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.

Familial hypercholesteromia: Why rely on cholesterol levels when more direct measures are available?

There are two forms of familial hypercholesteromia (FH), namely heterozygous and homozygous FH. In heterozygous FH only one copy of the gene that causes it is present, inherited either from the father or the mother. In homozygous FH, which is the most lethal form, two copies of the gene are present. FH is associated with early-onset cardiovascular disease (CVD).

Homozygous FH may happen if both the father and mother have heterozygous or homozygous FH. If both the father and mother have heterozygous FH, the likelihood that at least one in four children will have homozygous FH will be high. If both parents have homozygous FH the likelihood that all children will have homozygous FH will be high.

In fact, in the latter case, homozygous FH in the children is almost certain. One case in which it won’t occur is if the combining FH gene from the father or mother mutates into a non-FH gene before it is used in the assembly of the genome of the child. A gene mutation in a specific locus, only for the father or mother, is an unlikely event, and would lead to heterozygous FH. Two gene mutations at once in the same locus, for the father and mother, is a very unlikely event.

By the way, despite what many are led to believe based on fictional characters in movies and series like the X-Men and Hulk, mutations in functional genes usually lead to harmful traits. In our evolutionary past, those traits would have been largely removed from the gene pool by selection, making them rare or nonexistent in modern humans. Today we have modern medicine; a double-edged sword.

Mutations leading to super-human traits are very, very unlikely. The myostatin gene, for example, suppresses muscle growth. And yet the mutations that lead to little or no secretion of the related myostatin protein are very uncommon. Obviously they have not been favored by selection, even though their holders are very muscular – e.g., Germany’s “Incredible Hulky” ().

Okay, back to FH. Xanthelasmas are relatively common among those who suffer from FH (see photo below, from Globalskinatlas.com). They are skin deposits of cholesterol, have a genetic basis, and are NOT always associated with FH. This is important – several people have xanthelasmas but not FH.

FH is a fairly rare disease, even in its heterozygous form, with an overall incidence of approximately 0.2 percent. That is, about 1 in 500 people in the general population will have it. Genetically related groups will see a much higher or lower rate of incidence, as the disease is strongly influenced by a genetic mutation. This genetic mutation is apparently in the LDL receptor gene, located on the short arm of chromosome 19.

The table below, from a study by Miltiadous and colleagues (), paints a broad picture of the differences one would typically see between heterozygous FH sufferers and non-FH controls.

The main difference is in total cholesterol and in the relatively large contribution of LDL to total cholesterol. A large difference is also seen in Apolipoprotein B (indicated as "Apo B"), which acts as a LDL transporter (not to be confused with a LDL receptor). The LDL cholesterol shown on the table is calculated through the Friedewald equation, which is notoriously imprecise at low triglyceride levels ().

Looking at the total cholesterol row on the table, and assuming that the numbers after the plus/minus signs are standard deviations, we can conclude that: (a) a little more than two-thirds of the heterozygous FH sufferers had total cholesterol levels falling in between 280 and 446; and (b) a little more than two-thirds of the non-FH controls had total cholesterol levels falling in between 135 and 225.

Keep in mind that about 13.5 percent {calculated as: (95-68)/2} of the non-FH controls had total cholesterol levels between 225 and 270. This is a nontrivial percentage; i.e., these may be a minority but are not rare individuals. Heterozygous FH sufferers are rare, at 0.2 percent of the general population. Moreover, about 2 percent of the non-FH controls had non-pathological total cholesterol levels between 270 and 315. That is not so rare either, amounting to an “incidence” 10 times higher than heterozygous FH.

What would happen if people with heterozygous FH were to replace refined carbohydrates and sugars with saturated fat and cholesterol in their diets? Very likely their already high total cholesterol would go up higher, in part because their HDL cholesterol would go up (). Still, how could they be sure that CVD progression would accelerate if they did that?

According to some studies, the higher HDL cholesterol would either be generally protective or associated with protective factors, even among those with FH (). One of those protective factors may be a more nutrient-dense diet, as many foods rich in cholesterol are very nutrient-dense – e.g., eggs, organ meats, and seafood.

This brings me to my main point in this post. It is mainstream practice to diagnose people with FH based on total and/or LDL cholesterol levels. But the main problem with FH is that it leads to early onset of CVD, which can be measured more directly through simple tests, such as intima-media thickness and related ultrasound plaque tests (). These are noninvasive tests, done in 5 minutes or so, and often covered by insurance.

Even if simple direct tests are not perfect, it seems utterly nonsensical to rely on cholesterol measures to diagnose and treat FH, given the possible overlap between pathological and non-pathological high total cholesterol levels.

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.

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.