Health Correlator

The impressive nutrition value of whole dried small fish

2012-02-06T04:00:00.000-08:00

When I visited Japan a few years ago I noticed a variety of dried small fish for sale in grocery stores and supermarkets. They came in what seemed to be vacuum-packed flat plastic bags, often dried. The packing was a bit like that of beef jerky in the USA. Since I could not read the labels, I could not tell if preservatives or things like sugar were added. Beef jerky often has sugar added to it; at least the popular brands.

I have since incorporated dried or almost dried small fish, eaten whole, into my diet. My family eats it, but they don’t seem to like it as much as I do. The easiest small fish to find for sale where I live are smelts. A previous post has a recipe (). I can easily eat 200 g of smelts, about twice as much as on the plate below; not quite dried, but almost so. The veggies are a mix of lettuce and cabbage.

As you can see from the macronutrient composition below (from Nutritiondata.com, for a 100 g portion), 200 g of smelts have about 112 g of protein, and 36 g of fat. No carbohydrates; or a very small amount of them.

Unless you misguidedly think that they will “give you cholesterol”, the macronutrient to calorie ratio of a plate with 200 g of dried (or almost dried) smelts is very good. Let us take a look at the fat content, below (from Nutritiondata.com as well), which is for 100 g of dried smelts.

The “net” omega-3 content of 200 g of dried smelts, after subtracting the omega-6 content, is approximately 4.4 g. The concept of “net” omega-3 content was discussed in a previous post ().

So, the net omega-3 content of 200 g of dried smelts is the equivalent to the net omega-3 content of about 20 fish oil softgels. (Yes, you read it right!) And you would get a lot more omega-6 from the softgels.

Not to mention the fact that isolated omega-3 and omega-6 fats tend to become oxidized much more easily than when they come in “nature’s package”.

Below is the mineral content (also from Nutritiondata.com) of a 100 g portion. Dried smelts are clearly a very good source of selenium. The significant amount of calcium comes mostly from the bones, as with many varieties of small fish that are eaten whole. Combined with the above, we could say that, overall, the nutrient content is high up there next to beef liver as a super food; a natural multivitamin, if you will.

Smelts, like many small non-predatory fish, are not a significant source of toxic metals. Many people avoid seafood because of concerns about toxic metal contamination, particularly mercury. The infamous incident that led to a major scare in that respect – in Minamata, Japan – did involve consumption of small marine animals. But it also involved years of direct and indirect exposure to very high levels of methylmercury from untreated industrial waste.

Other cases have been reported among populations consuming large amounts of whale, shark, dogfish and other relatively large marine animals with tissues compromised via biomagnification. Generally speaking, large predatory fish and predatory aquatic mammals are best avoided as food. If they are consumed, they should be consumed very sporadically.

Many people would say that a plate like the one above, with smelts and veggies, is not very appetizing. But I can really devour it quickly and go for seconds. How come? I use a special spice that enhances the natural flavor or almost any combination of “natural” foods – foods that are not engineered by humans – making them taste delicious.

This special spice is “hunger”. This spice can be your best friend, or your worst enemy.

Kleiber's law and its possible implications for obesity

2012-01-30T04:00:00.000-08:00

Kleiber's law () is one of those “laws” of nature that is both derived from, and seems to fit quite well with, empirical data. It applies to most animals, including humans. The law is roughly summarized through the equation below, where E = energy expenditure at rest per day, and M = body weight in kilograms.

Because of various assumptions made in the original formulation of the law, the values of E do not translate very well to calories as measured today. What is important is the exponent, and what it means in terms of relative increases in weight. Since the exponent in the equation is 3/4, which is lower than 1, the law essentially states that as body weight increases animals become more efficient from an energy expenditure perspective. For example, the energy expenditure at rest of an elephant, per unit of body weight, is significantly lower than that of a mouse.

The difference in weight does not have to be as large as that between an elephant and a mouse for a clear difference in energy expenditure to be noticed. Moreover, the increase in energy efficiency predicted by the law is independent of what makes up the weight; whether it is more or less lean body mass, for example. And the law is very generic, also applying to different animals of the same species, and even the same animal at different developmental stages.

Extrapolating the law to humans is quite interesting. Let us consider a person weighing 68 kg (about 150 lbs). According to Kleiber's law, and using a constant multiplied to M to make it consistent with current calorie measurement assumptions (see Notes at the end of this post), this person’s energy expenditure at rest per day would be about 1,847 calories.

A person weighing 95 kg (about 210 lbs) would spend 2,374 calories at rest per day according to Kleiber's law. However, if we were to assume a linear increase based on the daily calorie expenditure at a weight of 68 kg, this person weighing 95 kg would spend 2,508 calories per day at rest. The difference of approximately 206 calories per day is a reflection of Kleiber's law.

This difference of 206 calories per day would translate into about 23 g of extra body fat being stored per day. Per month this would be about 688 g, a little more than 1.5 lbs. Not a negligible amount. So, as you become obese, your body becomes even more efficient on a weight-adjusted basis, from an energy expenditure perspective.

One more roadblock to go from obese to lean.

Now, here is the interesting part. It is unreasonable to assume that the extra mass itself has a significantly lower metabolic rate, with this fully accounting for the relative increase in efficiency. It makes more sense to think that the extra mass leads to systemic adaptations, which in turn lead to whole-body economies of scale (). In existing bodies, these adaptations should happen over time, as long-term compensatory adaptations ().

The implications are fascinating. One implication is that, if the compensatory adaptations that lead to a lower metabolic rate are long term, they should also take some time to undo. This is what some call having a “broken metabolism”; which may turn out not to be “broken”, but having some inertia to overcome before it comes back to a former state. Thus, lower metabolic rates should generally be observed in the formerly obese, with reductions compatible with Kleiber's law. Those reductions themselves should be positively correlated with the ratio of time spent in the obese and lean states.

Someone who was obese at 95 kg should have a metabolic rate approximately 5.6 percent lower than a never obese person, soon after reaching a weight of 68 kg (5.6 percent = [2,508 – 2,374] / 2,374). If the compensatory adaptation can be reversed, as I believe it can, we should see slightly lower percentage reductions in studies including formerly obese participants who had been lean for a while. This expectation is consistent with empirical evidence. For example, a study by Astrup and colleagues () concluded that: “Formerly obese subjects had a 3–5% lower mean relative RMR than control subjects”.

Another implication, which is related to the one above, is that someone who becomes obese and goes right back to lean should not see that kind of inertia. That is, that person should go right back to his or her lean resting metabolic rate. Perhaps Drew Manning’s Fit-2-Fat-2-Fit experiment () will shed some light on this possible implication.

A person becoming obese and going right back to lean is not a very common occurrence. Sometimes this is done on purpose, for professional reasons, such as before and after photos for diet products. Believed it or not, there is a market for this!

Notes

- Calorie expenditure estimation varies a lot depending on the equation used. The multiplier used here was 78, based on Cunningham’s equation, and assuming 10 percent body fat. The calorie expenditure for the same 68 kg person using Katch-McArdle’s equation, also assuming 10 percent body fat, would be about 1,692 calories. That would lead to a different multiplier.

- The really important thing to keep in mind, for the purposes of the discussion presented here, is the relative decrease in energy expenditure at rest, per unit of weight, as weight goes up. So we stuck with the 78 multiplier for illustration purposes.

- There is a lot of variation across individuals in energy expenditure at rest due to other factors such as nonexercise activity thermogenesis ().

All diets succeed at first, and eventually fail

2012-01-23T04:00:00.000-08:00

It is not very hard to find studies supporting one diet or another. Gardner and colleagues, for example, conducted a study in which the Atkins diet came out on top when compared with the Zone, Ornish, and LEARN diets (). In Dansinger and colleagues’ study (), on the other hand, following the Atkins diet led to relatively poor results compared with the Ornish, Weight Watchers, and Zone diets.

Often the diets compared have different macronutrient ratios, which end up becoming the focus of the comparison. Many consider Sacks and colleagues’ conclusion, based on yet another diet comparison study (), to be the most consistent with the body of evidence as a whole: “Reduced-calorie diets result in clinically meaningful weight loss regardless of which macronutrients they emphasize”.

I think there is a different conclusion that is even more consistent with the body of evidence out there. This conclusion is highlighted by the findings of almost all diet studies where participants were followed for more than 1 year. But the relevant findings are typically buried in the papers that summarize the studies, and are almost never mentioned in the abstracts. Take for example the study by Toubro and Astrup (); Figure 3 below is used by the authors to highlight the study’s main reported finding: “Ad lib, low fat, high carbohydrate diet was superior to fixed energy intake for maintaining weight after a major weight loss”.

But what does the figure above really tell us? It tells us, quite simply, that both diets succeeded at first, and then eventually failed. One failed slightly less miserably than the other, in this study. The percentage of subjects that maintained a weight loss above 25 kg (about 55 lbs) approached zero after 12 months, in both diets. This leads us to the conclusion below, which is always missing in diet studies even when the evidence is staring back at us. This is arguably the conclusion that is the most consistent with the body of evidence out there.

All diets succeed at first, and eventually fail.

In using the terms “succeed” and “fail” I am referring to the diets’ effects on the majority of the participants. This is in fact better demonstrated by the figure below, from the same study by Toubro and Astrup; it is labeled as Figure 2 there. Most of the participants at a certain weight, lose a lot of weight within a period of 1 year or so, and after 2 years (see the two points at the far right) are at the same original weight again. What is the average time to regain back the weight? From what I’ve seen in the literature, all the weight and some tends to be regained after 2-3 years.

The regained weight is not at all lean body mass. It is primarily, if not entirely, body fat. In fact, many studies suggest that those who diet tend to have a higher percentage of body fat when they regain their original weight; proportionally to how fast they regain the weight lost. Since the extra body fat tends to cause additional problems, which are compounded by the dieting process’ toll on the body, those dieters would have been slightly better off not having dieted in the first place.

Guyenet and Schwartz have recently authored an article that summarizes quite nicely what tends to happen with both obese and lean dieters (). Take a look at Figure 2 of the article below. The obese need to lose body fat to improve health markers, and avoid a number of downstream complications, such as type 2 diabetes and cancer (). Yet, with very few exceptions, the obese (and even the overweight) remain obese (or overweight) after dieting; regardless of the diet.

So what about those exceptions, what do they do to lose significant amounts of body fat and keep it off? Well, I rarely use myself as an example for anything in this blog, but this is something with which I unfortunately/fortunately have personal experience. I was obese, lost about 60 lbs of weight, and kept it off for quite a while already (). Like most of the formerly obese, I can very easily gain body fat back.

But I don’t seem to be gaining back the formerly lost body fat, and the reason is consistent with some of the studies based on data from the National Weight Control Registry, which stores information about adults who lost 30 lbs or more of weight and kept it off for at least 1 year (). I systematically measure my weight, body fat percentage, and a number of other variables; probably even more than the average National Weight Control Registry member. Based on those measurements, I try to understand how my body responds in the short and long term to stimuli such as different exercise, types of food, calorie restriction, sleep patterns etc.

And I act accordingly to keep any body fat gain from happening; by, for example, varying calorie intake, increasing exercise intensity, varying the types of food I eat etc. With a few exceptions (e.g., avoiding industrial seed oils), there is no generic formula. Customization based on individual responses and cyclical patterns seems to be a must.

Looking back, it was relatively easy for me to lose all that fat. This is consistent with the studies summarized in this post; all diets that rely on caloric reduction work marvelously at first for most people. The really difficult part is to keep the body fat off. I believe that this is especially true as the initial years go by, and becomes easier after that. This has something to do with initial inertia, which I will discuss soon in a post on metabolic rates and their relationship with overall body mass.

For people living in the wild, I can see one thing working in their favor. And that is not regular starvation; sapiens is too smart for that. It is laziness. Hunger has to reach a certain threshold for people to want to do some work to get their food; this acts as a natural body composition regulator, something that I intend to discuss in one of my next posts. It seems that people almost never become obese in the wild, without access to industrial foods.

As for living in the wild, in spite of the romantic portrayals of it, the experience is not as appealing after you really try it. The book Yanomamo: The Fierce People () is a solid, if not somewhat shocking, reminder of that. I had the opportunity to meet and talk at length with its author, the great anthropologist Nap Chagnon, at one of the Human Behavior and Evolution Society conferences. The man is a real-life Indiana Jones ().

In the formerly obese, the body seems to resort to “guerrilla warfare”, employing all kinds of physiological and psychological mechanisms, some more subtle than others, to make sure that the lost fat is recovered. Why? I have some ideas, which I have discussed indirectly in posts throughout this blog, but I still need to understand the whole process a bit better. My ideas build on the notion of compensatory adaptation ().

You might have heard some very smart people say that you do not need to measure anything to lose body fat and keep it off. Many of those people have never been obese. Those who have been obese often had not cleared the 2-3 year “danger zone” by the time they made those statements.

There are many obese or overweight public figures (TV show hosts, actors, even health bloggers) who embark on a diet and lose a dramatic amount of body fat. They talk and/or write for a year or so about their success, and then either “disappear” or start complaining about health issues. Those health issues are often part of the “guerrilla warfare” I mentioned above.

A few persistent public figures will gain the fat back, in part or fully, and do the process all over again. It makes for interesting drama, and at least keeps those folks in the limelight.

The China Study II: Wheat’s total effect on mortality is significant, complex, and highlights the negative effects of low animal fat diets

2012-01-16T04:00:00.000-08:00

The graph below shows the results of a multivariate nonlinear WarpPLS () analysis including the variables listed below. Each row in the dataset refers to a county in China, from the publicly available China Study II dataset (). As always, I thank Dr. Campbell and his collaborators for making the data publicly available. Other analyses based on the same dataset are also available ().
- Wheat: wheat flour consumption in g/d.
- Aprot: animal protein consumption in g/d.
- PProt: plant protein consumption in g/d.
- %FatCal: percentage of calories coming from fat.
- Mor35_69: number of deaths per 1,000 people in the 35-69 age range.
- Mor70_79: number of deaths per 1,000 people in the 70-79 age range.

Below are the total effects of wheat flour consumption, along with the number of paths used to calculate them, and the respective P values (i.e., probabilities that the effects are due to chance). Total effects are calculated by considering all of the paths connecting two variables. Identifying each path is a bit like solving a maze puzzle; you have to follow the arrows connecting the two variables. Version 3.0 of WarpPLS (soon to be released) does that automatically, and also calculates the corresponding P values.

To the best of my knowledge, this is the first time that total effects are calculated for this dataset. As you can see, the total effects of wheat flour consumption on mortality in the 35-69 and 70-79 age ranges are both significant, and fairly complex in this model, each relying on 7 paths. The P value for mortality in the 35-69 age range is 0.038; in other words, the probability that the effect is “real”, and thus not due to chance, is 96.2 percent (100-3.8=96.2). The P value for mortality in the 70-79 age range is 0.024; a 97.6 percent probability that the effect is “real”.

Note that in the model the effects of wheat flour consumption on mortality in both age ranges are hypothesized to be mediated by animal protein consumption, plant protein consumption, and fat consumption. These mediating effects have been suggested by previous analyses discussed on this blog (). The strongest individual paths are between wheat flour consumption and plant protein consumption, plant protein consumption and animal protein consumption, as well as animal protein consumption and fat consumption.

So wheat flour consumption contributes to plant protein consumption, probably by being a main source of plant protein (through gluten). Plant protein consumption in turn decreases animal protein consumption, which significantly decreases fat consumption. From this latter connection we can tell that most of the fat consumed likely came from animal sources.

How much fat and protein are we talking about? The graphs below tell us how much, and these graphs are quite interesting. They suggest that, in this dataset, daily protein consumption tended to be on average 60 g, whatever the source. If more protein came from plant foods, the proportion from animal foods went down, and vice-versa.

The more animal protein consumed, the more fat is also consumed in this dataset. And that is animal fat, which comes mostly in the form of saturated and monounsaturated fats, in roughly equal amounts. How do I know that it is animal fat? Because of the strong association with animal protein. By the way, with a few exceptions (e.g., some species of fatty fish) animal foods in general provide only small amounts of polyunsaturated fats – omega-3 and omega-6.

Individually, animal protein and wheat flour consumption have the strongest direct effects on mortality in both age ranges. Animal protein consumption is protective, and wheat flour consumption detrimental.

Does the connection between animal protein, animal fat, and longevity mean that a diet high in saturated and monounsaturated fats is healthy for most people? Not necessarily, at least without extrapolation, although the results do not suggest otherwise. Look at the amounts of fat consumed per day. They range from a little less than 20 g/d to a little over 90 g/d. By comparison, one steak of top sirloin (about 380 g of meat, cooked) trimmed to almost no visible fat gives you about 37 g of fat.

These results do suggest that consumption of animal fats, primarily saturated and monounsaturated fats, is likely to be particularly healthy in the context of a low fat diet. Or, said in a different way, these results suggest that longevity is decreased by diets that are low in animal fats.

How much fat should one eat? In this dataset, the more fat was consumed together with animal protein (i.e., the more animal fat was consumed), the better in terms of longevity. In other words, in this dataset the lowest levels of mortality were associated with the highest levels of animal fat consumption. The highest level of fat consumption in the dataset was a little over 90 g/d.

What about higher fat intake contexts? Well, we know that men on a high fat diet such as a variation of the Optimal Diet can consume on average a little over 170 g/d of animal fat (130 g/d for women), and their health markers remain generally good ().

One of the critical limiting factors, in terms of health, seems to be the amount of animal fat that one can eat and still remain relatively lean. Dietary saturated and monounsaturated fats are healthy. But when accumulated as excess body fat, beyond a certain level, they become pro-inflammatory.

Ground meat treats: Beef and bison meatza

2012-01-09T04:00:00.000-08:00

At the time of this writing, there was no Wikipedia article for the term “meatza”, which surprised me a bit given the number of recipes on the web. In fact, I could not find anything concrete about the dish’s tradition or history.

Another thing that surprised me about this dish is how much my family and I like it. It has become a regular weekend treat for us for quite some time now.

The recipe below is for a meal that feeds 4-8 people. Like in my previous recipe for a zucchini and onion meatloaf (), the ground beef used here has little fat, and thus a relatively low omega-6 content. Most of the fat comes from the ground bison, which has a higher omega-3 to omega-6 ratio.

- Prepare some dry seasoning powder by mixing sea salt, parsley flakes, garlic powder, chili powder, and a small amount of cayenne pepper.
- Mix 2 lb of very lean ground beef (96/4) with 1 lb of ground bison.
- Add the dry seasoning and a whole egg to the ground meat mix.
- Vigorously mix by hand until you get a homogeneous look.
- Place the mix into a sheet pan coated with olive oil. Richard’s suggestion of creating edges helps keep the sautéed vegetables on top, when they are added later ().
- Preheat oven to 375 degrees Fahrenheit.
- Bake the meatza for about 15 minutes.
- Grate 1 lb of aged cheese.
- Slice one tomato, half an onion, and one green bell pepper, and sauté them in olive oil.
- Drain the meatza after if comes out of the oven, and add the sautéed vegetables to the top, together with half a can of tomato sauce.
- Add the 1 lb of grated aged cheese on top of the vegetables and tomato sauce.
- Return meatza to the oven, still at 375 degrees Fahrenheit, and bake it for about 10 minutes.

The photo montage above shows a side dish of baked potatoes and zucchini. That is optional, as the meatza has vegetables added to it. I usually cut the meatza into 8 rectangular pieces. Each rectangle will have about 50 g of protein and 20 g of fat. The fat will be primarily saturated and monounsaturated (both healthy), with a good balance of omega-3 and omega-6 fats. Each piece of meatza will also be a good source of vitamins B12 and B6, niacin, calcium, zinc, selenium, and phosphorus.

HCE user experience: The anabolic range may be better measured in seconds than repetitions

2012-01-02T04:00:00.000-08:00

It is not uncommon for those who do weight training to see no gains over long periods of time for certain weight training exercises (e.g., overhead press), even while they experience gains in other types of exercise (e.g., regular squats).

HealthCorrelator for Excel (HCE) and its main outputs, coefficients of association and graphs (), have been helping some creative users identify the reasons why they see no gains, and break out of the stagnation periods.

It may be a good idea to measure the number of seconds of effort per set; in addition to other variables such as numbers of sets and repetitions, and the amount of weight lifted. In some cases, an inverted J curve, full or partial (just the left side of it), shows up suggesting that the number of seconds of effort in a particular type of weight training exercise is a better predictor of muscle gain than the number of repetitions used.

The inverted J curve is similar to the one discussed in a previous post on HCE used for weight training improvement, where the supercompensation phenomenon is also discussed ().

Repetitions in the 6-12 range are generally believed to lead to peak anabolic response, and this is generally true for weight training exercises conducted in good form and to failure. It is also generally believed that muscular effort should be maintained for 20 to 120 seconds for peak anabolic response.

The problem is that in certain cases not even 12 repetitions lead to at least 20 seconds of effort. This is usually the case when the repetitions are performed very quickly. There are a couple of good reasons why this may happen: the person has above-average muscular power, or the range of motion used is limited.

What is muscular power, and why would someone want to limit the range of motion used in a weight training exercise?

Muscular power is different from muscular strength, and is normally distributed (bell curve) across the population, like most human traints (). Muscular power is related to the speed with which an individual can move a certain amount of weight. Muscular strength is related to the amount of weight moved. Frequently people who perform amazing feats of strength, like Dennis Rogers (), have above-average muscular power.

As for limiting the range of motion used in a weight training exercise, one of the advantages of doing so is that it reduces the risk of injury, as a wise commenter pointed out here some time ago (). It also has the advantage of increasing the number of variations of an exercise that can be used at different points in time; which is desirable, as variation is critical for sustained supercompensation ().

The picture below is from a YouTube video clip showing champion natural bodybuilder Doug Miller performing 27 repetitions of the deadlift with 405 lbs (). Doug is one of the co-authors of the book Biology for Bodybuilders, which has been reviewed here ().

The point of showing the video clip above is that the range of repetitions used would be perceived as quite high by many bodybuilders, but is nevertheless the one leading to a peak anabolic response for Doug. If you pay careful attention to the video, you will notice that Doug completes the 27 repetitions in 45 seconds, well within the anabolic range. If he had completed only 12 repetitions, at about the same pace, he would have done that a few seconds before hitting the 20-second mark.

Doug completes those 27 repetitions relatively quickly, because he has above-average muscular power, in addition to having above-average muscular strength.

Ground meat treats: Zucchini and onion meatloaf

2011-12-26T04:00:00.000-08:00

A cousin of the meatball (), the meatloaf is a traditional German dish. The recipe below is for a meal that feeds 4-8 people. The ground beef used has little fat, and thus a relatively low omega-6 content. Most of the fat comes from the 1 lb of ground grass-fed lamb in the recipe, which has a higher omega-3 to omega-6 ratio than the regular (i.e., non-grass-fed) ground beef. The egg acts as a binder. Leave the potato out if you want to decrease the carbohydrate content; it does not add much (nutrient numbers are provided at the end of the post).

- Prepare some dry seasoning powder by mixing salt, parsley flakes, garlic powder, chili powder, and a small amount of cayenne pepper.
- Grate one zucchini squash and one peeled potato. Cut half an onion into small pieces of similar size.
- Mix 2 lb of very lean ground beef (96/4) with 1 lb of ground grass-fed lamb.
- Add the dry seasoning, zucchini, potato, onion and a whole egg to the ground meat mix.
- Vigorously mix by hand until you get a homogeneous look.
- Place the mix into a buttered casserole dish with the shape of a loaf.
- Preheat the oven to 375 degrees Fahrenheit.
- Bake the meatloaf for about 1 hour and a half.

It is a good idea to place the casserole dish within a tray, as in the photo above. The meatloaf will give off some of its juices as it bakes, which may overflow from the casserole dish and make a mess in your oven. Below is a slice of meatloaf served with a side of vegetables. The green spots in the meatloaf are the baked zucchini squash pieces.

A thick slice like the one on the photo above will have about 52 g of protein, 15 g of fat, and 6 g of carbohydrates (mostly from the potato). That'll be about 1/5 of the whole meatloaf; the slice will weigh a little less then 1/2 lb (approximately 200 g). The fat will be primairly saturated and monounsaturated (both healthy), with a good balance of omega-3 and omega-6 fats. The slice of meatloaf will also be a good source of vitamins B12 and B6, niacin, zinc, selenium, and phosphorus.

Protein powders before fasted weight training? Here is a more natural and cheaper alternative

2011-12-19T04:00:00.000-08:00

The idea that protein powders should be consumed prior to weight training has been around for a while, and is very popular among bodybuilders. Something like 10 grams or so of branched-chain amino acids (BCAAs) is frequently recommended. More recently, with the increase in popularity of intermittent fasting, it has been strongly recommended prior to “fasted weight training”. The quotation marks here are because, obviously, if you are consuming anything that contains calories prior to weight training, the weight training is NOT being done in a fasted state.

(Source: Ecopaper.com)

Most of the evidence available suggests that intermittent fasting is generally healthy. In fact, being able to fast for 16 hours or more, particularly without craving sweet foods, is actually a sign of a healthy glucose metabolism; which may complicate a cause-and-effect analysis between intermittent fasting and general health. The opposite, craving sweet foods every few hours, is generally a bad sign.

One key aspect of intermittent fasting that needs to be highlighted is that it is also arguably a form of liberation ().

Now, doing weight training in the fasted state may or may not lead to muscle loss. It probably doesn’t, even after a 24-hour fast, for those who fast and replenish their glycogen stores on a regular basis ().

However, weight training in a fasted state frequently induces an exaggerated epinephrine-norepinephrine (i.e., adrenaline-noradrenaline) response, likely due to depletion of liver glycogen beyond a certain threshold (the threshold varies for different people). The same is true for prolonged or particularly intense weight training sessions, even if they are not done in the fasted state. The body wants to crank up consumption of fat and ketones, so that liver glycogen is spared to ensure that it can provide the brain with its glucose needs.

Exaggerated epinephrine-norepinephrine responses tend to cause a few sensations that are not very pleasant. One of the first noticeable ones is orthostatic hypotension; i.e., feeling dizzy when going from a sitting to a standing position. Other related feelings are light-headedness, and a “pins and needles” sensation in the limbs (typically the arms and hands). Many believe that they are having a heart attack whey they have this “pins and needles” sensation, which can progress to a stage that makes it impossible to continue exercising.

Breaking the fast prior to weight training with dietary fat or carbohydrates is problematic, because those nutrients tend to blunt the dramatic rise in growth hormone that is typically experienced in response to weight training (). This is not good because the growth hormone response is probably one of the main reasons why weight training can be so healthy ().

Dietary protein, however, does not seem to significantly blunt the growth hormone response to weight training; even though it doesn't seem to increase it either (). Dietary protein seems to also suppress the exaggerated epinephrine-norepinephrine response to fasted weight training. And, on top of all that, it appears to suppress muscle loss, which may well be due to a moderate increase in circulating insulin ().

So everything points at the possibility that the ingestion of some protein, without carbohydrates or fat, is a good idea prior to fasted weight training. Not too much protein though, because insulin beyond a certain threshold is also likely to suppress the growth hormone response.

Does the protein have to be in the form of a protein powder? No.

Supplements are made from food, and this is true of protein powders as well. If you hard-boil a couple of large eggs, and eat only the whites prior to weight training, you will be getting about 8-10 grams of one of the highest quality protein "supplements" you can possibly get. Included are BCAAs. You will get a few extra nutrients with that too, but virtually no fat or carbohydrates.

Finding your sweet spot for muscle gain with HCE

2011-12-12T04:00:00.000-08:00

In order to achieve muscle gain, one has to repeatedly hit the “supercompensation” window, which is a fleeting period of time occurring at some point in the muscle recovery phase after an intense anaerobic exercise session. The figure below, from Vladimir Zatsiorsky’s and William Kraemer’s outstanding book Science and Practice of Strength Training () provides an illustration of the supercompensation idea. Supercompensation is covered in more detail in a previous post ().

Trying to hit the supercompensation window is a common denominator among HealthCorrelator for Excel (HCE) users who employ the software () to maximize muscle gain. (That is, among those who know and subscribe to the theory of supercompensation.) This post outlines what I believe is a good way of doing that while avoiding some pitfalls. The data used in the example that follows has been created by me, and is based on a real case. I disguised the data, simplified it, added error etc. to make the underlying method relatively easy to understand, and so that the data cannot be traced back to its “real case” user (for privacy).

Let us assume that John Doe is an intermediate weight training practitioner. That is, he has already gone through the beginning stage where most gains come from neural adaptation. For him, new gains in strength are a reflection of gains in muscle mass. The table below summarizes the data John obtained when he decided to vary the following variables in order to see what effects they have on his ability to increase the weight with which he conducted the deadlift () in successive exercise sessions:
- Number of rest days in between exercise sessions (“Days of rest”).
- The amount of weight he used in each deadlift session (“Deadlift weight”).
- The amount of weight he was able to add to the bar each session (“Delta weight”).
- The number of deadlift sets and reps (“Deadlift sets” and “Deadlift reps”, respectively).
- The total exercise volume in each session (“Deadlift volume”). This was calculated as follows: “Deadlift weight” x “Deadlift sets” x “Deadlift reps”.

John’s ability to increase the weight with which he conducted the deadlift in each session is measured as “Delta weight”. That was his main variable of interest. This may not look like an ideal choice at first glance, as arguably “Deadlift volume” is a better measure of total effort and thus actual muscle gain. The reality is that this does not matter much in his case, because: John had long rest periods within sets, of around 5 minutes; and he made sure to increase the weight in each successive session as soon as he felt he could, and by as much as he could, thus never doing more than 24 reps. If you think that the number of reps employed by John is too high, take a look at a post in which I talk about Doug Miller and his ideas on weight training ().

Below are three figures, with outputs from HCE: a table showing the coefficients of association between “Delta weight” and the other variables, and two graphs showing the variation of “Delta weight” against “Deadlift volume” and “Days of rest”. As you can see, nothing seems to be influencing “Delta weight” strongly enough to reach the 0.6 level that I recommend as the threshold for a “real effect” to be used in HCE analyses. There are two possibilities here: it is what it looks it is, that is, none of the variables influence “Delta weight”; or there are effects, but they do not show up in the associations table (as associations equal to or greater than 0.6) because of nonlinearity.

The graph of “Delta weight” against “Deadlift volume” is all over the place, suggesting a lack of association. This is true for the other variables as well, except “Days of rest”; the last graph above. That graph, of “Delta weight” against “Days of rest”, suggests the existence of a nonlinear association with the shape of an inverted J curve. This type of association is fairly common. In this case, it seems that “Delta weight” is maximized in the 6-7 range of “Days of rest”. Still, even varying things almost randomly, John achieved a solid gain over the time period. That was a 33 percent gain from the baseline “Deadlift weight”, a gain calculated as: (285-215)/215.

HCE, unlike WarpPLS (), does not take nonlinear relationships into consideration in the estimation of coefficients of association. In order to discover nonlinear associations, users have to inspect the graphs generated by HCE, as John did. Based on his inspection, John decided to changes things a bit, now working out on the right side of the J curve, with 6 or more “Days of rest”. That was difficult for John at first, as he was addicted to exercising at a much higher frequency; but after a while he became a “minimalist”, even trying very long rest periods.

Below are four figures. The first is a table summarizing the data John obtained for his second trial. The other three are outputs from HCE, analogous to those obtained in the first trial: a table showing the coefficients of association between “Delta weight” and the other variables, two graphs (side-by-side) showing “Delta weight” against “Deadlift sets” and “Deadlift reps”, and one graph of “Delta weight” against “Days of rest”. As you can see, “Days of rest” now influences “Delta weight” very strongly. The corresponding association is a very high -0.981! The negative sign means that “Delta weight” decreases as “Days of rest” increase. This does NOT mean that rest is not important; remember, John is now operating on the right side of the J curve, with 6 or more “Days of rest”.

The last graph above suggests that taking 12 or more “Days of rest” shifted things toward the end of the supercompensation window, in fact placing John almost outside of that window at 13 “Days of rest”. Even so, there was no loss of strength, and thus probably no muscle loss. Loss of strength would be suggested by a negative “Delta weight”, which did not occur (the “Delta weight” went down to zero, at 13 “Days of rest”). The two graphs shown side-by-side suggest that 2 “Deadlift sets” seem to work just as well for John as 3 or 4, and that “Deadlift reps” in the 18-24 range also work well for John.

In this second trial, John achieved a better gain over a similar time period than in the first trial. That was a 36 percent gain from the baseline “Deadlift weight”, a gain calculated as: (355-260)/260. John started with a lower baseline than in the end of the first trial period, probably due to detraining, but achieved a final “Deadlift weight” that was likely very close to his maximum potential (at the reps used). Because of this, the 36 percent gain in the period is a lot more impressive than it looks, as it happened toward the end of a saturation curve (e.g., the far right end of a logarithmic curve).

One important thing to keep in mind is that if an HCE user identifies a nonlinear relationship of the J-curve type by inspecting the graphs like John did, in further analyses the focus should be on the right or left side of the curve by either: splitting the dataset into two, and running a separate analysis for each new dataset; or running a new trial, now sticking with a range of variation on the right or left side of the curve, as John did. The reason is that nonlinear relationships tend to distort the linear coefficients calculated by HCE, hiding a real relationship between two variables.

This is a very simplified example. Most serious bodybuilders will measure variations in a number of variables at the same time, for a number of different exercise types and formats, and for longer periods. That is, their “HealthData” sheet in HCE will be a lot more complex. They will also have multiple instances of HCE running on their computer. HCE is a collection of sheets and code that can be copied, and saved with different names. The default is “HCE_1_0.xls” or “HCE_1_0.xlsm”, depending on which version you are using. Each new instance of HCE may contain a different dataset for analysis, stored in the “HealthData” sheet.

It is strongly recommended that you keep your data in a separate set of sheets, as a backup. That is, do not store all your data in the “HealthData” sheets in different HCE instances. Also, when you copy your data into the “HealthData” sheet in HCE, copy only the values and formats, and NOT the formulas. If you copy the formulas, you may end up having some problems, as some of the cells in the “HealthData” sheet will not be storing values. I also recommend storing values for other types variables, particularly perception-based variables.

Examples of perception-based variables are: “Perceived stress”, “Perceived delayed onset muscle soreness (DOMS)”, and “Perceived non-DOMS pain”. These can be answered on Likert-type scales, such as scales going from 1 (very strongly disagree) to 7 (very strongly agree) in response to self-prepared question-statements like “I feel stressed out” (for “Perceived stress”). If you find that a variable like “Perceived non-DOMS pain” is associated with working out at a particular volume range, that may help you avoid serious injury in the future, as non-DOMS pain is not a very good sign (). You also may find that working out in the volume range that is associated with non-DOMS pain adds nothing in terms of muscle gain.

Generally speaking, I think that many people will find out that their sweet spot for muscle gain involves less frequent exercise at lower volumes than they think. Still, each individual is unique; there is no one quite like John. The relationship between “Delta weight” and “Days of rest” varies from person to person based on age; older folks generally require more rest. It also varies based on whether the person is dieting or not; less food intake leads to longer recovery periods. Women will probably see visible lower-body muscle gain, but very little visible upper-body muscle gain (in the absence of steroid use), even as they experience upper-body strength gains. Other variables of interest for both men and women may be body weight, body fat percentage, and perceived muscle tone.

Want to make coffee less acidic? Add cream to it

2011-12-05T04:00:00.000-08:00

The table below is from a 2008 article by Ehlen and colleagues (), showing the amount of erosion caused by various types of beverages, when teeth were exposed to them 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.

As you can see, even diet drinks may cause tooth erosion. That is not to say that if you drink a diet soda occasionally you will destroy your teeth, but regular drinking may be a problem. I discussed this study in a previous post (). After that post was published here some folks asked me about coffee, so I decided to do some research.

Unfortunately coffee by itself can also cause some erosion, primarily because of its acidity. Generally speaking, you want a liquid substance that you are interested in drinking to have a pH as close to 7 as possible, as this pH is neutral (). Tap and mineral water have a pH that is very close to 7. Black coffee seems to have a pH of about 4.8.

Also problematic are drinks containing fermentable carbohydrates, such as sucrose, fructose, glucose, and lactose. These are fermented by acid-producing bacteria. Interestingly, when fermentable carbohydrates are consumed as part of foods that require chewing, such as fruits, acidity is either neutralized or significantly reduced by large amounts of saliva being secreted as a result of the chewing process.

So what to do about coffee?

One possible solution is to add heavy cream to it. A small amount, such as a teaspoon, appears to bring the pH in a cup of coffee to a little over 6. Another advantage of heavy cream is that it has no fermentable carbohydrates; it has no carbohydrates, period. You will have to get over the habit of drinking sweet beverages, including sweet coffee, if you were unfortunate enough to develop that habit (like so many people living in cities today).

It is not easy to find reliable pH values for various foods. I guess dentistry researchers are more interested in ways of repairing damage already done, and there doesn't seem to be much funding available for preventive dentistry research. Some pH testing results from a University of Cincinnati college biology page were available at the time of this writing; they appeared to be reasonably reliable the last time I checked them ().

Triglycerides, VLDL, and industrial carbohydrate-rich foods

2011-11-28T04:00:00.000-08:00

Below are the coefficients of association calculated by HealthCorrelator for Excel (HCE) for user John Doe. The coefficients of association are calculated as linear correlations in HCE (). The focus here is on the associations between fasting triglycerides and various other variables. Take a look at the coefficient of association at the top, with VLDL cholesterol, indicated with a red arrow. It is a very high 0.999.

Whoa! What is this – 0.999! Is John Doe a unique case? No, this strong association between fasting triglycerides and VLDL cholesterol is a very common pattern among HCE users. The reason is simple. VLDL cholesterol is not normally measured directly, but typically calculated based on fasting triglycerides, by dividing the fasting triglycerides measurement by 5. And there is an underlying reason for that - fasting triglycerides and VLDL cholesterol are actually very highly correlated, based on direct measurements of these two variables.

But if VLDL cholesterol is calculated based on fasting triglycerides (VLDL cholesterol = fasting triglycerides / 5), how come the correlation is 0.999, and not a perfect 1? The reason is the rounding error in the measurements. Whenever you see a correlation this high (i.e., 0.999), it is reasonable to suspect that the source is an underlying linear relationship disturbed by rounding error.

Fasting triglycerides are probably the most useful measures on standard lipid panels. For example, fasting triglycerides below 70 mg/dl suggest a pattern of LDL particles that is predominantly of large and buoyant particles. This pattern is associated with a low incidence of cardiovascular disease (). Also, chronically high fasting triglycerides are a well known marker of the metabolic syndrome, and a harbinger of type 2 diabetes.

Where do large and buoyant LDL particles come from? They frequently start as "big" (relatively speaking) blobs of fat, which are actually VLDL particles. The photo is from the excellent book by Elliott & Elliott (); it shows, on the same scale: (a) VLDL particles, (b) chylomicrons, (c) LDL particles, and (d) HDL particles. The dark bar at the bottom of each shot is 1000 A in length, or 100 nm (A = angstrom; nm = nanometer; 1 nm = 10 A).

If you consume an excessive amount of carbohydrates, my theory is that your liver will produce an abnormally large number of small VLDL particles (also shown on the photo above), a proportion of which will end up as small and dense LDL particles. The liver will do that relatively quickly, probably as a short-term compensatory mechanism to avoid glucose toxicity. It will essentially turn excess glucose, from excess carbohydrates, into fat. The VLDL particles carrying that fat in the form of triglycerides will be small because the liver will be in a hurry to clear the excess glucose in circulation, and will have no time to produce large particles, which take longer to produce individually.

This will end up leading to excess triglycerides hanging around in circulation, long after they should have been used as sources of energy. High fasting triglycerides will be a reflection of that. The graphs below, also generated by HCE for John Doe, show how fasting triglycerides and VLDL cholesterol vary in relation to refined carbohydrate consumption. Again, the graphs are not identical in shape because of rounding error; the shapes are almost identical.

Small and dense LDL particles, in the presence of other factors such as systemic inflammation, will contribute to the formation of atherosclerotic plaques. Again, the main source of these particles would be an excessive amount of carbohydrates. What is an excessive amount of carbohydrates? Generally speaking, it is an amount beyond your liver’s capacity to convert the resulting digestion byproducts, fructose and glucose, into liver glycogen. This may come from spaced consumption throughout the day, or acute consumption in an unnatural form (a can of regular coke), or both.

Liver glycogen is sugar stored in the liver. This is the main source of sugar for your brain. If your blood sugar levels become too low, your brain will get angry. Eventually it will go from angry to dead, and you will finally find out what awaits you in the afterlife.

Should you be a healthy athlete who severely depletes liver glycogen stores on a regular basis, you will probably have an above average liver glycogen storage and production capacity. That will be a result of long-term compensatory adaptation to glycogen depleting exercise (). As such, you may be able to consume large amounts of carbohydrates, and you will still not have high fasting triglycerides. You will not carry a lot of body fat either, because the carbohydrates will not be converted to fat and sent into circulation in VLDL particles. They will be used to make liver glycogen.

In fact, if you are a healthy athlete who severely depletes liver glycogen stores on a regular basis, excess calories will be just about the only thing that will contribute to body fat gain. Your threshold for “excess” carbohydrates will be so high that you will feel like the whole low carbohydrate community is not only misguided but also part of a conspiracy against people like you. If you are also an aggressive blog writer, you may feel compelled to tell the world something like this: “Here, I can eat 300 g of carbohydrates per day and maintain single-digit body fat levels! Take that you low carbohydrate idiots!”

Let us say you do not consume an excessive amount of carbohydrates; again, what is excessive or not varies, probably dramatically, from individual to individual. In this case your liver will produce a relatively small number of fat VLDL particles, which will end up as large and buoyant LDL particles. The fat in these large VLDL particles will likely not come primarily from conversion of glucose and/or fructose into fat (i.e., de novo lipogenesis), but from dietary sources of fat.

How do you avoid consuming excess carbohydrates? A good way of achieving that is to avoid man-made carbohydrate-rich foods. Another is adopting a low carbohydrate diet. Yet another is to become a healthy athlete who severely depletes liver glycogen stores on a regular basis; then you can eat a lot of bread, pasta, doughnuts and so on, and keep your fingers crossed for the future.

Either way, fasting triglycerides will be strongly correlated with VLDL cholesterol, because VLDL particles contain both triglycerides (“encapsulated” fat, not to be confused with “free” fatty acids) and cholesterol. If a large number of VLDL particles are produced by one’s liver, the person’s fasting triglycerides reading will be high. If a small number of VLDL particles are produced, even if they are fat particles, the fasting triglycerides reading will be relatively low. Neither VLDL cholesterol nor fasting triglycerides will be zero though.

Now, you may be wondering, how come a small number of fat VLDL particles will eventually lead to low fasting triglycerides? After all, they are fat particles, even though they occur in fewer numbers. My hypothesis is that having a large number of small-dense VLDL particles in circulation is an abnormal, unnatural state, and that our body is not well designed to deal with that state. Use of lipoprotein-bound fat as a source of energy in this state becomes somewhat less efficient, leading to high triglycerides in circulation; and also to hunger, as our mitochondria like fat.

This hypothesis, and the theory outlined above, fit well with the numbers I have been seeing for quite some time from HCE users. Note that it is a bit different from the more popular theory, particularly among low carbohydrate writers, that fat is force-stored in adipocytes (fat cells) by insulin and not released for use as energy, also leading to hunger. What I am saying here, which is compatible with this more popular theory, is that lipoproteins, like adipocytes, also end up holding more fat than they should if you consume excess carbohydrates, and for longer.

Want to improve your health? Consider replacing things like bread and cereal with butter and eggs in your diet (). And also go see you doctor (); if he disagrees with this recommendation, ask him to read this post and explain why he disagrees.

My transformation: How I looked 10 years ago next to a thin man called Royce Gracie

2011-11-21T04:00:00.000-08:00

The photos below were taken about 10 years ago. The first is at a restaurant near Torrance, California. (As you can see, the restaurant was about to close; we were the last customers.) I am standing next to Royce Grace, who had by then become a sensation (). He became a sensation by easily defeating nearly every champion fighter that was placed in front of him. In case you are wondering, Royce is 6’1” and I am 5’8”. The second photo also has Royce’s manager in it – that is his wife. Their children’s names both start with the letter “K”. I wonder how big they are right now.

I think that at the time these photos were taken I weighed around 200-210 lbs. Even though I am much shorter than Royce, I outweighed him by around 40 lbs. Now I weigh 150 lbs, at about 11 percent body fat, and look like the photo on the top-right area of this blog - essentially like a thin guy who does some manual labor for a living, I guess. A post is available discussing the "how" part of this transformation (). I only put a shirtless photo here after several readers told me that my previous photo looked out of place in this blog.

My day job is not even remotely related to fitness instruction. I am a college professor, and like to think of myself as a scholar. I don’t care much about my personal appearance; never did. At least in my mind, putting up shirtless photos on the web should not be done gratuitously. If you are a fitness instructor, or an athlete, that is fine. In my case, it is acceptable in the context of telling people that a few minutes of mid-day sun exposure, avoiding sunburn, yields 10,000 IU of skin-produced vitamin D, which is about 20 times more than one can get through most "fortified" industrial foods.

Royce is such a nice guy that, after much insistence, he paid for the dinner, and then we drove to his house and talked until about midnight. He had told me of a flight the next morning to Chicago, so I ended the interview and thanked him for the wonderful time we had spent together. I had to talk him out of driving ahead of me to I-405; he wanted to make sure I was not going to get lost at that time of the night. This was someone who was considered a demigod at the time in some circles. A humble, wonderful person.

Royce helped launch what is today the mega-successful Ultimate Fighting Championship franchise (), which was then still a no holders barred mixed martial arts tournament. At the time the photos were taken I was interviewing him for my book Compensatory Adaptation, which came out in print soon after (). The book has a full chapter on the famous Gracie Family, including his father Helio and his brother Rickson.

I talked before about the notion of compensatory adaptation and how it applies to our understanding of how we respond to diet and lifestyle changes (). In this context, I believe that the compensatory adaptation notion is far superior to that of hormesis (), which I think is interesting but overused and overrated.

The notion of compensatory adaptation has been picked up in the field of information systems, my main field of academic research. In this field, which deals with how people respond to technologies, it is part of a broader theory called media naturalness theory (). There are already several people who have received doctorates by testing this theory from novel angles. There are also several people today who call themselves experts in compensatory adaptation and media naturalness theory.

The above creates an odd situation, and something funny that happened with me a few times already. I do some new empirical research on compensatory adaptation, looking at it from a new angle, write an academic paper about it (often with one or more co-authors who helped me collect empirical data), and submit it to a selective refereed journal. Then an "expert" reviewer, who does not know who the authors of the paper are (this is called a "blind" review), recommends rejection of the paper because “the authors of this paper clearly do not understand the notion of compensatory adaptation”. Sometimes something like this is added: “the authors should read the literature on compensatory adaptation more carefully, particularly Kock (2004)” - an article that has a good number of citations to it ().

Oh well, the beauty of the academic refereeing process …

The China Study II: How gender takes us to the elusive and deadly factor X

2011-11-05T15:21:00.000-07:00

The graph below shows the mortality in the 35-69 and 70-79 age ranges for men and women for the China Study II dataset. I discussed other results in my two previous posts () (), all taking us to this post. The full data for the China Study II study is publicly available (). The mortality numbers are actually averages of male and female deaths by 1,000 people in each of several counties, in each of the two age ranges.

Men do tend to die earlier than women, but the difference above is too large.

Generally speaking, when you look at a set time period that is long enough for a good number of deaths (not to be confused with “a number of good deaths”) to be observed, you tend to see around 5-10 percent more deaths among men than among women. This is when other variables are controlled for, or when men and women do not adopt dramatically different diets and lifestyles. One of many examples is a study in Finland (); you have to go beyond the abstract on this one.

As you can see from the graph above, in the China Study II dataset this difference in deaths is around 50 percent!

This huge difference could be caused by there being significantly more men than women per county included the dataset. But if you take a careful look at the description of the data collection methods employed (), this does not seem to be the case. In fact, the methodology descriptions suggest that the researchers tried to have approximately the same number of women and men studied in each county. The numbers reported also support this assumption.

As I said before, this is a well executed research project, for which Dr. Campbell and his collaborators should be commended. I may not agree with all of their conclusions, but this does not detract even a bit from the quality of the data they have compiled and made available to us all.

So there must be another factor X causing this enormous difference in mortality (and thus longevity) among men and women in the China Study II dataset.

What could be this factor X?

This situation helps me illustrate a point that I have made here before, mostly in the comments under other posts. Sometimes a variable, and its effects on other variables, are mostly a reflection of another unmeasured variable. Gender is a variable that is often involved in this type of situation. Frequently men and women do things very differently in a given population due to cultural reasons (as opposed to biological reasons), and those things can have a major effect on their health.

So, the search for our factor X is essentially a search for a health-relevant variable that is reflected by gender but that is not strictly due to the biological aspects that make men and women different (these can explain only a 5-10 percent difference in mortality). That is, we are looking for a variable that shows a lot of variation between men and women, that is behavioral, and that has a clear impact on health. Moreover, as it should be clear from my last post, we are looking for a variable that is unrelated to wheat flour and animal protein consumption.

As it turns out, the best candidate for the factor X is smoking, particularly cigarette smoking.

The second best candidate for factor X is alcohol abuse. Alcohol abuse can be just as bad for one’s health as smoking is, if not worse, but it may not be as good a candidate for factor X because the difference in prevalence between men and women does not appear to be just as large in China (). But it is still large enough for us to consider it a close second as a candidate for factor X, or a component of a more complex factor X – a composite of smoking, alcohol abuse and a few other coexisting factors that may be reflected by gender.

I have had some discussions about this with a few colleagues and doctoral students who are Chinese (thanks William and Wei), and they mentioned stress to me, based on anecdotal evidence. Moreover, they pointed out that stressful lifestyles, smoking, and alcohol abuse tend to happen together - with a much higher prevalence among men than women.

What an anti-climax for this series of posts eh?

With all the talk on the Internetz about safe and unsafe starches, animal protein, wheat bellies, and whatnot! C’mon Ned, give me a break! What about insulin!? What about leucine deficiency … or iron overload!? What about choline!? What about something truly mysterious, related to an obscure or emerging biochemistry topic; a hormone du jour like leptin perhaps? Whatever, something cool!

Smoking and alcohol abuse!? These are way too obvious. This is NOT cool at all!

Well, reality is often less mysterious than we want to believe it is.

Let me focus on smoking from here on, since it is the top candidate for factor X, although much of the following applies to alcohol abuse and a combination of the two as well.

One gets different statistics on cigarette smoking in China depending on the time period studied, but one thing seems to be a common denominator in these statistics. Men tend to smoke in much, much higher numbers than women in China. And this is not a recent phenomenon.

For example, a study conducted in 1996 () states that “smoking continues to be prevalent among more men (63%) than women (3.8%)”, and notes that these results are very similar to those in 1984, around the time when the China Study II data was collected.

A 1995 study () reports similar percentages: “A total of 2279 males (67%) but only 72 females (2%) smoke”. Another study () notes that in 1976 “56% of the men and 12% of the women were ever-smokers”, which together with other results suggest that the gap increased significantly in the 1980s, with many more men than women smoking. And, most importantly, smoking industrial cigarettes.

So we are possibly talking about a gigantic difference here; the prevalence of industrial cigarette smoking among men may have been over 30 times the prevalence among women in the China Study II dataset.

Given the above, it is reasonable to conclude that the variable “SexM1F2” reflects very strongly the variable “Smoking”, related to industrial cigarette smoking, and in an inverse way. I did something that, grossly speaking, made the mysterious factor X explicit in the WarpPLS model discussed in my previous post. I replaced the variable “SexM1F2” in the model with the variable “Smoking” by using a reverse scale (i.e., 1 and 2, but reversing the codes used for “SexM1F2”). The results of the new WarpPLS analysis are shown on the graph below. This is of course far from ideal, but gives a better picture to readers of what is going on than sticking with the variable “SexM1F2”.

With this revised model, the associations of smoking with mortality in the 35-69 and 70-79 age ranges are a lot stronger than those of animal protein and wheat flour consumption. The R-squared coefficients for mortality in both ranges are higher than 20 percent, which is a sign that this model has decent explanatory power. Animal protein and wheat flour consumption are still significantly associated with mortality, even after we control for smoking; animal protein seems protective and wheat flour detrimental. And smoking’s association with the amount of animal protein and wheat flour consumed is practically zero.

Replacing “SexM1F2” with “Smoking” would be particularly far from ideal if we were analyzing this data at the individual level. It could lead to some outlier-induced errors; for example, due to the possible existence of a minority of female chain smokers. But this variable replacement is not as harmful when we look at county-level data, as we are doing here.

In fact, this is as good and parsimonious model of mortality based on the China Study II data as I’ve ever seen based on county level data.

Now, here is an interesting thing. Does the original China Study II analysis of univariate correlations show smoking as a major problem in terms of mortality? Not really.

The table below, from the China Study II report (), shows ALL of the statistically significant (P<0.05) univariate correlations with mortality in 70-79 age range. I highlighted the only measure that is directly related to smoking; that is “dSMOKAGEm”, listed as “questionnaire AGE MALE SMOKERS STARTED SMOKING (years)”.

The high positive correlation with “dSMOKAGEm” does not even make a lot of sense, as one would expect a negative correlation here – i.e., the earlier in life folks start smoking, the higher should be the mortality. But this reverse-signed correlation may be due to smokers who get an early start dying in disproportionally high numbers before they reach age 70, and thus being captured by another age range mortality variable. The fact that other smoking-related variables are not showing up on the table above is likely due to distortions caused by inter-correlations, as well as measurement problems like the one just mentioned.

As one looks at these univariate correlations, most of them make sense, although several can be and probably are distorted by correlations with other variables, even unmeasured variables. And some unmeasured variables may turn out to be critical. Remember what I said in my previous post – the variable “SexM1F2” was introduced by me; it was not in the original dataset. “Smoking” is this variable, but reversed, to account for the fact that men are heavy smokers and women are not.

Univariate correlations are calculated without adjustments or control. To correct this problem one can adjust a variable based on other variables; as in “adjusting for age”. This is not such a good technique, in my opinion; it tends to be time-consuming to implement, and prone to errors. One can alternatively control for the effects of other variables; a better technique, employed in multivariate statistical analyses. This latter technique is the one employed in WarpPLS analyses ().

Why don’t more smoking-related variables show up on the univariate correlations table above? The reason is that the table summarizes associations calculated based on data for both sexes. Since the women in the dataset smoked very little, including them in the analysis together with men lowers the strength of smoking-related associations, which would probably be much stronger if only men were included. It lowers the strength of the associations to the point that their P values become higher than 0.05, leading to their exclusion from tables like the one above. This is where the aggregation process that may lead to ecological fallacy shows its ugly head.

No one can blame Dr. Campbell for not issuing warnings about smoking, even as they came mixed with warnings about animal food consumption (). The former warnings, about smoking, make a lot of sense based on the results of the analyses in this and the last two posts.

The latter warnings, about animal food consumption, seem increasingly ill-advised. Animal food consumption may actually be protective in regards to the factor X, as it seems to be protective in terms of wheat flour consumption ().

The China Study II: Gender, mortality, and the mysterious factor X

2011-10-31T04:00:00.000-07:00

WarpPLS and HealthCorrelator for Excel were 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, visit the HealthCorrelator for Excel site and check under the sample datasets area. As always, I thank Dr. T. Colin Campbell and his collaborators for making the data publicly available for independent analyses.

In my previous post I mentioned some odd results that led me to additional analyses. Below is a screen snapshot summarizing one such analysis, of the ordered associations between mortality in the 35-69 and 70-79 age ranges and all of the other variables in the dataset. As I said before, this is a subset of the China Study II dataset, which does not include all of the variables for which data was collected. The associations shown below were generated by HealthCorrelator for Excel.

The top associations are positive and with mortality in the other range (the “M006 …” and “M005 …” variables). This is to be expected if ecological fallacy is not a big problem in terms of conclusions drawn from this dataset. In other words, the same things cause mortality to go up in the two age ranges, uniformly across counties. This is reassuring from a quantitative analysis perspective.

The second highest association in both age ranges is with the variable “SexM1F2”. This variable is a “dummy” variable coded as 1 for male sex and 2 for female, which I added to the dataset myself – it did not exist in the original dataset. The association in both age ranges is negative, meaning that being female is protective. They reflect in part the role of gender on mortality, more specifically the biological aspects of being female, since we have seen before in previous analyses that being female is generally health-protective.

I was able to add a gender-related variable to the model because the data was originally provided for each county separately for males and females, as well as through “totals” that were calculated by aggregating data from both males and females. So I essentially de-aggregated the data by using data from males and females separately, in which case the totals were not used (otherwise I would have artificially reduced the variance in all variables, also possibly adding uniformity where it did not belong). Using data from males and females separately is the reverse of the aggregation process that can lead to ecological fallacy problems.

Anyway, the associations with the variable “SexM1F2” got me thinking about a possibility. What if females consumed significantly less wheat flour and more animal protein in this dataset? This could be one of the reasons behind these strong associations between being female and living longer. So I built a more complex WarpPLS model than the one in my previous post, and ran a linear multivariate analysis on it. The results are shown below.

What do these results suggest? They suggest no strong associations between gender and wheat flour or animal protein consumption. That is, when you look at county averages, men and women consumed about the same amounts of wheat flour and animal protein. Also, the results suggest that animal protein is protective and wheat flour is detrimental, in terms of longevity, regardless of gender. The associations between animal protein and wheat flour are essentially the same as the ones in my previous post. The beta coefficients are a bit lower, but some P values improved (i.e., decreased); the latter most likely due to better resample set stability after including the gender-related variable.

Most importantly, there is a very strong protective effect associated with being female, and this effect is independent of what the participants ate.

Now, if you are a man, don’t rush to take hormones to become a woman with the goal of living longer just yet. This advice is not only due to the likely health problems related to becoming a transgender person; it is also due to a little problem with these associations. The problem is that the protective effect suggested by the coefficients of association between gender and mortality seems too strong to be due to men "being women with a few design flaws".

There is a mysterious factor X somewhere in there, and it is not gender per se. We need to find a better candidate.

One interesting thing to point out here is that the above model has good explanatory power in regards to mortality. I'd say unusually good explanatory power given that people die for a variety of reasons, and here we have a model explaining a lot of that variation. The model explains 45 percent of the variance in mortality in the 35-69 age range, and 28 percent of the variance in the 70-79 age range.

In other words, the model above explains nearly half of the variance in mortality in the 35-69 age range. It could form the basis of a doctoral dissertation in nutrition or epidemiology with important implications for public health policy in China. But first the factor X must be identified, and it must be somehow related to gender.

Next post coming up soon ...

The China Study II: Animal protein, wheat, and mortality … there is something odd here!

2011-10-24T04:00:00.000-07:00

WarpPLS and HealthCorrelator for Excel were used in the analyses below. For other China Study analyses, many using WarpPLS and HealthCorrelator for Excel, click here. For the dataset used, visit the HealthCorrelator for Excel site and check under the sample datasets area. I thank Dr. T. Colin Campbell and his collaborators at the University of Oxford for making the data publicly available for independent analyses.

The graph below shows the results of a multivariate linear WarpPLS analysis including the following variables: Wheat (wheat flour consumption in g/d), Aprot (animal protein consumption in g/d), Mor35_69 (number of deaths per 1,000 people in the 35-69 age range), and Mor70_79 (number of deaths per 1,000 people in the 70-79 age range).

Just a technical comment here, regarding the possibility of ecological fallacy. I am not going to get into this in any depth now, but let me say that the patterns in the data suggest that, with the possible exception of some variables (e.g., blood glucose, gender; the latter will get us going in the next few posts), ecological fallacy due to county aggregation is not a big problem. The threat of ecological fallacy exists, here and in many other datasets, but it is generally overstated (often by those whose previous findings are contradicted by aggregated results).

I have not included plant protein consumption in the analysis because plant protein consumption is very strongly and positively associated with wheat flour consumption. The reason is simple. Almost all of the plant protein consumed by the participants in this study was probably gluten, from wheat products. Fruits and vegetables have very small amounts of protein. Keeping that in mind, what the graph above tells us is that:

- Wheat flour consumption is significantly and negatively associated with animal protein consumption. This is probably due to those eating more wheat products tending to consume less animal protein.

- Wheat flour consumption is positively associated with mortality in the 35-69 age range. The P value (P=0.06) is just shy of the 5 percent (i.e., P=0.05) that most researchers would consider to be the threshold for statistical significance. More consumption of wheat in a county, more deaths in this age range.

- Wheat flour consumption is significantly and positively associated with mortality in the 70-79 age range. More consumption of wheat in a county, more deaths in this age range.

- Animal protein consumption is not significantly associated with mortality in the 35-69 age range.

- Animal protein consumption is significantly and negatively associated with mortality in the 70-79 age range. More consumption of animal protein in a county, fewer deaths in this age range.

Let me tell you, from my past experience analyzing health data (as well as other types of data, from different fields), that these coefficients of association do not suggest super-strong associations. Actually this is also indicated by the R-squared coefficients, which vary from 3 to 7 percent. These are the variances explained by the model on the variables above the R-squared coefficients. They are low, which means that the model has weak explanatory power.

R-squared coefficients of 20 percent and above would be more promising. I hate to disappoint hardcore carnivores and the fans of the “wheat is murder” theory, but these coefficients of association and variance explained are probably way less than what we would expect to see if animal protein was humanity's salvation and wheat its demise.

Moreover, the lack of association between animal protein consumption and mortality in the 35-69 age range is a bit strange, given that there is an association suggestive of a protective effect in the 70-79 age range.

Of course death happens for all kinds of reasons, not only what we eat. Still, let us take a look at some other graphs involving these foodstuffs to see if we can form a better picture of what is going on here. Below is a graph showing mortality at the two age ranges for different levels of animal protein consumption. The results are organized in quintiles.

As you can see, the participants in this study consumed relatively little animal protein. The lowest mortality in the 70-79 age range, arguably the range of higher vulnerability, was for the 28 to 35 g/d quintile of consumption. That was the highest consumption quintile. About a quarter to a third of 1 lb/d of beef, and less of seafood (in general), would give you that much animal protein.

Keep in mind that the unit of analysis here is the county, and that these results are based on county averages. I wish I had access to data on individual participants! Still I stand by my comment earlier on ecological fallacy. Don't worry too much about it just yet.

Clearly the above results and graphs contradict claims that animal protein consumption makes people die earlier, and go somewhat against the notion that animal protein consumption causes things that make people die earlier, such as cancer. But they do so in a messy way - that spike in mortality in the 70-79 age range for 21-28 g/d of animal protein is a bit strange.

Below is a graph showing mortality at the two age ranges (i.e., 35-69 and 70-79) for different levels of wheat flour consumption. Again, the results are shown in quintiles.

Without a doubt the participants in this study consumed a lot of wheat flour. The lowest mortality in the 70-79 age range, which is the range of higher vulnerability, was for the 300 to 450 g/d quintile of wheat flour consumption. The high end of this range is about 1 lb/d of wheat flour! How many slices of bread would this be equivalent to? I don’t know, but my guess is that it would be many.

Well, this is not exactly the smoking gun linking wheat with early death, a connection that has been reaching near mythical proportions on the Internetz lately. Overall, the linear trend seems to be one of decreased longevity associated with wheat flour consumption, as suggested by the WarpPLS results, but the relationship between these two variables is messy and somewhat weak. It is not even clearly nonlinear, at least in terms of the ubiquitous J-curve relationship.

Frankly, there is something odd about these results.

This oddity led to me to explore, using HealthCorrelator for Excel, all ordered associations between mortality in the 35-69 and 70-79 age ranges and all of the other variables in the dataset. That in turn led me to a more complex WarpPLS analysis, which I’ll talk about in my next post, which is still being written.

I can tell you right now that there will be more oddities there, which will eventually take us to what I refer to as the mysterious factor X. Ah, by the way, that factor X is not gender - but gender leads us to it.

Book review: Perfect Health Diet

2011-10-17T05:56:00.000-07:00

Perfect Health Diet is a book that one should own. It is not the type of book that you can get from your local library and just do a quick read over (and, maybe, write a review about it). If you do that, you will probably miss several important ideas that form the foundation of this book, which is a deep foundation.

The book is titled “Perfect Health Diet”, not “The Perfect Health Diet”. If you think that this is a mistake, consider that the most successful social networking web site of all time started as “The Facebook”, and then changed to simply “Facebook”; which was perceived later as a major improvement.

Moreover, “Perfect Health Diet” makes for a cool and not at all inappropriate acronym – “PHD”.

What people eat has an enormous influence on their lives, and also on the lives of those around them. Nutrition is clearly one of the most important topics in the modern world - it is the source of much happiness and suffering for entire populations. If Albert Einstein and Marie Curie were alive today, they would probably be interested in nutrition, as they were about important topics of their time that were outside their main disciplines and research areas (e.g., the consequences of war, and future war deterrence).

Nutrition attracts the interest of many bright people today. Those who are not professional nutrition researchers often fund their own research, spending hours and hours of their own time studying the literature and even experimenting on themselves. Several of them decide to think deeply and carefully about it. A few, like Paul Jaminet and Shou-Ching Jaminet, decide to write about it, and all of us benefit from their effort.

The Jaminets have PhDs (not copies of their books, degrees). Their main PhD disciplines are somewhat similar to Einstein’s and Curie’s; which is an interesting coincidence. What the Jaminets have written about nutrition is probably analogous, in broad terms, to what Einstein and Curie would have written about nutrition if they were alive today. They would have written about a “unified field theory” of nutrition, informed by chemistry.

To put it simply, the main idea behind this book is to find the “sweet spot” for each major macronutrient (e.g., protein and fat) and micronutrient (e.g., vitamins and minerals) that is important for humans. The sweet spot is the area indicated on the graph below. This is my own simplified interpretation of the authors' more complex graphs on marginal benefits from nutrients.

The book provides detailed information about each of the major nutrients that are important to humans, what their “sweet spot” levels are, and how to obtain them. In this respect the book is very thorough, and also very clear, including plenty of good arguments and empirical research results to back up the recommendations. But this book is much more than that.

Why do I refer to this book as proposing a “unified field theory” of nutrition? The reason is that this book clearly aims at unifying all of the current state of the art knowledge about nutrition, departing from a few fundamental ideas.

One of those fundamental ideas is that a good diet would provide nutrients in the same ratio as those provided by our own tissues when we “cannibalize” them – i.e., when we fast. Another is that human breast milk is a good basis for the estimation of the ratios of macronutrients a human adult would need for optimal health.

And here is where the depth and brilliance with which the authors address these issues can lead to misunderstandings.

For example, when our body “cannibalizes” itself (e.g., at the 16-h mark of a water fast), there is no digestion going on. And, as the authors point out, what you eat, in terms of nutrients, is often not what you get after digestion. It may surprise many to know that a diet rich in vegetables is actually a high fat diet (if you are surprised, you should read the book). One needs to keep these things in mind to understand that not all dietary macronutrient ratios will lead to the same ratios of nutrients after digestion, and that the dietary equivalent of “cannibalizing” oneself is not a beef-only diet.

Another example relates to the issue of human breast milk. Many seem to have misunderstood the authors as implying that the macronutrient ratios in human breast milk are optimal for adult humans. The authors say nothing of the kind. What they do is to use human breast milk as a basis for their estimation of what an adult human should get, based on a few reasonable assumptions. One of the assumptions is that a human adult’s brain consumes proportionally much less sugar than an infant’s.

Yet another example is the idea of “safe starches”, which many seem to have taken as a recommendation that diabetics should eat lots of white rice and potato. The authors have never said such a thing in the book; not even close. "Safe starches", like white rice and sweet potatoes (as well as white potatoes), are presented in the book as good sources of carbohydrates that are also generally free from harmful plant toxins. And they are, if consumed after cooking.

By the way, I have a colleague who has type 2 diabetes and can eat meat with white potatoes without experiencing hyperglycemia, as long as the amount of potato is very small and is eaten after a few bites of meat.

Do I disagree with some of the things that the authors say? Sure I do, but not in a way that would lead to significantly different dietary recommendations. And, who knows, maybe I am wrong.

For example, the authors seem to think that dietary advanced glycation end-products (AGEs) can be a problem for humans, and therefore recommend that you avoid cooking meat at high temperatures (no barbecuing, for example). I have not found any convincing evidence that this is true in healthy people, but following the authors’ advice will not hurt you at all. And if your digestive tract is compromised to the point that undigested food particles are entering your bloodstream, then maybe you should avoid dietary sources of AGEs.

Also, I think that humans tend to adapt to different macronutrient ratios in more fundamental ways than the authors seem to believe they can. These adaptations are long-term ones, and are better understood based on the notion of compensatory adaptation. For instance, a very low carbohydrate diet may bring about some problems in the short term, but long-term adaptations may reverse those problems, without a change in the diet.

The authors should be careful about small errors that may give a bad impression to some experts, and open them up to undue criticism; as experts tend to be very picky and frequently generalize based on small errors. Here is one. The authors seem to imply that eating coconut oil will help feed colon cells, which indeed seem to feed on short-chain fats; not exactly the medium-chain fats abundantly found in coconut oil, but okay. (This may be the main reason why indigestible fiber contributes to colon health, by being converted by bacteria to short-chain fats.) The main problem with the authors' implied claim is that coconut oil, as a fat, will be absorbed in the small intestine, and thus will not reach colon cells in any significant amounts.

Finally, I don’t think that increased animal protein consumption causes decreased longevity; an idea that the authors seem to lean toward. One reason is that seafood consumption is almost universally associated with increased longevity, even when it is heavily consumed, and seafood in general has a very high protein-to-fat ratio (much higher than beef). The connection between high animal protein consumption and decreased longevity suggested by many studies, some of which are cited in the book, is unlikely to be due to the protein itself, in my opinion. That connection is more likely to be due to some patterns that may be associated in certain populations with animal protein consumption (e.g., refined wheat and industrial seed oils consumption).

Thankfully, controversial issues and small errors can be easily addressed online. The authors maintain a popular blog, and they do so in such a way that the blog is truly an extension of the book. This blog is one of my favorites. Perhaps we will see some of the above issues addressed in the blog.

All in all, this seems like a bargain to me. For about 25 bucks (less than that, if you trade in quid; and more, if you do in Yuan), and with some self-determination, you may save thousands of dollars in medical bills. More importantly, you may change your life, and those of the ones around you, for the better.

Certain mental disorders may have evolved as costs of attractive mental traits

2011-10-10T04:00:00.000-07:00

I find costly traits fascinating, even though they pose a serious challenge to the notion that living as we evolved to live is a good thing. It is not that they always deny this notion; sometimes they do not, but add interesting and somewhat odd twists to it.

Costly traits have evolved in many species (e.g., the male peacock’s train) because they maximize reproductive success, even though they are survival handicaps. Many of these traits have evolved through nature’s great venture capitalist – sexual selection.

(Source: Vangoghart.org)

Certain harmful mental disorders in humans, such as schizophrenia and manic–depression, are often seen as puzzles from an evolutionary perspective. The heritability of those mental disorders and their frequency in the population at various levels of severity suggests that they may have been evolved through selection, yet they often significantly decrease the survival prospects of those afflicted by them (Keller & Miller, 2006; Nesse & Williams, 1994).

The question often asked is why have they evolved at all? Should not they have been eliminated, instead of maintained, by selective forces? It seems that the most straightforward explanation for the existence of certain mental disorders is that they have co-evolved as costs of attractive mental traits. Not all mental disorders, however, can be explained in this way.

The telltale signs of a mental disorder that is likely to be a cost associated with a trait used in mate choice are: (a) many of the individuals afflicted are also found to have an attractive mental trait; and (b) the mental trait in question is comparatively more attractive than other mental traits that have no apparent survival costs associated with them.

The broad category of mental disorders generally referred to as schizophrenia is a good candidate in this respect because:
- Its incidence in human males is significantly correlated with creative intelligence, the type of intelligence generally displayed by successful artists, which is an attractive mental trait (Miller & Tal, 2007; Nettle, 2006b).
- Creative intelligence is considered to be one of the most attractive mental traits in human males, to the point of females at the peak of their fertility cycles finding creative but poor males significantly more attractive than uncreative but wealthy ones (Haselton & Miller, 2006).

The same generally applies to manic–depression, and a few other related mental disorders.

By the way, creative intelligence is also strongly associated with openness, one of the "big five" personality traits. And, both creative intelligence and mental disorders are seen in men and women. This is so even though it is most likely that selection pressure for creative intelligence was primarily exerted by ancestral women on men, not ancestral men on women.

Crespi (2006), in a response to a thorough and provocative argument by Keller & Miller (2006) regarding the evolutionary bases of mental disorders, makes a point that is similar to the one made above (see, also, Nettle, 2006), and also notes that schizophrenia has a less debilitating effect on human females than males.

Ancestral human females, due to their preference for males showing high levels of creative intelligence, might have also selected a co-evolved cost that affects not only males but also the females themselves though gene correlation between the sexes (Gillespie, 2004; Maynard Smith, 1998).

There is another reason why ancestral women might have possessed certain traits that they selected for in ancestral men. Like anything that involves intelligence in humans, the sex applying selection pressure (i.e., female) must be just as intelligent as (if not more than) the sex to which selection pressure is applied (i.e., males). Peahens do not have to have big and brightly colored trains to select male peacocks that have them. That is not so with anything that involves intelligence (in any of its many forms, like creative and interpersonal intelligence), because intelligence must be recognized through communication and behavior, which itself requires intelligence.

Other traits that differentiate females from males may account for differences in the actual survival cost of schizophrenia in females and males. For example, males show a greater propensity toward risk-taking than females (Buss, 1999; Miller, 2000), and schizophrenia may positively moderate the negative relationship between risk-taking propensity and survival success.

Why were some of our ancestors in the Stone Age artists, creating elaborate cave paintings, sculptures, and other art forms? Maybe because a combination of genetic mutations and environmental factors made it a sexy thing to do from around 50,000 years ago or so, even though the underlying reason why the ancestral artists produced art may also have increased the chances that some of them suffered from mental disorders.

A heritable trait possessed by males and perceived as very sexy by females has a very good chance of evolving in any population. That is so even if the trait causes the males who possess it to die much earlier than other males. In the human species, a male can father literally hundreds of children in just a few years. Unlike men, women tend to be very selective of their sexual partners, which does not mean that they cannot all select the same partner (Buss, 1999).

So, if this is true, what is the practical value of knowing it?

It seems reasonable to believe that knowing the likely source of a strange and unpleasant view of the world is, in and of itself, therapeutic. A real danger, it seems, is in seeing the world in a strange and unpleasant way (e.g., as a schizophrenic may see it), and not knowing that the distorted view is caused by an underlying reason. The stress coming from this lack of knowledge may compound the problem; the symptoms of mental disorders are often enhanced by stress.

As one seeks professional help, it may also be comforting to know that something that is actually very good, like creative intelligence, may come together with the bad stuff.

Finally, is it possible that our modern diets and lifestyles significantly exacerbate the problem? The answer is "yes", and this is a theme that has been explored many times before by Emily Deans. (See also this post, by Emily, on the connection between mental disorders and creativity.)

Reference
(All cited references are listed in the article below. If you like mathematics, this article is for you.)

Kock, N. (2011). A mathematical analysis of the evolution of human mate choice traits: Implications for evolutionary psychologists. Journal of Evolutionary Psychology, 9(3), 219-247.

Great evolution thinkers you should know about

2011-10-03T04:00:00.001-07:00

If you follow a paleo diet, you follow a diet that aims to be consistent with evolution. This is a theory that has undergone major changes and additions since Alfred Russel Wallace and Charles Darwin proposed it in the 1800s. Wallace proposed it first, by the way, even though Darwin’s proposal was much more elaborate and supported by evidence. Darwin acknowledged Wallace's precedence, but received most of the credit for the theory anyway.

(Alfred Russel Wallace; source: Wikipedia)

What many people who describe themselves as paleo do not seem to know is how the theory found its footing. The original Wallace-Darwin theory (a.k.a. Darwin’s theory) had some major problems, notably the idea of blending inheritance (e.g., blue eye + brown eye = somewhere in between), which led it to be largely dismissed until the early 1900s. Ironically, it was the work of a Catholic priest that provided the foundation on which the theory of evolution would find its footing, and evolve into the grand theory that it is today. We are talking about Gregor Johann Mendel.

Much of the subsequent work that led to our current understanding of evolution sought to unify the theory of genetics, pioneered by Mendel, with the basic principles proposed as part of the Wallace-Darwin theory of evolution. That is where major progress was made. The evolution thinkers below are some of the major contributors to that progress.

Ronald A. Fisher. English statistician who proposed key elements of a genetic theory of natural selection in the 1910s, 1920s and 1930s. Fisher showed that the inheritance of discrete traits (e.g., flower color) described by Gregor Mendel has the same basis as the inheritance of continuous traits (e.g., human height) described by Francis Galton. He is credited, together with John B.S. Haldane and Sewall G. Wright, with setting the foundations for the development of the field of population genetics. In population genetics the concepts and principles of the theories of evolution (e.g., inheritance and natural selection of traits) and genetics (e.g., genes and alleles) have been integrated and mathematically formalized.

John B.S. Haldane. English geneticist who, together with Ronald A. Fisher and Sewall G. Wright, is credited with setting the foundations for the development of the field of population genetics. Much of his research was conducted in the 1920s and 1930s. Particularly noteworthy is the work by Haldane through which he mathematically modeled and explained the interactions between natural selection, mutation, and migration. He is also known for what is often referred to as Haldane’s principle, which explains the direction of the evolution of many species’ traits based on the body size of the organisms of the species. Haldane’s mathematical formulations also explained the rapid spread of traits observed in some actual populations of organisms, such as the increase in frequency of dark-colored moths from 2% to 95% in a little less than 50 years as a response to the spread of industrial soot in England in the late 1800s.

Sewall G. Wright. American geneticist and statistician who, together with Ronald A. Fisher and John B.S. Haldane, is credited with setting the foundations for the development of the field of population genetics. As with Fisher and Haldane, much of his original and most influential research was conducted in the 1920s and 1930s. He is believed to have discovered the inbreeding coefficient, related to the occurrence of identical genes in different individuals, and to have pioneered methods for the calculation of gene frequencies among populations of organisms. The development of the notion of genetic drift, where some of a population’s traits result from random genetic changes instead of selection, is often associated with him. Wright is also considered to be one of pioneers of the development of the statistical method known as path analysis.

Theodosius G. Dobzhansky. Ukrainian-American geneticist and evolutionary biologist who migrated to the United States in the late 1920s, and is believed to have been one of the main architects of the modern evolutionary synthesis. Much of his original research was conducted in the 1930s and 1940s. In the 1930s he published one of the pillars of the modern synthesis, a book titled Genetics and the Origin of Species. The modern evolutionary synthesis is closely linked with the emergence of the field of population genetics, and is associated with the integration of various ideas and predictions from the fields of evolution and genetics. In spite of Dobzhansky’s devotion to religious principles, he strongly defended Darwinian evolution against modern creationism. The title of a famous essay written by him is often cited in modern debates between evolutionists and creationists regarding the teaching of evolution in high schools: Nothing in Biology Makes Sense Except in the Light of Evolution.

Ernst W. Mayr. German taxonomist and ornithologist who spent most of his life in the United States, and is believed, like Theodosius G. Dobzhansky, to have been one of the main architects of the modern evolutionary synthesis. Mayr is credited with the development in the 1940s of the most widely accepted definition of species today, that of a group of organisms that are capable of interbreeding and producing fertile offspring. At that time organisms that looked alike were generally categorized as being part of the same species. Mayr served as a faculty member at Harvard University for many years, where he also served as the director of the Museum of Comparative Zoology. He lived to the age of 100 years, and was one of the most prolific scholars ever in the field of evolutionary biology. Unlike many evolution theorists, he was very critical of the use of mathematical approaches to the understanding of evolutionary phenomena.

William D. Hamilton. English evolutionary biologist (born in Egypt) widely considered one of the greatest evolution theorists of the 20th Century. Hamilton conducted pioneering research based on the gene-centric view of evolution, also know as the “selfish gene” perspective, which is based on the notion that the unit of natural selection is the gene and not the organism that carries the gene. His research conducted in the 1960s set the foundations for using this notion to understand social behavior among animals. The notion that the unit of natural selection is the gene forms the basis of the theory of kin selection, which explains why organisms often will instinctively behave in ways that will maximize the reproductive success of relatives, sometimes to the detriment of their own reproductive success (e.g., worker ants in an ant colony).

George C. Williams. American evolutionary biologist believed to have been a co-developer in the 1960s, together with William D. Hamilton, of the gene-centric view of evolution. This view is based on the notion that the unit of natural selection is the gene, and not the organism that carries the gene or a group of organisms that happens to share the gene. Williams is also known for his pioneering work on the evolution of sex as a driver of genetic variation, without which a species would adapt more slowly in response to environmental pressures, in many cases becoming extinct. He is also known for suggesting possible uses of human evolution knowledge in the field of medicine.

Motoo Kimura. Japanese evolutionary biologist known for proposing the neutral theory of molecular evolution in the 1960s. In this theory Kimura argued that one of the main forces in evolution is genetic drift, a stochastic process that alters the frequency of genotypes in a population in a non-deterministic way. Kimura is widely known for his innovative use of a class of partial differential equations, namely diffusion equations, to calculate the effect of natural selection and genetic drift on the fixation of genotypes. He has developed widely used equations to calculate the probability of fixation of genotypes that code for certain phenotypic traits due to genetic drift and natural selection.

George R. Price. American geneticist known for refining in the 1970s the mathematical formalizations developed by Ronald A. Fisher and William D. Hamilton, and thus making significant contributions to the development of the field of population genetics. He developed the famous Price Equation, which has found widespread use in evolutionary theorizing. Price is also known for introducing, together with John Maynard Smith, the concept of evolutionary stable strategy (ESS). The EES notion itself builds on the Nash Equilibrium, named after its developer John Forbes Nash (portrayed in the popular Hollywood film A Beautiful Mind). The concept of EES explains why certain evolved traits spread and become fixed in a population.

John Maynard Smith. English evolutionary biologist and geneticist credited with several innovative applications of game theory (which is not actually a theory, but an applied branch of mathematics) in the 1970s to the understanding of biological evolution. Maynard Smith is also known for introducing, together with George R. Price, the concept of evolutionary stable strategy (EES). As noted above, the EES notion builds on the Nash Equilibrium, and explains why certain evolved traits spread and become fixed in a population. The pioneering work by John Maynard Smith has led to the emergence of a new field of research within evolutionary biology known as evolutionary game theory.

Edward O. Wilson. American evolutionary biologist and naturalist who coined the term “sociobiology” in the 1970s to refer to the systematic study of the biological foundations of social behavior of animals, including humans. Wilson was one of the first evolutionary biologists to convincingly argue that human mental mechanisms are shaped as much by our genes as they are by the environment that surrounds us, setting the stage for the emergence of the field of evolutionary psychology. Many of Wilson’s theoretical contributions in the area of sociobiology are very general, and apply not only to humans but also to other species. Wilson has been acknowledged as one of the foremost experts in the study of ants’ and other insects’ social organizations. He is also known for his efforts to preserve earth’s environment.

Amotz Zahavi. Israeli evolutionary biologist best known for his widely cited handicap principle, proposed in the 1970s, which explains the evolution of fitness signaling traits that appear to be detrimental to the reproductive fitness of an organism. Zahavi argued that traits evolved to signal the fitness status of an organism must be costly in order to the reliable. An example is the large and brightly colored trains evolved by the males of the peacock species, which signal good health to the females of the species. The male peacock’s train makes it more vulnerable to predators, and as such is a costly indicator of survival success. Traits used for this type of signaling are often referred to as Zahavian traits.

Robert L. Trivers. American evolutionary biologist and anthropologist who proposed several influential theories in the 1970s, including the theories of reciprocal altruism, parental investment, and parent-offspring conflict. Trivers is considered to be one of the most influential living evolutionary theorists, and is a very active researcher and speaker. His most recent focus is on the study of body symmetry and its relationship with various traits that are hypothesized to have been evolved in our ancestral past. Trivers’s theories often explain phenomena that are observed in nature but are not easily understood based on traditional evolutionary thinking, and in some cases appear contradictory with that thinking. Reciprocal altruism, for example, is a phenomenon that is widely observed in nature and involves one organism benefiting another not genetically related organism, without any immediate gain to the organism (e.g., vampire bats regurgitating blood to feed non-kin).

There are many other more recent contributors that could arguably be included in the list above. Much recent progress has been made in interdisciplinary fields that could be seen as new fields of research inspired in evolutionary ideas. One such field is that of evolutionary psychology, which has emerged in the 1980s. New theoretical contributions tend to take some time to be recognized though, as will be the case with ideas coming off these new fields, because new theoretical contributions are invariably somewhat flawed and/or incomplete when they are originally proposed.

Calling self-experimentation N=1 is incorrect and misleading

2011-09-26T04:00:00.000-07:00

This is not a post about semantics. Using “N=1” to refer to self-experimentation is okay, as long as one understands that self-experimentation is one of the most powerful ways to improve one’s health. Typically the term “N=1” is used in a demeaning way, as in: “It is just my N=1 experience, so it’s not worth much, but …” This is the reason behind this post. Using the “N=1” term to refer to self-experimentation in this way is both incorrect and misleading.

Calling self-experimentation N=1 is incorrect

The table below shows a dataset that is discussed in this YouTube video on HealthCorrelator for Excel (HCE). Nearly all health-related datasets will look somewhat like this, with columns referring to health variables and rows referring to multiple measurements for the health variables. (This actually applies to datasets in general, including datasets about non-health-related phenomena.)

Often each individual measurement, or row, will be associated with a particular point in time, such as a date. This will characterize the measurement approach used as longitudinal, as opposed to cross-sectional. One example of the latter would be a dataset where each row referred to a different individual, with the data on all rows collected at the same point in time. Longitudinal health-related measurement is frequently considered superior to cross-sectional measurement in terms of the insights that it can provide.

As you can see, the dataset has 10 rows, with the top row containing the names of the variables. So this dataset contains nine rows of data, which means that in this dataset “N=9”. To call this an “N=1” experiment is incorrect.

As a side note, an empty cell, like that on the top row for HDL cholesterol, essentially means that a measurement for that variable was not taken on that date, or that it was left out because of obvious measurement error (e.g., the value received from the lab was “-10”, which would be a mistake since nobody has a negative HDL cholesterol level). The N of the dataset as a whole would still be technically 9 in a situation like this, with only one missing cell on the row in question. But the software would typically calculate associations for that variable (HDL cholesterol) based on a sample of 8.

Calling self-experimentation N=1 is misleading

Calling self-experimentation “N=1”, meaning that the results of self-experimentation are not a good basis for generalization, is very misleading. But there is a twist. Those results may indeed not be a good basis for generalization to other people, but they provide a particularly good basis for generalization for you. It is often much safer to generalize based on self-experimentation, even with small samples (e.g., N=9).

The reason, as I pointed out in this interview with Jimmy Moore, is that data about oneself only tends to be much more uniform than data about a sample of individuals. When multiple individuals are included in an analysis, the number of sources of error (e.g., confounding variables, measurement problems) is much higher than when the analysis is based on one single individual. Thus analyses based on data from one single individual yield results that are more uniform and stable across the sample.

Moreover, analyses of data about a sample of individuals are typically summarized through averages, and those averages tend to be biased by outliers. There are always outliers in any dataset; you might possibly be one of them if you were part of a dataset, which would render the average results at best misleading, and at worst meaningless, to you. This is a point that has also been made by Richard Nikoley, who has been discussing self-experimentation for quite some time, in this very interesting video.

Another person who has been talking about self-experimentation, and showing how it can be useful in personal health management, is Seth Roberts. He and the idea of self-experimentation were prominently portrayed in this article on the New York Times. Check this video where Dr. Roberts talks about how he found out through self-experimentation that, among other things, consuming butter reduced his arterial plaque deposits. Plaque reduction is something that only rarely happens, at least in folks who follow the traditional American diet.

HCE generates coefficients of association and graphs at the click of a button, making it relatively easy for anybody to understand how his or her health variables are associated with one another, and thus what modifiable health factors (e.g., consumption of certain foods) could be causing health effects (e.g., body fact accumulation). It may also help you identify other, more counter-intuitive, links; such as between certain thought and behavior patterns (e.g., wealth accumulation thoughts, looking at the mirror multiple times a day) and undesirable mental states (e.g., depression, panic attacks).

Just keep in mind that you need to have at least some variation in all the variables involved. Without variation there is no correlation, and thus causation may remain hidden from view.

Being glucose intolerant may make you live only to be 96, if you would otherwise live to be 100

2011-09-19T01:17:00.000-07:00

This comes also from the widely cited Brunner and colleagues study, published in Diabetes Care in 2006. They defined a person as glucose intolerant if he or she had a blood glucose level of 5.3-11 mmol/l after a 2-h post–50-g oral glucose tolerance test. For those using the other measurement system, like us here in the USA, that is a blood glucose level of approximately 95-198 mg/dl.

Quite a range, eh!? This covers the high end of normoglycemia, as well as pre- to full-blown type 2 diabetes.

In this investigation, called the Whitehall Study, 18,403 nonindustrial London-based male civil servants aged 40 to 64 years were examined between September 1967 and January 1970. These folks were then followed for over 30 years, based on the National Health Service Central Registry; essentially to find out whether they had died, and of what. During this period, there were 11,426 deaths from all causes; with 5,497 due to cardiovascular disease (48.1%) and 3,240 due to cancer (28.4%).

The graph below shows the age-adjusted survival rates against time after diagnosis. Presumably the N values refer to the individuals in the glucose intolerant (GI) and type 2 diabetic (T2DM) groups that were alive at the end of the monitoring period. This does not apply to the normoglycemic N value; this value seems to refer to the number of normoglycemic folks alive after the divergence point (5-10 years from diagnosis).

Note by the authors: “Survival by baseline glucose tolerance status diverged after 5-10 years of follow-up. Median survival differed by 4 years between the normoglycemic and glucose intolerant groups and was 10 years less in the diabetic compared with the glucose intolerant group.”

That is, it took between 5 and 10 years of high blood glucose levels for any effect on mortality to be noticed. One would expect at least some of the diagnosed folks to have done something about their blood glucose levels; a confounder that was not properly controlled for in this study, as far as I can tell. The glucose intolerant folks ended up living 4 years less than the normoglycemics, and 10 years more than the diabetics.

One implication of this article is that perhaps you should not worry too much if you experience a temporary increase in blood glucose levels due to compensatory adaptation to healthy changes in diet and lifestyle, such as elevated growth hormone levels. It seems unlikely that such temporary increase in blood glucose levels, even if lasting as much as 1 year, will lead to permanent damage to cells involved in glucose metabolism like the beta cells in the pancreas.

Another implication is that being diagnosed as pre-diabetic or diabetic is not a death sentence, as some people seem to take such diagnoses at first. Many of the folks in this study who decided to do something about their health following an adverse diagnosis probably followed the traditional advice for the treatment of pre-diabetes and diabetes, which likely made their health worse. (See Jeff O’Connell’s book Sugar Nation for a detailed discussion of what that advice entails.) And still, not everyone progressed from pre-diabetes to full-blow diabetes. Probably fewer refined foods available helped, but this does not fully explain the lack of progression to full-blow diabetes.

It is important to note that this study was conducted in the late 1960s. Biosynthetic insulin was developed in the 1970s using recombinant DNA techniques, and was thus largely unavailable to the participants of this study. Other treatment options were also largely unavailable. Arguably the most influential book on low carbohydrate dieting, by Dr. Atkins, was published in the early 1970s. The targeted use of low carbohydrate dieting for blood glucose control in diabetics was not widely promoted until the 1980s, and even today it is not adopted by mainstream diabetes doctors. To this I should add that, at least anecdotally and from living in an area where diabetes is an epidemic (South Texas), those people who carefully control their blood sugars after type 2 diabetes diagnoses, in many cases with the help of drugs, seem to see marked and sustained health improvements.

Finally, an interesting implication of this study is that glucose intolerance, as defined in the article, would probably not do much to change an outside observer’s perception of a long-living population. That is, if you take a population whose individuals are predisposed to live long lives, with many naturally becoming centenarians, they will likely still be living long lives even if glucose intolerance is rampant. Without carefully conducted glucose tolerance tests, an outside observer may conclude that a damaging diet is actually healthy by still finding many long-living individuals in a population consuming that diet.

Fasting blood glucose of 83 mg/dl and heart disease: Fact and fiction

2011-09-12T04:00:00.000-07:00

If you are interested in the connection between blood glucose control and heart disease, you have probably done your homework. This is a scary connection, and sometimes the information on the Internetz make people even more scared. You have probably seen something to this effect mentioned:

Heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl.

In fact, I have seen this many times, including on some very respectable blogs. I suspect it started with one blogger, and then got repeated over and over again by others; sometimes things become “true” through repetition. Frequently the reference cited is a study by Brunner and colleagues, published in Diabetes Care in 2006. I doubt very much the bloggers in question actually read this article. Sometimes a study by Coutinho and colleagues is also cited, but this latter study is actually a meta-analysis.

So I decided to take a look at the Brunner and colleagues study. It covers, among other things, the relationship between cardiovascular disease (they use the acronym CHD for this), and 2-hour blood glucose levels after a 50-g oral glucose tolerance test (OGTT). They tested thousands of men at one point in time, and then followed them for over 30 years, which is really impressive. The graph below shows the relationship between CHD and blood glucose in mmol/l. Here is a calculator to convert the values to mg/dl.

The authors note in the limitations section that: “Fasting glucose was not measured.” So these results have nothing to do with fasting glucose, as we are led to believe when we see this study cited on the web. Also, on the abstract, the authors say that there is “no evidence of nonlinearity”, but in the results section they say that the data provides “evidence of a nonlinear relationship”. The relationship sure looks nonlinear to me. I tried to approximate it manually below.

Note that CHD mortality really goes up more clearly after a glucose level of 5.5 mmol/l (100 mg/dl). But it also varies significantly more widely after that level; the magnitudes of the error bars reflect that. Also, you can see that at around 6.7 mmol/l (121 mg/dl), CHD mortality is on average about the same as at 5.5 mmol/l (100 mg/dl) and 3.5 mmol/l (63 mg/dl). This last level suggests an abnormally high insulin response, bringing blood glucose levels down too much at the 2-hour mark – i.e., reactive hypoglycemia, which the study completely ignores.

These findings are consistent with the somewhat chaotic nature of blood glucose variations in normoglycemic individuals, and also with evidence suggesting that average blood glucose levels go up with age in a J-curve fashion even in long-lived individuals.

We also know that traits vary along a bell curve for any population of individuals. Research results are often reported as averages, but the average individual does not exist. The average individual is an abstraction, and you are not it. Glucose metabolism is a complex trait, which is influenced by many factors. This is why there is so much variation in mortality for different glucose levels, as indicated by the magnitudes of the error bars.

In any event, these findings are clearly inconsistent with the statement that "heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl". The authors even state early in the article that another study based on the same dataset, to which theirs was a follow-up, suggested that:

…. [CHD was associated with levels above] a postload glucose of 5.3 mmol/l [95 mg/dl], but below this level the degree of glycemia was not associated with coronary risk.

Now, exaggerating the facts, to the point of creating fictitious results, may have a positive effect. It may scare people enough that they will actually check their blood glucose levels. Perhaps people will remove certain foods like doughnuts and jelly beans from their diets, or at least reduce their consumption dramatically. However, many people may find themselves with higher fasting blood glucose levels, even after removing those foods from their diets, as their bodies try to adapt to lower circulating insulin levels. Some may see higher levels for doing other things that are likely to improve their health in the long term. Others may see higher levels as they get older.

Many of the complications from diabetes, including heart disease, stem from poor glucose control. But it seems increasingly clear that blood glucose control does not have to be perfect to keep those complications at bay. For most people, blood glucose levels can be maintained within a certain range with the proper diet and lifestyle. You may be looking at a long life if you catch the problem early, even if your blood glucose is not always at 83 mg/dl (4.6 mmol/l). More on this on my next post.

Nonlinearity and the industrial seed oils paradox

2011-09-05T04:00:00.000-07:00

Most relationships among variables in nature are nonlinear, frequently taking the form of a J curve. The figure below illustrates this type of curve. In this illustration, the horizontal axis measures the amount of time an individual spends consuming a given dose (high) of a substance daily. The vertical axis measures a certain disease marker – e.g., a marker of systemic inflammation, such as levels of circulating tumor necrosis factor (TNF). This is just one of many measurement schemes that may lead to a J curve.

J-curve relationships and variants such as U-curve and inverted J-curve relationships are ubiquitous, and may occur due to many reasons. For example, a J curve like the one above may be due to the substance being consumed having at least one health-promoting attribute, and at least one health-impairing attribute. The latter has a delayed effect, and ends up overcoming the benefits of the former over time. In this sense, there is no “sweet spot”. People are better off not consuming the substance at all. They should look for other sources of the health-promoting factors.

So what does this have to do with industrial seed oils, like safflower and corn oil?

If you take a look at the research literature on the effects of industrial seed oils, you’ll find something interesting and rather paradoxical. Several studies show benefits, whereas several others hint at serious problems. The problems seem to be generally related to long-term consumption, and to be associated with a significant increase in the ratio of dietary omega-6 to omega-3 fats; this increase appears to lead to systemic inflammation. The benefits seem to be generally related to short-term consumption.

But what leads to the left side of the J curve, the health-promoting effects of industrial seed oils, usually seen in short-term studies?

It is very likely vitamin E, which is considered, apparently correctly, to be one of the most powerful antioxidants in nature. Oxidative stress is strongly associated with systemic inflammation. Seed oils are by far the richest sources of vitamin E around, in the form of both γ-Tocopherol and α-Tocopherol. Other good sources, with much less gram-adjusted omega-6 content, are what we generally refer to as “nuts”. And, there are many, many substances other than vitamin E that have powerful antioxidant properties.

Chris Masterjohn has talked about seed oils and vitamin E before, making a similar point (see here, and here). I acknowledged this contribution by Chris before; for example, in my June 2011 interview with Jimmy Moore. In fact, Chris has gone further and also argued that the vitamin E requirement goes up as body fat omega-6 content increases over time (see comments under this post, in addition to the links provided above).

If this is correct, I would speculate that it may create a vicious feedback-loop cycle, as the increased vitamin E requirement may lead to increased hunger for foods rich in vitamin E. For someone already consuming a diet rich in seed oils, this may drive a subconscious compulsion to add more seed oils to dishes. Not good!

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

2011-08-29T04:00:00.000-07:00

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

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

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

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

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

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

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

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

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

Refined carbohydrate-rich foods, palatability, glycemic load, and the Paleo movement

2011-08-22T04:00:00.000-07:00

A great deal of discussion has been going on recently revolving around the so-called “carbohydrate hypothesis of obesity”. I will use the acronym CHO to refer to this hypothesis. This acronym is often used to refer to carbohydrates in nutrition research; I hope this will not cause confusion.

The CHO could be summarized as this: a person consumes foods with “easily digestible” carbohydrates, those carbohydrates raise insulin levels abnormally, the abnormally high insulin levels drive too much fat into body fat cells and keep it there, this causes hunger as not enough fat is released from fat cells for use as energy, this hunger drives the consumption of more foods with “easily digestible” carbohydrates, and so on.

It is posited as a feedback-loop process that causes serious problems over a period of years. The term “easily digestible” is within quotes for emphasis. If it is taken to mean “refined”, which is still a bit vague, there is a good amount of epidemiological evidence in support of the CHO. If it is taken to mean simply “easily digestible”, as in potatoes and rice (which is technically a refined food, but a rather benign one), there is a lot of evidence against it. Even from an unbiased (hopefully) look at county-level data in the China Study.

Another hypothesis that has been around for a long time and that has been revived recently, which we could call the “palatability hypothesis”, is a competing hypothesis. It is an interesting and intriguing hypothesis, at least at first glance. There seems to be some truth to this hypothesis. The idea here is that we have not evolved mechanisms to deal with highly palatable foods, and thus end up overeating them. Therefore we should go in the opposite direction, and place emphasis on foods that are not very palatable to reach our optimal weight. You might think that to test this hypothesis it would be enough to find out if this diet works: “Eat something … if it tastes good, spit it out!”

But it is not so simple. To test this palatability hypothesis one could try to measure the palatability of foods, and see if it is correlated with consumption. The problem is that the formulations I have seen of the palatability hypothesis treat the palatability construct as static, when in fact it is dynamic – very dynamic. The perception of the reward associated with a specific food changes depending on a number of factors.

For example, we cannot assign a palatability score to a food without considering the particular state in which the individual who eats the food is. That state is defined by a number of factors, including physiological and psychological ones, which vary a lot across individuals and even across different points in time for the same individual. For someone who is hungry after a 20 h fast, for instance, the perceived reward associated with a food will go up significantly compared to the same person in the fed state.

Regarding the CHO, it seems very clear that refined carbohydrate-rich foods in general, particularly the highly modified ones, disrupt normal biological mechanisms that regulate hunger. Perceived food reward, or palatability, is a function of hunger. Abnormal glucose and insulin responses appear to be at the core of this phenomenon. There are undoubtedly many other factors at play as well. But, as you can see, there is a major overlap between the CHO and the palatability hypothesis. Refined carbohydrate-rich foods generally have higher palatability than natural foods in general. Humans are good engineers.

One meme that seems to be forming recently on the Internetz is that the CHO is incompatible with data from healthy isolated groups that consume a lot of carbohydrates, which are sometimes presented as alternative models of life in the Paleolithic. But in fact among influential proponents of the CHO are the intellectual founders of the Paleolithic dieting movement. Including folks who studied native diets high in carbohydrates, and found their users to be very healthy (e.g., the Kitavans). One thing that these intellectual founders did though was to clearly frame the CHO in terms of refined carbohydrate-rich foods.

Natural carbohydrate-rich foods are clearly distinguished from refined ones based on one key attribute; not the only one, but a very important one nonetheless. That attribute is their glycemic load (GL). I am using the term “natural” here as roughly synonymous with “unrefined” or “whole”. Although they are often confused, the GL is not the same as the glycemic index (GI). The GI is a measure of the effect of carbohydrate intake on blood sugar levels. Glucose is the reference; it has a GI of 100.

The GL provides a better way of predicting total blood sugar response, in terms of “area under the curve”, based on both the type and quantity of carbohydrate in a specific food. Area under the curve is ultimately what really matters; a pointed but brief spike may not have much of a metabolic effect. Insulin response is highly correlated with blood sugar response in terms of area under the curve. The GL is calculated through the following formula:

GL = (GI x the amount of available carbohydrate in grams) / 100

The GL of a food is also dynamic, but its range of variation is small enough in normoglycemic individuals so that it can be treated as a relatively static number. (Still, the reference are normoglycemic individuals.) One of the main differences between refined and natural carbohydrate-rich foods is the much higher GL of industrial carbohydrate-rich foods, and this is not affected by slight variations in GL and GI depending on an individual’s state. The table below illustrates this difference.

Looking back at the environment of our evolutionary adaptation (EEA), which was not static either, this situation becomes analogous to that of vitamin D deficiency today. A few minutes of sun exposure stimulate the production of 10,000 IU of vitamin D, whereas food fortification in the standard American diet normally provides less than 500 IU. The difference is large. So is the difference in GL of natural and refined carbohydrate-rich foods.

And what are the immediate consequences of that difference in GL values? They are abnormally elevated blood sugar and insulin levels after meals containing refined carbohydrate-rich foods. (Incidentally, the GL happens to be relatively low for the rice preparations consumed by Asian populations who seem to do well on rice-based diets.) Abnormal levels of other hormones, in a chronic fashion, come later, after many years consuming those foods. These hormones include adiponectin, leptin, and tumor necrosis factor. The authors of the article from which the table above was taken note that:

Within the past 20 y, substantial evidence has accumulated showing that long term consumption of high glycemic load carbohydrates can adversely affect metabolism and health. Specifically, chronic hyperglycemia and hyperinsulinemia induced by high glycemic load carbohydrates may elicit a number of hormonal and physiologic changes that promote insulin resistance. Chronic hyperinsulinemia represents the primary metabolic defect in the metabolic syndrome.

Who are the authors of this article? They are Loren Cordain, S. Boyd Eaton, Anthony Sebastian, Neil Mann, Staffan Lindeberg, Bruce A. Watkins, James H O’Keefe, and Janette Brand-Miller. The paper is titled “Origins and evolution of the Western diet: Health implications for the 21st century”. A full-text PDF is available here. For most of these authors, this article is their most widely cited publication so far, and it is piling up citations as I write. This means that not only members of the general public have been reading it, but that professional researchers have been reading it as well, and citing it in their own research publications.

In summary, the CHO and the palatability hypothesis overlap, and the overlap is not trivial. But the palatability hypothesis is more difficult to test. As Karl Popper noted, a good hypothesis is a testable hypothesis. Eating natural foods will make an enormous difference for the better in your health if you are coming from the standard American diet, and you can justify this statement based on the CHO, the palatability hypothesis, or even a few others – e.g., a nutrient density hypothesis, which would be closer to Weston Price's views. Even if you eat only plant-based natural foods, which I cannot fully recommend based on data I’ve reviewed on this blog, you will be better off.

Book review: Sugar Nation

2011-08-15T04:00:00.000-07:00

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

(Source: Bodybuilding.com)

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

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

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

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

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

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

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

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

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

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

Potassium deficiency in low carbohydrate dieting: High protein and fat alternatives that do not involve supplementation

2011-08-08T04:00:00.000-07:00

It is often pointed out, at least anecdotally, that potassium deficiency is common among low carbohydrate dieters. Potassium deficiency can lead to a number of unpleasant symptoms and health problems. This micronutrient is present in small quantities in meat and seafood; main sources are plant foods.

A while ago this has gotten me thinking and asking myself: what about isolated hunter-gatherers that seem to have thrived consuming mostly carnivorous diets with little potassium, such as various Native American tribes?

Another thought came to mind, which is that animal protein seems to be associated with increased bone mineralization, even when calcium intake is low. That seems to be due to animal protein being associated with increased absorption of calcium and other minerals that make up bone tissue.

Maybe animal protein intake is also associated with increased potassium absorption. If this is true, what could be the possible mechanism?

As it turns out, there is one possible and somewhat surprising connection, insulin seems to promote cell uptake of potassium. This is an argument made many years ago by Clausen and Kohn, and further discussed more recently by Benziane and Chibalin. See also this recent commentary by Clausen.

Protein is the only macronutrient that normally causes transient insulin elevation without any glucose response. And the insulin response to protein is nowhere near that associated with refined carbohydrate-rich foods. It is much lower, analogous to the response to natural carbohydrate-rich foods.

A very low carbohydrate diet with more animal protein, and less fat, would induce insulin responses after meals, possibly helping with the absorption of potassium, even if potassium intake were rather limited. Primarily carnivorous diets, like those of some traditional Native American groups, would fit the bill.

Also, a low carbohydrate diet with emphasis on fat, but that was not so low in carbohydrates from certain sources, would probably achieve the same effect. This latter sounds like Kwaśniewski’s Optimal Diet, where people are encouraged to eat a lot more fat than protein, but also a small amount of carbohydrates (e.g., 50-100 g/d) from things like potatoes.

Kwaśniewski’s suggestions may sound counterintuitive sometimes. But, as it turns out, potatoes are good sources of potassium. One potato may not be a lot, but that potato will also increase insulin levels, bringing potassium intake up at the cell level.

There is no doubt that abnormally elevated insulin is associated with body fat accumulation

2011-08-01T04:00:00.000-07:00

For as long as diets existed there have been influential proponents, or believers, who at some point had what they thought were epiphanies. From that point forward, they disavowed the diets that they formally endorsed. Low carbohydrate dieting seems to be in this situation now. Among other things, it has been recently “discovered” that the idea that insulin drives fat into body fat cells is “wrong”.

Based on some of the comments I have been receiving lately, apparently a few readers think that I am one of those “enlightened”. If you are interested in what I have been eating, for quite some time now, just click on the link at the top of this blog that refers to my transformation. It is essentially high in all macronutrients on days that I exercise, and low in carbohydrates and calories on days that I don’t. It is a cyclic approach that works for me; calorie surpluses on some days and calorie deficits on other days.

But let me set the record straight regarding what I think: there is no doubt that insulin is associated with body fat accumulation. I was told that an influential health blogger (whom I respect a lot) denied this recently, going to the extreme of saying that no professional metabolism or endocrinology researcher believes in it, but I couldn’t find any evidence of that statement. It is not hard at all to find professional metabolism and endocrinology researchers who have asserted that insulin is associated with body fat accumulation, based on very reliable evidence. Actually, this is Biochemistry 101.

What I think is truly unclear is whether insulin spikes associated with carbohydrate-rich foods in general are the cause of obesity. This idea is, indeed, probably wrong given the evidence we have from various human populations whose members consume plenty of non-industrialized carbohydrate-rich foods. On a related note, I particularly disagree with the notion that the pancreas gets tired over time due to having to secrete insulin in bursts, which seems to also be one of the foundations on which many low carbohydrate diet varieties rest.

As with almost everything related to health, the role of insulin in body fat gain is complex, and part of that complexity is due to the nonlinear relationship between body fat gain and postprandial insulin release. Industrial carbohydrate-rich foods have a much higher glycemic load than natural carbohydrate-rich foods, even though their glycemic index may be the same in some cases. In other words, the quantity of easily digestible carbohydrates per gram is much higher in industrial carbohydrate-rich foods.

In normoglycemic folks, this leads to an abnormally elevated insulin response, among other hormonal responses. For example, circulating growth hormone, which promotes body fat loss, is inversely correlated with circulating insulin. Insulin drives fat, typically from dietary sources of fat, into adipocytes. That fat may also come from excess carbohydrates, packaged into VLDL particles.

Under normal circumstances, that would be fine, since our body is designed to store fat and release it as needed. But the abnormal insulin response elicited by industrial carbohydrate-rich foods, together with other hormonal responses, leads to a little more body fat accumulation, and for longer, than it should. And I’m talking here about people without any metabolic damage. Saturated and monounsaturated fats are healthy when eaten, but when they are stored as excess body fat, they become pro-inflammatory.

Body fat is like an organ, secreting many hormones into the bloodstream, several of which are pro-inflammatory. One of those pro-inflammatory hormones, which I believe is closely linked with many diseases of civilization, is tumor necrosis factor. (The acronym is now TNF. Apparently the “-alpha” after its name and acronym has been dropped recently.) Dietary fat, particularly saturated fat, seems to be anti-inflammatory. In other words, body fat accumulation is the problem. You only need 30 g/d of excess body fat accumulation to gain around 24 lbs of fat per year. Over three years, that will add up to over 70 lbs of body fat.

In my view, ultimately it is excess inflammation (which is, in essence, a vascular response) that is at the source of most of the diseases of civilization.

That is where the nonlinearity comes in. Insulin is healthy up to a point. Beyond that, it starts causing health problems, over time. And one of the main mechanisms by which it does so is via excessive body fat accumulation, with different damage threshold levels for different people. Insulin may decrease appetite as it goes up, but it increases it if goes down too much. If it goes up abnormally, typically it will go down too much. As it reaches a trough it induces hypoglycemia, even if mildly.

Take a look at the graph below, from this post showing the glucose variations in normoglycemic individuals. There is a lot of variation among different individuals, but it is clear that the magnitude of the hypoglycemic dips is inversely correlated with the magnitude of the glucose spikes. That inverse correlation is due primarily to the effect of insulin. Under normal circumstances, a decrease in circulating insulin would promote an increase in free fatty acids in circulation, which would normally have a suppressing effect on hunger in the hours after a meal. But industrial carbohydrate-rich foods lead to increases and decreases in glucose and insulin that are too steep, causing the opposite effect.

You may ask: why do you keep talking about industrial carbohydrate-rich foods? Why not talk about industrial protein- or fat-rich foods as well? The reason is that the food industry has not been very successful at producing industrial protein- or fat-rich foods that are palatable without adding a lot of carbohydrate to them.

More often than not they need enough carbohydrate added in the form of sugar to become truly addictive.

Laser surgery for myopia early in life may create reading problems after 40

2011-07-25T04:00:00.000-07:00

Shortsightedness, or myopia, seems to be endemic in urban populations. The National Institutes of Health suggests that myopia cannot be prevented, and that neither reading nor watching television causes myopia. I find that doubtful, as reading is a rather unnatural activity, and there is evidence that myopia is significantly associated with amount of reading at early ages.

(Source: WebMD.com)

Trying to avoid reading early in life would not be a highly recommended Paleolithic-mimicking choice, except for those who later decide to live among hunter-gatherers. (In spite of our romantic views of hunter-gatherer life, it is very rare to see an urbanite do this outside the context of anthropological studies.) Education requires a lot of reading, and without education in urban environments one is likely to end up suffering from other diseases of civilization. Diabetes, for example, is strongly and inversely associated with education level in urban environments.

Also, keeping up with friends on Facebook, without which life as we know it now could go on, requires a lot of reading and writing.

A different theory, often associated with Cordain, is that myopia is due to consumption of industrial carbohydrate-rich foods. Interestingly, according to Cordain and colleagues, myopia is typically accompanied by higher stature, a finding that is supported by empirical evidence. The idea here is that industrial carbohydrate-rich foods promote abnormal growth patterns during developmental stages, which arguably include abnormal growth of the human eye and its various structures.

Avoiding industrial carbohydrate-rich foods during developmental stages is feasible, but currently very difficult given public health policies that strongly promote the consumption of some of those foods, during development stages, as healthy choices (e.g., cereals). In part as a result of those policies, and also due to budget constraints (those foods tend to be generally cheap), industrial carbohydrate-rich foods are frequently served as meals in schools.

Okay, now to the main topic of this post. Let us say a person has myopia, should he or she fix it surgically?

As one ages, the ability to read at a short-distance (as in reading from books, or from a computer screen) goes down, because the ability to focus on short-distance objects becomes impaired. This phenomenon is called presbyopia, and is also associated with excessive reading. Therefore it could be called a disease of civilization as well. Most college professors at the level of Associate Professor and higher I know (that is, older folks, like me) have developed it, sometimes as early as in their late 30s.

In the general population, normally presbyopia sets in between 40 and 50 years of age, requiring the use of "reading glasses" afterwards … except for those with myopia. This is sometimes called the “myopia payoff of presbyopia”. People with myopia are often able to read well, without the help of glasses, after presbyopia sets in. The reason is that myopia essentially opposes presbyopia at short distances.

Someone with myopia will still have it after presbyopia sets in, and thus will have difficulty seeing at long distances, but will frequently be able to read well at short distances.

So, if you undergo eye laser surgery (the most common type) to correct myopia early in life, you may create reading problems after 40.

P.S.: A friend of mine who has been studying this tells me that eye problems in general are caused by avoidance of indirect sunlight. I am planning on looking into this more deeply in the future.

Dietary protein does not become body fat if you are on a low carbohydrate diet

2011-07-18T04:00:00.000-07:00

By definition LC is about dietary carbohydrate restriction. If you are reducing carbohydrates, your proportional intake of protein or fat, or both, will go up. While I don’t think there is anything wrong with a high fat diet, it seems to me that the true advantage of LC may be in how protein is allocated, which seems to contribute to a better body composition.

LC with more animal protein and less fat makes particularly good sense to me. Eating a variety of unprocessed animal foods, as opposed to only muscle meat from grain-fed cattle, will get you that. In simple terms, LC with more protein, achieved in a natural way with unprocessed foods, means more of the following in one's diet: lean meats, seafood and vegetables. Possibly with lean meats and seafood making up more than half of one’s protein intake. Generally speaking, large predatory fish species (e.g., various shark species, including dogfish) are better avoided to reduce exposure to toxic metals.

Organ meats such as beef liver are also high in protein and low in fat, but should be consumed in moderation due to the risk of hypervitaminosis; particularly hypervitaminosis A. Our ancestors ate the animal whole, and organ mass makes up about 10-20 percent of total mass in ruminants. Eating organ meats once a week places you approximately within that range.

In LC liver glycogen is regularly depleted, so the amino acids resulting from the digestion of protein will be primarily used to replenish liver glycogen, to replenish the albumin pool, for oxidation, and various other processes (e.g., tissue repair, hormone production). If you do some moderate weight training, some of those amino acids will be used for muscle growth.

In this sense, the true “metabolic advantage” of LC, so to speak, comes from protein and not fat. “Calories in” still counts, but you get better allocation of nutrients. Moreover, in LC, the calorie value of protein goes down a bit, because your body is using it as a “jack of all trades”, and thus in a less efficient way. This renders protein the least calorie-dense macronutrient, yielding fewer calories per gram than carbohydrates; and significantly fewer calories per gram when compared with dietary fat and alcohol.

Dietary fat is easily stored as body fat after digestion. In LC, it is difficult for the body to store amino acids as body fat. The only path would be conversion to glucose and uptake by body fat cells, but in LC the liver will typically be starving and want all the extra glucose for itself, so that it can feed its ultimate master – the brain. The liver glycogen depletion induced by LC creates a hormonal mix that places the body in fat release mode, making it difficult for fat cells to take up glucose via the GLUT4 transporter protein.

Excess amino acids are oxidized for energy. This may be why many people feel a slight surge of energy after a high-protein meal. (A related effect is associated with alcohol consumption, which is often masked by the relaxing effect also associated with alcohol consumption.) Amino acid oxidation is not associated with cancer. Neither is fat oxidation. But glucose oxidation is; this is known as the Warburg effect.

A high-protein LC approach will not work very well for athletes who deplete major amounts of muscle glycogen as part of their daily training regimens. These folks will invariably need more carbohydrates to keep their performance levels up. Ultimately this is a numbers game. The protein-to-glucose conversion rate is about 2-to-1. If an athlete depletes 300 g of muscle glycogen per day, he or she will need about 600 g of protein to replenish that based only on protein. This is too high an intake of protein by any standard.

A recreational exerciser who depletes 60 g of glycogen 3 times per week can easily replenish that muscle glycogen with dietary protein. Someone who exercises with weights for 40 minutes 3 times per week will deplete about that much glycogen each time. Contrary to popular belief, muscle glycogen is only minimally replenished postprandially (i.e., after meals) based on dietary sources. Liver glycogen replenishment is prioritized postprandially. Muscle glycogen is replenished over several days, primarily based on liver glycogen. It is one fast-filling tank replenishing another slow-filling one.

Recreational exercisers who are normoglycemic and who do LC intermittently tend to increase the size of their liver glycogen tank over time, via compensatory adaptation, and also use more fat (and ketones, which are byproducts of fat metabolism) as sources of energy. Somewhat paradoxically, these folks benefit from regular high carbohydrate intake days (e.g., once a week, or on exercise days), since their liver glycogen tanks will typically store more glycogen. If they keep their liver and muscle glycogen tanks half empty all the time, compensatory adaptation suggests that both their liver and muscle glycogen tanks will over time become smaller, and that their muscles will store more fat.

One way or another, with the exception of those with major liver insulin resistance, dietary protein does not become body fat if you are on a LC diet.

Fasting for 24 hours and ending up with a bigger waist!? This may be a sign that you are losing abdominal fat

2011-07-11T04:00:00.000-07:00

This is such a common phenomenon that you’d expect to see it discussed more often – people fasting for a non-negligible number of hours and ending up with a bigger waist. However, it is very difficult to find anything published on it. (Lyle McDonald discussed a related phenomenon on this post on whooshes and squishy fat.) I am not talking about only a perceived bigger waist; I am talking about measurably bigger. This frequently happens with folks who were obese, lost a lot of body fat, and are trying to get rid of the stubborn lower abdominal fat.

(Source [ironically]: Gograins.com.au)

Fasting and ending up with a bigger waist; how is that possible?

Contrary to popular belief, this is very unlikely to be due to the body turning muscle protein into glucose, and then converting that glucose into fat for storage in fat cells around the waist. When you are fasting, one factor strongly opposes that transformation. The body is in net body fat release mode, due in part to low circulating insulin, and thus body fat cells are essentially rejecting glucose. Blood glucose levels are maintained, to feed the brain, but uptake by adipocytes via GLUT4 isn’t happening.

So where does the bigger waist come from?

When people fast they typically drink water, quite often lots of it. A reasonable explanation for the bigger waist is that body fat cells store water in place of fat, as fat becomes energy. Since water is denser than fat, the stronger gravitational pull will lead to a larger bulge around the lower abdominal area, increasing waist circumference at the point it is widest. The amount of fat mass, however, is going down due to fasting.

In the obese, body fat cells generally become insulin resistant, even though many people believe the opposite to be true. This leads to the creation of new body fat cells (hyperplasia) to store the extra fat. If body fat loss is maintained over time, I’d expect the body to get rid of those fat cells that were created through hyperplasia during the obese period. The literature, however, seems to suggest that the number of body fat cells is set before adulthood, and does not change afterwards. I am skeptical, as the body seems to be very good at getting rid of cells and tissues that are not used.

The loss of those extra body fat cells may bring the number of adipocytes to pre-obesity levels, but for many people quite some time is needed for that to happen. Often in the order of months; maintaining reasonably low body fat levels.

So don’t despair if you end up with a bigger waist around noon after skipping breakfast, or before dinner after fasting the whole day. That may be a good sign; a sign that you are actually losing abdominal fat.

Liver and meatballs separated by a wall of sweet potatoes

2011-07-04T04:00:00.000-07:00

A commenter wrote here some time ago that she liked to eat rice because rice can be easily used to separate food items on a plate. One can just as easily use sweet potatoes to do that; preparing the sweet potatoes in much less time than the rice. This post explains how, with a simple recipe.

- Cut up half of a sweet potato as shown on the first photo below, adding coconut oil or butter to prevent the pieces from sticking to a microwave-safe saucepan.
- Microwave the sweet potato pieces in high heat for about 5 minutes.
- Use the sweet potatoes to separate food items as in the second photo below, showing beef liver and meatballs with their respective sauces.
- Cover the dish with a wet paper towel to prevent spilling, and microwave it for as long as needed to heat up the meats. In this case, 2 minutes in high heat was enough. That will further cook the sweet potato, but not to the point of burning it.

The above assumes that the beef liver and meatballs are leftovers that had been cooked before. In this example, we have about ½ lb of meat and ½ of a sweet potato. As far as plant foods are concerned, sweet potatoes are at the very high end of the nutrition density scale. This is a very nutritious and satiating meal (for me) with over 55 g of protein, as well as a great mix of macro- and micro-nutrients.

Boring is another word for satiating

2011-06-27T04:00:00.000-07:00

Satiety is a common topic of discussion on this blog. In the last few posts it came up several times in the comments’ sections. Also, in my interview with Jimmy Moore, we did talk a bit about satiety. I told him what has been my perception and that of many people I know, which is that the least satiating foods tend to be foods engineered by humans.

(Source: Wellnessuncovered.com)

There is another component to satiety, which applies to natural foods, or foods that are not man-made. That other component is the nutrition value of those foods, and whether they meet our nutrition needs at a given point in time. If our body needs certain essential amino acids for tissue repair, subconscious mechanisms will make us crave those foods from which those amino acids can be extracted. In this context, eating is generally a good idea.

The problem is that we have not evolved mechanisms to differentiate “true” from “fake” nutrient starvation; one example of the latter would be fat starvation due to transient hyperinsulinemia induced by refined carbohydrate-rich foods.

Foods engineered by humans tend to lead to overeating because humans are good engineers. In modern society, business drives everything. Food business is predicated on consumption, so engineered foods are designed so that one person will want to consume many units of a food item – typically something that will come in a box or a plastic bag. There is no conspiracy involved; the underlying reason is profit maximization.

When we look at nature, we typically see the opposite. Prey animals do not want to be eaten; often they fight back. Eggs have to be stolen. Plants do not want their various parts, such as leaves and roots, to be eaten. Much less their seeds; so they have developed various defense mechanisms, including toxins. Fruits are exceptions to this rule; they are the only natural foods that are designed to be eaten by animals.

Plants want animals to eat their fruits so that they can disperse the plants’ seeds. So they must be somewhat alluring to animals. Sugar plays a role here, but it certainly is not the only factor. The chemical composition of fruits is quite complex, and they usually contain a number of health-promoting substances, such as vitamins. For example, most fruits contain vitamin C, which happens to be a powerful antioxidant, and also has the ability to reversibly bind to proteins at the sites where sugar-induced glycation would occur.

Many modern fruits have been bred to be resistant to diseases, more palatable, and larger (usually due to more water retention). But, fundamentally, fruits are products of evolution. So how come we don’t see fruits that are pure sugar? Watermelons, for example, are often referred to as “bags of sugar”, but they are only 6 percent sugar. Ice cream is 25 percent sugar.

Two things must be kept in mind regarding fruits and their evolution. One is that dead animals do not eat fruit, and thus cannot disperse seeds. Sick animals would probably not be good candidates for fruit dispersion either. So the co-evolution of fruits and animals must have led fruits to incorporate many health-promoting attributes. The other is that seed dispersion success is correlated with the number of different animals that consume fruits from a plant. In other words, plants do not want all of their fruits to be eaten by one single animal, which must have led fruits to incorporate satiety-promoting attributes.

Often combining foods, adding spices, and so on, is perceived as making those foods exciting. That is so even with natural foods. If you read the descriptions of the foods consumed by healthy isolated populations in Weston Price’s Nutrition and Physical Degeneration, you will probably find them a bit boring. A few very nutritious food items, consumed day in and day out, frequently without heavy preparation. Exciting foods, requiring elaborate and time-consuming preparation, were consumed in special occasions. They were not eaten regularly.

The members of those healthy isolated populations were generally thin and yet lacked no important nutrients in their diet. They were generally free from degenerative diseases. Their teeth were normally strong and healthy.

Just before writing this post, I took six whole sardines out of the freezer to thaw. I will prepare them as discussed on this post, and eat them with a side of steamed vegetables for lunch. (I tend to eat fruits only on the days I exercise; typically 3 days out of 7.) This lunch will be very nutrient-dense. I will be very hungry before lunch, since I’ll have been fasting for 16 hours, and after I’ll not be hungry until dinner. Frankly, eating the sardines will not be very exciting, since I’ve been doing this for years.

Boring is another word for satiating.

Maybe you should stop trying to be someone you are not

2011-06-20T04:00:00.000-07:00

Many people struggle to lose body fat, and never quite make it to their optimal. Fewer people manage to do so successfully, and, as soon as they do, they want more. It is human nature. Often they will start trying to become someone they are not, or cannot be. That may lead to a lot of stress and frustration, and also health problems.

Some women have an idealized look in mind, and keep losing weight well beyond their ideal, down to anorexic levels. That leads to a number of health problems. For example, hormones approach starvation levels, causing fatigue and mood swings; susceptibility to infectious diseases increases significantly; and the low weight leads to osteopenia, which is a precursor to osteoporosis.

In men, often what happens is the opposite. Guys who are successful getting body fat to healthy levels next want to become very muscular, and fast. They have an idealized look in mind, and think they know how much they should weigh to get there. Sometimes they want to keep losing body fat and gaining muscle at the same time.

I frequently see men who already look very healthy, but who think that they should weigh more than they do. Since muscle gain is typically very slow, they start eating more and simply gain body fat. The reality is that people have different body frames, and their muscles are built slightly differently; these are things that influence body weight.

There are many other things that also influence body weight, such as the length of arms and legs, bone density, organ mass, as well as the amount of glycogen and water stored throughout the body. As a result, you can weigh a lot less than you think you should weigh, and look very good. The photo below (from MMAjunkie.com) is of Donald Cerrone, weighing in at 145 lbs. He is 6 ft (183 cm) tall.

Mr. Cerrone is a professional mixed martial arts (MMA) fighter from Texas; one of the best in professional MMA at the moment. Yes, he is a bit dehydrated on the photo above. But also keep in mind that his bone density is probably well above that of the average person, like that of most MMA fighters, which pushes his weight up.

A man can be 6 ft tall, weigh 145 lbs, and be very healthy and look very good. That may well be his ideal weight. A woman may be 5’5”, weigh 145 lbs, and also be very healthy and look very good. Figuring out the optimal is not easy, but trying to be someone you are not will probably be a losing battle.

Alcohol intake increases LDL cholesterol, in some people

2011-06-13T04:00:00.000-07:00

Occasionally I get emails from people experiencing odd fluctuations in health markers, and trying to figure out what is causing those fluctuations. Spikes in LDL cholesterol without any change in diet seem to be a common occurrence, especially in men.

LDL cholesterol is a reflection of many things. It is one of the least useful measures in standard lipid profiles, as a predictor of future health problems. Nevertheless, if one’s diet is not changing, whether it is high or low in fat, significant fluctuations in LDL cholesterol may signal a change in inflammatory status. Generally speaking, the more systemic inflammation, the higher is the measured LDL cholesterol.

Corella and colleagues (2001) looked into alcohol consumption and its effect on LDL cholesterol, as part of the Framingham Offspring Study. They split the data into three genotypes, which are allele combinations. Alleles are genes variations; that is, they are variations in the sections of DNA that have been identified as coding for observable traits. The table below summarizes what they have found. Take a look at the last two columns on the right.

As you can see, for men with the E2 genotype, alcohol consumption significantly decreases LDL cholesterol. For men with the E4 genotype, alcohol consumption significantly increases LDL cholesterol. No significant effects were observed in women. The figure below illustrates the magnitude of the effects observed in men.

On average, alcohol consumption was moderate, around 15 g per day, and did not vary significantly based on genotype. This is important. Otherwise one could argue that a particular genotype predisposed individuals to drink more, which would be a major confounder in this study. Other confounders were also ruled out through multivariate controls - e.g., fat and calorie intake, and smoking.

Alcohol consumption in moderation seems, on average, to be beneficial. But for some individuals, particularly men with a certain genotype, it may be advisable to completely abstain from alcohol consumption. Who are those folks? They are the ones for whom LDL cholesterol goes up significantly following moderate alcohol consumption.

What is a good low carbohydrate diet? It is a low calorie one

2011-06-06T04:00:00.000-07:00

My interview with Jimmy Moore should be up on the day that this post becomes available. (I usually write my posts on weekends and schedule them for release at the beginning of the following weeks.) So the time is opportune for me to try to aswer this question: What is a good low carbohydrate diet?

For me, and many people I know, the answer is: a low calorie one. What this means, in simple terms, is that a good low carbohydrate diet is one with plenty of seafood and organ meats in it, and also plenty of veggies. These are low carbohydrate foods that are also naturally low in calories. Conversely, a low carbohydrate diet of mostly beef and eggs would be a high calorie one.

Seafood and organ meats provide essential fatty acids and are typically packed with nutrients. Because of that, they tend to be satiating. In fact, certain organ meats, such as beef liver, are so packed with nutrients that it is a good idea to limit their consumption. I suggest eating beef liver once or twice a week only. As for seafood, it seems like a good idea to me to get half of one’s protein from them.

Does this mean that the calories-in-calories-out idea is correct? No, and there is no need to resort to complicated and somewhat questionable feedback-loop arguments to prove that calories-in-calories-out is wrong. Just consider this hypothetical scenario; a thought experiment. Take two men, one 25 years of age and the other 65, both with the same weight. Put them on the same exact diet, on the same exact weight training regime, and keep everything else the same.

What will happen? Typically the 65-year-old will put on more body fat than the 25-year-old, and the latter will put on more lean body mass. This will happen in spite of the same exact calories-in-calories-out profile. Why? Because their hormonal mixes are different. The 65-year-old will typically have lower levels of circulating growth hormone and testosterone, both of which significantly affect body composition.

As you can see, it is not all about insulin, as has been argued many times before. In fact, average and/or fasting insulin may be the same for the 65- and 25-year-old men. And, still, the 65-year-old will have trouble keeping his body fat low and gaining muscle. There are other hormones involved, such as leptin and adiponectin, and probably several that we don’t know about yet.

A low carbohydrate diet appears to be ideal for many people, whether that is due to a particular health condition (e.g., diabetes) or simply due to a genetic makeup that favors this type of diet. By adopting a low carbohydrate diet with plenty of seafood, organ meats, and veggies, you will make it a low calorie diet. If that leads to a calorie deficit that is too large, you can always add a bit more of fat to it. For example, by cooking fish with butter and adding bacon to beef liver.

One scenario where I don’t see the above working well is if you are a competitive athlete who depletes a significant amount of muscle glycogen on a daily basis – e.g., 250 g or more. In this case, it will be very difficult to replenish glycogen only with protein, so the person will need more carbohydrates. He or she would need a protein intake in excess of 500 g per day for replenishing 250 g of glycogen only with protein.

Interview with Jimmy Moore, and basics of intima-media thickness and plaque tests

2011-05-30T04:00:00.000-07:00

Let me start this post by telling you that my interview with Jimmy Moore is coming up in about a week. Jimmy and I talk about evolution, statistics, and health – the main themes of this blog. We talk also about other things, and probably do not agree on everything. The interview was actually done a while ago, so I don’t remember exactly what we discussed.

From what I remember from mine and other interviews (I listen to Jimmy's podcasts regularly), I think I am the guest who has mentioned the most people during an interview – Gary Taubes, Chris Masterjohn, Carbsane, Petro (a.k.a., Peter “the Hyperlipid”), T. Colin Campbell, Denise Minger, Kurt Harris, Stephan Guyenet, Art De Vany, and a few others. What was I thinking?

In case you listen and wonder, my accent is a mix of Brazilian Portuguese, New Zealand English (where I am called “Need”), American English, and the dialect spoken in the “country” of Texas. The strongest influences are probably American English and Brazilian Portuguese.

Anyway, when medical doctors (MDs) look at someone’s lipid panel, one single number tends to draw their attention: the LDL cholesterol. That is essentially the amount of cholesterol in LDL particles.

One’s LDL cholesterol is a reflection of many factors, including: diet, amount of cholesterol produced by the liver, amount of cholesterol actually used by your body, amount of cholesterol recycled by the liver, and level of systemic inflammation. This number is usually calculated, and often very different from the number you get through a VAP test.

It is not uncommon for a high saturated fat diet to lead to a benign increase in LDL cholesterol. In this case the LDL particles will be large, which will also be reflected in a low “fasting triglycerides number” (lower than 70 mg/dl). While I say "benign" here, which implies a neutral effect on health, an increase in LDL cholesterol in this context may actually be health promoting.

Large LDL particles are less likely to cross the gaps in the endothelium, the thin layer of cells that lines the interior surface of blood vessels, and form atheromatous plaques.

Still, when an MD sees an LDL cholesterol higher than 100 mg/dl, more often than not he or she will tell you that it is bad news. Whether that is bad news or not is really speculation, even for high LDL numbers. A more reliable approach is to check one’s arteries directly. Interestingly, atheromatous plaques only form in arteries, not in veins.

The figure below (from: Novogen.com) shows a photomicrograph of carotid arteries from rabbits, which are very similar, qualitatively speaking, to those of humans. The meanings of the letters are: L = lumen; I = intima; M = media; and A = adventitia. The one on the right has significantly lower intima-media (I-M) thickness than the one on the left.

Atherosclerosis in humans tends to lead to an increase in I-M thickness; the I-M area being normally where atheromatous plaques grow. Aging also leads to an increase in I-M thickness. Typically one’s risk of premature death from cardiovascular complications correlates with one’s I-M thickness’ “distance” from that of low-risk individuals in the same sex and age group.

This notion has led to the coining of the term “vascular age”. For example, someone may be 30 years old, but have a vascular age of 80, meaning that his or her I-M thickness is that of an average 80-year-old. Conversely, someone may be 80 and have a vascular age of 30.

Nearly everybody’s I-M thickness goes up with age, even people who live to be 100 or more. Incidentally, this is true for average blood glucose levels as well. In long-living people they both go up slowly.

I-M thickness tests are noninvasive, based on external ultrasound, and often covered by health insurance. They take only a few minutes to conduct. Their reports provide information about one’s I-M thickness and its relative position in the same sex and age group, as well as the amount of deposited plaque. The latter is frequently provided as a bonus, since it can also be inferred with reasonable precision from the computer images generated via ultrasound.

Below is the top part of a typical I-M thickness test report (from: Sonosite.com). It shows a person’s average (or mean) I-M thickness; the red dot on the graph. The letter notations (A … E) are for reference groups. For the majority of the folks doing this test, the most important on this report are the thick and thin lines indicated as E, which are based on Aminbakhsh and Mancini’s (1999) study.

The reason why the thick and thin lines indicated as E are the most important for the majority of folks taking this test is that they are based on a study that provides one of the best reference ranges for people who are 45 and older, who are usually the ones getting their I-M thickness tested. Roughly speaking, if your red dot is above the thin line, you are at increased risk of cardiovascular disease.

Most people will fall in between the thick and thin lines. Those below the thick line (with the little blue triangles) are at very low risk, especially if they have little to no plaque. The person for whom this test was made is at very low risk. His red dot is below the thick line, when that line is extended to the little triangle indicated as D.

Below is the bottom part of the I-M thickness test report. The max I-M thickness score shown here tends to add little in terms of diagnosis to the mean score shown earlier. Here the most important part is the summary, under “Comments”. It says that the person has no plaque, and is at a lower risk of heart attack. If you do an I-M thickness test, your doctor will probably be able to tell you more about these results.

I like numbers, so I had an I-M thickness test done recently on me. When the doctor saw the results, which we discussed, he told me that he could guarantee two things: (1) I would die; and (2) but not of heart disease. MDs have an interesting sense of humor; just hang out with a group of them during a “happy hour” and you’ll see.

My red dot was below the thick line, and I had a plaque measurement of zero. I am 47 years old, eat about 1 lb of meat per day, and around 20 eggs per week - with the yolk. About half of the meat I eat comes from animal organs (mostly liver) and seafood. I eat organ meats about once a week, and seafood three times a week. This is an enormous amount of dietary cholesterol, by American diet standards. My saturated fat intake is also high by the same standards.

You can check the post on my transformation to see what I have been doing for years now, and some of the results in terms of levels of energy, disease, and body fat levels. Keep in mind that mine are essentially the results of a single-individual experiment; results that clearly contradict the lipid hypothesis. Still, they are also consistent with a lot of fairly reliable empirical research.

The China Study II: Wheat may not be so bad if you eat 221 g or more of animal food daily

2011-05-23T04:00:00.000-07:00

In previous posts on this blog covering the China Study II data we’ve looked at the competing effects of various foods, including wheat and animal foods. Unfortunately we have had to stick to the broad group categories available from the specific data subset used; e.g., animal foods, instead of categories of animal foods such as dairy, seafood, and beef. This is still a problem, until I can find the time to get more of the China Study II data in a format that can be reliably used for multivariate analyses.

What we haven’t done yet, however, is to look at moderating effects. And that is something we can do now. A moderating effect is the effect of a variable on the effect of another variable on a third. Sounds complicated, but WarpPLS makes it very easy to test moderating effects. All you have to do is to make a variable (e.g., animal food intake) point at a direct link (e.g., between wheat flour intake and mortality). The moderating effect is shown on the graph as a dashed arrow going from a variable to a link between two variables.

The graph below shows the results of an analysis where animal food intake (Afoods) is hypothesized to moderate the effects of wheat flour intake (Wheat) on mortality in the 35 to 69 age range (Mor35_69) and mortality in the 70 to 79 age range (Mor70_79). A basic linear algorithm was used, whereby standardized partial regression coefficients, both moderating and direct, are calculated based on the equations of best-fitting lines.

From the graph above we can tell that wheat flour intake increases mortality significantly in both age ranges; in the 35 to 69 age range (beta=0.17, P=0.05), and in the 70 to 79 age range (beta=0.24, P=0.01). This is a finding that we have seen before on previous posts, and that has been one of the main findings of Denise Minger’s analysis of the China Study data. Denise and I used different data subsets and analysis methods, and reached essentially the same results.

But here is what is interesting about the moderating effects analysis results summarized on the graph above. They suggest that animal food intake significantly reduces the negative effect of wheat flour consumption on mortality in the 70 to 79 age range (beta=-0.22, P<0.01). This is a relatively strong moderating effect. The moderating effect of animal food intake is not significant for the 35 to 69 age range (beta=-0.00, P=0.50); the beta here is negative but very low, suggesting a very weak protective effect.

Below are two standardized plots showing the relationships between wheat flour intake and mortality in the 70 to 79 age range when animal food intake is low (left plot) and high (right plot). As you can see, the best-fitting line is flat on the right plot, meaning that wheat flour intake has no effect on mortality in the 70 to 79 age range when animal food intake is high. When animal food intake is low (left plot), the effect of wheat flour intake on mortality in this range is significant; its strength is indicated by the upward slope of the best-fitting line.

What these results seem to be telling us is that wheat flour consumption contributes to early death for several people, perhaps those who are most sensitive or intolerant to wheat. These people are represented in the variable measuring mortality in the 35 to 69 age range, and not in the 70 to 79 age range, since they died before reaching the age of 70.

Those in the 70 to 79 age range may be the least sensitive ones, and for whom animal food intake seems to be protective. But only if animal food intake is above a certain level. This is not a ringing endorsement of wheat, but certainly helps explain wheat consumption in long-living groups around the world, including the French.

How much animal food does it take for the protective effect to be observed? In the China Study II sample, it is about 221 g/day or more. That is approximately the intake level above which the relationship between wheat flour intake and mortality in the 70 to 79 age range becomes statistically indistinguishable from zero. That is a little less than ½ lb, or 7.9 oz, of animal food intake per day.

Book review: Biology for Bodybuilders

2011-05-16T04:00:00.000-07:00

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.

(Source: www.dougmillerpro.com)

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!

Looking for a good orthodontist? My recommendation is Dr. Meat

2011-05-09T04:00:00.000-07:00

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).

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

2011-05-02T04:00:00.000-07:00

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

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

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

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

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

This is somewhat similar to the response to GH therapy.

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

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

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

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

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

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

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

2011-04-24T04:00:00.000-07:00

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.

Low bone mineral content in older Eskimos: Meat-eating or shrinking?

2011-04-18T04:00:00.000-07:00

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.

Beef meatballs, with no spaghetti

2011-04-11T04:00:00.000-07:00

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 China Study II: Carbohydrates, fat, calories, insulin, and obesity

2011-04-04T04:00:00.000-07:00

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.

Chew your calories and drink water: Industrial beverages and tooth erosion

2011-03-28T04:00:00.000-07:00

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.

Health markers varying inexplicably? Do some detective work with HCE

2011-03-21T05:00:00.000-07:00

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.

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

2011-03-14T05:00:00.000-07:00

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

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

The majority of the people who read this blog probably share a common ancestor who lived no more than 640 years ago.

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

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

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

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

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

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

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

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

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

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

The China Study II: Fruit consumption and mortality

2011-03-07T06:11:00.000-08:00

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.

Vitamin D production from UV radiation: The effects of total cholesterol and skin pigmentation

2011-02-28T05:00:00.000-08:00

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.

The China Study II: Wheat, dietary fat, and mortality

2011-02-21T05:00:00.000-08:00

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.

Does protein leach calcium from the bones? Yes, but only if it is plant protein

2011-02-13T21:28:00.000-08:00

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.

Is working standing up too expensive? It could cost you as little as $10

2011-02-06T14:33:00.000-08:00

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.

The China Study II: A look at mortality in the 35-69 and 70-79 age ranges

2011-01-30T07:59:00.000-08:00

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 free trial

2011-01-24T12:15:00.000-08:00

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.

Do you lose muscle if you lift weights after a 24-hour fast? Probably not if you do that regularly

2011-01-15T17:49:00.000-08:00

Compensatory adaptation (CA) is an idea that is useful in the understanding of how the body reacts to inputs like dietary intake of macronutrients and exercise. CA is a complex process, because it involves feedback loops, but it leads to adaptations that are fairly general, applying to a large cross-section of the population.

A joke among software developers is that the computer does exactly what you tell it to do, but not necessarily what you want it to do. Similarly, through CA your body responds exactly to the inputs you give it, but not necessarily in the way you would like it to respond. For example, a moderate caloric deficit may lead to slow body fat loss, while a very high caloric deficit may bring body fat loss to a halt.

Strength training seems to lead to various adaptations, which can be understood through the lens provided by CA. One of them is a dramatic increase in the ability of the body to store glycogen, in both liver and muscle. Glycogen is the main fuel used by muscle during anaerobic exercise. Regular strength training causes, over time, glycogen stores to more than double. And about 2.6 the amount of glycogen is also stored as water.

When one looks bigger and becomes stronger as a result of strength training, that is in no small part due to increases in glycogen and water stored. More glycogen stored in muscle leads to more strength, which is essentially a measure of one’s ability to move a certain amount of weight around. More muscle protein is also associated with more strength.

Thinking in terms of CA, the increase in the body’s ability to store glycogen is to be expected, as long as glycogen stores are depleted and replenished on a regular basis. By doing strength training regularly, you are telling your body that you need a lot of glycogen on a regular basis, and the body responds. But if you do not replenish your glycogen stores on a regular basis, you are also sending your body a conflicting message, which is that dietary sources of the substances used to make glycogen are not readily available. Among the substances that are used to make glycogen, the best seems to be the combination of fructose and glucose that one finds in fruits.

Let us assume a 160-lbs untrained person, John, who stored about 100 g of glycogen in his liver, and about 500 g in his muscle cells, before starting a strength training program. Let us assume, conservatively, that after 6 months of training he increased the size of his liver glycogen tank to 150 g. Muscle glycogen storage was also increased, but that is less relevant for the discussion in this post.

Then John fasted for 24 hours before a strength training session, just to see what would happen. While fasting he went about his business, doing light activities, which led to a caloric expenditure of about 100 calories per hour (equivalent to 2400 per day). About 20 percent of that, or 20 calories per hour, came from a combination of blood glucose and ketones. Contrary to popular belief, ketones can always be found in circulation. If only glucose were used, 5 g of glucose per hour would be needed to supply those 20 calories.

During the fast, John’s glucose needs, driven primarily by his brain’s needs, were met by conversion of liver glycogen to blood glucose. His muscle glycogen was pretty much “locked” during the fast; because he was doing only light activities, which rely primarily on fat as fuel. Muscle glycogen is “unlocked” through anaerobic exercise, of which strength training is an instance.

One of the roles of ketones is to spare liver glycogen, delaying the use of muscle protein to make glucose down the road, so the percentage of ketones in circulation in John’s body increased in a way that was inversely proportional to stored liver glycogen. According to this study, after 72 hours fasting about 25 percent of the body’s glucose needs are met by ketones. (This may be an underestimation.)

If we assume a linear increase in ketone concentration, this leads to a 0.69 percent increase in circulating ketones for every 2-hour period. (This is a simplification, as the increase is very likely nonlinear.) So, when we look at John’s liver glycogen tank, it probably went down in a way similar to that depicted on the figure below. The blue bars show liver glycogen at the end of each 2-hour period. The red bars show the approximate amount of glucose consumed during each 2-hour period. Glucose consumed goes down as liver glycogen decreases, because of the increase in blood ketones.

As you can see, after a 24-hour fast, John had about 35 g of glycogen left, which is enough for a few extra hours of fasting. At the 24-hour mark the body had no need to be using muscle protein to generate glucose. Maybe some of that happened, but probably not much if John was relaxed during the fast. (If he was stressed out, stress hormones would have increased blood glucose release significantly.) From the body’s perspective, muscle is “expensive”, whereas body fat is “cheap”. And body fat, converted to free fatty acids, is what is used to produce ketones during a fast.

Blood ketone concentration does not go up dramatically during a 24-hour fast, but it does after a 48-hour fast, when it becomes about 10 times higher. This major increase occurs primarily to spare muscle, including heart muscle. If the increase is much smaller during a 24-hour fast, one can reasonably assume that the body is not going to be using muscle during the fast. It can still rely on liver glycogen, together with a relatively small amount of ketones.

Then John did his strength training, after the 24-hour fast. When he did that, the muscles he used in the exercise session converted locally stored glycogen into lactate. A flood of lactate was secreted into the bloodstream, which was used by his liver to produce glucose and also to replenish liver glycogen a bit. Again, at this stage there was no need for John’s body to use muscle protein to generate glucose.

Counterintuitive as this may sound, the more different muscles John used, the more lactate was made available. If John did 20 sets of isolated bicep curls, for example, his body would not have released enough lactate to meet its glucose needs or replenish liver glycogen. As a result, stress hormones would go up a lot, and his body would send him some alarm signals. One of those signals is a feeling of “pins and needles”, which is sometimes confused with the symptoms of a heart attack.

John worked out various muscle groups for 30 minutes or so, and he did not even feel fatigued. He felt energetic, in part because his blood glucose went up a lot, peaking at 150 mg/dl, to meet muscle needs. This elevated blood glucose was caused by his liver producing blood glucose based on lactate and releasing it into his blood. Muscle glycogen was depleted as a result of that.

Do you lose any muscle if you lift weights after a 24-hour fast?

I don’t think so, if you deplete your glycogen stores by doing strength training on a regular basis, and also replenish them on a regular basis. In fact, your liver glycogen tank will increase in size, and you may find yourself being able to fast for many hours without feeling hungry.

You will feel hungry after the strength training session following the fast though; probably ravenous.

References

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

Wilmore, J.H., Costill, D.L., & Kenney, W.L. (2007). Physiology of sport and exercise. Champaign, IL: Human Kinetics.

How come evolution hasn’t made us immortal? Death, like sex, helps animal populations avoid extinction

2011-01-10T06:51:00.000-08:00

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”.

(Human chromosomes capped by telomeres, the white areas at the ends. Telomere shortening is caused by oxidative stress, and seems to be associated with death of cells and organisms. Source: Wikipedia.)

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.

Does strength exercise increase nitrogen balance?

2011-01-06T06:46:00.000-08:00

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.

(Strength exercise combined with a small calorie deficit may be one of the best approaches for body fat loss in women. Photo source: complete-strength-training.com)

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.

How much protein does one need to be in nitrogen balance?

2010-12-30T07:51:00.000-08:00

The figure below, from Brooks et al. (2005), shows a graph relating nitrogen balance and protein intake. A nitrogen balance of zero is a state in which body protein mass is stable; that is, it is neither increasing nor decreasing. The graph was taken from this classic study by Meredith et al. The participants in the study were endurance exercisers. As you can see, age is not much of a factor for nitrogen balance in this group.

Nitrogen balance is greater than zero (i.e., an anabolic state) for the vast majority of the participants at 1.2 g of protein per kg of body weight per day. To convert lbs to kg, divide by 2.2. A person weighing 100 lbs (45 kg) would need 55 g/d of protein; a person weighing 155 lbs (70 kg) would need 84 g/d; someone weighing 200 lbs (91 kg) would need 109 g/d.

The above numbers are overestimations of the amounts needed by people not doing endurance exercise, because endurance exercise tends to lead to muscle loss more than rest or moderate strength training. One way to understand this is compensatory adaptation; the body adapts to endurance exercise by shedding off muscle, as muscle is more of a hindrance than an asset for this type of exercise.

Total calorie intake has a dramatic effect on protein requirements. The above numbers assume that a person is getting just enough calories from other sources to meet daily caloric needs. If a person is in caloric deficit, protein requirements go up. If in caloric surplus, protein requirements go down. Other factors that increase protein requirements are stress and wasting diseases (e.g., cancer).

But what if you want to gain muscle?

Wilson & Wilson (2006) conducted an extensive review of the literature on protein intake and nitrogen balance. That review suggests that a protein intake beyond 25 percent of what is necessary to achieve a nitrogen balance of zero would have no effect on muscle gain. That would be 69 g/d for a person weighing 100 lbs (45 kg); 105 g/d for a person weighing 155 lbs (70 kg); and 136 g/d for someone weighing 200 lbs (91 kg). For the reasons explained above, these are also overestimations.

What if you go well beyond these numbers?

The excess protein will be used primarily as fuel; that is, it will be oxidized. In fact, a large proportion of all the protein consumed on a daily basis is used as fuel, and does not become muscle. This happens even if you are a gifted bodybuilder that can add 1 lb of protein to muscle tissue per month. So excess protein can make you gain body fat, but not by protein becoming body fat.

Dietary protein does not normally become body fat, but will typically be used in place of dietary fat as fuel. This will allow dietary fat to be stored. Dietary protein also leads to an insulin response, which causes less body fat to be released. In this sense, protein has a fat-sparing effect, preventing it from being used to supply the energy needs of the body. As long as it is available, dietary protein will be favored over dietary or body fat as a fuel source.

Having said that, if you were to overeat anything, the best choice would be protein, in the absence of any disease that would be aggravated by this. Why? Protein contributes fewer calories per gram than carbohydrates; many fewer when compared with dietary fat. Unlike carbohydrates or fat, protein almost never becomes body fat under normal circumstances. Dietary fat is very easily converted to body fat; and carbohydrates become body fat when glycogen stores are full. Finally, protein seems to be the most satiating of all macronutrients, perhaps because natural protein-rich foods are also very nutrient-dense.

It is not very easy to eat a lot of protein without getting also a lot of fat if you get your protein from natural foods; as opposed to things like refined seed/grain products or protein supplements. Exceptions are organ meats and seafood, which generally tend to be quite lean and protein-rich.

References

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

Wilson, J., & Wilson, G.J. (2006). Contemporary issues in protein requirements and consumption for resistance trained athletes. Journal of the International Society of Sports Nutrition, 3(1), 7-27.

How much dietary protein can you store in muscle? About 15 g/d if you are a gifted bodybuilder

2010-12-28T07:00:00.000-08:00

Let us say you are one of the gifted few who are able to put on 1 lb of pure muscle per month, or 12 lbs per year, by combining strength training with a reasonable protein intake. Let us go even further and assume that the 1 lb of muscle that we are talking about is due to muscle protein gain, not glycogen or water. This is very uncommon; one has to really be genetically gifted to achieve that.

And you do that by eating a measly 80 g of protein per day. That is little more than 0.5 g of protein per lb of body weight if you weigh 155 lbs; or 0.4 per lb if you weigh 200 lbs. At the end of the year you are much more muscular. People even think that you’ve been taking steroids; but that just came naturally. The figure below shows what happened with the 80 g of protein you consumed every day. About 15 g became muscle (that is 1 lb divided by 30) … and 65 g “disappeared”!

Is that an amazing feat? Yes, it is an amazing feat of waste, if you think that the primary role of protein is to build muscle. More than 80 percent of the protein consumed was used for something else, notably to keep your metabolic engine running.

A significant proportion of dietary protein also goes into the synthesis of albumin, to which free fatty acids bind in the blood. (Albumin is necessary for the proper use of fat as fuel.) Dietary protein is also used in the synthesis of various body tissues and hormones.

Dietary protein does not normally become body fat, but can be used in place of fat as fuel and thus allow more dietary fat to be stored. It leads to an insulin response, which causes less body fat to be released. In this sense, dietary protein has a fat-sparing effect, preventing it from being used to supply the energy needs of the body.

Nevertheless, the fat-sparing effect of protein is lower than that of the other two macronutrients – alcohol and carbohydrates. That is, alcohol takes precedence over carbohydrates for use as fuel, and carbohydrates take precedence over protein. Neither alcohol nor protein typically becomes body fat. Carbohydrates can become body fat, but only when glycogen stores are full.

What does this mean?

As it turns out, a reasonably high protein intake seems to be quite healthy, and there is nothing wrong with the body using protein to feed its metabolism.

Having said that, one does not need enormous amounts of protein to keep or even build muscle if one is getting enough calories from other sources.

In my next post I’ll talk a little bit more about that.