The 2012 Arch Intern Med red meat-mortality study: Eating 234 g/d of red meat could reduce mortality by 23 percent

As we have seen in an earlier post on the China Study data (), which explored relationships hinted at by Denise Minger’s previous and highly perceptive analysis (), one can use a multivariate analysis tool like WarpPLS () to explore relationships based on data reported by others. This is true even when the dataset available is fairly small.

So I entered the data reported in the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality into WarpPLS to do some exploratory analyses. I discussed the study in my previous post; it was conducted by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine (). The data I used is from Table 1 of the article; it reports figures on several variables along 5 quintiles, based on separate analyses of two samples, called “Health Professionals” and “Nurses Health” samples. The Health Professionals sample comprised males; the Nurses Health sample, females.

Below is an interesting exploratory model, with results. It includes a number of hypotheses, represented by arrows, which seem to make sense. This is helpful, because a model incorporating hypotheses that make sense allows for easy identification of nonsense results, and thus rejection of the model or the data. (Refutability is one of the most important characteristics of good theoretical models.) Keep in mind that the sample size here is very small (N=10), as the authors of the study reported data along 5 quintiles for the Health Professionals sample, together with 5 quintiles for the Nurses Health sample. In a sense, this is somewhat helpful, because a small sample tends to be “unstable”, leading nonsense results and other signs of problems to show up easily – one example would be multivariate coefficients of association (the beta coefficients reported near the arrows) greater than 1 due to collinearity ().

So what does the model above tell us? It tells us that smoking (Smokng) is associated with reduced physical activity (PhysAct); beta = -0.92. It tells us that smoking (Smokng) is associated with reduced food intake (FoodInt); beta = -0.36. It tells us that physical activity (PhysAct) is associated with reduced incidence of diabetes (Diabetes); beta = -0.25. It tells us that increased food intake (FoodInt) is associated with increased incidence of diabetes (Diabetes); beta = 0.93. It tells us that increased food intake (FoodInt) is associated with increased red meat intake (RedMeat); beta = 0.60. It tells us that increased incidence of diabetes (Diabetes) is associated with increased mortality (Mort); beta = 0.61. It tells us that being female (SexM1F2) is associated with reduced mortality (Mort); beta = -0.67.

Some of these betas are a bit too high (e.g., 0.93), due to the level of collinearity caused by such a small sample. Due to being quite high, they are statistically significant even in a small sample. Betas greater than 0.20 tend to become statistically significant when the sample size is 100 or greater; so all of the coefficients above would be statistically significant with a larger sample size. What is the common denominator of all of the associations above? The common denominator is that all of them make sense, qualitatively speaking; there is not a single case where the sign is the opposite of what we would expect. There is one association that is shown on the graph and that is missing from my summary of associations above; and it also makes sense, at least to me. The model also tells us that increased red meat intake (RedMeat) is associated with reduced mortality (Mort); beta = -0.25. More technically, it tells us that, when we control for biological sex (SexM1F2) and incidence of diabetes (Diabetes), increased red meat intake (RedMeat) is associated with reduced mortality (Mort).

How do we roughly estimate this effect in terms of amounts of red meat consumed? The -0.25 means that, for each standard deviation in the amount of red meat consumed, there is a corresponding 0.25 standard deviation reduction of mortality. (This interpretation is possible because I used WarpPLS’ linear analysis algorithm; a nonlinear algorithm would lead to a more complex interpretation.) The standard deviation for red meat consumption is 0.897 servings. Each serving has about 84 g. And the highest number of servings in the dataset is 3.1 servings, or 260 g/d (calculated as: 3.1*84). To stay a bit shy of this extreme, let us consider a slightly lower intake amount, which is 3.1 standard deviations, or 234 g/d (calculated as: 3.1*0.897*84). Since the standard deviation for mortality is 0.3 percentage points, we can conclude that an extra 234 g of red meat per day is associated with a reduction in mortality of approximately 23 percent (calculated as: 3.1*0.25*0.3).

Let me repeat for emphasis: the data reported by the authors suggests that, when we control for biological sex and incidence of diabetes, an extra 234 g of red meat per day is associated with a reduction in mortality of approximately 23 percent. This is exactly the opposite, qualitatively speaking, of what was reported by the authors in the article. I should note that this is also a minute effect, like the effect reported by the authors. (The mortality rates in the article are expressed as percentages, with the lowest being around 1 percent. So this 23 percent is a percentage of a percentage.) If you were to compare a group of 100 people who ate little red meat with another group of the same size that ate 234 g more of red meat every day, over a period of more than 20 years, you would not find a single additional death in either group. If you were to compare matched groups of 1,000 individuals, you would find only 2 additional deaths among the folks who ate little red meat.

At the same time, we can also see that excessive food intake is associated with increased mortality via its effect on diabetes. The product beta coefficient for the mediated effect FoodInt --> Diabetes --> Mort is 0.57. This means that, for each standard deviation of food intake in grams, there is a corresponding 0.57 standard deviation increase in mortality, via an increase in the incidence of diabetes. This is very likely at levels of food consumption where significantly more calories are consumed than spent, ultimately leading to many people becoming obese. The standard deviation for food intake is 355 calories. The highest daily food intake quintile reported in the article is 2,396 calories, which happens to be associated with the highest mortality (and is probably an underestimation); the lowest is 1,202 (also probably underestimated).

So, in summary, the data suggests that, for the particular sample studied (made up of two subsamples): (a) red meat intake is protective in terms of overall mortality, through a direct effect; and (b) the deleterious effect of overeating on mortality is stronger than the protective effect of red meat intake. These conclusions are consistent with those of my previous post on the same study (). The difference is that the previous post suggested a possible moderating protective effect; this post suggests a possible direct protective effect. Both effects are small, as was the negative effect reported by the authors of the study. Neither is statistically significant, due to sample size limitations (secondary data from an article; N=10). And all of this is based on a study that categorized various types of processed meat as red meat, and that did not distinguish grass-fed from non-grass-fed meat.

By the way, in discussions of red meat intake’s effect on health, often iron overload is mentioned. What many people don’t seem to realize is that iron overload is caused primarily by hereditary haemochromatosis. Another cause is “blood doping” to improve athletic performance (). Hereditary haemochromatosis is a very rare genetic disorder; rare enough to be statistically “invisible” in any study that does not specifically target people with this disorder.

The 2012 red meat-mortality study (Arch Intern Med): The data suggests that red meat is protective

I am not a big fan of using arguments such as “food questionnaires are unreliable” and “observational studies are worthless” to completely dismiss a study. There are many reasons for this. One of them is that, when people misreport certain diet and lifestyle patterns, but do that consistently (i.e., everybody underreports food intake), the biasing effect on coefficients of association is minor. Measurement errors may remain for this or other reasons, but regression methods (linear and nonlinear) assume the existence of such errors, and are designed to yield robust coefficients in their presence. Besides, for me to use these types of arguments would be hypocritical, since I myself have done several analyses on the China Study data (), and built what I think are valid arguments based on those analyses.

My approach is: Let us look at the data, any data, carefully, using appropriate analysis tools, and see what it tells us; maybe we will find evidence of measurement errors distorting the results and leading to mistaken conclusions, or maybe not. With this in mind, let us take a look at the top part of Table 3 of the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality, authored by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine (). This is a prominent journal, with an average of over 270 citations per article according to Google Scholar. The study has received much media attention recently.

Take a look at the area highlighted in red, focusing on data from the Health Professionals sample. That is the multivariate-adjusted cardiovascular mortality rate, listed as a normalized percentage, in the highest quintile (Q5) of red meat consumption from the Health Professionals sample. The non-adjusted percentages are 1.4  percent mortality in Q5 and 1.13 in Q1 (from Table 1 of the same article); so the multivariate adjustment-normalization changed the values of the percentages somewhat, but not much. The highlighted 1.35 number suggests that for each group of 100 people who consumed a lot of red meat (Q5), when compared with a group of 100 people who consumed little red meat (Q1), there were on average 0.35  more deaths over the same period of time (more than 20 years).

The heavy red meat eaters in Q5 consumed 972.37 percent more red meat than those in Q1. This is calculated with data from Table 1 of the same article, as: (2.36-0.22)/0.22. In Q5, the 2.36 number refers to the number of servings of red meat per day, with each serving being approximately 84 g. So the heavy red meat eaters ate approximately 198 g per day (a bit less than 0.5 lb), while the light red meat eaters ate about 18 g per day. In other words, the heavy red meat eaters ate 9.7237 times more, or 972.37 percent more, red meat.

So, just to be clear, even though the folks in Q5 consumed 972.37 percent more red meat than the folks in Q1, in each matched group of 100 you would not find a single additional death over the same time period. If you looked at matched groups of 1,000 individuals, you would find 3 more deaths among the heavy red meat eaters. The same general pattern, of a minute difference, repeats itself throughout Table 3. As you can see, all of the reported mortality ratios are 1-point-something. In fact, this same pattern repeats itself in all mortality tables (all-cause, cardiovascular, cancer). This is all based on a multivariate analysis that according to the authors controlled for a large number of variables, including baseline history of diabetes.

Interestingly, looking at data from the same sample (Health Professionals), the incidence of diabetes is 75 percent higher in Q5 than in Q1. The same is true for the second sample (Nurses Health), where the Q5-Q1 difference in incidence of diabetes is even greater - 81 percent. This caught my eye, being diabetes such a prototypical “disease of affluence”. So I entered the whole data reported in the article into HCE () and WarpPLS (), and conducted some analyses. The graphs below are from HCE. The data includes both samples – Health Professionals and Nurses Health.

HCE calculates bivariate correlations, and so does WarpPLS. But WarpPLS stores numbers with a higher level of precision, so I used WarpPLS for calculating coefficients of association, including correlations. I also double-checked the numbers with other software, just in case (e.g., SPSS and MATLAB). Here are the correlations calculated by WarpPLS, which refer to the graphs above: 0.030 for red meat intake and mortality; 0.607 for diabetes and mortality; and 0.910 for food intake and diabetes. Yes, you read it right, the correlation between red meat intake and mortality is a very low and non-significant 0.030 in this dataset. Not a big surprise when you look at the related HCE graph, with the line going up and down almost at random. Note that I included the quintiles data from both the Health Professionals and Nurses Health samples in one dataset.

Those folks in Q5 had a much higher incidence of diabetes, and yet the increase in mortality for them was significantly lower, in percentage terms. A key difference between Q5 and Q1 being what? The Q5 folks ate a lot more red meat. This looks suspiciously suggestive of a finding that I came across before, based on an analysis of the China Study II data (). The finding was that animal food consumption (and red meat is an animal food) was protective, actually reducing the negative effect of wheat flour consumption on mortality. That analysis actually suggested that wheat flour consumption may not be so bad if you eat 221 g or more of animal food daily.

So, I built the model below in WarpPLS, where red meat intake (RedMeat) is hypothesized to moderate the relationship between diabetes incidence (Diabetes) and mortality (Mort). Below I am also including the graphs for the direct and moderating effects; the data is standardized, which reduces estimation error, particularly in moderating effects estimation. I used a standard linear algorithm for the calculation of the path coefficients (betas next to the arrows) and jackknifing for the calculation of the P values (confidence = 1 – P value). Jackknifing is a resampling technique that does not require multivariate normality and that tends to work well with small samples; as is the case with nonparametric techniques in general.

The direct effect of diabetes on mortality is positive (0.68) and almost statistically significant at the P < 0.05 level (confidence of 94 percent), which is noteworthy because the sample size here is so small – only 10 data points, 5 quintiles from the Health Professionals sample and 5 from the Nurses Health sample. The moderating effect is negative (-0.11), but not statistically significant (confidence of 61 percent). In the moderating effect graphs (shown side-by-side), this negative moderation is indicated by a slightly less steep inclination of the regression line for the graph on the right, which refers to high red meat intake. A less steep inclination means a less strong relationship between diabetes and mortality – among the folks who ate the most red meat.

Not too surprisingly, at least to me, the results above suggest that red meat per se may well be protective. Although we should consider a least two other possibilities. One is that red meat intake is a marker for consumption of some other things, possibly present in animal foods, that are protective - e.g., choline and vitamin K2. The other possibility is that red meat is protective in part by displacing other less healthy foods. Perhaps what we are seeing here is a combination of these.

Whatever the reason may be, red meat consumption seems to actually lessen the effect of diabetes on mortality in this sample. That is, according to this data, the more red meat is consumed, the fewer people die from diabetes. The protective effect might have been stronger if the participants had eaten more red meat, or more animal foods containing the protective factors; recall that the threshold for protection in the China Study II data was consumption of 221 g or more of animal food daily (). Having said that, it is also important to note that, if you eat excess calories to the point of becoming obese, from red meat or any other sources, your risk of developing diabetes will go up – as the earlier HCE graph relating food intake and diabetes implies.

Please keep in mind that this post is the result of a quick analysis of secondary data reported in a journal article, and its conclusions may be wrong, even though I did my best not to make any mistake (e.g., mistyping data from the article). The authors likely spent months, if not more, in their study; and have the support of one of the premier research universities in the world. Still, this post raises serious questions. I say this respectfully, as the authors did seem to try their best to control for all possible confounders.

I should also say that the moderating effect I uncovered is admittedly a fairly weak effect on this small sample and not statistically significant. But its magnitude is apparently greater than the reported effects of red meat on mortality, which are not only minute but may well be statistical artifacts. The Cox proportional hazards analysis employed in the study, which is commonly used in epidemiology, is nothing more than a sophisticated ANCOVA; it is a semi-parametric version of a special case of the broader analysis method automated by WarpPLS.

Finally, I could not control for confounders because, given the small sample, inclusion of confounders (e.g., smoking) leads to massive collinearity. WarpPLS calculates collinearity estimates automatically, and is particularly thorough at doing that (calculating them at multiple levels), so there is no way to ignore them. Collinearity can severely distort results, as pointed out in a YouTube video on WarpPLS (). Collinearity can even lead to changes in the signs of coefficients of association, in the context of multivariate analyses - e.g., a positive association appears to be negative. The authors have the original data – a much, much larger sample - which makes it much easier to deal with collinearity.

Moderating effects analyses () – we need more of that in epidemiological research eh?

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.

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. 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.

Men who are skinny-fat: There are quite a few of them

The graph below (from Wikipedia) plots body fat percentage (BF) against body mass index (BMI) for men. The data is a bit old: 1994. The top-left quadrant refers to men with BF greater than 25 percent and BMI lower than 25. A man with a BF greater than 25 has crossed into obese territory, even though a BMI lower than 25 would suggest that he is not even overweight. These folks are what we could call skinny-fat men.

The data is from the National Health and Nutrition Examination Survey (NHANES), so it is from the USA only. Interesting that even though this data is from 1994, we already could find quite a few men with more than 25 percent BF and a BMI of around 20. One example of this would be a man who is 5’11’’, weighing 145 lbs, and who would be technically obese!

About 8 percent of the entire sample of men used as a basis for the plot fell into the area defined by the top-left quadrant – the skinny-fat men. (That quadrant is one in which the BMI measure is quite deceiving; another is the bottom-right quadrant.) Most of us would be tempted to conclude that all of these men were sick or on the path to becoming so. But we do not know this for sure. On the standard American diet, I think it is a reasonably good guess that these skinny-fat men would not fare very well.

What is most interesting for me regarding this data, which definitely has some measurement error built in (e.g., zero BF), is that it suggests that the percentage of skinny-fat men in the general population is surprisingly high. (And this seems to be the case for women as well.) Almost too high to characterize being skinny-fat as a disease per se, much less a genetic disease. Genetic diseases tend to be rarer.

In populations under significant natural selection pressure, which does not include modern humans living in developed countries, genetic diseases tend to be wiped out by evolution. (The unfortunate reality is that modern medicine helps these diseases spread, although quite slowly.)  Moreover, the prevalence of diabetes in the population was not as high as 8 percent in 1994, and is not that high today either; although it tends to be concentrated in some areas and cluster with obesity as defined based on both BF and BMI.

And again, who knows, maybe these folks (the skinny-fat men) were not even the least healthy in the whole sample, as one may be tempted to conclude.

Maybe being skinny-fat is a trait, passed on across generations, not a disease. Maybe such a trait was useful at some point in the not so distant past to some of our ancestors, but leads to degenerative diseases in the context of a typical Western diet. Long-living Asians with low BMI tend to gravitate more toward the skinny-fat quadrant than many of their non-Asian counterparts. That is, long-living Asians generally tend have higher BF percentage at the same BMI (see a discussion about the Okinawans on this post).

Evolution is a deceptively simple process, which can lead to very odd results.

This “trait-not-disease” idea may sound like semantics, but it has major implications. It would mean that many of the folks who are currently seen as diseased or disease-prone, are in fact simply “different”. At a point in time in our past, under a unique set of circumstances, they might have been the ones who would have survived. The ones who would have been perceived as healthier than average.

Book review: Sugar Nation

Jeff O’Connell is the Editor-in-Chief for Bodybuilding.com, a former executive writer for Men’s Health, and former Editor-in-Chief of Muscle & Fitness. He is also the author of a few bestselling books on fitness.

(Source: Bodybuilding.com)

It is obvious that Jeff is someone who can write, and this comes across very clearly in his new book, Sugar Nation.

Now, with a title like this, Sugar Nation, I was expecting a book discussing trends of sugar consumption in the USA, and the related trends in various degenerative diseases. So when I started reading the book I was slightly put off by what seemed to be a book about a very personal journey, written in the first person by the author.

Yet, after reading it for a while I was hooked, and literally could not put the book down. Jeff has managed to write something of a page-turner, combining a harrowing personal account with carefully researched scientific information, about a relatively rare form of type 2 diabetes.

Jeff has a genetic propensity to insulin resistance, just like his father did. What makes Jeff’s case a little unusual is that Jeff is thin, and apparently has difficulty gaining weight. The most common type of diabetes is type 2, and most of those who develop type 2 diabetes do so via the metabolic syndrome. Typically this involves becoming obese or overweight before getting diagnosed as a diabetic.

In fact, in a thin person who is insulin resistant it seems that body fat cells become resistant to the normal actions of insulin much sooner than in the obese. This essentially means that they start rejecting fat. This is a problem, because fat should either be stored in fat cells (adipocytes) or used for energy; as opposed to being deposited in other tissues or remaining in circulation. Apparently this makes it even more difficult for them to control glucose levels once insulin resistance sets in; there is no “cushion”, so to speak.

Still, Jeff appears to believe that his case was that of a skinny-fat person, where body fat percentage is a lot higher than expected based on a low body mass index, and where excess visceral fat is a main culprit. In fact, Jeff seems to think that most cases of thin folks who developed type 2 diabetes are like this, as they follow the metabolic syndrome progression pattern. Fasting triglycerides go up and HDL cholesterol goes down, among other things, but in a skinny-fat body.

Somewhat predictably, what Jeff found out is that, in his case, adopting a low carbohydrate diet made an enormous difference. In fact, it made the difference between having a fairly normal life versus constantly suffering through hypoglycemic episodes. And, at the stage in which Jeff caught the problem, he did not have to avoid all natural carbohydrate-rich foods, not even things like apples. (He had to control portions though.) It is the refined carbohydrate-rich foods that were the problem for him.

I must say that I disagree with a few of the statements in the book. For example, the author seems to believe that excess saturated fat and salt may be quite unhealthy. I think that foods rich in refined carbohydrates and sugars are much more of a problem; cut them out and often excess saturated fat and salt either cease to be a problem, or become healthy. Jeff doesn’t seem to think that excess omega-6 fats can also cause diabetes; I believe the opposite to be true, via a pro-inflammatory path.

Still, this is a great book on so many levels. Jeff meticulously records his experience dealing with doctors, most of whom seem to be clueless as to what to do to prevent the damage that is caused by abnormally high glucose levels. This happens even though diabetes is those doctors’ main area of expertise. He talks about himself with complete abandon, and manages to mix that up with quite a lot of relevant research on diabetes. He gives us an insider’s view of the professional bodybuilding culture, including its use of insulin injections. His description of the Amish is very interesting and somewhat surprising.

For these reasons and a few others, I think this is a great book, and highly recommend it!

Strength training plus fasting regularly, and becoming diabetic!? No, it is just compensatory adaptation at work

One common outcome of doing glycogen-depleting exercise (e.g., strength training, sprinting) in combination with intermittent fasting is an increase in growth hormone (GH) levels. See this post for a graph showing the acute effect on GH levels of glycogen-depleting exercise. This effect applies to both men and women, and is generally healthy, leading to improvements in mood and many health markers.

It is a bit like GH therapy, with GH being “administered” to you by your own body. Both glycogen-depleting exercise and intermittent fasting increase GH levels; apparently they have an additive effect when done together.

Still, a complaint that one sees a lot from people who have been doing glycogen-depleting exercise and intermittent fasting for a while is that their fasting blood glucose levels go up. This is particularly true for obese folks (after they lose body fat), as obesity tends to be associated with low GH levels, although it is not restricted to the obese. In fact, many people decide to stop what they were doing because they think that they are becoming insulin resistant and on their way to developing type 2 diabetes. And, surely enough, when they stop, their blood glucose levels go down.

Guess what? If your blood glucose levels are going up quite a bit in response to glycogen-depleting exercise and intermittent fasting, maybe you are one of the lucky folks who are very effective at increasing their GH levels. The blood glucose increase effect is temporary, although it can last months, and is indeed caused by insulin resistance. An HbA1c test should also show an increase in hemoglobin glycation.

Over time, however, you will very likely become more insulin sensitive. What is happening is compensatory adaptation, with different short-term and long-term responses. In the short term, your body is trying to become a more efficient fat-burning machine, and GH is involved in this adaptation. But in the short term, GH leads to insulin resistance, probably via actions on muscle and fat cells. This gradually improves in the long term, possibly through a concomitant increase in liver insulin sensitivity and glycogen storage capacity.

This is somewhat similar to the response to GH therapy.

The figure below is from Johannsson et al. (1997). It shows what happened in terms of glucose metabolism when a group of obese men were administered recombinant GH for 9 months. The participants were aged 48–66, and were given in daily doses the equivalent to what would be needed to bring their GH levels to approximately what they were at age 20. For glucose, 5 mmol is about 90 mg, 5.5 is about 99, and 6 is about 108. GDR is glucose disposal rate; a measure of how quickly glucose is cleared from the blood.

As you can see, insulin sensitivity initially goes down for the GH group, and fasting blood glucose goes up quite a lot. But after 9 months the GH group has better insulin sensitivity. Their GDR is the same as in the placebo group, but with lower circulating insulin. The folks in the GH group also have significantly less body fat, and have better health markers, than those who took the placebo.

There is such a thing as sudden-onset type 2-like diabetes, but it is very rare (see Michael’s blog). Usually type 2 diabetes “telegraphs” its arrival through gradually increasing fasting blood glucose and HbA1c. However, those normally come together with other things, notably a decrease in HDL cholesterol and an increase in fasting triglycerides. Folks who do glycogen-depleting exercise and intermittent fasting tend to see the opposite – an increase in HDL cholesterol and a decrease in triglycerides.

So, if you are doing things that have the potential to increase your GH levels, a standard lipid panel can help you try to figure out whether insulin resistance is benign or not, if it happens.

By the way, GH and cortisol levels are correlated, which is often why some associate responses to glycogen-depleting exercise and intermittent fasting with esoteric nonsense that has no basis in scientific research like “adrenal fatigue”. Cortisol levels are meant to go up and down, but they should not go up and stay up while you are sitting down.

Avoid chronic stress, and keep on doing glycogen-depleting exercise and intermittent fasting; there is overwhelming scientific evidence that these things are good for you.

Alcohol consumption, gender, and type 2 diabetes: Strange … but true

Let me start this post with a warning about spirits (hard liquor). Taken on an empty stomach, they cause an acute suppression of liver glycogenesis. In other words, your liver becomes acutely insulin resistant for a while. How long? It depends on how much you drink; possibly as long as a few hours. So it is not a very good idea to consume them immediately before eating carbohydrate-rich foods, natural or not, or as part of sweet drinks. You may end up with near diabetic blood sugar levels, even if your liver is insulin sensitive under normal circumstances.

The other day I was thinking about this, and the title of this article caught my attention: Alcohol Consumption and the Risk of Type 2 Diabetes Mellitus. This article is available here in full text. In it, Kao and colleagues show us a very interesting table (Table 4), relating alcohol consumption in men and women with incidence of type 2 diabetes. I charted the data from Model 3 in that table, and here is what I got:

I used the data from Model 3 because it adjusted for a lot of things: age, race, education, family history of diabetes, body mass index, waist/hip ratio, physical activity, total energy intake, smoking history, history of hypertension, fasting serum insulin, and fasting serum glucose. Whoa! As you can see, Model 3 even adjusted for preexisting insulin resistance and impaired glucose metabolism.

So, according to the charts, the more women drink, the lower is the risk of developing type 2 diabetes, even if they drink more than 21 drinks per week. For men, the sweet spot is 7-14 drinks per week; after 21 drinks per week the risk goes up significantly.

A drink is defined as: a 4-ounce glass of wine, a 12-ounce bottle or can of beer, or a 1.5-ounce shot of hard liquor. The amounts of ethanol vary, with more in hard liquor: 4 ounces of wine = 10.8 g of ethanol, 12 ounces of beer = 13.2 g of ethanol, and 1.5 ounces of spirits = 15.1 g of ethanol.

Initially I thought that these results were due to measurement error, particularly because the study relies on questionnaires. But I did some digging and checking, and now think they are not. In fact, there are plausible explanations for them. Here is what I think, and it has to do with a fundamental difference between men and women – sex hormones.

In men, alcohol consumption, particularly in large quantities, suppresses testosterone production. And testosterone levels are inversely associated with diabetes in men. Heavy alcohol consumption also increases estrogen production in men, which is not good news either.

In women, alcohol consumption, particularly in large quantities, increases estrogen production. And estrogen levels are (you guessed it) inversely associated with diabetes in women. Unnatural suppression of testosterone levels in women is not good either, as this hormone also plays important roles in women; e.g., it influences mood and bone density.

What if we were to disregard the possible negative health effects of suppressing testosterone production in women; should women start downing 21 drinks or more per week? The answer is “no”, because alcohol consumption, particularly in large quantities, increases the risk of breast cancer in women. So, for women, alcohol consumption in moderation may also provide overall health benefits, as it does for men; but for different reasons.

Blood glucose levels in birds are high yet HbA1c levels are low: Can vitamin C have anything to do with this?

Blood glucose levels in birds are often 2-4 times higher than those in mammals of comparable size. Yet birds often live 3 times longer than mammals of comparable size. This is paradoxical. High glucose levels are generally associated with accelerated senescence, but birds seem to age much slower than mammals. Several explanations have been proposed for this, one of which is related to the formation of advanced glycation endproducts (AGEs).

Glycation is a process whereby sugar molecules “stick” to protein or fat molecules, impairing their function. Glycation leads to the formation of AGEs, which seem to be associated with a host of diseases, including diabetes, and to be implicated in accelerated aging (or “ageing”, with British spelling).

The graphs below, from Beuchat & Chong (1998), show the glucose levels (at rest and prior to feeding) and HbA1c levels (percentage of glycated hemoglobin) in birds and mammals. HbA1c is a measure of the degree of glycation of hemoglobin, a protein found in red blood cells. As such HbA1c (given in percentages) is a good indicator of the rate of AGE formation within an animal’s body.

The glucose levels are measured in mmol/l; they should be multiplied by 18 to obtain the respective measures in mg/dl. For example, the 18 mmol/l glucose level for the Anna’s (a hummingbird species) is equivalent to 324 mg/dl. Even at that high level, well above the level of a diabetic human, the Anna’s hummingbird species has an HbA1c of less than 5, which is lower than that for most insulin sensitive humans.

How can that be?

There are a few possible reasons. Birds seem to have evolved better mechanisms to control cell permeability to glucose, allowing glucose to enter cells very selectively. Birds also seem to have a higher turnover of cells where glycation and thus AGE formation results. The lifespan of red blood cells in birds, for example, is only 50 to 70 percent that of mammals.

But one of the most interesting mechanisms is vitamin C synthesis. Not only is vitamin C a powerful antioxidant, but it also has the ability to reversibly bind to proteins at the sites where glycation would occur. That is, vitamin C has the potential to significantly reduce glycation. The vast majority of birds and mammals can synthesize vitamin C. Humans are an exception. They have to get it from their diet.

This may be one of the many reasons why isolated human groups with traditional diets high in fruits and starchy tubers, which lead to temporary blood glucose elevations, tend to have good health. Fruits and starchy tubers in general are good sources of vitamin C.

Grains and seeds are not.

References

Beuchat, C.A., & Chong, C.R. (1998). Hyperglycemia in hummingbirds and its consequences for hemoglobin glycation. Comparative Biochemistry and Physiology Part A, 120(3), 409–416.

Holmes D.J., Flückiger, R., & Austad, S.N. (2001). Comparative biology of aging in birds: An update. Experimental Gerontology, 36(4), 869-883.

How to become diabetic in 6 hours!? Thanks Dr. Delgado for bringing science to the masses!

(Note: My apologies for the sarcastic tone of this post. I am not really congratulating anybody here!)

Dr. Nick Delgado shows us in this YouTube video how to "become diabetic" in 6 hours!

I must admit that I liked the real-time microscope imaging, and wish he had shown us more of that.

But really!

After consulting with my mentor, the MIMIW, I was reminded that there is at least one post on this blog that shows how one can "become diabetic" in just over 60 minutes – that is, about 6 times faster than using the technique described by Dr. Delgado.

The technique used in the post mentioned above is called "intense exercise", which is even believed to be health-promoting! (Unlike drinking olive oil as if it was water, or eating white bread.)

The advantage of this technique is that one can "become diabetic" by doing something healthy!

Thanks Dr. Delgado, your video ranks high up there, together with this Ali G. video, as a fine example of how to bring real science to the masses.

Lipotoxicity or tired pancreas? Abnormal fat metabolism as a possible precondition for type 2 diabetes

The term “diabetes” is used to describe a wide range of diseases of glucose metabolism; diseases with a wide range of causes. The diseases include type 1 and type 2 diabetes, type 2 ketosis-prone diabetes (which I know exists thanks to Michael Barker’s blog), gestational diabetes, various MODY types, and various pancreatic disorders. The possible causes include genetic defects (or adaptations to very different past environments), autoimmune responses, exposure to environmental toxins, as well as viral and bacterial infections; in addition to obesity, and various other apparently unrelated factors, such as excessive growth hormone production.

Type 2 diabetes and the “tired pancreas” theory

Type 2 diabetes is the one most commonly associated with the metabolic syndrome, which is characterized by middle-age central obesity, and the “diseases of civilization” brought up by Neolithic inventions. Evidence is mounting that a Neolithic diet and lifestyle play a key role in the development of the metabolic syndrome. In terms of diet, major suspects are engineered foods rich in refined carbohydrates and refined sugars. In this context, one widely touted idea is that the constant insulin spikes caused by consumption of those foods lead the pancreas (figure below from Wikipedia) to get “tired” over time, losing its ability to produce insulin. The onset of insulin resistance mediates this effect.

Empirical evidence against the “tired pancreas” theory

This “tired pancreas” theory, which refers primarily to the insulin-secreting beta-cells in the pancreas, conflicts with a lot of empirical evidence. It is inconsistent with the existence of isolated semi/full hunter-gatherer groups (e.g., the Kitavans) that consume large amounts of natural (i.e., unrefined) foods rich in easily digestible carbohydrates from tubers and fruits, which cause insulin spikes. These groups are nevertheless generally free from type 2 diabetes. The “tired pancreas” theory conflicts with the existence of isolated groups in China and Japan (e.g., the Okinawans) whose diets also include a large proportion of natural foods rich in easily digestible carbohydrates, which cause insulin spikes. Yet these groups are generally free from type 2 diabetes.

Humboldt (1995), in his personal narrative of his journey to the “equinoctial regions of the new continent”, states on page 121 about the natives as a group that: "… between twenty and fifty years old, age is not indicate by wrinkling skin, white hair or body decrepitude [among natives]. When you enter a hut is hard to differentiate a father from son …" A large proportion of these natives’ diets included plenty of natural foods rich in easily digestible carbohydrates from tubers and fruits, which cause insulin spikes. Still, there was no sign of any condition that would suggest a prevalence of type 2 diabetes among them.

At this point it is important to note that the insulin spikes caused by natural carbohydrate-rich foods are much less pronounced than the ones caused by refined carbohydrate-rich foods. The reason is that there is a huge gap between the glycemic loads of natural and refined carbohydrate-rich foods, even though the glycemic indices may be quite similar in some cases. Natural carbohydrate-rich foods are not made mostly of carbohydrates. Even an Irish (or white) potato is 75 percent water.

More insulin may lead to abnormal fat metabolism in sedentary people

The more pronounced spikes may lead to abnormal fat metabolism because more body fat is force-stored than it would have been with the less pronounced spikes, and stored body fat is not released just as promptly as it should be to fuel muscle contractions and other metabolic processes. Typically this effect is a minor one on a daily basis, but adds up over time, leading to fairly unnatural patterns of fat metabolism in the long run. This is particularly true for those who lead sedentary lifestyles. As for obesity, nobody gets obese in one day. So the key problem with the more pronounced spikes may not be that the pancreas is getting “tired”, but that body fat metabolism is not normal, which in turn leads to abnormally high or low levels of important body fat-derived hormones (e.g., high levels of leptin and low levels of adiponectin).

One common characteristic of the groups mentioned above is absence of obesity, even though food is abundant and often physical activity is moderate to low. Repeat for emphasis: “… even though food is abundant and often physical activity is moderate to low”. Note that having low levels of activity is not the same as spending the whole day sitting down in a comfortable chair working on a computer. Obviously caloric intake and level of activity among these groups were/are not at the levels that would lead to obesity. How could that be possible? See this post for a possible explanation.

Excessive body fat gain, lipotoxicity, and type 2 diabetes

There are a few theories that implicate the interaction of abnormal fat metabolism with other factors (e.g., genetic factors) in the development of type 2 diabetes. Empirical evidence suggests that this is a reasonable direction of causality. One of these theories is the theory of lipotoxicity.

Several articles have discussed the theory of lipotoxicity. The article by Unger & Zhou (2001) is a widely cited one. The theory seems to be widely based on the comparative study of various genotypes found in rats. Nevertheless, there is mounting evidence suggesting that the underlying mechanisms may be similar in humans. In a nutshell, this theory proposes the following steps in the development of type 2 diabetes:

    (1) Abnormal fat mass gain leads to an abnormal increase in fat-derived hormones, of which leptin is singled out by the theory. Some people seem to be more susceptible than others in this respect, with lower triggering thresholds of fat mass gain. (What leads to exaggerated fat mass gains? The theory does not go into much detail here, but empirical evidence from other studies suggests that major culprits are refined grains and seeds, as well as refined sugars; other major culprits seem to be trans fats, and vegetable oils rich in linoleic acid.)

    (2) Resistance to fat-derived hormones sets in. Again, leptin resistance is singled out as the key here. (This is a bit simplistic. Other fat-derived hormones, like adiponectin, seem to clearly interact with leptin.) Since leptin regulates fatty acid metabolism, the theory argues, leptin resistance is hypothesized to impair fatty acid metabolism.

    (3) Impaired fat metabolism causes fatty acids to “spill over” to tissues other than fat cells, and also causes an abnormal increase in a substance called ceramide in those tissues. These include tissues in the pancreas that house beta-cells, which secrete insulin. In short, body fat should be stored in fat cells (adipocytes), not outside them.

    (4) Initially fatty acid “spill over” to beta-cells enlarges them and makes them become overactive, leading to excessive insulin production in response to carbohydrate-rich foods, and also to insulin resistance. This is the pre-diabetic phase where hypoglycemic episodes happen a few hours following the consumption of carbohydrate-rich foods. Once this stage is reached, several natural carbohydrate-rich foods also become a problem (e.g., potatoes and bananas), in addition to refined carbohydrate-rich foods.

    (5) Abnormal levels of ceramide induce beta-cell apoptosis in the pancreas. This is essentially “death by suicide” of beta cells in the pancreas. What follows is full-blown type 2 diabetes. Insulin production is impaired, leading to very elevated blood glucose levels following the consumption of carbohydrate-rich foods, even if they are unprocessed.

It is widely known that type 2 diabetics have impaired glucose metabolism. What is not so widely known is that usually they also have impaired fatty acid metabolism. For example, consumption of the same fatty meal is likely to lead to significantly more elevated triglyceride levels in type 2 diabetics than non-diabetics, after several hours. This is consistent with the notion that leptin resistance precedes type 2 diabetes, and inconsistent with the “tired pancreas” theory.

Weak and strong points of the theory of lipotoxicity

A weakness of the theory of lipotoxicity is its strong lipophobic tone; at least in the articles that I have read. See, for example, this article by Roger H. Unger in the Journal of the American Medical Association. There is ample evidence that eating a lot of the ultra-demonized saturated fat, per se, is not what makes people obese or type 2 diabetic. Yet overconsumption of trans fats and vegetable oils rich in linoleic acid does seem to be linked with obesity and type 2 diabetes. (So does the consumption of refined grains and seeds, and refined sugars.) The theory of lipotoxicity does not seem to make these distinctions.

In defense of the theory of lipotoxicity, it does not argue that there cannot be thin diabetics. Many type 1 diabetics are thin. Type 2 diabetics can also be thin, even though that is much less common. In certain individuals, the threshold of body fat gain that will precipitate lipotoxicity may be quite low. In others, the same amount of body fat gain (or more) may in fact increase their insulin sensitivity under certain circumstances – e.g., when growth hormone levels are abnormally low.

Autoimmune disorders, perhaps induced by environmental toxins, or toxins found in certain refined foods, may cause the immune system to attack the beta-cells in the pancreas. This may lead to type 1 diabetes if all beta cells are destroyed, or something that can easily be diagnosed as type 2 (or type 1.5) diabetes if only a portion of the cells are destroyed, in a way that does not involve lipotoxicity.

Nor does the theory of lipotoxicity predict that all those who become obese will develop type 2 diabetes. It only suggests that the probability will go up, particularly if other factors are present (e.g., genetic propensity). There are many people who are obese during most of their adult lives and never develop type 2 diabetes. On the other hand, some groups, like Hispanics, tend to develop type 2 diabetes more easily (often even before they reach the obese level). One only has to visit the South Texas region near the Rio Grande border to see this first hand.

What the theory proposes is a new way of understanding the development of type 2 diabetes; a way that seems to make more sense than the “tired pancreas” theory. The theory of lipitoxicity may not be entirely correct. For example, there may be other mechanisms associated with abnormal fat metabolism and consumption of Neolithic foods that cause beta-cell “suicide”, and that have nothing to do with lipotoxicity as proposed by the theory. (At least one fat-derived hormone, tumor necrosis factor-alpha, is associated with abnormal cell apoptosis when abnormally elevated. Levels of this hormone go up immediately after a meal rich in refined carbohydrates.) But the link that it proposes between obesity and type 2 diabetes seems to be right on target.

Implications and thoughts

Some implications and thoughts based on the discussion above are the following. Some are extrapolations based on the discussion in this post combined with those in other posts. At the time of this writing, there were 90 posts on this blog, in addition to many comments. See under "Labels" at the bottom-right area of this blog for a summary of topics addressed. It is hard to ignore things that were brought to light in previous posts.

    - Let us start with a big one: Avoiding natural carbohydrate-rich foods in the absence of compromised glucose metabolism is unnecessary. Those foods do not “tire” the pancreas significantly more than protein-rich foods do. While carbohydrates are not essential macronutrients, protein is. In the absence of carbohydrates, protein will be used by the body to produce glucose to supply the needs of the brain and red blood cells. Protein elicits an insulin response that is comparable to that of natural carbohydrate-rich foods on a gram-adjusted basis (but significantly lower than that of refined carbohydrate-rich foods, like doughnuts and bagels). Usually protein does not lead to a measurable glucose response because glucagon is secreted together with insulin in response to ingestion of protein, preventing hypoglycemia.

    - Abnormal fat gain should be used as a general measure of one’s likelihood of being “headed south” in terms of health. The “fitness” level for men and women shown on the table in this post seem like good targets for body fat percentage. The problem here, of course, is that this is not as easy as it sounds. Attempts at getting lean can lead to poor nutrition and/or starvation. These may make matters worse in some cases, leading to hormonal imbalances and uncontrollable hunger, which will eventually lead to obesity. Poor nutrition may also depress the immune system, making one susceptible to a viral or bacterial  infection that may end up leading to beta-cell destruction and diabetes. A better approach is to place emphasis on eating a variety of natural foods, which are nutritious and satiating, and avoiding refined ones, which are often addictive “empty calories”. Generally fat loss should be slow to be healthy and sustainable.

    - Finally, if glucose metabolism is compromised, one should avoid any foods in quantities that cause an abnormally elevated glucose or insulin response. All one needs is an inexpensive glucose meter to find out what those foods are. The following are indications of abnormally elevated glucose and insulin responses, respectively: an abnormally high glucose level 1 hour after a meal (postprandial hyperglycemia); and an abnormally low glucose level 2 to 4 hours after a meal (reactive hypoglycemia). What is abnormally high or low? Take a look at the peaks and troughs shown on the graph in this post; they should give you an idea. Some insulin resistant people using glucose meters will probably realize that they can still eat several natural carbohydrate-rich foods, but in small quantities, because those foods usually have a low glycemic load (even if their glycemic index is high).

Lucy was a vegetarian and Sapiens an omnivore. We apparently have not evolved to be pure carnivores, even though we can be if the circumstances require. But we absolutely have not evolved to eat many of the refined and industrialized foods available today, not even the ones marketed as “healthy”. Those foods do not make our pancreas “tired”. Among other things, they “mess up” fat metabolism, which may lead to type 2 diabetes through a complex process involving hormones secreted by body fat.

References

Humboldt, A.V. (1995). Personal narrative of a journey to the equinoctial regions of the new continent. New York, NY: Penguin Books.

Unger, R.H., & Zhou, Y.-T. (2001). Lipotoxicity of beta-cells in obesity and in other causes of fatty acid spillover. Diabetes, 50(1), S118-S121.

Growth hormone, insulin resistance, body fat accumulation, and glycogen depletion: Making sense of a mysterious hormone replacement therapy outcome

Hormone replacement therapies are prescribed in some cases, for medical reasons. They usually carry some risks. The risks come in part from the body down-regulating its own production of hormones when hormones are taken orally or injected. This could be seen as a form of compensatory adaptation, as the body tries to protect itself from abnormally high hormone levels.

More often than not the down-regulation can be reversed by interrupting the therapy. In some cases, the down-regulation becomes permanent, leading to significant health deterioration over the long run. One can seriously regret having started the hormone replacement therapy in the first place. The same is true (if not more) for hormone supplementation for performance enhancement, where normal hormone secretion levels are increased to enhance (mostly) athletic performance.

Rosenfalck and colleagues (1999) conducted an interesting study linking growth hormone (GH) replacement therapy with insulin resistance. Their conclusions are not very controversial. What I find interesting is what their data analysis unveiled and was not included in their conclusions. Also, they explain their main findings by claiming that there was a deterioration of beta cell function. (Beta cells are located in the pancreas, and secrete insulin.) While they may be correct, their explanation is not very plausible, as you will see below.

Let us take a quick look at what past research says about GH therapy and insulin resistance. One frequent finding is a significant but temporary impairment of insulin sensitivity, which usually normalizes after a period of a few months (e.g., 6 months). Another not so frequent finding is a significant and permanent impairment of insulin sensitivity; this is not as frequent in healthy individuals.

The researchers did a good job at reviewing this literature, and concluded that in many cases GH therapy is not worth the risk. They also studied 24 GH-deficient adults (18 males and 6 females). All of them had known pituitary pathology, which caused the low GH levels. The participants were randomly assigned to two groups. One received 4 months treatment with biosynthetic GH daily (n=13); the other received a placebo (n=11).

The table below (click on it to enlarge) shows various measures before and after treatment. Note the significant reduction in abdominal fat mass in the GH group. Also note that, prior to the treatment, the GH group folks (who were GH-deficient) were overall much heavier and much fatter, particular at the abdominal area, than the folks in the placebo (or control) group.

From the measures above one could say that the treatment was a success. But the researchers point out that it was not, because insulin sensitivity was significantly impaired. They show some graphs (below), and that is where things get really interesting, but not in the way intended by the researchers.

On the figure above, the graphs on the left refer to the placebo group, and on the right to the GH group. The solid lines reflect pre-treatment numbers and dotted lines post-treatment numbers. Indeed, GH therapy is making the GH-deficient folks significantly more insulin resistant.

But look carefully. The GH folks are more insulin sensitive than the controls prior to the treatment, even though they are much fatter, particularly in terms of abdominal fat. The glucose response is significantly lower for the GH-deficient folks, and that is not due to them secreting more insulin. The insulin response is also significantly lower. This is confirmed by glucose and insulin “area under the curve” measures provided by the researchers.

In fact, after treatment both groups seem to have generally the same insulin and glucose responses. This means that the GH treatment made insulin-sensitive folks a bit more like their normal counterparts in the placebo group. But obviously the change for the worse occurred only in the GH group, which is what the researchers concluded.

Now to the really interesting question, at least in my mind: What could have improved insulin sensitivity in the GH-deficient group prior to the treatment?

The GH-deficient folks had more body fat, particularly around the abdominal area. High serum GH is usually associated with low body fat, particularly around the abdominal area, because high GH folks burn it easily. So, looking at it from a different perspective, the GH-deficient folks seem to have been more effective at making body fat, and less effective at burning it.

Often we talk about insulin sensitivity as though there was only one type. But there is more than one type of insulin sensitivity. Insulin signals to the liver to take up glucose from the blood and turn it into glycogen or fat. Insulin also signals to body fat tissue to take up glucose from the blood and make fat with it. (GLUT 4 is an insulin-sensitive glucose transporter present in both fat and muscle cells.)

Therefore, it is reasonable to assume that folks with fat cells that are particularly insulin-sensitive would tend to make body fat quite easily based on glucose. While this is a type of insulin sensitivity that most people probably do not like to have, it may play an important role in reducing blood glucose levels under certain conditions. This appears to be true in the short term. Down the road, having very insulin-sensitive fat cells seems to lead to obesity, the metabolic syndrome, and diabetes.

In fact, in individuals without pituitary pathology, increased insulin sensitivity in fat cells could be a compensatory adaptation in response to a possible decrease in liver and muscle glucose uptake. Lack of exercise will shift the burden of glucose clearance to tissues other than liver and muscle, because with glycogen stores full both liver and muscle will usually take up much less blood glucose than they would otherwise.

I am speculating here, but I think that in individuals without pituitary pathology, an involuntary decrease in endogenous GH secretion may actually be at the core of this compensatory adaptation mechanism. In these individuals, low GH levels may be an outcome, not a cause of problems. This would explain two apparently contradictory findings: (a) GH levels drop dramatically in the 40s, particularly for men; and (b) several people in their 50s and 60s, including men, have much higher levels of circulating GH than people in their 40s, and even than much younger folks.

Vigorous exercise increases blood glucose uptake, inside and outside the exercise window; this is an almost universal effect among humans. Exercise depletes muscle and liver glycogen. (Fasting and low-carbohydrate dieting alone deplete liver, but not muscle, glycogen.) As glycogen stores become depleted, the activity of glycogen synthase (an enzyme involved in the conversion of glucose to glycogen) increases acutely. This activity remains elevated for several days in muscle tissue; the liver replenishes its glycogen in a matter of hours. With glycogen synthase activity elevated, glucose is quickly used to replenish glycogen stores, and not to make fat.

Depleting glycogen stores on a regular basis (e.g., once every few days) may over time reverse the adaptations that made fat cells particularly insulin-sensitive in the first place. Those adaptations become a protection that is not only no longer needed but also detrimental to health, since they lead to obesity. This could be the reason why many people initially find it difficult to lower their body fat set point, but once they lose body fat and stay lean for a while, they seem to become able to maintain their leanness without much effort.

Well, perhaps glycogen-depleting exercise is more important than many people think. It can help make you thin, but through a circuitous path.

And, incidentally, glycogen-depleting exercise causes a temporary but dramatic spike in GH secretion. This natural increase in GH secretion does not seem to be associated with any significant impairment in overall insulin sensitivity, even though glycogen-depleting exercise increases blood glucose levels a lot during the exercise window. This is a temporary and physiological, not pathological, phenomenon.

Reference:

Rosenfalck A.M., Fisker, S., Hilsted, J., Dinesen, B., Vølund, A., Jørgensen, J.O., Christiansen, J.S., & Madsbad, S. (1999). The effect of the deterioration of insulin sensitivity on beta-cell function in growth-hormone-deficient adults following 4-month growth hormone replacement therapy. Growth Hormone & IGF Research, 9(2), 96–105.