Hormonal reductionism is as myopic as biochemical reductionism

Biochemistry-based arguments can be very misleading. Yet, biochemistry can be extremely useful in the elucidation of diet and lifestyle effects that are suggested by well-designed studies of humans. If you start with a biochemistry-based argument though, and ignore actual studies of humans, you can easily convince someone that glycogen-depleting exercise (e.g., weight training) is unhealthy, because many health markers change for the worse after that type of exercise. But it is the damage caused by glycogen-depleting exercise that leads to health improvements, via short- and long-term compensatory adaptations ().

Biochemistry is very helpful in terms of providing “pieces for the puzzle”, but biochemical reductionism is a problem. Analogous to biochemical reductionism, and perhaps one example of it, is hormonal reductionism – trying to argue that all diet and lifestyle effects are mediated by a single hormone. A less extreme position, but still myopic, is to argue that all diet and lifestyle effects are mostly mediated by a single hormone.

One of my own “favorite” hormones is adiponectin, which I have been discussing for years in this blog (). Increased serum adiponectin has been found to be significantly associated with: decreased body fat (particularly decreased visceral fat), decreased risk of developing diabetes type 2, and decreased blood pressure. Adiponectin appears to also have anti-inflammatory and athero-protective properties.

As a side note, typically women have higher levels of serum adiponectin than men, particularly young women. Culturally we have a tendency to see young women as “delicate” and “vulnerable”. Guess what? Young women are the closest we get to “indestructible” in the human species. And there is an evolutionary reason for that, which is that fertile women have been in our evolutionary past, and still are, the bottleneck of any population. A population of 100 individuals, where 99 are men and 1 is a woman, will quickly disappear. If it is 99 women and 1 fertile man, the population will grow; but there will also be some problems due to inbreeding. Even if the guy is ugly the population will grow; without competition, he will look very cute.

Jung and colleagues measured various hormone levels in 78 obese people who had visited obesity clinics at five university hospitals (Ajou, Ulsan, Catholic, Hanyang and Yonsei) in Korea (). Those folks restricted their caloric intake to 500 calories less than their usual intake, and exercised, for 12 weeks. Below are the measured changes in tumor necrosis factor α (TNF-α, now called only TNF), interleukin-6 (IL-6), resistin, leptin, adiponectin, and interleukin-10 (IL-10).

We see from the table above that the hormonal changes were all significant (all at the P equal to or lower than 0.001 level except one, at the P lower than 0.05 level), and all indicative of health improvements. The serum concentrations of all hormones decreased, with two exceptions – adiponectin and interleukin-10, which increased. Interleukin-10 is an anti-inflammatory hormone produced by white blood cells. The most significant increase of the two was by far in adiponectin (P = .001, versus P = .041 for interleukin-10).

Now, should we try to find a way of producing synthetic adiponectin then? My guess is that doing that will not lead to very positive results in human trials; because, as you can see from the table, hormones vary in concert. At the moment, the only way to “supplement” adiponectin is to lose body fat, and that leads to concurrent changes in many other hormones (e.g., TNF decreases).

Trying to manipulate one single hormone, or build an entire health-improvement approach based on its effects, is myopic. But that is what often happens. Leptin is a relatively recent example.

One reason why biochemistry is so complex, with so many convoluted processes, is that evolution is a tinkerer that is “blind” to complexity. Traits appear at random in populations and spread if they increase reproductive success; even if they decrease survival success, by the way ().

Evolution is not an engineer, and is not even our “friend” (). To optimize our health, we need to “hack” evolution.

If your NEAT is low, maybe you should chill

I wrote most of this post a while ago, and then forgot about it. The recent blogosphere storm of comments regarding cold-induced thermogenesis caught me by surprise (), and provided a motivation to get this post out. Contrary to popular perception, I guess, cold-induced thermogenesis is an extensively researched topic. Some reasonably well cited references are linked here.

Let us backtrack a bit. When people say that they want to lose weight, usually what they really want is to lose is body fat. However, they frequently do things that make them lose what they do not want – muscle glycogen, water, and even some muscle protein. Physical activity in general depletes muscle glycogen; even aerobic physical activity.

Walking, for example, depletes muscle glycogen; but slowly, and proportionally to how fast one walks. Weight training and sprints deplete muscle glycogen much faster. Whatever depletes muscle glycogen also lowers the amount of water stored in myocytes (muscle cells), effectively reducing muscle mass. Depleted muscle glycogen needs to be replenished; protein and carbohydrates are the sources. If you deplete muscle glycogen through strength training, this will provide a strong stimulus for glycogen replenishment and thus muscle growth, even beyond the original level – a phenomenon called supercompensation ().

In conjunction with strength training, situations in which one burns mostly fat, and very little glycogen, should be at the top of the list for those wishing to lose weight by losing body fat and nothing else. These are not very common though. One example is nonexercise activity thermogenesis (NEAT), or heat generation from nonexercise activities such as fidgeting (). There is a great deal of variation in NEAT across individuals; for some it is high, for others it is annoyingly low.

Walking slowly is almost as good as NEAT for body fat burning, when done in conjunction with strength training. Up the pace a bit though, and you’ll be burning more muscle glycogen. But if you walk slowly you don’t burn that much body fat per unit of time. If you walk a bit faster you’ll burn more fat, but also more glycogen. C’mon, there is no way to win in this game!

This is why being physically active, in a “non-exercise way”, seems to be so important for health; together with strength training, limiting calorie intake, and all the while having a nutritious diet. These are not very common things in modern urban environments. Long term, there isn’t a lot of margin for error. It is ultimately a game of small numbers in the short term, played over long periods of time.

But there is an alternative if your NEAT is low – just chill. That is, another situation in which one can burn mostly fat, and very little glycogen, is exposure to mildly cold temperatures, but above the level that induces shivering (mild cold: 16 degrees Celsius or so; about 60 degrees Fahrenheit). Shivering in general, and particularly intense shivering, is associated with levels of muscle activity that would induce glycogen depletion () (). If muscle glycogen depletion happens while one is fasting, liver glycogen will be used to replenish muscle glycogen, and also to supply the needs of the brain – which is always hungry for glucose.

As the liver glycogen tank goes down beyond a certain point, and no protein or carbohydrates are eaten, the body will use amino acids from muscle to produce glucose. Muscle glycogen will be locked until it is needed. Interesting eh!? The body sacrifices muscle protein but doesn’t tap into muscle glycogen, which is only used to fuel violent muscle contractions. We are talking about fight-or-flight responses here. From an evolutionary perspective, sacrificing some muscle beats losing a lot of it to a predator any day.

Cold-induced thermogenesis is a very interesting phenomenon. The figure below, where open circles represent lean and closed circles obese folks, shows that it leads to different responses in lean and obese folks, and also that it presents a lot of variation across different individuals (like NEAT). This type of thermogenesis actually seems to be strongly associated with an increase in NEAT (); although it seems to also be associated with futile cycles used by the body to generate heat without any movement, as in thermogenesis during hibernation in certain animals () (). Having more brown fat as an adult, or being able to make brown fat more easily, is associated with more cold-induced thermogenesis; and also with a lower obesity risk.

In fact, cold-induced thermogenesis leads to an increase in energy expenditure that is comparable with that of another major energy sinkhole – overfeeding () (). Unlike overfeeding though, cold-induced thermogenesis does not require calories to go in. And, no, you don’t burn more than you take in with overfeeding.

How can one burn fat via cold-induced thermogenesis? Here are some ideas. Set the home thermostat to a mildly cold temperature in the winter (this will also save you some money). When it is a little cooler than normal, don’t wear heavy clothes. Take mildly cold showers, or end a warm shower with some mildly cold water.

What about more extreme cold exposure? It should be no surprise that one would feel pretty good after a dip in ice-cold water; that is, if the person does not suffer from a glycogen storage disease (e.g., McArdle's disease). At least in theory, that type of cold exposure should induce whole-body muscle glycogen depletion, just like an intense whole-body exercise session, with the resulting hormonal changes ().

Growth hormone should be up after that, perhaps for hours. Done right after weight training, or intense exercise, it may have a boosting effect on the hormonal response. But if you do that in the recovery phase (e.g., several hours after the weight training session), it should impair muscle recovery. It would be a bit like doing another strength training session, when the body is trying to recover from the previous one.

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?