Could we have evolved traits that are detrimental to our survival?

Let us assume that we collected data on the presence or absence of a trait (e.g., propensity toward risky behavior) in a population of individuals, as well as on intermediate effects of the trait, downstream effects on mating and survival success, and ultimately on reproductive success (a.k.a. “fitness”, in evolutionary biology).

The data would have been collected over several generations. Let us also assume that we conducted a multivariate analysis on this data, of the same type as the analyses employing WarpPLS that were discussed here in previous posts (). The results are summarized through the graph below.



Each of the numbers next to the arrows in the graph below represents the strength of a cause-effect relationship. The number .244 linking “a” and “y” means that a one standard deviation variation in “a” causes a .244 standard deviation increase in “y”. It also means that a one standard deviation variation in “a” causes a 24.4 percent increase in “y” considering the average “y” as the baseline.

This type of mathematical view of evolution may look simplistic. This is an illusion. It is very general, and encompasses evolution in all living organisms, including humans. It also applies to theoretical organisms where multiple (e.g., 5, 6 etc.) sexes could exist. It even applies to non-biological organisms, as long as these organisms replicate - e.g., replicating robots.

So the trait measured by “a” has a positive effect on the intermediate effect “y”. This variable, “y” in turn has a negative effect on survival success (“s”), and a strong one at that: -.518. Examples: “a” = propensity toward risky behavior, measured as 0 (low) and 1 (high); and “y” = hunting success, measured in the same way. (That is, “a” and “y” are correlated, but “a”=1 does not always mean “y”=1.) Here the trait “a” has a negative effect on survival via its intermediate effect on “y”. If I calculate the total effect of “a” on “w” via the 9 paths that connect these two variables, I will find that it is .161.

The total effect on reproductive success is positive, which means that the trait will tend to spread in the population. In other words, the trait will evolve in the population, even though it has a negative effect on survival. This type of trait is what has been referred to as a “costly” trait ().

Say what? Do you mean to say that we have evolved traits that are unhealthy for us? Yes, I mean exactly that. Is this a “death to paleo” post? No, it is not. I discussed this topic here before, several years ago (). But the existence of costly traits is one of the main reasons why I don’t think that mimicking our evolutionary past is necessarily healthy. For example, many of our male ancestors were warriors, and they died early because of that.

What type of trait will present this evolutionary pattern – i.e., be a costly trait? One answer is: a trait that is found to be attractive by members of the other sex, and that is not very healthy. For example, a behavior that is perceived as “sexy”, but that is also associated with increased mortality. This would likely be a behavior prominently displayed by males, since in most species, including humans, sexual selection pressure is much more strongly applied by females than by males.

Examples would be aggressiveness and propensity toward risky behavior, especially in high-stress situations such as hunting and intergroup conflict (e.g., a war between two tribes) where being aggressive is likely to benefit an individual’s group. In warrior societies, both aggressiveness and propensity toward risky behavior are associated with higher social status and a greater ability to procure mates. These traits are usually seen as male traits in these societies.

Here is something interesting. Judging from our knowledge of various warrior societies, including American plains Indians societies, the main currency of warrior societies were counts of risky acts, not battle effectiveness. Slapping a fierce enemy warrior on the face and living to tell the story would be more valuable, in terms of “counting coup”, than killing a few inexperienced enemy warriors in an ambush.

Greater propensity toward risky behavior among men is widespread and well documented, and is very likely the result of evolutionary forces, operating on costly traits. Genetic traits evolved primarily by pressure on one sex are often present in the other (e.g., men have nipples). There are different grades of risky behavior today. At the high end of the scale would be things that can kill suddenly like race car driving and free solo climbing (, ). (If you'd like to know the source of the awesome background song of the second video linked, here it is: Radical Face's "Welcome Home".)

One interesting link between risky behavior and diet refers to the consumption of omega-6 and omega-3 fats. Risky behavior may be connected with aggressive behavior, which may in turn be encouraged by greater consumption of foods rich in omega-6 fats and avoidance of foods rich in omega-3 fats (, ). This may be behind our apparent preference for foods rich in omega-6 fats, even though tipping the balance toward more foods rich in omega-3 fats would be beneficial for survival. We would be "calmer" though - not a high priority among most men, particularly young men.

This evolved preference may also be behind the appeal of industrial foods that are very rich in omega-6 fats. These foods seem to be particularly bad for us in the long term. But when the sources of omega-6 fats are unprocessed foods, the negative effects seem to become "invisible" to statistical tests.

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

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

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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. Genealogical records often reveal interesting connections - the figure below has been cropped from a larger one from Pinterest.

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

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

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

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

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

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

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

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

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

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

The Neurobiology of the Obesity Epidemic

I recently read an interesting review paper by Dr. Edmund T. Rolls titled "Taste, olfactory and food texture reward processing in the brain and the control of appetite" that I'll discuss in this post (1).  Dr. Rolls is a prolific neuroscience researcher at Oxford who focuses on "the brain mechanisms of perception, memory, emotion and feeding, and thus of perceptual, memory, emotional and appetite disorders."  His website is here.

The first half of the paper is technical and discusses some of Dr. Rolls' findings on how specific brain areas process sensory and reward information, and how individual neurons can integrate multiple sensory signals during this process.  I recommend reading it if you have the background and interest, but I'm not going to cover it here.  The second half of the paper is an attempt to explain the obesity epidemic based on what he knows about the brain and other aspects of human biology.

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The bipolar disorder pendulum: Depression as a compensatory adaptation

As far as explaining natural phenomena, Darwin was one of the best theoretical researchers of all time. Yet, there were a few phenomena that puzzled him for many years. One was the evolution of survival-impairing traits such as the peacock’s train, the large and brightly colored tail appendage observed in males.

Tha male peacock’s train is detrimental to the animal’s survival, and yet it is clearly an evolved trait ().

This type of trait is known as a “costly” trait – a trait that enhances biological fitness (or reproductive success, not to be confused with “gym fitness”), and yet is detrimental to the survival of the individuals who possess it (). Many costly traits have evolved in animals because of sexual selection. That is, they have evolved because they are sexy.

Costly traits seem like a contradiction in terms, but the mechanisms by which they can evolve become clear when evolution is modeled mathematically (, ). There is evidence that mental disorders may have evolved as costs of attractive mental traits (); one in particular, bipolar disorder (a.k.a. manic-depression), fits this hypothesis quite well.

Ironically, a key contributor to the mathematics used to understand costly traits, George R. Price (), might have suffered from severe bipolar disorder. Most of Price’s work in evolutionary biology was done in the 1970s; toward the end of his life, which was untimely ended by Price himself. For many years he was known mostly by evolutionary biologists, but this has changed recently with the publication of Oren Harman’s superb biographical book titled “The Price of Altruism: George Price and the Search for the Origins of Kindness” ().

Bipolar disorder is a condition characterized by disruptive mood swings. These swings are between manic and depressed states, and are analogous to the movement of a pendulum in that they alternate, seemingly gravitating around the "normal" state. See the figurative pendulum representation below, adapted from a drawing on Thinkquest.org.

Bipolar disorder is generally associated with creative intelligence, which is a very attractive trait (). Moreover, the manic state of the disorder is associated with hypersexuality and exaggerated generosity (). So one can clearly see how having bipolar disorder may lead to greater reproductive success, even as it creates long-term survival problems.

On one hand, a person may become very energetic and creative while in the manic state. This could be one of the reasons why many who suffer from bipolar disorder have fairly successful careers in fields that require creative intelligence (), which are many and not restricted to fields related to the fine and performing arts. Creative intelligence is highly valued in most knowledge-intensive professions ().

On the other hand, sustained acute mania or depression are frequently associated with serious health problems (). This is why the clinical treatment of bipolar disorder often starts with an attempt to keep the pendulum from moving too far in one direction or another. This may require medication, such as clinical doses of the elemental salt lithium, prior to cognitive behavioral therapy. The focus of cognitive behavioral therapy is on changing the way one sees and thinks about the world, particularly one’s “social world”.

Prolonged acute mania, usually accompanied by severely impaired sleep, may lead to psychosis. This, psychosis, is an extreme state characterized by hallucinations and/or delusions, leading to hospitalization in most cases. It has been theorized that depression is an involuntary compensatory adaptation () aimed at moving the pendulum in the other direction, out of the manic state, before more damage ensues ().

Elaborate approaches have been devised to treat and manage bipolar disorder treatment that involve the identification of mania and depression “prodromes” (), which are signs that a full-blown manic or depressive episode is about to start. Once prodromes are identified, cognitive behavioral therapy techniques are employed to prevent the pendulum from moving further in one direction or the other. The main goal of these techniques is to change one’s way of thinking about various issues (e.g., fears, pessimism). These techniques take years of practice to be used effectively.

Identification of prodromes and subsequent use of cognitive behavioral therapy seems to be particularly effective when dutifully applied with respect to manic episodes (). The reason for this may be related to one interesting fact related to bipolar disorder: manic episodes are not normally dreaded as much as depression episodes.

In fact, many sufferers avoid taking medication because they do not want to give up the creative and energetic bursts that come with manic episodes, even though they absolutely do not want the pendulum to go in the other direction. The problem is that, if depression is indeed a compensatory adaptation to mania, it seems reasonable to assume that extreme manic episodes are likely to be followed by extreme episodes of depression. Perhaps the key to avoid prolonged acute depression is to avoid prolonged acute mania.

As someone with bipolar disorder becomes more and more excited with novel and racing thoughts (a prodrome of mania), it would probably make sense to identify and carry out calming activities – to avoid a fall into despairing depression afterwards.

Ancestral Health Symposium 2012

I recently returned from AHS12 and a little side trip to visit family.  The conference was hosted at Harvard University through the Harvard Food Law Society.  Many thanks to all the organizers who made it happen.  By and large, it went smoothly.

The science as expected ranged from outstanding to mediocre, but I was really encouraged by the presence and enthusiastic participation of a number of quality researchers and clinicians. The basic concept of ancestral health is something almost anyone can get behind: many of our modern health problems are due to a mismatch between the modern environment and what our bodies "expect".  The basic idea is really just common sense, but of course the devil is in the details when you start trying to figure out what exactly our bodies expect, and how best to give it to them.  I think our perspective as a community is moving in the right direction.

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Beyond Ötzi: European Evolutionary History and its Relevance to Diet. Part III

In previous posts, I reviewed some of the evidence suggesting that human evolution has accelerated rapidly since the development of agriculture (and to some degree, before it).  Europeans (and other lineages with a long history of agriculture)  carry known genetic adaptations to the Neolithic diet, and there are probably many adaptations that have not yet been identified.  In my final post in this series, I'll argue that although we've adapted, the adaptation is probably not complete, and we're left in a sort of genetic limbo between the Paleolithic and Neolithic state. 

Recent Genetic Adaptations are Often Crude

It may at first seem strange, but many genes responsible for common genetic disorders show evidence of positive selection.  In other words, the genes that cause these disorders were favored by evolution at some point because they presumably provided a survival advantage.  For example, the sickle cell anemia gene protects against malaria, but if you inherit two copies of it, you end up with a serious and life-threatening disorder (1).  The cystic fibrosis gene may have been selected to protect against one or more infectious diseases, but again if you get two copies of it, quality of life and lifespan are greatly curtailed (2, 3).  Familial Mediterranean fever is a very common disorder in Mediterranean populations, involving painful inflammatory attacks of the digestive tract, and sometimes a deadly condition called amyloidosis.  It shows evidence of positive selection and probably protected against intestinal disease due to the heightened inflammatory state it confers to the digestive tract (4, 5).  Celiac disease, a severe autoimmune reaction to gluten found in some grains, may be a by-product of selection for protection against bacterial infection (6).  Phenylketonuria also shows evidence of positive selection (7), and the list goes on.  It's clear that a lot of our recent evolution was in response to new disease pressures, likely from increased population density, sendentism, and contact with domestic animals.

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Beyond Ötzi: European Evolutionary History and its Relevance to Diet. Part I

In the previous post, I explained that Otzi descended in large part from early adopters of agriculture in the Middle East or nearby.  What I'll explain in further posts is that Otzi was not a genetic anomaly: he was part of a wave of agricultural migrants that washed over Europe thousands of years ago, spreading their genes throughout.  Not only that, Otzi represents a halfway point in the evolutionary process that transformed Paleolithic humans into modern humans.

Did Agriculture in Europe Spread by Cultural Transmission or by Population Replacement?

There's a long-standing debate in the anthropology community over how agriculture spread throughout Europe.  One camp proposes that agriculture spread by a cultural route, and that European hunter-gatherers simply settled down and began planting grains.  The other camp suggests that European hunter-gatherers were replaced (totally or partially) by waves of agriculturalist immigrants from the Middle East that were culturally and genetically better adapted to the agricultural diet and lifestyle.  These are two extreme positions, and I think almost everyone would agree at this point that the truth lies somewhere in between: modern Europeans are a mix of genetic lineages, some of which originate from the earliest Middle Eastern agriculturalists who expanded into Europe, and some of which originate from indigenous hunter-gatherer groups including a small contribution from neanderthals.  We know that modern-day Europeans are not simply Paleolithic mammoth eaters who reluctantly settled down and began farming. 

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Lessons From Ötzi, the Tyrolean Ice Man. Part III

There are two reasons why I chose this time to write about Otzi.  The first is that I've been looking for a good excuse to revisit human evolutionary history, particularly that of Europeans, and what it does and doesn't tell us about the "optimal" human diet.  The second is that Otzi's full genome was sequenced and described in a recent issue of Nature Communications (1).  A "genome" is the full complement of genes an organism carries.  So what that means is that researchers have sequenced almost all of his genes. 

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Lessons From Ötzi, the Tyrolean Ice Man. Part II

Otzi's Diet

Otzi's digestive tract contains the remains of three meals.  They were composed of cooked grains (wheat bread and wheat grains), meat, roots, fruit and seeds (1, 2).  The meat came from three different animals-- chamois, red deer and ibex.  The "wheat" was actually not what we would think of as modern wheat, but an ancestral variety called einkorn.

Isotope analysis indicates that Otzi's habitual diet was primarily centered around plant foods, likely heavily dependent on grains but also incorporating a variety of other plants (3).  He died in the spring with a belly full of einkorn wheat.  Since wheat is harvested in the fall, this suggests that his culture stored grain and was dependent on it for most if not all of the year.  However, he also clearly ate meat and used leather made from his prey.  Researchers are still debating the quantity of meat in his diet, but it was probably secondary to grains and other plant foods. It isn't known whether or not he consumed dairy.

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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.

Eocene Diet Follow-up

Now that WHS readers around the globe have adopted the Eocene Diet and are losing weight at an alarming rate, it's time to explain the post a little more.  First, credit where credit is due: Melissa McEwen made a similar argument in her 2011 AHS talk, where she rolled out the "Cambrian Explosion Diet", which beats the Eocene Diet by about 470 million years.  It was probably in the back of my head somewhere when I came up with the idea.

April Fools day is good for a laugh, but humor often has a grain of truth in it.  In this case, the post was a jumping off point for discussing human evolution and what it has to say about the "optimal" human diet, if such a thing exists.  Here's a preview: evolution is a continuous process that has shaped our ancestors' genomes for every generation since the beginning of life.  It didn't end with the Paleolithic, in fact it accelerated, and most of us today carry meaningful adaptations to the Neolithic diet and lifestyle. 

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

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

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

(Source: Vangoghart.org)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Great evolution thinkers you should know about

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.

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

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

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.

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

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.

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.