Health Tips

Niacin turbocharges the growth hormone response to anaerobic exercise: A delayed effect

2011-12-06T01:36:00.060-08:00

Weird News : Niacin is also known as vitamin B3, or nicotinic acid. It is an essential vitamin whose deficiency leads to pellagra. In large doses of 1 to 3 g per day it has several effects on blood lipids, including an increase in HDL cholesterol and a marked decreased in fasting triglycerides. Niacin is also a powerful antioxidant.

Among niacin’s other effects, when taken in large doses

Cortisol, stress, excessive gluconeogenesis, and visceral fat accumulation

2011-12-06T01:36:00.059-08:00

Weird News : Cortisol is a hormone that plays several very important roles in the human body. Many of these are health-promoting, under the right circumstances. Others can be disease-promoting, especially if cortisol levels are chronically elevated.

Among the disease-promoting effects of chronically elevated blood cortisol levels are that of excessive gluconeogenesis, causing high blood glucose levels even while a person is fasting. This also causes muscle wasting, as muscle tissue is used to elevate blood glucose levels.

Cortisol also seems to transfer body fat from subcutaneous to visceral areas. Presumably cortisol promotes visceral fat accumulation to facilitate the mobilization of that fat in stressful “fight-or-flight” situations. Visceral fat is much easier to mobilize than subcutaneous fat, because visceral fat deposits are located in areas where vascularization is higher, and are closer to the portal vein.

The problem is that modern humans often experience stress without the violent muscle contractions of a “fight-or-flight” response that would have normally occurred among our hominid ancestors. Arguably those muscle contractions would have normally been in the anaerobic range (like a weight training set) and be fueled by both glycogen and fat. Recovery from those anaerobic "workouts" would induce aerobic metabolic responses, for which the main fuel would be fat.

Coates and Herbert (2008) studied hormonal responses of a group of London traders. Among other interesting results, they found that a trader’s blood cortisol level rises with the volatility of the market. The figure below (click to enlarge) shows the variation in cortisol levels against a measure of market volatility.

On a day of high volatility cortisol levels can be significantly higher than those on a day with little volatility. The correlation between cortisol levels and market volatility in this study was a very high 0.93. This is almost a perfectly linear association. Market volatility is associated with traders’ stress levels; stress that is experienced without heavy physical exertion.

Cortisol levels go up a lot with stress. And modern humans live in hyper-stressful environments. Unfortunately stress in modern urban environments is often experienced while sitting down. In the majority of cases stress is experienced without any vigorous physical activity in response to it.

As Geoffrey Miller pointed out in his superb book, The Mating Mind, the lives of our Paleolithic ancestors would probably look rather boring to a modern human. But that is the context in which our endocrine responses evolved.

Our insatiable appetite for over stimulation may be seen as a disease. A modern disease. A disease of civilization.

Well, it is no wonder that heavy physical activity is NOT a major trigger of death by sudden cardiac arrest. Bottled up modern human stress likely is.

We need to learn how to make stress management techniques work for us.

Visiting New Zealand at least once and watching this YouTube video clip often to remind you of the experience does not hurt either! Note the “honesty box” at around 50 seconds into the clip.

References:

Coates, J.M., & Herbert, J. (2008). Endogenous steroids and financial risk taking on a London trading floor. Proceedings of the National Academic of Sciences of the U.S.A., 105(16), 6167–6172.

Elliott, W.H., & Elliott, D.C. (2009). Biochemistry and molecular biology. 4th Edition. New York: NY: Oxford University Press.

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Fructose in fruits may be good for you, especially if you are low in glycogen

2011-12-06T01:36:00.058-08:00

Weird News : Excessive dietary fructose has been shown to cause an unhealthy elevation in serum triglycerides. This and other related factors are hypothesized to have a causative effect on the onset of the metabolic syndrome. Since fructose is found in fruits (see table below, from Wikipedia; click to enlarge), there has been some concern that eating fruit may cause the metabolic syndrome.

Vegetables also have fructose. Sweet onions, for example, have more free fructose than peaches, on a gram-adjusted basis. Sweet potatoes have more sucrose than grapes (but much less overall sugar), and sucrose is a disaccharide derived from glucose and fructose. Sucrose is broken down to fructose and glucose in the human digestive tract.

Dr. Robert Lustig has given a presentation indicting fructose as the main cause of the metabolic syndrome, obesity, and related diseases. Yet, even he pointed out that the fructose in fruits is pretty harmless. This is backed up by empirical research.

The problem is over-consumption of fructose in sodas, juices, table sugar, and other industrial foods with added sugar. Table sugar is a concentrated form of sucrose. In these foods the fructose content is unnaturally high; and it comes in an easily digestible form, without any fiber or health-promoting micronutrients (vitamins and minerals).

Dr. Lustig’s presentation is available from this post by Alan Aragon. At the time of this writing, there were over 450 comments in response to Aragon’s post. If you read the comments you will notice that they are somewhat argumentative, as if Lustig and Aragon were in deep disagreement with one other. The reality is that they agree on a number of issues, including that the fructose found in fruits is generally healthy.

Fruits are among the very few natural plant foods that have been evolved to be eaten by animals, to facilitate the dispersion of the plants’ seeds. Generally and metaphorically speaking, plants do not “want” animals to eat their leaves, seeds, or roots. But they “want” animals to eat their fruits. They do not “want” one single animal to eat all of their fruits, which would compromise seed dispersion and is probably why fruits are not as addictive as doughnuts.

From an evolutionary standpoint, the idea that fruits can be unhealthy is somewhat counterintuitive. Given that fruits are made to be eaten, and that dead animals do not eat, it is reasonable to expect that fruits must be good for something in animals, at least in one important health-related process. If yes, what is it?

Well, it turns out that fructose, combined with glucose,

Soccer as play and exercise: Resistance and endurance training at the same time

2011-12-06T01:36:00.057-08:00

Weird News : Many sports combine three key elements that make them excellent fitness choices: play, resistance exercise, and endurance exercise; all at the same time. Soccer is one of those sports. Its popularity is growing, even in the US! The 2010 FIFA World Cup, currently under way in South Africa, is a testament to that. It helps that the US team qualified and did well in its first game against England.

Pelé is almost 70 years old in the photo below, from Wikipedia. He is widely regarded as the greatest soccer player of all time. But not by Argentineans, who will tell you that Pelé is probably the second greatest soccer player of all time, after Maradona.

Even though Brazil is not a monarchy, Pelé is known there as simply “The King”. How serious are Brazilians about this? Well, consider this. Fernando Henrique Cardoso was one of the most popular presidents of Brazil. He was very smart; he appointed Pelé to his cabinet. But when Cardoso had a disagreement with Pelé he was broadly chastised in Brazil for disrespecting “The King”, and was forced to publicly apologize or blow his political career!

Arguably soccer is a very good choice of play activity to be used in combination with resistance exercise. When used alone it is likely to lead to much more lower- than upper-body muscle development. Unlike before the 1970s, most soccer players today use whole body resistance exercise as part of their training. Still, you often see very developed leg muscles and relatively slim upper bodies.

What leads to leg muscle gain are the sprints. Interestingly, it is the eccentric part of the sprints that add the most muscle, by causing the most muscle damage. That is, it not the acceleration, but the deceleration phase that leads to the largest gains in leg muscle.

This eccentric phase effect is true for virtually all types of anaerobic exercise, and a well known fact among bodybuilders and exercise physiologists (see, e.g.,

Pretty faces are average faces: Genetic diversity and health

2011-12-06T01:36:00.056-08:00

Weird News : Many people think that the prettiest faces are those with very unique features. Generally that is not true. Pretty faces are average faces. And that is not only because they are symmetrical, even though symmetry is an attractive facial trait. Average faces are very attractive, which is counterintuitive but makes sense in light of evolution and genetics.

The faces in the figure below (click to enlarge) are from a presentation I gave at the University of Houston in 2008. The PowerPoint slides file for the presentation is available here. The photos were taken from the German web site Beautycheck.de. This site summarizes a lot of very interesting research on facial attractiveness.

The face on the right is a composite of the two faces on the left. It simulates what would happen if you were to morph the features of the two faces on the left into the face on the right. That is, the face on the right is the result of an “averaging” of the two faces on the left.

If you show these photos to a group of people, like I did during my presentation in Houston, most of the people in the group will say that the face on the right is the prettiest of the three. This happens even though most people will also say that each of the three faces is pretty, if shown each face separately from the others.

Why are average faces more beautiful?

The reason may be that we have brain algorithms that make us associate a sense of “beauty” with features that suggest an enhanced resistance to disease. This is an adaptation to the environments our ancestors faced in our evolutionary past, when disease would often lead to observable distortions of facial and body traits. Average faces are the result of increased genetic mixing, which leads to increased resistance to disease.

This interpretation is a variation of Langlois and Roggman’s “averageness hypothesis”, published in a widely cited 1990 article that appeared in the journal Psychological Science.

By the way, many people think that the main survival threats ancestral humans faced were large predators. I guess it is exciting to think that way; our warrior ancestors survived due to their ability to fight off predators! The reality is that, in our ancestral past, as today, the biggest killer of all by far was disease. The small organisms, the ones our ancestors couldn’t see, were the most deadly.

People from different populations, particularly those that have been subjected to different diseases, frequently carry genetic mutations that protect them from those diseases. Those are often carried as dominant alleles (i.e., variations of a gene). When two people with diverse genetic protections have children, the children inherit the protective mutations of both parents. The more genetic mixing, the more likely it is that multiple protective genetic mutations will be carried. The more genetic mixing, the higher is the "averageness" score of the face.

The opposite may happen when people who share many genes (e.g., cousins) have children. The term for this is inbreeding. Since alleles that code for diseases are often carried in recessive form, a child of closely related parents has a higher chance of having a combination of two recessive disease-promoting alleles. In this case, the child will be homozygous recessive for the disease, which will increase dramatically its chances of developing the disease.

In a nutshell: gene mixing = health; inbreeding = disease.

Finally, if you have some time, make sure to take a look at this page on the Virtual Miss Germany!

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What about some offal? Boiled tripes in tomato sauce

2011-12-06T01:36:00.055-08:00

Weird News : Tripe dishes are made with the stomach of various ruminants. The most common type of tripe is beef tripe from cattle. Like many predators, our Paleolithic ancestors probably ate plenty of offal, likely including tripe. They certainly did not eat only muscle meat. It would have been a big waste to eat only muscle meat, particularly because animal organs and other non-muscle parts are very rich in vitamins and minerals.

The taste for tripe is an acquired one. Many national cuisines have traditional tripe dishes, including the French, Chinese, Portuguese, and Mexican cuisines – to name only a few. The tripe dish shown in the photo below was prepared following a simple recipe. Click on the photo to enlarge it.

Here is the recipe:

- Cut up about 2 lbs of tripe into rectangular strips. I suggest rectangles of about 5 by 1 inches.
- Boil the tripe strips in low heat for 5 hours.
- Drain the boiled tripe strips, and place them in a frying or sauce pan. You may use the same pan you used for boiling.
- Add a small amount of tomato sauce, enough to give the tripe strips color, but not to completely immerse them in the sauce. Add seasoning to taste. I suggest some salt, parsley, garlic powder, chili powder, black pepper, and cayenne pepper.
- Cook the tripe strips in tomato sauce for about 15 minutes.

Cooked tripe has a strong, characteristic smell, which will fill your kitchen as you boil it for 5 hours. Not many people will be able to eat many tripe strips at once, so perhaps this should not be the main dish of a dinner with friends. I personally can only eat about 5 strips at a time. I know folks who can eat a whole pan full of tripe strips, like the one shown on the photo in this post. But these folks are not many.

In terms of nutrition, 100 g of tripe prepared in this way will have approximately 12 g of protein, 4 g of fat, 157 g of cholesterol, and 2 g of carbohydrates. You will also be getting a reasonable amount of vitamin B12, zinc, and selenium.

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Compensatory adaptation as a unifying concept: Understanding how we respond to diet and lifestyle changes

2011-12-06T01:36:00.054-08:00

Weird News : Trying to understand each body response to each diet and lifestyle change, individually, is certainly a losing battle. It is a bit like the various attempts to classify organisms that occurred prior to solid knowledge about common descent. Darwin’s theory of evolution is a theory of common descent that makes classification of organisms a much easier and logical task.

Compensatory adaptation (CA) is a broad theoretical framework that hopefully can help us better understand responses to diet and lifestyle changes. CA is a very broad idea, and it has applications at many levels. I have discussed CA in the context of human behavior in general (Kock, 2002), and human behavior toward communication technologies (Kock, 2001; 2005; 2007). Full references and links are at the end of this post.

CA is all about time-dependent adaptation in response to stimuli facing an organism. The stimuli may be in the form of obstacles. From a general human behavior perspective, CA seems to be at the source of many success stories. A few are discussed in the Kock (2002) book; the cases of Helen Keller and Stephen Hawking are among them.

People who have to face serious obstacles sometimes develop remarkable adaptations that make them rather unique individuals. Hawking developed remarkable mental visualization abilities, which seem to be related to some of his most important cosmological discoveries. Keller could recognize an approaching person based on floor vibrations, even though she was blind and deaf. Both achieved remarkable professional success, perhaps not as much in spite but because of their disabilities.

From a diet and lifestyle perspective, CA allows us to make one key prediction. The prediction is that compensatory body responses to diet and lifestyle changes will occur, and they will be aimed at maximizing reproductive success, but with a twist – it’s reproductive success in our evolutionary past! We are stuck with those adaptations, even though we live in modern environments that differ in many respects from the environments where our ancestors lived.

Note that what CA generally tries to maximize is reproductive success, not survival success. From an evolutionary perspective, if an organism generates 30 offspring in a lifetime of 2 years, that organism is more successful in terms of spreading its genes than another that generates 5 offspring in a lifetime of 200 years. This is true as long as the offspring survive to reproductive maturity, which is why extended survival is selected for in some species.

We live longer than chimpanzees in part because our ancestors were “good fathers and mothers”, taking care of their children, who were vulnerable. If our ancestors were not as caring or their children not as vulnerable, maybe this blog would have posts on how to control blood glucose levels to live beyond the ripe old age of 50!

The CA prediction related to responses aimed at maximizing reproductive success is a straightforward enough prediction. The difficult part is to understand how CA works in specific contexts (e.g., Paleolithic dieting, low carbohydrate dieting, calorie restriction), and what we can do to take advantage (or work around) CA mechanisms. For that we need a good understanding of evolution, some common sense, and also good empirical research.

One thing we can say with some degree of certainty is that CA leads to short-term and long-term responses, and that those are likely to be different from one another. The reason is that a particular diet and lifestyle change affected the reproductive success of our Paleolithic ancestors in different ways, depending on whether it was a short-term or long-term change. The same is true for CA responses at different stages of one’s life, such as adolescence and middle age; they are also different.

This is the main reason why many diets that work very well in the beginning (e.g., first months) frequently cease to work as well after a while (e.g., a year).

Also, CA leads to psychological responses, which is one of the key reasons why most diets fail. Without a change in mindset, more often than not one tends to return to old habits. Hunger is not only a physiological response; it is also a psychological response, and the psychological part can be a lot stronger than the physiological one.

It is because of CA that a one-month moderately severe calorie restriction period (e.g., 30% below basal metabolic rate) will lead to significant body fat loss, as the body produces hormonal responses to several stimuli (e.g., glycogen depletion) in a compensatory way, but still “assuming” that liberal amounts of food will soon be available. Do that for one year and the body will respond differently, “assuming” that food scarcity is no longer short-term and thus that it requires different, and possibly more drastic, responses.

Among other things, prolonged severe calorie restriction will lead to a significant decrease in metabolism, loss of libido, loss of morale, and physical as well as mental fatigue. It will make the body hold on to its fat reserves a lot more greedily, and induce a number of psychological responses to force us to devour anything in sight. In several people it will induce psychosis. The results of prolonged starvation experiments, such as the Biosphere 2 experiments, are very instructive in this respect.

It is because of CA that resistance exercise leads to muscle gain. Muscle gain is actually a body’s response to reasonable levels of anaerobic exercise. The exercise itself leads to muscle damage, and short-term muscle loss. The gain comes after the exercise, in the following hours and days (and with proper nutrition), as the body tries to repair the muscle damage. Here the body “assumes” that the level of exertion that caused it will continue in the near future.

If you increase the effort (by increasing resistance or repetitions, within a certain range) at each workout session, the body will be constantly adapting, up to a limit. If there is no increase, adaptation will stop; it will even regress if exercise stops altogether. Do too much resistance training (e.g., multiple workout sessions everyday), and the body will react differently. Among other things, it will create deterrents in the form of pain (through inflammation), physical and mental fatigue, and even psychological aversion to resistance exercise.

CA processes have a powerful effect on one’s body, and even on one’s mind!

References:

Kock, N. (2001). Compensatory Adaptation to a Lean Medium: An Action Research Investigation of Electronic Communication in Process Improvement Groups. IEEE Transactions on Professional Communication, 44(4), 267-285.

Kock, N. (2002). Compensatory Adaptation: Understanding How Obstacles Can Lead to Success. Infinity Publishing, Haverford, PA. (Additional link.)

Kock, N. (2005). Compensatory adaptation to media obstacles: An experimental study of process redesign dyads. Information Resources Management Journal, 18(2), 41-67.

Kock, N. (2007). Media Naturalness and Compensatory Encoding: The Burden of Electronic Media Obstacles is on Senders. Decision Support Systems, 44(1), 175-187.

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Exercise and blood glucose levels: Insulin and glucose responses to exercise

2011-12-06T01:36:00.053-08:00

Weird News : The notion that exercise reduces blood glucose levels is widespread. That notion is largely incorrect. Exercise appears to have a positive effect on insulin sensitivity in the long term, but also increases blood glucose levels in the short term. That is, exercise, while it is happening, leads to an increase in circulating blood glucose. In normoglycemic individuals, that increase is fairly small compared to the increase caused by consumption of carbohydrate-rich foods, particularly foods rich in refined carbohydrates and sugars.

The figure below, from the excellent book by Wilmore and colleagues (2007), shows the variation of blood insulin and glucose in response to an endurance exercise session. The exercise session’s intensity was at 65 to 70 percent of the individuals’ maximal capacity (i.e., their VO2 max). The session lasted 180 minutes, or 3 hours. The full reference to the

Power napping, stress management, and jet lag

2011-12-06T01:36:00.052-08:00

Weird News : Many animals take naps during the day. Our ancestors probably napped during the day too. They certainly did not spend as many hours as we do under mental stress. In fact, the lives of our Paleolithic ancestors would look quite boring to a modern human. Mental stress can be seen as a modern poison. We need antidotes for that poison. Power napping seems to be one of them.

(Source: Squidoo.com)

Power napping is a topic that I have done some research on, but unfortunately I do not have access to the references right now. I am posting this from Europe, where I arrived a few days ago. Thus I am labeling this post “my experience”. Hopefully I will be able to write a more research-heavy post on this topic in the near future. I am pretty sure that there is a strong connection between power napping and stress hormones. Maybe our regular and knowledgeable commenters can help me fill this gap in their comments on this post.

Surprisingly, jet lag has been only very minor this time for me. The time difference between most of Europe and Texas is about 8 hours, which makes adaptation very difficult, especially coming over to Europe. In spite of that, I slept during much of my first night here. The same happened in the following nights, even though I can feel that my body is still not fully adapted to the new time zone.

How come? I am all but sure that this is a direct result of my recent experience with power napping.

I have been practicing power napping for several months now. Usually in the middle of the afternoon, between 3 and 4 pm, I lie down for about 15 minutes in a sleeping position on a yoga mat. I use a pillow for the head. I close my eyes and try to clear my mind of all thoughts, focusing on my breathing, as in meditation. When I feel like I am about to enter deep sleep, I get up. This usually happens 15 minutes after I lie down. The sign that I am about to enter deep sleep is having incoherent thoughts, like in dreaming. Often I have muscle jerks, called hypnic jerks, which are perfectly normal. Hypnic jerks are also a sign that it is time for me to get up.

After getting up I always feel very refreshed and relaxed. My ability to do intellectual work is also significantly improved. If I make the mistake of going further, and actually entering a deep sleep stage, I get up feeling very groggy and sleepy. So the power nap has to end at around 15 minutes for me. For most people, this time ranges from 10 to 20 minutes. It seems that once one enters a deep sleep phase, it is better to then sleep for at least a few hours.

Power napping is not as easy as it sounds. If one cannot enter a state of meditation at the beginning, the onset of sleep does not happen. You have to be able to clear your mind of thoughts. Focusing on your breathing helps. Interestingly, once you become experienced at power napping, you can then induce actual sleep in almost any situation – e.g., on a flight or when you arrive in another country. That is what happened with me during this trip. Even though I have been waking up at night since I arrived in Europe, I have been managing to go right back to sleep. Previously, in other trips to Europe, I would be unable to go back to sleep after I woke up in the middle of the night.

Power napping seems to also be an effective tool for stress management. In our busy modern lives, with many daily stressors, it is common for significant mental stress to set in around 8 to 9 hours after one wakes up in the morning. For someone waking up at 7 am, this will be about 3 to 4 pm in the afternoon. Power napping, when done right, seems to be very effective at relieving that type of stress.

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Our body’s priority is preventing hypoglycemia, not hyperglycemia

2011-12-06T01:36:00.051-08:00

Weird News : An adult human has about 5 l of blood in circulation. Considering a blood glucose concentration of 100 mg/dl, this translates into a total amount of glucose in the blood of about 5 g (5 l x 0.1 g / 0.1 l). That is approximately a teaspoon of glucose. If a person’s blood glucose goes down to about half of that, the person will enter a state of hypoglycemia. Severe and/or prolonged hypoglycemia can cause seizures, comma, and death.

In other words, the disappearance of about 2.5 g of glucose from the blood will lead to hypoglycemia. Since 2.5 g of glucose yields about 10 calories, it should be easy to see that it does not take much to make someone hypoglycemic in the absence of compensatory mechanisms. An adult will consume on average 6 to 9 times as many calories just sitting quietly, and a proportion of those calories will come from glucose.

While hypoglycemia has severe negative health effects in the short term, including the most severe of all - death, hyperglycemia has primarily long-term negative health effects. Given this, it is no surprise that our body’s priority is to prevent hypoglycemia, not hyperglycemia.

The figure below, from the outstanding book by Brooks and colleagues (2005), shows two graphs. The graph at the top shows the variation of arterial glucose in response to exercise. The graph at the bottom shows the variation of whole-body and muscle glucose uptake, plus hepatic glucose production, in response to exercise. The full reference to the Brooks and colleagues book is at the end of this post.

Note how blood glucose increases dramatically as the intensity of the exercise session increases, which means that muscle tissue consumption of glucose is also increasing. This is particularly noticeable as arm exercise is added to leg exercise, bringing the exercise intensity to 82 percent of maximal capacity. This blood glucose elevation is similar to the elevation one would normally see in response to all-out sprinting and weight training within the anaerobic range (with enough weight to allow only 6 to 12 repetitions).

The dashed line at the bottom graph represents whole-body glucose uptake, including what would be necessary for the body to function in the absence of exercise. This is why whole-body glucose uptake is higher than muscle glucose uptake induced by exercise; the latter was measured through a glucose tracing method. The top of the error bars above the points on the dashed line represent hepatic glucose production, which is always ahead of whole-body glucose uptake. This is our body doing what it needs to do to prevent hypoglycemia.

One point that is important to make here is that at the beginning of an anaerobic exercise session muscle uses up primarily local glycogen stores (not liver glycogen stores), and can completely deplete them in a very localized fashion. Muscle glycogen stores add up to 500 g, but intense exercise depletes glycogen stores locally, only within the muscles being used. Still, muscle glycogen use generates lactate as a byproduct, which is then used by the liver to produce glucose (gluconeogenesis) to prevent hypoglycemia. The liver also

Free running and primal workouts: Both look awesome, and dangerous

2011-12-06T01:36:00.050-08:00

Weird News : The other day I showed a YouTube MovNat video clip to one of my sons, noting the serious fitness of Erwan Le Corre. I also noted that the stunts were somewhat dangerous, and that they tried to replicate some of the movements that our Paleolithic ancestors had to do on a regular basis. That is, those movements are part of what one could call a primal workout.

My son looked at me and laughed, as if asking me if I was really being serious. Why? Well, he is into breakdancing (a.k.a. b-boying), and also does a bit of something called "free running". If you don’t know what free running is, take a look at this Wikipedia article.

Here are a couple of YouTube video clips on free running: clip 1, and clip 2. The moves do look a lot more hardcore than the ones on the MovNat video clip. (The reason for my son's reaction.) But, to be fair, the environments and goals are different. And, in terms of danger, some of these free running moves are really at the high end of the scale.

And, if you are interested, here are a couple of instructional YouTube video clips prepared by my sons: this one by my oldest, and this by my second oldest. (We have four children.) I have been telling them to be careful with those “airchairs” – the moves where all the weight is placed on one hand. It just looks like too much pressure on the joints of one single arm.

Two of the things that I like the most about primal workouts like the MovNat ones are the variety of movements, and the proximity to nature. Those two elements can potentially help with sticking to an exercise program in the long run, which is what matters most. Most people get very bored of exercising after a few months. Free running seems to be more competitive, and more dangerous.

Both free running and primal workouts are practiced by some people as their main form of exercise. In those cases, they appear to lead to body types that are similar to those of the hunter-gatherers on this post. I cannot help but notice that those body types are more like that of a sprinter than that of a typical bodybuilder.

The feats that those body types enable are feats of relative, not absolute, strength. This makes sense, as our Paleolithic ancestors were too smart to hunt prey or fight off predators (or even each other) with their bare hands. Spears and stones were formidable weapons. Paleolithic ancestors who were very adept at using weapons would probably be like skilled gunfighters in the American Old West – menacing, with the advantage of being able to use their skills to feed themselves and others.

Being lean, strong, and agile – all at the same time – arguably was one of the keys to survival in the Paleolithic.

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The China Study: With a large enough sample, anything is significant

2011-12-06T01:36:00.049-08:00

Weird News : There have been many references recently on diet and lifestyle blogs to the China Study. Except that they are not really references to the China Study, but to a blog post by Denise Minger. This post is indeed excellent, and brilliant, and likely to keep Denise from “having a life” for a while. That it caused so much interest is a testament to the effect that a single brilliant post can have on the Internet. Many thought that the Internet would lead to a depersonalization and de-individualization of communication. Yet, most people are referring to Denise’s post, rather than to “a great post written by someone on a blog.”

Anyway, I will not repeat what Denise said on her post here. My goal with this post is bit more general, and applies to the interpretation of quantitative research results in general. This post is a warning regarding “large” studies. These are studies whose main claim to credibility is that they are based on a very large sample. The China Study is a good example. It prominently claims to have covered 2,400 counties and 880 million people.

There are many different statistical analysis techniques that are used in quantitative analyses of associations between variables, where the variables can be things like dietary intakes of certain nutrients and incidence of disease. Generally speaking, statistical analyses yield two main types of results: (a) coefficients of association (e.g., correlations); and (b) P values (which are measures of statistical significance). Of course there is much more to statistical analyses than these two types of numbers, but these two are usually the most important ones when it comes to creating or testing a hypothesis. The P values, in particular, are often used as a basis for claims of significant associations. P values lower than 0.05 are normally considered low enough to support those claims.

In analyses of pairs of variables (known as "univariate", or "bivariate" analyses), the coefficients of association give an idea of how strongly the variables are associated. The higher these coefficients are, the more strongly the variables are associated. The P values tell us whether an apparent association is likely to be due to chance, given a particular sample. For example, if a P value is 0.05, or 5 percent, the likelihood that the related association is due to chance is 5 percent. Some people like to say that, in a case like this, one has a 95 percent confidence that the association is real.

One thing that many people do not realize is that P values are very sensitive to sample size. For example, with a sample of 50 individuals, a correlation of 0.6 may be statistically significant at the 0.01 level (i.e., its P value is lower than 0.01). With a sample of 50,000 individuals, a much smaller correlation of 0.06 may be statistically significant at the same level. Both correlations may be used by a researcher to claim that there is a significant association between two variables, even though the first association (correlation = 0.6) is 10 times stronger than the second (correlation = 0.06).

So, with very large samples, cherry-picking results is very easy. It has been argued sometimes that this is not technically lying, since one is reporting associations that are indeed statistically significant. But, by doing this, one may be omitting other associations, which may be much stronger. This type of practice is sometimes referred to as “lying with statistics”.

With a large enough sample one can easily “show” that drinking water causes cancer.

This is why I often like to see the coefficients of association together with the P values. For simple variable-pair correlations, I generally consider a correlation around 0.3 to be indicative of a reasonable association, and a correlation at or above 0.6 to be indicative of a strong association. These conclusions are regardless of P value. Whether these would indicate causation is another story; one has to use common sense and good theory.

If you take my weight from 1 to 20 years of age, and the price of gasoline in the US during that period, you will find that they are highly correlated. But common sense tells me that there is no causation whatsoever between these two variables.

There are a number of other issues to consider which I am not going to cover here. For example, relationships may be nonlinear, and standard correlation-based analyses are “blind” to nonlinearity. This is true even for advanced correlation-based statistical techniques such as multiple regression analysis, which control for competing effects of several variables on one main dependent variable. Ignoring nonlinearity may lead to misleading interpretations of associations, such as the association between total cholesterol and cardiovascular disease.

Note that this post is not an indictment of quantitative analyses in general. I am not saying “ignore numbers”. Denise’s blog post in fact uses careful quantitative analyses, with good ol’ common sense, to debunk several claims based on, well, quantitative analyses. If you are interested in this and other more advanced statistical analysis issues, I invite you to take a look at my other blog. It focuses on WarpPLS-based robust nonlinear data analysis.

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Subcutaneous versus visceral fat: How to tell the difference?

2011-12-06T01:36:00.048-08:00

Weird News : The photos below, from Wikipedia, show two patterns of abdominal fat deposition. The one on the left is predominantly of subcutaneous abdominal fat deposition. The one on the right is an example of visceral abdominal fat deposition, around internal organs, together with a significant amount of subcutaneous fat deposition as well.

Body fat is not an inert mass used only to store energy. Body fat can be seen as a “distributed organ”, as it secretes a number of hormones into the bloodstream. For example, it secretes leptin, which regulates hunger. It secretes adiponectin, which has many health-promoting properties. It also secretes tumor necrosis factor-alpha (more recently referred to as simply “tumor necrosis factor” in the medical literature), which promotes inflammation. Inflammation is necessary to repair damaged tissue and deal with pathogens, but too much of it does more harm than good.

How does one differentiate subcutaneous from visceral abdominal fat?

Subcutaneous abdominal fat shifts position more easily as one’s body moves. When one is standing, subcutaneous fat often tends to fold around the navel, creating a “mouth” shape. Subcutaneous fat is easier to hold in one’s hand, as shown on the left photo above. Because subcutaneous fat tends to “shift” more easily as one changes the position of the body, if you measure your waist circumference lying down and standing up, and the difference is large (a one-inch difference can be considered large), you probably have a significant amount of subcutaneous fat.

Waist circumference is a variable that reflects individual changes in body fat percentage fairly well. This is especially true as one becomes lean (e.g., around 14-17 percent or less of body fat for men, and 21-24 for women), because as that happens abdominal fat contributes to an increasingly higher proportion of total body fat. For people who are lean, a 1-inch reduction in waist circumference will frequently translate into a 2-3 percent reduction in body fat percentage. Having said that, waist circumference comparisons between individuals are often misleading. Waist-to-fat ratios tend to vary a lot among different individuals (like almost any trait). This means that someone with a 34-inch waist (measured at the navel) may have a lower body fat percentage than someone with a 33-inch waist.

Subcutaneous abdominal fat is hard to mobilize; that is, it is hard to burn through diet and exercise. This is why it is often called the “stubborn” abdominal fat. One reason for the difficulty in mobilizing subcutaneous abdominal fat is that the network of blood vessels is not as dense in the area where this type of fat occurs, as it is with visceral fat. Another reason, which is related to degree of vascularization, is that subcutaneous fat is farther away from the portal vein than visceral fat. As such, it has to travel a longer distance to reach the main “highway” that will take it to other tissues (e.g., muscle) for use as energy.

In terms of health, excess subcutaneous fat is not nearly as detrimental as excess visceral fat. Excess visceral fat typically happens together with excess subcutaneous fat; but not necessarily the other way around. For instance, sumo wrestlers frequently have excess subcutaneous fat, but little or no visceral fat. The more health-detrimental effect of excess visceral fat is probably related to its proximity to the portal vein, which amplifies the negative health effects of excessive pro-inflammatory hormone secretion. Those hormones reach a major transport “highway” rather quickly.

Even though excess subcutaneous body fat is more benign than excess visceral fat, excess body fat of any kind is unlikely to be health-promoting. From an evolutionary perspective, excess body fat impaired agile movement and decreased circulating adiponectin levels; the latter leading to a host of negative health effects. In modern humans, negative health effects may be much less pronounced with subcutaneous than visceral fat, but they will still occur.

Based on studies of isolated hunger-gatherers, it is reasonable to estimate “natural” body fat levels among our Stone Age ancestors, and thus optimal body fat levels in modern humans, to be around 6-13 percent in men and 14–20 percent in women.

If you think that being overweight probably protected some of our Stone Age ancestors during times of famine, here is one interesting factoid to consider. It will take over a month for a man weighing 150 lbs and with 10 percent body fat to die from starvation, and death will not be typically caused by too little body fat being left for use as a source of energy. In starvation, normally death will be caused by heart failure, as the body slowly breaks down muscle tissue (including heart muscle) to maintain blood glucose levels.

References:

Arner, P. (2005). Site differences in human subcutaneous adipose tissue metabolism in obesity. Aesthetic Plastic Surgery, 8(1), 13-17.

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

Fleck, S.J., & Kraemer, W.J. (2004). Designing resistance training programs. Champaign, IL: Human Kinetics.

Taubes, G. (2007). Good calories, bad calories: Challenging the conventional wisdom on diet, weight control, and disease. New York, NY: Alfred A. Knopf.

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My transformation: I cannot remember the last time I had a fever

2011-12-06T01:36:00.047-08:00

Weird News : The two photos below (click to enlarge) were taken 4 years apart. The one on the left was taken in 2006, when I weighed 210 lbs (95 kg). Since my height is 5 ft 8 in, at that weight I was an obese person, with over 30 percent body fat. The one on the right was taken in 2010, at a weight of 150 lbs (68 kg) and about 13 percent body fat. I think I am a bit closer to the camera on the right, so the photos are not exactly on the same scale.

My lipids improved from borderline bad to fairly good numbers, as one would expect, but the two main changes that I noticed were in terms of illnesses and energy levels. I have not had a fever in a long time. I simply cannot remember when it was the last time that I had to stay in bed because of an illness. I only remember that I was fat then. Also, I used to feel a lot more tired when I was fat. Now I seem to have a lot of energy, almost all the time.

In my estimation, I was obese or overweight for about 10 years, and was rather careless about it. A lot of that time I weighed in the 190s; with a peak weight of 210 lbs. Given that, I consider myself lucky not to have had major health problems by now, like diabetes or cancer. A friend of mine who is a doctor told me that I probably had some protection due to the fact that, when I was fat, I was fat everywhere. My legs, for example, were fat. So were my arms and face. In other words, I lot of the fat was subcutaneous, and reasonably distributed. In fact, most people do not believe me when I say that I weighed 210 lbs when that photo was taken in 2006; but maybe they are just trying to be nice.

If you are not obese, you should do everything you can to avoid reaching that point. Among other things, your chances of having cancer will skyrocket.

So, I lost a whopping 60 lbs (27 kg) over about 2-3 years. That is not so radical; about 1.6-2.5 lbs per month. There were plateaus with no weight loss, and even a few periods with weight gain. Perhaps because of that and the slow weight loss, I had none of the problems usually associated with body responses to severe calorie restriction, such as hypothyroidism. I remember a short period when I felt a little weak and miserable; I was doing exercise after long fasts (20 h or so), and not eating enough afterwards. I did that for a couple of weeks and decided against the idea.

There are no shortcuts with body fat loss, it seems. Push it too hard and the body will react; compensatory adaptation at work.

My weight has been stable, at around 150 lbs, for a little less than 2 years now.

What did I do to lose 60 lbs? I did a number of things at different points in time. I measured various variables (e.g., intake of macronutrients, weight, body fat, HDL cholesterol etc.) and calculated associations, using a prototype version of HealthCorrelator for Excel (HCE). Based on all that, I am pretty much convinced that the main factors were the following:

- Complete removal of foods rich in refined carbohydrates and sugars from my diet, plus almost complete removal of plant foods that I cannot eat raw. (I do cook some plant foods, but avoid the ones I cannot eat raw; with a few exceptions like sweet potato.) That excluded most seeds and grains from my diet, since they can only be eaten after cooking.

- Complete removal of vegetable oils rich in omega-6 fats from my diet. I cook primarily with butter and organic coconut oil. I occasionally use olive oil, often with water, for steam cooking.

- Consumption of plenty of animal products, with emphasis on eating the animal whole. All cooked. This includes small fish (sardines and smelts) eaten whole about twice a week, and offal (usually beef liver) about once or twice a week. I also eat eggs, about 3-5 per day.

- Practice of moderate exercise (2-3 sessions a week) with a focus on resistance training and high-intensity interval training (e.g., sprints).

- Adoption of more natural eating patterns; by eating more when I am hungry, usually on days I exercise, and less (including fasting) when I am not hungry. I estimate that this leads to a caloric surplus on days that I exercise, and a caloric deficit on days that I do not (without actually controlling caloric intake).

- A few minutes (15-20 min) of direct skin exposure to sunlight almost every day, when the sun is high, to get enough of the all-important vitamin D. This is pre-sunburn exposure, usually in my backyard. When traveling I try to find a place where people jog, and walk shirtless for 15-20 min.

- Stress management, including some meditation and power napping.

- Face-to-face social interaction, in addition to online interaction. Humans are social animals, and face-to-face social interaction contributes to promoting the right hormonal balance.

When I was fat, my appetite was a bit off. I was hungry at the wrong times, it seemed. Then slowly, after a few months eating essentially whole foods, my hunger seemed to start “acting normally”. That is, my hunger slowly fell into a pattern of increasing after physical exertion, and decreasing with rest. Protein and fat are satiating, but so seem to be fruits and vegetables. Never satiating for me were foods rich in refined carbohydrates and sugars – white bread, bagels, doughnuts, pasta etc.

Looking back, it almost seems too easy. Whole foods taste very good, especially if you are hungry.

But I will never want to each a peach after I have a doughnut. The peach will be tasteless!

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The China Study again: A multivariate analysis suggesting that schistosomiasis rules!

2011-12-06T01:36:00.046-08:00

Weird News : In the comments section of Denise Minger’s post on July 16, 2010, which discusses some of the data from the China Study (as a follow up to a previous post on the same topic), Denise herself posted the data she used in her analysis. This data is from the China Study. So I decided to take a look at that data and do a couple of multivariate analyzes with it using WarpPLS (warppls.com).

First I built a model that explores relationships with the goal of testing the assumption that the consumption of animal protein causes colorectal cancer, via an intermediate effect on total cholesterol. I built the model with various hypothesized associations to explore several relationships simultaneously, including some commonsense ones. Including commonsense relationships is usually a good idea in exploratory multivariate analyses.

The model is shown on the graph below, with the results. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) The arrows explore causative associations between variables. The variables are shown within ovals. The meaning of each variable is the following: aprotein = animal protein consumption; pprotein = plant protein consumption; cholest = total cholesterol; crcancer = colorectal cancer.

The path coefficients (indicated as beta coefficients) reflect the strength of the relationships; they are a bit like standard univariate (or Pearson) correlation coefficients, except that they take into consideration multivariate relationships (they control for competing effects on each variable). A negative beta means that the relationship is negative; i.e., an increase in a variable is associated with a decrease in the variable that it points to.

The P values indicate the statistical significance of the relationship; a P lower than 0.05 means a significant relationship (95 percent or higher likelihood that the relationship is real). The R-squared values reflect the percentage of explained variance for certain variables; the higher they are, the better the model fit with the data. Ignore the “(R)1i” below the variable names; it simply means that each of the variables is measured through a single indicator (or a single measure; that is, the variables are not latent variables).

I should note that the P values have been calculated using a nonparametric technique, a form of resampling called jackknifing, which does not require the assumption that the data is normally distributed to be met. This is good, because I checked the data, and it does not look like it is normally distributed. So what does the model above tell us? It tells us that:

- As animal protein consumption increases, colorectal cancer decreases, but not in a statistically significant way (beta=-0.13; P=0.11).

- As animal protein consumption increases, plant protein consumption decreases significantly (beta=-0.19; P<0.01). This is to be expected.

- As plant protein consumption increases, colorectal cancer increases significantly (beta=0.30; P=0.03). This is statistically significant because the P is lower than 0.05.

- As animal protein consumption increases, total cholesterol increases significantly (beta=0.20; P<0.01). No surprise here. And, by the way, the total cholesterol levels in this study are quite low; an overall increase in them would probably be healthy.

- As plant protein consumption increases, total cholesterol decreases significantly (beta=-0.23; P=0.02). No surprise here either, because plant protein consumption is negatively associated with animal protein consumption; and the latter tends to increase total cholesterol.

- As total cholesterol increases, colorectal cancer increases significantly (beta=0.45; P<0.01). Big surprise here!

Why the big surprise with the apparently strong relationship between total cholesterol and colorectal cancer? The reason is that it does not make sense, because animal protein consumption seems to increase total cholesterol (which we know it usually does), and yet animal protein consumption seems to decrease colorectal cancer.

When something like this happens in a multivariate analysis, it usually is due to the model not incorporating a variable that has important relationships with the other variables. In other words, the model is incomplete, hence the nonsensical results. As I said before

The China Study one more time: Are raw plant foods giving people cancer?

2011-12-06T01:36:00.045-08:00

Weird News : In this previous post I analyzed some data from the China Study that included counties where there were cases of schistosomiasis infection. Following one of Denise Minger’s suggestions, I removed all those counties from the data. I was left with 29 counties, a much smaller sample size. I then ran a multivariate analysis using WarpPLS (warppls.com), like in the previous post, but this time I used an algorithm that identifies nonlinear relationships between variables.

Below is the model with the results. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) As in the previous post, the arrows explore associations between variables. The variables are shown within ovals. The meaning of each variable is the following: aprotein = animal protein consumption; pprotein = plant protein consumption; cholest = total cholesterol; crcancer = colorectal cancer.

What is total cholesterol doing at the right part of the graph? It is there because I am analyzing the associations between animal protein and plant protein consumption with colorectal cancer, controlling for the possible confounding effect of total cholesterol.

I am not hypothesizing anything regarding total cholesterol, even though this variable is shown as pointing at colorectal cancer. I am just controlling for it. This is the type of thing one can do in multivariate analyzes. This is how you “control for the effect of a variable” in an analysis like this.

Since the sample is fairly small, we end up with insignificant beta coefficients that would normally be statistically significant with a larger sample. But it helps that we are using nonparametric statistics, because they are still robust in the presence of small samples, and deviations from normality. Also the nonlinear algorithm is more sensitive to relationships that do not fit a classic linear pattern. We can summarize the findings as follows:

- As animal protein consumption increases, plant protein consumption decreases significantly (beta=-0.36; P<0.01). This is to be expected and helpful in the analysis, as it differentiates somewhat animal from plant protein consumers. Those folks who got more of their protein from animal foods tended to get significantly less protein from plant foods.

- As animal protein consumption increases, colorectal cancer decreases, but not in a statistically significant way (beta=-0.31; P=0.10). The beta here is certainly high, and the likelihood that the relationship is real is 90 percent, even with such a small sample.

- As plant protein consumption increases, colorectal cancer increases significantly (beta=0.47; P<0.01). The small sample size was not enough to make this association insignificant. The reason is that the distribution pattern of the data here is very indicative of a real association, which is reflected in the low P value.

Remember, these results are not confounded by schistosomiasis infection, because we are only looking at counties where there were no cases of schistosomiasis infection. These results are not confounded by total cholesterol either, because we controlled for that possible confounding effect. Now, control variable or not, you would be correct to point out that the association between total cholesterol and colorectal cancer is high (beta=0.58; P=0.01). So let us take a look at the shape of that association:

Does this graph remind you of the one on this post; the one with several U curves? Yes. And why is that? Maybe it reflects a tendency among the folks who had low cholesterol to have more cancer because the body needs cholesterol to fight disease, and cancer is a disease. And maybe it reflects a tendency among the folks who have high total cholesterol to do so because total cholesterol (and particularly its main component, LDL cholesterol) is in part a marker of disease, and cancer is often a culmination of various metabolic disorders (e.g., the metabolic syndrome) that are nothing but one disease after another.

To believe that total cholesterol causes colorectal cancer is nonsensical because total cholesterol is generally increased by consumption of animal products, of which animal protein consumption is a proxy. (In this reduced dataset, the linear univariate correlation between animal protein consumption and total cholesterol is a significant and positive 0.36.) And animal protein consumption seems to be protective again colorectal cancer in this dataset (negative association on the model graph).

Now comes the part that I find the most ironic about this whole discussion in the blogosphere that has been going on recently about the China Study; and the answer to the question posed in the title of this post: Are raw plant foods giving people cancer? If you think that the answer is “yes”, think again. The variable that is strongly associated with colorectal cancer is plant protein consumption.

Do fruits, veggies, and other plant foods that can be consumed raw have a lot of protein?

With a few exceptions, like nuts, they do not. Most raw plant foods have trace amounts of protein, especially when compared with foods made from refined grains and seeds (e.g., wheat grains, soybean seeds). So the contribution of raw fruits and veggies in general could not have influenced much the variable plant protein consumption. To put this in perspective, the average plant protein consumption per day in this dataset was 63 g; even if they were eating 30 bananas a day, the study participants would not get half that much protein from bananas.

Refined foods made from grains and seeds are made from those plant parts that the plants absolutely do not “want” animals to eat. They are the plants’ “children” or “children’s nutritional reserves”, so to speak. This is why they are packed with nutrients, including protein and carbohydrates, but also often toxic and/or unpalatable to animals (including humans) when eaten raw.

But humans are so smart; they learned how to industrially refine grains and seeds for consumption. The resulting human-engineered products (usually engineered to sell as many units as possible, not to make you healthy) normally taste delicious, so you tend to eat a lot of them. They also tend to raise blood sugar to abnormally high levels, because industrial refining makes their high carbohydrate content easily digestible. Refined foods made from grains and seeds also tend to cause leaky gut problems, and autoimmune disorders like celiac disease. Yep, we humans are really smart.

Thanks again to Dr. Campbell and his colleagues for collecting and compiling the China Study data, and to Ms. Minger for making the data available in easily downloadable format and for doing some superb analyses herself.

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Too much complexity! I like the simplicity of Ricky’s Weather Forecasting Stone

2011-12-06T01:36:00.044-08:00

Weird News : Too much complexity in the last few posts and related comments: multivariate analyses, path coefficients, nonparametric statistics, competing and interaction effects, explained variance, plant protein and colorectal cancer, the China Study, raw plant foods possibly giving people cancer unless they don’t …

I like simplicity though, and so does my mentor. I really like the simplicity of Ricky’s Weather Forecasting Stone. (See photo below, from … I will tell you in the comments section. Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.)

Can you guess who the gentleman on the photo is?

A few hints. He is a widely read and very smart blogger. He likes to eat a lot of saturated fat, and yet is very lean. If you do not read his blog, you should. Reading his blog is like heavy resistance exercise, for the brain. It is not much unlike doing an IQ test with advanced biology and physiology material mixed in, and a lot of joking around.

Like heavy resistance exercise, reading his blog is hard, but you fell pretty good after doing it.

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

2011-12-06T01:36:00.043-08:00

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I am speculating here, but I think that in individuals without pituitary pathology, an involuntary decrease in endogenous GH secretion may actually be at the core of this compensatory adaptation mechanism. In these individuals, low GH levels may be an outcome, not a cause of problems. This would explain two apparently

The baffling rise in seasonal allergies: Global warming or obesity?

2011-12-06T01:36:00.042-08:00

Weird News : The July 26, 2010 issue of Fortune has an interesting set of graphs on page 14. It shows the rise of allergies in the USA, together with figures on lost productivity, doctor visits, and medical expenditures. (What would you expect? This is Fortune, and money matters.) It also shows some cool maps with allergen concentrations, and how they are likely to increase with global warming. (See below; click on it to enlarge; use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.)

The implication: A rise in global temperatures is causing an increase in allergy cases. Supposedly the spring season starts earlier, with more pollen being produced overall, and thus more allergy cases.

Really!?

I checked their numbers against population growth, because as the population of a country increases, so will the absolute number of allergy cases (as well as cancer cases, and cases of almost any disease). What is important is whether there has been an increase in allergy rates, or the percentage of the population suffering from allergies. Well, indeed, allergy rates have been increasing.

Now, I don’t know about your neck of the woods, but temperatures have been unusually low this year in South Texas. Global warming may be happening, but given recent fluctuations in temperature, I am not sure global warming explains the increases in allergy rates. Particularly the spike in allergy rates in 2010; this seems to be very unlikely to be caused by global warming.

And I have my own experience of going from looking like a seal to looking more like a human being. When I was a seal (i.e., looked like one), I used to have horrible seasonal pollen allergies. Then I lost 60 lbs, and my allergies diminished dramatically. Why? Body fat secretes a number of pro-inflammatory hormones (see, e.g., this post, and also

Cortisol, surprise-enhanced cognition, and flashbulb memories: Scaring people with a snake screen and getting a PhD for it!

2011-12-06T01:36:00.041-08:00

Weird News : Cortisol is a hormone that has a number of important functions. It gets us out of bed in the morning, it cranks up our metabolism in preparation for intense exercise, and it also helps us memorize things and even learn. Yes, it helps us learn. Memorization in particular, and cognition in general, would be significantly impaired without cortisol. When you are surprised, particularly with something unpleasant, cortisol levels increase and enhance cognition. This is in part what an interesting study suggests; a study in which I was involved. The study was properly “sanctified” by the academic peer-review process (Kock et al., 2009; full reference and link at the end of this post).

The main hypothesis tested through this study is also known as the “flashbulb memorization” hypothesis. Interestingly, up until this study was conducted no one seemed to have used evolution to provide a basis on which flashbulb memorization can be explained. The basic idea here is that enhanced cognition within the temporal vicinity of animal attacks (i.e., a few minutes before and after) allowed our hominid ancestors to better build and associate memories related to the animals and their typical habitat markers (e.g., vegetation, terrain, rock formations), which in turn increased their survival chances. Their survival chances increased because the memories helped them avoid a second encounter; if they survived the first, of course. And so flashbulb memorization evolved. (In fact, it might have evolved earlier than at the hominid stage, and it may also have evolved in other species.)

The study involved 186 student participants. The participants were asked to review web-based learning modules and subsequently take a test on what they had learned. Data from 6 learning modules in 2 experimental conditions were contrasted. In the treatment condition a web-based screen with a snake in attack position was used to surprise the participants; the snake screen was absent in the control condition. See schematic figure below (click on it to enlarge). The “surprise zone” in the figure comprises the modules immediately before and after the snake screen (modules 3 and 4); those are the modules in which higher scores were predicted.

The figure below (click on it to enlarge) shows a summary of the results. The top part of the figure shows the percentage differences between average scores obtained by participants in the treatment and control conditions. The bottom part of the figure shows the average scores obtained by participants in both conditions, as well as the scores that the participants would have obtained by chance. The chance scores would likely have been the ones obtained by the participants if their learning had been significantly impaired for any of the modules; this could have happened due to distraction, for example. As you can see, the scores for all modules are significantly higher than chance.

In summary, the participants who were surprised with the snake screen obtained significantly higher scores for the two modules immediately before (about 20 percent higher) and after (about 40 percent higher) the snake screen. The reason is that the surprise elicited by the snake screen increased cortisol levels, which in turn improved learning for modules 3 and 4. Adrenaline and noradrenaline (epinephrine and norepinephrine) may also be involved. This phenomenon is so odd that it seems to defy the laws of physics; note that Module 3 was reviewed before the snake screen. And, depending on the size of a test, this could have turned a “C” into an “A” grade!

Similarly, it is because of this action of cortisol that Americans reading this post, especially those who lived in the East Coast in 2001, remember vividly where they were, what they were doing, and who they were with, when they first heard about the September 11, 2001 Attacks. I was living in Philadelphia at the time, and I remember those details very vividly, even though the Attacks happened almost 10 years ago. That is one of the fascinating things that cortisol does; it instantaneously turns short-term contextual memories temporally associated with a surprise event (i.e., a few minutes before and after the event) into vivid long-term memories.

This study was part of the PhD research project of one of my former doctoral students, and now Dr. Ruth Chatelain-Jardon. Her PhD was granted in May 2010. She expanded the study through data collection in two different countries, and a wide range of analyses. (It is not that easy to get a PhD!) Her research provides solid evidence that flashbulb memorization is a real phenomenon, and also that it is a human universal. Thanks are also due to Dr. Jesus Carmona, another former doctoral student of mine who worked on a different PhD research project, but who also helped a lot with this project.

Reference:

Kock, N., Chatelain-Jardón, R., & Carmona, J. (2009). Scaring them into learning!? Using a snake screen to enhance the knowledge transfer effectiveness of a web interface. Decision Sciences Journal of Innovative Education, 7(2), 359-375.

: Nouvelles de Téléphone et Gadgets

Nonexercise activities like fidgeting may account for a 1,000 percent difference in body fat gain! NEAT eh?

2011-12-06T01:36:00.040-08:00

Weird News : Some studies become classics in their fields and yet are largely missed by the popular media. This seems to be what happened with a study by Levine and colleagues (1999; full reference and link at the end of this post), which looked at the role that nonexercise activity thermogenesis (NEAT) plays in fat gain suppression. Many thanks go to Lyle McDonald for posting on this.

You have probably seen on the web claims that overeating leads to fat loss, because overeating increases one’s basal metabolic rate. There are also claims that food has a powerful thermic effect, due to the energy needed for digestion, absorption and storage of nutrients; this is also claimed to lead to fat loss. There is some truth to these claims, but the related effects are very small compared with the effects of NEAT.

Ever wonder why there are some folks who seem to eat whatever they want, and never get fat? As it turns out, it may be primarily due to NEAT!

NEAT is associated with fidgeting, maintenance of posture, shifting position, pacing, and other involuntary light physical activities. The main finding of this study was that NEAT accounted for a massive amount of the difference in body fat gain among the participants in the study. The participants were 12 males and 4 females, ranging in age from 25 to 36 years. These healthy and lean participants were fed 1,000 kilocalories per day in excess of their weight-maintenance requirements, for a period of 8 weeks. See figure below; click on it to enlarge.

Fat gain varied more than 10-fold among the participants (or more than 1,000 percent), ranging from a gain of only 0.36 kg (0.79 lbs) to a gain of 4.23 kg (9.33 lbs). As you can see, NEAT explains a lot of the variance in the fat gain variable, which is indicated by the highly statistically significant negative correlation (-0.77). Its effect dwarfs those related to basal metabolic rate and food-induced thermogenesis, neither of which was statistically significant.

How can one use this finding in practice? This research indirectly suggests that moving often throughout the day may have a significant additive long term effect on fat gain suppression. It is reasonable to expect a similar effect on fat loss. And this effect may be stealthy enough to prevent the body from reacting to fat loss by significantly lowering its basal metabolic rate. (Yes, while the increase in basal metabolic rate is trivial in response to overfeeding, the decrease in this rate is nontrivial in response to underfeeding. Essentially the body is much more “concerned” about starving than fattening up.)

The bad news is that it is not easy to mimic the effects of NEAT through voluntary activities. The authors of the study estimated that the maximum increase in NEAT detected in the study (692 kcal/day) would be equivalent to a 15-minute walk every waking hour of every single day! (This other study focuses specifically on fidgeting.) Clearly NEAT has a powerful effect on weight loss, which is not easy to match with voluntary pacing, standing up etc. Moreover, females seem to benefit less from NEAT, because they seem to engage in fewer NEAT-related activities than men. The four lowest NEAT values in the study corresponded to the four female participants.

Nevertheless, if you have a desk job, like I do, you may want to stand up and pace for a few seconds every 30 minutes. You may also want to stand up while you talk on the phone. You may want to shift position from time to time; e.g., sitting at the edge of the chair for a few minutes every hour, without back support. And so on. These actions may take you a bit closer to the lifestyle of our Paleolithic ancestors, who were not sitting down motionless the whole day. Try also eating more like they did and, over a year, the results may be dramatic!

Reference:

James A. Levine, Norman L. Eberhardt, Michael D. Jensen (1999). Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science, 283(5399), 212-214.

: Nouvelles de Téléphone et Gadgets

The evolution of costly traits: Competing for women can be unhealthy for men

2011-12-06T01:36:00.039-08:00

Weird News : There are human traits that evolved in spite of being survival handicaps. These counterintuitive traits are often called costly traits, or Zahavian traits (in animal signaling contexts), in honor of the evolutionary biologist Amotz Zahavi (Zahavi & Zahavi, 1997). I have written a post about this type of traits, and also an academic article (Kock, 2009). The full references and links to these publications are at the end of this post.

The classic example of costly trait is the peacock’s train, which is used by males to signal health to females. (Figure below from: animals.howstuffworks.com.) The male peacock’s train (often incorrectly called “tail”) is a costly trait because it impairs the ability of a male to flee predators. It decreases a male’s survival success, even though it has a positive net effect on the male’s reproductive success (i.e., the number of offspring it generates). It is used in sexual selection; the females find big and brightly colored trains with many eye spots "sexy".

So costly traits exist in many species, including the human species, but we have not identified them all yet. The implication for human diet and lifestyle choices is that our ancestors might have evolved some habits that are bad for human survival, and moved away from others that are good for survival. And I am not only talking about survival among modern humans; I am talking about survival among our human ancestors too.

The simple reason for the existence of costly traits in humans is that evolution tends to maximize reproductive success, not survival, and that applies to all species. (Inclusive fitness theory goes a step further, placing the gene at the center of the selection process, but this is a topic for another post.) If that were not the case, rodent species, as well as other species that specialize in fast reproduction within relatively short life spans, would never have evolved.

Here is an interesting piece of news about research done at the University of Michigan. (I have met the lead researcher, Dan Kruger, a couple of times at HBES conferences. My impression is that his research is solid.) The research illustrates the evolution of costly traits, from a different angle. The researchers argue, based on the results of their investigation, that competing for a woman’s attention is generally bad for a man’s health!

Very romantic ...

References:

Kock, N. (2009). The evolution of costly traits through selection and the importance of oral speech in e-collaboration. Electronic Markets, 19(4), 221-232.

Zahavi, A. & Zahavi, A. (1997). The Handicap Principle: A missing piece of Darwin’s puzzle. Oxford, England: Oxford University Press.

: Nouvelles de Téléphone et Gadgets

The theory of supercompensation: Strength training frequency and muscle gain

2011-12-06T01:36:00.038-08:00

Weird News :

Moderate strength training has a number of health benefits, and is viewed by many as an important component of a natural lifestyle that approximates that of our Stone Age ancestors. It increases bone density, muscle mass, and improves a number of health markers. Done properly, it may decrease body fat percentage.

Generally one would expect some muscle gain as a result of strength training. Men seem to be keen on upper-body gains, while women appear to prefer lower-body gains. Yet, many people do strength training for years, and experience little or no muscle gain.

Paradoxically, those people experience major strength gains, both men and women, especially in the first few months after they start a strength training program. However, those gains are due primarily to neural adaptations, and come without any significant gain in muscle mass. This can be frustrating, especially for men. Most men are after some noticeable muscle gain as a result of strength training. (Whether that is healthy is another story, especially as one gets to extremes.)

After the initial adaptation period, of “beginner” gains, typically no strength gains occur without muscle gains.

The culprits for the lack of anabolic response are often believed to be low levels of circulating testosterone and other hormones that seem to interact with testosterone to promote muscle growth, such as growth hormone. This leads many to resort to anabolic steroids, which are drugs that mimic the effects of androgenic hormones, such as testosterone. These drugs usually increase muscle mass, but have a number of negative short-term and long-term side effects.

There seems to be a better, less harmful, solution to the lack of anabolic response. Through my research on compensatory adaptation I often noticed that, under the right circumstances, people would overcompensate for obstacles posed to them. Strength training is a form of obstacle, which should generate overcompensation under the right circumstances. From a biological perspective, one would expect a similar phenomenon; a natural solution to the lack of anabolic response.

This solution is predicted by a theory that also explains a lack of anabolic response to strength training, and that unfortunately does not get enough attention outside the academic research literature. It is the theory of supercompensation, which is discussed in some detail in several high-quality college textbooks on strength training. (Unlike popular self-help books, these textbooks summarize peer-reviewed academic research, and also provide the references that are summarized.) One example is the excellent book by Zatsiorsky & Kraemer (2006) on the science and practice of strength training.

The figure below, from Zatsiorsky & Kraemer (2006), shows what happens during and after a strength training session. The level of preparedness could be seen as the load in the session, which is proportional to: the number of exercise sets, the weight lifted (or resistance overcame) in each set, and the number of repetitions in each set. The restitution period is essentially the recovery period, which must include plenty of rest and proper nutrition.

Note that toward the end there is a sideways S-like curve with a first stretch above the horizontal line and another below the line. The first stretch is the supercompensation stretch; a window in time (e.g., a 20-hour period). The horizontal line represents the baseline load, which can be seen as the baseline strength of the individual prior to the exercise session. This is where things get tricky. If one exercises again within the supercompensation stretch, strength and muscle gains will likely happen. (Usually noticeable upper-body muscle gain happens in men, because of higher levels of testosterone and of other hormones that seem to interact with testosterone.) Exercising outside the supercompensation time window may lead to no gain, or even to some loss, of both strength and muscle.

Timing strength training sessions correctly can over time lead to significant gains in strength and muscle (see middle graph in the figure below, also from Zatsiorsky & Kraemer, 2006). For that to happen, one has not only to regularly “hit” the supercompensation time window, but also progressively increase load. This must happen for each muscle group. Strength and muscle gains will occur up to a point, a point of saturation, after which no further gains are possible. Men who reach that point will invariably look muscular, in a more or less “natural” way depending on supplements and other factors. Some people seem to gain strength and muscle very easily; they are often called mesomorphs. Others are hard gainers, sometimes referred to as endomorphs (who tend to be fatter) and ectomorphs (who tend to be skinnier).

It is not easy to identify the ideal recovery and supercompensation periods. They vary from person to person. They also vary depending on types of exercise, numbers of sets, and numbers of repetitions. Nutrition also plays a role, and so do rest and stress. From an evolutionary perspective, it would seem to make sense to work all major muscle groups on the same day, and then do the same workout after a certain recovery period. (Our Stone Age ancestors did not do isolation exercises, such as bicep curls.) But this will probably make you look more like a strong hunter-gatherer than a modern bodybuilder.

To identify the supercompensation time window, one could employ a trial-and-error approach, by trying to repeat the same workout after different recovery times. Based on the literature, it would make sense to start at the 48-hour period (one full day of rest between sessions), and then move back and forth from there. A sign that one is hitting the supercompensation time window is becoming a little stronger at each workout, by performing more repetitions with the same weight (e.g., 10, from 8 in the previous session). If that happens, the weight should be incrementally increased in successive sessions. Most studies suggest that the best range for muscle gain is that of 6 to 12 repetitions in each set.

The discussion above is not aimed at professional bodybuilders. There are a number of factors that can influence strength and muscle gain other than supercompensation. (Still, supercompensation seems to be a “biggie”.) Things get trickier over time with trained athletes, as returns on effort get progressively smaller. Even natural bodybuilders appear to benefit from different strategies at different levels of proficiency. For example, changing the workouts on a regular basis seems to be a good idea, and there is a science to doing that properly. See the “Interesting links” area of this web site for several more focused resources of strength training.

Reference:

Zatsiorsky, V., & Kraemer, W.J. (2006). Science and practice of strength training. Champaign, IL: Human Kinetics.

: Nouvelles de Téléphone et Gadgets

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

2011-12-06T01:36:00.037-08:00

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

Type 2 diabetes and the “tired pancreas” theory

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

Empirical evidence against the “tired pancreas” theory

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

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

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

More insulin may lead to abnormal fat metabolism in sedentary people

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

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

Excessive body fat gain, lipotoxicity, and type 2 diabetes

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

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

Heavy physical activity may significantly reduce heart disease deaths, especially after age 45

2011-12-06T01:36:00.036-08:00

Weird News : The idea that heavy physical activity is a main trigger of heart attacks is widespread. Often endurance running and cardio-type activities are singled out. Some people refer to this as “death by running”. Others think that strength training has a higher lethal potential. We know based on the Oregon Sudden Unexpected Death Study that this is a myth.

Here is some evidence that heavy physical activity in fact has a significant protective effect. The graph below, from Brooks et al. (2005) shows the number of deaths from coronary heart disease, organized by age group, in longshoremen (dock workers). The shaded bars represent those whose level of activity at work was considered heavy. The unshaded bars represent those whose level of activity at work was considered moderate or light (essentially below the “heavy” level).

The data is based on an old and classic study of 6351 men, aged 35 to 74 years, who were followed either for 22 years, or to death, or to the age of 75. It shows a significant protective effect of heavy activity, especially after age 45. The numbers atop the unshaded bars reflect the relative risk of death from coronary heart disease in each age group. For example, in the age group 65-74, the risk among those not in the heavy activity group is 110 percent higher (2.1 times higher) than in the

How to lose fat and gain muscle at the same time? Strength training plus a mild caloric deficit

2011-12-06T01:36:00.035-08:00

Weird News : Ballor et al. (1996) conducted a classic and interesting study on body composition changes induced by aerobic and strength training. This study gets cited a lot, but apparently for the wrong reasons. One of these reasons can be gleaned from this sentence in the abstract:

Low omega-6 to omega-3 ratio: Grain-fed meats or industrial vegetable oils?

2011-12-06T01:36:00.034-08:00

Weird News : Just a little note on the use of language. Clearly there is no such a thing as grain-fed or grass-fed beef, because one does not feed beef anything. One feeds cattle grain or grass, and then the resulting beef is said to be “grain-fed” or “grass-fed”. It is a manner of speaking that facilitates discourse, which is why it is used here.

To compensate for this digression, let me show you a graph, which pretty much summarizes the "punch line" of this post. The graph below shows the omega-6 fat contents of 1 lb (454 g) of grain-fed beef and 1 tablespoon (roughly 14 g) of a typical industrial vegetable oil (safflower oil). As you can see, there is a lot more omega-6 in the much smaller amount of industrial vegetable oil. A gram-for-gram comparison would practically make the beef content bar disappear.

It has been estimated that our Paleolithic ancestors consumed a diet with an omega-6 to omega-3 ratio of about 1. While other estimates exist, the general consensus seems to be that that ratio was not much greater than 5. Western diets, in contrast, typically have omega-6 to omega-3 ratios of between 15 and 40. In some cases, the ratio is even higher.

Omega-6 fats are essential fats, meaning that they must be part of one’s diet. Fats make up about 60 percent of our brain. About 20 percent is made up of omega-6 and omega-3 fats. The primary omega-6 fat found in our brain is arachidonic acid, which is either synthesized by our body based on linoleic acid from plant foods or obtained directly from animal foods such as meat and eggs. The predominant omega 3 fat found in our brain is docosahexaenoic acid (DHA), of which certain types of fish and algae are rich sources.

Inflammation is an important process in the human body, without which wounds would never heal. Incidentally, muscle gain would not occur without inflammation either. Strength training causes muscle damage and inflammation, after which recovery leads to muscle gain. Omega-6 fats play an important role in inflammation. Generally, they are pro-inflammatory.

Too much inflammation, particularly in a chronic fashion, is believed to be very detrimental to our health. A very high omega-6 to omega-3 ratio seems to cause excessive and chronic inflammation. The reason is that omega-3 fats are generally anti-inflammatory, counteracting the pro-inflammatory action of omega-6 fats. Over time, a very high omega-6 to omega-3 ratio is believed to cause a number of Western diseases. Among them are cardiovascular complications, cancer, and various autoimmune diseases.

So, should you worry about too much omega-6 from grain-fed meats?

If you think that the answer is “yes”, consider this. Apparently the (arguably) longest-living group in the world, the non-Westernized Okinawans, consume plenty of pork. Pork is a staple of their traditional diet. It is true that the average cut will have an omega-6 to omega-3 ratio of more than 7, which is not very favorable. Pork in general, whether grain-fed or not, is relatively high in omega-6 fats. As a side note, pork is not a good source of linoleic acid (found in plants), even though it is a rich source of arachidonic acid, the omega-6 fat synthesized from linoleic acid by various animals.

It is difficult to estimate the exact amounts of omega-6 and omega-3 fats from grain-fed cuts of meat; different sources provide different estimates. Here are some reasonable estimates based on various sources, including Nutritiondata.com. A typical 100 g portion of grain-fed pork should contain about 690 mg of omega-6 fats, and 120 mg of omega-3 fats. A typical 100 g portion of grain-fed beef should have about 234 mg of omega-6 fats, and 12 mg of omega-3 fats. It does not take that much omega-3 to counterbalance the omega-6 obtained from grain-fed pork or beef, even if one eats a lot of them. Two softgels of fish oil will normally contain about 720 mg of omega-3 fats (they will also come with 280 mg of omega-6 fats). Three sardines will have over 2 g of omega-3 fats, and less than 200 mg of omega-6 fats.

Industrial vegetable oils (made from, e.g., safflower seeds, soybean, and sunflower seeds) are very, very rich sources of omega-6 fats, in the form of linoleic acid. There is a lot more omega-6 in them than in grain-fed meats. One tablespoon of safflower oil contains over 10 g of omega-6 fats, in the form of linoleic acid, and virtually zero omega-3 fats. About 2 kg (4.4 lbs) of grain-fed pork, and 5 kg (11 lbs) of grain-fed beef will give you that much omega-6; but they will also come with omega-3.

How much fish oil does one need to neutralize 10 g of pure omega-6 fats? A lot! And there is a problem. Excessive fish oil consumption may be toxic to the liver.

If you cook with industrial vegetable oils rich in linoleic acid (this excludes olive and coconut oils), or eat out a lot in restaurants that use them (the vast majority), you will probably be consuming significantly more than 10 g of omega-6 fats per day. The likely negative health effects of eating grain-fed meats pales in comparison with the likely negative health effects of this much omega-6 fats from industrial vegetable oils.

You should reduce as much as possible your consumption of industrial vegetable oils rich in linoleic acid, as well as other products that use them (e.g., margarine). Keep in mind that industrial vegetable oils are in many, many industrialized foods; even canned sardines, if they are canned with soybean oil.

It is also advisable to couple this with moderate consumption of fish rich in omega-3, such as sardines and salmon. (See this post for a sardine recipe.) Taking large doses of fish oil every day may not be such a good idea.

Should you also consume only grass-fed meat? Do it if you can. But, if you cannot, maybe you shouldn’t worry too much about it. This also applies to eggs, dairy, and other animal products.

References:

Elliott, W.H., & Elliott, D.C. (2009). Biochemistry and molecular biology. New York: NY: Oxford University Press.

Ramsden, C.E., Faurot, K.R., Carrera-Bastos, P., Cordain, L., De Lorgeril, M., & Sperling (2009). Dietary fat quality and coronary heart disease prevention: A unified theory based on evolutionary, historical, global, and modern perspectives. Current Treatment Options in Cardiovascular Medicine, 11(4), 289-301.

Schmidt, M.A. (1997). Smart fats: How dietary fats and oils affect mental, physical and emotional intelligence. Berkeley, CA: North Atlantic Books.

: Nouvelles de Téléphone et Gadgets

The China Study II: Cholesterol seems to protect against cardiovascular disease

2011-12-06T01:36:00.033-08:00

Weird News : First of all, many thanks are due to Dr. Campbell and his collaborators for collecting and compiling the data used in this analysis. This data is from this site, created by those researchers

The China Study II: Wheat flour, rice, and cardiovascular disease

2011-12-06T01:36:00.032-08:00

Weird News : In my

Strong causation can exist without any correlation: The strange case of the chain smokers, and a note about diet

2011-12-06T01:36:00.031-08:00

Weird News : Researchers like to study samples of data and look for associations between variables. Often those associations are represented in the form of correlation coefficients, which go from -1 to 1. Another popular measure of association is the path coefficient, which usually has a narrower range of variation. What many researchers seem to forget is that the associations they find depend heavily on the sample they are looking at, and on the ranges of variation of the variables being analyzed.

A forgotten warning: Causation without correlation

Often those who conduct multivariate statistical analyses on data are unaware of certain limitations. Many times this is due to lack of familiarity with statistical tests. One warning we do see a lot though is: Correlation does not imply causation. This is, of course, absolutely true. If you take my weight from 1 to 20 years of age, and the price of gasoline in the US during that period, you will find that they are highly correlated. But common sense tells me that there is no causation whatsoever between these two variables.

So correlation does not imply causation alright, but there is another warning that is rarely seen: There can be strong causation without any correlation. Of course this can lead to even more bizarre conclusions than the “correlation does not imply causation” problem. If there is strong causation between variables B and Y, and it is not showing as a correlation, another variable A may “jump in” and “steal” that “unused correlation”; so to speak.

The chain smokers “study”

To illustrate this point, let us consider the following fictitious case, a study of “100 cities”. The study focuses on the effect of smoking and genes on lung cancer mortality. Smoking significantly increases the chances of dying from lung cancer; it is a very strong causative factor. Here are a few more details. Between 35 and 40 percent of the population are chain smokers. And there is a genotype (a set of genes), found in a small percentage of the population (around 7 percent), which is protective against lung cancer. All of those who are chain smokers die from lung cancer unless they die from other causes (e.g., accidents). Dying from other causes is a lot more common among those who have the protective genotype.

(I created this fictitious data with these associations in mind, using equations. I also added uncorrelated error into the equations, to make the data look a bit more realistic. For example, random deaths occurring early in life would reduce slightly any numeric association between chain smoking and cancer deaths in the sample of 100 cities.)

The table below shows part of the data, and gives an idea of the distribution of percentage of smokers (Smokers), percentage with the protective genotype (Pgenotype), and percentage of lung cancer deaths (MLCancer). (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) Each row corresponds to a city. The rest of the data, up to row 100, has a similar distribution.

The graphs below show the distribution of lung cancer deaths against: (a) the percentage of smokers, at the top; and (b) the percentage with the protective genotype, at the bottom. Correlations are shown at the top of each graph. (They can vary from -1 to 1. The closer they are to -1 or 1, the stronger is the association, negative or positive, between the variables.) The correlation between lung cancer deaths and percentage of smokers is slightly negative and statistically insignificant (-0.087). The correlation between lung cancer deaths and percentage with the protective genotype is negative, strong, and statistically significant (-0.613).

Even though smoking significantly increases the chances of dying from lung cancer, the correlations tell us otherwise. The correlations tell us that lung cancer does not seem to cause lung cancer deaths, and that having the protective genotype seems to significantly decrease cancer deaths. Why?

If there is no variation, there is no correlation

The reason is that the “researchers” collected data only about chain smokers. That is, the variable “Smokers” includes only chain smokers. If this was not a fictitious case, focusing the study on chain smokers could be seen as a clever strategy employed by researchers funded by tobacco companies. The researchers could say something like this: “We focused our analysis on those most likely to develop lung cancer.” Or, this could have been the result of plain stupidity when designing the research project.

By restricting their study to chain smokers the researchers dramatically reduced the variability in one particular variable: the extent to which the study participants smoked. Without variation, there can be no correlation. No matter what statistical test or software is used, no significant association will be found between lung cancer deaths and percentage of smokers based on this dataset. No matter what statistical test or software is used, a significant and strong association will be found between lung cancer deaths and percentage with the protective genotype.

Of course, this could lead to a very misleading conclusion. Smoking does not cause lung cancer; the real cause is genetic.

A note about diet

Consider the analogy between smoking and consumption of a particular food, and you will probably see what this means for the analysis of observational data regarding dietary choices and disease. This applies to almost any observational study, including the China Study. (Studies employing experimental control manipulations would presumably ensure enough variation in the variables studied.) In the China Study, data from dozens of counties were collected. One may find a significant association between consumption of food A and disease Y.

There may be a much stronger association between food B and disease Y, but that association may not show up in statistical analyses at all, simply because there is little variation in the data regarding consumption of food B. For example, all those sampled may have eaten food B; about the same amount. Or none. Or somewhere in between, within a rather small range of variation.

Statistical illiteracy, bad choices, and taxation

Statistics is a “necessary evil”. It is useful to go from small samples to large ones when we study any possible causal association. By doing so, one can find out whether an observed effect really applies to a larger percentage of the population, or is actually restricted to a small group of individuals. The problem is that we humans are very bad at inferring actual associations from simply looking at large tables with numbers. We need statistical tests for that.

However, ignorance about basic statistical phenomena, such as the one described here, can be costly. A group of people may eliminate food A from their diet based on coefficients of association resulting from what seem to be very clever analyses, replacing it with food B. The problem is that food B may be equally harmful, or even more harmful. And, that effect may not show up on statistical analyses unless they have enough variation in the consumption of food B.

Readers of this blog may wonder why we explicitly use terms like “suggests” when we refer to a relationship that is suggested by a significant coefficient of association (e.g., a linear correlation). This is why, among other reasons.

One does not have to be a mathematician to understand basic statistical concepts. And doing so can be very helpful in one’s life in general, not only in diet and lifestyle decisions. Even in simple choices, such as what to be on. We are always betting on something. For example, any investment is essentially a bet. Some outcomes are much more probable than others.

Once I had an interesting conversation with a high-level officer of a state government. I was part of a consulting team working on an information technology project. We were talking about the state lottery, which was a big source of revenue for the state, comparing it with state taxes. He told me something to this effect:

Our lottery is essentially a tax on the statistically illiterate.

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Low nonexercise activity thermogenesis: Uncooperative genes or comfy furniture?

2011-12-06T01:36:00.030-08:00

Weird News : The degree of nonexercise activity thermogenesis (NEAT) seems to a major factor influencing the amount of fat gained or lost by an individual. It also seems to be strongly influenced by genetics, because NEAT is largely due to involuntary activities like fidgeting.

But why should this be?

The degree to which different individuals will develop diseases of civilization in response to consumption of refined carbohydrate-rich foods can also be seen as influenced by genetics. After all, there are many people who eat those foods and are thin and healthy, and that appears to be in part a family trait. But whether we consume those products or not is largely within our control.

So, it is quite possible that NEAT is influenced by genetics, but the fact that NEAT is low in so many people should be a red flag. In the same way that the fact that so many people who eat refined carbohydrate-rich foods are obese should be a red flag. Moreover, modern isolated hunter-gatherers tend to have low levels of body fat. Given the importance of NEAT for body fat regulation, it is not unreasonable to assume that NEAT is elevated in hunter-gatherers, compared to modern urbanites. Hunter-gatherers live more like our Paleolithic ancestors than modern urbanites.

True genetic diseases, caused by recent harmful mutations, are usually rare. If low NEAT were truly a genetic “disease”, those with low NEAT should be a small minority. That is not the case. It is more likely that the low NEAT that we see in modern urbanites is due to a maladaptation of our Stone Age body to modern life, in the same way that our Stone Age body is maladapted to the consumption of foods rich in refined grains and seeds.

What could have increased NEAT among our Paleolithic ancestors, and among modern isolated hunter-gatherers?

One thing that comes to mind is lack of comfortable furniture, particularly comfortable chairs (photo below from: prlog.org). It is quite possible that our Paleolithic ancestors invented some rudimentary forms of furniture, but they would have been much less comfortable than modern furniture used in most offices and homes. The padding of comfy office chairs is not very easy to replicate with stones, leaves, wood, or even animal hides. You need engineering to design it; you need industry to produce that kind of thing.

I have been doing a little experiment with myself, where I do things that force me to sit tall and stand while working in my office, instead of sitting back and “relaxing”. Things like putting a pillow on the chair so that I cannot rest my back on it, or placing my computer on an elevated surface so that I am forced to work while standing up. I tend to move a lot more when I do those things, and the movement is largely involuntary. These are small but constant movements, a bit like fidgeting. (It would be interesting to tape myself and actually quantify the amount of movement.)

It seems that one can induce an increase in NEAT, which is largely due to involuntary activities, by doing some voluntary things like placing a pillow on a chair or working while standing up.

Is it possible that the unnaturalness of comfy furniture, and particularly of comfy chairs, is contributing (together with other factors) to not only making us fat but also having low-back problems?

Both obesity and low-back problems are widespread among modern urbanites. Yet, from an evolutionary perspective, they should not be. They likely impaired survival success among our ancestors, and thus impaired their reproductive success. Evolution “gets angry” at these things; over time it wipes them out. In my reading of studies of hunter-gatherers, I don’t recall a single instance in which obesity and low-back problems were described as being widespread.

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Income, obesity, and heart disease in US states

2011-12-06T01:36:00.029-08:00

Weird News : The figure below combines data on median income by state (bottom-left and top-right), as well as a plot of heart disease death rates against percentage of population with body mass index (BMI) greater than 30 percent. The data are recent, and have been provided by CNN.com and creativeclass.com, respectively.

Heart disease deaths and obesity are strongly associated with each other, and both are inversely associated with median income. US states with lower median income tend to have generally higher rates of obesity and heart disease deaths.

The reasons are probably many, complex, and closely interconnected. Low income is usually associated with high rates of stress, depression, smoking, alcoholism, and poor nutrition. Compounding the problem, these are normally associated with consumption of cheap, addictive, highly refined foods.

Interestingly, this is primarily an urban phenomenon. If you were to use hunter-gatherers as your data sources, you would probably see the opposite relationship. For example, non-westernized hunter-gatherers have no income (at least not in the “normal” sense), but typically have a lower incidence of obesity and heart disease than mildly westernized ones. The latter have some income.

Tragically, the first few generations of fully westernized hunter-gatherers usually find themselves in the worst possible spot.

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The China Study II: Does calorie restriction increase longevity?

2011-12-06T01:36:00.028-08:00

Weird News : The idea that calorie restriction extends human life comes largely from studies of other species. The most relevant of those studies have been conducted with primates, where it

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

2011-12-06T01:36:00.027-08:00

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

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

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

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

How can that be?

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

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

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

Grains and seeds are not.

References

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

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

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Slow-cooked meat: Round steak, not grilled, but slow-cooked in a frying pan

2011-12-06T01:36:00.026-08:00

Weird News : I am yet to be convinced that grilled meat is truly unhealthy in the absence of leaky gut problems. I am referring here to high heat cooking-induced Maillard reactions and the resulting advanced glycation endproducts (AGEs). If you are interested, see this post and the comments under it, where I looked into some references provided by an anonymous commenter. In short, I am more concerned about endogenous (i.e., inside the body) formation of AGEs than with exogenous (e.g., dietary) intake.

Still, the other day I had to improvise when cooking meat, and used a cooking method that is considered by many to be fairly healthy – slow-cooking at a low temperature. I seasoned a few pieces of beef tenderloin (filet mignon) for the grill, but it started raining, so I decided to slow-cook them in a frying pan with water and some olive oil. After about 1 hour of slow-cooking, and somewhat to my surprise, they tasted more delicious than grilled!

I have since been using this method more and more, with all types of cuts of meat. It is great for round steak and top sirloin, for example, as well as cuts that come with bone. The pieces of meat come off the bone very easily, are soft, and taste great. So does much of the marrow. You also end up with a delicious sauce. Almost any cut of beef end up very soft when slow-cooked, even cuts that would normally come out from a grill a bit hard. Below is a simple recipe, for round steak (a.k.a. eye round).

- Prepare some dry seasoning powder by mixing sea salt, black pepper, dried garlic bits, chili powder, and a small amount of cayenne pepper.
- Season the round steak pieces at least 2 hours prior to placing them in the pan.
- Add a bit of water and olive oil to one or more frying pans. Two frying pans may be needed, depending on their size and the amount of meat.
- Place the round steak pieces in the frying pan, and add more water, almost to the point of covering them.
- Cook on low fire covered

The amounts of water, carbohydrates, fat, and protein lost during a 30-day fast

2011-12-06T01:36:00.025-08:00

Weird News : When it comes to losing fat and maintaining muscle, at the same time, there are no shortcuts. The process generally has to be slow to be healthy. When one loses a lot of weight in a few days, most of what is being lost is water, followed by carbohydrates. (Carbohydrates are stored as liver and muscle glycogen.) Smaller amounts of fat and protein are also lost. The figure below, from Wilmore et al. (2007), shows the weights in grams of stored water, carbohydrates (glycogen), fat, and protein lost during a 30-day water fast.

On the first few days of the fast a massive amount of water is lost, even though drinking water is allowed in this type of fast. A significant amount of glycogen is lost as well. This is no surprise. About 2.6 g of water are lost for each 1 g of glycogen lost. That is, water is stored by the body proportionally to the amount of glycogen stored. People who do strength training on a regular basis tend to store more glycogen, particular in muscle tissue; this is a compensatory adaptation. Those folks also tend to store more water.

Not many people will try a 30-day fast. Still, the figure above has implications for almost everybody.

One implication is that if you use a bioimpedance scale to measure your body fat, you can bet that it will give you fairly misleading results if your glycogen stores are depleted. Your body fat percentage will be overestimated, because water and glycogen are lean body mass. This will happen with low carbohydrate dieters who regularly engage in intense physical exercise, aerobic or anaerobic. The physical exercise will deplete glycogen stores, which will typically not be fully replenished due to the low intake of carbohydrates.

Light endurance exercise (e.g., walking) is normally easier to maintain with a depleted “glycogen tank” than strength training, because light endurance exercise relies heavily on fat oxidation. It uses glycogen, but more slowly. Strength training, on the other hand, relies much more heavily on glycogen while it is being conducted (significant fat oxidation occurs after the exercise session), and is difficult to do effectively with a depleted “glycogen tank”.

Strength training practitioners often will feel fatigued, and will probably be unable to generate supercompensation, if their “glycogen tank” is constantly depleted. Still, compensatory adaptation can work its “magic” if one persists, and lead to long term adaptations that make athletes rely much more heavily on fat than the average person as a fuel for strength training and other types of anaerobic exercise. Some people seem to be naturally more likely to achieve this type of compensatory adaptation; others may never do so, no matter how hard they try.

Another implication is that you should not worry about short-term weight variations if your focus is on losing body fat. Losing stored water and glycogen may give you an illusion of body fat loss, but it will be only that – an illusion. You may recall this post, where body fat loss coupled with muscle gain led to some weight gain and yet

Amino acids in skeletal muscle: Are protein supplements as good as advertised?

2011-12-06T01:36:00.024-08:00

Weird News : When protein-rich foods, like meat, are ingested they are first broken down into peptides through digestion. As digestion continues, peptides are broken down into amino acids, which then enter circulation, becoming part of the blood plasma. They are then either incorporated into various tissues, such as skeletal muscle, or used for other purposes (e.g., oxidation and glucose generation). The table below shows the amino acid composition of blood plasma and skeletal muscle. It was taken from Brooks et al. (2005), and published originally in a classic 1974 article by Bergström and colleagues. Essential amino acids, shown at the bottom of the table, are those that have to be consumed through the diet. The human body cannot synthesize them. (Tyrosine is essential in children; in adults tryptophan is essential.)

The data is from 18 young and healthy individuals (16 males and 2 females) after an overnight fast. The gradient is a measure that contrasts the concentration of an amino acid in muscle against its concentration in blood plasma. Amino acids are transported into muscle cells by amino acid transporters, such as the vesicular glutamate transporter 1 (VGLUT1). Transporters exist because without them a substance’s gradient higher or lower than 1 would induce diffusion through cell membranes; that is, without transporters anything would enter or leave cells.

Research suggests that muscle uptake of amino acids is positively correlated with the concentration of the amino acids in plasma (as well as the level of activity of transporters) and that this effect is negatively moderated by the gradient. This is especially true after strength training, when protein synthesis is greatly enhanced. In other words, if the plasma concentration of an amino acid such as alanine is high, muscle uptake will be increased (with the proper stimulus; e.g., strength training). But if a lot of alanine is already present in muscle cells when compared to plasma (which is normally the case, since alanine’s 7.3 gradient is relatively high), more plasma alanine will be needed to increase muscle uptake.

The amino acid makeup of skeletal muscle is a product of evolutionary forces, which largely operated on our Paleolithic ancestors. Those ancestors obtained their protein primarily from meat, eggs, vegetables, fruits, and nuts. Vegetables and fruits today are generally poor sources of protein; that was probably the case in the Paleolithic as well. Also, only when very young our Paleolithic ancestors obtained their protein from human milk. It is very unlikely that they drank the milk of other animals. Still, many people today possess genetic adaptations that enable them to consume milk (and dairy products in general) effectively due to a more recent (Neolithic) ancestral heritage. A food-related trait can evolve very fast – e.g., in a few hundred years.

One implication of all of this is that protein supplements in general may not be better sources of amino acids than natural protein-rich foods, such as meat or eggs. Supplements may provide more of certain amino acids than others sources, but given the amino acid makeup of skeletal muscle, a supplemental overload of a particular amino acid is unlikely to be particularly healthy. That overload may induce an unnatural increase in amino acid oxidation, or an abnormal generation of glucose through gluconeogenesis. Depending on one’s overall diet, those may in turn lead to elevated blood glucose levels and/or a caloric surplus. The final outcome may be body fat gain.

Another implication is that man-made foods that claim to be high in protein, and that are thus advertised as muscle growth supplements, may actually be poor sources of those amino acids whose concentration in muscle are highest. (You need to check the label for the amino acid composition, and trust the manufacturer.) Moreover, if they are sources of nonessential amino acids, they may overload your body if you consume a balanced diet. Interestingly, nonessential amino acids are synthesized from carbon sources. A good source of carbon is glucose.

Among the essential amino acids are a group called branched-chain amino acids (BCAA) – leucine, isoleucine, and valine. Much is made of these amino acids, but their concentration in muscle in adults is not that high. That is, they do not contribute significantly as building blocks to protein synthesis in skeletal muscle. What makes BCAAs somewhat unique is that they are highly ketogenic, and somewhat glucogenic (via gluconeogenesis). They also lead to insulin spikes. Ingestion of BCAAs increases the blood concentration of two of the three human ketone bodies (acetone and acetoacetate). Ketosis is both protein and glycogen sparing (but gluconeogenesis is not), which is among the reasons why ketosis is significantly induced by exercise (blood ketones concentration is much more elevated after exercise than after a 20 h fast). This is probably why some exercise physiologists and personal trainers recommend consumption of BCAAs immediately prior to or during anaerobic exercise.

Why do carnivores often consume prey animals whole? (Consumption of eggs is not the same, but similar, because an egg is the starting point for the development of a whole animal.) Carnivores consume prey animals whole arguably because prey animals have those tissues (muscle, organ etc. tissues) that carnivores also have, in roughly the same amounts. Prey animals that are herbivores do all the work of converting their own prey (plants) to tissues that they share with carnivores. Carnivores benefit from that work, paying back herbivores by placing selective pressures on them that are health-promoting at the population level. (Carnivores usually target those prey animals that show signs of weakness or disease.)

Supplements would be truly natural if they provided nutrients that mimicked eating an animal whole. Most supplements do not get even close to doing that; and this includes protein supplements.

Reference

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

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High-heat cooking will AGE you, if you eat food deep-fried with industrial vegetable oils

2011-12-06T01:36:00.023-08:00

Weird News : As I said before on this blog, I am yet to be convinced that grilled meat is truly unhealthy in the absence of leaky gut problems. I am referring here to high heat cooking-induced Maillard reactions (browning) and the resulting advanced glycation endproducts (AGEs). Whenever you cook a food in high heat, to the point of browning it, you generate a Maillard reaction. Searing and roasting meat usually leads to that.

Elevated levels of serum AGEs presumably accelerate the aging process in humans. This is supported by research with uncontrolled diabetics, who seem to have elevated levels of serum AGEs. In fact, a widely used measure in the treatment of diabetes, the HbA1c (or percentage of glycated hemoglobin), is actually a measure of endogenous AGE formation. (Endogenous = generated by our own bodies.)

Still, evidence that a person with an uncompromised gut can cause serum levels of AGEs to go up significantly by eating AGEs is weak, and evidence that any related serum AGE increases lead the average person to develop health problems is pretty much nonexistent. The human body can handle AGEs, as long as their concentration is not too high. We cannot forget that a healthy HbA1c in humans is about 5 percent; meaning that AGEs are created and dealt with by our bodies. A healthy HbA1c in humans is not 0 percent.

Thanks again to Justin for sending me the full text version of the Birlouez-Aragon et al. (2010) article, which is partially reviewed here. See this post and the comments under it for some background on this discussion. The article is unequivocally titled: “A diet based on high-heat-treated foods promotes risk factors for diabetes mellitus and cardiovascular diseases.”

This article is recent, and has already been cited by news agencies and bloggers as providing “definitive” evidence that high-heat cooking is bad for one’s health. Interestingly, quite a few of those citations are in connection with high-heat cooking of meat, which is not even the focus of the article.

In fact, the Birlouez-Aragon et al. (2010) article provides no evidence that high-heat cooking of meat leads to AGEing in humans. If anything, the article points at the use of industrial vegetable oils for cooking as the main problem. And we know already that industrial vegetable oils are not healthy, whether you cook with them or drink them cold by the tablespoon.

But there are a number of good things about this article. For example, the authors summarize past research on AGEs. They focus on MRPs, which are “Maillard reaction products”. One of the summary statements supports what I have said on this blog before:

"The few human intervention trials […] that reported on health effects of dietary MRPs have all focused on patients with diabetes or renal failure."

That is, there is no evidence from human studies that dietary AGEs cause health problems outside the context of preexisting conditions that themselves seem to be associated with endogenous AGE production. To that I would add that gut permeability may also be a problem, as in celiacs ingesting large amounts of AGEs.

As you can see from the quote below, the authors decided to focus their investigation on a particular type of AGE, namely CML or carboxymethyllysine.

"...we decided to specifically quantify CML, as a well-accepted MRP indicator ..."

As I noted in my comments under this post (the oven roasted pork tenderloin post), one particular type of diet seems to lead to high serum CML levels – a vegetarian diet.

So let us see what the authors studied:

"... we conducted a randomized, crossover, intervention trial to clarify whether a habitual diet containing high-heat-treated foods, such as deep-fried potatoes, cookies, brown crusted bread, or fried meat, could promote risk factors of type 2 diabetes or cardiovascular diseases in healthy people."

Well, “deep-fried potatoes” is a red flag, don’t you think? They don’t say what oil was used for deep-frying, but I bet it was not coconut or olive oil. Cheap industrial vegetable oils (corn, safflower etc.) are the ones normally used (and re-used) for deep-frying. This is in part because these oils are cheap, and in part because they have high “smoke points” (the temperature at which the oil begins to generate smoke).

Let us see what else the authors say about the dietary conditions they compared:

"The STD was prepared by using conventional techniques such as grilling, frying, and roasting and contained industrial food known to be highly cooked, such as extruded corn flakes, coffee, dry cookies, and well-baked bread with brown crust. In contrast, the STMD comprised some raw food and foods that were cooked with steam techniques only. In addition, convenience products were chosen according to the minimal process applied (ie, steamed corn flakes, tea, sponge cakes, and mildly baked bread) ..."

The STD diet was the one with high-heat preparation of foods; in the STMD diet the foods were all steam-cooked at relatively low temperatures. Clearly these diets were mostly of plant-based foods, and of the unhealthy kind!

The following quote, from the results, pretty much tells us that the high omega-6 content of industrial oils used for deep frying was likely to be a major confounder, if not the main culprit:

"... substantial differences in the plasma fatty acid profile with higher plasma concentrations of long-chain omega-3 fatty acids […] and lower concentrations of omega-6 fatty acids […] were analyzed in the STMD group compared with in the STD group."

That is, the high-heat cooking group had higher plasma concentrations of omega-6 fats, which is what you would expect from a group consuming a large amount of industrial vegetable oils. One single tablespoon per day is already a large amount; these folks were

Your mind as an anabolic steroid

2011-12-06T01:36:00.022-08:00

Weird News : The figure below, taken from Wilmore et al. (2007), is based on a classic 1972 study conducted by Ariel and Saville. The study demonstrated the existence of what is referred to in exercise physiology as the “placebo effect on muscular strength gains”. The study had two stages. In the first stage, fifteen male university athletes completed a 7-week strength training program. Gains in strength occurred during this period, but were generally small as these were trained athletes.

In the second stage the same participants completed a 4-week strength training program, very much like the previous one (in the first stage). The difference was that

Human traits are distributed along bell curves: You need to know yourself, and HCE can help

2011-12-06T01:36:00.021-08:00

Weird News : Most human traits (e.g., body fat percentage, blood pressure, propensity toward depression) are influenced by our genes; some more than others. The vast majority of traits are also influenced by environmental factors, the “nurture” part of the “nature-nurture” equation. Very few traits are “innate”, such as blood type.

This means that manipulating environmental factors, such as diet and lifestyle, can strongly influence how the traits are finally expressed in humans. But each individual tends to respond differently to diet and lifestyle changes, because each individual is unique in terms of his or her combination of “nature” and “nurture”. Even identical twins are different in that respect.

When plotted, traits that are influenced by our genes are distributed along a bell-shaped curve. For example, a trait like body fat percentage, when measured in a population of 1000 individuals, will yield a distribution of values that will look like a bell-shaped distribution. This type of distribution is also known in statistics as a “normal” distribution.

Why is that?

The additive effect of genes and the bell curve

The reason is purely mathematical. A measurable trait, like body fat percentage, is usually influenced by several genes. (Sometimes individual genes have a very marked effect, as in genes that “switch on or off” other genes.) Those genes appear at random in a population, and their various combinations spread in response to selection pressures. Selection pressures usually cause a narrowing of the bell-shaped curve distributions of traits in populations.

The genes interact with environmental influences, which also have a certain degree of randomness. The result is a

HealthCorrelator for Excel 1.0 (HCE): Call for beta testers

2011-12-06T01:36:00.020-08:00

Weird News : This call is closed. Beta testing has been successfully completed. 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.

To download a free trial version – as well as get the User Manual, view demo YouTube videos, and download and try sample datasets – visit the HealthCorrelator.com web site.

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How lean should one be?

2011-12-06T01:36:00.019-08:00

Weird News : Loss of muscle mass is associated with aging. It is also associated with the metabolic syndrome, together with excessive body fat gain. It is safe to assume that having low muscle and high fat mass, at the same time, is undesirable.

The extreme opposite of that, achievable though natural means, would be to have as much muscle as possible and as low body fat as possible. People who achieve that extreme often look a bit like “buff skeletons”.

This post assumes that increasing muscle mass through strength training and proper nutrition is healthy. It looks into body fat levels, specifically how low body fat would have to be for health to be maximized.

I am happy to acknowledge that quite often I am working on other things and then become interested in a topic that is brought up by Richard Nikoley, and discussed by his readers (I am one of them). This post is a good example of that.

Obesity and the diseases of civilization

Obesity is strongly associated with the diseases of civilization, of which the prototypical example is perhaps type 2 diabetes. So much so that sometimes the impression one gets is that without first becoming obese, one cannot develop any of the diseases of civilization.

But this is not really true. For example, diabetes type 1 is also one of the diseases of civilization, and it often strikes thin people. Diabetes type 1 results from the destruction of the beta cells in the pancreas by a person’s own immune system. The beta cells in the pancreas produce insulin, which regulates blood glucose levels.

Still, obesity is undeniably a major risk factor for the diseases of civilization. It seems reasonable to want to move away from it. But how much? How lean should one be to be as healthy as possible? Given the ubiquity of U-curve relationships among health variables, there should be a limit below which health starts deteriorating.

Is the level of body fat of the gentleman on the photo below (from: ufcbettingtoday.com) low enough? His name is Fedor; more on him below. I tend to admire people who excel in narrow fields, be they intellectual or sport-related, even if I do not do anything remotely similar in my spare time. I admire Fedor.

Let us look at some research and anecdotal evidence to see if we can answer the question above.

The buff skeleton look is often perceived as somewhat unattractive

Being in the minority is not being wrong, but should make one think. Like Richard Nikoley’s, my own perception of the physique of men and women is that, the leaner they are, the better; as long as they also have a reasonable amount of muscle. That is, in my mind, the look of a stage-ready competitive natural bodybuilder is close to the healthiest look possible.

The majority’s opinion, however, seems different, at least anecdotally. The majority of women that I hear or read voicing their opinions on this matter seem to find the “buff skeleton” look somewhat unattractive, compared with a more average fit or athletic look. The same seems to be true for perceptions of males about females.

A little side note. From an evolutionary perspective, perceptions of ancestral women about men must have been much more important than perceptions of ancestral men about women. The reason is that the ancestral women were the ones applying sexual selection pressures in our ancestral past.

For the sake of discussion, let us define the buff skeleton look as one of a reasonably muscular person with a very low body fat percentage; pretty much only essential fat. That would be 10-13 percent for women, and 5-8 percent for men.

The average fit look would be 21-24 percent for women, and 14-17 percent for men. Somewhere in between, would be what we could call the athletic look, namely 14-20 percent for women, and 6-13 percent for men. These levels are exactly the ones posted on this Wikipedia article on body fat percentages, at the time of writing.

From an evolutionary perspective, attractiveness to members of the opposite sex should be correlated with health. Unless we are talking about a costly trait used in sexual selection by our ancestors; something analogous to the male peacock’s train.

But costly traits are usually ornamental, and are often perceived as attractive even in exaggerated forms. What prevents male peacock trains from becoming the size of a mountain is that they also impair survival. Otherwise they would keep growing. The peahens find them sexy.

Being ripped is not always associated with better athletic performance

Then there is the argument that if you carried some extra fat around the waist, then you would not be able to fight, hunt etc. as effectively as you could if you were living 500,000 years ago. Evolution does not “like” that, so it is an unnatural and maladaptive state achieved by modern humans.

Well, certainly the sport of mixed martial arts (MMA) is not the best point of comparison for Paleolithic life, but it is not such a bad model either. Look at this photo of Fedor Emelianenko (on the left, clearly not so lean) next to Andrei Arlovski (fairly lean). Fedor is also the one on the photo at the beginning of this post.

Fedor weighed about 220 lbs at 6’; Arlovski 250 lbs at 6’4’’. In fact, Arlovski is one of the leanest and most muscular MMA heavyweights, and also one of the most highly ranked. Now look at Fedor in action (see this YouTube video), including what happened when Fedor fought Arlovski, at around the 4:28 mark. Fedor won by knockout.

Both Fedor and Arlovski are heavyweights; which means that they do not have to “make weight”. That is, they do not have to lose weight to abide by the regulations of their weight category. Since both are professional MMA fighters, among the very best in the world, the weight at which they compete is generally the weight that is associated with their best performance.

Fedor was practically unbeaten until recently, even though he faced a very high level of competition. Before Fedor there was another professional fighter that many thought was from Russia, and who ruled the MMA heavyweight scene for a while. His name is Igor Vovchanchyn, and he is from the Ukraine. At 5’8’’ and 230 lbs in his prime, he was a bit chubby. This YouTube video shows him in action; and it is brutal.

A BMI of about 25 seems to be the healthiest for long-term survival

Then we have this post by Stargazey, a blogger who likes science. Toward the end the post she discusses a study suggesting that a body mass index (BMI) of about 25 seems to be the healthiest for long-term survival. That BMI is between normal weight and overweight. The study suggests that both being underweight or obese is unhealthy, in terms of long-term survival.

The BMI is calculated as an individual’s body weight divided by the square of the individual’s height. A limitation of its use here is that the

Pressure-cooked meat: Top sirloin

2011-12-06T01:36:00.018-08:00

Weird News : Pressure cooking relies on physics to take advantage of the high temperatures of liquids and vapors in a sealed container. The sealed container is the pressure-cooking pan. Since the sealed container does not allow liquids or vapors to escape, the pressure inside the container increases as heat is applied to the pan. This also significantly increases the temperature of the liquids and vapors inside the container, which speeds up cooking.

Pressure cooking is essentially a version of high-heat steaming. The food inside the cooker tends to be very evenly cooked. Pressure cooking is also considered to be one of the most effective cooking methods for killing food-born pathogens. Since high pressure reduces cooking time, pressure cooking is usually employed in industrial food processing.

When cooking meat, the amount of pressure used tends to affect amino-acid digestibility; more pressure decreases digestibility. High pressures in the cooker cause high temperatures. The content of some vitamins in meat and plant foods is also affected; they go down as pressure goes up. Home pressure cookers are usually set at 15 pounds per square inch (psi). Significant losses in amino-acid digestibility occur only at pressures of 30 psi or higher.

My wife and I have been pressure-cooking for quite some time. Below is a simple recipe, for top sirloin.

- Prepare some dry seasoning powder by mixing sea salt, garlic power, chili powder, and a small amount of cayenne pepper.
- Season the top sirloin pieces at least 2 hours prior to placing them in the pressure cooking pan.
- Place the top sirloin pieces in the pressure cooking pan, and add water, almost to the point of covering them.
- Cook on very low fire, after the right amount of pressure is achieved, for 1 hour. The point at which the right amount of pressure is obtained is signaled by the valve at the top of the pan making a whistle-like noise.

As with slow cooking in an open pan, the water around the cuts should slowly turn into a fatty and delicious sauce, which you can pour on the meat when serving, to add flavor. The photos below show the seasoned top sirloin pieces, the (old) pressure-cooking pan we use, and some cooked pieces ready to be eaten together with some boiled yam.

A 100 g portion will have about 30 g of protein. (That is a bit less than 4 oz, cooked.) The amount of fat will depend on how trimmed the cuts are. Like most beef cuts, the fat will be primarily saturated and monounsatured, with approximately equal amounts of each. It will provide good amounts of the following vitamins and minerals: iron, magnesium, niacin, phosphorus, potassium, zinc, selenium, vitamin B6, and vitamin B12.

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Strength training: A note about Scooby and comments by Anon

2011-12-06T01:36:00.017-08:00

Weird News : Let me start this post with a note about Scooby, who is a massive bodybuilder who has a great website with tips on how to exercise at home without getting injured. Scooby is probably as massive a bodybuilder as anyone can get naturally, and very lean. He says he is a natural bodybuilder, and I am inclined to believe him. His dietary advice is “old school” and would drive many of the readers of this blog crazy – e.g., plenty of grains, and six meals a day. But it obviously works for him. (As far as muscle gain is concerned, a lot of different approaches work. For some people, almost any reasonable approach will work; especially if they are young men with high testosterone levels.)

The text below is all from an anonymous commenter’s notes on this post discussing the theory of supercompensation. Many thanks to this person for the detailed and thoughtful comment, which is a good follow-up on the note above about Scooby. In fact I thought that the comment might have been from Scooby; but I don’t think so. My additions are within “[ ]”. While the comment is there under the previous post for everyone to see, I thought that it deserved a separate post.

***

I love this subject [i.e., strength training]. No shortages of opinions backed by research with the one disconcerting detail that they don't agree.

First one opening general statement. If there was one right way we'd all know it by now and we'd all be doing it. People's bodies are different and what motivates them is different. (Motivation matters as a variable.)

My view on one set vs. three is based on understanding what you're measuring and what you're after in a training result.

Most studies look at one rep max strength gains as the metric but three sets [of repetitions] improves strength/endurance. People need strength/endurance more typically than they need maximal strength in their daily living. The question here becomes what is your goal?

The next thing I look at in training is neural adaptation. Not from the point of view of simple muscle strength gain but from the point of view of coordinated muscle function, again, something that is transferable to real life. When you exercise the brain is always learning what it is you are asking it to do. What you need to ask yourself is how well does this exercise correlate with a real life requirements.

[This topic needs a separate post, but one can reasonably argue that your brain works a lot harder during a one-hour strength training session than during a one-hour session in which you are solving a difficult mathematical problem.]

To this end single legged squats are vastly superior to double legged squats. They invoke balance and provoke the activation of not only the primary movers but the stabilization muscles as well. The brain is acquiring a functional skill in activating all these muscles in proper harmony and improving balance.

I also like walking lunges at the climbing wall in the gym (when not in use, of course) as the instability of the soft foam at the base of the wall gives an excellent boost to the basic skill by ramping up the important balance/stabilization component (vestibular/stabilization muscles). The stabilization muscles protect joints (inner unit vs. outer unit).

The balance and single leg components also increase core activation naturally. (See single legged squat and quadratus lumborum for instance.) [For more on the quadratus lumborum muscle, see here.]

Both [of] these exercises can be done with dumbbells for increased strength[;] and though leg exercises strictly speaking, they ramp up the core/full body aspect with weights in hand.

I do multiple sets, am 59 years old and am stronger now than I have ever been (I have hit personal bests in just the last month) and have been exercising for decades. I vary my rep ranges between six and fifteen (but not limited to just those two extremes). My total exercise volume is between two and three hours a week.

Because I have been at this a long time I have learned to read my broad cycles. I push during the peak periods and back off during the valleys. I also adjust to good days and bad days within the broader cycle.

It is complex but natural movements with high neural skill components and complete muscle activation patterns that have moved me into peak condition while keeping me from injury.

I do not exercise to failure but stay in good form for all reps. I avoid full range of motion because it is a distortion of natural movement. Full range of motion with high loads in particular tends to damage joints.

Natural, functional strength is more complex than the simple study designs typically seen in the literature.

Hopefully these things that I have learned through many years of experimentation will be of interest to you, Ned, and your readers, and will foster some experimentation of your own.

Anonymous

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What is a reasonable vitamin D level?

2011-12-06T01:36:00.016-08:00

Weird News : The figure and table below are from Vieth (1999); one of the most widely cited articles on vitamin D. The figure shows the gradual increase in blood concentrations of 25-Hydroxyvitamin, or 25(OH)D, following the start of daily vitamin D3 supplementation of 10,000 IU/day. The table shows the average levels for people living and/or working in sun-rich environments; vitamin D3 is produced by the skin based on sun exposure.

25(OH)D is also referred to as calcidiol. It is a pre-hormone that is produced by the liver based on vitamin D3. To convert from nmol/L to ng/mL, divide by 2.496. The figure suggests that levels start to plateau at around 1 month after the beginning of supplementation, reaching a point of saturation after 2-3 months. Without supplementation or sunlight exposure, levels should go down at a comparable rate. The maximum average level shown on the table is 163 nmol/L (65 ng/mL), and refers to a sample of lifeguards.

From the figure we can infer that people on average will plateau at approximately 130 nmol/L, after months of 10,000 IU/d supplementation. That is 52 ng/mL. Assuming a normal distribution with a standard deviation of about 20 percent of the range of average levels, we can expect about 68 percent of the population to be in the 42 to 63 ng/mL range.

This might be the range most of us should expect to be in at an intake of 10,000 IU/d. This is the equivalent to the body’s own natural production through sun exposure.

Approximately 32 percent of the population can be expected to be outside this range. A person who is two standard deviations (SDs) above the mean (i.e., average) would be at around 73 ng/mL. Three SDs above the mean would be 83 ng/mL. Two SDs below the mean would be 31 ng/mL.

There are other factors that may affect levels. For example, being overweight tends to reduce them. Excess cortisol production, from stress, may also reduce them.

Supplementing beyond 10,000 IU/d to reach levels much higher than those in the range of 42 to 63 ng/mL may not be optimal. Interestingly, one cannot overdose through sun exposure, and the idea that people do not produce vitamin D3 after 40 years of age is a myth.

One would be taking in about 14,000 IU/d of vitamin D3 by combining sun exposure with a supplemental dose of 4,000 IU/d. Clear signs of toxicity may not occur until one reaches 50,000 IU/d. Still, one may develop other complications, such as kidney stones, at levels significantly above 10,000 IU/d.

See this post by Chris Masterjohn, which makes a different argument, but with somewhat similar conclusions. Chris points out that there is a point of saturation above which the liver is unable to properly hydroxylate vitamin D3 to produce 25(OH)D.

How likely it is that a person will develop complications like kidney stones at levels above 10,000 IU/d, and what the danger threshold level could be, are hard to guess. Kidney stone incidence is a sensitive measure of possible problems; but it is, by itself, an unreliable measure. The reason is that it is caused by factors that are correlated with high levels of vitamin D, where those levels may not be the problem.

There is some evidence that kidney stones are associated with living in sunny regions. This is not, in my view, due to high levels of vitamin D3 production from sunlight. Kidney stones are also associated with chronic dehydration, and populations living in sunny regions may be at a higher than average risk of chronic dehydration. This is particularly true for sunny regions that are also very hot and/or dry.

Reference

Vieth, R. (1999). Vitamin D supplementation, 25-hydroxyvitamin D concentrations, and safety. American Journal of Clinical Nutrition, 69(5), 842-856.

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Maknig to mayn tipos? Myabe ur teh boz

2011-12-06T01:36:00.015-08:00

Weird News : Undoubtedly one of the big differences between life today and in our Paleolithic past is the level of stress that modern humans face on a daily basis. Much stress happens at work, which is very different from what our Paleolithic ancestors would call work. Modern office work, in particular, would probably be seen as a form of slavery by our Paleolithic ancestors.

Some recent research suggests that organizational power distance is a big factor in work-related stress. Power distance is essentially the degree to which bosses and subordinates accept wide differences in organizational power between them (Hofstede, 2001).

(Source: talentedapps.wordpress.com)

I have been studying the topic of information overload for a while. It is a fascinating topic. People who experience it have the impression that they have more information to process than they can handle. They also experience significant stress as a result of it, and both the quality of their work and their productivity goes down.

Recently some colleagues and I conducted a study that included employees from companies in New Zealand, Spain, and the USA (Kock, Del Aguila-Obra & Padilla-Meléndez, 2009). These are countries whose organizations typically display significant differences in power distance. We found something unexpected. Information overload was much more strongly associated with power distance than with the actual amount of information employees had to process on a daily basis.

While looking for explanations to this paradoxical finding, I recalled an interview I gave way back in 2001 to the Philadelphia Inquirer, commenting on research by Dr. David A. Owens. His research uncovered an interesting phenomenon. The higher up in the organizational pecking order one was, the less the person was concerned about typos on emails to subordinates.

There is also some cool research by Carlson & Davis (1998) suggesting that bosses tend to pick the communication media that are the most convenient for them, and don’t care much about convenience for the subordinates. One example would be calling a subordinate on the phone to assign a task, and then demanding a detailed follow-up report by email.

As a side note, writing a reasonably sized email takes a lot longer than conveying the same ideas over the phone or face-to-face (Kock, 2005). To be more precise, it takes about 10 times longer when the word count is over 250 and the ideas being conveyed are somewhat complex. For very short messages, a written medium like email is fairly convenient, and the amount of time to convey ideas may be even shorter than by using the phone or doing it face-to-face.

So a picture started to emerge. Bosses choose the communication media that are convenient for them when dealing with subordinates. If the media are written, they don’t care about typos at all. The subordinates use the media that are imposed on them, and if the media are written they certainly don’t want something with typos coming from them to reach their bosses. It would make them look bad.

The final result is this. Subordinates experience significant information overload, particularly in high power distance organizations. They also experience significant stress. Work quality and productivity goes down, and they get even more stressed. They get fat, or sickly thin. Their health deteriorates. Eventually they get fired, which doesn’t help a bit.

What should you do, if you are not the boss? Here are some suggestions:

- Try to tactfully avoid letting communication media being imposed on you all the time by your boss (and others). Explicitly state, in a polite way, the media that would be most convenient for you in various circusmtances, both as a receiver and sender. Generally, media that support oral speech are better for discussing complex ideas. Written media are better for short exchanges. Want an evolutionary reason for that? As you wish: Kock (2004).

- Discuss the ideas in this post with your boss; assuming that the person cares. Perhaps there is something that can be done to reduce power distance, for example. Making the work environment more democratic seems to help in some cases.

- And ... dot’n wrory soo mach aobut tipos ... which could be extrapolated to: don’t sweat the small stuff. Most bosses really care about results, and will gladly take an email with some typos telling them that a new customer signed a contract. They will not be as happy with an email telling them the opposite, no matter how well written it is.

Otherwise, your organizational demise may come sooner than you think.

References

Carlson, P.J., & Davis, G.B. (1998). An investigation of media selection among directors and managers: From "self" to "other" orientation. MIS Quarterly, 22(3), 335-362.

Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions, and organizations across nations. Thousand Oaks, CA: Sage.

Kock, N. (2004). The psychobiological model: Towards a new theory of computer-mediated communication based on Darwinian evolution. Organization Science, 15(3), 327-348.

Kock, N. (2005). Business process improvement through e-collaboration: Knowledge sharing through the use of virtual groups. Hershey, PA: Idea Group Publishing.

Kock, N., Del Aguila-Obra, A.R., & Padilla-Meléndez, A. (2009). The information overload paradox: A structural equation modeling analysis of data from New Zealand, Spain and the U.S.A. Journal of Global Information Management, 17(3), 1-17.

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Nuts by numbers: Should you eat them, and how much?

2011-12-06T01:36:00.014-08:00

Weird News : Nuts are generally seen as good sources of protein and magnesium. The latter plays a number of roles in the human body, and is considered critical for bone health. Nuts are also believed to be good sources of vitamin E. While there is a lot of debate about vitamin E’s role in health, it is considered by many to be a powerful antioxidant. Other than in nuts, vitamin E is not easily found in foods other than seeds and seed oils.

Some of the foods that we call nuts are actually seeds; others are legumes. For simplification, in this post I am calling nuts those foods that are generally protected by shells (some harder than others). This protective layer is what makes most people call them nuts.

Let us see how different nuts stack up against each other in terms of key nutrients. The quantities listed below are per 1 oz (28 g), and are based on data from Nutritiondata.com. All are raw. Roasting tends to reduce the vitamin content of nuts, often by half, and has little effect on the mineral content. Protein and fat content are also reduced, but not as much as the vitamin content.

These two figures show the protein, fat, and carbohydrate content of nuts (on the left); and the omega-6 and omega-3 fat content (on the right).

When we talk about nuts, walnuts are frequently presented in a very positive light. The reason normally given is that walnuts have a high omega-3 content; the plant form of omega-3, alpha-linolenic acid (ALA). That is true. But look at the large amount of omega-6 in walnuts. The difference between the omega-6 and omega-3 content in walnuts is about 8 g! And this is in only 1 oz of walnuts. That is 8 g of pro-inflammatory omega-6 fats to be “neutralized”. It would take many fish oil softgels to achieve that.

Walnuts should be eaten in moderation. Most studies looking at the health effects of nuts, including walnuts, show positive results in short-term interventions. But they usually involve moderate consumption, often of 1 oz per day. Eat several ounces of walnuts every day, and you are entering industrial see oil territory in terms of omega-6 fats consumption. Maybe other nutrients in walnuts have protective effects, but still, this looks like dangerous territory; “diseases of civilization” territory.

A side note. Focusing too much on the omega-6 to omega-3 ratio of individual foods can be quite misleading. The reason is that a food with a very small amount of omega-6 (e.g., 50 mg) but close zero omega-3 will have a very high ratio. (Any number divided by zero yields infinity.) Yet, that food will contribute little omega-6 to a person’s diet. It is the ratio at the end of the day that matters, when all foods that have been eaten are considered.

The figures below show the magnesium content of nuts (on the left); and the vitamin E content (on the right).

Let us say that you are looking for the best combination of protein, magnesium, and vitamin E. And you also want to limit your intake of omega-6 fats, which is a very wise thing to do. Then what is the best choice? It looks like it is almonds. And even they should be eaten in small amounts, as 1 oz has more than 3 g of omega-6 fats.

Macadamia nuts don’t have much omega-6; their fats are mostly monounsaturated, which are very good. Their protein to fat ratio is very low, and they don’t have much magnesium or vitamin E. Coconuts (i.e., their meat) have mostly medium-chain saturated fats, which are also very good. Coconuts have little protein, magnesium, and vitamin E. If you want to increase your intake of healthy fats, both macadamia nuts and coconuts are good choices, with macadamia nuts providing about 3 times more fat.

There are many other dietary sources of magnesium around. In fact, magnesium is found in many foods. Examples are, in approximate descending order of content: salmon, spinach, sardine, cod, halibut, banana, white potato, sweet potato, beef, chicken, pork, liver, and cabbage. This is by no means a comprehensive list.

As for vitamin E, it likes to hide in seeds. While it may be a powerful antioxidant, I wonder whether Mother Nature really had it “in mind” as she tinkered with our DNA for the last few million years.

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38 g of sardines or 2 fish oil softgels? Let us look at the numbers

2011-12-06T01:36:00.013-08:00

Weird News : The bar chart below shows the fat content of 1 sardine (38 g) canned in tomato sauce, and 2 fish oil softgels of the Nature Made brand. (The sardine is about 1/3 of the content of a typical can, and the data is from Nutritiondata.com. The two softgels are listed as the “serving size” on the Nature Made bottle.) Both the sardine and softgels have some vegetable oil added; presumably to increase their vitamin E content and form a more stable oil mix. This chart is a good reminder that looking at actual numbers can be quite instructive sometimes. Even though the chart focuses on fat content, it is worth noting that the 38 g sardine also contains 8 g of high quality protein.

If your goal with the fish oil is to “neutralize” the omega-6 fat content of your diet, which is most people’s main goal, you should consider this. A rough measure of the omega-6 neutralization “power” of a food portion is, by definition, its omega-3 minus omega-6 content. For the 1 canned sardine, this difference is 596 mg; for the 2 fish oil softgels, 440 mg. The reason is that the two softgels have more omega-6 than the sardine.

In case you are wondering, the canning process does not seem to have much of an effect on the nutrient composition of the sardine. There is some research suggesting that adding vegetable oil (e.g., soy) helps preserve the omega-3 content during the canning process. There is also research suggesting that not much is lost even without any vegetable oil being added.

Fish oil softgels, when taken in moderation (e.g., two of the type discussed in this post, per day), are probably okay as “neutralizers” of omega-6 fats in the diet, and sources of a minimum amount of omega-3 fats for those who do not like seafood. For those who can consume 1 canned sardine per day, which is only 1/3 of a typical can of sardines, the sardine is not only a more effective source of omega-3, but also a good source of protein and many other nutrients.

As far as balancing dietary omega-6 fats is concerned, you are much better off reducing your consumption of foods rich in omega-6 fats in the first place. Apparently nothing beats avoiding industrial seed oils in that respect. It is also advisable to eat certain types of nuts with high omega-6 content, like walnuts, in moderation.

Both omega-6 and omega-3 fats are essential; they must be part of one’s diet. The actual minimum required amounts are fairly small, probably much lower than the officially recommended amounts. Chances are they would be met by anyone on a balanced diet of whole foods. Too much of either type of fat in synthetic or industrialized form can cause problems. A couple of instructive posts on this topic are this post by Chris Masterjohn, and this one by Chris Kresser.

Even if you don’t like canned sardines, it is not much harder to gulp down 38 g of sardines than it is to gulp down 2 fish oil softgels. You can get the fish oil for $12 per bottle with 300 softgels; or 8 cents per serving. You can get a can of sardines for 50 cents; which gives 16.6 cents per serving. The sardine is twice as expensive, but carries a lot more nutritional value.

You can also buy wild caught sardines, like I do. I also eat canned sardines. Wild caught sardines cost about $2 per lb, and are among the least expensive fish variety. They are not difficult to prepare; see this post for a recipe.

I don’t know how many sardines go into the industrial process of making 2 fish oil softgels, but I suspect that it is more than one. So it is also probably more ecologically sound to eat the sardine.

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

2011-12-06T01:36:00.012-08:00

Weird News : 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.

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

2011-12-06T01:36:00.011-08:00

Weird News : 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.

Does strength exercise increase nitrogen balance?

2011-12-06T01:36:00.010-08:00

Weird News : This previous post

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

2011-12-06T01:36:00.009-08:00

Weird News : 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.

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

2011-12-06T01:36:00.008-08:00

Weird News : Compensatory adaptation (CA)

HealthCorrelator for Excel (HCE) is now publicly available for free trial

2011-12-06T01:36:00.007-08:00

Weird News : HealthCorrelator for Excel (HCE)

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

2011-12-06T01:36:00.006-08:00

Weird News : 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

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

2011-12-06T01:36:00.005-08:00

Weird News : 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.

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

2011-12-06T01:36:00.004-08:00

Weird News : 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.

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

2011-12-06T01:36:00.003-08:00

Weird News : 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.

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

2011-12-06T01:36:00.002-08:00

Weird News : 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.

The China Study II: Fruit consumption and mortality

2011-12-06T01:36:00.001-08:00

Weird News : 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.

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

2011-12-06T01:36:00.000-08:00

Weird News : 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.

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Health markers varying inexplicably? Do some detective work with HCE

2011-12-06T01:35:00.034-08:00

Weird News : 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)

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

2011-12-06T01:35:00.033-08:00

Weird News : 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.

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

2011-12-06T01:35:00.032-08:00

Weird News : 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

Beef meatballs, with no spaghetti

2011-12-06T01:35:00.031-08:00

Weird News : 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.

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

2011-12-06T01:35:00.030-08:00

Weird News : 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)

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

2011-12-06T01:35:00.029-08:00

Weird News : 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.

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

2011-12-06T01:35:00.028-08:00

Weird News : The figure below is one of many in Weston Price’s outstanding book Nutrition and Physical Degeneration

Book review: Biology for Bodybuilders

2011-12-06T01:35:00.027-08:00

Weird News : 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

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

2011-12-06T01:35:00.026-08:00

Weird News : 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

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

2011-12-06T01:35:00.025-08:00

Weird News : 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

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

2011-12-06T01:35:00.024-08:00

Weird News : 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.)

Alcohol intake increases LDL cholesterol, in some people

2011-12-06T01:35:00.023-08:00

Weird News : 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.

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Maybe you should stop trying to be someone you are not

2011-12-06T01:35:00.022-08:00

Weird News : 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.

: Nouvelles de Téléphone et Gadgets

Boring is another word for satiating

2011-12-06T01:35:00.021-08:00

Weird News : 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.

: Nouvelles de Téléphone et Gadgets

Liver and meatballs separated by a wall of sweet potatoes

2011-12-06T01:35:00.020-08:00

Weird News : 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.

: Nouvelles de Téléphone et Gadgets

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

2011-12-06T01:35:00.019-08:00

Weird News : 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

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

2011-12-06T01:35:00.018-08:00

Weird News : 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

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

2011-12-06T01:35:00.017-08:00

Weird News : 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

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

2011-12-06T01:35:00.016-08:00

Weird News : 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.

Book review: Sugar Nation

2011-12-06T01:35:00.015-08:00

Weird News : 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!

: Nouvelles de Téléphone et Gadgets

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

2011-12-06T01:35:00.014-08:00

Weird News : 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

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

2011-12-06T01:35:00.013-08:00

Weird News : 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.)

Nonlinearity and the industrial seed oils paradox

2011-12-06T01:35:00.012-08:00

Weird News : 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

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

2011-12-06T01:35:00.011-08:00

Weird News : 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

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

2011-12-06T01:35:00.010-08:00

Weird News : 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

Calling self-experimentation N=1 is incorrect and misleading

2011-12-06T01:35:00.009-08:00
Weird News : 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

Great evolution thinkers you should know about

2011-12-06T01:35:00.008-08:00
Weird News : 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

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

2011-12-06T01:35:00.007-08:00
Weird News : 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:

Book review: Perfect Health Diet

2011-12-06T01:35:00.006-08:00
Weird News : 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

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

2011-12-06T01:35:00.005-08:00
Weird News : WarpPLS

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

2011-12-06T01:35:00.004-08:00
Weird News : WarpPLS

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

2011-12-06T01:35:00.003-08:00
Weird News : 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 ().
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My transformation: How I looked 10 years ago next to a thin man called Royce Gracie

2011-12-06T01:35:00.002-08:00
Weird News : The photos below were taken about 10 years ago. The first is at a restaurant near Torrance, California. 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 …
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Triglycerides, VLDL, and industrial carbohydrate-rich foods

2011-12-06T01:35:00.001-08:00
Weird News : 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.