Waist-to-weight ratios in pictures: The John Stone transformation

John Stone is a bodybuilder and founder of a bodybuilding and fitness web site (). There he has provided pictures and stats of his remarkable transformation, which were used to prepare the montage below.



John’s height is reported as 5' 11.5". Below the photos are the months in which they were taken, the waist circumferences in inches, the weights in lbs, and the waist-to-weight ratios (WWRs). Abhi was kind enough to provide a more detailed plot of John Stone’s WWRs ().

Assuming that minimizing one’s WWR is healthy, an idea whose rationale was explained here before (), we could say that John was at his most unhealthy in the photo on the left.

The second photo from the left shows a slightly more healthy state, at a reported 8 percent body fat (his lowest). The two photos on the right represent states in which John’s WWR is at its lowest, namely 0.1544. That is, in these two photos John minimized his WWR; at a reported 14 and 13.8 percent body fat, respectively.

When we look at the WWRs in these photos, it seems that he is only marginally healthier in the second photo from the left than in the leftmost photo. In the two photos on the right, the WWRs are much lower (they are the same), suggesting that he was significantly healthier in those photos.

Interestingly, in both photos on the right John reported to have been at the end of bulking periods. Whenever he entered a cutting period his WWR started going up. This suggests that his ratio of lean body mass to total mass started decreasing just as soon as he started cutting. I suspect the same would happen if he continued gaining weight.

Which of the two photos on the right represents the best state? Assuming that both states are sustainable, over the long run I would argue that the best state is the one where the WWR was minimized with the lowest weight. There whole-day joint stress is lower. This corresponds to the photo at the far right.

By sustainable states I mean states that are not reached through approaches that are unhealthy in the long term; e.g., approaches that place organs under such an abnormal stress that they are damaged over time. This kind of damage is essentially what happens when we become obese – i.e., too fat. One can also become too muscular for his or her own good.

Could we have evolved traits that are detrimental to our survival?

Let us assume that we collected data on the presence or absence of a trait (e.g., propensity toward risky behavior) in a population of individuals, as well as on intermediate effects of the trait, downstream effects on mating and survival success, and ultimately on reproductive success (a.k.a. “fitness”, in evolutionary biology).

The data would have been collected over several generations. Let us also assume that we conducted a multivariate analysis on this data, of the same type as the analyses employing WarpPLS that were discussed here in previous posts (). The results are summarized through the graph below.



Each of the numbers next to the arrows in the graph below represents the strength of a cause-effect relationship. The number .244 linking “a” and “y” means that a one standard deviation variation in “a” causes a .244 standard deviation increase in “y”. It also means that a one standard deviation variation in “a” causes a 24.4 percent increase in “y” considering the average “y” as the baseline.

This type of mathematical view of evolution may look simplistic. This is an illusion. It is very general, and encompasses evolution in all living organisms, including humans. It also applies to theoretical organisms where multiple (e.g., 5, 6 etc.) sexes could exist. It even applies to non-biological organisms, as long as these organisms replicate - e.g., replicating robots.

So the trait measured by “a” has a positive effect on the intermediate effect “y”. This variable, “y” in turn has a negative effect on survival success (“s”), and a strong one at that: -.518. Examples: “a” = propensity toward risky behavior, measured as 0 (low) and 1 (high); and “y” = hunting success, measured in the same way. (That is, “a” and “y” are correlated, but “a”=1 does not always mean “y”=1.) Here the trait “a” has a negative effect on survival via its intermediate effect on “y”. If I calculate the total effect of “a” on “w” via the 9 paths that connect these two variables, I will find that it is .161.

The total effect on reproductive success is positive, which means that the trait will tend to spread in the population. In other words, the trait will evolve in the population, even though it has a negative effect on survival. This type of trait is what has been referred to as a “costly” trait ().

Say what? Do you mean to say that we have evolved traits that are unhealthy for us? Yes, I mean exactly that. Is this a “death to paleo” post? No, it is not. I discussed this topic here before, several years ago (). But the existence of costly traits is one of the main reasons why I don’t think that mimicking our evolutionary past is necessarily healthy. For example, many of our male ancestors were warriors, and they died early because of that.

What type of trait will present this evolutionary pattern – i.e., be a costly trait? One answer is: a trait that is found to be attractive by members of the other sex, and that is not very healthy. For example, a behavior that is perceived as “sexy”, but that is also associated with increased mortality. This would likely be a behavior prominently displayed by males, since in most species, including humans, sexual selection pressure is much more strongly applied by females than by males.

Examples would be aggressiveness and propensity toward risky behavior, especially in high-stress situations such as hunting and intergroup conflict (e.g., a war between two tribes) where being aggressive is likely to benefit an individual’s group. In warrior societies, both aggressiveness and propensity toward risky behavior are associated with higher social status and a greater ability to procure mates. These traits are usually seen as male traits in these societies.

Here is something interesting. Judging from our knowledge of various warrior societies, including American plains Indians societies, the main currency of warrior societies were counts of risky acts, not battle effectiveness. Slapping a fierce enemy warrior on the face and living to tell the story would be more valuable, in terms of “counting coup”, than killing a few inexperienced enemy warriors in an ambush.

Greater propensity toward risky behavior among men is widespread and well documented, and is very likely the result of evolutionary forces, operating on costly traits. Genetic traits evolved primarily by pressure on one sex are often present in the other (e.g., men have nipples). There are different grades of risky behavior today. At the high end of the scale would be things that can kill suddenly like race car driving and free solo climbing (, ). (If you'd like to know the source of the awesome background song of the second video linked, here it is: Radical Face's "Welcome Home".)

One interesting link between risky behavior and diet refers to the consumption of omega-6 and omega-3 fats. Risky behavior may be connected with aggressive behavior, which may in turn be encouraged by greater consumption of foods rich in omega-6 fats and avoidance of foods rich in omega-3 fats (, ). This may be behind our apparent preference for foods rich in omega-6 fats, even though tipping the balance toward more foods rich in omega-3 fats would be beneficial for survival. We would be "calmer" though - not a high priority among most men, particularly young men.

This evolved preference may also be behind the appeal of industrial foods that are very rich in omega-6 fats. These foods seem to be particularly bad for us in the long term. But when the sources of omega-6 fats are unprocessed foods, the negative effects seem to become "invisible" to statistical tests.

We share an ancestor who probably lived no more than 640 years ago

This post is a revised version of a previous post. The original post has been or will be deleted, with the comments preserved. Typically this is done with posts that attract many visits at the time they are published, and whose topics become particularly relevant or need to be re-addressed at a later date.

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We all evolved from one single-celled organism that lived billions of years ago. I don’t see why this is so hard for some people to believe, given that all of us also developed from a single fertilized cell in just 9 months.

However, our most recent common ancestor is not that first single-celled organism, nor is it the first Homo sapiens, or even the first Cro-Magnon.

The majority of the people who read this blog probably share a common ancestor who lived no more than 640 years ago. Genealogical records often reveal interesting connections - the figure below has been cropped from a larger one from Pinterest.

You and I, whoever you are, have each two parents. Each of our parents have (or had) two parents, who themselves had two parents. And so on.

If we keep going back in time, and assume that you and I do not share a common ancestor, there will be a point where the theoretical world population would have to be impossibly large.

Assuming a new generation coming up every 20 years, and going backwards in time, we get a theoretical population chart like the one below. The theoretical population grows in an exponential, or geometric, fashion.

As we move back in time the bars go up in size. Beyond a certain point their sizes go up so fast that you have to segment the chart. Otherwise the bars on the left side of the chart disappear in comparison to the ones on the right side (as several did on the chart above). Below is the section of the chart going back to the year 1371.

The year 1371 is a mere 640 years ago. And what is the theoretical population in that year if we assume that you and I have no common ancestors? The answer is: more than 8.5 billion people. We know that is not true.

Admittedly this is a somewhat simplistic view of this phenomenon, used here primarily to make a point. For example, it is possible that a population of humans became isolated 15 thousand years ago, remained isolated to the present day, and that one of their descendants just happened to be around reading this blog today.

Perhaps the most widely cited article discussing this idea is this one by Joseph T. Chang, published in the journal Advances in Applied Probability. For a more accessible introduction to the idea, see this article by Joe Kissell.

Estimates vary based on the portion of the population considered. There are also assumptions that have to be made based on migration and mating patterns, as well as the time for each generation to emerge and the stability of that number over time.

Still, most people alive today share a common ancestor who lived a lot more recently than they think. In most cases that common ancestor probably lived less than 640 years ago.

And who was that common ancestor? That person was probably a man who, due to a high perceived social status, had many consorts, who gave birth to many children. Someone like Genghis Khan.

Could grain-fed beef liver be particularly nutritious?

There is a pervasive belief today that grain-fed beef is unhealthy, a belief that I addressed before in this blog () and that I think is exaggerated. This general belief seems to also apply to a related meat, one that is widely acknowledged as a major micronutrient “powerhouse”, namely grain-fed beef liver.

Regarding grain-fed beef liver, the idea is that cattle that are grain-fed tend to develop a mild form of fatty liver disease. This I am inclined to agree with.

However, I am not convinced that this is such a bad thing for those who eat grain-fed beef liver.

In most animals, including Homo sapiens, fatty liver disease seems to be associated with extra load being put on the liver. Possible reasons for this are accelerated growth, abnormally high levels of body fat, and ingestion of toxins beyond a certain hormetic threshold (e.g., alcohol).

In these cases, what would one expect to see as a body response? The extra load is associated with high oxidative stress and rate of metabolic work. In response, the body should shuttle more antioxidants and metabolism catalysts to the organ being overloaded. Fat-soluble vitamins can act as antioxidants and catalysts in various metabolic processes, among other important functions. They require fat to be stored, and can then be released over time, which is a major advantage over water-soluble vitamins; fat-soluble vitamins are longer-acting.

So you would expect an overloaded liver to have more fat in it, and also a greater concentration of fat-soluble vitamins. This would include vitamin A, which would give the liver an unnatural color, toward the orange-yellow range of the spectrum.

Grain-fed beef liver, like the muscle meat of grain-fed cattle, tends to have more fat than that of grass-fed animals. One function of this extra fat could be to store fat-soluble vitamins. This extra fat appears to have a higher omega-6 fat content as well. Still, beef liver is a fairly lean meat; with about 5 g of fat per 100 g of weight, and only 20 mg or so of omega-6 fat. Clearly consumption of beef liver in moderation is unlikely to lead to a significant increase in omega-6 fat content in one’s diet (). By consumption in moderation I mean approximately once a week.

The photo below, from Wikipedia, is of a dish prepared with foie gras. That is essentially the liver of a duck or goose that has been fattened through force-feeding, until the animal develops fatty liver disease. This “diseased” liver is particularly rich in fat-soluble vitamins; e.g., it is the best known source of the all-important vitamin K2.



Could the same happen, although to a lesser extent, with grain-fed beef liver? I don’t think it is unreasonable to speculate that it could.

How can carrying some extra body fat be healthy?

Most of the empirical investigations into the association between body mass index (BMI) and mortality suggest that the lowest-mortality BMI is approximately on the border between the normal and overweight ranges. Or, as Peter put it (): "Getting fat is good."

As much as one may be tempted to explain this based only on the relative contribution of lean body mass to total weight, the evidence suggests that both body fat and lean body mass contribute to this phenomenon. In fact, the evidence suggests that carrying some extra body fat may be healthy for many.

Yet, the scientific evidence strongly suggests that body fat accumulation beyond a certain point is unhealthy. There seems to be a sweet spot of body fat percentage, and that sweet spot may vary a lot across different individuals.

One interesting aspect of most empirical investigations of the association between BMI and mortality is that the participants live in urban or semi-urban societies. When you look at hunter-gatherer societies, the picture seems to be a bit different. The graph below shows the distribution of BMIs among males in Kitava and Sweden, from a study by Lindeberg and colleagues ().



In Sweden, a lowest mortality BMI of 26 would correspond to a point on the x axis that would rise up approximately to the middle of the distribution of data points from Sweden in the graph. It is reasonable to assume that this would also happen in Kitava, in which case the lowest mortality BMI would be around 20.

One of the key differences between urbanites and hunter-gatherers is the greater energy expenditure among the latter; hunter-gatherers generally move more. This provides a clue as to why some extra body fat may be healthy among urbanites. Hunter-gatherers spend more energy, so they have to consume more “natural” food, and thus more nutrients, to maintain their lean body mass.

A person’s energy expenditure is strongly dependent on a few variables, including body weight and physical activity. Let us assume that a hunter-gatherer, due to a reasonably high level of physical activity, maintains a BMI of 20 while consuming 3,000 kilocalories (a.k.a. calories) per day. An urbanite with the same height, but a lower level of physical activity, may need a higher body weight, and thus a higher BMI, to consume 3,000 calories per day at maintenance.

And why would someone want to consume 3,000 calories per day? Why not 1,500? The reason is nutrient intake, particularly micronutrient intake – intake of vitamins and minerals that are used by the body in various processes. Unfortunately it seems that micronutrient supplementation (e.g., a multivitamin pill) is largely ineffective except in cases of pathological deficiency.

Urbanites may need to carry a bit of extra body fat to be able to have an appropriate intake of micronutrients to maintain their lean body structures in a healthy state. Obviously the type of food eaten matters a lot. A high nutrient-to-calorie ratio is generally desirable. However, we cannot forget that we also need to eat fat, in part because without it we cannot properly absorb the all-important fat-soluble vitamins. And dietary fat is the most calorie-dense nutrient of all.

Why not putting on extra muscle instead of carrying the extra fat? For one, that is not easy when you are a sedentary urbanite. Particularly after a certain age, if you try too hard you end up getting injured. But there is another interesting angle to consider. Humans, like many other animals, have genetic “protections” against high muscularity, such as the protein myostatin. Myostatin is produced mostly in muscle cells; it acts on muscle, by inhibiting its growth.

Say what? Why would evolution favor something like myostatin? Big, muscular humans could be at the top of the food chain by physical strength alone; they could kill a lion with their bare hands. Well, it is possible. (Many men like to think of themselves as warriors, probably because most of them are not.) But evolution favors what works best given the ecological niches available. In our case, it favored bigger and more plastic brains to occupy what Steve Pinker called a “cognitive niche”.

Even though fat mass is not inert, secreting a number of hormones into the bloodstream, the micronutrient “need” of fat mass is likely much lower than the micronutrient need of non-fat mass. That is, a kilogram of lean mass likely puts a higher demand on micronutrients than a kilogram of fat mass. This should be particularly the case for organs, such as the liver, but also applies to muscle tissue.

While gaining muscle mass through moderate exercise is extremely healthy, bulking up beyond one’s natural limitations may actually backfire. It could increase the demand for micronutrients above what a person can actually consume and absorb through a healthy nutritious diet. Some extra fat mass allows for a higher level of micronutrient intake at weight maintenance, with a lower demand for micronutrients than the same amount of extra lean mass.

Some people are naturally more muscular. Their frame and underlying organ-based capabilities probably support that. It is often visibly noticeable when they go beyond their organ-based capabilities. A common trait among many professional bodybuilders, who usually go beyond the genetic gifts that they naturally have, is an abnormal swelling of internal organs.

What complicates this discussion is that all of this seems to vary from individual to individual. People have to find their sweet spots, and doing that may not be the simplest of tasks. For example, even measuring body fat percentage with some precision is difficult and costly. Also, certain types of fat are less desirable than others – visceral versus subcutaneous body fat. It is not easy differentiating one from the other ().

How do you find your sweet spot in terms of body fat percentage? One of the most promising approaches is to find the point at which your waist-to-weight ratio is minimized ().

An illustration of the waist-to-weight ratio theory: The fit2fat2fit experiment

In my previous blog post, I argued that one’s optimal weight may be the one that minimizes one’s waist-to-weight ratio. I built this argument based on the fact that body fat percentage is associated with lean body mass (and also weight) in a nonlinear way.

The fit2fat2fit experiment (), provides what seems to be an interestingly way to put this optimal waist-to-weight ratio theory to test. This is due to a fortuitous event, as I explain in this post.

In this experiment, Drew Manning, a personal trainer, decided to undergo a transformation where he went from what he argued was his fittest level, all the way to obese, and then back to fit again. He said that he wanted to do that so that he could better understand his clients’ struggles. This may be true, but it looks like he planned very well his experiment from a marketing perspective.

His fittest level was at the start, with a weight of 193 lbs, at a height of 6 ft 2 in. That was his fittest level according to his own opinion. At that point, he had a waist of 34.5 in, and looked indeed very fit (). At his fattest level, he reached the weight of 264.8 pounds, with a 47.5 waist.

As he moved back to fit, one interesting thing happened. Toward the end of this journey back to fit, he moved past the level that he felt was his optimal. He dropped down to 190.1 lbs, and a 34 in waist; which he perceived as too skinny. He talks about this in a video ().

As a self-defined “fanatic” personal trainer, I figured that he knew when he had gone too far. That is, he is probably as qualified as one can get to identify the point at which he moved past his optimal. So I thought that this would be an interesting way of putting my optimal waist-to-weight ratio theory to the test.

Below is a bar chart showing variations in waist-to-weight ratio against weight for Drew Manning during his fit2fat2fit experiment. I included only three data points in this chart because I would have to view all of his video clips to get all of the data points.



As you can see, at the point at which he felt he was too thin, his waist-to-weight ratio clearly started going up from what seems to have been its optimal at 34.5 in / 193 lbs. This is exactly what you would expect based on my optimal waist-to-weight ratio theory. You probably can’t tell that something was not right at that point, because he looked very fit.

But apparently he felt that something was not entirely right. And that is consistent with the idea that he had passed his optimal waist-to-weight ratio, and became too lean for his own good. Note that his waist decreased, and probably could go down even further, even though that was no longer optimal.

What is your optimal weight? Maybe it is the one that minimizes your waist-to-weight ratio

There is a significant amount of empirical evidence suggesting that, for a given individual and under normal circumstances, the optimal weight is the one that maximizes the ratio below, where: L = lean body mass, and T = total mass.

L / T

L is difficult and often costly to measure. T can be measured easily, as one’s total weight.

Through some simple algebraic manipulations, you can see below that the ratio above can be rewritten in terms of one’s body fat mass (F).

L / T = (T – F) / T = 1 – F / T

Therefore, in order to maximize L / T, one should maximize 1 – F / T. This essentially means that one should minimize the second term, or the ratio below, which is one’s body fat mass (F) divided by one’s weight (T).

F / T

So, you may say, all I have to do is to minimize my body fat percentage. The problem with this is that body fat percentage is very difficult to measure with precision, and, perhaps more importantly, body fat percentage is associated with lean body mass (and also weight) in a nonlinear way.

In English, it becomes increasingly difficult to retain lean body mass as one's body fat percentage goes down. Mathematically, body fat percentage (F / T) is a nonlinear function of T, where this function has the shape of a J curve.

This is what complicates matters, making the issue somewhat counterintuitive. Six-pack abs may look good, but many people would have to sacrifice too much lean body mass for their own good to get there. Genetics definitely plays a role here, as well as other factors such as age.

Keep in mind that this (i.e., F / T) is a ratio, not an absolute measure. Given this, and to facilitate measurement, we can replace F with a variable that is highly correlated with it, and that captures one or more important dimensions particularly well. This new variable would be a proxy for F. One the most widely used proxies in this type of context is waist circumference. We’ll refer to it as W.

W may well be a very good proxy, because it is a measure that is particularly sensitive to visceral body fat mass, an important dimension of body fat mass. W likely captures variations in visceral body fat mass at the levels where this type of body fat accumulation seems to cause health problems.

Therefore, the ratio that most of us would probably want to minimize is the following, where W is one’s waist circumference, and T is one’s weight.

W / T = waist / weight

Based on the experience of HCE () users, variations in this ratio are likely to be small and require 4-decimals or more to be captured. If you want to avoid having so many decimals, you can multiply the ratio by 1000. This will have no effect on the use of the ratio to find your optimal weight; it is analogous to multiplying a ratio by 100 to express it as a percentage.

Also based on the experience of HCE users, there are fluctuations that make the ratio look like it is changing direction when it is not actually doing that. Many of these fluctuations may be due to measurement error.

If you are obese, as you lose weight through dieting, the waist / weight ratio should go down, because you will be losing more body fat mass than lean body mass, in proportion to your total body mass.

It would arguably be wise to stop losing weight when the waist / weight ratio starts going up, because at that point you will be losing more lean body mass than body fat mass, in proportion to your total body mass.

One’s lowest waist / weight ratio at a given point in time should vary depending on a number of factors, including: diet, exercise, general lifestyle, and age. This lowest ratio will also be dependent on one’s height and genetic makeup.

Mathematically, this lowest ratio is the ratio at which d(W / T) / dT = 0 and d(d(W / T) / dT) / dT > 0. That is, the first derivative of W / T with respect to T equals zero, and the second derivative is greater than zero.

The lowest waist / weight ratio is unique to each individual, and can go up and down over time (e.g., resistance exercise will push it down). Here I am talking about one's lowest waist / weight ratio at a given point in time, not one's waist / weight ratio at a given point in time.

This optimal waist / weight ratio theory is one of the most compatible with evidence regarding the lowest mortality body mass index (, ). Nevertheless, it is another ratio that gets a lot of attention in the health-related literature. I am talking about the waist / hip ratio (). In this literature, waist circumference is often used alone, not as part of a ratio.

Dr. Jekyll dieted and became Mr. Hyde

One of the most fascinating topics for an independent health researcher is the dichotomy between short- and long-term responses in successful dieters. In the short term, dieters that manage to lose a significant amount of fat mass, tend to feel quite well. Many report that their energy levels go through the roof.

A significant loss of fat mass could be considered one of 30 lbs, or 13.6 kg. This is the threshold for weight loss used in the National Weight Control Registry. Ideally you want to lose body fat, not lean mass, both of which contribute to weight loss.

So, in the short term, significant body fat loss feels pretty good for the dieters. In the long term, however, successful dieters tend to experience the symptoms of chronic stress. This should be no surprise because some of the same hormones that induce a sense of elation and high energy are the ones associated with chronic stress. These are generally referred to as “stress hormones”, of which the most prominent seem to be cortisol, epinephrine (adrenaline), and norepinephrine (noradrenaline).

Stress hormones display acute elevations during intense exercise as well ().

This is all consistent with evolution, and with the idea that our hominid ancestors would not go hungry for too long, at least not on a regular basis. High energy levels, combined with hunger, would make them succeed at hunting-gathering activities, leading to a period of feast before a certain threshold of sustained caloric restriction (with or without full fasting) would be reached. This would translate into a regular and cyclical hunger-feast process, with certain caloric costs having to be met for successful hunting-gathering.

After a certain period of time under sustained caloric restriction, it would probably be adaptive among our ancestors to experience significant mental and physical discomfort. That would compel our hominid ancestors to more urgently engaged in hunting-gathering activities.

And here is a big difference between those ancestors and modern urbanites: our ancestors would actually be working towards getting food for a feast, not restraining themselves from eating what they have easily available at home or from a grocery store nearby. There are major psychological differences here. Dieting, in the sense of not eating when food is easily available, is as unnatural as obesity, if not more.

So what are some of the mechanisms by which the body dials up stress, leading to the resulting mental and physical discomfort? Here is one that seems to play a key role: hypoglycemia.

Of the different types of hypoglycemia, there is one that is quite interesting in this type of context, because it refers to hypoglycemia in response to intake of any food item that raises insulin levels; that is, food that contains protein and/or carbohydrates. More specifically, we are referring here to reactive hypoglycemia, of the same general type as that experienced by those on their way to type II diabetes.

But reactive hypoglycemia in successful dieters is often different from that of prediabetics, as it is caused by something that would sound surprising to many: successful dieters appear to become too insulin sensitive for their own good!

There is ongoing debate as to what is considered a blood glucose level that is low enough to characterize hypoglycemia. Several factors influence that, including measurement method and age. One important factor related to measurement method is this: commercial fingerstick glucose meters tend to grossly underestimate low glucose levels (e.g., 50 mg/dl shows as 30 mg/dl).

Having said that, glucose levels below 60 mg/dl are generally considered low.

Luyckx and Lefebvre selected 47 cases of reactive hypoglycemia for a study, from a total of 663 standard four-hour oral glucose tolerance tests (OGTT). They classified these 47 cases as follows, with the number of cases in each class within parentheses: obesity (11), obesity with chemical diabetes (9), postgastrectomy syndrome (3), chemical diabetes without obesity (1), renal glycosuria (7), and isolated reactive hypoglycemia (16).

Postgastrectomy is the period following a gastrectomy, which is removal of part of one’s stomach. The modern term for this stomach amputation procedure is “bariatric surgery”; admittedly a broader term, which many people say they would do as if they were referring to a walk in the park!

In the cases of isolated reactive hypoglycemia, the individuals had normal weight, normal glucose tolerance, and no glycosuria (excretion of glucose in the urine). As you can see in the paragraph above, this, isolated reactive hypoglycemia, was the category with the largest number of individuals. The figure below illustrates what happened in these cases.



The cases in question are represented in the left part of the graph with dashed lines (the full lines are for normal controls). There a reasonably normal insulin response, lower in fact in terms of area under the curve (AUC) than for the controls, leads to an abnormal reduction in blood glucose levels. They are 9 out of 16, the majority of the isolated reactive hypoglycemia cases. In those 9 individuals, insulin became “more potent”, so to speak.

Reactive hypoglycemia is frequently associated with obesity, in which case it is also associated with hyperinsulinemia, and caused by an exaggerated insulin response. About 40 percent of the reactive hypoglycemia cases in the study were classified as happening in obese individuals.

This study suggests that, if you are not obese, and you are diagnosed with reactive hypoglycemia following an OGTT, chances are that the diagnosis is due to high insulin sensitivity – as opposed to low insulin sensitivity, coupled with hyperinsulinemia. A follow-up test should focus on insulin levels, to see if they are elevated; i.e., to try to detect hyperinsulinemia.

I have been blogging here long enough to hear from people who have gone the full fat2fit2fat cycle, sometimes more than once. They start dieting, go from obese to lean, feel good at first but then miserable, drop the diet, become obese or almost obese again, then start dieting again …

Quite a few are folks who do things like ditching industrial foods, regularly eating organ meats, and doing resistance exercise. How can you go wrong doing all of these, generally healthy, things? Well, they all increase your insulin sensitivity. If you don’t build in plateaus to slow down your progress, you may not give your body enough time to adapt.

You may become too lean, too fast, for your own good. The more successful the diet, the bigger is the risk. No wonder the paleo diet is being targeted lately as a “bad” diet. How can you go wrong on a diet of whole foods; “real” whole foods, not “whole wheat”? Well, here is how you can go wrong. The diet, if not managed properly, may be too successful for your own good; too much of a good thing can be a problem, you know!

See the graph below, from a previous post on a related topic (). I intend to discuss a method to identify the point at which weight loss should stop, in a future post. This method builds on the calculation of a simple index, which is unique to each individual. Let me just say now that I suspect that, with exceptions, frequently people are hurting their health by trying to have six pack abs.



But what does all this have to do with stress hormones? The connection is this. Hypoglycemia is only “felt”, as something unpleasant, due to the body’s frequent acute stress hormone response to it. Elevated levels of stress hormones also increase blood glucose levels, countering hypoglycemia. Our body’s priority is preventing hypoglycemia, not hyperglycemia ().

And here is an interesting pattern, based on anecdotal evidence from HCE () users. It seems that folks who have abnormally high insulin sensitivity, also have medium-to-high HbA1c (a measure of glycation) and fasting blood glucose levels. By medium-to-high HbA1c levels I mean 5.7 to even as high as 6.2.

Since cortisol is elevated, one would expect higher fasting blood glucose levels – the “dawn phenomenon”. But higher HbA1c, how? I am not sure, but I believe that HbA1c will be found in the future to be something a bit more complicated than what it is believed to be: a measure of average blood glucose over a period of time. I am not talking here about cases of anemia.

One indication of this complicated nature of the HbA1c is the fact that blood glucose levels in birds are high yet HbA1c levels are low, and birds live much longer than mammals of comparable size (). Some birds have extremely high glucose levels, even carnivorous birds who consume no or very small amounts of carbohydrate (e.g. hawks), with fairly low HbA1c levels.

The title of this post is inspired in the classic short novel “Strange Case of Dr. Jekyll and Mr. Hyde” by the Scottish author Robert Louis Stevenson; who also authored another famous novel, “Treasure Island”. In “Dr. Jekyll and Mr. Hyde”, gentle Dr. Jekyll becomes nasty Mr. Hyde (see poster below, from Wikipedia).



Mr. Hyde had a bad temper, impaired judgment, and was prone to criminal behavior. Hypoglycemia has long been associated with bad temper, impaired judgment, and criminal behavior (, ).

Sudden cholesterol increase? It may be psychological

There are many published studies with evidence that cholesterol levels are positively associated with heart disease. In multivariate analyses the effects are usually small, but they are still there. On the other hand, there is also plenty of evidence that cholesterol is beneficial in terms of health. Here of course I am referring to the health of humans, not of the many parasites that benefit from disease.

For example, there is evidence () that cholesterol levels are negatively associated with mortality (i.e., higher cholesterol leading to lower mortality), and are positively associated with vitamin D production from skin exposure to sunlight ().

Most of the debris accumulated in atheromas are made up of macrophages, which are specialized cells that “eat” cell debris (ironically) and some pathogens. The drug market is still hot for cholesterol-lowering drugs, often presented in TV and Internet ads as effective tools to prevent formation of atheromas.

But what about macrophages? What about calcium, another big component of atheromas? If drugs were to target macrophages for atheroma prevention, drug users may experience major muscle wasting and problems with adaptive immunity, as macrophages play a key role in muscle repair and antibody formation. If drugs were to target calcium, users may experience osteoporosis.

So cholesterol is the target, because there is a “link” between cholesterol and atheroma formation. There is also a link between the number of house fires in a city and the amount of firefighting activity in the city, but we don’t see mayors announcing initiatives to reduce the number of firefighters in their cities to prevent house fires.

When we talk about variations in cholesterol, we usually mean variations in cholesterol carried by LDL particles. That is because LDL cholesterol seems to be very “sensitive” to a number of factors, including diet and disease, presenting quite a lot of sudden variation in response to changes in those factors.

LDL particles seem to be intimately involved with disease, but do not be so quick to conclude that they cause disease. Something so widespread and with so many functions in the human body could not be primarily an agent of disease that needs to be countered with statins. That makes no sense.

Looking at the totally of evidence linking cholesterol with health, it seems that cholesterol is extremely important for the human body, particularly when it is under attack. So the increases in LDL cholesterol associated with various diseases, notably heart disease, may not be because cholesterol is causing disease, but rather because cholesterol is being used to cope with disease.

LDL particles, and their content (including cholesterol), may be used by the body to cope with conditions that themselves cause heart disease, and end up being blamed in the process. The lipid hypothesis may be a classic case of reverse causation. A case in point is that of cholesterol responses to stress, particularly mental stress.

Grundy and Griffin () studied the effects of academic final examinations on serum cholesterol levels in 2 groups of medical students in the winter and spring semesters (see table below). During control periods, average cholesterol levels in the two groups were approximately 213 and 216 mg/dl. During the final examination periods, average cholesterol levels were 248 and 240 mg/dl. These measures were for winter and spring, respectively.



One could say that even the bigger increase from 213 to 248 is not that impressive in percentage terms, approximately 16 percent. However, HDL cholesterol does not go up significantly response to sustained (e.g., multi-day) stress, it actually goes down, so the increases reported can be safely assumed to be chiefly due to LDL cholesterol. For most people, LDL particles are the main carriers of cholesterol in the human body. Thus, in percentage terms, the increases in LDL cholesterol are about twice those reported for total cholesterol.

A 32-percent increase (16 x 2) in LDL cholesterol would not go unnoticed today. If one’s LDL cholesterol were to be normally 140 mg/dl, it would jump to 185 mg/dl with a 32-percent increase. It looks like the standard deviations were more than 30 in the study. (This is based on the standard errors reported, and assuming that the standard deviation equals the standard error multiplied by the square root of the sample size.) So we can guess that several people might go from 140 to 215 or more (this is LDL cholesterol, in mg/dl) in response to the stress from exams.

And the effects above were observed with young medical students, in response to the stress from exams. What about a middle-aged man or woman trying to cope with chronic mental stress for months or years, due to losing his or her job, while still having to provide for a family? Or someone who has just been promoted, and finds himself or herself overwhelmed with the new responsibilities?

Keep in mind that sustained dieting can be a major stressor for some people, particular when one gets to that point in the dieting process where he or she gets regularly into negative nitrogen balance (muscle loss). So you may have heard from people saying that, after months or years of successful dieting, their cholesterol levels are inexplicably going up. Well, this post provides one of many possible explanations for that.

The finding that cholesterol goes up with stress has been replicated many times. It has been known for a long time, with studies dating back to the 1950s. Wertlake and colleagues () observed an increase in average cholesterol levels from 214 to 238 (in mg/dl); also among medical students, in response to the mental and emotional stress of an examination week. A similar study to the one above.

Those enamored with the idea of standing up the whole day, thinking that this will make them healthy, should know that performing cognitively demanding tasks while standing up is a known stressor. It is often used in research where stress must be induced to create an experimental condition. Muldoon and colleagues () found that people performing a mental task while standing experienced an increase in serum cholesterol of approximately 22 points (in mg/dl).

What we are not adapted for is sitting down for long hours in very comfortable furniture (, ). But our anatomy clearly suggests adaptations for sitting down, particularly when engaging in activities that resemble tool-making, a hallmark of the human species. Among modern hunter-gatherers, tool-making is part of daily life, and typically it is much easier to accomplish sitting down than standing up.

Modern urbanites could be seen as engaging in activities that resemble tool-making when they produce things at work for internal or external customers, whether those things are tangible or intangible.

So, stress is associated with cholesterol levels, and particularly with LDL cholesterol levels. Diehard lipid hypothesis proponents may argue that this is how stress is associated with heart disease: stress increases cholesterol which increases heart disease. Others may argue that one of the reasons why LDL cholesterol levels are sometimes found to be associated with heart disease-related conditions, such as chronic stress, and other health conditions is that the body is using LDL cholesterol to cope with those conditions.

Specifically regarding mental stress, a third argument has been put forth by Patterson and colleagues, who claimed that stress-mediated variations in blood lipid concentrations are a secondary result of decreased plasma volume. The cause, in their interpretation, was unspecified – “vascular fluid shifts”. However, when you look at the numbers reported in their study, you still see a marked increase in LDL cholesterol, even controlling for plasma volume. And this is all in response to “10 minutes of mental arithmetic with harassment” ().

I tend to think that the view that cholesterol increases with stress because cholesterol is used by the body to cope with stress is the closest to the truth. Among other things, stress increases the body’s overall protein demand, and cholesterol is used in the synthesis of many proteins. This includes proteins used for signaling, also known as hormones.

Cholesterol also seems to be a diet marker, tending to go up in high fat diets. This is easier to explain. High fat diets increase the demand for bile production, as bile is used in the digestion of fat. Most of the cholesterol produced by the human body is used to make bile.

Trip to South Korea: Hidden reasons for the leanness of its people

In September last year (2012) I went to South Korea to speak about nonlinear data analysis with WarpPLS (), initially for business and engineering faculty and students at Korea University in Seoul, and then as a keynote speaker at the HumanCom 2012 Conference () in Gwangju. Since Seoul is in the north part of the country, and Gwangju in the south, I had the opportunity to see quite a lot of the land and the people in this beautiful country.


(Korea University’s main entrance, Anam campus)


(In front of Korea University’s main Business School building)

Korea University is one of the most prestigious universities in South Korea. In the fields of business and engineering, it is arguably the most prestigious. It also has a solid international reputation, attracting a large number of highly qualified foreign students.

I wanted to take this opportunity and try to understand why obesity prevalence is so low in South Korea, which is a common characteristic among Southeast Asian countries, even though the caloric intake of South Koreans seems to be relatively high. Foods that are rich in carbohydrates, such as rice, are also high-calorie foods. At 4 calories per gram, carbohydrates are not as calorie-dense as fats (9 calories per gram), but they sure add up and can make one obese.

Based on my observations, explanations for the leanness that are too obvious or that focus on a particular dietary item (e.g., kimchi, green tea etc.) tend to miss the point.

Let us take for example a typical South Korean meal, like the one depicted in the photos below, which we had at a restaurant in Seoul. If you are a foreigner, this type of meal would be difficult to have without a local accompanying you, because it is not easy to make yourself understood in a traditional restaurant in South Korea speaking anything other than Korean.


(Main items of a traditional South Korean meal)


(You cook your own meal)

The meal started with thin-sliced meat (with some fat, but not much) and vegetables, with the obligatory side dishes, notably kimchi (). This part of the meal was low in calories and high in nutrients. Then we had two high-calorie low-nutrient items: noodles and rice. The rice was used in the end to soak up the broth left in the pot, so it ended adding to the nutrition value of the meal.

Because we started the meal with the low-calorie high-nutrient items, the meat and vegetables, our consumption of noodles and rice was not as high as if we had started the meal with those items. In a meal like this, a good chunk of calories would come from the carbohydrate-rich items. Still, it seems to me that we ingested plenty of calories, enough to make one fat over the long run, eating these types of meals regularly.

A side note. As I said here before, the caloric value of protein is less than the commonly listed 4 calories per gram, essentially because protein is a multi-purpose macronutrient.

In our meal, the way in which at least one of the carbohydrate-rich items was prepared possibly decreased its digestible carbohydrate content, and thus its calorie content, in a significant way. I am referring to the rice, which had been boiled, cooled and stored, way before it was re-heated and served. This likely turned some of its starch content into resistant starch (). Resistant starch is essentially treated by our digestive system as fiber.

Another factor to consider is the reduction in the glycemic load (not to be confused with glycemic index) of the rice. As I noted, the rice was used to soak up the broth from the pot. This soaking up process significantly reduces the rice’s glycemic load, because of a unique property of rice. It has an amazing capacity of absorbing liquid and swelling in the process.

This was one of several traditional Korean meals I had, and all of them followed a similar pattern in terms of the order in which the food items were consumed, and the way in which the carbohydrate-rich items were prepared. The order in which you eat foods affects your calorie intake because if you eat high nutrient-to-calorie ratio foods before, and leave the low nutrient-to-calorie ones for later, my experience is that you will eat less of the latter.

Another possible hidden reason for the low rate of obesity in South Korea is what seems to be a cultural resistance to industrialized foods, particularly among older generations; a sort of protective cultural inertia, if you will. Those foods are slowly being adopted – my visit left me with that impression – by not as quickly as in other countries. And there is overwhelming evidence that consumption of highly industrialized foods, especially those rich in refined carbohydrates and sugars, is a major cause obesity and a host of other problems.

Cultural resistance to, or cultural inertia against the adoption of, highly industrialized foods among pregnant mothers limits one’s exposure to those foods at a particularly critical time in one’s life – the 9-month gestation period in the mother’s womb. This could have a major impact on a person’s propensity to become obese or have other metabolic derangements later on in life. Some refer to this phenomenon as a classic example of modern epigenetics, whereby acquired traits appear to induce innate traits across generations.

Another reason I was excited about this trip to South Korea was my interest in table tennis. I wanted to know more about their table tennis “culture”, and how it was influenced by their general culture. China dominates modern table tennis, with such prodigies as Ma Lin, Ma Long, Wang Hao, Wang Liqin, and Zhang Jike. South Korea is not far behind; two of my all-time favorite South Korean players are Kim Taek-Soo and former Olympic champion Ryu Seung-Min.

Another side note. The best table tennis player of all time is arguably Jan-Ove Waldner (), from Sweden. I talked about him in my book on compensatory adaptation (). Waldner has been one of the few players outside China to be able to consistently beat the best Chinese players at times when they were at the top of the games, including Ma Lin ().

But, as I soon learned, as far as sports are concerned, it is not table tennis that most South Koreans are interested in these days. It is soccer.

A nice surprise during this trip was a tour in Gwangju in which we visited a studio that converted standard movies to stereoscopic three-dimensional ones (photo below). These folks were getting a lot of business, particularly from the USA, in a market that is very competitive.


(A standard-to-3D movie conversion studio in Gwangju)

Let’s get back to the health angle of the post. So there you have it, two possible “hidden” reasons for the low prevalence of obesity in South Korea, and maybe in other Southeast Asian countries. One is the way in which foods are prepared and consumed, and the other is cultural inertia. These are not very widely discussed, but future research may change that.

Cabeza de Vaca: Supernaturalism and psychosomatic disorders

Andrew Weil, a major proponent of the idea of self-healing (), has repeatedly acknowledged the influence of osteopaths such as Robert C. Fulford () on him, particularly regarding his philosophy of health management. Self-healing is not about completely autonomous healing; it is about healing by stimulation of the body's self-repair processes, which in some cases can be achieved by simply reducing stress.

Interestingly, there are many reported cases of osteopaths curing people from various diseases by doing things like cranial manipulation and other forms of touching. We also have much evidence of health improvement through prescription of drugs that don’t appear to have any health benefits, which is arguably a similar phenomenon.

The number of such reported cases highlights what seems to be a reality about diseases in general, which is that they often have a psychosomatic basis. Their “cure” involves making the person affected believe that someone can cure him, a healer, with or without drugs. The healer then cures the person essentially by her power of suggestion.

Paleoanthropological evidence suggests that this healer-induced phenomenon has always been widespread among hunter-gatherer cultures, so much so that it may well have been the result of evolutionary pressures. If this is correct, how does it relate to health in our modern world?

I am very interested in hunter-gatherer cultures, and I have also been living in Texas for almost 10 years now. So it is only natural for me to try to learn more about the former hunter-gatherer groups in Texas, particularly those who lived in the area prior to the introduction of horses by the Europeans.

There are parks, museums, and other resources on the topic in various parts of Texas, which are at driving distance. Unfortunately much has been lost, as the Plains Indians of Texas (e.g., Comanches and Kiowas) who succeeded those pre-horse native groups have largely been forcibly relocated to reservations in Oklahoma.

Anthropological evidence suggests that the earliest migrations to America have occurred via the Bering Strait, initially from Siberia into Alaska, and then gradually spreading southward to most of the Americas between 13,000 and 10,000 years ago.

Much of what is known about the early Texas Indians is due to Álvar Núñez Cabeza de Vaca, a Spanish explorer who survived a shipwreck and lived among the Amerindians in and around Texas between 1528 and 1536. He later wrote a widely cited report about his experiences ().


(Cabeza de Vaca and his companions; source: Biography.com)

In Spanish, “cabeza de vaca” means, literally, “cow’s head”. This odd surname, Cabeza de Vaca, clearly had a flavor of nobility to it in Spain at the time.

You may have heard that early American Indians were uniformly of short stature, not unlike most people at the time, but certainly shorter than the average American today. Cabeza de Vaca dispels this idea with his description of the now extinct Karankawas, a description that has been born out by anthropological evidence. The male members “towered above the Spaniards”, often 6 ft or taller in height, in addition to being muscular.

The Karankawas were a distinct indigenous group that shared the same environment and similar food sources with other early groups of much lower stature. This strongly suggests a genetic basis for their high stature and muscular built, probably due to the “founder effect”, well known among population geneticists.

Cabeza de Vaca and three companions, two Spaniards and one Moroccan slave, were believed by the Amerindians to be powerful healers. This enabled them to survive among early Texas Indians for several years. Cabeza de Vaca and his colleagues at times acknowledged that they were probably curing people through what we would refer today as a powerful placebo effect.

Having said that, Cabeza de Vaca has also come to believe, at least to a certain extent, that he was indeed able to perform miraculous cures. He repeatedly stated his conviction that those cures were primarily through divine intervention, as he was a devout Christian, although there are many contradictory statements in this respect in his reports (possibly due to fear from the Spanish Inquisition). He also performed simple surgeries.

Much has been written about Cabeza de Vaca’s life among the early Indians of Texas and surrounding areas, including the report by Cabeza de Vaca himself. One of my favorites is the superb book “A Land So Strange” () by Andrés Reséndez, a professor of history at the University of California at Davis ().

The Spanish explorer’s experiences have been portrayed in the film “Cabeza de Vaca” (), which focuses primarily on the supernatural angle, with a lot of artistic license. I must admit that I was a bit disappointed with this film, as I expected it to show more about the early Indians’ culture and lifestyle. Juan Diego, the Spanish actor portraying Cabeza de Vaca, was razor thin in this film - a fairly realistic aspect of the portrayal.

It is quite possible that modern humans have an innate tendency to believe in and rely on the supernatural, a tendency that is the product of evolution. We know from early and more recent evidence from hunter-gatherer societies that supernatural beliefs help maintain group cohesion and, perhaps quite importantly, mitigate the impact that the knowledge of certain death has on the mental health of hunter-gatherers.

Homo sapiens is unique among animals in its awareness of its own mortality, which may be a byproduct of its also unique ability to make causal inferences. Supernatural beliefs among hunter-gatherers almost universally address this issue, by framing death as a threshold between this existence and the afterlife, essentially implying immortality.

Yet, supernatural beliefs seem to also have a history of exploitation, where they are used to manipulate others. Cabeza de Vaca himself implies that, at points, he and his companions took personal advantage of the beliefs in their healing powers by the various indigenous groups with which they came into contact.

Modern humans who are convinced that they have no supernatural beliefs often perceive that to be a major advantage. But there could be disadvantages. One is that they may have more difficulty dealing with psychosomatic disorders. The conscious knowledge that they are psychosomatic could possibly pale in comparison with the belief in supernatural healing, in terms of curative power. Another potential disadvantage is a greater likelihood of suffering from mental disorders.

Finally, those who are sure that they have no supernatural beliefs; are they really correct? Well, subconsciously things may be different. Perhaps a good test would be to go to a “convincing” movie (i.e., not a laughable “B-level” one; for lack of a better word) about supernatural things, such as possession or infestation by evil spirits, and see if it has any effect on you.

If the experience does have an effect on you, even a small one, couldn't this suggest that your subconscious belief in the supernatural may not be so easy to control in a conscious way? I suspect that having no supernatural beliefs is unnatural and unhealthy. In most cases it probably creates a conscious-subconscious conflict, and a fairly pessimist view of the world.

My guess is that it is better to have those beliefs, in some form or another, and be on guard against exploitation.

Dried meat: Homemade beef jerky

You can dry many types of meat, including beef, pork, goat, deer, and even some types of seafood, such as mussels. Drying meat tends to significantly increase the meat’s protein content per gram, often more than doubling it. It also helps preserve the meat, as bacteria need an aqueous environment to grow; adding salt helps further prevent bacterial growth.

Dried meat preparation and consumption was common among the Plains Indians (e.g., of the Cheyenne, Comanche, and Lakota tribes), and also a valuable trade item for them. They often ground the dried meat into a powder, mixing fat and berries with them; the result of which was pemmican. Many other hunter-gatherer cultures around the world have incorporated dried meat into their diets.

Below is a recipe for homemade beef jerky, which is very close in terms of nutrition content to the dried meat of the Plains Indians's time; that is, the time when the Plains Indians subsisted mostly on bison. Commercial beef jerky typically has a lower nutrient-to-calorie ratio, in part because sugar is added to it. The recipe is for beef jerky, but can be used to make jerky with bison meat as well.

- Cut about 3 lbs of beef muscle into thin strips (see photo below). Ideally you should buy it partially cut already, with most of the fat trimmed. Cutting with or against the grain doesn’t seem to make much difference, at least to me.

- Prepare some dry seasoning powder by mixing salt and cayenne pepper.

- Season the strips and place them on a tray with a grid on top, so that the fat that will come off the meat is captured by the tray and doesn’t drip into the oven.

- Preheat the oven to about 180 degrees Fahrenheit, and place the strips in it until you can easily pull a piece of the meat off with your fingers (see photos below, for an idea of how they would look). This should take about 1 hour or so. You will not technically be “baking” or "cooking" the meat at this temperature, although the digestibility of the final product will be comparable to that of cooked meat – i.e., greater digestibility than raw meat.

- Leave the strips in the oven until they are cold, this will dry them further.







Homemade beef jerky, prepared as above, is supposed to be eaten cold. In this sense, it could be thought of as a bit like salami, but with a higher protein-to-fat ratio. If your kids eat this on a regular basis, I suspect that their future orthodontist needs will be significantly reduced. Homemade beef jerky, like the commercial one, requires some serious chewing.

The dried strips of meat can be kept outside the fridge for a long time, but if you intend to keep them for more than a few weeks, I would suggest that you keep them in the fridge. Interestingly, adding sugar apparently increases the non-refrigerated shelf life of beef jerky even further. It doesn’t improve the flavor though, in my opinion.

This is a zero-carbohydrate food item, which may be a good choice for those who are insulin resistant or diabetic, and also for those on low-carbohydrate or just-enough-carbohydrate diets. Often I hear bodybuilders who eat multiple meals per day to say that it is hard for them to prepare high-protein snacks that they can easily carry with them. Well, beef jerky is one option.

Drs. Francisco Cervantes and Marivic Torregosa, and the 2013 Ancestral Health Symposium

Last year I traveled to South Korea to give presentations on nonlinear structural equation modeling and WarpPLS (). These are an advanced statistical analysis technique and related software tool, respectively, which have been used extensively in this blog to analyze health data, notably data related to the China Study.

I gave a couple of presentations at Korea University, which is in Seoul, and a keynote address at a conference in Gwangju, in the south part of the country. So I ended up seeing quite a lot of this beautiful country, and meeting many people. Some of my impressions regarding health and lifestyle issues need separate blog posts, which are forthcoming.

One issue that kept me thinking, as it did when I visited Japan a few years ago as well, was the obvious leanness of the South Koreans, compared with Americans, even though you don’t see a lot of emphasis on dieting there. Interestingly, this phenomenon also poses a challenge to many dietary schools of thought. For example, consumption of high-glycemic-index carbohydrates seems to be relatively high in South Korea.

The relative leanness of South Koreans is probably due to a combination of factors. A major one, it seems, is often forgotten. It is related to epigenetics. This term, “epigenetics”, is often assigned different meanings depending on the context in which it is used. Here it is used to refer to innate predispositions that don’t have a primarily genetic basis.

Epigenetic phenomena often give the impression that acquired characteristics can be inherited, and are frequently, and misguidedly, used as examples in support of a theory often associated with Jean-Baptiste Pierre Antoine de Monet, better known as Lamarck.

A classic example of epigenetics, in this context, is that of a mother with type II diabetes giving birth to a child that will develop type II diabetes at a young age. Typically type II diabetes develops in adults, but its incidence in children has been increasing lately, particularly in certain areas. And I think that this classic example is in part related to the general leanness of South Koreans and of people in other cultures where adoption of highly industrialized foods has been relatively slow.

In other words, I think that it is possible that a major protection in South Korea, as well as in Japan and other countries, is the cultural resistance, particularly among older generations, against adopting modern diets and lifestyles that deviate from their traditional ones.

This brings me to Drs. Francisco Cervantes and Marivic Torregosa (pictured below). Dr. Cervantes is the Chief Director of Laredo Pediatrics and Neonatology, a pediatrician who studied and practiced in a variety of places, including Mexico, New Jersey, and Texas. Dr. Torregosa is a colleague of mine, a college professor and nurse practitioner in Laredo, with a Ph.D. in nursing and a research interest in child obesity.



As it turns out, Laredo, a city in Southwestern Texas near the border with Mexico, seems like the opposite of South Korea in terms of health, and this may well be related to epigenetics. This presents an enormous opportunity for research, and for helping people who really need help.

In Laredo, as well as in other areas where insulin resistance and type II diabetes are rampant, there is a great deal of variation in health. There are very healthy folks in Laredo, and very sick ones. This great deal of variation is very useful in the identification of causative factors through advanced statistical analyses. Lack of variation tends to have the opposite effect, often “hiding” causative effects.

Drs. Cervantes, Torregosa, and I had a presentation accepted for the 2013 Ancestral Health Symposium (). It is titled “Gallbladder Disease in Children: Separating Myths from Facts”. It is entirely based on data collected and analyzed by Dr. Cervantes, who is very knowledgeable about statistics. Below is the abstract.

Cholesterol’s main role in the body is to serve as raw material for bile acids; the conversion of cholesterol to bile acids by the liver accounts for approximately 70 percent of the daily disposal of cholesterol. Bile acids are then stored in the gallbladder and secreted to aid in the digestion of dietary fat. It is often believed that high cholesterol levels cause gallbladder disease. In this presentation, we will discuss various aspects of gallbladder disease, with a focus on children. The presentation will be based on data from 2116 patients of the Laredo Pediatrics & Neonatology. The patients, 1041 boys and 1075 girls, are largely first generation American-born children of Hispanic descent; a group at very high risk of developing gallbladder disease. This presentation will dispel several myths, and lay out a case for a strong association between gallbladder disease and abnormally high body fat levels. Gallbladder disease appears to be largely preventable in children through diet and lifestyle modifications, some of which will be discussed during the presentation.

Many people seem to be unaware of the fact that cholesterol production and disposal are strongly associated with secretion of bile acids. Most of the body's cholesterol is used to produce bile acids, which are reabsorbed from the gut, in a cyclical process. This is the reason behind the use of "bile acid sequestrants" to reduce cholesterol levels.

The focus on gallbladder disease in the presentation comes from an interest by Dr. Cervantes, based on his many years of clinical experience, in using gallbladder disease markers to identify and prevent other conditions, including several conditions associated with what we refer to as diseases of affluence or civilization.

Dr. Cervantes is unique among clinical practitioners in that he spends a lot of time analyzing data from his patients. His knowledge of data analyses techniques rivals that of many professional researchers I know. And he does that at his own expense, something that most clinical practitioners are unwilling to do. Dr. Cervantes and I will be co-authoring blog posts here in the future.

The 2013 PLoS ONE sugar and diabetes study: Sugar from fruits is harmless

A new study linking sugar consumption with diabetes prevalence has gained significant media attention recently. The study was published in February 2013 in the journal PLoS ONE (). The authors are Sanjay Basu, Paula Yoffe, Nancy Hills and Robert H. Lustig.

Among the claims made by the media is that “… sugar consumption — independent of obesity — is a major factor behind the recent global pandemic of type 2 diabetes” (). As it turns out, the effects revealed by the study seem to be very small, which may actually be a side effect of data aggregation; I will discuss this further below.

Fruits are exonerated

Let me start by saying that this study also included in the analysis the main natural source of sugar, fruit, as a competing variable (competing with the effects of sugar itself), and found it to be unrelated to diabetes. As the authors note: “None of the other food categories — including fiber-containing foods (pulses, nuts, vegetables, roots, tubers), fruits, meats, cereals, and oils — had a significant association with diabetes prevalence rates”.

This should not surprise anyone who has actually met and talked with Dr. Lustig, the senior author of the study and a very accessible man who has been reaching out to the public in a way that few in his position do. He is a clinician and senior researcher affiliated with a major university; public outreach, in the highly visible way that he does it, is probably something that he does primarily (if not solely) to help people. Dr. Lustig was at the 2012 Ancestral Health Symposium, and he told me, and anyone who asked him, that sugar in industrialized foods was his target, not sugar in fruits.

As I noted here before, the sugar combination of fruits, in their natural package, may in fact be health-promoting (). The natural package probably promotes enough satiety to prevent overconsumption.

Both (unnatural) sugar and obesity have effects, but they are tiny in this study

The Diabetes Report Card 2012 () provides a wealth of information that can be useful as a background for our discussion here.

In the USA, general diabetes prevalence varies depending on state, with some states having higher prevalence than others. The vast majority of diabetes cases are of type 2 diabetes, which is widely believed to be strongly associated with obesity.

In 2012, the diabetes prevalence among adults (aged 20 years or older) in Texas was 9.8 percent. This rate is relatively high compared to other states, although lower than in some. So, among a random group of 1,000 adult Texans, you would find approximately 98 with diabetes.

Prevalence increases with age. Among USA adults in general, prevalence of diabetes is 2.6 percent within ages 20–44, 11.7 percent within ages 45–64, and 18.9 percent at age 64 or older. So the numbers above for Texas, and prevalence in almost any population, are also a reflection of age distribution in the population.

According to the 2013 study published in PLoS ONE, a 1 percent increase in obesity prevalence is associated with a 0.081 percent increase in diabetes prevalence. This comes directly from the table below, fifth column on the right. That is the column for the model that includes all of the variables listed on the left.



We can translate the findings above in more meaningful terms by referring to hypothetical groups of 1,000 people. Let us say we have two groups of 1,000 people. In one of them we have 200 obese people (20 percent); and no obese person in the other. We would find only between 1 and 2 people with diabetes in the group with 200 obese people.

The authors also considered overweight prevalence as a cause of diabetes prevalence. A section of the table with the corresponding results in included below. They also found a significant effect, of smaller size than for obesity – which itself is a small effect.



The study also suggests that consumption of the sugar equivalent of a 12 oz. can of regular soft drink per person per day was associated with a 1.1 percent rise in diabetes prevalence. The effect here is about the same as that of a 1 percent increase in obesity.

That is, let us say we have two groups of 1,000 people. In one of them we have 200 people (20 percent) consuming one 12 oz. can of soft drink per day; and no one consuming sugar in the other. (Sugar from fruits is not considered here.) We would find only about 2 people with diabetes in the group with 200 sugary soda drinkers.

In other words, the effects revealed by this study are very small. They are so small that their corresponding effect sizes make them borderline irrelevant for predictions at the individual level. Based on this study, obesity and sugar consumption combined would account for no more than 5 out of each 100 cases of diabetes (a generous estimate, based on the results discussed above).

Even being weak, the effects revealed by this study are not irrelevant for policy-making, because policies tend to influence the behavior of very large numbers of people. For example, if the number of people that could be influenced by policies to curb consumption of refined sugar were 100 million, the number of cases of diabetes that could be prevented would be 200 thousand, notwithstanding the weak effects revealed by this study.

Why are the effects so small?

The effects in this study are based on data aggregated by country. When data is aggregated by population, the level of variation in the data is reduced; sometimes dramatically, a problem that is proportional to the level of aggregation (e.g., the problem is greater for country aggregation than for city aggregation).

Because there can be no association without correlation, and no correlation without variation, coefficients of association tend to be reduced when data aggregation occurs. This is, in my view, the real problem behind what statisticians often refer to, in “statospeech”, as “ecological fallacy”. The effects in aggregated data are weaker than the effects one would get without aggregation.

So, I suspect that the effects in this study, which are fairly weak at the level of aggregation used (the country level), reflect much stronger effects at the individual level of analysis.

Bottom line

Should you avoid getting obese? Should you avoid consuming industrialized products with added sugar? I think so, and I would still have recommended these without this study. There seems to be no problem with natural foods containing sugar, such as fruits.

This study shows evidence that sugar in industrialized foods is associated with diabetes, independently from obesity, but it does not provide evidence that obesity doesn’t matter. It shows that both matter, independently of one another, which is an interesting finding that backs up Dr. Lustig’s calls for policies to specifically curb refined sugar consumption.

Again, what the study refers to as sugar, as availability but implying consumption, seems to refer mostly to industrialized foods where sugar was added to make them more enticing. Fruit consumption was also included in the study, and found to have no significant effect on diabetes prevalence.

Here is a more interesting question. If a group of people have a predisposition toward developing diabetes, due to any reason (genetic, epigenetic, environmental), what would be the probability that they would develop diabetes if they became obese and/or consumed unnatural sugar-added foods?

This type of question can be answered with a moderating effects analysis, but as I noted here before (), moderating effects analyses are not conducted in health research.