Lifeworld

The photos below show Doug Miller and his wife, Stephanie Miller. Doug is one of the most successful natural bodybuilders in the U.S.A. today. He is also a manager at an economics consulting firm and an entrepreneur. As if these were not enough, now he can add book author to his list of accomplishments. His book, Biology for Bodybuilders, has just been published.


Doug studied biochemistry, molecular biology, and economics at the undergraduate level. His co-authors are Glenn Ellmers and Kevin Fontaine. Glenn is a regular commenter on this blog, a professional writer, and a certified Strength and Conditioning Specialist. Dr. Fontaine is an Associate Professor at the Johns Hopkins University’s School of Medicine and Bloomberg School of Public Health.

Biology for Bodybuilders is written in the first person by Doug, which is one of the appealing aspects of the book. This also allows Doug to say that his co-authors disagree with him sometimes, even as he outlines what works for him. Both Glenn and Kevin are described as following Paleolithic dieting approaches. Doug follows a more old school bodybuilding approach to dieting – e.g., he eats grains, and has multiple balanced meals everyday.

This relaxed approach to team writing neutralizes criticism from those who do not agree with Doug, at least to a certain extent. Maybe it was done on purpose; a smart idea. For example, I do not agree with everything Doug says in the book, but neither do Doug’s co-authors, by his own admission. Still, one thing we all have to agree with – from a competitive sports perspective, no one can question success.

At less than 120 pages, the book is certainly not encyclopedic, but it is quite packed with details about human physiology and metabolism for a book of this size. The scientific details are delivered in a direct and simple manner, through what I would describe as very good writing.

Doug has interesting ideas on how to push his limits as a bodybuilder. For example, he likes to train for muscle hypertrophy at around 20-30 lbs above his contest weight. Also, he likes to exercise at high repetition ranges, which many believe is not optimal for muscle growth. He does that even for mass building exercises, such as the deadlift. In this video he deadlifts 405 lbs for 27 repetitions.

Here it is important to point out that whether one is working out in the anaerobic range, which is where muscle hypertrophy tends to be maximized, is defined not by the number of repetitions but by the number of seconds a muscle group is placed under stress. The anaerobic range goes from around 20 to 120 seconds. If one does many repetitions, but does them fast, he or she will be in the anaerobic range. Incidentally, this is the range of strength training at which glycogen depletion is maximized.

I am not a bodybuilder, nor do I plan on becoming one, but I do admire athletes that excel in narrow sports. Also, I strongly believe in the health-promoting effects of moderate glycogen-depleting exercise, which includes strength training and sprints. Perhaps what top athletes like Doug do is not exactly optimal for long-term health, but it certainly beats sedentary behavior hands down. Or maybe top athletes will live long and healthy lives because the genetic makeup that allows them to be successful athletes is also conducive to great health.

In this respect, however, Doug is one of the people who have gotten the closest to convincing me that genes do not influence so much what one can achieve as a bodybuilder. In the book he shows a photo of himself at age 18, when he apparently weighed not much more than 135 lbs. Now, in his early 30s, he weighs 210-225 lbs during the offseason, at a height of 5'9". He has achieved this without taking steroids. Maybe he is a good example of compensatory adaptation, where obstacles lead to success.

If you are interested in natural bodybuilding, and/or the biology behind it, this book is highly recommended!

The figure below is one of many in Weston Price’s outstanding book Nutrition and Physical Degeneration showing evidence of teeth crowding among children whose parents moved from a traditional diet of minimally processed foods to a Westernized diet.


Tooth crowding and other forms of malocclusion are widespread and on the rise in populations that have adopted Westernized diets (most of us). Some blame it on dental caries, particularly in early childhood; dental caries are also a hallmark of Westernized diets. Varrela (2007), however, in a study of Finnish skulls from the 15th and 16th centuries found evidence of dental caries, but not of malocclusion, which Varrela reported as fairly high in modern Finns.

Why does malocclusion occur at all in the context of Westernized diets? Lombardi (1982) put forth an evolutionary hypothesis:

“In modern man there is little attrition of the teeth because of a soft, processed diet; this can result in dental crowding and impaction of the third molars. It is postulated that the tooth-jaw size discrepancy apparent in modern man as dental crowding is, in primitive man, a crucial biologic adaptation imposed by the selection pressures of a demanding diet that maintains sufficient chewing surface area for long-term survival. Selection pressures for teeth large enough to withstand a rigorous diet have been relaxed only recently in advanced populations, and the slow pace of evolutionary change has not yet brought the teeth and jaws into harmonious relationship.”

So what is one to do? Apparently getting babies to eat meat is not a bad idea. They may well just chew on it for a while and spit it out. The likelihood of meat inducing dental caries is very low, as most low carbers can attest. (In fact, low carbers who eat mostly meat often see dental caries heal.)

Concerned about the baby choking on meat? At the time of this writing a Google search yielded this: No results found for “baby choked on meat”. Conversely, Google returned 219 hits for “baby choked on milk”.

What if you have a child with crowded teeth as a preteen or teen? Too late? Should you get him or her to use “cute” braces? Our daughter had crowded teeth a few years ago, as a preteen. It overlapped with the period of my transformation, which meant that she started having a lot more natural foods to eat. There were more of those around, some of which require serious chewing, and less industrialized soft foods. Those natural foods included hard-to-chew beef cuts, served multiple times a week.

We noticed improvement right away, and in a few years the crowding disappeared. Now she has the kind of smile that could land her a job as a toothpaste model:


The key seems to be to start early, in developmental years. If you are an adult with crowded teeth, malocclusion may not be solved by either tough foods or braces. With braces, you may even end up with other problems (see this).

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.

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.

Mazess & Mather (1974) is probably the most widely cited article summarizing evidence that bone mineral content in older North Alaskan Eskimos was lower (10 to 15 percent) than that of United States whites. Their finding has been widely attributed to the diet of the Eskimos, which is very high in animal protein. Here is what they say:

“The sample consisted of 217 children, 89 adults, and 107 elderly (over 50 years). Eskimo children had a lower bone mineral content than United States whites by 5 to 10% but this was consistent with their smaller body and bone size. Young Eskimo adults (20 to 39 years) of both sexes were similar to whites, but after age 40 the Eskimos of both sexes had a deficit of from 10 to 15% relative to white standards.”

Note that their findings refer strictly to Eskimos older than 40, not Eskimo children or even young adults. If a diet very high in animal protein were to cause significant bone loss, one would expect that diet to cause significant bone loss in children and young adults as well. Not only in those older than 40.

So what may be the actual reason behind this reduced bone mineral content in older Eskimos?

Let me make a small digression here. If you want to meet quite a few anthropologists who are conducting, or have conducted, field research with isolated or semi-isolated hunter-gatherers, you should consider attending the annual Human Behavior and Evolution Society (HBES) conference. I have attended this conference in the past, several times, as a presenter. That gave me the opportunity to listen to some very interesting presentations and poster sessions, and talk with many anthropologists.

Often anthropologists will tell you that, as hunter-gatherers age, they sort of “shrink”. They lose lean body mass, frequently to the point of becoming quite frail in as early as their 60s and 70s. They tend to gain body fat, but not to the point of becoming obese, with that fat replacing lean body mass yet not forming major visceral deposits. Degenerative diseases are not a big problem when you “shrink” in this way; bigger problems are  accidents (e.g., falls) and opportunistic infections. Often older hunter-gatherers have low blood pressure, no sign of diabetes or cancer, and no heart disease. Still, they frequently die younger than one would expect in the absence of degenerative diseases.

A problem normally faced by older hunter-gatherers is poor nutrition, which is both partially caused and compounded by lack of exercise. Hunter-gatherers usually perceive the Western idea of exercise as plain stupidity. If older hunter-gatherers can get youngsters in their prime to do physically demanding work for them, they typically will not do it themselves. Appetite seems to be negatively affected, leading to poor nutrition; dehydration often is a problem as well.

Now, we know from this post that animal protein consumption does not lead to bone loss. In fact, it seems to increase bone mineral content. But there is something that decreases bone mineral content, as well as muscle mass, like nothing else – lack of physical activity. And there is something that increases bone mineral content, as well as muscle mass, in a significant way – vigorous weight-bearing exercise.

Take a look at the figure below, which I already discussed on a previous post. It shows a clear pattern of benign ventricular hypertrophy in Eskimos aged 30-39. That goes down dramatically after age 40. Remember what Mazess & Mather (1974) said in their article: “… after age 40 the Eskimos of both sexes had a deficit of from 10 to 15% relative to white standards”.


Benign ventricular hypertrophy is also known as athlete's heart, because it is common among athletes, and caused by vigorous physical activity. A prevalence of ventricular hypertrophy at a relatively young age, and declining with age, would suggest benign hypertrophy. The opposite would suggest pathological hypertrophy, which is normally induced by obesity and chronic hypertension.

So there you have it. The reason older Eskimos were found to have lower bone mineral content after 40 is likely not due to their diet.  It is likely due to the same reasons why they "shrink", and also in part because they "shrink". Not only does physical activity decrease dramatically as Eskimos age, but so does lean body mass.

Obese Westerners tend to have higher bone density on average, because they frequently have to carry their own excess body weight around, which can be seen as a form of weight-bearing exercise. They pay the price by having a higher incidence of degenerative diseases, which probably end up killing them earlier, on average, than osteoporosis complications.

Reference

Mazess R.B., & Mather, W.W. (1974). Bone mineral content of North Alaskan Eskimos. American Journal of Clinical Nutrition, 27(9), 916-925.

There are pizza restaurants, whose specialty is pizza, even though they usually have a few side dishes. Not healthy enough?

Well, don’t despair, there are meatball restaurants too. I know of at least one, The Meatball Shop, on 84 Stanton Street, in New York City.

Finally a restaurant that elevates the "lowly" meatball to its well deserved place!

Meatballs are delicious, easy to prepare, and you can use quite a variety of meats to do them. Below is a simple recipe. We used ground grass-fed beef, not because of omega-6 concerns (see this post), but because of the different taste.

- Prepare some dry seasoning powder by mixing sea salt, parsley, garlic power, chili powder, and a small amount of cayenne pepper.
- Thoroughly mix 1 pound of ground beef, one or two eggs, and the seasoning powder.
- Make about 10 meatballs, and place them in a frying pan with a small amount of water (see picture below).
- Cover the pan and cook on low fire for about 1 hour.


There is no need for any oil in the pan. On a low fire the small amount of water at the bottom will heat up, circulate, and essentially steam the meatballs. The water will also prevent the meatballs from sticking to the pan. Some moisture will also be released by the meat.

Part of the fat from the meat will be released and accumulate at the bottom of the pan. If you add tomato sauce and mix, the fat will become part of the resulting red sauce. This sauce will add moisture back to the dish, as the meatballs sometimes get a bit dry from the cooking.

Five meatballs of the type that we used (about 15 percent fat) will have about 57 g of protein and 32 g of fat; the latter mostly saturated and monounsaturated (both healthy). They will also be a good source of vitamins B12 and B6, niacin, zinc, selenium, and phosphorus.

Add a fruit or a sweet potato as a side dish to 3-5 meatballs and you have a delicious and nutritious meal that may eve impress some people!

The “great blogosphere debate” rages on regarding the effects of carbohydrates and insulin on health. A lot of action has been happening recently on Peter’s blog, with knowledgeable folks chiming in, such as Peter himself, Dr. Harris, Dr. B.G. (my sista from anotha mista), John, Nigel, CarbSane, Gunther G., Ed, and many others.

I like to see open debate among people who hold different views consistently, are willing to back them up with at least some evidence, and keep on challenging each other’s views. It is very unlikely that any one person holds the whole truth regarding health matters. Unfortunately this type of debate also confuses a lot of people, particularly those blog lurkers who want to get all of their health information from one single source.

Part of that “great blogosphere debate” debate hinges on the effect of low or high carbohydrate dieting on total calorie consumption. Well, let us see what the China Study II data can tell us about that, and about a few other things.

WarpPLS was used to do the analyses below. For other China Study analyses, many using WarpPLS as well as HealthCorrelator for Excel, click here. For the dataset used here, visit the HealthCorrelator for Excel site and check under the sample datasets area.

The two graphs below show the relationships between various foods, carbohydrates as a percentage of total calories, and total calorie consumption. A basic linear analysis was employed here. As carbohydrates as a percentage of total calories go up, the diet generally becomes a high carbohydrate diet. As it goes down, we see a move to the low carbohydrate end of the scale.


The left parts of the two graphs above are very similar. They tell us that wheat flour consumption is very strongly and negatively associated with rice consumption; i.e., wheat flour displaces rice. They tell us that fruit consumption is positively associated with rice consumption. They also tell us that high wheat flour consumption is strongly and positively associated with being on a high carbohydrate diet.

Neither rice nor fruit consumption has a statistically significant influence on whether the diet is high or low in carbohydrates, with rice having some effect and fruit practically none. But wheat flour consumption does. Increases in wheat flour consumption lead to a clear move toward the high carbohydrate diet end of the scale.

People may find the above results odd, but they should realize that white glutinous rice is only 20 percent carbohydrate, whereas wheat flour products are usually 50 percent carbohydrate or more. Someone consuming 400 g of white rice per day, and no other carbohydrates, will be consuming only 80 g of carbohydrates per day. Someone consuming 400 g of wheat flour products will be consuming 200 g of carbohydrates per day or more.

Fruits generally have much less carbohydrate than white rice, even very sweet fruits. For example, an apple is about 12 percent carbohydrate.

There is a measure that reflects the above differences somewhat. That measure is the glycemic load of a food; not to be confused with the glycemic index.

The right parts of the graphs above tell us something else. They tell us that the percentage of carbohydrates in one’s diet is strongly associated with total calorie consumption, and that this is not the case with percentage of fat in one’s diet.

Given the above, one may be interested in looking at the contribution of individual foods to total calorie consumption. The graph below focuses on that. The results take nonlinearity into consideration; they were generated using the Warp3 algorithm option of WarpPLS.


As you can see, wheat flour consumption is more strongly associated with total calories than rice; both associations being positive. Animal food consumption is negatively associated, somewhat weakly but statistically significantly, with total calories. Let me repeat for emphasis: negatively associated. This means that, as animal food consumption goes up, total calories consumed go down.

These results may seem paradoxical, but keep in mind that animal foods displace wheat flour in this dataset. Note that I am not saying that wheat flour consumption is a confounder; it is controlled for in the model above.

What does this all mean?

Increases in both wheat flour and rice consumption lead to increases in total caloric intake in this dataset. Wheat has a stronger effect. One plausible mechanism for this is abnormally high blood glucose elevations promoting abnormally high insulin responses. Refined carbohydrate-rich foods are particularly good at raising blood glucose fast and keeping it elevated, because they usually contain a lot of easily digestible carbohydrates. The amounts here are significantly higher than anything our body is “designed” to handle.

In normoglycemic folks, that could lead to a “lite” version of reactive hypoglycemia, leading to hunger again after a few hours following food consumption. Insulin drives calories, as fat, into adipocytes. It also keeps those calories there. If insulin is abnormally elevated for longer than it should be, one becomes hungry while storing fat; the fat that should have been released to meet the energy needs of the body. Over time, more calories are consumed; and they add up.

The above interpretation is consistent with the result that the percentage of fat in one’s diet has a statistically non-significant effect on total calorie consumption. That association, although non-significant, is negative. Again, this looks paradoxical, but in this sample animal fat displaces wheat flour.

Moreover, fat leads to no insulin response. If it comes from animals foods, fat is satiating not only because so much in our body is made of fat and/or requires fat to run properly; but also because animal fat contains micronutrients, and helps with the absorption of those micronutrients.

Fats from oils, even the healthy ones like coconut oil, just do not have the latter properties to the same extent as unprocessed fats from animal foods. Think slow-cooking meat with some water, making it release its fat, and then consuming all that fat as a sauce together with the meat.

In the absence of industrialized foods, typically we feel hungry for those foods that contain nutrients that our body needs at a particular point in time. This is a subconscious mechanism, which I believe relies in part on past experience; the reason why we have “acquired tastes”.

Incidentally, fructose leads to no insulin response either. Fructose is naturally found mostly in fruits, in relatively small amounts when compared with industrial foods rich in refined sugars.

And no, the pancreas does not get “tired” from secreting insulin.

The more refined a carbohydrate-rich food is, the more carbohydrates it tends to pack per unit of weight. Carbohydrates also contribute calories; about 4 calories per g. Thus more carbohydrates should translate into more calories.

If someone consumes 50 g of carbohydrates per day in excess of caloric needs, that will translate into about 22.2 g of body fat being stored. Over a month, that will be approximately 666.7 g. Over a year, that will be 8 kg, or 17.6 lbs. Over 5 years, that will be 40 kg, or 88 lbs. This is only from carbohydrates; it does not consider other macronutrients.

There is no need to resort to the “tired pancreas” theory of late-onset insulin resistance to explain obesity in this context. Insulin resistance is, more often than not, a direct result of obesity. Type 2 diabetes is by far the most common type of diabetes; and most type 2 diabetics become obese or overweight before they become diabetic. There is clearly a genetic effect here as well, which seems to moderate the relationship between body fat gain and liver as well as pancreas dysfunction.

It is not that hard to become obese consuming refined carbohydrate-rich foods. It seems to be much harder to become obese consuming animal foods, or fruits.

Dental erosion is a different problem from dental caries. Dental erosion is defined as the removal of minerals from the tooth structure via chemicals. Dental caries are the result of increased site-specific acidity due to bacterial fermentation of sugars.

Still, both have the same general result, destruction of teeth structure.

Losing teeth probably significantly accelerated death among our Paleolithic ancestors, as it does with modern hunter-gatherers. It is painful and difficult to eat nutritious foods when one has teeth problems, and chronic lack of proper nutrition is the beginning of the end.

The table below, from Ehlen et al. (2008), shows the amount of erosion that occurred when teeth were exposed to beverages for 25 h in vitro. Erosion depth is measured in microns. The third row shows the chance probabilities (i.e., P values) associated with the differences in erosion of enamel and root. These are not particularly enlightening; enamel and root are both significantly eroded.


These results reflect a broader trend. Nearly all industrial beverages cause erosion, even the “healthy” fruit juices. This is due in part, but not entirely, to the acidity of the beverages. Other chemicals contribute to erosion as well. For example, Coke has a lower pH than Gatorade, but the latter causes more erosion of both enamel and root. Still, both pHs are lower than 4.0. The pH of pure water is a neutral 7.0.

Coke is how my name is pronounced, by the way.

This was a study in vitro. Is there evidence of tooth erosion by industrial beverages in people who drink them? Yes, there is quite a lot of evidence, and this evidence dates back many years. You would not guess it by looking at beverage commercials. See, for example, this article.

What about eating the fruits that are used to make the erosion-causing fruit juices? Doesn’t that cause erosion as well? Apparently not, because chewing leads to the release of a powerful protective substance, which is also sometimes exchanged by pairs of people who find each other attractive.

Reference

Leslie A. Ehlen, Teresa A. Marshall, Fang Qian, James S. Wefel, and John J. Warren (2008). Acidic beverages increase the risk of in vitro tooth erosion. Nutrition Research, 28(5), 299–303.

John was overweight, out of shape, and experiencing fatigue. What did he do? He removed foods rich in refined carbohydrates and sugars from his diet. He also ditched industrial seed oils and started exercising. He used HealthCorrelator for Excel (HCE) to keep track of several health-related numbers over time (see figure below).


Over the period of time covered in the dataset, health markers steadily improved. For example, John’s HDL cholesterol went from a little under 40 mg/dl to just under 70; see chart below, one of many generated by HCE.


However, John’s blood pressure varied strangely during that time, as you can see on the chart below showing the variation of systolic blood pressure (SBP) against time. What could have been the reason for that? Salt intake is an unlikely culprit, as we’ve seen before.


As it turns out, John knew that heart rate could influence blood pressure somewhat, and he also knew that his doctor’s office measured his heart rate regularly. So he got the data from his doctor's office. When he entered heart rate as a column into HCE, the reason for his blood pressure swings became clear, as you can see on the figure below.


On the left part of the figure above are the correlations between SBP and each of the other health-related variables John measured, which HCE lists in order of strength. Heart rate shows up at the top, with a high 0.946 correlation with SBP. On the right part of the figure is the chart of SBP against heart rate.

As you can see, John's heart rate, measured at the doctor's office, varied from 61 to 90 bpm. Given that, John decided to measure his resting heart rate. John’s resting heart rate, measured after waking up using a simple wrist watch, was 61 bpm.

Mystery solved! John’s blood pressure fluctuations were benign, and caused by fluctuations in heart rate.

If John's SBP had been greater than 140, which did not happen, this could be seen as an unusual example of irregular white coat hypertension.

If you are interested, this YouTube video clip discusses in more detail the case above, from HCE’s use perspective. It shows how the heart rate column was added to the dataset in HCE, how the software generated correlations and graphs, and how they were interpreted.

Reference

Kock, N. (2010). HealthCorrelator for Excel 1.0 User Manual. Laredo, Texas: ScriptWarp Systems.

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

I ran several analyses on the effects of fruit consumption on mortality on the China Study II dataset using WarpPLS. For other China Study analyses, many using WarpPLS as well as HCE, click here.

The results are pretty clear – fruit consumption has no significant effect on mortality.

The bar charts figure below shows what seems to be a slight downward trend in mortality, in the 35-69 and 70-79 age ranges, apparently due to fruit consumption.


As it turns out, that slight trend may be due to something else: in the China Study II dataset, fruit consumption is positively associated with both animal protein and fat consumption. And, as we have seen from previous analyses (e.g., this one), the latter two seem to be protective.

So, if you like to eat fruit, maybe you should also make sure that you eat animal protein and fat as well.

Our body naturally produces as much as 10,000 IU of vitamin D based on a few minutes of sun exposure when the sun is high. Getting that much vitamin D from dietary sources is very difficult, even after “fortification”.

The above refers to pre-sunburn exposure. Sunburn is not associated with increased vitamin D production; it is associated with skin damage and cancer.

Solar ultraviolet (UV) radiation is generally divided into two main types: UVB (wavelength: 280–320 nm) and UVA (320–400 nm). Vitamin D is produced primarily based on UVB radiation. Nevertheless, UVA is much more abundant, amounting to about 90 percent of the sun’s UV radiation.

UVA seems to cause the most skin damage, although there is some debate on this. If this is correct, one would expect skin pigmentation to be our body’s defense primarily against UVA radiation, not UVB radiation. If so, one’s ability to produce vitamin D based on UVB should not go down significantly as one’s skin becomes darker.

Also, vitamin D and cholesterol seem to be closely linked. Some argue that one is produced based on the other; others that they have the same precursor substance(s). Whatever the case may be, if vitamin D and cholesterol are indeed closely linked, one would expect low cholesterol levels to be associated with low vitamin D production based on sunlight.

Bogh et al. (2010) recently published a very interesting study. The link to the study was provided by Ted Hutchinson in the comments sections of a previous post on vitamin D. (Thanks Ted!) The study was published in a refereed journal with a solid reputation, the Journal of Investigative Dermatology.

The study by Bogh et al. (2010) is particularly interesting because it investigates a few issues on which there is a lot of speculation. Among the issues investigated are the effects of total cholesterol and skin pigmentation on the production of vitamin D from UVB radiation.

The figure below depicts the relationship between total cholesterol and vitamin D production based on UVB radiation. Vitamin D production is referred to as “delta 25(OH)D”. The univariate correlation is a fairly high and significant 0.51.


25(OH)D is the abbreviation for calcidiol, a prehormone that is produced in the liver based on vitamin D3 (cholecalciferol), and then converted in the kidneys into calcitriol, which is usually abbreviated as 1,25-(OH)2D3. The latter is the active form of vitamin D.

The table below shows 9 columns; the most relevant ones are the last pair at the right. They are the delta 25(OH)D levels for individuals with dark and fair skin after exposure to the same amount of UVB radiation. The difference in vitamin D production between the two groups is statistically indistinguishable from zero.


So there you have it. According to this study, low total cholesterol seems to be associated with impaired ability to produce vitamin D from UVB radiation. And skin pigmentation appears to have little  effect on the amount of vitamin D produced.

I hope that there will be more research in the future investigating this study’s claims, as the study has a few weaknesses. For example, if you take a look at the second pair of columns from the right on the table above, you’ll notice that the baseline 25(OH)D is lower for individuals with dark skin. The difference was just short of being significant at the 0.05 level.

What is the problem with that? Well, one of the findings of the study was that lower baseline 25(OH)D levels were significantly associated with higher delta 25(OH)D levels. Still, the baseline difference does not seem to be large enough to fully explain the lack of difference in delta 25(OH)D levels for individuals with dark and fair skin.

A widely cited dermatology researcher, Antony Young, published an invited commentary on this study in the same journal issue (Young, 2010). The commentary points out some weaknesses in the study, but is generally favorable. The weaknesses include the use of small sub-samples.

References

Bogh, M.K.B., Schmedes, A.V., Philipsen, P.A., Thieden, E., & Wulf, H.C. (2010). Vitamin D production after UVB exposure depends on baseline vitamin D and total cholesterol but not on skin pigmentation. Journal of Investigative Dermatology, 130(2), 546–553.

Young, A.R. (2010). Some light on the photobiology of vitamin D. Journal of Investigative Dermatology, 130(2), 346–348.

In this post on the China Study II data we have seen that wheat apparently displaces dietary fat a lot, primarily fat from animal sources. We have also seen in that post that wheat is strongly and positively associated with mortality in both the 35-69 and 70-79 age ranges, whereas dietary fat is strongly and negatively associated with mortality in those ranges.

This opens the door for the hypothesis that wheat increased mortality in the China Study II sample mainly by displacing dietary fat, and not necessarily by being a primary cause of health problems. In fact, given the strong displacement effect discussed in the previous post, I thought that this hypothesis was quite compelling. I was partly wrong, as you’ll see below.

A counterintuitive hypothesis no doubt, given that wheat is unlikely to have been part of the diet of our Paleolithic ancestors, and thus the modern human digestive tract may be maladapted to it. Moreover, wheat’s main protein (gluten) is implicated in celiac disease, and wheat contains plant toxins such as wheat germ agglutinin.

Still, we cannot completely ignore this hypothesis because: (a) the data points in its general direction; and (b) wheat-based foods are found in way more than trivial amounts in the diets of populations that have relatively high longevity, such as the French.

Testing the hypothesis essentially amounts to testing the significance of two mediating effects; of fat as a mediator of the effects of wheat on mortality, in both the 35-69 and 70-79 age ranges. There are two main approaches for doing this. One is the classic test discussed by Baron & Kenny (1986). The other is the modern test discussed by Preacher & Hayes (2004), and extended by Hayes & Preacher (2010) for nonlinear relationships.

I tested the meditating effects using both approaches, including the nonlinear variation. I used the software WarpPLS for this; the results below are from WarpPLS outputs. Other analyses of the China Study data using WarpPLS can be found here (calorie restriction and longevity), and here (wheat, rice, and cardiovascular disease). For yet other studies, click here.

The graphs below show the path coefficients and chance probabilities of two models. The one at the top-left suggests that wheat flour consumption seems to be associated with a statistically significant increase in mortality in the 70-79 age range (beta=0.23; P=0.04). The effect in the 35-69 age range is almost statistically significant (beta=0.22; P=0.09); the likelihood that it is due to chance is 9 percent (this is the meaning of the P=0.09=9/100=9%).


The graph at the bottom-right suggests that the variable “FatCal”, which is the percentage of calories coming from dietary fat, is indeed a significant mediator of the relationships above between wheat and mortality, in both ranges. But “FatCal” is only a partial mediator.

The reason why “FatCal” is not a “perfect” mediator is that the direct effects of wheat on mortality in both ranges are still relatively strong after “FatCal” is added to the model (i.e., controlled for). In fact, the effects of wheat on mortality don’t change that much with the introduction of the variable “FatCal”.

This analysis suggests that, in the China Study II sample, one of wheat’s main sins might indeed have been to displace dietary fat from animal sources. Wheat consumption is strongly and negatively associated with dietary fat (beta=-0.37; P<0.01), and dietary fat is relatively strongly and negatively associated with mortality in both ranges (more in the 70-79 age range).

Why is dietary fat more protective in the 70-79 than in the 35-69 age range, with the latter effect only being significant at the P=0.10 level (a 10 percent chance probability)? My interpretation is that, as with almost any dietary habit, it takes years for a chronically low fat diet to lead to problems. See graph below; fat was not a huge contributor to the total calorie intake in this sample.


The analysis suggests that wheat also caused problems via other paths. What are them? We can’t say for sure based on this dataset. Perhaps the paths involve lectins and/or gluten. One way or another, the relationship is complex. As you can see from the graph below, the relationship between wheat consumption and mortality is nonlinear for the 70-79 age range, most likely due to confounding factors. The effect size is small for the 35-69 age range, even though it looks linear or quasi-linear in that range.


As you might recall from this post, rice does NOT displace dietary fat, and it seems to be associated with increased longevity. Carbohydrate content per se does not appear to be the problem here. Both rice and wheat foods are rich in them, and have a high glycemic index. Wheat products tend to have a higher glycemic load though.

And why is dietary fat so important as to be significantly associated with increased longevity? This is not a trivial question, because if too much of that fat is stored as body fat it will actually decrease longevity. Dietary fat is very calorie-dense, and can be easily stored as body fat.

Dietary fat is important for various reasons, and probably some that we don’t know about yet. It leads to the formation of body fat, which is not only found in adipocytes or used only as a store of energy. Fat is a key component of a number of important tissues, including 60 percent of our brain. Since fat in the human body undergoes constant turnover, more in some areas than others, lack of dietary fat may compromise the proper functioning of various organs.

Without dietary fat, the very important fat-soluble vitamins (A, D, E and K) cannot be properly absorbed. Taking these vitamins in supplemental form will not work if you don’t consume fat as well. A very low fat diet is almost by definition a diet deficient in fat-soluble vitamins, even if those vitamins are consumed in large amounts via supplements.

Moreover, animals store fat-soluble vitamins in their body fat (as well as in organs), so we get these vitamins in one of their most natural and potent forms when we consume animal fat. Consuming copious amounts of olive and/or coconut oil will not have just the same effect.

References

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality & Social Psychology, 51(6), 1173-1182.

Preacher, K.J., & Hayes, A.F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36 (4), 717-731.

Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45(4), 627-660.

The idea that protein leaches calcium from the bones has been around for a while. It is related to the notion that protein, especially from animal foods, increases blood acidity. The body then uses its main reservoir of calcium, the bones, to reduce blood acidity. Chris Masterjohn does not agree with this idea. This post generally supports Chris’s view, and adds a twist to it, related to plant protein consumption.

The “eat-meat-lose-bone” idea has apparently become popular due to the position taken by Loren Cordain on the topic. Dr. Cordain has also made several important and invaluable contributions to our understanding of the diets of our Paleolithic ancestors. He has argued in his book, The Paleo Diet, and elsewhere (see, e.g., here) that to counter the acid load of protein one should eat fruits and vegetables. The latter are believed to have an alkaline load.

If the idea that protein leaches calcium from the bones is correct, one would expect to see a negative association between protein consumption and bone mineral density (BMD). This negative association should be particularly strong in people aged 50 and older, who are more vulnerable to BMD losses.

As it turns out, this idea appears to be correct only for plant protein. Animal protein seems to be associated with an increase in BMD, at least according to a study by Promislow et al. (2002). The study shows that there is a positive multivariate association between animal protein consumption and BMD; an association that becomes negative when plant protein consumption is considered.

The study focused on 572 women and 388 men aged 55–92 years living in Rancho Bernardo, California. Food frequency questionnaires were administered in the 1988–1992 period, and BMD was measured 4 years later. The bar chart below shows the approximate increases in BMD (in g/cm^2) for each 15 g/d increment in protein intake.


The authors reported increments in BMD for different increments of protein (15 and 5 g/d), so the results above are adjusted somewhat from the original values reported in the article. Keeping that in mind, the increment in BMD for men due to animal protein was not statistically significant (P=0.20). That is the smallest bar on the left.

Does protein leach calcium from the bones? Based on this study, the reasonable answers to this question are yes for plant protein, and no for animal protein. For animal protein, it seems to be quite the opposite.

Even more interesting, calcium intake did not seem to be much of a factor. BMD gains due to animal protein seemed to converge to similar values whether calcium intake was high, medium or low. The convergence occurred as animal protein intake increased, and the point of convergence was between 85-90 g/d of animal protein intake.

And high calcium intakes did not seem to protect those whose plant protein consumption was high.

The authors do not discuss specific foods, but one can guess the main plant protein that those folks likely consumed. It was likely gluten from wheat products.

Are the associations above due to: (a) the folks eating animal protein consuming more fruits and vegetables than the folks eating plant protein; or (b) something inherent to animal foods that stimulates an increase in the absorption of dietary calcium, even in small amounts?

This question cannot be answered based on this study; it should have controlled for fruit and vegetable consumption for that.

But if I were to bet, I would bet on (b).

Reference

Promislow, J.H.E., Goodman-Gruen, D., Slymen, D.J., & Barrett-Connor, E. (2002). Protein consumption and bone mineral density in the elderly. American Journal of Epidemiology, 155(7), 636–644.

Spending too much time sitting down is clearly unnatural, particularly if you sit down on very comfortable chairs. Sitting down per se is probably natural, given the human anatomy, but not sitting down for hours in the same position. Also, comfortable furniture is an apparently benign Neolithic invention, but over several years it may stealthily contributed to the metabolic syndrome and the diseases of civilization.

Getting an elevated workstation may be a bit expensive. At work, you may have to go through a bit of a battle with your employer to get it (unless you are "teh boz"), only to find out that having to work standing up all the time is not what you really wanted. That may not be very natural either. So what is one to do? One possible solution is to buy a small foldable plastic table (or chair) like the one on the figure below, which may cost you less than $10, and put it on your work desk. I have been doing this for quite a while now, and it works fine for me.



The photo above shows a laptop computer. Nevertheless, you can use this table-over-table approach with a desktop computer as well. And you still keep the space under the foldable table, which you can use to place other items. With a desktop computer this approach would probably require two foldable tables to elevate the screen, keyboard, and mouse. This approach also works for reading documents and writing with a pen or pencil; just put a thick sheet of paper on the foldable table to make a flat surface (if the foldable table’s surface is not flat already). And you don’t have to be standing up all the time; you can sit down as well after removing the foldable table. It takes me about 5 seconds to do or undo this setup.

When you sit down, you may want to consider using a pillow like the one on the photo to force yourself to sit upright. (You can use it as shown, or place the pillow flat on the chair and sit on its edge.) Sitting on a very comfy chair with back support prevents you from using the various abdominal and back muscles needed to maintain posture. As a result, you may find yourself unusually prone to low back injuries and suffering from “mysterious” abdominal discomfort. You will also very likely decrease your nonexercise activity thermogenesis (NEAT), which is a major calorie expenditure regulator.

With posture stabilization muscles, as with almost everything else in the human body, the reality is this: if you don’t use them, you lose them.

This post is based on an analysis of a subset of the China Study II data, using HealthCorrelator for Excel (HCE), which is publicly available for download and use on a free trial basis. You can access the original data on the HCE web site, under “Sample datasets”.

HCE was designed to be used with small and individual personal datasets, but it can also be used with larger datasets for multiple individuals.

This analysis focuses on two main variables from the China Study II data: mortality in the 35-69 age range, and mortality in the 70-79 range. The table below shows the coefficients of association calculated by HCE for those two variables. The original variable labels are shown.


One advantage of looking at mortality in these ranges is that they are more likely to reflect the impact of degenerative diseases. Infectious diseases likely killed a lot of children in China at the time the data was being collected. Heart disease, on the other hand, is likely to have killed more people in the 35-69 and 70-79 ranges.

It is also good to have data for both ranges, because factors that likely increased longevity were those that were associated with decreased mortality in both ranges. For example, a factor that was strongly associated with mortality in the 35-69 range, but not the 70-79 range, might simply be very deadly in the former range.

The mortalities in both ranges are strongly correlated with each other, which is to be expected. Next, at the very top for both ranges, is sex. Being female is by far the variable with the strongest, and negative, association with mortality.

While I would expect females to live longer, the strengths of the associations make me think that there is something else going on here. Possibly different dietary or behavioral patterns displayed by females. Maybe smoking cigarettes or alcohol abuse was a lot less prevalent among them.

Markedly different lifestyle patterns between males and females may be a major confounding variable in the China Study sample.

Some of the variables are redundant; meaning that they are highly correlated and seem to measure the same thing. This is clear when one looks at the other coefficients of association generated by HCE.

For example, plant food consumption is strongly and negatively correlated with animal food consumption; so strongly that you could use either one of these two variables to measure the other, after inverting the scale. The same is true for consumption of rice and white flour.

Plant food consumption is not strongly correlated with plant protein consumption; many plant foods have little protein in them. The ones that have high protein content are typically industrialized and seed-based. The type of food most strongly associated with plant protein consumption is white flour, by far. The correlation is .645.

The figure below is based on the table above. I opened a separate instance of Excel, and copied the coefficients generated by HCE into it. Then I built two bar charts with them. The variable labels were replaced with more suggestive names, and some redundant variables were removed. Only the top 7 variables are shown, ordered from left to right on the bar charts in order of strength of association. The ones above the horizontal axis possibly increase mortality in each age range, whereas the ones at the bottom possibly decrease it.


When you look at these results as a whole, a few things come to mind.

White flour consumption doesn’t seem to be making people live longer; nor does plant food consumption in general. For white flour, it is quite the opposite. Plant food consumption reflects white flour consumption to a certain extent, especially in counties where rice consumption is low. These conclusions are consistent with previous analyses using more complex statistics.

Total food is positively associated with mortality in the 35-69 range, but not the 70-79 range. This may reflect the fact that folks who reach the age of 70 tend to naturally eat in moderation, so you don’t see wide variations in food consumption among those folks.

Eating in moderation does not mean practicing severe calorie restriction. This post suggests that calorie restriction doesn't seem to be associated with increased longevity in this sample. Eating well, but not too much, is.

The bar for rice (consumption) on the left chart is likely a mirror reflection of the white flour consumption, so it may appear to be good in the 35-69 range simply because it reflects reduced white flour consumption in that range.

Green vegetables seem to be good when you consider the 35-69 range, but not the 70-79 range.

Neither rice nor green vegetables seem to be bad either. For overall longevity they may well be neutral, with the benefits likely coming from their replacement of white flour in the diet.

Dietary fat seems protective overall, particularly together with animal foods in the 70-79 range. This may simply reflect a delayed protective effect of animal fat and protein consumption.

The protective effect of dietary fat becomes clear when we look at the relationship between carbohydrate calories and fat calories. Their correlation is -.957, which essentially means that carbohydrate intake seriously displaces fat intake.

Carbohydrates themselves may not be the problem, even if coming from high glycemic foods (except wheat flour, apparently). This post shows that they are relatively benign if coming from high glycemic rice, even at high intakes of 206 to 412 g/day. The problem seems to be caused by carbohydrates displacing nutrient-dense animal foods.

Interestingly, rice does not displace animal foods or fat in the diet. It is positively correlated with them. Wheat flour, on the other hand, displaces those foods. Wheat flour is negatively and somewhat strongly correlated with consumption of animal foods, as well as with animal fat and protein.

There are certainly several delayed effects here, which may be distorting the results somewhat.  Degenerative diseases don’t develop fast and kill folks right away. They often require many years of eating and doing the wrong things to be fatal.

HealthCorrelator for Excel (HCE) is now publicly available for download and use on a free trial basis. For those users who decide to buy it after trying, licenses are available for individuals and organizations. If you are a gym member, consider asking your gym to buy an organizational site license; this would allow the gym to distribute individual licenses at no cost to you and your colleagues.

HCE is a user-friendly Excel-based software that unveils important associations among health variables at the click of a button. Here are some of its main features:

- Easy to use yet powerful health management software.

- Estimates associations among any number of health variables.

- Automatically orders associations by decreasing absolute strength.

- Graphs relationships between pairs of health variables, for all possible combinations.

The beta testing was successfully completed, with fairly positive results. (Thank you beta testers!) Among beta testers were Mac users. The main request from beta testers was for more illustrative material on how to use HCE for specific purposes, such as losing body fat or managing blood glucose levels. This will be coming in the future in the form of posts and linked material.

To download a free trial version, good for 30 use sessions (which is quite a lot!), please visit the HealthCorrelator.com web site. There you will also find the software’s User Manual and various links to demo YouTube videos. You can also download sample datasets to try the software’s main features.

Genes do not evolve, nor do traits that are coded for our genes. We say that they evolve to facilitate discourse, which is alright. Populations evolve. A new genotype appears in a population and then either spreads or disappears. If it spreads, then the population is said to be evolving with respect to that genotype. A genotype may spread to an entire population; in population genetics, this is called “fixation”.


Asexual reproduction is very uncommon among animals. The most accepted theory to explain this is that animal populations live in environments that change very quickly, and thus need a great deal of genetic diversity within them to cope with the change. Otherwise they disappear, and so do their genes. Asexual reproduction leads to dramatically less genetic diversity in populations than sexual reproduction.

Asexual reproduction is similar to cloning. Each new individual looks a lot like its single parent. This does not work well in populations where individuals live relatively long lives. And even 1 year may be too long in this respect. It is just too much time to wait for a possible new mutation that will bring in some genetic diversity. To complicate matters, genetic mutation does not occur very often, and most genetic mutations are neutral with respect to the phenotype (i.e., they don’t code for any trait).

This is not so much of a problem for species whose members reproduce extremely fast; e.g., produce a new generation in less than 1 hour. A fast-reproducing species usually has a short lifespan as well. Accordingly, asexual reproduction is common among short-lived and fast-reproducing unicellular organisms and pathogens that have no cell structure like viruses.

Bacteria and viruses, in particular, form a part of the environment in which animals live that require animal populations to have a large amount of genetic diversity. Animal populations with low genetic diversity are unlikely to be able to cope with the barrage of diseases caused by these fast-mutating parasites.

We make sex chiefly because of the parasites.

And what about death? What does it bring to the table for a population?

Let us look at the other extreme – immortality. Immortality is very problematic in evolutionary terms because a population of immortal individuals would quickly outgrow its resources. That would happen too fast for the population to evolve enough intelligence to be able to use resources beyond those that were locally available.

In this post I assume that immortality is not the same as indestructibility. Here immortality is equated to the absence of aging as we know it. In this sense, immortals can still die by accident or due to disease. They simply do not age. For immortals, susceptibility to disease does not go up with age.

One could argue that a population of immortal individuals who did not reproduce would have done just fine. But that is not correct, because in this case immortality would be akin to cloning, but worse. Genetic diversity would not grow, as no mutations would occur. The fixed population of immortals would be unable to cope with fast-mutating parasites.

There is so much selection pressure against immortality in nature that it is no surprise that animals of very few species live more than 60 years on average. Humans are at the high end of the longevity scale. They are there for a few reasons. One is that our ancestors had offspring that required extra care, which led to an increase in the parents’ longevity. The offspring required extra care chiefly because of their large brains.

That increase in longevity was likely due to genetic mutations that helped our ancestors extend a lifespan that was programmed to be relatively short. Immortality is not a sound strategy for population survival, and thus there are probably many mechanisms through which it is prevented.

Death is evolution’s main ally. Sex is a very good helper. Both increase genetic diversity in populations.

We can use our knowledge of evolution to live better today. The aging clock can be slowed significantly via evolutionarily sound diet and lifestyle changes, essentially because some of our modern diet and lifestyle choices accelerate aging a lot. But diet and lifestyle changes probably will not make people live to 150.

If we want to become immortal, as we understand it in our current human form, ultimately we may want to beat evolution. In this sense, only very intelligent beings can become immortal.

Maybe we can achieve that by changing our genes, or by learning how to transfer our consciousness “software” into robots. In doing so, however, we may become something different; something that is not human and thus doesn’t see things in the same way as a human does. A conscious robot, without the hormones that so heavily influence human behavior, may find that being alive is pointless.

There is another problem. What if the only natural way to achieve some form of immortality is through organic death, but in a way that we don’t understand? This is not a matter of faith or religion. There are many things that we don’t know for sure. This is probably the biggest mystery of all; one that we cannot unravel in our current human state.

This previous post looks at the amounts of protein needed to maintain a nitrogen balance of zero. It builds on data about individuals doing endurance exercise, which increases the estimates a bit. The post also examines the issue of what happens when more protein than is needed in consumed; including by people doing strength exercise.

What that post does not look into is whether strength exercise, performed at the anaerobic range, increases nitrogen balance. If it did, it may lead to a counterintuitive effect: strength exercise, when practiced at a certain level of intensity, might enable individuals in calorie deficit to retain their muscle, and lose primarily body fat. That is, strength exercise might push the body into burning more body fat and less muscle than it would normally do under calorie deficit conditions.



Under calorie deficit people normally lose both body fat and muscle to meet caloric needs. About 25 percent of lean body mass is lost in sedentary individuals, and 33 percent or more in individuals performing endurance exercise. I suspect that strength exercise has the potential to either bring this percentage down to zero, or to even lead to muscle gain if the calorie deficit is very small. One of the reasons is the data summarized on this post.

Two other reasons are related to what happens with children, and the variation in spontaneous hunger up-regulation in response to various types of exercise. The first reason can be summarized as this: it is very rare for children to be in negative nitrogen balance (Brooks et al., 2005); even when they are under some, not extreme, calorie deficit. It is rare for children to be in negative nitrogen balance even when their daily consumption of protein is below 0.5 g per kg of body weight.

This suggests that, when children are in calorie deficit, they tend to hold on to protein stores (which are critical for growth), and shift their energy consumption to fat more easily than adults. The reason is that developmental growth powerfully stimulates protein synthesis. This leads to a hormonal mix that causes the body to be in anabolic state, even when other forces (e.g., calorie deficit, low protein intake) are pushing it into a catabolic state. In a sense, the tissues of children are always hungry for their building blocks, and they do not let go of them very easily.

The second reason is an interesting variation in the patterns of spontaneous hunger up-regulation in various athletes. The increase in hunger is generally lower for strength than endurance activities. The spontaneous increase for bodybuilders is among the lowest. Since being in a catabolic state tends to have a strong effect on hunger, increasing it significantly, these patterns suggest that strength exercise may actually contribute to placing one in an anabolic state. The duration of this effect is approximately 48 h. Some increase in hunger is expected, because of the increased calorie expenditure during and after strength exercise, but that is counterbalanced somewhat by the start of an anabolic state.

What is going on, and what does this mean for you?

One way to understand what is happening here is to think in terms of compensatory adaptation. Strength exercise, if done properly, tells the body that it needs more muscle protein. Calorie deficit, as long as it is short-term, tells the body that food supply is limited. The body’s short-term response is to keep muscle as much as possible, and use body fat to the largest extent possible to supply the body’s energy needs.

If the right stimuli are supplied in a cyclical manner, no long-term adaptations (e.g., lowered metabolism) will be “perceived” as necessary by the body. Let us consider a 2-day cycle where one does strength exercise on the first day, and rests on the second. A surplus of protein and calories on the first day would lead to both muscle and body fat gain. A deficit on the second day would lead to body fat loss, but not to muscle loss, as long as the deficit is not too extreme. Since only body fat is being lost, more is lost on the second day than on the first.

In this way, one can gain muscle and lose body fat at the same time, which is what seems to have happened with the participants of the Ballor et al. (1996) study. Or, one can keep muscle (not gaining any) and lose more body fat, with a slightly higher calorie deficit. If the calorie deficit is too high, one will enter negative nitrogen balance and lose both muscle and body fat, as often happens with natural bodybuilders in the pre-tournament “cutting” phase.

In a sense, the increase in protein synthesis stimulated by strength exercise is analogous to, although much less strong than, the increase in protein synthesis stimulated by the growth process in children.

References

Ballor, D.L., Harvey-Berino, J.R., Ades, P.A., Cryan, J., & Calles-Escandon, J. (1996). Contrasting effects of resistance and aerobic training on body composition and metabolism after diet-induced weight loss. Metabolism, 45(2), 179-183.

Brooks, G.A., Fahey, T.D., & Baldwin, K.M. (2005). Exercise physiology: Human bioenergetics and its applications. Boston, MA: McGraw-Hill.