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

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Selasa, 06 Desember 2011

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
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Cortisol, stress, excessive gluconeogenesis, and visceral fat accumulation

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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

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

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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.,
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Pretty faces are average faces: Genetic diversity and health

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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

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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

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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

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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
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Power napping, stress management, and jet lag

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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

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

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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

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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?

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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

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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!

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

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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

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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

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

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

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

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

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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.
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The theory of supercompensation: Strength training frequency and muscle gain

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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.
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