’ in an adverse manner; I have talked about this topic on this blog before. A diabetic might be denied coverage of glucose testing materials by his private insurer, and that might mean that the diabetic instead gets hospitalized for a foreseeable and avoidable complication (hypoglycemic coma due to misdosing), but because it might not be the same people paying for the testing material and the subsequent hospitalization it might not matter to the people denying coverage of the testing materials, and/so they won’t take it into account when they’re making their coverage decisions. That sort of thing is quite common in the health care sector – different entities pay for and receive payments for different things, and this is once again a problem to keep in mind if you’re interested in health care evaluation; interventions which seem to lower cost may not do so in reality, because the intervention lead to higher health care utilization elsewhere in the system. If incentives are not well-aligned things may go badly wrong, and they are often not well-aligned in the health care sector. When both the private and public sectors are involved in either the financial arrangements and/or actual health service provision – which is the default health care system setup for developed societies – this usually leads to highly complex systems, where the scope for such problems to appear seems magnified, rather than the opposite. I would assume that in many cases it matters a lot more that incentives are well-aligned than which specific entity is providing insurance or health care in the specific context, in part a conclusion drawn from the coverage included in Simmons, Wenzel & Zgibor‘s book.

In terms of the incentive structures of the people involved in the health care sector, this stuff is of course also adding another layer of complexity. In all sectors of the economy you have people with different interests who interact with each other, and when incentives change outcomes change. Outcomes may be car batteries, or baseball bats, or lectures. Evaluating outcomes is easier in some settings than in others, and I have already touched upon some of the problems that might be present when you’re trying to evaluate outcomes in the health care context. How easy it is to evaluate outcomes will naturally vary across sub-sectors of the health care sector but a general problem which tends to surface here is the existence of various forms of asymmetrical information. There are multiple layers, but a few examples are worth mentioning. To put it bluntly, the patient tends to know his symptoms and behavioural patterns – which may be disease-relevant, and this aspect is certainly important to include when discussing preventative illnesses caused at least in part by behaviours which increase the risk of said illnesses – better than his doctor, and the doctor will in general tend to know much more about the health condition and potential treatment options than will the patient. The patient wants to get better, but he also wants to look good in the eyes of the doctor, which means he might not be completely truthful when interacting with the doctor; he might downplay how much alcohol he drinks, misrepresent how often he exercises, or he may lie about smoking habits or about how much he weighs. These things make risk-assessments more difficult than they otherwise might have been. As for the GPs, usually we here have some level of regulation which restricts their behaviour to some extent, and part of the motivation for such regulation is to reduce the level of induced demand which might otherwise be the result of information asymmetry in the context of stuff like relevant treatment effects. If a patient is not sufficiently competent to evaluate the treatments he receives (‘did the drug the doctor ordered really work, or would I have gotten better without it?’), there’s a risk he might be talked into undergoing needless procedures or take medications for which he has no need, especially if the doctor who advises him has a financial interest in the treatment modality on offer.

General physicians have different incentives from nurses and specialists working in hospitals, and all of these groups may experience conflicts of interests when they’re dealing with insurance providers and with each other. Patients as mentioned have their own set of incentives, which may not align perfectly with those of the health care providers. Different approaches to how to deal with such problems lead to different organizational setups, all of which influence both the quantity and quality of care, subject to various constraints. It’s an active area of research whether decreasing competition between stakeholders/service providers may decrease costs; one thing that is relatively clear from diabetes research with which I have familiarized myself is that when different types of care providers coordinate activities, this tends to lead to better outcomes (and sometimes, but not always, lower costs), because some of the externalized costs become internalized by virtue of the coordination. It seems very likely to me that conclusions to such questions will be different for different subsectors of the health care sector. A general point might be that more complex diseases should be expected to be more likely to generate cost savings from increased coordination than should relatively simple diseases (if you’re fuzzy about what the concept of disease complexity refers to, this post includes some relevant observations). This may be important, because complex diseases also should probably tend to be more expensive to treat in general, because the level of need in patients is higher.

It’s perhaps hardly surprising, considering the problems I’ve already discussed related to how difficult it may be to properly assess costs, that there’s a big discussion to be had about how to even estimate costs (and benefits) in specific contexts, and that people write books about these kinds of things. A lot of things have already been said on this topic and a lot more could be said, but one general point perhaps worth repeating is that it may in the health care sector be very difficult to figure out what things (‘truly’) cost (/’is worth’). If you only have a public sector entity dealing with a specific health problem and patients are not charged for receiving treatment, it may be very difficult to figure out what things ‘should’ cost because relevant prices are simply missing from the picture. You know what the government entity paid the doctors in wages and what it paid for the drugs, but the link between payment and value is sometimes a bit iffy here. There are ways to at least try to address some of these issues, but as already noted people write books about these kinds of things so I’m not going to provide all the highlights here – I refer to the previous posts I’ve written on these topics instead.

Another important related point is that medical expenditures and medical costs are not synonyms. There are many costs associated with illness which are not directly related to e.g. a payment to a doctor. People who are ill may be less productive while they are at work, they may have more sick-days, they may retire earlier, their spouse may cut down on work hours to take care of them instead of going to work, a family caretaker may become ill as a result of the demands imposed by the caretaker role (for example Alzheimer’s disease significantly increases the risk of depression in the spouse). Those costs are relevant, there are literatures on these things, and in some contexts such ‘indirect costs’ (e.g. lower productivity at work and early retirement) may make up a very substantial proportion of the total costs of a health condition. I have seen diabetes cost estimates which indicated that the indirect costs may account for as much as 50 % of the total costs.

If there’s a significant disconnect between total costs and medical expenditures then minimizing expenditures may not be desirable from an economic viewpoint. A reasonable assessment model will/should in the context of models of outlays include both a monetary cost parameter and a quality/quantity (ideally both) parameter; if you neglect to take account of the latter, in some sense you’re only dealing with what you pay out, not what you get for that payment (which is relevant). If you don’t take into account indirect costs you implicitly allow cost switching practices to potentially muddle the picture and make assessments more difficult; for example if you provide fewer long-term care facilities then the number of people involved in ‘informal care’ (e.g. family members having to take care of granny) will go up, and that is going to have secondary effects downstream which should also be assessed (you improve the budget in the context of the long-term care facilities, but you may at the same time increase demands on e.g. psychiatric institutions and marginally lower especially the female labour market participation rate. The net effect may still be positive, but the point is that an evaluation will/should include costs like these in the analysis, at least if you want anything remotely close to the full picture).

Let’s return to those smokers we talked about earlier. A general point not mentioned yet is that if you don’t cover smokers in the public sector because of cost considerations, many of them may also not be covered by private insurance either. This is because a group of individuals that is high risk and expensive to treat will be demanded high premiums (or the insurance providers would go out of business), and for the sake of this discussion we’re now assuming smokers are expensive. If that is so, many of them probably would not be able to afford the premiums demanded. Now, one of the health problems which are very common in smokers is chronic obstructive pulmonary disease (COPD). Admission rates for COPD patients differ as much as 10-fold between European countries, and one of the most important parameters regarding pharmacoeconomics is the hospitalization rate (both observations are from this text). What does this mean? It means that we know that admission rate from COPD is highly responsive to the treatment regime; populations well-treated have much fewer hospitalizations. 4% of all Polish hospitalizations are due to COPD. If you remove the public sector subsidies, the most likely scenario you get seems to me to be a poor-outcomes scenario with lots of hospitalizations. Paying for those is likely to be a lot more expensive than it is to treat the COPD pharmacologically in the community. And if smokers aren’t going to be paying for it, someone else will have to do that. If you both deny them health insurance and refuse them treatment if they cannot pay for it they may just die of course, but in most cost-assessment models that’s a high-cost outcome, not a low-cost outcome (e.g. due to lost work-life productivity etc. Half of people with COPD are of working age, see the text referred to above.). This is one example where the ‘more fair’ option might lead to higher costs, rather than lower costs. Some people might still consider such an outcome desirable, it depends on the maximand of interest, but such outcomes are worth considering when assessing the desirability of different systems.

A broadly similar dynamic, in the context of post-diagnosis behaviour and links to complications and costs, may be present in the context of type 2 diabetes. I know much more about diabetes than I do about respirology, but certainly in the case of diabetes this is a potentially really big problem. Diabetics who are poorly regulated tend to die a lot sooner than other people, they develop horrible complications, they stop being able to work, etc. etc. Some of those costs you can ignore if you’re willing to ‘let them die in the streets’ (as the expression goes), but a lot of those costs are indirect costs due to lower productivity, and those costs aren’t going anywhere, regardless of who may or may not be paying the medical bills of these people. Even if they have become sick due to a high-risk behaviour of their own choosing, their health care costs post-diagnosis will still be highly dependent upon their future medical care and future health insurance coverage. Denying them coverage for all diabetes-related costs post-diagnosis may, paradoxical though it may seem to some, not be the cost-minimizing option.

I already talked about information asymmetries. Another problematic aspect linked to information management also presents itself here in a model of this nature (‘deny all diabetes-related coverage to known diabetics’); people who suspect they might be having type 2 diabetes may choose not to disclose this fact to a health care provider because of the insurance aspect (denial of coverage problems). Insurance providers can of course (and will try to) counter this by things like mandatory screening protocols, but this is expensive, and even assuming they are successful you again not only potentially neglect to try to minimize the costs of the high-cost individuals in the population (the known diabetics, who might be cheaper long-term if they had some coverage), you also price a lot of non-diabetics out of the market (because premiums went up to pay for the screening). And some of those non-diabetics are diabetics to-be, who may get a delayed diagnosis as a result, with an associated higher risk of (expensive) complications. Again, as in the smoking context if the private insurer does not cover the high-cost outcomes someone else will have to do that, and the blind diabetic in a wheel-chair is not likely to be able to pay for his dialysis himself.

More information may in some situations lead to a breakdown in insurance markets. This is particularly relevant in the context of genetics and genetic tests. If you have full information, or close to it, the problem you have to some extent stops being an insurance problem and instead becomes a problem of whether or not to, and to which extent you want to-, explicitly compensate people for having been dealt a bad hand by nature. To put it in very general terms, insurance is a better framework for diseases which can in principle be cured than it is for chronic conditions where future outlays are known with a great level of certainty; the latter type of disease tends to be difficult to handle in an insurance context.

People who have one disease may develop other diseases as time progresses, and having disease X may increase or decrease the risk of disease Y. People study such disease variability patterns, and have done so for years, but there’s still a lot of stuff we don’t know – here’s a recent post on these topics. Such patterns are interesting for multiple reasons. One major motivation for studying these things is that ‘different’ diseases may have common mechanisms, and the identification of these mechanisms may lead to new treatment options. A completely different motivation for studying these things relate rather to the kind of stuff covered in this post, where you instead wonder about economic aspects; for example, if the smoker stops smoking he may gain weight and eventually develop type 2 diabetes instead of developing some smoking-related condition. Is this outcome better or worse than the other? It’s important to keep in mind when evaluating changes in compensation schedules/insurance structures that diseases are not independent, and this is a problem regardless of whether you’re interested in total costs or ‘just’ direct outlays. Say you’re ‘only’ worried about outlays and you are trying to figure out if it is a good idea to deny coverage to smokers, and you know that ex-smokers are likely to gain weight and have an increased risk of type 2 diabetes. Then the relevant change in cost is not the money you save on smoking-related illness, it’s the cost change you arrive at when after you account for those savings also account for the increased cost of treating type 2 diabetes. Disease interdependencies are probably as complex as risk factor interdependencies – the two phenomena are to some extent representing the same basic phenomenon – so this makes true cost evaluation even harder than it already was. Not all relevant costs at the societal level are of course medical costs; if people live longer, and they rely partly on a pension scheme to which they are no longer contributing, that cost is also relevant.

If a group of people who live longer cost more than a group of people who do not live as long, and you need to cover the associated shortfall, then – as we concluded in the beginning – there are really only two ways to handle this: Make them pay more than the people who do not live as long, or make the people who do not live as long pay more to cover the shortfall. Another way to look at this is that in this situation you can either tax people ‘for not living long enough’, or you can tax people for ‘not dying at the appropriate time’. On the other hand (?), if a group of people who die early turns out to be the higher-cost group in the relevant comparison (perhaps because they have shorter working lives and so pay into the system for a shorter amount of time), then you can deal with this problem by… either taxing them for ‘not living long enough’ or by punishing the people who live long lives for ‘not dying at the appropriate time’. No, of course it doesn’t matter which group is high cost, the solution mechanism is the same in both cases – make one of the groups pay more. And every time you tweak things you change the incentives of various people, and implicit effects like these hide somewhere in the background.

March 31, 2017 Posted by | Cancer/oncology, Diabetes, Economics, health care, rambling nonsense | Leave a comment

Biodemography of aging (II)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Biodemography of aging (I)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Ageing Immune System and Health (II)

Here’s the first post about the book. I finished it a while ago but I recently realized I had not completed my intended coverage of the book here on the blog back then, and as some of the book’s material sort-of-kind-of relates to material encountered in a book I’m currently reading (Biodemography of Aging) I decided I might as well finish my coverage of the book now in order to review some things I might have forgot in the meantime, by providing coverage here of some of the material covered in the second half of the book. It’s a nice book with some interesting observations, but as I also pointed out in my first post it is definitely not an easy read. Below I have included some observations from the book’s second half.


“The aged lung is characterised by airspace enlargement similar to, but not identical with acquired emphysema [4]. Such tissue damage is detected even in non-smokers above 50 years of age as the septa of the lung alveoli are destroyed and the enlarged alveolar structures result in a decreased surface for gas exchange […] Additional problems are that surfactant production decreases with age [6] increasing the effort needed to expand the lungs during inhalation in the already reduced thoracic cavity volume where the weakened muscles are unable to thoroughly ventilate. […] As ageing is associated with respiratory muscle strength reduction, coughing becomes difficult making it progressively challenging to eliminate inhaled particles, pollens, microbes, etc. Additionally, ciliary beat frequency (CBF) slows down with age impairing the lungs’ first line of defence: mucociliary clearance [9] as the cilia can no longer repel invading microorganisms and particles. Consequently e.g. bacteria can more easily colonise the airways leading to infections that are frequent in the pulmonary tract of the older adult.”

“With age there are dramatic changes in neutrophil function, including reduced chemotaxis, phagocytosis and bactericidal mechanisms […] reduced bactericidal function will predispose to infection but the reduced chemotaxis also has consequences for lung tissue as this results in increased tissue bystander damage from neutrophil elastases released during migration […] It is currently accepted that alterations in pulmonary PPAR profile, more precisely loss of PPARγ activity, can lead to inflammation, allergy, asthma, COPD, emphysema, fibrosis, and cancer […]. Since it has been reported that PPARγ activity decreases with age, this provides a possible explanation for the increasing incidence of these lung diseases and conditions in older individuals [6].”


“Age is an important risk factor for cancer and subjects aged over 60 also have a higher risk of comorbidities. Approximately 50 % of neoplasms occur in patients older than 70 years […] a major concern for poor prognosis is with cancer patients over 70–75 years. These patients have a lower functional reserve, a higher risk of toxicity after chemotherapy, and an increased risk of infection and renal complications that lead to a poor quality of life. […] [Whereas] there is a difference in organs with higher cancer incidence in developed versus developing countries [,] incidence increases with ageing almost irrespective of country […] The findings from Surveillance, Epidemiology and End Results Program [SEERincidentally I likely shall at some point discuss this one in much more detail, as the aforementioned biodemography textbook covers this data in a lot of detail.. – US] [6] show that almost a third of all cancer are diagnosed after the age of 75 years and 70 % of cancer-related deaths occur after the age of 65 years. […] The traditional clinical trial focus is on younger and healthier patient, i.e. with few or no co-morbidities. These restrictions have resulted in a lack of data about the optimal treatment for older patients [7] and a poor evidence base for therapeutic decisions. […] In the older patient, neutropenia, anemia, mucositis, cardiomyopathy and neuropathy — the toxic effects of chemotherapy — are more pronounced […] The correction of comorbidities and malnutrition can lead to greater safety in the prescription of chemotherapy […] Immunosenescence is a general classification for changes occurring in the immune system during the ageing process, as the distribution and function of cells involved in innate and adaptive immunity are impaired or remodelled […] Immunosenescence is considered a major contributor to cancer development in aged individuals“.

Neurodegenerative diseases:

“Dementia and age-related vision loss are major causes of disability in our ageing population and it is estimated that a third of people aged over 75 are affected. […] age is the largest risk factor for the development of neurodegenerative diseases […] older patients with comorbidities such as atherosclerosis, type II diabetes or those suffering from repeated or chronic systemic bacterial and viral infections show earlier onset and progression of clinical symptoms […] analysis of post-mortem brain tissue from healthy older individuals has provided evidence that the presence of misfolded proteins alone does not correlate with cognitive decline and dementia, implying that additional factors are critical for neural dysfunction. We now know that innate immune genes and life-style contribute to the onset and progression of age-related neuronal dysfunction, suggesting that chronic activation of the immune system plays a key role in the underlying mechanisms that lead to irreversible tissue damage in the CNS. […] Collectively these studies provide evidence for a critical role of inflammation in the pathogenesis of a range of neurodegenerative diseases, but the factors that drive or initiate inflammation remain largely elusive.”

“The effect of infection, mimicked experimentally by administration of bacterial lipopolysaccharide (LPS) has revealed that immune to brain communication is a critical component of a host organism’s response to infection and a collection of behavioural and metabolic adaptations are initiated over the course of the infection with the purpose of restricting the spread of a pathogen, optimising conditions for a successful immune response and preventing the spread of infection to other organisms [10]. These behaviours are mediated by an innate immune response and have been termed ‘sickness behaviours’ and include depression, reduced appetite, anhedonia, social withdrawal, reduced locomotor activity, hyperalgesia, reduced motivation, cognitive impairment and reduced memory encoding and recall […]. Metabolic adaptation to infection include fever, altered dietary intake and reduction in the bioavailability of nutrients that may facilitate the growth of a pathogen such as iron and zinc [10]. These behavioural and metabolic adaptions are evolutionary highly conserved and also occur in humans”.

“Sickness behaviour and transient microglial activation are beneficial for individuals with a normal, healthy CNS, but in the ageing or diseased brain the response to peripheral infection can be detrimental and increases the rate of cognitive decline. Aged rodents exhibit exaggerated sickness and prolonged neuroinflammation in response to systemic infection […] Older people who contract a bacterial or viral infection or experience trauma postoperatively, also show exaggerated neuroinflammatory responses and are prone to develop delirium, a condition which results in a severe short term cognitive decline and a long term decline in brain function […] Collectively these studies demonstrate that peripheral inflammation can increase the accumulation of two neuropathological hallmarks of AD, further strengthening the hypothesis that inflammation i[s] involved in the underlying pathology. […] Studies from our own laboratory have shown that AD patients with mild cognitive impairment show a fivefold increased rate of cognitive decline when contracting a systemic urinary tract or respiratory tract infection […] Apart from bacterial infection, chronic viral infections have also been linked to increased incidence of neurodegeneration, including cytomegalovirus (CMV). This virus is ubiquitously distributed in the human population, and along with other age-related diseases such as cardiovascular disease and cancer, has been associated with increased risk of developing vascular dementia and AD [66, 67].”


“Frailty is associated with changes to the immune system, importantly the presence of a pro-inflammatory environment and changes to both the innate and adaptive immune system. Some of these changes have been demonstrated to be present before the clinical features of frailty are apparent suggesting the presence of potentially modifiable mechanistic pathways. To date, exercise programme interventions have shown promise in the reversal of frailty and related physical characteristics, but there is no current evidence for successful pharmacological intervention in frailty. […] In practice, acute illness in a frail person results in a disproportionate change in a frail person’s functional ability when faced with a relatively minor physiological stressor, associated with a prolonged recovery time […] Specialist hospital services such as surgery [15], hip fractures [16] and oncology [17] have now begun to recognise frailty as an important predictor of mortality and morbidity.

I should probably mention here that this is another area where there’s an overlap between this book and the biodemography text I’m currently reading; chapter 7 of the latter text is about ‘Indices of Cumulative Deficits’ and covers this kind of stuff in a lot more detail than does this one, including e.g. detailed coverage of relevant statistical properties of one such index. Anyway, back to the coverage:

“Population based studies have demonstrated that the incidence of infection and subsequent mortality is higher in populations of frail people. […] The prevalence of pneumonia in a nursing home population is 30 times higher than the general population [39, 40]. […] The limited data available demonstrates that frailty is associated with a state of chronic inflammation. There is also evidence that inflammageing predates a diagnosis of frailty suggesting a causative role. […] A small number of studies have demonstrated a dysregulation of the innate immune system in frailty. Frail adults have raised white cell and neutrophil count. […] High white cell count can predict frailty at a ten year follow up [70]. […] A recent meta-analysis and four individual systematic reviews have found beneficial evidence of exercise programmes on selected physical and functional ability […] exercise interventions may have no positive effect in operationally defined frail individuals. […] To date there is no clear evidence that pharmacological interventions improve or ameliorate frailty.”


“[A]s we get older the time and intensity at which we exercise is severely reduced. Physical inactivity now accounts for a considerable proportion of age-related disease and mortality. […] Regular exercise has been shown to improve neutrophil microbicidal functions which reduce the risk of infectious disease. Exercise participation is also associated with increased immune cell telomere length, and may be related to improved vaccine responses. The anti-inflammatory effect of regular exercise and negative energy balance is evident by reduced inflammatory immune cell signatures and lower inflammatory cytokine concentrations. […] Reduced physical activity is associated with a positive energy balance leading to increased adiposity and subsequently systemic inflammation [5]. […] Elevated neutrophil counts accompany increased inflammation with age and the increased ratio of neutrophils to lymphocytes is associated with many age-related diseases including cancer [7]. Compared to more active individuals, less active and overweight individuals have higher circulating neutrophil counts [8]. […] little is known about the intensity, duration and type of exercise which can provide benefits to neutrophil function. […] it remains unclear whether exercise and physical activity can override the effects of NK cell dysfunction in the old. […] A considerable number of studies have assessed the effects of acute and chronic exercise on measures of T-cell immunesenescence including T cell subsets, phenotype, proliferation, cytokine production, chemotaxis, and co-stimulatory capacity. […] Taken together exercise appears to promote an anti-inflammatory response which is mediated by altered adipocyte function and improved energy metabolism leading to suppression of pro-inflammatory cytokine production in immune cells.”

February 24, 2017 Posted by | Biology, Books, Cancer/oncology, Epidemiology, Immunology, Medicine, Neurology | Leave a comment

Role of Biomarkers in Medicine

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

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

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

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

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

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

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

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

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

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

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

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

Human Drug Metabolism (III)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Human Drug Metabolism (II)

My first post covering Coleman’s excellent book can be found here, and here you can read my goodreads review of the book; I think it makes sense to read those things before reading this post, if you have not already done that. As I believe I’ve previously mentioned (?) most non-fiction books I read, including those I do not blog, usually get a goodreads review, and actually I’m much more active on goodreads these days than I am on this blog. I have considered cross-posting goodreads reviews here on the blog, but I decided it might be best to just keep these things separate for the time being. I might change my mind about this, though; I don’t like how inactive the blog has become during the last few months, and goodreads reviews I’ve already written take almost no work to cross-post, so this would be an easy way to at least get some ‘activity’ here.

The book includes a lot of information that really pretty much everybody would be likely to benefit from knowing (how many people for example live their entire lives without consuming any alcohol, tobacco, or medical drugs? If you’ve ever consumed any of these things, the book has material of relevance included in the coverage…). I repeat myself here, but  some of the general observations included in the following seem to me to be important takeaways from the book: Drugs work (sometimes very) differently in different people, they interact with different things, including innocuous things like what you eat and drink and whether you exercise or not; drugs may interact with each other, in a very confusing variety of ways; some drugs are metabolized differently in people who have taken the drug for a while (‘induction’), compared to how the drug might be metabolized in someone who’s not taken the drug before (drug-naïve), and sometimes the ability to metabolize the drug faster/more efficiently may be lost (inhibition) because of a third factor, such as e.g. another drug or a dietary factor, which can be very dangerous (an improved ability to metabolize the drug because of habituation may also be lost due to non-consumption of the drug for some time, leading to a ‘reset’ of the metabolic pathway of relevance, an important factor in an abuse context where this can lead to overdose); there are huge racial and genetic differences in terms of how specific drugs are metabolized; the consequences of getting too much of a specific drug (toxicity) tend to be foreseeably different from the consequences of getting not enough of a drug (drug failure); efficient metabolism of a drug may depend upon the body’s ability not just to transform the xenobiotic compound into something useful, but also the ability to get rid of sometimes really quite toxic metabolites which might be created along the way as the body tries to get rid of that thing you just injected/ingested/etc. Many people don’t consider herbal remedies to be ‘real drugs’ and so neglect to tell their medical practitioner that they’re taking them/have recently stopped taking them, despite some of these having the potential to cause quite serious drug interactions (even if nothing is taken but herbal remedies; St. John’s Wort + kava kava = acute hepatitis? As noted in the book, “One point important to emphasize, is that assuming various herbal remedies do contain active and potent substituents, there is virtually nothing known clinically about what effects mixing herbal remedies might have, in terms of pharmacology and toxicity. This area is unfortunately left for patients to discover for themselves”).

This book is not ‘the whole story’ about drug metabolism and related stuff, it just scratches the surface, but the coverage serves to make it clear to you just how much stuff is to be found ‘below the surface’, and this is something I really like about the book. It makes you appreciate how little you know and how complex this stuff is. People write 500+ page textbooks like this one simply about CYP subtypes (I came across a different 1000+ page textbook also about a CYP subtype while reading the book so I know this one is hardly unique, but unfortunately I did not bookmark the book and I didn’t find the book after a brief search for it – but take my word for it, those books are out there…) and alcohol metabolism, they write 700 page textbooks about the side effects of psychiatric drugs (not the intended effects, that is – the side effects!) they write 800 page textbooks about aspirin and related drugs and about how drugs affect the liver… I know that in some circles it’s somewhat common for people to ‘experiment’ with various drugs and substances, illicit or otherwise; I also assume that most people who do this sort of thing have little idea what they’re actually doing and are likely taking a lot of risks the very existence of which they’re likely not aware of. Simply because there’s just so much stuff you need to know to even have a proper concept of what you’re doing when you’re dealing with how the human body works and how it responds to foreign substances we might choose to introduce into it. It might be that they wouldn’t care even if they knew because you’re probably rather low in risk aversion if you engage in that sort of experimentation in the first place (I incidentally am highly risk averse), but I do find it curious.

I have added some observations from the middle of the book below.

“Although there is growing awareness of the clinical problems posed by P-gp [P-glycoprotein] inhibition on drug bioavailability and toxicity, until recently it was very difficult to generalize and predict which classes of drug might be inhibitors of P-gp. […] There are dozens of drugs which are known inhibitors of P-gp […] it is often difficult to establish what contribution cellular transport systems make to bioavailability. Indeed, it is emerging that one of the reasons for the very wide variety of drug bioavailability in modern medicine could be the sheer number of possible inhibitors and substrates that exist for P-gp in the diet, such as a number of natural products like the flavonols, which can be as potent as cyclosporine or verapamil as P-gp inhibitors. Natural dietary inhibitors have advantages in their general lack of toxicity, but the basic problem of a lack of predictability in their effects on P-gp substrates remains. Since no two people’s diets are identical, the impact of P-gp modulation on drug absorption could be simply too complex to unravel.”

“the objectives of metabolizing systems could be summed up thus:
• To terminate the pharmacological effect of the molecule.
• Make the molecule so water-soluble that it cannot escape clearance, preferably by more than one route to absolutely guarantee its removal.
These objectives could be accomplished by:
• Changing the molecular shape so it no longer binds to its receptors.
• Changing the molecular lipophilicity to hydrophilicity to ensure high water solubility.
• Making the molecule larger and heavier, so it can be eliminated in bile as well as urine.
• Efflux pump systems, which ensure that a highly water-soluble metabolite actually leaves the cell to enter the bloodstream, before it is excreted in bile and urine. […]
CYP-mediated metabolism can increase hydrophilicity, but it does not always increase it enough and it certainly does not make the molecule any bigger and heavier, indeed, sometimes the molecule becomes lighter […] CYP-mediated metabolism does not always alter the pharmacological effects of the drug either […] However, CYPs do perform two essential tasks: the initial destabilization of the molecule, creating a ‘handle’ on it. […] CYPs also ‘unmask’ groups that could be more reactive for further metabolism. […] CYP-mediated preparation can make the molecule vulnerable to the attachment of a very water-soluble and plentiful agent to the drug or steroid, which accomplishes the objectives of metabolism. This is achieved through the attachment of a modified glucose molecule (glucuronidation), or a soluble salt such as a sulphate (sulphation) [see also this] to the prepared site. Both adducts usually make the drug into a stable, heavier and water-soluble ex-drug. […] with many drugs, their stability and lipophilicity mean that their clearance must take more than one metabolic operation to make them water-soluble.”

PXR [Pregnane X receptor], CAR [constitutive androstane receptor] and FXR [Farnesoid X receptor] are […] part of the process whereby the liver can sense whether its own metabolic capacity and physical size is sufficient to respond to homeostatic demands. Hence, alongside various growth factors, the NRs [nuclear receptors] facilitate the amazing process whereby the liver regenerates itself after areas of the organ are removed or damaged. […] As CYPs, UGTs [Glucuronosyltransferases], other biotransforming systems and efflux transporters are meeting the same xenobiotic or endobiotic stimuli in different tissues and degrees of exposure, it is logical that the […] receptor systems integrate and coordinate their responses. […] These multi-receptor mechanisms enable levels of induction to be customized for individual tissues to deal with different chemical threats. Essentially, according to diet, chemical and drug exposure, each individual will possess a unique expression array of UGTs and CYPs which will be constantly fine-tuned throughout life.”

“Sulphonation is accomplished by a set of enzyme systems known as sulphotransferases (SULTs) and they are found in most tissues to varying degrees of activity. […] The general aim of sulphonation is to make the substrate more water-soluble and usually less active pharmacologically. Sulphonated molecules are more readily eliminated in bile and urine. […] All SULTs are subject to genetic polymorphisms, with a high degree of individual variation in their expression and catalytic activities […] Regarding classification of the superfamily of SULTs, it is assumed that 47 per cent amino acid sequence homology is indicative of same family members and 60 per cent homology for subfamily members. To date, there are 47 mammalian SULT isoforms so far discovered, which are derived from ten human sulphotransferase gene families […] knowledge of the role of NRs and AhR [Aryl hydrocarbon receptor] in human SULT expression has progressed in animals but not really in humans. This is partly due to the fact that rodent SULT profiles are quite different to ours […] Many studies have been carried out in rodents, which have produced rather contradictory results […] It seems that whilst SULTs in general are not as responsive to inducers as CYPs and UGTs, their basal expression is much higher, although interindividual expression does vary considerably and this may have severe toxicological consequences, in terms of xenobiotic toxicity and carcinogenicity. There is also some evidence that diet is a strong influence on individual SULT profiles.”

“One of the main problems with the oxidation of various molecules by CYP enzymes is that they are often destabilized and sometimes form highly reactive products. […] CYPs occasionally form metabolites so reactive that they immediately destroy the enzyme by reacting with it, changing its structure and, therefore, its function. […] The most dangerous forms of reactive species are those that evade UGTs and SULT enzymes, or are inadvertently created by conjugation processes. These species escape into the cytosol and even into the nucleus, where potentially carcinogenic events may result. […] CYPs are not the only source of reactive species generated within cells. Around 75 per cent of our food intake is directed at maintaining our body temperature and a great deal of energy must be liberated from the food to accomplish this. Cells derive the vast majority of their energy through oxidative phosphorylation and this takes place in […] the mitochondria. […] In cells almost all the oxygen we breathe is consumed in oxidative phosphorylation, forming ATP, heat and reactive oxidant species in the mitochondria that could cause severe damage to the structure and function of the cell if they were allowed to escape. So all cells, particularly hepatocytes, have evolved a separate system to accommodate such reactive toxic products and this is based on a three amino acid (cysteine, glycine and glutamate) thiol known as glutathione, or GSH. Thiols in general are extremely effective at reducing and thus ‘quenching’ highly reactive, electrophilic species. […] if cells are depleted of GSH by blocking its synthesis (by using buthionine sulphoxime), cell death follows and the organism itself will die in a few days, due to uncontrolled activity of endogenous radicals. […] If GSH levels are not maintained in the cell over a long period of time, the cell wears out more quickly; for example, diabetic complications and HIV infection are linked with poor GSH maintenance.” [I did not know this…]

“There are several enzymes that promote and catalyze the reaction of GSH with potential toxins to ensure that reactive species are actively dealt with, rather than just passive GSH-mediated reduction. Probably the most important from the standpoint of drug metabolism are the GSH-S-transferases [‘GSTs’, which] are the key cellular defence against electrophilic agents formed from endogenous or xenobiotic oxidative metabolism. […] The GSTs are found in humans in several major classes. […] The classes contain several subfamilies […] These enzymes are polymorphic […] and their individual expression ranges from complete absence in some isoforms to overabundance as a response to anticancer therapy. […] The upregulation of GST is a serious problem within cancer therapeutics and resistance to a range of drugs including melphalan and doxorubicin is linked with GST detoxification. Much research has been directed at inhibitors of GST isoforms to reverse or even prevent the development of resistance to anti-neoplastic agents. Unfortunately this strategy has not been successful”

“once xenobiotics have been converted into low-toxicity, higher-molecular-weight and high-water-solubility metabolites by the combination of CYPs, UGTs, SULTs and GSTs, this appears at first sight to be ‘mission accomplished’. However, these conjugates must be transported against a concentration gradient out of the cell into the interstitial space between cells. Then they will enter the capillary system and thence to the main bloodstream and filtration by the kidneys. The biggest hurdle is the transport out of the cell, which is a tall order, as once a highly water-soluble entity has been created, it will effectively be ‘ion-trapped’ in the cell, as the cell membrane is highly lipophilic and is an effective barrier to the exit as well as entry of most hydrophilic molecules. […] failure to remove the hydrophilic products of conjugation reactions [from the cells] can lead to:
• toxicity of conjugates to various cell components;
• hydrolysis of conjugates back to the original reactive species;
• inhibition of conjugating enzymes.
If the cell can manage to transport them out, then they should be excreted in urine or bile and detoxification can proceed at a maximal rate. […] Consequently, an impressive array of multi-purpose membrane bound transport carrier systems has evolved which can actively remove hydrophilic metabolites and many other low molecular weight drugs and toxins from cells. The relatively recent […] term of Phase III metabolism has been applied to the study of this essential arm of the detoxification process. […] The main thrust of research into efflux transporters has been directed at the ABC-type transporters [this link actually has quite a bit of content, unlike some of the other wiki articles on these topics], of which there are 48 genes that code of a variety of ATP-powered pumps.”

“it is clear that the whole process of detection, metabolism and elimination of endobiotic and xenobiotic agents is minutely coordinated and is responsive to changes in load in individual tissues. The CYPs, UGTs, MRPs [Multidrug Resistance Proteins] and P-gp are all tightly regulated through the NR system of PXR, CAR, FXE, PPAR α, LXR etc, as well as the AhR receptor system [does it even make sense to keep adding links here? I’m not sure it does…]. Some enzyme/pump processes are closely linked, such as CYP3A4 and P-gp, as inducers powerfully increase both systems capacity. The reactive species protection ‘arm’ of biotransformation is also controlled through a separate but almost certainly ‘cross-talking’ Nrf2/Keap1 system which coordinates not only the interception of reactive species by GSTs, but also the supply of their GSH substrate, UGTs and the MRPs. This latter coordination is particularly relevant in resistance to cancer chemotherapy and happens because overexpression of any one entity alone cannot rid the cell of the toxin. […] The MRPs, GSH production and GST/UGT activity must be induced in concert. […] much of the integration and coordination of detoxification processes remains to be uncovered”.

Chapter 7, about ‘factors affecting drug metabolism’, has some very interesting stuff, but I think this post is quite long enough as it is. I might talk about that stuff in detail later on, but I make no promises.

August 9, 2016 Posted by | Books, Cancer/oncology, Medicine, Pharmacology | Leave a comment

Oxford Handbook of Clinical Medicine (IV)

Here you can read my first three posts about the book. In this post I’ll talk a little bit about the book’s coverage of surgery; this is a major chapter with a lot of content.

In a surgical context prophylactic antibiotics are very often given to counter the risk of wound infection, especially in the gastrointestinal surgical context. The authors of the chapter don’t discuss the demerits of this approach at all, but I’ve read other people before who are critical of this way of doing things and before moving on to what the book has to say about related matters I thought I should remind you of some of the problems associated with the widespread prophylactic use of antibiotics in the surgical context – here’s part of what Gould and van der Meer had to say about this topic:

“Surgical prophylaxis is a common area of overuse [of antibiotics] as shown in many publications. Measured by total DDDs [defined daily doses], it can amount to around one third of a hospital’s total antibiotic use. This illustrates the potential for ecological damage although surgeons often ask whether 24 h or even single dose prophylaxis can really select for resistance. The simple answer is yes, but of course much of the problem is extension of prophylaxis beyond the perioperative period, often for several days in critical patients, perhaps until all lines and drains are removed. There is no evidence base in favour of such practices.” (link to further blog coverage of related topics here)

Omissions like these is incidentally one of several reasons why I did not give the Oxford handbook a higher rating than I did. With that out of the way let’s get back to the Oxford handbook coverage. They note in the surgery chapter that wound infection occurs in roughly one in five cases of elective GI surgery, and in up to 60 per cent of emergency surgery settings. Infections in surgical patients are not trivial events; they can lead to bleeding, wounds that reopen, and they can ultimately kill the patient. Another major risk associated with surgery in many different surgical contexts is the risk of deep vein thrombosis (-DVT). According to the book DVTs occur in 25-50% of surgical patients. That said, almost two-thirds of below-knee DVTs are asymptomatic and these rarely embolize to the lungs. Aside from surgery some other DVT risk factors worth knowing about include age (older patients are at higher risk), pregnancy, trauma, synthetic oestrogen (i.e., oral contraceptives), past DVT, cancer, obesity, and immobility.

As for DVTs in non-surgical contexts, I found it interesting that the book observes that “the evidence linking air travel to an increased risk of DVT is still largely circumstantial” – it also adds some additional data to contextualize the risk. For someone in the general population, the risk of DVT from a long-distance flight is estimated to be somewhere between one in 10.000 to one in 40.000, however for people in high-risk subgroups the incidence of DVT from flights lasting longer than 10 hours has been estimated at 4-6%. They argue in the book that travelers with multiple risk factors should consider compression stockings and/or a single prophylactic dose of low molecular-weight heparin for flights lasting longer than 6 hours; other ways to minimize risk include leg exercises, increased water intake and refraining from alcohol or caffeine during the flight. “There is no evidence to support the use of prophylactic aspirin.”

Even though I think a common impression is that surgeons always want to cut people open whereas internal medicine people will often think this is not necessary, ‘even surgeons’ are sometimes hesitant to cut you open. There are many reasons for this – the book covers a lot of surgical complications, but a perhaps particularly important long-term problem is this:
“Any surgical procedure that breaches the abdominal or pelvic cavities can predispose to the formation of adhesions [‘Adhesions are fibrous bands[1] that form between tissues and organs, often as a result of injury during surgery. They may be thought of as internal scar tissue that connects tissues not normally connected’], which are found in up to 90% of those with previous abdominal surgery; this is why we do not rush to operate on small bowel obstruction: the operation predisposes to yet more adhesions. Handling of the serosal surface of the bowel causes inflammation, which over weeks to years can lead to the formation of fibrous bands that tether the bowel to itself or adjacent structures […] Their main sequelae are intestinal obstruction (the cause in ~60% of cases […]) and chronic abdominal or pelvic pain.”

Appendicitis is a lot more common than I’d thought; lifetime incidence is 6%, with risk peaking during the second decade of life; according to the book it is the most common surgical emergency. A diagnosis of appendicitis is often wrong; in up to one in five patients a healthy appendix is removed. Another very common surgical procedure is surgical repair of an inguinal hernia; more than 100.000 of these surgeries are performed in the UK each year.

Though the book has a separate chapter specifically dealing with the topic of oncology (and palliative care), the surgical chapter of course also covers various cancers and their treatments. You’ll encounter the usual encouraging remarks about diseases with a ‘gloomy prognosis and non-specific presentation’, ‘[m]ost patients […] present with locally advanced (inoperable) or metastatic disease’ (both quotes are on the topic of carcinoma of the stomach); ‘[s]urvival rates are poor with or without treatment’ (carcinoma of the oesophagus); ‘rare, have an overall poor prognosis and are difficult to diagnose’ (bile duct and gallbladder cancers), ‘~80% present with inoperable disease’ (bile duct cancer). It’s sort of hard to find it encouraging that colorectal carcinoma, another cancer covered in that chapter, in general tend to have lower mortality than these others (“Overall 5yr survival is ~50%”) when you also keep in mind that it’s one of the most common cancers (it is the second most common cause of cancer deaths in the United Kingdom, and the third most common cancer), and so kill a lot more people overall (16.000 deaths/year). Another thing to note is that the survival rate of patients with metastatic disease in this context is still really terrible; the treated 5-year survival rate for patients with distant metastases is reported to be 6.6%, compared to e.g. a 48% survival rate in treated cases with ‘only’ regional lymph node involvement. They observe in their coverage that “[l]aparoscopic surgery has revolutionized surgery for colon cancer. It is as safe as open surgery and there is no difference in overall survival or disease recurrence.”

There are many bodily changes which take place in people as they age, and some of the potentially problematic changes only occasionally cause symptoms despite their presence in a large number of people. One example is gastrointestinal diverticula. These are outpouchings of the gut wall which are present in many people but do not always cause problems. According to the authors, diverticulosis is a term used to indicate that diverticula are present, whereas diverticular disease implies they the diverticula are symptomatic; the term diverticulitis is used when there’s inflammation of the diverticula. 30 % of people at the age of 60 living in the West are estimated to have diverticulosis, but the majority are asymptomatic – they are a common incidental finding when people have colonoscopies. Although they often do not cause problems they can cause perforation and hemorrhage (e.g. large rectal bleeds); the former complication has a high mortality, ~40%. Lack of dietary fiber is thought to be implicated in the pathophysiological processes leading to diverticulosis. Gallstones is another example of a common condition many people have without knowing it; gallstone prevalence is estimated at 8% at the age of 40. Risk is increasing in age and is higher in obese people. 90% remain asymptomatic. Smoking is known to increase the risk that gallstones become symptomatic. Renal stones are also common, with lifetime incidence estimated to be ‘up to’ (?) 15%. However males are three times as likely to get renal stones as are females, so in males in particular these things are very common. In the case of small stones (<5mm in lower ureter) ~90-95 % pass spontaneously on their own. The simplest and easiest way to lower risk of kidney stones is to drink plenty of fluids (but keep in mind that tea increases oxalate levels and thus may contribute to stone formation…). They note that calculi may be asymptomatic but do not provide estimates of how often this is the case; I assume one reason is that it’s really very difficult to get a good estimate of how often people pass stones they did not know they had – you mostly learn about these things when they cause trouble. Making a brief jump back to the topic of cancers it should perhaps be noted that although cancer is not usually thought of as a really not very worrisome asymptomatic condition, some forms of cancer actually sometimes may be just that; autopsy studies have indicated that 80% of men above the age of 80 have some form of prostate cancer.

Stress incontinence is leakage from an incompetent sphincter for example when intra-abdominal pressure rises, which it may do when people laugh or cough. It is very common in pregnancy and following birth, and it “occurs to some degree in ~50% of post-menopausal women”.

Although I didn’t think much of the epidemiology chapter, I did want to include a few observations from the chapter in this post:

“In one study looking at recommendations of meta-analyses where there was a later ‘definitive’ big trial, it turned out that meta-analyses got it wrong 30% of the time”.

“During the time it takes you to read this page, your better-connected patients may have checked out the latest recommendations of Guatemalan Guidelines on Gynaecomastia, or the NICE’S Treatise on Toxoplasmosis. Patients have time and motivation, whereas we have little time and our motivation may be flickering. This can seem threatening to the doctor who sees himself as a dispenser of wisdom and precious remedies. It is less threatening if we consider ourselves to be in partnership with our patients. The evidence is that those who use the internet to question their therapy receive a better service.” (A lot of related topics were incidentally covered in the Cochrane handbook The Knowledgeable Patient: Communication and Participation in Health – see this post for data on and discussion of these things).


January 18, 2016 Posted by | Books, Cancer/oncology, Epidemiology, Medicine, Pharmacology | Leave a comment

Oxford Handbook of Clinical Medicine (III)

Here are my first two posts about the book, which I have now finished. I gave the book three stars on goodreads, but I’m close to a four star rating and I may change my opinion later – overall it’s a pretty good book. I’ve read about many of the topics covered before but there was also quite a bit of new stuff along the way; as a whole the book spans very widely, but despite this the level of coverage of individual topics is not bad – I actually think the structure of the book makes it more useful as a reference tool than is McPhee et al. (…in terms of reference books which one might find the need to refer to in order to make sense of medical tests and test results, I should of course add that no book can beat Newman & Kohn). I have tried to take this into account along the way in terms of the way I’ve been reading the book, in the sense that I’ve tried to make frequent references in the margin to other relevant works going into more detail about specific topics whenever this seemed like it might be useful, and I think if one does something along those lines systematically a book like this one can become a really powerful tool – you get the short version with the most important information (…or at least what the authors considered to be the most important information) here almost regardless of what topic you’re interested in – I should note in this context that the book has only very limited coverage of mental health topics, so this is one area where you definitely need to go elsewhere for semi-detailed coverage – and if you need more detail than what’s provided in the coverage you’ll also know from your notes where to go next.

In my last post I talked a bit about which topics were covered in the various chapters in the book – I figured it might make sense here to list the remaining chapter titles in this post. After the (long) surgery chapter, the rest of the chapters deal with epidemiology (I thought this was a poor chapter and the authors definitely did not consider this topic to be particularly important; they spent only 12 pages on it), clinical chemistry (lab results, plasma proteins, topics like ‘what is hypo- and hypernatremia’, …), eponymous syndromes (a random collection of diseases, many of which are quite rare), radiology (MRI vs X-ray? When to use, or not use, contrast material? Etc.), ‘reference intervals etc.‘ (the ‘etc.’ part covered drug therapeutic ranges for some commonly used drugs, as well as some important drug interactions – note to self: The effects of antidiabetic drugs are increased by alcohol, beta-blockers, bezafibrate, and MAOIs, and are decreased by contraceptive steroids, corticosteroids, diazoxide, diuretics, and possibly also lithium), practical procedures (I was considering skipping this chapter because I’m never going to be asked to e.g. insert a chest drain and knowing how to do it seems to be of limited benefit to me, but I figured I might as well read it anyway; there were some details about what can go wrong in the context of specific procedures and what should be done when this happens, and this seemed like valuable information. Also, did you know that “There is no evidence that lying flat post procedure prevents headache” in the context of lumbar punctures? I didn’t, and a lot of doctors probably also don’t. You can actually go even further than that: “Despite years of anecdotal advice to the contrary, none of the following has ever been shown to be a risk factor [for post-LP headache]: position during or after the procedure; hydration status before, during, or after; amount of CSF removed; immediate activity or rest post-LP.”), and emergencies.

In this post I won’t cover specific chapters of the book in any detail, rather I’ll talk about a few specific topics and observations I could be bothered to write some stuff about here. Let’s start with some uplifting news about the topic of liver tumours: Most of these (~90%) are secondary (i.e. metastatic) tumours with an excellent prognosis (“Often ↑3yr survival to 59% from 13%; but ~50% have recurrence by 3yrs.[3] Liver transplant gives a 5yr survival rate of 70%.” It should be noted in a disease impact context that this type of cancer is far more common in areas of the world with poorly developed health care systems like Africa and China.

Alcoholism is another one of the causes of liver tumors. In the book they include the observation that the lifetime prevalence of alcoholism is around 10% for men and 4% for women, but such numbers are of course close to being completely meaningless almost regardless of where they’re coming from. Alcoholism is dangerous; in cases with established cirrhosis roughly half (52%) of people who do not stop drinking will be dead within 5 years, whereas this is also the case for 23% of the people who do stop drinking. Excessive alcohol consumption can cause alcoholic hepatitis; “[m]ild episodes hardly affect mortality” but in severe cases half will be dead in a month, and in general 40% of people admitted to the hospital for alcoholic hepatitis will be dead within one year of admission. Alcohol can cause portal hypertension (80% of cases are caused by cirrhosis in the UK), which may lead to the development of abnormal blood vessels e.g. in the oesophagus which will have a tendency to cause bleeding, which can be fatal.  Roughly 30% of cirrhotics with varices bleed, and rebleeding is common: “After a 1st variceal bleed, 60% rebleed within 1yr” and “40% of rebleeders die of complications.” Alcoholism can kill you in a variety of different ways (acute poisonings and accidents should probably also be included here as well), and many don’t survive long enough to develop cancer.

As mentioned in the first post about the book acute kidney injury is common in a hospital setting. In the following I’ve added a few more observations about renal disease. “Renal pain is usually a dull ache, constant and in the loin.” But renal disease don’t always cause pain, and in general: “There is often a poor correlation between symptoms and severity of renal disease. Progression [in chronic disease] may be so insidious that patients attribute symptoms to age or a minor illnesses. […] Serious renal failure may cause no symptoms at all.” The authors note that odd chronic symptoms like fatigue should not be dismissed without considering a renal function test first. The book has a nice brief overview of the pathophysiology of diabetic nephropathy – this part is slightly technical, but I decided to include it here anyway before moving on to a different topic:
“Early on, glomerular and tubular hypertrophy occur, increasing GFR [glomerular filtration rate, an indicator variable used to assess kidney function] transiently, but ongoing damage from advanced glycosylation end-products (AGE—caused by non-enzymatic glycosylation of proteins from chronic hyperglycaemia) triggers more destructive disease. These AGE trigger an inflammatory response leading to deposition of type IV collagen and mesangial expansion, eventually leading to arterial hyalinization, thickening of the mesangium and glomerular basement membrane and nodular glomerulosclerosis (Kimmelstiel–Wilson lesions). Progression generally occurs in four stages:
1 GFR elevated: early in disease renal blood flow increases, increasing the GFR and leading to microalbuminuria. […]
2 Glomerular hyperfiltration: in the next 5–10yrs mesangial expansion gradually occurs and hyperfiltration at the glomerulus is seen without microalbuminuria.
3 Microalbuminuria: as soon as this is detected it indicates progression of disease, GFR may be raised or normal. This lasts another 5–10yrs.
4 Nephropathy: GFR begins to decline and proteinuria increases.
Patients with type 2 DM may present at the later stages having had undetected hyperglycaemia for many years before diagnosis.”

Vitamin B12 deficiency is quite common, the authors note that it occurs in up to 15% of older people. Severe B12 deficiency is not the sort of thing which will lead to you feeling ‘a bit under the weather’ – it can lead to permanent brain damage and damage to the spinal cord. “Vitamin B12 is found in meat, fish, and dairy products, but not in plants.” It’s important to note that “foods of non-animal origin contain no B12 unless fortified or contain bacteria.” The wiki article incidentally includes even higher prevalence estimates (“It is estimated to occur in about 6% of those under the age of 60 and 20% of those over the age of 60. Rates may be as high as 80% in parts of Africa and Asia.”) than the one included in the book – this vitamin deficiency is common, and if severe it can have devastating consequences.

On bleeding disorders: “After injury, 3 processes halt bleeding: vasoconstriction, gap-plugging by platelets, and the coagulation cascade […]. Disorders of haemostasis fall into these 3 groups. The pattern of bleeding is important — vascular and platelet disorders lead to prolonged bleeding from cuts, bleeding into the skin (eg easy bruising and purpura), and bleeding from mucous membranes (eg epistaxis [nose bleeds], bleeding from gums, menorrhagia). Coagulation disorders cause delayed bleeding into joints and muscle.” An important observation in the context of major bleeds is incidentally this: “Blood should only be given if strictly necessary and there is no alternative. Outcomes are often worse after a transfusion.” The book has some good chapters about the leukaemias, but they’re relatively rare diseases and some of them are depressing (e.g. acute myeloid leukaemia: according to the book coverage death occurs in ~2 months if untreated, and roughly four out of five treated patients are dead within 3 years) so I won’t talk a lot about them. One thing I found somewhat interesting about the blood disorders covered in the book is actually how rare they are, all things considered: “every day each of us makes 175 billion red cells, 70 billion granulocytes, and 175 billion platelets”. There are lots of opportunities for things to go wrong here…

Some ways to prevent traveller’s diarrhea: “If in doubt, boil all water. Chlorination is OK, but doesn’t kill amoebic cysts (get tablets from pharmacies). Filter water before purifying. Distinguish between simple gravity filters and water purifiers (which also attempt to sterilize chemically). […] avoid surface water and intermittent tap supplies. In Africa assume that all unbottled water is unsafe. With bottled water, ensure the rim is clean & dry. Avoid ice. […] Avoid salads and peel your own fruit. If you cannot wash your hands, discard the part of the food that you are holding […] Hot, well-cooked food is best (>70°C for 2min is no guarantee; many pathogens survive boiling for 5min, but few last 15min)”

An important observation related to this book’s coverage about how to control hospital acquired infection: “Cleaning hospitals: Routine cleaning is necessary to ensure that the hospital is visibly clean and free from dust and soiling. 90% of microorganisms are present within ‘visible dirt’, and the purpose of routine cleaning is to eliminate this dirt. Neither soap nor detergents have antimicrobial activity, and the cleaning process depends essentially on mechanical action.”

Falciparum malaria causes one million deaths/year, according to the book, and mortality is close to 100% in untreated severe malaria – treatment reduces this number to 15-20%. Malaria in returning travellers is not particularly common, but there are a couple thousand cases in the UK each year. Malaria prophylaxis does not give full protection, and “[t]here is no good protection for parts of SE Asia.” Multidrug resistance is common.

November 8, 2015 Posted by | alcohol, Books, Cancer/oncology, Epidemiology, Infectious disease, Medicine, Nephrology | Leave a comment

Oxford Handbook of Clinical Medicine (II)

Here’s my first post about the book. I’ve read roughly 75% of the book at this point (~650 pages). The chapters I’ve read so far have dealt with the topics of: ‘thinking about medicine’ (an introductory chapter), ‘history and examination’, cardiovascular medicine, chest medicine, endocrinology, gastroenterology, renal medicine, haematology, infectious diseases, neurology, oncology and palliative care, rheumatology, and surgery (this last one is a long chapter – ~100 pages – which I have not yet finished). In my first post I (…mostly? I can’t recall if I included one or two observations made later in the coverage as well…) talked about observations included in the first 140 pages of the book, which relate only to the first three topics mentioned above; the chapter about chest medicine starts at page 154. In this post I’ll move on and discuss stuff covered in the chapters about cardiovascular medicine, chest medicine, and endocrinology.

In the previous post I talked a little bit about heart failure, acute coronary syndromes and a few related topics, but there’s a lot more stuff in the chapter about cardiovascular medicine and I figured I should add a few more observations – so let’s talk about aortic stenosis. The most common cause is ‘senile calcification’. The authors state that one should think of aortic stenosis in any elderly person with problems of chest pain, shortness of breath during exercise (exertional dyspnoea), and fainting episodes (syncope). Symptomatic aortic stenosis tends to be bad news; “If symptomatic, prognosis is poor without surgery: 2–3yr survival if angina/syncope; 1–2yr if cardiac failure. If moderate-to-severe and treated medically, mortality can be as high as 50% at 2yrs”. Surgery can improve the prognosis quite substantially; they note elsewhere in the coverage that a xenograft (e.g. from a pig) aortic valve replacement can last (“may require replacement at…”) 8-10 years, whereas a mechanical valve lasts even longer than that. Though it should also be noted in that context that the latter type requires life-long anticoagulation, whereas the former only requires this if there is atrial fibrilation.

Next: Infective endocarditis. Half of all cases of endocarditis occur on normal heart valves; the presentation in that case is one of acute heart failure. So this is one of those cases where your heart can be fine one day, and not many days later it’s toast and you’ll die unless you get treatment (often you’ll die even if you do get treatment as mortality is quite high: “Mortality: 5–50% (related to age and embolic events)”; mortality relates to which organism we’re dealing with: “30% with staphs [S. Aureus]; 14% if bowel organisms; 6% if sensitive streptococci.”). Multiple risk factors are known, but some of those are not easily preventable (renal failure, dermatitis, organ transplantation…); don’t be an IV drug (ab)user, and try to avoid getting (type 2) diabetes.. The authors note that: “There is no proven association between having an interventional procedure (dental or non-dental) and the development of IE”, and: “Antibiotic prophylaxis solely to prevent IE is not recommended”.

Speaking of terrible things that can go wrong with your heart for no good reason, hypertrophic cardiomyopathy (-HCM) is the leading cause of sudden cardiac death in young people, with an estimated prevalence of 1 in 500. “Sudden death may be the first manifestation of HCM in many patients”. Yeah…

The next chapter in the book as mentioned covers chest medicine. At the beginning of the chapter there’s some stuff about what the lungs look like and some stuff about how to figure out whether they’re working or not, or why they’re not working – I won’t talk about that here, but I would note that lung problems can relate to stuff besides ‘just’ lack of oxygen; they can also for example be related to retention of carbon dioxide and associated acidosis. In general I won’t talk much about this chapter’s coverage as I’m aware that I have covered many of the topics included in the book before here on the blog in other posts. It should perhaps be noted that whereas the chapter has two pages about lung tumours and two pages about COPD, it has 6 pages about pneumonia; this is still a very important disease and a major killer. Approximately one in five (the number 21% is included in the book) patients with pneumonia in a hospital setting die. Though it should perhaps also be observed that maybe one reason why more stuff is not included about lung cancer in that chapter is that this disease is just depressing and doctors can’t really do all that much. Carcinoma of the bronchus make up ~19% of all cancers and 27% of cancer deaths in the UK. In terms of prognosis, non-small cell lung cancer has a 50% 2-year mortality in cases where the cancer was not spread at presentation and a 90% 2-year mortality in cases with spread. That’s ‘the one you would prefer’: Small cell lung cancer is worse as small cell tumours “are nearly always disseminated at presentation” – here the untreated median survival is 3 months, increasing to 1-1,5 years if treated. The authors note that only 5% (of all cases, including both types) are ‘cured’ (they presumably use those citation marks for a reason). Malignant mesothelioma, a cancer strongly linked to asbestos exposure most often developing in the pleura, incidentally also has a terrible prognosis (”

5-8% of people in the UK have asthma; I was surprised the number was that high. Most people who get it during childhood either grow out of it or suffer much less as adults, but on the other hand there are also many people who develop chronic asthma late in life. In 2009 approximately 1000 people in the UK died of asthma – unless this number is a big underestimate, it would seem to me that asthma at least in terms of mortality is a relatively mild disease (if 5% of the UK population has asthma, that’s 3 million people – and 1000 deaths among 3 million people is not a lot, especially not considering that half of those deaths were in people above the age of 65). COPD is incidentally another respiratory disease which is more common than I had thought; they note that the estimated prevalence in people above the age of 40 in the UK is 10-20%.

The endocrinology chapter has 10 pages about diabetes, and I won’t talk much about that coverage here as I’ve talked about many of these things before on the blog – however a few observations are worth including and discussing here. The authors note that 4% of all pregnancies are complicated by diabetes, with the large majority of cases (3.5%) being new-onset gestational diabetes. In a way the 0,5% could be considered ‘good news’ because they reflect the fact that outcomes have improved so much that a female diabetic can actually carry a child to term without risking her own life or running a major risk that the fetus dies (“As late as 1980, physicians were still counseling diabetic women to avoid pregnancy” – link). But the 3,5%? That’s not good: “All forms [of diabetes] carry an increased risk to mother and foetus: miscarriage, pre-term labour, pre-eclampsia, congenital malformations, macrosomia, and a worsening of diabetic complications”. I’m not fully convinced this statement is actually completely correct, but there’s no doubt that diabetes during pregnancy is not particularly desirable. As to which part of the statement I’m uncertain about, I think gestational diabetes ‘ought to’ have somewhat different effects than type 1 especially in the context of congenial malformations. Based on my understanding of these things, gestational diabetes should be less likely to cause congenital malformations than type 1 diabetes in the mother; diabetes-related congenital malformations tend to happen/develop very early in pregnancy (for details, see the link above) and gestational pregnancy is closely related to hormonal changes and changing metabolic demands which happen over time during pregnancy. Hormonal changes which occur during pregnancy play a key role in the pathogenesis of gestational diabetes, as the hormonal changes in general increase insulin resistance significantly, which is what causes some non-diabetic women to become diabetic during pregnancy; these same processes incidentally also causes the insulin demands of diabetic pregnant women to increase a lot during pregnancy. You’d expect the inherently diabetogenic hormonal and metabolic processes which happen in pregnancy to play a much smaller role in the beginning of the pregnancy than they do later on, especially as women who develop gestational diabetes during their pregnancy would be likely to be able to compensate early in pregnancy, where the increased metabolic demands are much less severe than they are later on. So I’d expect the risk contribution from ‘classic gestational diabetes’ to be larger in the case of macrosomia than in the case of neural tube defects, where type 1s should probably be expected to dominate – a sort of ‘gestational diabetics don’t develop diabetes early enough in pregnancy for the diabetes to be very likely to have much impact on organogenesis’-argument. This is admittedly not a literature I’m intimately familiar with and maybe I’m wrong, but from my reading of their diabetes-related coverage I sort of feel like the authors shouldn’t be expected to be intimately familiar with the literature either, and I’m definitely not taking their views on these sorts of topics to be correct ‘by default’ at this point. This NHS site/page incidentally seems to support my take on this, as it’s clear that the first occasion for even testing for gestational diabetes is at week 8-12, which is actually after a substantial proportion of diabetes-related organ damage would already be expected to have occurred in the type 1 diabetes context (“There is an increased prevalence of congenital anomalies and spontaneous abortions in diabetic women who are in poor glycemic control during the period of fetal organogenesis, which is nearly complete by 7 wk postconception.” – Sperling et al., see again the link provided above. Note that that entire textbook is almost exclusively about type 1 diabetes, so ‘diabetes’ in the context of that quote equals T1DM), and a glucose tolerance test/screen does not in this setting take place until weeks 24-28.

The two main modifiable risk factors in the context of gestational diabetes are weight and age of pregnancy; the risk of developing gestational diabetes  increases with weight and is higher in women above the age of 25. One other sex/gender-related observation to make in the context of diabetes is incidentally that female diabetics are at much higher risk of cardiovascular disease than are non-diabetic females: “DM [diabetes mellitus] removes the vascular advantage conferred by the female sex”. Relatedly, “MI is 4-fold commoner in DM and is more likely to be ‘silent’. Stroke is twice as common.” On a different topic in which I’ve been interested they provided an observation which did not help much: “The role of aspirin prophylaxis […] is uncertain in DM with hypertension.”

They argue in the section about thyroid function tests (p. 209) that people with diabetes mellitus should be screened for abnormalities in thyroid function on the annual review; I’m not actually sure this is done in Denmark and I think it’s not – the DDD annual reports I’ve read have not included this variable, and if it is done I know for a fact that doctors do not report the results to the patient. I’m almost certain they neglected to include a ‘type 1’ in that recommendation, because it makes close to zero sense to screen type 2 diabetics for comorbid autoimmune conditions, and I’d say I’m probably also a little skeptical, though much less skeptical, about annual screenings of all type 1s being potentially cost-effective. Given that autoimmune comorbidities (e.g. Graves’ disease and Hashimoto’s) are much more common in women than in men and that they often present in middle-aged individuals (and given that they’re more common in people who develop diabetes relatively late, unlike me – see Sperling) I would assume I’m relatively low risk and that it would probably not make sense to screen someone like me annually from a cost-benefit/cost-effectiveness perspective; but it might make sense to ask the endocrinologist at my next review about how this stuff is actually being done in Denmark, if only to satisfy my own curiosity. Annual screening of *female*, *type 1* diabetics *above (e.g.) the age of 30* might be a great idea and perhaps less restrictive criteria than that can also be justified relatively easily, but this is an altogether very different recommendation from the suggestion that you should screen all diabetics annually for thyroid problems, which is what they recommend in the book – I guess you can add this one to the list of problems I have with the authors’ coverage of diabetes-related topics (see also my comments in the previous post). The sex- and age-distinction is likely much less important than the ‘type’ restriction and maybe you can justify screening all type 1 diabetics (For example: “Hypothyroid or hyperthyroid AITD [autoimmune thyroid disease] has been observed in 10–24% of patients with type 1 diabetes” – Sperling. Base rates are important here: Type 1 diabetes is rare, and Graves’ disease is rare, but if the same HLA mutation causes both in many cases then the population prevalence is not informative about the risk an individual with diabetes and an HLA mutation has of developing Graves’) – but most diabetics are not type 1 diabetics, and it doesn’t make sense to screen a large number of people without autoimmune disease for autoimmune comorbidities they’re unlikely to have (autoimmunity in diabetes is complicated – see the last part of this comment for a few observations of interest on that topic – but it’s not that complicated; most type 2 diabetics are not sick because of autoimmunity-related disease processes, and type 2 diabetics make up the great majority of people with diabetes mellitus in all patient populations around the world). All this being said, it is worth keeping in mind that despite overt thyroid disease being relatively rare in general, subclinical hypothyroidism is common in middle-aged and elderly individuals (“~10% of those >55yrs”); and the authors recommend treating people in this category who also have DM because they are more likely to develop overt disease (…again it probably makes sense to add a ‘T1’ in front of that DM).

Smoking is sexy, right? (Or at least it used to be…). And alcohol makes other people look sexy, right? In a way I find it a little amusing that alcohol and smoking are nevertheless two of the three big organic causes of erectile dysfunction (the third is diabetes).

How much better does it feel to have sex, compared to how it feels to masturbate? No, they don’t ask that question in the book (leave that to me…) but they do provide part of the answer because actually there are ways to quantify this, sort of: “The prolactin increase ( and ) after coitus is ~400% greater than after masturbation; post-orgasmic prolactin is part of a feedback loop decreasing arousal by inhibiting central dopaminergic processes. The size of post-orgasmic prolactin increase is a neurohormonal index of sexual satisfaction.”

November 1, 2015 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Epidemiology, Immunology, Medicine | Leave a comment

Peripheral Neuropathy & Neuropathic Pain: Into the light (II)

Here’s my first post about the book. As I mentioned in that post, I figured I should limit detailed coverage to the parts of the book dealing with stuff related to diabetic/metabolic neuropathies. There’s a chapter specifically about ‘diabetic and uraemic neuropathies’ in the book and most of the coverage below relates to content covered in that chapter, but I have also included some related observations from other parts of the book as they seemed relevant.

It is noted in the book’s coverage that diabetes is the commonest cause of neuropathy in industrialized countries. There are many ways in which diabetes can affect the nervous system, and not all diabetes-related neuropathies affect peripheral nerves. Apart from distal symmetric polyneuropathy, which can probably in this context be thought of as ‘classic diabetic neuropathy’, focal or multifocal involvement of the peripheral nervous system is also common, and so is autonomic neuropathy. Diabetics are also at increased risk of inflammatory neuropathies such as CIDP – chronic inflammatory demyelinating polyneuropathy (about which the book also has a chapter). Late stage complications of diabetes usually relate to some extent to vessel wall abnormalities and their effects, and the blood vessels supplying the peripheral nerves can be affected just like all other blood vessels; in that context it is of interest to note that the author mentions elsewhere in the book that “tissue ischaemia is more likely to be symptomatic in nerves than in most other organs”. According to the author there isn’t really a great way to classify all the various manifestations of diabetic neuropathy, but most of them fall into one of three groups – distal symmetrical sensorimotor (length-dependent) polyneuropathy (DSSP); autonomic neuropathy; and focal- and multifocal neuropathy. The first one of these is by far the most common, and it is predominantly a sensory neuropathy (‘can you feel this?’ ‘does this hurt?’ ‘Is this water hot or cold?’ – as opposed to motor neuropathy: ‘can you move your arm?’) with no motor deficit.

Neuropathies in diabetics are common – how common? The author notes that the prevalence in several population-based surveys has been found to be around 30% “in studies using restrictive definitions”. The author does not mention this, but given that diabetic neuropathy usually has an insidious onset and given that diabetes-related sensory neuropathy “can be totally asymptomatic”, survey-based measures are if anything likely to underestimate prevalence. Risk increases with age and duration of diabetes; the prevalence of diabetic peripheral neuropathy is more than 50% in type 1 diabetics above the age of 60.

DSSP may lead to numbness, burning feet, a pins and needles sensation and piercing/stabbing pain in affected limbs. The ‘symmetric’ part of the abbreviation means that it usually affects both sides of the body, instead of e.g. just one foot or hand. The length-dependence mentioned in the parenthesis earlier relates in a way to the pathophysiological process. The axons of the peripheral nervous system lack ribosomes, and this means that essential proteins and enzymes needed in distal regions of the nervous system need to be transported great distances through the axons – which again means that neurons with long axons are particularly vulnerable to toxic or metabolic disturbances (introducing a length-dependence aspect in terms of which nerves are affected) which may lead to so-called dying-back axonal degeneration. The sensory loss can be restricted to the toes, extend over the feet, or it can migrate even further up the limbs – when sensory loss extends above the knee, signs and symptoms of nerve damage will usually also be observed in the fingers/hands/forearms. In generalized neuropathies a distinction can be made in terms of which type of nerve fibres are predominantly involved. When small fibres are most affected, sensory effects relating to pain- and temperature perception predominate, whereas light touch, position and vibratory senses are relatively preserved; on the other hand abnormalities of proprioception and sensitivity to light touch, often accompanied by motor deficits, will predominate if larger myelinated fibres are involved. DSSP is a small fibre neuropathy.

One of the ‘problems’ in diabetic neuropathy is actually that whereas sensation is affected, motor function often is not. This might be considered much better than the alternative, but unimpaired motor function actually relates closely to how damage often occurs. Wounds/ulcers developing on the soles of the feet (plantar ulcers) are very common in conditions in which there is sensation loss but no motor involvement/loss of strength; people with absent pain sensation will not know when their feet get hurt, e.g. because of a stone in the shoe or other forms of micro-trauma, but they’re still able to walk around relatively unimpaired and the absence of protective sensation in the limbs can thus lead to overuse of joints and accidental self-injury. A substantial proportion of diabetics with peripheral neuropathy also have lower limb ischaemia from peripheral artery disease, which further increases risk, but even in the absence of ischaemia things can go very wrong (for more details, see Edmonds, Foster, and Sanders – I should perhaps warn that the picture in that link is not a great appetite-stimulant). Of course one related problem here is that you can’t just stop moving around in order to avoid these problems once you’re aware that you have peripheral sensory neuropathy; inactivity will lead to muscle atrophy and ischaemia, and that’s not good for your feet either. The neuropathy may not ‘just’ lead to ulcers, but may also lead to the foot becoming deformed – the incidence of neuroarthropathy is approximately 2%/year in diabetics with peripheral neuropathy. Foot deformity is sometimes of acute onset and may be completely painless, despite leading to (painless) fractures and disorganization of joints. In the context of ulcers it is important that foot ulcers often take a *very* long time to heal, and so they provide excellent entry points for bacteria which among other things can cause chronic osteomyelitis (infection and inflammation of the bone and bone marrow). Pronounced motor involvement is as mentioned often absent in DSSP, but it does sometimes occur, usually at a late stage.

The author notes repeatedly in the text that peripheral neuropathy is sometimes the presenting symptom in type 2 diabetes, and I thought I should include that observation here as well. The high blood glucose may not be what leads the patient to see a doctor – sometimes the fact that he can no longer feel his toes is. At that point the nerve damage which has already occurred will of course usually be irreversible.

When the autonomic nervous system is affected (this is called Diabetic Autonomic Neuropathy, -DAN), this can lead to a variety of different symptoms. Effects of orthostatic hypotension (-OH) are frequent complaints; blackouts, faintness and dizziness or visual obscuration on standing are not always due to side effects of blood pressure medications. The author notes that OH can be aggravated by tricyclic antidepressants which are often used for treating chronic neuropathic pain (diabetics with autonomous nervous system disorder will often have, sometimes painful, peripheral neuropathy as well). Neurogenic male impotence seems to be “extremely common”; this leads to the absence of an erection at any time under any circumstances. The bladder may also be involved, which can lead to increased intervals between voiding and residual urine in the bladder after voiding, which can lead to UTIs. It is noted that retrograde ejaculation is frequent in people with bladder atony. The gastrointestinal system can be affected; this is often asymptomatic, but may lead to diarrhea and constipation causing weight loss and malnutrition. Associated diarrhea may be accompanied by fecal incontinence. DAN can lead to hypoglycemia unawareness, making glycemic control more difficult to accomplish. Sweating disorders are common in the feet. When a limb is affected by neuropathy the limb may lose its ability to sweat, and this may lead to other parts of the body (e.g. the head or upper trunk) engaging in ‘compensatory sweating’ to maintain temperature control. Abnormal pupil responses, e.g. in the form of reduced light reflexes and constricted pupils (miosis), are common in diabetics.

Focal (one nerve) and occasionally also multi-focal (more than one nerve) neuropathic syndromes also occur in the diabetic setting. The book spends quite a bit of time talking about what different nerves do and what happens when they stop working, so it’s hard to paint a broad picture of how these types of problems may present – it all depends on which nerve(s) is (are) affected. Usually in the setting of these disorders the long-term prognosis is good, or at least better than in the setting of DSSP; nerve damage is often not permanent. It seems that in terms of cranial nerve involvement, oculomotor nerve palsies are the most common, but still quite rare, affecting 1-2% of diabetics. Symptoms are rapid onset pain followed by double vision, and “spontaneous and complete recovery invariably occurs within 2-3 months” – I would like to note that as far as diabetes complications go, this is probably about as good as it gets… In so-called proximal diabetic neuropathy (-PDN), another type of mononeuropathy/focal neuropathy, the thighs are involved, with numbness or pain, often of a burning character which is worse at night, as well as muscle wasting. That syndrome progresses over weeks or months, after which the condition usually stabilizes and the pain improves, though residual muscle weakness seems to be common. Unlike in the case of DSSP, deficits in PDN are usually asymmetric, and both motor involvement and gradual recovery is common – it’s important to note in this context that DSSP virtually never improves spontaneously and often has a progressive course. Multi-focal neuropathies affect only a small proportion of diabetics, and in terms of outcome patterns they might be said to lie somewhere in between mononeuropathies and DSSP; outcomes are better than in the case of DSSP, but long-term sequelae are common.

Diabetics are at increased risk of developing pressure palsies in general. According to the author carpal tunnel syndrome occurs in 12% of diabetic patients, and “the incidence of ulnar neuropathy due to microlesions at the elbow level is high”.

In diabetics with renal failure caused by diabetic nephropathy (or presumably for that matter renal failure caused by other things as well, but most diabetics with kidney failure will have diabetic nephropathy) neuropathy is common and often severe. Renal failure impairs nerve function and is responsible for sometimes severe motor deficits in these patients. “Recovery from motor deficits is usually good after kidney transplant”. Carpal tunnel syndrome is very common in patients on long-term dialysis; 20 to 50 % of patients dialysed for 10 years or more are reported to have carpal tunnel syndrome. The presence of neuropathy in renal patients is closely related to renal function; the lower renal function, the more likely neurological symptoms become.

As you’ll learn from this book, a lot of things can cause peripheral neuropathies – and so the author notes that “In focal neuropathy occurring in diabetic patients, a neuropathy of another origin must always be excluded.” It’s not always diabetes, and sometimes missing the true cause can be a really bad thing; for example cancer-associated paraneoplastic syndromes are often associated with neuropathy (“paraneoplastic syndromes affect the PNS [Peripheral Nervous System] in up to one third of patients with solid tumors”), and so missing ‘the true cause’ in the case of a focal neuropathy may mean missing a growing tumour.

In terms of treatment options, “There is no specific treatment for distal symmetric polyneuropathy.” Complications can be treated/ideally prevented, but we have no drugs the primary effects of which are to specifically stop the nerves from dying. Treatment of autonomic neuropathy mostly relates to treating symptoms, in particular symptomatic OH. Treatment of proximal diabetic neuropathy, which is often very painful, relates only to pain management. Multifocal diabetic neuropathy can be treated with corticosteroids, minimizing inflammation.

Due to how common diabetic neuropathy is, most controlled studies on treatment options for neuropathic pain have involved patients with distal diabetic polyneuropathy. Various treatment options exist in the context of peripheral neuropathies, including antidepressants, antiepileptic drugs and opioids, as well as topical patches. In general pharmacological treatments will not cause anywhere near complete pain relief: “For patients receiving pharmacological treatment, the average pain reduction is about 20-30%, and only 20-35% of patients will achieve at least a 50% pain reduction with available drugs. […] often only partial pain relief from neuropathic pain can be expected, and […] sensory deficits are unlikely to respond to treatment.” Treatment of neuropathic pain is often a trial-and-error process.

October 17, 2015 Posted by | Books, Cancer/oncology, Diabetes, Epidemiology, Medicine, Neurology, Pharmacology | Leave a comment

An Introduction to Medical Diagnosis (4)

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.”

The eye:

“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.”

April 7, 2015 Posted by | Books, Cancer/oncology, Diabetes, Infectious disease, Medicine, Neurology | Leave a comment

A Systematic Review… (II)

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 [11]. 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 [3].
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) [4].” [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 [4], 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 [7]. […] 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 [14].”

“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 [37]. 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 [39], although this figure is higher in high-income countries (e.g. the figure of 6 % has been proposed for UK [9] 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 [19]. 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 [27].”

“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 [102].”

“The number of new cases of cancer worldwide in 2008 has been estimated at about 12,700,000 [3]. 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 [9].”

“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 [9]. 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 % [9] to 5 % [34]. 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.”

March 30, 2015 Posted by | alcohol, Books, Cancer/oncology, Cardiology, Data, Diabetes, Epidemiology, Infectious disease, Medicine | Leave a comment

An Introduction to Medical Diagnosis (3)

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.”

March 24, 2015 Posted by | Books, Cancer/oncology, Diabetes, Epidemiology, Medicine | Leave a comment

An Introduction to Medical Diagnosis (1)

“The student of medicine has to learn both the ‘bottom up’ approach of constructing a differential diagnosis from individual clinical findings, and the ‘top down’ approach of learning the key features pertaining to a particular diagnosis. In this textbook we have integrated both approaches into a coherent working framework that will assist the reader in preparing for academic and professional examinations, and in every day practice. […] We have split this textbook into three sections. The first section introduces the basic skills underpinning much of what follows – how to take a history and perform an examination, how to devise a differential diagnosis and select appropriate investigations, and how to record your findings in the case notes and present cases on ward rounds. The second section takes a systems-based approach to history taking and examining patients, and also includes information on relevant diagnostic tests and common diagnoses for each system. Each chapter begins with the individual ‘building blocks’ of the history and examination, and ends by drawing these elements together into relevant diagnoses. […] The third and final section of the book covers ‘special situations’, including the assessment of the newborn, infants and children, the acutely ill patient, the patient with impaired consciousness, the older patient and death and the dying patient.”

The above quote is from the preface of the book. This is a medical textbook with 500 pages and 26 chapters written by 27 contributors, so it has a lot of stuff; I’ve been conflicted about how to blog it for this reason. It has as lot of stuff which is useful to know but which most people don’t, and I think it’s the sort of book I might be tempted to ‘consult’ later on; the various 100 Cases… books I’ve read include some similar useful observations, but I think it’d be more natural to consult this book first because it’s much more likely that this book will at least have something about the medical condition you’re curious/can’t remember the details about. I think it was somewhat easier to read than was McPhee et al., and I’m not sure this is only because I read the former first (while I was reading McPhee et al. I was learning part of the vocabulary which is needed to read this book).

In the coverage below I have not talked about the stuff included in the first part; I don’t need to e.g. be able to take a medical history and navigate medical records, and if some of my readers do I’ll assume they have the necessary skills already, or know where/how to obtain such skills. In this post I’ll focus on the coverage of major systems in part two, with my coverage focused on ‘key variables’, and, well, ‘stuff I found interesting’ – which also means that I won’t talk about stuff like ‘this is how you palpate a liver’ and ‘this is how you grade heart murmurs’ (the book also covers that kind of stuff in some detail). Nor will I tell you what Buerger’s test or Trendelenburg’s test are used for, or give you a full account of the many, many different types of ‘named medical signs’ included and described in the book (Charcot’s triad, Cullen’s sign, Grey Turner’s sign, Murphy’s sign, Courvoisier’s sign, Kussmaul’s sign, Levine’s sign, etc. …).

I may in my coverage of this book tend to focus more on acute conditions than on chronic conditions, in part because it seems more useful to me to know/remember whether or not someone is, say, having a heart attack than whether or not someone with chronic kidney failure will be bothered by pitting edema. I think this approach makes sense.

The book has split the systems coverage in part 2 up into 15 chapters – there are specific chapters about: *The cardiovascular system, *the respiratory system, *the gastrointestinal system, *the renal system, *the genitourinary system, *the nervous system, *psychiatric assessment, *the musculoskeletal system, *the endocrine system, *the breast, *the haematological system, *skin, nails and hair, *the eye, *ear, nose and throat, and *infectious and tropical diseases. Most of the book coverage is devoted to this treatment of individual systems, as these 15 chapters make up roughly 350 pages of the total. I found it, interesting, that there was close to zero overlap between the coverage in this book and Newman and Kohn’s text; I’m not quite sure what to think about that.

In this post I’ll mostly talk about the first three ‘systems’ chapters. When dealing with cardiovascular disease, the major symptoms are chest discomfort, breathlessness, palpitation (an awareness of the heartbeat), dizziness and syncope (‘transient loss of consciousness resulting from transient global cerebral hypoperfusion’), and peripheral oedema (usually ankle swelling, most often associated with heart failure, often worse in the evening). An important observation is that myocardial ischemia (‘the heart muscle doesn’t get enough blood/oxygen’) can cause breathlessness and chest discomfort, and “in many cases breathlessness is the predominant symptom (particularly in women).” Deep vein thrombosis can be asymptomatic, but it commonly causes pain and swelling in the affected leg – the main acute risk factor associated with the condition (which is not particularly rare among elderly people) is that the blood clot travels to the lungs and causes a pulmonary embolism.

Next, the respiratory system: “respiratory conditions are common – accounting for more than 13 per cent of all emergency admissions and more than 20 per cent of general practitioner consultations”. I was very surprised the number was that high! I can’t provide a source as the authors did not provide a source; there are no inline citations in this book, which is part of the reason why the book did not get five stars on goodreads. Six key symptoms of respiratory diseases are chest pain (that may be extended to chest sensations), dyspnoea (shortness of breath/breathlessness), cough (“the commonest symptom that is associated with pure respiratory disease”), wheeze, sputum production, and haemoptysis (coughing up blood/blood in the sputum – this is, perhaps unsurprisingly, often, but not always, a ‘red flag symptom’: “Current recommendations indicate that urgent referral to a hospital clinic should be made when patients have haemoptysis, are over the age of 40, and are current or ex-smokers. However, a young patient who has a small amount of streak (lines in sputum) haemoptysis in the context of an upper respiratory tract infection usually will not require referral”).

In respiratory medicine, cough duration is an important variable in the diagnostic context; I was surprised that even simple respiratory tract infections may cause cough for up to three weeks, and that this is not necessarily something to worry about. Longer than that and it’s however less likely to be due to a self-limiting condition, and is more likely to be due to either lung cancer or one of the many causes of chronic cough (cough is not chronic until it’s lasted longer than 6 months) – these causes include, but are not limited to, astma, COPD, and GERD. As should be clear from the above, both heart and lung conditions may cause shortness of breath, so you can’t always conclude that shortness of breath is a lung issue. This is of course far from the only symptom which may present in different disease contexts, and the heart and lungs are connected in other ways as well; for example problems in both systems may cause clubbing. When dealing with a case of pneumonia it’s useful to be familiar with the CURB 65 score to assess risk/severity. Lung cancer can be either ‘non-small cell’ or ‘small cell’ lung cancer – in terms of presenting symptoms they’re reasonably similar, but the latter is more often associated with paraneoplastic syndromes (though these are still rare in an absolute sense, presenting in 5% of small cell lung cancers and 1% of non-small cell lung cancers, according to the book). The most common symptom is a cough, followed by persistent ‘chest infections’ (which are of course not infections) and bloody sputum/coughing up blood – but “some patients have remarkably few signs.” In the context of acute conditions affecting the lungs, pleuritic chest pain is an important symptom; this means pain which is made worse by breathing and which often has a sharp and stabbing quality to it – acute onset pleuritic chest pain can be due to a pulmonary embolism (60% of patients with PE have acute onset pleuritic chest pain; in another 25% there is a sudden onset of acute breathlessness) or a pneumothorax (‘collapsed lung’ – may also cause acute breathlessness). Although the two conditions are different, if you have either of them you want to get to a hospital, fast – sudden onset pleuritic chest pain seems to me a very good reason to call for an ambulance/visit the local emergency department.

“The gastrointestinal system includes the alimentary tract from mouth to anus, the liver, hepatobiliary structures including the gallbladder, pancreas and the biliary and pancreatic ductal systems.” This is a big system. And it’s often hard to get a good look at what’s the problem: “Almost half of gastrointestinal problems are not associated with physical signs or positive test results. Hence, the diagnosis and management is often based entirely on the inferences drawn from a patient’s symptoms.” Difficulty swallowing is a ‘red flag’ symptom, because “many patients with this symptom will have clinically significant pathology.” Weight loss combined with worsening difficulty swallowing (solids first, liquids later) means that oesophageal cancer is likely to be the cause (this one has a really bad prognosis). A useful observation when it comes to distinguishing between angina (‘heart issue’) and heartburn (‘gastrointestinal issue’), which may cause somewhat similar symptoms, is that whereas angina is often worsened by physical exertion, heartburn is not and often occurs at rest. It’s worth noting that when dealing with gastrointestinal disorders, you can learn a lot by figuring out where exactly the pain is coming from – stomach pain isn’t just stomach pain. Pain localized to one specific section of the stomach is much more likely to be due to condition X than condition Y (e.g., pain in the right upper quadrant = maybe biliary obstruction or hepatomegaly; pain in the left lower quadrant = maybe diverticulitis or infectious colitis). This may not be particularly useful for people in general to know, but I thought it was interesting. Duration of pain is a key variable: “Sudden onset of well-localized severe pain is likely to be due to catastrophic events [and] [p]ain present for weeks to months is often less life-threatening than pain presenting within hours of symptom onset.” The authors point out that the severity of abdominal pain can be underestimated in elderly people, very young patients, people who are immunosuppressed and diabetics (the latter presumably due to autonomous-/diabetes-associated enteric neuropathy). “Presence of blood in the stool points towards either inflammatory bowel disease or malignancy, but in those with infective diarrhoea it is highly specific for infections with a invasive organism.” The authors mention a few pointers to specific nutritional deficiencies which are probably useful to know about – iron deficiency may cause a flat angle or ‘spooning‘ of the nails, and it may also (together with vitamin B12-deficiency) cause soreness/redness of the tongue. Redness and cracks at the angles of the mouth are also associated with deficiencies of iron and vitamin-B12, as well as deficiencies of riboflavin, and folate.

January 31, 2015 Posted by | Books, Cancer/oncology, Cardiology, Medicine | Leave a comment

Evidence-Based Diagnosis

“Evidence-Based Diagnosis is a textbook about diagnostic, screening, and prognostic tests in clinical medicine. The authors’ approach is based on many years of experience teaching physicians in a clinical research training program. Although requiring only a minimum of mathematics knowledge, the quantitative discussions in this book are deeper and more rigorous than those in most introductory texts. […] It is aimed primarily at clinicians, particularly those who are academically minded, but it should be helpful and accessible to anyone involved with selection, development, or marketing of diagnostic, screening, or prognostic tests. […] Our perspective is that of skeptical consumers of tests. We want to make proper diagnoses and not miss treatable diseases. Yet, we are aware that vast resources are spent on tests that too frequently provide wrong answers or right answers of little value, and that new tests are being developed, marketed, and sold all the time, sometimes with little or no demonstrable or projected benefit to patients. This book is intended to provide readers with the tools they need to evaluate these tests, to decide if and when they are worth doing, and to interpret the results.”

I simply could not possibly justify not giving this book a shot considering the amazon ratings – it has an insane average rating of five stars, based on nine ratings. I agree with the reviewers: This is a really nice book. It covers a lot of stuff I’ve seen before, e.g. in Fletcher and Fletcher, Petrie and Sabin, Juth and Munthe, Borenstein, Hedges et al., Adam, Baltussen et al. (listing all of these suddenly made me realize how much stuff I’ve actually read about these sorts of topics in the past…), as well as in stats courses I’ve taken, but as the book is focusing specifically on medical testing aspects there is also a lot of new stuff as well. It should be noted that some people will benefit a lot more from reading the book than I did; I’ve spent weeks dealing with related aspects of subtopics they cover in just a few pages, and there were a lot of familiar concepts, distinctions, etc. in the book. Even so, this book is remarkably well-written and these guys really know their stuff. If you want to read a book about the basics of how to make sense of the results of medical tests and stuff like that, this is the book you’ll want to read.

Let’s say you have a test measuring some variable which might be useful in a diagnostic context. How would we know it might be useful? Well, one might come up with some criteria such a test should meet; like that the results of the test doesn’t depend on who’s doing the testing, perhaps it also shouldn’t matter when the test is done. You might also want the test to be somewhat accurate. But what do we even mean by that? There are various approaches to thinking about accuracy, and some may be better than others. So the book covers familiar topics like sensitivity and specificity, likelihood ratios, and receiver operating characteristic (ROC) curves. A test might be accurate, but if the results of a test does not change clinical decision-making it might not be worth it to do the test; so the question of whether a test is accurate or not is different from whether it’s also useful. In terms of usefulness concepts like positive- and negative predictive value and distinctions such as that between absolute and relative risk become important. It might not even be a good idea to use a test even if it distinguishes reasonably well between people who are sick and people who are not, because a very accurate test might be too expensive to be justified undertaking; the book also has a bit of stuff on cost-effectiveness. Of course costs associated with getting tested for a health condition are not limited to monetary costs; a test might be uncomfortable, and it may also for example be the case that a false positive or a false negative result might sometimes have quite severe consequences (e.g. in the context of cancer screening). In such contexts concepts like the number needed to treat might be useful. It might also on the other hand be that a test gives answers which are wrong so often that even if it’s very cheap to do, it still might not be worth doing. There’s stuff in the book about how to think about, and come up with decision-rules about, how to identify things like treatment-thresholds; variables which will be determined by probability of disease and costs associated with testing (/and treatment). A variable like the cost of a treatment might in an analytical framework involve both the costs of treating people with the health condition as well as the costs of treating people who tested positive without being sick and the costs of not treating sick people who tested negative. One might think in one context that it would be twice as bad to miss a diagnosis than it would be to treat someone who does not have the disease, which would lead to one set of decision-rules in terms of when to test and when to treat, whereas in another context it might be a lot worse to miss a diagnosis, so we’d be less worried about treating someone without the disease. There may be more than one relevant threshold in the diagnostic setting; usually there’ll be some range of prior probabilities of disease for which the test will add enough information to change decision-making, but at either end of the range the test might not add enough information to be justified. To be more specific, if you’re almost certain the patient has some specific disease, you’ll want to treat him because the test result will not change anything; and if on the other hand you’re almost certain that he does not have the disease, based e.g. on the prevalence rate and the clinical presentation, then you’ll want to refrain from testing if the test has costs (including time costs, inconvenience, etc.). The book includes formal and reasonably detailed analysis of such topics.

In terms of how to interpret the results of a test it matters who you’re testing, and as already indicated the authors apply a Bayesian approach to these matters and repeatedly emphasize the importance of priors when evaluating test results (or for that matter findings from the literature). In that context some important notions are included about what you can and can’t use e.g. variables like prevalence and incidence for, how best to use such variables to inform decision-making, and things like how the study design might impact which variables are available to you for analysis (don’t try to estimate prevalence if you’re dealing with a case-control setup, where this variable is determined by the study design).

Of course medical most tests don’t just give two results. Dichotomization adds simplicity compared to more complex scenarios, so that’s where the book starts out, but it doesn’t stop there. If you have a test involving a continuous variable then dichotomizing the results will reduce the value of the test; this is equivalent to using pair-wise comparisons to make sense of continuous data in other contexts. However it’s sometimes useful to do it anyway because you may be in a situation where you need to quickly/easily separate ‘normal’ from ‘abnormal’. Likelihood ratios are really useful in the context of multi-level tests. In the simple dichotomous test, the LR for a test result is the probability of the result in a patient with disease divided by the probability of the result in a patient without disease. If you have lots of possible test results however, you’ll not be limited to two likelihood ratios; you’ll have as many likelihood ratios as there are results of the test. Those likelihood ratios are useful because the LR in the context of a multi-level test is equal to the slope of the ROC curve over the relevant interval. The ROC curve in some sense displays the tradeoff between sensitivity (‘true positive’) and specificity (‘true negative’); each point on the curve represents a different cut-off for calling a test positive. Such curves are quite useful in terms of figuring out if a test adds information or not, how well it distinguishes between patients. If you want to compare different tests and how they perform, Bland-Altman plots also seem to be useful tools.

Sometimes the results of more than one test will be relevant to decision-making, and a key question to ask here is the extent to which tests, and test results, are independent. If tests are not independent, one should be careful about how to update the probability of disease based on a new laboratory finding, and about which conclusions can be drawn regarding the extent to which an additional test might or might not be necessary/useful/required. The book does not go into too much detail, but enough is said on this topic to make it clear that test dependence is a potential issue one should keep in mind when evaluating multiple test results. They do talk a bit about how to come up with decision-rules about which tests to prefer in situations where multiple interdependent tests are available for analysis.

Sometimes blinding is difficult. The book tells us that it’s particularly important to blind when outcomes are subjective (like pain), and when prognostic factors may affect treatment in the study setting.

Medical tests can be used for different things, and not all tests are equal. One important distinction they talk about in the book is the distinction between diagnostic tests, which are done on sick people to figure out why they’re sick, and screening tests, which are mostly done on healthy people with a low prior probability of disease. There are different types of screening tests. One type of test is screening for symptomatic disease, which is sometimes done because people may be sick and have symptoms without being aware of the fact that they’re sick; screening for depression might be an example of this (that may even sometimes be cost-effective). These tests are reasonably similar to traditional diagnostic tests, and so can be evaluated in a similar manner. However most screening tests are of a different kind; they’re aimed at identifying risk factors, rather than ‘actual disease’ (a third kind is screening for presymptomatic disease). This generally tends to make them harder to justify undertaking, for reasons covered in much greater detail in Juth and Munthe (see the link over the word ‘may’ above). There are other differences as well; concepts such as sensitivity and specificity are for example difficult to relate to screening tests aimed at identifying risk factors, as such screening tests have as a goal to estimate incidence, rather than prevalence, which will often make it hard to compare such tests with the established ‘gold standard’ (as is usually the case). I decided to include a few quotes from this part of the coverage:

“the general public tends to be supportive of screening programs. Part of this is wishful thinking. We would like to believe that bad things happen for a reason, and that there are things we can do to prevent them […] .We also tend to be much more swayed by stories of individual patients (either those whose disease was detected early or those in whom it was found “too late”) than by boring statistics about risks, costs, and benefits […]. Because, at least in the U.S., there is no clear connection between money spent on screening tests and money not being available to spend on other things, the public tends not to be swayed by arguments about cost efficacy […]. In fact, in the general public’s view of screening, even wrong answers are not necessarily a bad thing. Schwartz et al. (2004) did a national telephone survey of attitudes about cancer screening in the U.S. They found that 38% of respondents had experienced at least one false-positive screening test. Although more than 40% of these subjects referred to that experience as “very scary” or the “scariest time of my life,” 98% were glad they had the screening test! […] Another disturbing result of the survey by Schwartz et al. was that, even though (as of 2002) the U.S. Preventive Health Services Task Force felt that evidence was insufficient to recommend prostate cancer screening, more than 60% of respondents said that a 55-year-old man who did not have a routine PSA test was “irresponsible,” and more than a third said this for an 80-year old […] Thus, regardless of the efficacy of screening tests, they have become an obligation if one does not wish to be blamed for getting some illnesses.”

There are many reasons why there may be problems with using observational studies to evaluate screening tests, and they talk a bit about those. One is what they call ‘volunteer bias’, which is just basic selection bias. Then there are the familiar problems of lead-time bias and length time bias. It should perhaps be noted here that both of the two latter problems can be handled in the context of a randomized controlled trial; neither lead-time bias nor length time bias are issues if the study is an RCT which compares the entire screened group with the entire unscreened group. Yet another problem is stage-migration bias, which for example can be a problem when more sensitive tests allow for earlier detection which changes how people are staged; this may lead to changes in stage-specific mortality rates, without actually improving overall mortality at all. A final problem they talk about is overdiagnoses related to the problem of pseudodisease, which is disease that would never have affected the patient if it had not been diagnosed by the screening procedure. Again a quote might be in order:

“It is difficult to identify pseudodisease in an individual patient, because it requires completely ignoring the diagnosis. (If you treat pseudodisease, the treatment will always appear to be curative, and you won’t realize the patient had pseudodisease rather than real disease!) In some ways, pseudodisease is an extreme type of stage migration bias. Patients who were not previously diagnosed as having the disease are now counted as having it. Although the incidence of the disease goes up, the prognosis of those who have it improves. […] Lack of understanding of pseudodisease, including the lack of people who know they have had it, is a real problem, because most of us understand the world through stories […]. Patients whose pseudodisease has been “cured” become strong proponents of screening and treatment and can tell a powerful and easily understood story about their experience. On the other hand, there aren’t people who can tell a compelling story of pseudodisease – men who can say, “I had a completely unnecessary prostatectomy,” or women who say, “I had a completely unnecessary mastectomy,” even though we know statistically that many such people exist.
The existence of pseudo–lung cancer was strongly suggested by the results of the Mayo Lung Study, a randomized trial of chest x-rays and sputum cytology to screen for lung cancer among 9,211 male cigarette smokers (Marcus et al. 2000).”

I included the last part also to indicate that this is actually a real problem also in situations where you’d be very likely to imagine it couldn’t possibly be a problem; even a disease as severe as lung cancer is subject to this kind of issue. There are also problems that may make screening tests look worse than they really are; like power issues, unskilled medical personnel doing the testing, and lack of follow-up (if a positive test result does not lead to any change in health care provision, there’s no good reason to assume earlier diagnosis as a result of screening will impact e.g. disease-specific mortality. On a related note there’s some debate about which mortality metric (general vs disease-specific) is to be preferred in the screening context, and they talk a bit about that as well).

I expected to write more about the book in this post than I have so far and perhaps include a few more quotes, but my computer broke down while I was writing this post yesterday so this is what you get. However as already mentioned this is a great book, and if you think you might like it based on the observations included in this post you should definitely read it.

December 22, 2014 Posted by | Books, Cancer/oncology, Epidemiology, Medicine, Statistics | Leave a comment