Hunger is your best friend: It makes natural foods taste delicious and promotes optimal nutrient partitioning

One of the biggest problems with modern diets rich in industrial foods is that they promote unnatural hunger patterns. For example, hunger can be caused by hypoglycemic dips, coupled with force-storage of fat in adipocytes, after meals rich in refined carbohydrates. This is a double-edged post-meal pattern that is induced by, among other things, abnormally elevated insulin levels. The resulting hunger is a rather unnatural type of hunger.

By the way, I often read here and there, mostly in blogs, that “insulin suppresses hunger”. I frankly don’t know where this idea comes from. What actually happens is that insulin is co-secreted with a number of other hormones. One of those, like insulin also secreted by the beta-cells in the pancreas, is amylin – a powerful appetite suppressor. Amylin deficiency leads to hunger even after a large carbohydrate-rich meal, when insulin levels are elevated.

Abnormally high insulin levels – like those after a “healthy” breakfast of carbohydrate-rich cereals, pancakes etc. – lead to abnormal blood glucose dips soon after the meal. What I am talking about here is a fall in glucose levels that is considerable, and that also happens very fast – illustrated by the ratio between the lengths of the vertical and horizontal black lines on the figure below, from a previous post ().

Those hypoglycemic dips induce hunger, because the hormonal changes necessary to apply a break to the fall in glucose levels (which left unchecked would lead to death) leave us with a hormonal mix that ends up stimulating hunger, in an unnatural way. At the bottom of those dips, insulin levels are much lower than before. I am not talking about diabetics here. I am talking about normoglycemic folks, like the ones whose glucose levels are show on the figure above.

On a diet primarily of natural foods, or foods that are not heavily modified from their natural state, hunger patterns tend to be better synchronized with nutrient deficiencies. This is one of the main advantages of a natural foods diet. By nutrients, I do not mean only micronutrients such as vitamins and minerals, but also macronutrients such as amino and fatty acids.

On a natural diet, nutrient deficiencies should happen regularly. Our bodies are designed for sporadic nutrient intake, remaining most of the time in the fasted state. Human beings are unique in that they have very large brains in proportion to their overall body size, brains that run primarily on glucose – the average person’s brain consumes about 5 g/h of glucose. This latter characteristic makes it very difficult to extrapolate diet-based results based on other species to humans.

As hunger becomes better synchronized with nutrient deficiencies, it should promote optimal nutrient partitioning. This means that, among other things: (a) you should periodically feel hungry for different types of food, depending on your nutrient needs at that point in time; (b) if you do weight training, and fell hungry, some muscle gain should follow; and (c) if you let hunger drive food consumption, on a diet of predominantly natural foods, body fat levels should remain relatively low.

In this sense, hunger becomes your friend – and the best spice!

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 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.