Book review: Perfect Health Diet

Perfect Health Diet is a book that one should own. It is not the type of book that you can get from your local library and just do a quick read over (and, maybe, write a review about it). If you do that, you will probably miss several important ideas that form the foundation of this book, which is a deep foundation.

The book is titled “Perfect Health Diet”, not “The Perfect Health Diet”. If you think that this is a mistake, consider that the most successful social networking web site of all time started as “The Facebook”, and then changed to simply “Facebook”; which was perceived later as a major improvement.

Moreover, “Perfect Health Diet” makes for a cool and not at all inappropriate acronym – “PHD”.

What people eat has an enormous influence on their lives, and also on the lives of those around them. Nutrition is clearly one of the most important topics in the modern world - it is the source of much happiness and suffering for entire populations. If Albert Einstein and Marie Curie were alive today, they would probably be interested in nutrition, as they were about important topics of their time that were outside their main disciplines and research areas (e.g., the consequences of war, and future war deterrence).

Nutrition attracts the interest of many bright people today. Those who are not professional nutrition researchers often fund their own research, spending hours and hours of their own time studying the literature and even experimenting on themselves. Several of them decide to think deeply and carefully about it. A few, like Paul Jaminet and Shou-Ching Jaminet, decide to write about it, and all of us benefit from their effort.

The Jaminets have PhDs (not copies of their books, degrees). Their main PhD disciplines are somewhat similar to Einstein’s and Curie’s; which is an interesting coincidence. What the Jaminets have written about nutrition is probably analogous, in broad terms, to what Einstein and Curie would have written about nutrition if they were alive today. They would have written about a “unified field theory” of nutrition, informed by chemistry.

To put it simply, the main idea behind this book is to find the “sweet spot” for each major macronutrient (e.g., protein and fat) and micronutrient (e.g., vitamins and minerals) that is important for humans. The sweet spot is the area indicated on the graph below. This is my own simplified interpretation of the authors' more complex graphs on marginal benefits from nutrients.

The book provides detailed information about each of the major nutrients that are important to humans, what their “sweet spot” levels are, and how to obtain them. In this respect the book is very thorough, and also very clear, including plenty of good arguments and empirical research results to back up the recommendations. But this book is much more than that.

Why do I refer to this book as proposing a “unified field theory” of nutrition? The reason is that this book clearly aims at unifying all of the current state of the art knowledge about nutrition, departing from a few fundamental ideas.

One of those fundamental ideas is that a good diet would provide nutrients in the same ratio as those provided by our own tissues when we “cannibalize” them – i.e., when we fast. Another is that human breast milk is a good basis for the estimation of the ratios of macronutrients a human adult would need for optimal health.

And here is where the depth and brilliance with which the authors address these issues can lead to misunderstandings.

For example, when our body “cannibalizes” itself (e.g., at the 16-h mark of a water fast), there is no digestion going on. And, as the authors point out, what you eat, in terms of nutrients, is often not what you get after digestion. It may surprise many to know that a diet rich in vegetables is actually a high fat diet (if you are surprised, you should read the book). One needs to keep these things in mind to understand that not all dietary macronutrient ratios will lead to the same ratios of nutrients after digestion, and that the dietary equivalent of “cannibalizing” oneself is not a beef-only diet.

Another example relates to the issue of human breast milk. Many seem to have misunderstood the authors as implying that the macronutrient ratios in human breast milk are optimal for adult humans. The authors say nothing of the kind. What they do is to use human breast milk as a basis for their estimation of what an adult human should get, based on a few reasonable assumptions. One of the assumptions is that a human adult’s brain consumes proportionally much less sugar than an infant’s.

Yet another example is the idea of “safe starches”, which many seem to have taken as a recommendation that diabetics should eat lots of white rice and potato. The authors have never said such a thing in the book; not even close. "Safe starches", like white rice and sweet potatoes (as well as white potatoes), are presented in the book as good sources of carbohydrates that are also generally free from harmful plant toxins. And they are, if consumed after cooking.

By the way, I have a colleague who has type 2 diabetes and can eat meat with white potatoes without experiencing hyperglycemia, as long as the amount of potato is very small and is eaten after a few bites of meat.

Do I disagree with some of the things that the authors say? Sure I do, but not in a way that would lead to significantly different dietary recommendations. And, who knows, maybe I am wrong.

For example, the authors seem to think that dietary advanced glycation end-products (AGEs) can be a problem for humans, and therefore recommend that you avoid cooking meat at high temperatures (no barbecuing, for example). I have not found any convincing evidence that this is true in healthy people, but following the authors’ advice will not hurt you at all. And if your digestive tract is compromised to the point that undigested food particles are entering your bloodstream, then maybe you should avoid dietary sources of AGEs.

Also, I think that humans tend to adapt to different macronutrient ratios in more fundamental ways than the authors seem to believe they can. These adaptations are long-term ones, and are better understood based on the notion of compensatory adaptation. For instance, a very low carbohydrate diet may bring about some problems in the short term, but long-term adaptations may reverse those problems, without a change in the diet.

The authors should be careful about small errors that may give a bad impression to some experts, and open them up to undue criticism; as experts tend to be very picky and frequently generalize based on small errors. Here is one. The authors seem to imply that eating coconut oil will help feed colon cells, which indeed seem to feed on short-chain fats; not exactly the medium-chain fats abundantly found in coconut oil, but okay. (This may be the main reason why indigestible fiber contributes to colon health, by being converted by bacteria to short-chain fats.) The main problem with the authors' implied claim is that coconut oil, as a fat, will be absorbed in the small intestine, and thus will not reach colon cells in any significant amounts.

Finally, I don’t think that increased animal protein consumption causes decreased longevity; an idea that the authors seem to lean toward. One reason is that seafood consumption is almost universally associated with increased longevity, even when it is heavily consumed, and seafood in general has a very high protein-to-fat ratio (much higher than beef). The connection between high animal protein consumption and decreased longevity suggested by many studies, some of which are cited in the book, is unlikely to be due to the protein itself, in my opinion. That connection is more likely to be due to some patterns that may be associated in certain populations with animal protein consumption (e.g., refined wheat and industrial seed oils consumption).

Thankfully, controversial issues and small errors can be easily addressed online. The authors maintain a popular blog, and they do so in such a way that the blog is truly an extension of the book. This blog is one of my favorites. Perhaps we will see some of the above issues addressed in the blog.

All in all, this seems like a bargain to me. For about 25 bucks (less than that, if you trade in quid; and more, if you do in Yuan), and with some self-determination, you may save thousands of dollars in medical bills. More importantly, you may change your life, and those of the ones around you, for the better.

Certain mental disorders may have evolved as costs of attractive mental traits

I find costly traits fascinating, even though they pose a serious challenge to the notion that living as we evolved to live is a good thing. It is not that they always deny this notion; sometimes they do not, but add interesting and somewhat odd twists to it.

Costly traits have evolved in many species (e.g., the male peacock’s train) because they maximize reproductive success, even though they are survival handicaps. Many of these traits have evolved through nature’s great venture capitalist – sexual selection.

(Source: Vangoghart.org)

Certain harmful mental disorders in humans, such as schizophrenia and manic–depression, are often seen as puzzles from an evolutionary perspective. The heritability of those mental disorders and their frequency in the population at various levels of severity suggests that they may have been evolved through selection, yet they often significantly decrease the survival prospects of those afflicted by them (Keller & Miller, 2006; Nesse & Williams, 1994).

The question often asked is why have they evolved at all? Should not they have been eliminated, instead of maintained, by selective forces? It seems that the most straightforward explanation for the existence of certain mental disorders is that they have co-evolved as costs of attractive mental traits. Not all mental disorders, however, can be explained in this way.

The telltale signs of a mental disorder that is likely to be a cost associated with a trait used in mate choice are: (a) many of the individuals afflicted are also found to have an attractive mental trait; and (b) the mental trait in question is comparatively more attractive than other mental traits that have no apparent survival costs associated with them.

The broad category of mental disorders generally referred to as schizophrenia is a good candidate in this respect because:
    - Its incidence in human males is significantly correlated with creative intelligence, the type of intelligence generally displayed by successful artists, which is an attractive mental trait (Miller & Tal, 2007; Nettle, 2006b).
    - Creative intelligence is considered to be one of the most attractive mental traits in human males, to the point of females at the peak of their fertility cycles finding creative but poor males significantly more attractive than uncreative but wealthy ones (Haselton & Miller, 2006).

The same generally applies to manic–depression, and a few other related mental disorders.

By the way, creative intelligence is also strongly associated with openness, one of the "big five" personality traits. And, both creative intelligence and mental disorders are seen in men and women. This is so even though it is most likely that selection pressure for creative intelligence was primarily exerted by ancestral women on men, not ancestral men on women.

Crespi (2006), in a response to a thorough and provocative argument by Keller & Miller (2006) regarding the evolutionary bases of mental disorders, makes a point that is similar to the one made above (see, also, Nettle, 2006), and also notes that schizophrenia has a less debilitating effect on human females than males.

Ancestral human females, due to their preference for males showing high levels of creative intelligence, might have also selected a co-evolved cost that affects not only males but also the females themselves though gene correlation between the sexes (Gillespie, 2004; Maynard Smith, 1998).

There is another reason why ancestral women might have possessed certain traits that they selected for in ancestral men. Like anything that involves intelligence in humans, the sex applying selection pressure (i.e., female) must be just as intelligent as (if not more than) the sex to which selection pressure is applied (i.e., males). Peahens do not have to have big and brightly colored trains to select male peacocks that have them. That is not so with anything that involves intelligence (in any of its many forms, like creative and interpersonal intelligence), because intelligence must be recognized through communication and behavior, which itself requires intelligence.

Other traits that differentiate females from males may account for differences in the actual survival cost of schizophrenia in females and males. For example, males show a greater propensity toward risk-taking than females (Buss, 1999; Miller, 2000), and schizophrenia may positively moderate the negative relationship between risk-taking propensity and survival success.

Why were some of our ancestors in the Stone Age artists, creating elaborate cave paintings, sculptures, and other art forms? Maybe because a combination of genetic mutations and environmental factors made it a sexy thing to do from around 50,000 years ago or so, even though the underlying reason why the ancestral artists produced art may also have increased the chances that some of them suffered from mental disorders.

A heritable trait possessed by males and perceived as very sexy by females has a very good chance of evolving in any population. That is so even if the trait causes the males who possess it to die much earlier than other males. In the human species, a male can father literally hundreds of children in just a few years. Unlike men, women tend to be very selective of their sexual partners, which does not mean that they cannot all select the same partner (Buss, 1999).

So, if this is true, what is the practical value of knowing it?

It seems reasonable to believe that knowing the likely source of a strange and unpleasant view of the world is, in and of itself, therapeutic. A real danger, it seems, is in seeing the world in a strange and unpleasant way (e.g., as a schizophrenic may see it), and not knowing that the distorted view is caused by an underlying reason. The stress coming from this lack of knowledge may compound the problem; the symptoms of mental disorders are often enhanced by stress.

As one seeks professional help, it may also be comforting to know that something that is actually very good, like creative intelligence, may come together with the bad stuff.

Finally, is it possible that our modern diets and lifestyles significantly exacerbate the problem? The answer is "yes", and this is a theme that has been explored many times before by Emily Deans. (See also this post, by Emily, on the connection between mental disorders and creativity.)

Reference
(All cited references are listed in the article below. If you like mathematics, this article is for you.)

Kock, N. (2011). A mathematical analysis of the evolution of human mate choice traits: Implications for evolutionary psychologists. Journal of Evolutionary Psychology, 9(3), 219-247.

Great evolution thinkers you should know about

If you follow a paleo diet, you follow a diet that aims to be consistent with evolution. This is a theory that has undergone major changes and additions since Alfred Russel Wallace and Charles Darwin proposed it in the 1800s. Wallace proposed it first, by the way, even though Darwin’s proposal was much more elaborate and supported by evidence. Darwin acknowledged Wallace's precedence, but received most of the credit for the theory anyway.

(Alfred Russel Wallace; source: Wikipedia)

What many people who describe themselves as paleo do not seem to know is how the theory found its footing. The original Wallace-Darwin theory (a.k.a. Darwin’s theory) had some major problems, notably the idea of blending inheritance (e.g., blue eye + brown eye = somewhere in between), which led it to be largely dismissed until the early 1900s. Ironically, it was the work of a Catholic priest that provided the foundation on which the theory of evolution would find its footing, and evolve into the grand theory that it is today. We are talking about Gregor Johann Mendel.

Much of the subsequent work that led to our current understanding of evolution sought to unify the theory of genetics, pioneered by Mendel, with the basic principles proposed as part of the Wallace-Darwin theory of evolution. That is where major progress was made. The evolution thinkers below are some of the major contributors to that progress.

Ronald A. Fisher. English statistician who proposed key elements of a genetic theory of natural selection in the 1910s, 1920s and 1930s. Fisher showed that the inheritance of discrete traits (e.g., flower color) described by Gregor Mendel has the same basis as the inheritance of continuous traits (e.g., human height) described by Francis Galton. He is credited, together with John B.S. Haldane and Sewall G. Wright, with setting the foundations for the development of the field of population genetics. In population genetics the concepts and principles of the theories of evolution (e.g., inheritance and natural selection of traits) and genetics (e.g., genes and alleles) have been integrated and mathematically formalized.

John B.S. Haldane. English geneticist who, together with Ronald A. Fisher and Sewall G. Wright, is credited with setting the foundations for the development of the field of population genetics. Much of his research was conducted in the 1920s and 1930s. Particularly noteworthy is the work by Haldane through which he mathematically modeled and explained the interactions between natural selection, mutation, and migration. He is also known for what is often referred to as Haldane’s principle, which explains the direction of the evolution of many species’ traits based on the body size of the organisms of the species. Haldane’s mathematical formulations also explained the rapid spread of traits observed in some actual populations of organisms, such as the increase in frequency of dark-colored moths from 2% to 95% in a little less than 50 years as a response to the spread of industrial soot in England in the late 1800s.

Sewall G. Wright. American geneticist and statistician who, together with Ronald A. Fisher and John B.S. Haldane, is credited with setting the foundations for the development of the field of population genetics. As with Fisher and Haldane, much of his original and most influential research was conducted in the 1920s and 1930s. He is believed to have discovered the inbreeding coefficient, related to the occurrence of identical genes in different individuals, and to have pioneered methods for the calculation of gene frequencies among populations of organisms. The development of the notion of genetic drift, where some of a population’s traits result from random genetic changes instead of selection, is often associated with him. Wright is also considered to be one of pioneers of the development of the statistical method known as path analysis.

Theodosius G. Dobzhansky. Ukrainian-American geneticist and evolutionary biologist who migrated to the United States in the late 1920s, and is believed to have been one of the main architects of the modern evolutionary synthesis. Much of his original research was conducted in the 1930s and 1940s. In the 1930s he published one of the pillars of the modern synthesis, a book titled Genetics and the Origin of Species. The modern evolutionary synthesis is closely linked with the emergence of the field of population genetics, and is associated with the integration of various ideas and predictions from the fields of evolution and genetics. In spite of Dobzhansky’s devotion to religious principles, he strongly defended Darwinian evolution against modern creationism. The title of a famous essay written by him is often cited in modern debates between evolutionists and creationists regarding the teaching of evolution in high schools: Nothing in Biology Makes Sense Except in the Light of Evolution.

Ernst W. Mayr. German taxonomist and ornithologist who spent most of his life in the United States, and is believed, like Theodosius G. Dobzhansky, to have been one of the main architects of the modern evolutionary synthesis. Mayr is credited with the development in the 1940s of the most widely accepted definition of species today, that of a group of organisms that are capable of interbreeding and producing fertile offspring. At that time organisms that looked alike were generally categorized as being part of the same species. Mayr served as a faculty member at Harvard University for many years, where he also served as the director of the Museum of Comparative Zoology. He lived to the age of 100 years, and was one of the most prolific scholars ever in the field of evolutionary biology. Unlike many evolution theorists, he was very critical of the use of mathematical approaches to the understanding of evolutionary phenomena.

William D. Hamilton. English evolutionary biologist (born in Egypt) widely considered one of the greatest evolution theorists of the 20th Century. Hamilton conducted pioneering research based on the gene-centric view of evolution, also know as the “selfish gene” perspective, which is based on the notion that the unit of natural selection is the gene and not the organism that carries the gene. His research conducted in the 1960s set the foundations for using this notion to understand social behavior among animals. The notion that the unit of natural selection is the gene forms the basis of the theory of kin selection, which explains why organisms often will instinctively behave in ways that will maximize the reproductive success of relatives, sometimes to the detriment of their own reproductive success (e.g., worker ants in an ant colony).

George C. Williams. American evolutionary biologist believed to have been a co-developer in the 1960s, together with William D. Hamilton, of the gene-centric view of evolution. This view is based on the notion that the unit of natural selection is the gene, and not the organism that carries the gene or a group of organisms that happens to share the gene. Williams is also known for his pioneering work on the evolution of sex as a driver of genetic variation, without which a species would adapt more slowly in response to environmental pressures, in many cases becoming extinct. He is also known for suggesting possible uses of human evolution knowledge in the field of medicine.

Motoo Kimura. Japanese evolutionary biologist known for proposing the neutral theory of molecular evolution in the 1960s. In this theory Kimura argued that one of the main forces in evolution is genetic drift, a stochastic process that alters the frequency of genotypes in a population in a non-deterministic way. Kimura is widely known for his innovative use of a class of partial differential equations, namely diffusion equations, to calculate the effect of natural selection and genetic drift on the fixation of genotypes. He has developed widely used equations to calculate the probability of fixation of genotypes that code for certain phenotypic traits due to genetic drift and natural selection.

George R. Price. American geneticist known for refining in the 1970s the mathematical formalizations developed by Ronald A. Fisher and William D. Hamilton, and thus making significant contributions to the development of the field of population genetics. He developed the famous Price Equation, which has found widespread use in evolutionary theorizing. Price is also known for introducing, together with John Maynard Smith, the concept of evolutionary stable strategy (ESS). The EES notion itself builds on the Nash Equilibrium, named after its developer John Forbes Nash (portrayed in the popular Hollywood film A Beautiful Mind). The concept of EES explains why certain evolved traits spread and become fixed in a population.

John Maynard Smith. English evolutionary biologist and geneticist credited with several innovative applications of game theory (which is not actually a theory, but an applied branch of mathematics) in the 1970s to the understanding of biological evolution. Maynard Smith is also known for introducing, together with George R. Price, the concept of evolutionary stable strategy (EES). As noted above, the EES notion builds on the Nash Equilibrium, and explains why certain evolved traits spread and become fixed in a population. The pioneering work by John Maynard Smith has led to the emergence of a new field of research within evolutionary biology known as evolutionary game theory.

Edward O. Wilson. American evolutionary biologist and naturalist who coined the term “sociobiology” in the 1970s to refer to the systematic study of the biological foundations of social behavior of animals, including humans. Wilson was one of the first evolutionary biologists to convincingly argue that human mental mechanisms are shaped as much by our genes as they are by the environment that surrounds us, setting the stage for the emergence of the field of evolutionary psychology. Many of Wilson’s theoretical contributions in the area of sociobiology are very general, and apply not only to humans but also to other species. Wilson has been acknowledged as one of the foremost experts in the study of ants’ and other insects’ social organizations. He is also known for his efforts to preserve earth’s environment.

Amotz Zahavi. Israeli evolutionary biologist best known for his widely cited handicap principle, proposed in the 1970s, which explains the evolution of fitness signaling traits that appear to be detrimental to the reproductive fitness of an organism. Zahavi argued that traits evolved to signal the fitness status of an organism must be costly in order to the reliable. An example is the large and brightly colored trains evolved by the males of the peacock species, which signal good health to the females of the species. The male peacock’s train makes it more vulnerable to predators, and as such is a costly indicator of survival success. Traits used for this type of signaling are often referred to as Zahavian traits.

Robert L. Trivers. American evolutionary biologist and anthropologist who proposed several influential theories in the 1970s, including the theories of reciprocal altruism, parental investment, and parent-offspring conflict. Trivers is considered to be one of the most influential living evolutionary theorists, and is a very active researcher and speaker. His most recent focus is on the study of body symmetry and its relationship with various traits that are hypothesized to have been evolved in our ancestral past. Trivers’s theories often explain phenomena that are observed in nature but are not easily understood based on traditional evolutionary thinking, and in some cases appear contradictory with that thinking. Reciprocal altruism, for example, is a phenomenon that is widely observed in nature and involves one organism benefiting another not genetically related organism, without any immediate gain to the organism (e.g., vampire bats regurgitating blood to feed non-kin).

There are many other more recent contributors that could arguably be included in the list above. Much recent progress has been made in interdisciplinary fields that could be seen as new fields of research inspired in evolutionary ideas. One such field is that of evolutionary psychology, which has emerged in the 1980s. New theoretical contributions tend to take some time to be recognized though, as will be the case with ideas coming off these new fields, because new theoretical contributions are invariably somewhat flawed and/or incomplete when they are originally proposed.

Calling self-experimentation N=1 is incorrect and misleading

This is not a post about semantics. Using “N=1” to refer to self-experimentation is okay, as long as one understands that self-experimentation is one of the most powerful ways to improve one’s health. Typically the term “N=1” is used in a demeaning way, as in: “It is just my N=1 experience, so it’s not worth much, but …” This is the reason behind this post. Using the “N=1” term to refer to self-experimentation in this way is both incorrect and misleading.

Calling self-experimentation N=1 is incorrect

The table below shows a dataset that is discussed in this YouTube video on HealthCorrelator for Excel (HCE). It refers to one single individual. Nearly all health-related datasets will look somewhat like this, with columns referring to health variables and rows referring to multiple measurements for the health variables. (This actually applies to datasets in general, including datasets about non-health-related phenomena.)

Often each individual measurement, or row, will be associated with a particular point in time, such as a date. This will characterize the measurement approach used as longitudinal, as opposed to cross-sectional. One example of the latter would be a dataset where each row referred to a different individual, with the data on all rows collected at the same point in time. Longitudinal health-related measurement is frequently considered superior to cross-sectional measurement in terms of the insights that it can provide.

As you can see, the dataset has 10 rows, with the top row containing the names of the variables. So this dataset contains nine rows of data, which means that in this dataset “N=9”, even though the data is for one single individual. To call this an “N=1” experiment is incorrect.

As a side note, an empty cell, like that on the top row for HDL cholesterol, essentially means that a measurement for that variable was not taken on that date, or that it was left out because of obvious measurement error (e.g., the value received from the lab was “-10”, which would be a mistake since nobody has a negative HDL cholesterol level). The N of the dataset as a whole would still be technically 9 in a situation like this, with only one missing cell on the row in question. But the software would typically calculate associations for that variable (HDL cholesterol) based on a sample of 8.

Calling self-experimentation N=1 is misleading

Calling self-experimentation “N=1”, meaning that the results of self-experimentation are not a good basis for generalization, is very misleading. But there is a twist. Those results may indeed not be a good basis for generalization to other people, but they provide a particularly good basis for generalization for you. It is often much safer to generalize based on self-experimentation, even with small samples (e.g., N=9).

The reason, as I pointed out in this interview with Jimmy Moore, is that data about oneself only tends to be much more uniform than data about a sample of individuals. When multiple individuals are included in an analysis, the number of sources of error (e.g., confounding variables, measurement problems) is much higher than when the analysis is based on one single individual. Thus analyses based on data from one single individual yield results that are more uniform and stable across the sample.

Moreover, analyses of data about a sample of individuals are typically summarized through averages, and those averages tend to be biased by outliers. There are always outliers in any dataset; you might possibly be one of them if you were part of a dataset, which would render the average results at best misleading, and at worst meaningless, to you. This is a point that has also been made by Richard Nikoley, who has been discussing self-experimentation for quite some time, in this very interesting video.

Another person who has been talking about self-experimentation, and showing how it can be useful in personal health management, is Seth Roberts. He and the idea of self-experimentation were prominently portrayed in this article on the New York Times. Check this video where Dr. Roberts talks about how he found out through self-experimentation that, among other things, consuming butter reduced his arterial plaque deposits. Plaque reduction is something that only rarely happens, at least in folks who follow the traditional American diet.

HCE generates coefficients of association and graphs at the click of a button, making it relatively easy for anybody to understand how his or her health variables are associated with one another, and thus what modifiable health factors (e.g., consumption of certain foods) could be causing health effects (e.g., body fact accumulation). It may also help you identify other, more counter-intuitive, links; such as between certain thought and behavior patterns (e.g., wealth accumulation thoughts, looking at the mirror multiple times a day) and undesirable mental states (e.g., depression, panic attacks).

Just keep in mind that you need to have at least some variation in all the variables involved. Without variation there is no correlation, and thus causation may remain hidden from view.

Being glucose intolerant may make you live only to be 96, if you would otherwise live to be 100

This comes also from the widely cited Brunner and colleagues study, published in Diabetes Care in 2006 (linked here). They defined a person as glucose intolerant if he or she had a blood glucose level of 5.3-11 mmol/l after a 2-h post–50-g oral glucose tolerance test. For those using the other measurement system, like us here in the USA, that is a blood glucose level of approximately 95-198 mg/dl.

Quite a range, eh!? This covers the high end of normoglycemia, as well as pre- to full-blown type 2 diabetes.

In this investigation, called the Whitehall Study, 18,403 nonindustrial London-based male civil servants aged 40 to 64 years were examined between September 1967 and January 1970. These folks were then followed for over 30 years, based on the National Health Service Central Registry; essentially to find out whether they had died, and of what. During this period, there were 11,426 deaths from all causes; with 5,497 due to cardiovascular disease (48.1%) and 3,240 due to cancer (28.4%).

The graph below shows the age-adjusted survival rates against time after diagnosis. Presumably the N values refer to the individuals in the glucose intolerant (GI) and type 2 diabetic (T2DM) groups that were alive at the end of the monitoring period. This does not apply to the normoglycemic N value; this value seems to refer to the number of normoglycemic folks alive after the divergence point (5-10 years from diagnosis).

Note by the authors: “Survival by baseline glucose tolerance status diverged after 5-10 years of follow-up. Median survival differed by 4 years between the normoglycemic and glucose intolerant groups and was 10 years less in the diabetic compared with the glucose intolerant group.”

That is, it took between 5 and 10 years of high blood glucose levels for any effect on mortality to be noticed. One would expect at least some of the diagnosed folks to have done something about their blood glucose levels; a confounder that was not properly controlled for in this study, as far as I can tell. The glucose intolerant folks ended up living 4 years less than the normoglycemics, and 10 years more than the diabetics.

One implication of this article is that perhaps you should not worry too much if you experience a temporary increase in blood glucose levels due to compensatory adaptation to healthy changes in diet and lifestyle, such as elevated growth hormone levels. It seems unlikely that such temporary increase in blood glucose levels, even if lasting as much as 1 year, will lead to permanent damage to cells involved in glucose metabolism like the beta cells in the pancreas.

Another implication is that being diagnosed as pre-diabetic or diabetic is not a death sentence, as some people seem to take such diagnoses at first. Many of the folks in this study who decided to do something about their health following an adverse diagnosis probably followed the traditional advice for the treatment of pre-diabetes and diabetes, which likely made their health worse. (See Jeff O’Connell’s book Sugar Nation for a detailed discussion of what that advice entails.) And still, not everyone progressed from pre-diabetes to full-blow diabetes. Probably fewer refined foods available helped, but this does not fully explain the lack of progression to full-blow diabetes.

It is important to note that this study was conducted in the late 1960s. Biosynthetic insulin was developed in the 1970s using recombinant DNA techniques, and was thus largely unavailable to the participants of this study. Other treatment options were also largely unavailable. Arguably the most influential book on low carbohydrate dieting, by Dr. Atkins, was published in the early 1970s. The targeted use of low carbohydrate dieting for blood glucose control in diabetics was not widely promoted until the 1980s, and even today it is not adopted by mainstream diabetes doctors. To this I should add that, at least anecdotally and from living in an area where diabetes is an epidemic (South Texas), those people who carefully control their blood sugars after type 2 diabetes diagnoses, in many cases with the help of drugs, seem to see marked and sustained health improvements.

Finally, an interesting implication of this study is that glucose intolerance, as defined in the article, would probably not do much to change an outside observer’s perception of a long-living population. That is, if you take a population whose individuals are predisposed to live long lives, with many naturally becoming centenarians, they will likely still be living long lives even if glucose intolerance is rampant. Without carefully conducted glucose tolerance tests, an outside observer may conclude that a damaging diet is actually healthy by still finding many long-living individuals in a population consuming that diet.

Fasting blood glucose of 83 mg/dl and heart disease: Fact and fiction

If you are interested in the connection between blood glucose control and heart disease, you have probably done your homework. This is a scary connection, and sometimes the information on the Internetz make people even more scared. You have probably seen something to this effect mentioned:

Heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl.

In fact, I have seen this many times, including on some very respectable blogs. I suspect it started with one blogger, and then got repeated over and over again by others; sometimes things become “true” through repetition. Frequently the reference cited is a study by Brunner and colleagues, published in Diabetes Care in 2006. I doubt very much the bloggers in question actually read this article. Sometimes a study by Coutinho and colleagues is also cited, but this latter study is actually a meta-analysis.

So I decided to take a look at the Brunner and colleagues study. It covers, among other things, the relationship between cardiovascular disease (they use the acronym CHD for this), and 2-hour blood glucose levels after a 50-g oral glucose tolerance test (OGTT). They tested thousands of men at one point in time, and then followed them for over 30 years, which is really impressive. The graph below shows the relationship between CHD and blood glucose in mmol/l. Here is a calculator to convert the values to mg/dl.

The authors note in the limitations section that: “Fasting glucose was not measured.” So these results have nothing to do with fasting glucose, as we are led to believe when we see this study cited on the web. Also, on the abstract, the authors say that there is “no evidence of nonlinearity”, but in the results section they say that the data provides “evidence of a nonlinear relationship”. The relationship sure looks nonlinear to me. I tried to approximate it manually below.

Note that CHD mortality really goes up more clearly after a glucose level of 5.5 mmol/l (100 mg/dl). But it also varies significantly more widely after that level; the magnitudes of the error bars reflect that. Also, you can see that at around 6.7 mmol/l (121 mg/dl), CHD mortality is on average about the same as at 5.5 mmol/l (100 mg/dl) and 3.5 mmol/l (63 mg/dl). This last level suggests an abnormally high insulin response, bringing blood glucose levels down too much at the 2-hour mark – i.e., reactive hypoglycemia, which the study completely ignores.

These findings are consistent with the somewhat chaotic nature of blood glucose variations in normoglycemic individuals, and also with evidence suggesting that average blood glucose levels go up with age in a J-curve fashion even in long-lived individuals.

We also know that traits vary along a bell curve for any population of individuals. Research results are often reported as averages, but the average individual does not exist. The average individual is an abstraction, and you are not it. Glucose metabolism is a complex trait, which is influenced by many factors. This is why there is so much variation in mortality for different glucose levels, as indicated by the magnitudes of the error bars.

In any event, these findings are clearly inconsistent with the statement that "heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl". The authors even state early in the article that another study based on the same dataset, to which theirs was a follow-up, suggested that:

…. [CHD was associated with levels above] a postload glucose of 5.3 mmol/l [95 mg/dl], but below this level the degree of glycemia was not associated with coronary risk.

Now, exaggerating the facts, to the point of creating fictitious results, may have a positive effect. It may scare people enough that they will actually check their blood glucose levels. Perhaps people will remove certain foods like doughnuts and jelly beans from their diets, or at least reduce their consumption dramatically. However, many people may find themselves with higher fasting blood glucose levels, even after removing those foods from their diets, as their bodies try to adapt to lower circulating insulin levels. Some may see higher levels for doing other things that are likely to improve their health in the long term. Others may see higher levels as they get older.

Many of the complications from diabetes, including heart disease, stem from poor glucose control. But it seems increasingly clear that blood glucose control does not have to be perfect to keep those complications at bay. For most people, blood glucose levels can be maintained within a certain range with the proper diet and lifestyle. You may be looking at a long life if you catch the problem early, even if your blood glucose is not always at 83 mg/dl (4.6 mmol/l). More on this on my next post.

Nonlinearity and the industrial seed oils paradox

Most relationships among variables in nature are nonlinear, frequently taking the form of a J curve. The figure below illustrates this type of curve. In this illustration, the horizontal axis measures the amount of time an individual spends consuming a given dose (high) of a substance daily. The vertical axis measures a certain disease marker – e.g., a marker of systemic inflammation, such as levels of circulating tumor necrosis factor (TNF). This is just one of many measurement schemes that may lead to a J curve.

J-curve relationships and variants such as U-curve and inverted J-curve relationships are ubiquitous, and may occur due to many reasons. For example, a J curve like the one above may be due to the substance being consumed having at least one health-promoting attribute, and at least one health-impairing attribute. The latter has a delayed effect, and ends up overcoming the benefits of the former over time. In this sense, there is no “sweet spot”. People are better off not consuming the substance at all. They should look for other sources of the health-promoting factors.

So what does this have to do with industrial seed oils, like safflower and corn oil?

If you take a look at the research literature on the effects of industrial seed oils, you’ll find something interesting and rather paradoxical. Several studies show benefits, whereas several others hint at serious problems. The problems seem to be generally related to long-term consumption, and to be associated with a significant increase in the ratio of dietary omega-6 to omega-3 fats; this increase appears to lead to systemic inflammation. The benefits seem to be generally related to short-term consumption.

But what leads to the left side of the J curve, the health-promoting effects of industrial seed oils, usually seen in short-term studies?

It is very likely vitamin E, which is considered, apparently correctly, to be one of the most powerful antioxidants in nature. Oxidative stress is strongly associated with systemic inflammation. Seed oils are by far the richest sources of vitamin E around, in the form of both γ-Tocopherol and α-Tocopherol. Other good sources, with much less gram-adjusted omega-6 content, are what we generally refer to as “nuts”. And, there are many, many substances other than vitamin E that have powerful antioxidant properties.

Chris Masterjohn has talked about seed oils and vitamin E before, making a similar point (see here, and here). I acknowledged this contribution by Chris before; for example, in my June 2011 interview with Jimmy Moore. In fact, Chris has gone further and also argued that the vitamin E requirement goes up as body fat omega-6 content increases over time (see comments under this post, in addition to the links provided above).

If this is correct, I would speculate that it may create a vicious feedback-loop cycle, as the increased vitamin E requirement may lead to increased hunger for foods rich in vitamin E. For someone already consuming a diet rich in seed oils, this may drive a subconscious compulsion to add more seed oils to dishes. Not good!