Hormonal reductionism is as myopic as biochemical reductionism

Biochemistry-based arguments can be very misleading. Yet, biochemistry can be extremely useful in the elucidation of diet and lifestyle effects that are suggested by well-designed studies of humans. If you start with a biochemistry-based argument though, and ignore actual studies of humans, you can easily convince someone that glycogen-depleting exercise (e.g., weight training) is unhealthy, because many health markers change for the worse after that type of exercise. But it is the damage caused by glycogen-depleting exercise that leads to health improvements, via short- and long-term compensatory adaptations ().

Biochemistry is very helpful in terms of providing “pieces for the puzzle”, but biochemical reductionism is a problem. Analogous to biochemical reductionism, and perhaps one example of it, is hormonal reductionism – trying to argue that all diet and lifestyle effects are mediated by a single hormone. A less extreme position, but still myopic, is to argue that all diet and lifestyle effects are mostly mediated by a single hormone.

One of my own “favorite” hormones is adiponectin, which I have been discussing for years in this blog (). Increased serum adiponectin has been found to be significantly associated with: decreased body fat (particularly decreased visceral fat), decreased risk of developing diabetes type 2, and decreased blood pressure. Adiponectin appears to also have anti-inflammatory and athero-protective properties.

As a side note, typically women have higher levels of serum adiponectin than men, particularly young women. Culturally we have a tendency to see young women as “delicate” and “vulnerable”. Guess what? Young women are the closest we get to “indestructible” in the human species. And there is an evolutionary reason for that, which is that fertile women have been in our evolutionary past, and still are, the bottleneck of any population. A population of 100 individuals, where 99 are men and 1 is a woman, will quickly disappear. If it is 99 women and 1 fertile man, the population will grow; but there will also be some problems due to inbreeding. Even if the guy is ugly the population will grow; without competition, he will look very cute.

Jung and colleagues measured various hormone levels in 78 obese people who had visited obesity clinics at five university hospitals (Ajou, Ulsan, Catholic, Hanyang and Yonsei) in Korea (). Those folks restricted their caloric intake to 500 calories less than their usual intake, and exercised, for 12 weeks. Below are the measured changes in tumor necrosis factor α (TNF-α, now called only TNF), interleukin-6 (IL-6), resistin, leptin, adiponectin, and interleukin-10 (IL-10).

We see from the table above that the hormonal changes were all significant (all at the P equal to or lower than 0.001 level except one, at the P lower than 0.05 level), and all indicative of health improvements. The serum concentrations of all hormones decreased, with two exceptions – adiponectin and interleukin-10, which increased. Interleukin-10 is an anti-inflammatory hormone produced by white blood cells. The most significant increase of the two was by far in adiponectin (P = .001, versus P = .041 for interleukin-10).

Now, should we try to find a way of producing synthetic adiponectin then? My guess is that doing that will not lead to very positive results in human trials; because, as you can see from the table, hormones vary in concert. At the moment, the only way to “supplement” adiponectin is to lose body fat, and that leads to concurrent changes in many other hormones (e.g., TNF decreases).

Trying to manipulate one single hormone, or build an entire health-improvement approach based on its effects, is myopic. But that is what often happens. Leptin is a relatively recent example.

One reason why biochemistry is so complex, with so many convoluted processes, is that evolution is a tinkerer that is “blind” to complexity. Traits appear at random in populations and spread if they increase reproductive success; even if they decrease survival success, by the way ().

Evolution is not an engineer, and is not even our “friend” (). To optimize our health, we need to “hack” evolution.

Great evolution thinkers you should know about

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

(Alfred Russel Wallace; source: Wikipedia)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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.

Maknig to mayn tipos? Myabe ur teh boz

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.

How lean should one be?

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 BMI is a more reliable proxy for body fat percentage for women than for men, and can be particularly misleading when applied to muscular men.

The traditional Okinawans are not super lean

The traditional Okinawans (here is a good YouTube video) are the longest living people in the world. Yet, they are not super lean, not even close. They are not obese either. The traditional Okinawans are those who kept to their traditional diet and lifestyle, which seems to be less and less common these days.

There are better videos on the web that could be used to illustrate this point. Some even showing shirtless traditional karate instructors and students from Okinawa, which I had seen before but could not find again. Nearly all of those karate instructors and students were a bit chubby, but not obese. By the way, karate was invented in Okinawa.

The fact that the traditional Okinawans are not ripped does not mean that the level of fat that is healthy for them is also healthy for someone with a different genetic makeup. It is important to remember that the traditional Okinawans share a common ancestry.

What does this all mean?

Some speculation below, but before that let me tell this: as counterintuitive as it may sound, excessive abdominal fat may be associated with higher insulin sensitivity in some cases. This post discusses a study in which the members of a treatment group were more insulin sensitive than the members of a control group, even though the former were much fatter; particularly in terms of abdominal fat.

It is possible that the buff skeleton look is often perceived as somewhat unattractive because of cultural reasons, and that it is associated with the healthiest state for humans. However, it seems a bit unlikely that this applies as a general rule to everybody.

Another possibility, which appears to be more reasonable, is that the buff skeleton look is healthy for some, and not for others. After all, body fat percentage, like fat distribution, seems to be strongly influenced by our genes. We can adapt in ways that go against genetic pressures, but that may be costly in some cases.

There is a great deal of genetic variation in the human species, and much of it may be due to relatively recent evolutionary pressures.

Life is not that simple!

References

Buss, D.M. (1995). The evolution of desire: Strategies of human mating. New York, NY: Basic Books.

Cartwright, J. (2000). Evolution and human behavior: Darwinian perspectives on human nature. Cambridge, MA: The MIT Press.

Miller, G.F. (2000). The mating mind: How sexual choice shaped the evolution of human nature. New York, NY: Doubleday.

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

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

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 massive combined randomness. It is this massive randomness that leads to the bell-curve distribution. The bell curve itself is not random at all, which is a fascinating aspect of this phenomenon. From “chaos” comes “order”. A bell curve is a well-defined curve that is associated with a function, the probability density function.

The underlying mathematical reason for the bell shape is the central limit theorem. The genes are combined in different individuals as combinations of alleles, where each allele is a variation (or mutation) of a gene. An allele set, for genes in different locations of the human DNA, forms a particular allele combination, called a genotype. The alleles combine their effects, usually in an additive fashion, to influence a trait.

Here is a simple illustration. Let us say one generates 1000 random variables, each storing 10 random values going from 0 to 1. Then the values stored in each of the 1000 random variables are added. This mimics the additive effect of 10 genes with random allele combinations. The result are numbers ranging from 1 to 10, in a population of 1000 individuals; each number is analogous to an allele combination. The resulting histogram, which plots the frequency of each allele combination (or genotype) in the population, is shown on the figure bellow. Each allele configuration will “push for” a particular trait range, making the trait distribution also have the same bell-shaped form.

The bell curve, research studies, and what they mean for you

Studies of the effects of diet and exercise on health variables usually report their results in terms of average responses in a group of participants. Frequently two groups are used, one control and one treatment. For example, in a diet-related study the control group may follow the Standard American Diet, and the treatment group may follow a low carbohydrate diet.

However, you are not the average person; the average person is an abstraction. Research on bell curve distributions tells us that there is about a 68 percentage chance that you will fall within a 1 standard deviation from the average, to the left or the right of the “middle” of the bell curve. Still, even a 0.5 standard deviation above the average is not the average. And, there is approximately a 32 percent chance that you will not be within the larger -1 to 1 standard deviation range. If this is the case, the average results reported may be close to irrelevant for you.

Average results reported in studies are a good starting point for people who are similar to the studies’ participants. But you need to generate your own data, with the goal of “knowing yourself through numbers” by progressively analyzing it. This is akin to building a “numeric diary”. It is not exactly an “N=1” experiment, as some like to say, because you can generate multiple data points (e.g., N=200) on how your body alone responds to diet and lifestyle changes over time.

HealthCorrelator for Excel (HCE)

I think I have finally been able to develop a software tool that can help people do that. I have been using it myself for years, initially as a prototype. You can see the results of my transformation on this post. The challenge for me was to generate a tool that was simple enough to use, and yet powerful enough to give people good insights on what is going on with their body.

The software tool is called HealthCorrelator for Excel (HCE). It runs on Excel, and generates coefficients of association (correlations, which range from -1 to 1) among variables and graphs at the click of a button.

This 5-minute YouTube video shows how the software works in general, and this 10-minute video goes into more detail on how the software can be used to manage a specific health variable. These two videos build on a very small sample dataset, and their focus is on HDL cholesterol management. Nevertheless, the software can be used in the management of just about any health-related variable – e.g., blood glucose, triglycerides, muscle strength, muscle mass, depression episodes etc.

You have to enter data about yourself, and then the software will generate coefficients of association and graphs at the click of a button. As you can see from the videos above, it is very simple. The interpretation of the results is straightforward in most cases, and a bit more complicated in a smaller number of cases. Some results will probably surprise users, and their doctors.

For example, a user who is a patient may be able to show to a doctor that, in the user’s specific case, a diet change influences a particular variable (e.g., triglycerides) much more strongly than a prescription drug or a supplement. More posts will be coming in the future on this blog about these and other related issues.

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

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.

Our body’s priority is preventing hypoglycemia, not hyperglycemia

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, or a time under tension of about 30 to 70 seconds).

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 makes some glycogen (glycogenesis) during this time. This means that it is not only pre-exercise liver glycogen that is being used to maintain blood glucose levels above whole-body glucose uptake. This makes sense, since the liver stores only about 100 g of glycogen.

The need to prevent hypoglycemia at all costs is the main reason why there are several hormones that increase blood glucose, while apparently there is only one that decreases blood glucose. Examples of hormones that increase blood glucose are cortisol, adrenaline, noradrenaline, growth hormone, and, notably, glucagon. The only hormone that decreases blood glucose levels in a significant way is insulin. These hormones do not increase or decrease blood glucose directly; they signal to various tissues to either secrete or absorb glucose.

Evolution typically prioritizes processes that have a higher impact on reproductive success, and one must be alive to successfully reproduce. Hypoglycemia causes death. Often those processes that have a significant effect on reproductive success rely on redundant mechanisms. So our evolved mechanisms to deal with hypoglycemia are redundant. Evolution is not an engineer; it is a tinkerer!

What about hyperglycemia – doesn’t it cause death? Well, not in the short term, so related selection pressures were fairly small compared to those associated with hypoglycemia. Besides, there were no foods rich in refined carbohydrates and sugars in the Paleolithic - e.g., white bread, bagels, doughnuts, pasta, cereals, fruit juices, regular sodas, table sugar. Those are the foods that contribute the most to hyperglycemia.

Reference:

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

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

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.

Pretty faces are average faces: Genetic diversity and health

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!

Intermittent fasting, engineered foods, leptin, and ghrelin

Engineered foods are designed by smart people, and the goal is not usually to make you healthy; the goal is to sell as many units as possible. Some engineered foods are “fortified” with the goal of making them as healthy as possible. The problem is that food engineers are competing with many millions of years of evolution, and evolution usually leads to very complex metabolic processes. Evolved mechanisms tend to be redundant, leading to the interaction of many particles, enzymes, hormones etc.

Natural foods are not designed to make you eat them nonstop. Animals do not want to be eaten (even these odd-looking birds below). Most plants do not “want” their various nutritious parts to be eaten. Fruits are exceptions, but plants do not want one single individual to eat all their fruits. That compromises seed dispersion. Multiple individual fruit eaters enhance seed dispersion. Plants "want" one individual animal to eat some of their fruits and then move on, so that other individuals can also eat.

(Source: Teamsugar.com)

It is safe to assume that doughnut manufacturers want one single individual to eat as many doughnuts as possible, and many individuals to want to do that. That takes some serious food engineering, and a lot of testing. Success will increase the manufacturers' revenues, the real bottom line for them. The medical establishment will then take care of those individuals, and prolong their miserable lives so that they can continue eating doughnuts for as long as possible. It is self-perpetuating system.

As mentioned in this previous post, to succeed in the practice of intermittent fasting, one has to stop worrying about food, and one good step in that direction is to avoid engineered foods. In this sense, intermittent fasting can be seen as a form of liberation. Doing something enjoyable and forgetting about food. Like children playing outdoors; they do not care as much about food as they do about play. Even sleeping will do; most people forget about eating when they are asleep.

Intermittent fasting as a religious and/or social activity, as in the Great Lent and Ramadan, also seems to work well. Any activity that brings people together with a common goal, especially if the goal is not to do something evil, has a lot of potential for success.

If you approach intermittent fasting as another thing to worry about, then it will be tough – one fast per week, on the same day of the week, from 7.33 pm of one day to 3.17 pm of the next day. I exaggerate a bit. Anyway, if you approach it as another obligation, another modern stressor, you will probably fail in the medium to long term. It is just commonsense. Maybe you will be able to do it for a while, but not for long enough to reap some serious benefits. A few fasts are not going to make you lose a lot of weight; the body will adapt in a compensatory way during the fast, slowing down your metabolism a bit and conserving calories. On top of that, you will feel very, very hungry. That will make you binge when you break your fast. Compensatory adaptation (a very general phenomenon) is something that our body is very good at, regardless of what we want it to do.

From a more pragmatic perspective, for most people it is easier to fast at night and in the morning. Eating a big meal right after you wake up is not a very natural activity; several hormones that promote body fat catabolism are often elevated in the morning, causing mild physiological insulin resistance.

If you have dinner at 7 pm, skip breakfast, and then have brunch the next day at 10 am, you will have fasted for 15 h. If you skip breakfast and brunch, and have lunch at noon the next day, you will have fasted for 17 h.

On the other hand, if you have breakfast at 8 am, skip lunch, and then have dinner at 6 pm, you will have fasted only for 10 h.

Leptin levels seem to go down significantly after 12 h of fasting, leading to increased body fat catabolism and leptin sensitivity. This is a good thing, since leptin resistance seems to frequently precede insulin resistance.

Many people think that skipping breakfast will make them fat, for various reasons, including that being what sumo wrestlers do to put on enormous amounts of body fat. Well, skipping breakfast probably will make people fat if, when they break the fast, they stuff themselves to the point of almost throwing up, combine plenty of easily digestible carbohydrates (e.g., multiple bowls of rice) with a lot of dietary fat, and then go to sleep. That is what sumo wrestlers normally do.

Eating fat is great, but not together with lots of easily digestible carbohydrates. Even eating a lot of fat by itself will make it difficult for you to shed enough fat to look like the hunter-gatherers in this post. But your body fat set point will be much lower if you eat a lot of fat by itself than if you eat a lot of fat with a lot of easily digestible carbohydrates.

Anyway, if people skip breakfast and eat what they normally eat at lunch, they will not gain more body fat than they would have if they had breakfast. If they do anything to boost their metabolism in the morning, they will most certainly lose body fat in a noticeable way over several weeks, as long as they have enough fat to lose. For example, they can add some light activity in the morning (such as walking), or have a metabolism-boosting drink (e.g., coffee, green tea), or both.

Our hunter-gatherer ancestors, living outdoors, probably spent most of their day performing light activities that involved little stress. Those activities increase metabolism and fat burning, while keeping stress hormone levels at low ranges. Hunger suppression was the result, making intermittent fasting fairly easy.

Again, intermittent fasting should be approached as a form of liberation. You are no longer a slave of food.

It helps staying away from engineered foods as much as possible, because, again, they are usually engineered with food addiction in mind. I am talking primarily about foods rich in refined carbohydrates and sugars. They come in boxes and plastic bags with labels describing calories and macronutrient composition, which are often wrong or misleading.

Let us say we could transport a group of archaic Homo sapiens to a modern city, and feed them white bread, bagels, doughnuts, potato chips industrially fried in vegetable oils, and the like. Would they say “Yuck, how can these people eat this?” No, they would not. It would be heaven for them; they would want nothing else for the rest of their gustatorily happy but health-wise miserable lives.

While practicing intermittent fasting, it is probably a good idea to have fixed meal times, and skipping them from time to time. The reason is the hunger hormone ghrelin, secreted by the stomach (mostly) and pancreas to stimulate hunger and possibly prepare the digestive tract for optimal or quasi-optimal absorption of food. Its secretion appears to follow the pattern of habitual meals adopted by a person.

References:

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

Fuhrman, J., & Barnard, N.D. (1995). Fasting and eating for health: A medical doctor's program for conquering disease. New York, NY: St. Martin’s Press.

Atheism is a recent Neolithic invention: Ancestral humans were spiritual people

For the sake of simplicity, this post treats “atheism” as synonymous with “non-spiritualism”. Technically, one can be spiritual and not believe in any deity or supernatural being, although this is not very common. This post argues that atheism is a recent Neolithic invention; an invention that is poorly aligned with our Paleolithic ancestry.

Our Paleolithic ancestors were likely very spiritual people; at least those belonging to the Homo sapiens species. Earlier ancestors, such as the Australopithecines, may have lacked enough intelligence to be spiritual. Interestingly, often atheism is associated with high intelligence and a deep understanding of science. Many well-known, and brilliant, evolution researchers are atheists (e.g., Richard Dawkins).

Well, when we look at our ancestors, spirituality seems to have emerged as a result of increased intelligence.

Spirituality can be seen in cave paintings, such as the one below, from the Chauvet Cave in southern France. The Chauvet Cave is believed to have the earliest known cave paintings, dating back to about 30 to 40 thousand years ago. The painting below is on the cover of the book Dawn of art: The Chauvet Cave. (See the full reference for this publication and others at the end of this post.)

The most widely accepted theory of the origin of cave paintings is that they were used in shamanic or religious rituals. By and large, they were not used to convey information (e.g., as maps); and they are often found deep in caves, in areas that are almost inaccessible, ruling out a “decorative” artistic purpose. As De La Croix and colleagues (1991) note:

Researchers have evidence that the hunters in the caves, perhaps in a frenzy stimulated by magical rites and dances, treated the painted animals as if they were alive. Not only was the quarry often painted as pierced by arrows, but hunters actually may have thrown spears at the images, as sharp gouges in the side of the bison at Niaux suggest.

Niaux is another cave in southern France. Like the Chauvet Cave, it is full of prehistoric paintings. Even though those paintings are believed to be more recent, dating back to the end of the Paleolithic, they follow the same patterns seen almost everywhere in prehistoric art. The patterns point at a life that gravitates around spiritual rituals.

Isolated hunter-gatherers also provide a glimpse at our spiritual Paleolithic past. No isolated hunter-gatherer group has ever been found in which atheism was the predominant belief among its members. In fact, the life of most isolated hunter-gatherer groups that have been studied appears to have revolved around religious rituals. In many of these groups, shamans held a very high social status, and strongly influenced group decisions.

Finally, there is solid empirical evidence from human genetics and the study of modern human groups that: (a) “religiosity” may be coded into our genes, to a larger extent in some individuals than in others; and (b) those who are spiritual, particularly those who belong to a spiritual or religious group, have generally better health and experience lower levels of depression and stress (which likely influence health) than those who do not.

There was once an ape that became smart. It invented weapons, which greatly multiplied the potential for death and destruction of the ape’s natural propensity toward violence; violence often motivated by different religious and cultural beliefs held by different groups. It also invented delicious foods rich in refined carbohydrates and sugars, which slowly poisoned the ape’s body.

Could the recent invention of atheism have been just as unhealthy?

Surely religion has been at the source of conflicts that have caused much death and destruction. But is religion, or spirituality, really to be blamed? Many other factors can lead to a great deal of death and destruction, sometimes directly, other times indirectly – e.g., poverty and illiteracy.

References:

Brown, D.E. (1991). Human universals. New York, NY: McGraw-Hill.

Chauvet, J.M., Deschamps, E.B., & Hillaire, C. (1996). Dawn of art: The Chauvet Cave. New York, NY: Harry N. Abrams.

De La Croix, H., Tansey, R.G., & Kirkpatrick, D. (1991). Gardner’s art through the ages: Ancient, medieval, and non-European art. Philadelphia, PA: Harcourt Brace.

Gombrich, E.H. (2006). The story of art. London, England: Pheidon Press.

Murdock, G.P. (1958). Outline of world cultures. New Haven, CN: Human Relations Area Files Press.