There are more geniuses among men than among women, and more idiots too

Deary and colleagues (2007) conducted an interesting study on differences in intelligence scores among men and women. In the context of this blog, this study highlights yet one more counterintuitive and intriguing aspect of Darwinian evolution, adding to points previously made in other posts (see here, and here). Evolution may look simple at first glance, but that is a bit of a mirage. In my opinion, to really understand it one has to understand the mathematics underlying it, a lot of which comes from the field of population genetics.

What makes the study by Deary and colleagues (2007) particularly interesting is that its participants were opposite-sex siblings. This helped control for the influence of environmental factors. The downside is that the effect sizes might have been decreased, because of the high gene correlation among siblings, so we could expect larger differences between unrelated groups of men women. The differences, as you will see, are not in overall scores, but in score dispersion.

Let us get straight to the point made by the study. On average, men and women seem to score equally well on intelligence tests. The main difference is that there is more variation in the scores achieved by men than by women, which leads to an interesting effect: there are more geniuses and more idiots among men than among women.

This does NOT mean that a man’s genius is of a higher order; just that there is a tendency for more men to be geniuses (and idiots) than women in any random population sample. The women who are geniuses can be super geniuses, like two-time Nobel Prize winner Marie Curie, the first PERSON to receive such an honor. Albert Einstein is said that have greatly admired her intelligence.

As an illustration of this score dispersion effect, Deary and colleagues (2007) note that: “… for example, in terms of indices of scientific achievement, men were awarded 545 out of the 557 Nobel prizes awarded for science.” On the “idiot” end of the scale: there are a lot more men than women in prison, and one common denominator of prison inmates is that they tend to score very low on intelligence tests. (This is not to say that all criminals have low intelligence; perhaps mostly the ones that get caught do.)

Having said that, it is important to acknowledge that there are multiple types of intelligence, and even multi-indicator intelligence coefficients are usually poor approximations of an overall measure of intelligence (if there is one). This does not invalidate the main point of this post, which is related to score variability.

The table below (from: Deary and colleagues, 2007; click on it to enlarge; full reference at the end of this post) shows scores obtained by men and women (1,292 pairs of opposite-sex siblings) in various subtests of the Armed Services Vocational Aptitude Battery (ASVAB) test.

Note that nearly all of the differences between means (i.e., averages) are significant, but the direction of the differences (captured by the signs of the Cohen’s d coefficients, which are measures of effect size) varies a lot. That is, on several subtests (e.g., “Arithmetic”) men score higher, but in others (e.g., “Numerical operations”) women score higher. It all comes down to men and women scoring equally well overall.

Now look at the columns showing the standard deviations (“SD”) for men and women. In all subtests but two (“Coding speed” and “Numerical operations”) the standard deviation is higher for men; in many cases significantly higher (e.g., 44 percent higher for “Mechanical comprehension”). The standard deviations are about the same for “Coding speed” and “Numerical operations”. What this means is that variability in scores is nearly always higher, often significantly higher, among men than among women. I prepared the schematic figure below to illustrate the effect that this has on the numbers of individuals at the extremes.

The figure above shows two (badly drawn) quasi-normal distributions of scores. (This post shows a better illustration of a normal distribution.) The red curve refers to a distribution with a lower standard deviation than the blue curve; the latter is flatter. Each point on a curve reflects the number of individuals obtaining a particular score, which would be indicated on the horizontal axis. The number of individuals with that score is on the vertical axis. As you can see, the numbers of individuals scoring very high and low (geniuses and idiots, if the scores reflected intelligence) are greater for the blue curve, which is the curve with the higher standard deviation (higher dispersion of scores). The farther one goes to the left or right (the extremes), the bigger this difference becomes.

What does this have to do with evolution?

Well, there are a few possibilities, two of which appear to be particularly compelling. Maybe this effect is due to a combination of these two.

One is that ancestral women, like women today, selected mating partners based on a wide range of traits. Ancestral men on the other hand, like modern men, focused on a much smaller set of traits (Buss, 1995). The end result is more variation in traits, generally speaking, among men than among women. This refers to traits in general, not only intelligence. For example, there seems to be more variation in height among men than among women.

The other possible explanation is that, in our ancestral past, staying out of the extremes of intelligence was associated with higher survival success in both sexes. It seems that the incidence of certain types of mental disease (e.g., schizophrenia) is quite high among geniuses. This leads to more deaths due to related issues – suicide, depression leading to the metabolic syndrome, etc. And this is today, where geniuses can find many opportunities to “shine” in our complex urban societies. In our ancestral past the cognitive demands would have been much lower, and so would the practical value of being a genius.

If staying out of the extremes has indeed enhanced survival success in our evolutionary past, then it is reasonable to expect more women to fit that pattern than men. As with almost any “thing” that enhances survival success, women (especially pre-menopausal) naturally have more of that “thing” than men (e.g., HDL cholesterol).

The reason is that women are more important for the survival of any population than men; today and 1 million years ago. A population of 99 women and 1 man can potentially generate 99 children every few years. Here inbreeding in subsequent generations will be a problem, but that is better than extinction. A population with 99 women and 99 men (or even 1,000 men) will not generate significantly more children.

Reference:

Buss, D.M. (2003). The evolution of desire: Strategies of human mating. New York, NY: Basic Books.

Deary, I.J., Irwing, P., Der, G., & Bates, T.C. (2007). Brother–sister differences in the g factor in intelligence: Analysis of full, opposite-sex siblings from the NLSY1979. Intelligence, 35(5), 451-456.

Book review: The Eclipse of a Mind

The Eclipse of a Mind () is a 722-page book published in 1942 that describes the life of Alonzo Graves. Alonzo is also listed as the author of the book, even though the narrative is not that of a typical autobiography.

The book is an in-depth study of manic depression. Alonzo is the sufferer. He is a very intelligent college dropout journalist who narrates his lifelong struggle for mental balance. We are taken through World War I, the great Great Depression (iconic photo below: ), the various treatments of bacterial diseases prior to antibiotics, among a variety of other topics; all through Alonzo’s eyes.

This book is rather “dense”, and not very easy to read in a linear fashion – i.e., from beginning to end. Since it is annotated, with comments by various psychiatrists and medical doctors who examined and treated Alonzo, the book is fairly repetitive at points. Nevertheless, it is a fascinating read.

Alonzo delves into important historical events that many today are likely unaware of, such as the Bonus Expeditionary Force movement in the 1930s. World War I veterans had been issued paper money that they could not exchange for real money until 1945. Out-of-work veterans revolted during the Great Depression, demanding early payment. Alonzo was right in the middle of this movement, acting as a journalist and taking the side of the veterans. The ensuing stress caused a manic episode that eventually led to Alonzo's hospitalization.

Manic episodes are characterized by euphoric states and increased levels of activity. The episodes are often triggered by stress. Some people become creative and highly productive during manic states, whereas others become irritable and prone to engaging in risky behavior. Frequently manic episodes are followed by debilitating depression ().

Alonzo’s falls into manic states usually started with benign increases in work-related activity. However, as that high-energy state was maintained for various consecutive days, causing periods of very poor sleep, it often led to psychotic or near-psychotic episodes. This produced a total of five hospitalizations, all of which are described in detail in the book. The book ends with Alonzo moving to Russia, whose government ideology he admired, and never being heard of again.

One of the most interesting aspects of this book is Alonzo’s insights into other people’s mental illnesses, some of whom were manic depressive, combined with his inability to recognize the signs of his own illness. Notably, Alonzo was unable to recognize early signs, or “prodomes” (), which made it difficult for him to avoid entering manic states.

As noted earlier, this book is not an easy read. And it is an old book, copies of which are probably difficult to find today. Nevertheless, it is unique in its tell-it-all style, with detailed narratives from both the patient and doctors about a mental illness that is widespread today. Manic depression is an eminently treatable condition that tends to be highly correlated with creative intelligence ().

A frequently unrecognized reality is put forth by this book. Manic depression is not a “new” condition, even though it may be a “disease of civilization”. The levels of sustained stress found in urban societies are probably much higher than those experienced by our ancestors during most of our evolutionary history, and stress is a trigger of manic depression symptoms. The Eclipse of a Mind is a goldmine of insights into this condition.

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

Let us say you are one of the gifted few who are able to put on 1 lb of pure muscle per month, or 12 lbs per year, by combining strength training with a reasonable protein intake. Let us go even further and assume that the 1 lb of muscle that we are talking about is due to muscle protein gain, not glycogen or water. This is very uncommon; one has to really be genetically gifted to achieve that.

And you do that by eating a measly 80 g of protein per day. That is little more than 0.5 g of protein per lb of body weight if you weigh 155 lbs; or 0.4 per lb if you weigh 200 lbs. At the end of the year you are much more muscular. People even think that you’ve been taking steroids; but that just came naturally. The figure below shows what happened with the 80 g of protein you consumed every day. About 15 g became muscle (that is 1 lb divided by 30) … and 65 g “disappeared”!

Is that an amazing feat? Yes, it is an amazing feat of waste, if you think that the primary role of protein is to build muscle. More than 80 percent of the protein consumed was used for something else, notably to keep your metabolic engine running.

A significant proportion of dietary protein also goes into the synthesis of albumin, to which free fatty acids bind in the blood. (Albumin is necessary for the proper use of fat as fuel.) Dietary protein is also used in the synthesis of various body tissues and hormones.

Dietary protein does not normally become body fat, but can be used in place of fat as fuel and thus allow more dietary fat to be stored. It leads to an insulin response, which causes less body fat to be released. In this sense, dietary protein has a fat-sparing effect, preventing it from being used to supply the energy needs of the body.

Nevertheless, the fat-sparing effect of protein is lower than that of another "macronutrient" – alcohol. That is, alcohol takes precedence over protein and carbohydrates for use as fuel. Protein takes precedence over carbohydrates. Neither alcohol nor protein typically becomes body fat. Carbohydrates can become body fat, but only when glycogen stores are full.

What does this mean?

As it turns out, a reasonably high protein intake seems to be quite healthy, and there is nothing wrong with the body using protein to feed its metabolism.

Having said that, one does not need enormous amounts of protein to keep or even build muscle if one is getting enough calories from other sources.

Wheat flour, rice and vascular diseases in the China Study II data: Article on Cliodynamics

My article on volume 6, number 2, of the journal Cliodynamics has recently been published; it is titled “Wheat flour versus rice consumption and vascular diseases: Evidence from the China Study II data” (). While this is an academic article, I think that the main body of the article is fairly easy to read. More technical readers may want to check under “Supporting material”, which is one of the links on the left, where they will find a detailed description of the data used and the results of some specialized statistical tests.

In the past I have discussed in this blog the associations with vascular diseases, in the China Study dataset, of wheat flour and rice consumption. The interest in the possible effects of wheat flour AND rice consumption comes from the fact that these foods are similar in some important respects – e.g., they tend to raise insulin levels in similar ways. But as you will see in the article, their associations with vascular diseases are clearly different, particularly when we conduct nonlinear analyses.

While I do not think that wheat flour consumption per se is particularly healthy, the results of the analysis go somewhat against the idea that wheat flour intake is the primary culprit with respect to vascular diseases. The results also go somewhat against the “insulin theory of obesity”, at least in a narrow sense, and call for a broader explanation that includes cultural elements. These points are further elaborated in the article. There is speculation in the article, and also a discussion of possible limitations.

Enjoy!

PLS Applications Symposium; 13 - 15 April 2016; Laredo, Texas

PLS Applications Symposium; 13 - 15 April 2016; Laredo, Texas
(Abstract submissions accepted until 19 February 2015)

*** Health researchers ***

The research techniques discussed in this Symposium are finding growing use among health researchers. This is in part due to steady growth in the use of the software WarpPLS (visit: http://warppls.com) among those researchers. For those interested in learning more, a full-day workshop will be conducted (see below).

*** Only abstracts are needed for the submissions ***

The partial least squares (PLS) method has increasingly been used in a variety of fields of research and practice, particularly in the context of PLS-based structural equation modeling (SEM). The focus of this Symposium is on the application of PLS-based methods, from a multidisciplinary perspective. For types of submissions, deadlines, and other details, please visit the Symposium’s web site:

http://plsas.net

*** Workshop on PLS-SEM ***

On 13 April 2015 a full-day workshop on PLS-SEM will be conducted by Dr. Ned Kock, using the software WarpPLS. This workshop will be hands-on and interactive. To participate in the workshop, please indicate your interest when making your registration for the Symposium.

The following topics, among others, will be covered - Running a Full PLS-SEM Analysis - Conducting a Moderating Effects Analysis - Viewing Moderating Effects via 3D and 2D Graphs - Creating and Using Second Order Latent Variables - Viewing Indirect and Total Effects - Viewing Skewness and Kurtosis of Manifest and Latent Variables - Conducting a Multi-group Analysis with Range Restriction - Viewing Nonlinear Relationships - Conducting a Factor-Based PLS-SEM Analysis - Viewing and Changing Missing Data Imputation Settings - Isolating Mediating Effects - Identifying and Dealing with Outliers - Solving Indicator Problems - Solving Collinearity Problems.

*** Proceedings of the Symposium ***

Accepted submissions will be published in the online proceedings of the Symposium, subject to the following registration requirements. At least one of the authors listed for a presentation must register for the Symposium. Panels must have 3-5 participants, all of whom must register for the Symposium. Abstracts must have 150-500 words. Below is an example of submission.

------------ Example of submission ------------

Using PLS in medical technology studies: What if I have only one group and one condition?

Type of submission: Presentation

John Doe
Professor of Medicine
Division of General Internal Medicine
ABC University
1234 University Boulevard
University City, Texas, USA
Tel: +1-956-333-1234
Fax: +1-956-333-4321
Email: johndoe@abcu.edu
Web site: http://www.abcu.edu/johndoe

Jane Doe
Professor of Medicine
Division of General Internal Medicine
ABC University
1234 University Boulevard
University City, Texas, USA
Tel: +1-956-333-2345
Fax: +1-956-333-5432
Email: janedoe@abcu.edu
Web site: http://www.abcu.edu/janedoe

Abstract
What if a researcher obtains empirical data by asking questions to gauge the effect of a medical technology on task performance, but does not obtain data on the extent to which the medical technology is used? This characterizes what is referred to here as a scenario with one group and one condition, where the researcher is essentially left with only one column of data to be analyzed. When this happens, often researchers do not know how to analyze the data, or analyze the data making incorrect assumptions and using unsuitable techniques. Some of the PLS method’s features make it particularly useful in this type of scenario, such as its support for small samples and the use of data that does not meet parametric assumptions. The main goal of this presentation is to help medical technology researchers use the PLS method to analyze data in this type of scenario, where only one group and one condition are available. Two other scenarios are also discussed – a typical scenario, and a scenario with one group and two before-after technology introduction conditions. While the focus here is on medical technology use, the recommendations apply to many other fields.

Keywords: Multivariate Statistics, Partial Least Squares, Structural Equation Modeling, Field Research, Action Research, Medical Technology

-----------------------------------------------------------
Ned Kock
Symposium Chair
http://plsas.net

Should you drink your coffee filtered?

Coffee is one of the most widely consumed beverages in the world. Arguably a key reason for this is that coffee has psychoactive properties that we may be hardwired to value, even if subconsciously. For example, it increases alertness; possibly a fitness-enhancing effect in our evolutionary past. Here the term “fitness” in “fitness-enhancing effect” means “reproductive success”, and does not mean having great athletic ability or having shredded abs.

The two most common sources of coffee beans, which are roasted and ground prior to brewing, are the widely favored Coffea arabica, and the "robusta" form Coffea canephora. The arabica form accounts for 80 percent or so of world consumption. The graph below, from a study by Bonita and colleagues (), shows the per capita consumption of coffee in various countries. As you can see, Scandinavian countries are big consumers.



Most people probably drink filtered coffee. However, there are many unfiltered coffee preparation methods that are also widely used. Greek coffee, Turkish coffee, coffee prepared with a French press, and “cowboy coffee” are all unfiltered.

In the photo below (from: Goldenstate.wordpress.com), illustrating cowboy coffee, note that the coffee pot is placed near but not over the fire.



What is “cowboy coffee”? This method of preparation has many variations. A simple one involves mixing ground coffee with hot water, and then keeping the coffee simmering on very low fire for a while. It is called cowboy coffee due to its association with coffee drank by cowboys around a campfire.

After brewed, coffee tends to rise and spill out of the pot if heated at a high temperature. To avoid this, one should turn off the fire just prior to the coffee boiling, heat the coffee in a pot on very low fire, or heat the coffee by placing the pot near but not too close to a campfire. The same is generally true for tea.

With cowboy coffee you need significantly less coffee per measure of water, and the coffee ends up with a stronger flavor – if prepared properly. You also keep two key oily components of the coffee, namely the diterpenes known as kahweol and cafestol; its polyphenols, most notably chlorogenic acid; and some of the coffee particles.

Both kahweol and cafestol seem to be associated with reduction in certain types of cancer in humans, and show strong anti-cancer effects in rats (). The same seems to be generally true for chlorogenic acid (). The coffee particles, if ingested, would probably be treated as indigestible fiber and promote colon health. This is usually the fate of indigestible and partially digestible plant matter.

Why is filtered coffee often recommended? Well, unfiltered coffee is believed to promote heart disease. But that is not primarily due to any strong association having been found between unfiltered coffee consumption and heart disease. In fact, the absence of evidence in favor of this hypothesis in long-term studies is rather conspicuous ().

The belief that unfiltered coffee can promote heart disease is due to evidence showing that consumption of 4 cups per day of unfiltered coffee raises total cholesterol by up to 10 mg/dl ().

Only diehard proponents of the lipid hypothesis would look at total cholesterol increase as a marker of heart disease, in part because total cholesterol may increase due to an increase in HDL cholesterol – a much more reliable marker, but of protection against heart disease, particularly within certain ranges. And yes, unfiltered coffee consumption is associated with an increase in HDL cholesterol ().

Moreover, some of the metabolites of caffeine, 1-methyxanthine and 1-methyluric acid, appear to help prevent LDL oxidation; caffeine metabolites also seem to have potent anti-inflammatory properties ().

Some research provides evidence of the importance of moderation in coffee consumption as an important factor in its relationship with health. In this respect, coffee is like almost anything that can be ingested, including water – the dose makes the poison. In a study of 40,000 post-menopausal women in the US reviewed by Bonita and colleagues (), the hazard ratio of death attributed to heart disease was 0.76 for consumption of 1–3 cups/day, 0.81 for 4–5 cups/day, and 0.87 for ≥6 cups/day. Interestingly, the same study reported that the hazard ratio for death from other inflammatory diseases was 0.72 for consumption of 1–3 cups/day, 0.67 for 4–5 cups/day, and 0.68 for ≥6 cups/day.

Frequently you hear about the possible connection between coffee consumption and gastritis. The most widely cited study I could find that looked into this link found no association between coffee consumption and reflux-associated gastritis ().

By the way, if you have gastritis, you should consider getting tested for Helicobacter pylori (), especially if you like eating raw fish.

Stress and coffee consumption may have similar effects in those who test positive for Helicobacter pylori (see, e.g., ). In those individuals, past research has found a link between: (a) stress, coffee consumption, and other purported “stomach irritants”; and (b) exacerbation of gastritis symptoms, stomach ulcers, and stomach cancer.

This discussion on gastritis is largely unrelated to the issue of drinking unfiltered coffee. It is unclear based on the past studies that I reviewed whether coffee filtration has anything to do with any possible connection between coffee consumption and exacerbation of gastritis symptoms caused by other factors.

As a side note, it is important to keep in mind that the acidity of coffee is nowhere near the acidic of gastric acid, which the stomach is uniquely designed to handle.

I may be wrong, but from what I can see, if you drink coffee regularly and it causes no problems for you, drinking unfiltered coffee is not a bad idea at all.

Hypervitaminosis A and sweet potatoes

Can consumption of sweet potatoes cause hypervitaminosis A? The answer is “no”, even if you eat ten or more sweet potatoes per day. Sweet potatoes do have high vitamin A content, more than almost any other food. However, most of it is in the form of β-carotene, which is used by the body to produce the active form of vitamin A, retinal (yes, with an “a”), only if the body’s vitamin A status is low.

The graph below shows the vitamin A content of different foods, together with the recommended daily allowance. It was prepared with information from Nutritiondata.com (), with the horizontal axis in international units (). The graph also takes into consideration some key research findings related to the bioavailability of vitamin A. For example, the sweet potato is assumed to be taken with some fat to facilitate the absorption of vitamin A.

Primarily, vitamin A is available either as retinol, from animal foods; or β-carotene, from plant foods. There are other carotenes available from plant foods, but their vitamin A contribution is relatively small compared with β-carotene. High β-carotene content is “advertised” by plant foods to animals via a characteristic orange color. The main sources of β-carotene throughout human evolution have probably been fruits, which plants “want” animals to eat so that the plants’ seeds are dispersed.

Retinol also needs to be converted by the body to retinal, and when consumed in excess it tends to be stored in body fat reserves – hence lean individuals tend to store less retinol than fat ones. It seems that intake of retinol from sources like beef liver is naturally controlled via satiety. In the case of plant sources, like sweet potatoes, a key control mechanism is limited internal production of retinal. My impression is that most people, if given the chance, would prefer to eat a lot of sweet potato than a lot of beef liver.

Like all of the fat-soluble vitamins, the bioavailability of vitamin A from foods is dependent on whether they are consumed together with fat. For example, a lot more vitamin A will be absorbed from a sweet potato if it is eaten with butter than if it is eaten by itself (again, if the body’s vitamin A status is low). I should note that butter is itself a good source of vitamin A, in addition to providing the fat needed for absorption. Beef liver is low in fat, which means that the vitamin A content in the graph above may be an overestimation.

Hypervitaminosis is a fat-soluble vitamin phenomenon, and it is usually associated with consumption of supplements (e.g., cod liver oil). Generally speaking, one does not develop noticeable hypervitaminosis symptoms from consumption of natural food sources. This is probably due to a combination of satiety and internal regulation of the production of the active forms of the vitamins.