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Chewing the Cud

by Scott Jameson

RaceRealist and I have been ruminating on a lot of stuff lately. Here’s a fun one: what economic system works best relative to what we know about human health? In my mind there are two approaches: the libertarian approach, and quasi-fascism.

In the libertarian approach, there’s no regulation of sugar placed in our food. That’s already the case. But here’s an improvement: you don’t have to pay for anyone’s gastric bypass after they overeat that sugar.

In the fascist approach, there is regulation of sugar, because a fascist state does not allow people to poison each other for profit. You still have to pay for others’ medical expenses, but those expenses will be lower.

Here’s an advantage to the libertarian approach. In that society, the people who stuff their faces and refuse to get off the couch- who are dumber and lazier on average, probably- will have a higher mortality rate on average. Eugenics need not cost a dime.

But you run into a snag, sand in the gears of your hands-off system, when Big Food kicks out a whole bunch of crappy dietary advice, at which point a minority of reasonably intelligent people will be led astray, perhaps to the grave. How could a libertarian society stop that from taking place? Would it even bother? Could the system broadly work in spite of this snag?

A libertarian society doesn’t pay for idiots to have children. That’s good, but half of your population (women) are unlikely to ever support it. Women don’t do libertarianism; observe Rand Paul’s demographic Achilles Heel on page 25. When women asked men what to do about so-and-so’s eighth unpaid for child, we’d have to look them in the eyes and give a deadpan “let’s hope private charity can handle it.” There was a time, before FDR, when women would’ve accepted that answer. They were still in the kitchen back then, and I don’t know how to put them back there.

A fascist society has more hands-on eugenics, possibly genome editing or embryo selection. Also good. Expensive, but obviously worth it.

We welcome your input on these issues.

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As an aside, White men are well-known as the most conservative, small government, nationalist group out there in our current political atmosphere. I always hear people spewing the schmaltziest nonsense about the values of the Founding Fathers. They were, relative to our political compass, nationalist libertarians. Accordingly, modern nationalists and libertarians do best with the exact same demographics that used to vote on candidates back then: property-owning White men. The sole reason that Ron and Rand Paul couldn’t get elected is that they are too similar to the Founding Fathers. Any other candidate who blathers on about the Founding values is simply a liar, and their obvious lies show a disrespect of your intelligence.

If you’re a libertarian, but not an ethno-nationalistic and patriarchal thinker, then you simply haven’t gotten the memo: women and minorities do not want to create the same world that you do, nor will they ever. Evolution gave us women who want social safety nets and other races which are better off if they parasitize off of your tax dollars. All of the most libertarian societies that ever existed (early US, ancient Athens, Roman Republic) were entirely run by White men, and adding women to the electorate gave us the welfare state. Aristophanes was right.

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We’re also ruminating on the difference between IQ and expertise. I know of no mentally complicated task of which one can be a master without being intelligent. Take the IQs of chess grandmasters and you will find no morons.

Contrast that with purely physical activities. I bet you there are some really stupid people out there who are great at dancing for example. A prodigiously capable cerebellum may not predict an equally capable frontal lobe.

Discounting tasks which exclusively require things like simple physical coordination, muscle memory, etc, I ought to think that IQ is the biggest component of expertise.

Between-group Differences in Obesity Rates

By Scott Jameson

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I’ve been active in the blogosphere for around 24 hours now and I’ve already gotten a negative response from someone who happens to be wrong. That’s a win in my book.
The argument we’re having is, as best I can tell, why some populations out there just don’t have obesity as an observed phenotype amongst their members. TL;DR: Pumpkin Person and Robert Lindsay believe that genetics explain why there are no obese New Guineans. But it ain’t so.

The original context is an old Pumpkin Person post. Much of what he’s saying here doesn’t seem too off-base; for example he says that behavioral genetics may explain much of the differences in BMI between individuals within the same population. True. It is possible that some people are genetically inclined to eat more or unhealthier foods, rather than simply being genetically inclined to putting on weight regardless of what they do.

As an aside, genotypes that affect how you digest things also probably explain part of the BMI gap between skinny folks and fat folks within the first world. The APOA2 gene for example has a recessive allele that is associated with higher BMI in people who eat more saturated fats. The interactions between genes and environment which determine BMI are complicated and not yet fully understood, but I’m willing to bet that being genetically worse at processing certain nutrients is a part of the problem, and that being genetically inclined to stuff your face is a part of the problem as well. PP is probably right about that issue.

Where he and Lindsay get it wrong is using examples of people from Podunk, New Guinea as evidence for obesity “being genetic” (relative term). Obesity is a gene-environment interaction such that, without certain environmental inputs, you simply won’t get the phenotype. History tells us that that input is processed carbohydrates.

There was a time when people could have used Australian Aboriginals or Inuit or Pima Indians as examples of groups of people who just don’t have obese folks amongst their numbers, just as Lindsay did with a few populations. Homo sans lardicus. Then the White Devils showed up with their refined Einkorn wheat products and their firewater and so on. Now those populations have fat people in them.

There’s an ongoing debate as to whether some populations are more resistant to the fattening effects of processed carbs or not. My guess is, the answer’s yes (and you’d look at Europeans and East Asians to see the more carb-resistant people, in theory) but that topic would merit its own post. That being said, every population in the world will almost assuredly have obese people in it after you introduce processed carbs. All of the populations that were introduced to this diet, now have fat people in them.

Heritability of BMI is high within the first world because the relevant environmental input is pretty uniform: everybody has access to potatoes, everybody has access to broccoli. As PP points out, which you’re likely to eat and how much you’re likely to eat likely depends on your genetics. As I point out, how your body processes the nutrients also has a likely genetic component. But the environmental contribution to our within-population differences in BMI is low (~20%) because we all have access to roughly the same stuff.

Rural New Guineans, lacking a bunch of processed carbs, could hardly get fat if they tried their best to. That’s a big between-population, nonheritable cause for a phenotypic difference; this means that environment probably explains most of the BMI gap between them and us. If I wanted evidence to refute Lindsay’s assertion that New Guineans are skinnier thanks to genetics, I’d find a population of urbanized New Guineans somewhere with higher average BMI. Such a group would have New Guinean genetics but a “developed” environment vaguely similar to ours; if they were fatter than their rural ken, then Lindsay’s hypothesis that New Guineans are just genetically obesity-free would be falsified.

If only such research existed!

Fatty Acids and PISA Math Performance

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There are much more interesting theories of the evolution of hominin intelligence other than the tiring (yawn) cold winter theory. Last month I wrote on why men are attracted to a low waist-to-hip ratio in women. However, the relationship between gluteofemoral fat (fat in the thighs and buttocks) is only part of the story on how DHA and fatty acids (FAs) drove our brain growth and our evolution as a whole. Tonight I will talk about how fatty acids predict ‘cognitive performance’ (it’s PISA, ugh) in a sample of 28 countries, particularly the positive relationship between n-3 (Omega-3s) and intelligence and the negative relationship between n-6 and intelligence. I will then talk about the traditional Standard American Diet (the SAD diet [apt name]) and how it affects American intelligence on a nation-wide level. Finally, I will talk about the best diet to maximize cognition in growing babes and women.

Lassek and Gaulin (2013) used the 2009 PISA data to infer cognitive abilities for 28 countries (ugh, I’d like to see a study like this done with actual IQ tests). They also searched for studies that showed data providing “maternal milk DHA DHA values as percentages of total fatty acids in 50 countries”. Further, to control for SES influences on cognitive performance, they controlled for GDP/PC (gross domestic product per country) and “educational expenditures per pupil.” They further controlled for the possible effect of macronutrients on maternal milk DHA levels, they included estimates for each country of the average amount of kcal consumed from protein, fat, and carbohydrates. To explore the relationship between DHA and cognitive ability, they included foodstuffs high in n-3—fish, eggs, poultry, red meat, and milk which also contain DPA depending on the type of feed the animal is given. There is also a ‘metabolic competition’ between n-3 and n-6 fatty acids, so they also included total animal and vegetable fat as well as vegetable oils.

Lassek and Gaulin (2013) found that GDP/PC, expenditures per student and DHA were significant predictors of (PISA) math scores, whereas macronutrient content showed no correlation.

The predictive value of milk DHA on cognitive ability is only weak when either two of the SES variables are added in the multiple regression. When milk arachidonic (a type of Omega-6 fatty acid) is added to the regression, it is negatively correlated with math scores but not significantly (so it wasn’t added to the table below).

pisadha

So countries with lower maternal milk levels of DHA score lower on the maths section of the PISA exam (not an IQ test, but it’s ‘good enough’). Knowing what is known about the effects of DHA on cognitive abilities, countries who have higher maternal milk levels of DPA do score higher on the maths section of the PISA exam.

dhafoodcorrelation

Table 2 shows the correlations between grams per capita per day of food consumption in the data set they used and maternal milk DHA. As you can see, total fish and seafood consumption are substantially correlated with total milk DHA, while foods that are high in n-6 show medium negative correlations with maternal milk DHA. The combination of foods that explain the most of the variance in maternal milk DHA is total fat consumed and total fish consumed. This explained 61 percent of the variance in maternal milk DHA across countries.

Not surprisingly, foodstuffs high in n-6 showed significant negative correlations on maternal milk DHA. “Any regression including total fish or seafood, and vegetable oils, animal fat or milk consistently explains at least half of the variance in milk DHA, with fish or seafood having positive beta coefficients and the remainder having negative beta coefficients.”

The study showed that a country’s balance of n-3 and n-6 was strongly related to the students’ math performance on the PISA. This relationship between milk DHA and cognitive performance remains sufficient even after controlling for national wealth, macro intake and investment in education. The availability of DHA in populations is a better predictor of test scores than are SES factors (which I’ve covered here on Italian IQ), though SES explains a considerable portion of the variance, it’s not as much as the overall DHA levels by country. Furthermore, maternal DHA levels are strongly correlated to per capita fish and seafood consumption while a negative correlation was noticed with the intake of more vegetable oils, fat, and beef, which suggests ‘metabolic competition’ between the n-3 and n-6 fatty acids.

There are, of course, many possible errors with the study such as maternal milk DHA values not reflecting the total DHA in that population as a whole; measures of extracting milk fatty acids differed between studies; test results being due to sampling error; and finally the per capita consumption of foods is based on food disappearance, not amount of food consumed. However, even with the faults of the study, it’s still very interesting and I hope they do further work with actual measures of cognitive ability. Despite the pitfalls of the study (the main one being the use of PISA to test ‘cognitive abilities’), this is a very interesting study. I eventually hope that a study similar to this one is undertaken with actual measures of cognitive ability and not PISA scores.

We now know that n-6 is negatively linked with brain performance, and that n-3 is positively linked. What does this say about America?

As I’m sure all of you are aware of, America is one of the fattest nations in the world. Not surprisingly, Americans consume extremely low levels of seafood (very high in DPA) and more foods high in n-6 (Papanikolaou et al, 2014). High levels of n-3 (which we do not get enough of in America) and n-6 are correlated with obesity (Simopoulos, 2016). So not only do we have a current dysgenic effect in America due to decreased fertility of the more intelligent (which is also part of the reason why we have the effect of dysgenic fertility in America), obesity is also driven by high levels of n-6 in the Western diet, which then causes obesity down the generations (Massiera et al, 2010).

I also previously wrote on agriculture and diseases of civilization. Our hunter-gatherer ancestors were all around healthier than we were. This, clearly, is due to the fact that they ate a more natural diet and not one full of processed, insulin-spiking carbohydrates, among other things. Our hunter-gatherer ancestors consumed n-3 and n-6 at equal amounts (1:1) (Kris-Etherson, et al 2000). As I documented in my article on agriculture and disease, HGs had low to nonexistent rates of the diseases that plague us in our modern societies today. However, around 140 years ago, we entered the Industrial Revolution. The paradigm shift that this caused was huge. We began consuming less n-3 (fish and other assorted seafood and nuts among other foods) while n-6 intake increased (beef, grains, carbohydrates) (Kris-Etherson, et al 2000). Moreover, the ratio of n-6 to n-3 from the years 1935 to 1939 were 8.4 to 1, whereas from the years 1935 to 1985, the ratio increased to about 10 percent (Raper et al, 2013). We Americans also consume 20 percent of our daily kcal from one ‘food’ source—soybean oil—with almost 9 percent of the total kcal coming from n-6 linoleic acids (United States Department of Agriculture, 2007). The typical American diet contains about 26 percent more n-6 than n-3, and people wonder why we are slowly getting dumber (which is, obviously, a side effect of civilization). So our n-6 consumption is about 26 percent higher than it was when we were still hunter-gatherers. Does anyone still wonder why diseases of civilization exist and why hunter-gatherers have low to nonexistent rates of the diseases that plague us?

The bioavailability of n-6 is dependent on the amount of n-3 in fatty tissue (Hibbeln et al, 2006). This goes back to the ‘metabolic competition’ mentioned earlier. N-3 also makes up 10 percent of the overall brain weight since the first neurons evolved in an environment high in n-3. N-3 fatty acids were positively related to test scores in both men and women, while n-6 showed the reverse relationship (with a stronger effect in females). Furthermore, in female children, the effect of n-3 intake were twice as strong in comparison to male children, which also exceeded the negative effects of lead exposure, suggesting that higher consumption of foods rich in n-3 while consuming fewer foods rich in n-6 will improve cognitive abilities (Lassek and Gaulin, 2011).

The preponderance of evidence suggests that if parents want to have the healthiest and smartest babes that a pregnant woman should consume a lot of seafood while avoiding vegetable oils, total fat and milk (fat, milk and beef moreso from animals that are grain-fed) Grassfed beef has higher levels of n-3, which will balance out the levels of n-6 in the beef. So if you want your family to have the highest cognition possible, eat more fish and less grain-fed beef and animal products.

In sum, if you want the healthiest, most intelligent family you can possibly have, the most important factor is…diet. Diets high in n-3 and low in n-6 are extremely solid predictors of cognitive performance. Due to the ‘meatbolic competition’ between the two fatty acids. This is because n-6 accumulates in the blood and tissue lipids exacerbating the competiiton between linolic acid (the most common form of n-6) and n-3 for metabolism and acylation into tissue lipds (Innis, 2014). Our HG ancestors had lower rates of n-6 in their diets than we do today, along with low to nonexistent disease rates. This is due to the availability of n-6 in the modern diet, which was unknown to our ancestors. Yes, seafood intake had the biggest effect on the PISA math scores, which, in my opinion (I need to look at the data), is due in part to poverty. I’m very critical of PISA, especially as a measure of cognitive abilities, but this study is solid, even though it has pitfalls. I hope a study using an actual IQ test is done (and not Richard Lynn IQ tests that use children, a robust adult sample is the only thing that will satisfy me) to see if the results will be replicated.

I also think it’d be extremely interesting to get a representative sample from each country studied and somehow make it so that all maternal DHA levels are the same and then administer the tests. This way, we can see how all groups perform with the same amounts of DHA (and to see how much of an effect that DHA really does have). Furthermore, nutritonally impoverished countries will not have access to the high-quality foods with more DHA and healthy fatty acids that lead to higher cognitive function.

It’s clear: if you want the healthiest family you could possibly have, consume more seafood.

Agriculture and Diseases of Civilization

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It is assumed that since the advent of agriculture that we’ve been better nourished than our hunter-gatherer ancestors. This assumption stems from the past 130 years since the advent of the Industrial Revolution and the increase in the quality of life of those who had the benefit of the Revolution. However, over a longer period of time, the advent of agriculture is linked to poorer health, vectors of disease and lower quality of life (in terms of intractable disease). Despite what I have claimed in the past about hunter-gatherer societies, they do have lower or nonexistent rates of the diseases that currently plague our first-world societies. Why do we have such extremely high rates of disease that they don’t?

Contrary to popular belief, agriculture has caused decreases in many facets of our lives. These diseases, more aptly termed ‘diseases of civilization‘ are directly caused by agricultural and societal ways of living. This increases disease rates as it’s easier for diseases to spread faster through bigger populations. Moreover, we haven’t had time to evolve to the current diet we now eat in first-world countries which has lead to what is termed an ‘evolutionary mismatch‘ between genes and environment. We evolved to eat a certain diet and the introduction of easily digestible carbohydrates which spike insulin the highest. Since insulin causes weight gain, and carbohydrate intake has dramatically increased since the 70s, obesity has increased as a result as countries begin to industrialize and more processed foods are available to the populace.

However, since the Industrial Revolution, height has increased along with IQ. Researchers argue that in first-world countries, high rates of obesity are not preventable due to the excess amounts of highly refined and processed foods. There is data for this theory. In first-world countries, the heritability of BMI is between .76 and .85. Since first-world countries are industrialized, we would expect them to hit their ‘genetic height and weight’ along with having the ability to reach their IQ potential. However, with the excess amount of highly processed and refined foods, this would also, in theory, have the population hit their ‘genetic weights’. This is what we see in first-world countries.

To see how first-world, industrialized societies cause these gene-environment mismatches, we can compare the disease acquisition rate—or lack thereof—to that of Europeans eating an industrialized, first-world diet (high in carbohydrates).

In his 2013 book The Story of the Human Body: Evolution, Health, and DiseasePaleoanthropologist Daniel Lieberman talks at length about evolutionary mismatches. The easiest way to think about this is to think about how one evolved to their environment and think how the processes that alter the environment. A perfect example is African farmers. They may dig a trench to divert water to better irrigate their crops, but this then would cause a higher rate of mosquitoes due to the increase in still water and then selection for genes that protect against malaria would be selected for. This is one example of an evolutionary mismatch turning into an advantage for a population. Most mismatch diseases are caused by changes in the environment which change how the body functions. In other words, the current first-world diet is correlated very highly with diseases of civilization and drive most of the mismatch diseases. Most likely, you will die from one of these mismatch diseases.

If you’re born in a hotter environment, you will have more sweat glands than if you were born in a cooler environment. If you grow up eating soft, processed food, your face will be smaller than if you ate harder foods. These are two ways in which ‘cultural evolution’ (cultural change) have an effect on how the human body grows and adapts to certain stimuli based on the environment around it.

The largest cause of the higher disease rate between industrialized peoples and those in hunter-gatherer societies is shifts in life history. As our life spans increased through modernization, so to did our chance of acquiring more diseases. Of course living longer affects how many children you have but it also raises your chances of acquiring an evolutionary mismatch and your chances of dying from one.

Daniel Lieberman writes on page 190 of his book The Story of the Human Body:

A typical hunter-gatherer adult female will manage to collect 2,000 calories a day and a male can hunt between 3,000 and 6,000 calories a day. (24) A hunter-gatherer groups combined efforts yield just enough food to feed small families. In contrast, a household of early Neolithic farmers from Europe using solely manual labor before the invention of the plow could produce an average if 12,800 calories per day over the course of a year, enough to feed families of six. (25) In other words, the first farmers could double their family size.

Thusly, you can see how evolutionary mismatches would occur with the advent of an agricultural diet that we didn’t evolve to be accustomed to. This is one of the biggest examples of the negative effects of agriculture, our inability to adapt quickly to our new diets which then accelerated after the Industrial Revolution. Further, hunter-gatherers will eat anything edible while agricultural societies will largely eat only what they grow. This would have huge implications for farmers if a few pests ruined their crops since they relied on a few crops to survive.

The thing about farming is that as the Agricultural Revolution began, this increased the population size as well as making that population pretty much stable in terms of migrating. This, then, led to higher rates of disease as larger populations foster new kinds of infectious diseases. Large populations didn’t happen until the advent of farming, and with it came the first plagues. The first farming villages were small, but “as the Reverend Malthus pointed out in 1798, even modest increases in a population’s birthrate will cause rapid increases in overall population size in just a few generations.” (Lieberman, 2013: 197) So as even small increases in population size would cause a boom in future generations, which along with it would drive disease acquisition and plagues in that new and stationary society.

Lieberman further writes on pages 199-200:

Not surprisingly, farming ushered in an era of epidemics, including tuberculosis, leprosy, syphilis, plague, smallpox and influenza. (44) This is not to say that hunter-gatherers did not get sick, but before farming, human societies primarily suffered from parasites such as lice, pinworms they acquired from contaminated food, and viruses or bacteria, such as herpes simplex, which they got from contact with mammals. (45) Diseases such as malaria and yaws (the nonvenereal precursor of syphilis) were probably also present among hunter-gatherers, but at much lower rates than in farmers. In fact, epidemics could not exist prior to the Neolithic because hunter-gatherer populations are below one person per square kilometer, which is below the threshold necessary for virulent diseases to spread. Smallpox, for example, is an ancient viral disease that humans apparently acquired from monkeys or rodents (the disease’s origins are unresolved) that was able to spread appreciably until the growth of large, dense settlements. (46)

Moreover, another evolutionary mismatch is the lack of sanitation that comes with stationary societies. Hunter-gatherers could just go and defecate in a bush, whereas with the advent of civilization, waste and refuse began to pile up in the area. As noted above, when farmers clear space for irrigation to plant crops, this introduces mosquitoes into the area which then causes more disease. Furthermore, we have also acquired about 50 diseases from living near animals (Lieberman, 2013: 201). There are more than 100 evolutionary mismatch diseases that agriculture has brought to humanity.

We can compare disease rates of people in industrialized societies and people in modern-day hunter-gatherer societies. In his 2008 book Good Calories, Bad CaloriesGary Taubes documents numerous instances of hunter-gatherer societies that have no to low rates of the same modern diseases that we have:

In 1914, Hoffman himself had surveyed physicians working for the Bureau of Indian Affairs. “Among some 63,000 Indians of all tribes,” he reported, “there occurred only 2 deaths from cancer as medically observed from the year 1914.” (Taubes, 2008: 92)

“There are no known reasons why cancer should not occasionally occur among any race of people, even though it be below the lowest degree of savagery and barbarism,” Hoffman wrote. (Taubes, 2008: 92)

“Granting the practical difficulties of determining with accuracy the causes of death among the non-civilized races, it is nevertheless a safe assumption that the large number of medical missionaries and other trained medical observers, living for years among native races throughout the world, would long ago have provided a substantial basis of fact regarding the frequency of malignant disease among the so-called “uncivilized” races, if cancer were met with among them to anything like the degree common to practically all civilized countries. Quite the contrary, the negative evidence is convincing that in the opinion of qualified medical observers cancer is exceptionally rare among the primitive peoples.” (Taubes, 2008: 92)

These reports, often published in the British Medical Journal, The Lancet or local journals like the East African Medical Journal, would typically include the length of service the author had undergone among the natives, the size of the local native population served by the hospital in question, the size of the local European population, and the number of cancers involved in both. F.P. Fouch, for instance, district surgeon of the Orange Free State in South Africa, reported to the BMJ in 1923 that he had spent six years at a hospital that served fourteen thousand natives. “I never saw a single case of gastric or duodenal ulcer, colitis, appendicitis, or cancer in any form in a native, although these diseases were frequently seen among the white or European population.” (Taubes, 2008: 92)

As a result of these modern processed foods, noted Hoffman, “far-reaching changes in bodily functioning and metabolism are introduced which, extending over many years, are the causes or conditions predisposing to the development of malignant new growths, and in part at least explain the observed increase in cancer death rate of practically all civilized and highly urbanized countries.” (Taubes, 2008: 96)

The preponderance of evidence shows that these people have low rates of disease that are endemic to our societies due to the advent of agriculture. There is one large difference between hunter-gatherer societies and industrialized ones: the type and amount of food we eat.

Along with the boom of agriculture, we see a slight decrease in height the longer people live in these types of societies. As the Neolithic began 11,500 years ago, height increased about 1.5 inches for males and slightly less for females. But around 7,500 years ago, stature began to decrease and we began noticing evidence of nutritional stress and skeletal markers of disease. There is evidence that as maize was introduced into eastern Tennessee about 1,000 years ago, a decrease of .87 inches in men and 2.4 inches in women were seen. Further, the height of early farmers in China and Japan decreased by 3.1 inches as rice farming progressed, with similar height decreases being seen in Mesoamerica in men (2.2 inches) and women (3.1 inches).

Anti-hereditarian Jared Diamond asks the question “Was farming worth it?” in which he writes:

With agriculture came the gross social and sexual inequality, the disease and despotism, that curse our existence.

The first two things he brings up are pretty Marxist in nature, though they are true. He implies that agriculture causes so-called ‘sexual inequalities’ in which women are made ‘beasts of burden’, made to do the work while men walk by ’empty handed’. This seems to be one negative to a society that is, supposedly, smarter than Europeans.

Regular readers may remember me criticizing Andrew Anglin and his stance on the paleo diet—with how it’s ‘how European man evolved to eat’. However, I am a data-driven person and I try to not let any bias get involved in my thought processes. I know do believe that we should eat a diet that closely mimics our hunter-gatherer ancestors, though we shouldn’t go overboard like certain people in the paleo community, we should be mindful of the quality of food we do it as we will greatly increase our life expectancy along with our quality of life. Indeed, researchers have proposed that we should adopt diets that are close in composition to what our hunter-gatherer ancestors ate in order to battle diseases of civilization. Based on what I’ve read over the past few months, I am inclined to agree. Indeed, evidence for this is seen in a sample of ten Australian Aborigines who were introduced back to their traditional lifestyle (O’Dea, 1984). In a 7 week period, they showed improvement in carbohydrate and lipid metabolism, effectively becoming diabetes-free in almost 2 months.

In sum, there were obviously both positive and negative effects on human life due to the advent of agriculture (leaning more towards negative). These range from diseases to increased population size, to ‘social inequalities’ to higher rates of obesity (this evolutionary mismatch will be extensively covered in the future) to a whole myriad of other diseases. These then lower the quality of life of the individual inflicted. However, the rates of these diseases are low to non-existent in hunter-gatherer societies due to them being nomadic and eating more plentiful foods. Agricultural societies become dependent on a few staple crops so when an endemic occurs, there is mass death since they do not know how to subsist on anything but what they have become accustomed to. The advent of agriculture leads to a decrease in stature as well as brain size. Further, agriculture and the processed foods that came with it caused us to become more susceptible to obesity, which was further exacerbated by the industrial revolutions and the ‘nutritional guidelines’ of the 60s and 70s that led to higher rates of coronary heart disease. It is the lifestyle change from agriculture that we have not adapted to yet that causes disease these diseases of civilization that shorten our life expectancies. I do now believe that all people should eat a diet as close to hunter-gatherer diet as possible, as that’s what the preponderance of evidence shows.

By the way, to my knowledge, contrary to what The Alternative Hypothesis says, there are no differences in carbohydrate metabolism between races (save for a few populations such as the Pima).

Why Are Men Attracted To Low Waist-to-Hip Ratios?

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Why are men attracted to low waist-to-hip ratios (WHR)? Like with a lot of our preferences, there is an evolutionary reason why men are attracted to low WHR. I came across a paper the other day by M.D. William Lassek, “Assistant Professor of Epidemiology and Research Associate in the department of Anthropology at the University of California, Santa Barbara” and co-author P.h.D. Steven Gaulin, Professor of Anthropology with specific research interests in “evolutionary psychology, cognitive adaptations, the human voice, sexual selection, evolution of sex differences, lipid metabolism and brain evolution.” This paper fascinates me because it talks about the evolution of human intelligence through a lens of nutrition and micronutrients, something that I’m well-read on due to my career. First, I will discuss the benefits of fish oil and the main reason for taking them: omega-3 fatty acids and DHA. Then I will discuss the WHR/intelligence theory.

Fish Oils, DPA/EPA, and Omega-3 Fatty Acids

Misinformation about fish oils is rampant, specifically in the HBD-sphere, specifically with Steve Sailer’s article HBD and Diet AdviceThe study he cites (with no reference)  I assume is this study by Yano et al (1978) in which they found that Japanese men who ate more carbohydrates had less of a chance to die of cardiovascular heart disease (CHD). He says that the first generation ate mostly rice and no fat while the second generation “ate cheeseburgers and had higher rates of coronary disease than their parents.” He then says that these diet recommendations (low-fat, high-carb) were put onto all populations with no proven efficacy for all ethnies/racial groups. These diet recommendations began around two decades before the 80s, however.

He then quotes an article by the NYT science write, Carl Zimmer, talking about how the Inuit study has “added a new twist to the omega-3 fatty acid story”. Now, I read papers on nutrition every day due to my career, I don’t know what kind of literature they read on the subject, but fish oil, more specifically DPA/EPA and omega-3s are hugely important for optimal brain growth, health, and function.

Controlled studies clearly show that omega-3 consumption had a positive influence on n-3 (fatty acid) intake. N-3 has also been recognized as a modulator of inflammation as well as the fact that omega-3 fatty acids down-regulate genes involved in chronic inflammation, which show that n-3 is may be good for atherosclerosis.

An increase in omega-3 consumption leads to decreased damage from heart attacks.

Omega-3 may also reduce damage after a stroke.

Dietary epidemiology has also shown a link between n-3 and mental disorders such as Alzheimers and depression. N-3 intake is also linked to intelligence, vision and mood. Infants who don’t get enough n-3 prenatally are at risk for developing vision and nerve problems. Other studies have shown n-3’s effects on tumors, in particular, breast, colon and prostate cancer.

Omega-3’s are also great for muscle growth. Omega-3 intake in obese individuals along with exercise show a speed up in fat-loss for that individual.

Where do these people get their information from? Not only are omega-3’s good for damage reduction after a stroke and a heart attack, they’re also good for muscle growth, breast, colon and prostate tumor reduction, infants deficient in omega-3 prenatally are at risk for developing nerve and vision problems. Increase in omega-3 consumption is also linked to increases in cognition, reduces chronic inflammation and is linked to lower instances of depression.

Clearly, fish oils have a place in everyone’s diet, not only Inuits’.

This also reminds me of The Alternative Hypothesis’s argument that there are differing CHO metabolisms based on geographic origin (not true, to the best of my knowledge).

WHR and Intelligence

Most of the theories of the increase in brain size and intelligence have to do with climate, in one way or another, along with sexual selection. Though recently, I’ve been rethinking my position on cold winters having that big of an effect on intelligence due to some new information I’ve come across. The paper titled Waist-hip ratio and cognitive ability: is gluteofemoral fat a privileged store of neurodevelopmental resources? by Lassek and Gaudin (2008) posits a very sensible theory about the evolution of human intelligence: mainly that men prefer hour-glass figures due to an evolutionary adaptation.

Why may this be the case? One of the most important reasons I can think of is that women with high WHR have a higher chance of rate of death. The Nurses Health Study followed 44,000 women for 16 years and found that women who had waists bigger than 35 inches had a two times higher risk of dying from heart disease when compared to women with the lowest waist size of less than 28 inches. Clearly, men prefer women with low WHR since they will live longer, conceive more children and be around longer to take care of said children. So while a low WHR is not correlated with fertility per se, it is correlated with longevity, so the woman can have more children to spread more of her genes.

Lassek and Gaulin also bring up the ‘thrifty gene hypothesis’, which states that these genes evolved in populations that experienced nutritional stress, i.e., famines. I’ve read a lot of books on nutrition and human evolution (I highly recommend The Story of the Human Body: Evolution, Health, and Diseaseover the years and most of them discredit the idea of the thrifty gene hypothesis. However, recent research has shown the existence of these ‘thrifty genes’ in populations such as the Samoans and ‘Native’ Americans. It’s simple, really. Stop eating carbohydrates and the problems will fade away. (Hunter-gatherers don’t have these disease rates that we do in the West; it’s clear that the only difference is our diet and lifestyle. I will cover this in a future post titled “Diseases of Civilization”.)

Lassek and Gaulin pursued the hypothesis that gluteofemoral fat (fat stored in the thighs and buttocks) was the cause for the difference in the availability of neurodevelopmental nutrients available to a fetus. If correct, this could show why men prefer women with a low WHR and could show why we underwent such rapid brain growth: due to the availability of neurodevelopmental nutrients in the mother’s fat stores. Gluteofemoral body fat is the main source of long-chain polyunsaturated fatty acids (LPUFA) for children, along with another pertinent nutrient for fetal development: DHA. Lassek and Gaulin also state that 10 to 20 percent of the fat stored by a young woman during puberty is gluteofemoral fat, obviously priming her for childbearing. Even with caloric restriction, the gluteofemoral fat is not tapped utilized until late pregnancy/lactation when the baby needs nutrients such as DPA/EPA and omega-3s.

Further, 10 to 20 percent of the dry weight of the brain is made up of LCPUFA, which shows how important this one nutrient is for proper brain development in-vitro as well as the first few years of life. Lassek and Gaulin state:

A recent meta-analysis estimates that a child’s IQ increases by 0.13 point for every 100-mg increase in daily maternal prenatal intake of DHA (Cohen, Bellinger, Connor, & Shaywitz, 2005), and a recent study in England shows a similar positive relationship between a mother’s prenatal consumption of seafood (high in DHA) and her child’s verbal IQ (Hibbeln et al., 2007).

Along with what I cited above about these nutrients and their effects on our bodies while we’re in our adolescence and even adulthood, this is yet another huge reason WHY we should be consuming more fish oils, not only for the future intelligence of our offspring, but for our own brain health as a whole. Lassek and Gaulin state on pg. 3:

Each cycle of pregnancy and lactation draws down the gluteofemoral fat store deposited in early life; in many poorly nourished populations, this fat is not replaced, and women become progressively thinner with each pregnancy, which is termed “maternal depletion” (Lassek & Gaulin, 2006). We have recently shown that even well-nourished American women experience a relative loss of gluteofemoral fat with parity (Lassek & Gaulin, 2006). In parallel, parity is inversely related to the amount of DHA in the blood of mothers and neonates (Al, van Houwelingen, & Hornstra, 1997).

That critical fatty acids are depleted with parity is also consistent with studies showing that cognitive functioning is impaired with parity. IQ is negatively correlated with birth order (Downey, 2001), and twins have decreased DHA (McFadyen, Farquharson, & Cockburn, 2001) and compromised neurodevelopment compared to singletons (Ronalds, De Stavola, & Leon, 2005). The mother’s brain also typically decreases in size during pregnancy (Oatridge et al., 2002).

This also could explain why first born children are more intelligent than their siblings: because they have first dibs on the neurodevelopmental nutrients from the gluteofemoral fat, which aids in their brain growth and intelligence. What also lends credence to the theory is how the mother’s brain size typically decreases during pregnancy, due to the neurodevelopmental nutrients going to the child. (I also can’t help but wonder if this has any effect on Chinese IQ, since they had a nice increase in intelligence due to the Flynn Effect from 1982 to 2012. I will cover that in the future.)

“This hypothesis,” the authors write, “thus unites two derived (evolutionarily novel) features of Homo sapiens: sexually dimorphic fat distributions and large brains. On this view, a low WHR signals the availability of critical brain-building resources and should therefore have consequences for cognitive performance.”

The authors put forth three predictions for their study: 1) that a woman’s WHR should be negatively correlated with the cognitive ability of her offspring, 2) a woman’s WHR should be negatively correlated with her own intelligence since a woman passes on DPA as well as her own genes for low WHR to female offspring and 3) “cognitive development should be impaired in women whose first birth occurred early as well as in her future offspring, but lower WHRs, which indicate large stores of LCPUFA should be significantly protective for both” the mother and the child.

Lassek and Gaulin used data from the NHANES (National Health and Nutrition Examination Survey) III which included over 16,000 females with a mean age of 29.9 years. Measurements were taken on waist and hip circumference, WHR, BMI, and body fat as measured from bioelectrical impedance.*

For 752 “nulligravidas” (medical term for a woman who has never been pregnant), WHR explained 23 percent of the variance in total body fat estimated from the bioelectrical impedance (ugh, such a horrible measure). Moreover, “controlling for age and race/ethnicity” showed an increase of “0.01 in WHR increases total body fat by .83 kg” (1.82 pounds in freedom units). They also discovered that WHR explains 28 percent of the variance in BMI, with an increase of .47 kg per square meter, increasing the WHR by 0.01. BMI also explained 89 percent of the variance in body fat (garbage ‘body fat measuring instrument’ aside) with an increase of 1 kg per square meter increasing fat by 1.8 kg (close to 4 pounds in freedom units), but when added to the regression model, WHR made no additional contribution.

Lassek and Gaulin’s first hypothesis was corroborated when they found that the mother’s WHR was negatively correlated with the child’s intelligence on 4 cognitive tests. WHR accounted for 2.7 percent of the variation in test scores, “with a decrease of 0.01 in the mother’s current WHR increasing the child’s mean cognitive score by 0.061 points”. In the first subsample, they controlled for mother’s age, parental education, family income and race/ethnicity. Even when these variables were controlled for, WHR was still negatively correlated with the cognitive score. When these variables were controlled for, a decrease of 0.01 in WHR increased the average score by 0.024 points.

Their second hypothesis was also confirmed: that women with lower WHR would be more intelligent than women with higher WHRs. In girls aged 14-16, the WHR accounted for 3.6 percent of the variance in the average of the four cognitive tests. Also discovered was that in women aged 18 to 49, WHR accounted for 7 percent of the variance in years of education and 6 percent of the variance in two tests of cognitive ability. Even when controlling for age, parity, family income, age at first birth, and race/ethnicity, the negative correlation was still seen in 14 to 16-year-old girls.

There is also competition neurodevelopmental resources between mother and child. As I showed earlier in this article, a woman’s brain size decreases during pregnancy. This decrease in brain size during pregnancy is due to the babe getting more of the neurodevelopmental nutrients for brain growth from the mother. Clearly, as the mother’s stores of brain-growing nutrients become depleted, so does her brain size as te nutrients from her stored fat goes to developing the fetuses’ brain.

Lassek and Gaulin confirmed their hypothesis that a woman with a lower WHR would be more intelligent as well as have more intelligent children. WHR predicts the cognitive ability of the offspring while BMI does not. However, controlling for family income and parental education decreases the effect of WHR on the child’s intelligence, the effect still remains giving strong support to the hypothesis that women with low WHR pass on genes for low WHR as well as nutrients needed for neurodevelopment. Further, controlling for parental cognitive ability may mask the effects of the WHR. It’s well known that the mother’s intelligence is the best predictor for her offspring’s intelligence, which is due to the mother and grandmother passing on genes that augment the effect of LCPUFAs, along with the genes for lower WHR.

Women with a lower WHR were found to be more intelligent, and a lower WHR helps to protect cognitive resources (neurodevelopmental nutrients) for the mother and child. The mother’s body has a dilemma, though: it has to store nutrients for the mother’s own cognition; store resources for future pregnancies; and provide nutrients for their growing fetus. Obviously, especially in young mothers, this poses a problem as there is a conflict for what the brain should do with the nutrients the mother ingests. Children born to teenaged mothers have lower cognitive test scores, but, they are protected from this fate if the mother has a low WHR. This shows, definitively, that young mothers who are still growing will show no negative effects on their growth when pregnant if they have a low WHR which signals they have a large amount of LCPUFAs and other essential neurodevelopmental nutrients for the baby’s brain growth.

LCPUFAs are scarce in human diets. Thusly, an evolutionary preference for low WHR evolved for men so their children can have optimal nutrients while growing in the mother’s womb. The study confirmed that large brains, and along with it higher intelligence, and sexually dimorphic fat distribution have a strong link. Clearly, if a mother doesn’t have adequate levels of LCPUFAs, neurodevelopment will be impeded since the babe will not be getting the optimal nutrients for brain growth. Moreover, diets low in omega-3s should have consequences for intelligence and brain size of a baby, since when a baby is in the womb that is the most important time for it to get optimal brain nutrients. Is there any type of environment we can make ourselves and lifestyle choices we can take for ourselves, spouses and children to foster higher intelligence in them? I will cover that in the future.

Men love hour-glass figures, a low WHR. As I’ve shown in this article, there is an evolutionary reason for this. Men were asked to rate women who had surgery to move fat to their buttocks. Body weight stayed the same, but the fat was redistributed. It was found in brain scans of the men that the same parts of the brain related to reward lit up, including regions associated with drugs and alcohol. (more information here)

Conclusion

I’ve long known of the tons of positive benefits of omega-3 fatty acids and fish oil on human brain development. Fish oils and the nutrients in them are imperative for a healthy and growing brain. Without it, brain development will suffer. As a man, I can say firsthand that a low WHR is the most attractive. Now I understand the evolutionary reason behind it: fostering high intelligence due to the mothers lower-body fat stores. Omega-3s and LCPUFA are extremely important for optimal fetal brain growth. Moreover, the current American diet is low in omega-3s, while high in omega-6s. There is evidence of high omega-6 intake being related to obesity, metabolic syndromes, a progressive increase in body fat over the generationsThe omega-6 and -3 ratios in the body also play a role in obesity, with a lower omega-3 ratio and higher omega-6 ratio being related to obesity. This is due to adipogenesis, browning of the fat tissue, lipid homeostasis, and systemic inflammation. Clearly, as shown in this article, it’s imperative to have a balance of omega-3 and omega-6 fatty acids. This could also have to do with the hyperactivity of the cannabinoid system (which we all know what that’s involved with: eating more) and that could also be a cause for obesity with out-of-whack omega-6 to -3 fatty acid levels in the body. That’s for another day, though.

The totality of evidence is clear. If you want healthy children, choose a mate with a low WHR. She and her offspring will be more likely to be more intelligent. Clearly, if you’re reading this, you’re interested in intelligence as well as having the best possible life and life outcomes for your children. Well, choose a woman with a low WHR and you’ll be more likely to have more intelligent children!

* I have one problem with this study. They assessed body fat with bioelectrical impedance. The machine sends a light electrical current through the body and measures the degree of resistance to the flow of the current, which body fat can then be estimated. Problems with measuring body fat this way are as follows: it depends on how hydrated you are, whether you exercised that day, when you last ate, even whether your feet are calloused. Most importantly, they vary depending on the machine as well. Two differing machines will give two differing estimates. This is my only problem with the study. I would like if, in a follow-up study, they would use the DXA scan or hydrostatic weighing. These two techniques would be much better than using bioelectrical impedance, as the variables that prevent bioelectrical impedance from being a good way to measure body fat don’t exist with the DXA scan or hydrostatic weighing.

(Also see Eternal Curves by the Lassek and Gaulin and their book Why Women Need Fat for more information.)

Agriculture and Evolution: A Reply to The Alternative Hypothesis

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I love nutrition science. So much so that I read a new book on it every week. The Alternative Hypothesis has a pretty old video on agriculture and evolution. I strongly disagree with his main thesis. I strongly disagree with his denigration of Gary Taubes. Most of all, I strongly disagree with what he says about the East Asian rice eaters because since that video has been made, the carbohydrate/insulin hypothesis of obesity has changed to the insulin hypothesis of obesity.

In the very beginning of the video he brings up Gary Taubes’s research on low-carb diets and how people tend to be healthier than those who eat higher carb diets. He brings up the East Asians who eat a lot of rice. However, it’s clear he doesn’t know that the percent of carbohydrate intake is nowhere near as important as the absolute amount of carbohydrate consumed:

  1. They consume a fraction of the sugar we do.  More sugar consumption leads to greater insulin resistance, more fat creation, less fat breakdown, and more fat accumulation.
  2. They consume less total glucose, AND the glucose they consume is accompanied by less sugar (and less omega-6 PUFA, if it matters).
  3. They consume a ratio of omega-6 to omega-3 PUFA that is much lower than we do.  This mayfurther reduce any insulin resistance brought on by the glucose they do consume (in smaller doses and with less sugar).

The fact that East Asians didn’t have high rates of diabesity (diabetes and obesity) was a big blow to the carbohydrate insulin hypothesis. However, the East Asian rice paradox is simply explained by low, if non-existent, consumption of refined carbohydrates. Those populations actually consume fewer total carbohydrates than Western diets, and have lower levels of glycemic load as a result. To quote Mark Sisson:

Before recently, Asians ate less refined sugar and used animal fats for cooking. Sugar intake is rising now, of course, and cooking oils made from corn and soybean have largely replaced lard and tallow, but rice in the context of a low-sugar, no-HFCS (remember, the oft-cited 55/45 fructose/glucose breakdown for HFCS is highly misleading and actually quite often incorrect), low-vegetable oil, nose-to-tail nutrient-dense diet is (or was) acceptable. You can’t reduce a food down to its constituent parts and focus on, say, the bit of fructose in a blueberry and then condemn the entire berry because of it. Similarly, you can’t reduce a diet down to a single constituent food and condemn – or praise – it based on that single food. You have to look at the entire picture, and the Asian diet is largely a nutritious one.

These paradoxes where one population seems to disprove a certain hypothesis are pretty easily explainable with the existing data. There are numerous reasons why East Asian rice eaters have lower rates of diabesity. Dr. Jason Fung also explains why:

Wheat, particularly in the modern iteration may be particularly fattening for numerous reasons.  The high level of amylopectin means that most of the starch contained in flour is efficiently converted to glucose.  This deadly combination of wheat and sugar has been introduced into the Chinese diet.  The result is a Chinese diabetes catastrophe.  The prevalence of diabetes in China has now even outstripped the USA.

This is the answer to the paradox of the Asian Rice eater puzzle.  Why didn’t the Chinese have a diabetes epidemic in 1990 with all their white rice?  Well, because they didn’t eat any sugar (fructose), they were not developing insulin resistance.  Because they were not snacking all the time, they had periods of low insulin level that helped prevent the development of insulin resistance.  So the high rice intake by itself was not enough to cause either of diabetes or obesity.

Then he says that whites intake more total carbs in comparison to blacks and ‘Hispanics’ (1:32 in the video). This is wrong.

Diaz et al (2005) showed that minority populations are more likely to be affected by diabetes mellitus which may be due to less healthy diets and/or genetic factors. Using the National Health and Nutrition Survey for 1999-2000, they analyzed overweight, healthy adults, calculating dietary intake variables and insulin sensitivity by ethnicity. They characterized insulin resistance with fasted insulin, as those who are more likely to become insulin resistant have higher fasted insulin levels (levels taken after waking, with the subject being told not to eat the night before as to get a better reading of fasted insulin levels). Non-‘Hispanic’ whites had higher energy and fat intake while ‘Hispanics’ had higher carb intake with blacks having lower fiber intake.  Blacks and ‘Hispanics’ were more likely to have lower insulin sensitivity. However, ‘Hispanics’ were more likely to have lower insulin sensitivity even after controlling for diet, showing that metabolic differences exist between ethnicities that affect carbohydrate metabolism which leads to higher rates of diabetes in those populations.

Diaz et al state in the results of the study:

Dietary differences are seen by ethnicity, with non-Hispanic whites having higher
energy, saturated fat and total fat intake, while Hispanics had higher carbohydrate intake and African-Americans had lower fibre intake.Both African-Americans and Hispanics had higher levels of fasting insulin, demonstrating lower insulin sensitivity in comparison with non-Hispanic whites.
Non-‘Hispanic’ whites have higher overall energy and fat intake. This means that carbohydrates are less of a percent of the overall diet. In comparison, blacks had lower fiber intake. This means that they eat more processed foods. The same with ‘Hispanics’. Since they constantly spike their insulin with refined carbs, they have higher rates of fasted insulin and thus, have lower insulin sensitivity which is a risk factor for pre-diabetes.
Moreover, from my own personal work with people’s diets, whites eat less refined carbs than blacks or ‘Hispanics’, and while an anecdote, I’ve worked with hundreds of people.
Table 2 of the study shows that whites have a higher total kcal intake in comparison to blacks and ‘Hispanics’, ‘Hispanics’ have a statistically significant higher carb consumption, and blacks eat less fiber. Since whites eat more dietary fat and have a higher fiber intake, that protects them against higher rates of the *average population* that will be obese. Blacks consume less fiber. Dietary fiber actually protects against obesity, so the fact that blacks don’t eat more fiber shows that they eat more refined foods (it’s easily explainable why ‘Hispanics’ eat more fiber. They consume more beans and other fibrous, whole foods). However, since ‘Hispanics’ are more likely to be poor (correlated with low intelligence), they then cannot afford higher quality foods.
Drewnowski and Specter (2004) showed that 1) the highest rates of obesity are found in populations with the lowest incomes and education (correlated with IQ); 2) an inverse relationship between energy density and energy cost; 3) sweets and fats have higher energy density and are more palatable (food scientists work feverishly in labs to find out different combinations of foods to make them more palatable so we will eat more of them); and 4) poverty and food insecurity are associated with lower food expenditures, lower fruit and vegetable intake, and lower-quality diet. All of these data points show that those who are poor are more likely to be obese due to more energy-dense food being cheaper and fats and sugars being more palatable. it’s worth noting that dietary fat combined with carbohydrates and the subsequent insulin spike stores the dietary fat as body fat as well as the insulin telling the body to not burn fat.

I’ve written more on this here and here.

So now that I’ve established that blacks and ‘Hispanics’ consume more total carbohydrates from refined foods, now I’ll show the physiologic effects of insulin.

Insulin inhibits the breakdown of fat in the adipose tissue by inhibiting the lipase that hydrolyzes (the chemical breakdown of a compound due to a reaction with water) the fat out of the cell. Since insulin facilitates the entry of glucose into the cell, when this occurs, the glucose is synthesized into glycerol. Along with the fatty acids in the liver, they both are synthesized into triglycerides in the liver. Due to these mechanisms, insulin is directly involved with the shuttling of more fat into the adipocyte. Since insulin has this effect on fat metabolism in the body, it has a fat-sparing effect. Insulin drives most cells to prefer carbohydrates for energy. Putting it all together, insulin indirectly stimulates the accumulation of fat into the adipose tissue.

Do you see why blacks and ‘Hispanics’ are more susceptible to obesity?

Another glaring error he commits is not separating refined carb consumption with natural carb consumption. Refined carbs spike insulin much more than those foods with natural carbohydrates. East Asians do not have a “higher carbohydrate tolerance than Europeans” (2:06 in the video). This one huge error he commits completely discredits his hypothesis.

He then goes on to talk about India’s diabetes rates. But why is it increasing? Because of Western diets. It’s not about a “lower carbohydrate tolerance” as he says at 3:07, it’s about consuming more refined carbohydrates.

Then at 5:05, he says that he’s “solved Gary Taubes’s race problem in regards to diet”. He did nothing of the sort.

I, of course, have no problem with his IQ data. I have a problem with the conclusions he jumps to in regards to carbohydrates and diabetes. He clearly didn’t look at other factors that would explain why East Asians have lower rates of diabesity (which is increasing as they adopt a Western lifestyle… Weird…). The same thing explains it with the Australian Aborigines.

I have absolutely no problem with the second half of his video. My problem is the first half of it–his denigration of Taubes, non-understanding of insulin spikes in comparison to the quality of carbohydrate ingested and not controlling for refined carbs– as it’s clear he didn’t do extensive research into these populations (which Taubes and others have) to show why they don’t have higher rates of diabesity.

What he doesn’t touch on are “obesogenic environments” which is defined as “the sum of influences that the surroundings, opportunities, or conditions of life have on promoting obesity in individuals or populations”. What a huge coincidence that most of the populations he cited today with higher rates of diabesity live in first-world nations, otherwise known as obesogenic environments.

He should have spoken about the Pima Indians and their rates of diabesity. They didn’t have rates of diabesity as high 100 years ago. Why? The introduction of the obesogenic environment. Prisancho (2003) in his study on the Pima and reduced fat oxidation in first-world countries showed how the Pima preferentially burn carbs and not body fat for energy. Fat-burning would account for 9 kcal lost and CHO for 4. Since they preferentially burn carbs for energy and not fat, this shows why they have higher rates of diabesity. It’s not that it’s a genetic susceptibility to burn CHO for energy over fat (there may be a small genetic component, but it doesn’t override the effects of the actual diet). I’ve shown insulin’s role in fat storage above, do you see why the Pima have this diabesity epidemic after the introduction of refined carbohydrates and the obesogenic environment?

Added sugars and salts in foods causes us to want more of those foods. As I alluded to above, food scientists continuously work to find out which combinations of sugar, salt and fat will be more hyperpalatable to us and make us eat them more. Whites nor East Asians have a ‘higher carb tolerance’, they just eat different types of carbs (mostly unrefined, in comparison to blacks and ‘Hispanics’ anyway). If any individual were to overeat on high carb foods they would become diabetic and obese. Whites nor East Asians are exempt from that.

In sum, he didn’t look at where the carbs came from, only total carb intake. Refined carbs and unrefined carbs do different things in the body. The whiter a processed food is, the more it is refined. The more a food is processed, the more its natural nutrients such as fiber are taken out. These low-fat refined foods are one cause of obesity. However, it’s way too complicated to say that only refined carbohydrates cause diabesity.

I strongly recommend he read Taubes’s and Fung’s books. If he did, he wouldn’t have said what he said about Taubes’s theory and completely disregard the absolute total amount of carb intake and not the total amount of carbohydrates ingested.

Are Flynn Losses in France Due to Immigration?

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Over a ten year period in France, from 1999 to 2008-9, IQ has declined in France by almost 4 points. What is the cause? Immigration? Dysgenics? A reversal of the Flynn Effect? No doubt that numerous people would attribute the decline in intelligence in France due to MENA and SSA immigration. But is this true?

Lynn and Dutton (2015) show how differing studies show both positive and negative gains in IQ. To prevent further evidence of these negative Flynn gains, they looked to the IQ of France from 1999-2009.

The WAIS-III was standardized in France in 1999 while the WAIS-IV was standardized in 2009. This was a great opportunity to see if the intelligence of the French dropped using the new WAIS-IV. The sample was of 79 people who were of a different sample than that of the broader WAIS-IV French standardization. The average age of the sample was 45, ranging between 30 and 63 years of age. Half of this sample took the WAIS-IV first while the other half took the WAIS-III first to control for practice effects. They used a separate sample to compare the norms of generated by the two standardizes samples.

wais-iii

The above table from the paper, table 3, shows the comparison between the two WAIS tests. Positive ds indicate lower scaled scores on the III in comparison to the IV and thusly higher scores. What these data show is that the IV is harder than the III and IQ declined because the test got ‘tougher’ (because full-scale intelligence declined). As noted above, this phenomenon of decreasing IQ scores has been noticed for about 20 years now. The symbol search showed the smallest decline while there was no change in digit span. The biggest gain was in vocabulary.

waisiv

This is pretty shocking. In ten years, verbal comp decreased by 4 points, perceptual reasoning index by 3.1 points, no change in working memory index, processing speed index decreased by .7 points, perceptual organization index decreased by 3.9 and the whole full-scale IQ decreased by 3.8 points. Lynn and Dutton discuss the results:

In addition, the Full Scale IQ on the WAIS IV sample of 79 subjects was calculated based on a comparison with the WAIS IV sample of 876 subjects, which was representative of the French population on key variables such as education and region. The scores of this sample of 876 subjects were set at 100 and a comparison made with the sample of 79 subjects. As can be seen in Table 4, on this basis the IQ of the sample of 79 subjects was 101.1 with an SD of 14.7, where the French norm would be 100 and the SD 15. As such, the smaller sample can be regarded as representative of the French population in terms of intelligence.

So this small sample can be regarded as representative of the French population. Lynn and Dutton say that the digit span showing no increase corroborates findings from another researcher that showed that there was no change in forward or backward digit span in 85 years. They then say:

. . .improvements in the quality of nutrition during the twentieth century made a major contribution to increasing IQs. But it seems improbable that the quality of nutrition declined in recent years in France and in the other economically developed countries in which declining IQs have been reported.

So one possible cause is that nutrition has declined in France. From Dubuisson et al, 2010:

These repeated surveys highlighted the fact that trends in French food habits have moved towards an average European diet at the crossroads between Mediterranean and Northern diets, and that food consumption changes impacted, to a lesser extent, nutritional intake.

It shows that the French diet is in between Med and Nord diets. Really, as Lynn and Dutton asserted, there was no decline in nutritional quality for the French.

Another possible cause is a decrease in quality of schools. Flynn says a part of the reason for the rise in IQ was due to the advent of scientific thinking. However, this is not a good explanation either since school quality seems to not have been affected.

Flynn also talks about the media and its role. Lynn and Dutton say:

However, this would not explain declines in other forms of intelligence and, moreover, it might be argued that the desire and ability to read such literature would be underpinned by general intelligence and so a decline in the consumption of such literature would partly reflect a decline in general intelligence, as vocabulary is a measure of intelligence.

It is also worth noting that, apparently, reading may actually increase general intelligence (post coming on that soon).

Now, finally, the theory we’ve all been waiting for: Is it increased immigration?

Lynn and Dutton state:

This increase has occurred throughout western Europe and a number of studies have shown that immigrants from North Africa and south-west Asia typically have an average IQ of around 85 to 90 (Lynn, 2006, Lynn, 2008, Lynn and Vanhanen, 2012 and Rindermann and Thompson, 2014; for a large meta-analysis see te Nijenhuis, de Jong, Evers, & van der Flier, 2004). This conclusion has been confirmed by Kirkegaard (2013) who has shown that in Denmark the number of non-European immigrants increased from approximately 50,000 in 1980 to 400,000 in 2012 and the IQ of non-European immigrants in 18–19 year old military conscripts was 86.3, relative to 100 for indigenous Danes. These immigrants are likely to have had some impact on reducing the average IQ of the populations, but it is doubtful whether the increase in the number of immigrants with lower IQs has been sufficiently great to have had a major effect.

I personally don’t think that migration into Europe from MENA and SSA countries has been enough to put that big of a dent (over 1/3rd of an SD) in average IQ in France, and Europe as a whole. Since people are coming from areas closer to the equator and have higher rates of children since they are r-selected, could this be why France has seen a decrease in intelligence?

No.

Woodley of Menie and Dunkel (2015) reviewed Lynn and Dutton’s paper and said:

Replacement migration in France involving populations exhibiting lower means of IQ and higher rates of total fertility, such as Algerians, Moroccans, Tunisians and Roma (Čvorić, 2014 and Lynn and Vanhanen, 2012) may be increasing the rate of secular losses at the level of g, consistent with speculations advanced in Dutton and Lynn (2015), however the additional loss in g due to this process is anticipated to be very small. Based on a simulation, Nyborg (2012) estimates that in Denmark, replacement migration may be reducing heritable g by .28 points per decade, which would increase the overall loss in to 1.51 points per decade ( Woodley of Menie, 2015), this still being only 37.75% of the loss observed in the French cohort.

As you can see, the rate of increase in is not consistent with the speculations advanced by Lynn and Dutton. Though Nyborg (2012) estimates that migratin may only be reducing by only 2.8 percent a decade. Trivially small. Except we still need an explanation for why the native French have this IQ decrease.
Finally, the last theory is dysgenic fertility. This occurs when lower IQ people have more children than those with higher IQs. This is seen in the CLASH model (r/K selection theory). CLASH predicts that K-selected peoples will have fewer children, and, as a result, the r-selected people within the K-selected population will have more children and thus dysgenic fertility will begin.

An Environmental Explanation?

Since we still need an explanation for 62.25 percent of the 3.8 decrease in full-scale IQ other than dysgenic fertility, are there any environmental explanations? Environmental explanations can be anything from child abuse, to poor schooling, to poor nutrition, etc. Was there an increase in any of these or other variables that negatively affect IQ which would explain the 3.8 point decline in IQ?

One of the most likely candidates is nutrition. Lack of certain vitamins, especially in childhood, would prevent the brain from receiving the proper nourishment to grow.

The INCA study took record of food consumption from 2,373 people aged 4 to 92 from a 7-week period and from that they saw which nutrients they were deficient in (Touvier et al 2006). To measure if and how much they were nutrient deficient, they used the Estimated Average Requirement (EAR). The vitamins used were calcium, magnesium, iron, vitamins C, A, B6, and B12, thiamin, riboflavin, niacin, pantothenic acid, and folate. A lot of these have to do with proper brain functioning and ability to reach its full-size potential. For instance like the B vitamins and iron. Being deficient in those nutrients depresses brain size and with it IQ. For instance, being deficient in vitamin B 12 and folate leads to decreased  brain size in childhood. The negative effects of being deficient in these nutrients may partially explain some of the 3.8 point decrease in full-scale IQ.

Regarding the prevalence of the aforementioned nutrient deficiencies in these populations, the authors state:

We also calculated daily consumption of 44 food groups by age and gender. This paper shows how the combination of both data sets, i.e., inadequacy and food consumption data, allows a preliminary screening of potential food vehicles in order to optimize fortification. The prevalence of inadequacy was particularly high for the following groups: for calcium, females aged 10-19 years (73.5%) or aged 55-90 years (67.8%), and males aged 15-19 years (62.4%) or aged 65-92 years (65.4%); for magnesium, males aged 15-92 years (71.7%) and females aged 10-90 years (82.5%); for iron, females aged 15-54 years (71.1%); and for vitamin C, females aged 15-54 years (66.2%). Two examples are provided to illustrate the proposed method for the optimization of fortification.

Most vitamins and minerals have positive effects on brain functioning, some more than others, but notice the prevalence of iron defieciency in the females aged 15-54 years (71.1 percent). With the cohort cited by Lynn and Dutton (2015) and Woodley of Menie and Dunkel (2015) being aged 30 to 63 with an average age of 45, the prevalence of iron deficiencies in the INCA study, along with the other deficiencies in the cohort, may partially be responsible for the decline in IQ.

The Flynn Effect

PumpkinPerson describes it well here:

One of the biggest mysteries in psychology is the Flynn Effect; the fact that over the 20th century, people have been performing better and better on IQ tests.  Of course, the average IQ in Western countries by definition is always about 100, however because people keep scoring higher every decade, the tests routinely have to be made more difficult and the norms must be regularly updated to keep the mean IQ from rising far above 100.

However, in first-world countries, in the past 20 or so years, it has been in decline, particularly in France. It’s due to a mix of dysgenic fertility and nutrient deficiencies. Since Flynn gains are largely due to advancements in better nutrition, Flynn loses would then be attributed in part to nutrient deficiencies as well as dysgenic fertility.

The cause for the 3.8 decrease in IQ in France is low fertility rates amongst the French population as well as nutrient deficiencies. Clearly, ameliorating this decrease in IQ can be reversed by the K-selected having more children and healthier eating habits. Drops in IQ won’t be attributed to MENA and SSA populations until the future, but for now, the cause for the decrease is the French themselves.

Ethnic Differences in Sleep, Obesity, and Metabolic Syndromes

2300 words

Ethnic differences in the prevalence of obesity occur, majorly in part due to differences in the rates of metabolic syndrome (which is actually a few variables including high blood pressure, high blood sugar which leads to insulin resistance, excess visceral fat around the waist which is the ‘skinny fat‘ phenomenon, and abnormal blood pressure levels) and obesity. Ethnic differences in these variables do, in part, show how the three ethnies differ in rates of obesity. I will discuss the differences between each ethny in regards to metabolic syndrome and sleep and how it leads to the differences in ethnic obesity rates.

Sleep Differences

There is a ‘missing hour of sleep‘ when comparing blacks and whites. On average, blacks get 6.05 hours of sleep while whites get 6.85 hours of sleep. Of course, the same old racism argument comes up, which, if one ‘percieves’ discrimination, I wouldn’t doubt that it would have an effect on sleep due to a rise in cortisol, which affects sleep due to the raised levels making you restless and not able to fall asleepInsulin levels then rise due to the rise in cortisol, which is the cause of obesity.

Some studies may try to say that racism and other forms of discrimination are a factor, without even thinking of genetic factors. Another study that Frost cites says that duration of deep sleep and duration of stage 2 (light sleep) is correlated correlated in African Americans with perceived discrimination. The authors defined ‘perceived discrimination’ as the extent to which one believes that their ethnic group have been discriminated against by society. Still even when controlling for discrimination, there were still marked differences between blacks and whites and how long they slept.

Frost then talks about how sleep patterns are heritable and cites studies done on Africans in Africa. One study found that there was an hour sleep difference between Ghanaians and Norwegians on the week days and between a quarter to half hour less on weekends. He shows another study showing that Nigerian college students sleep 6.2 hours a day while getting 70-minute naps in the afternoon.

Frost concludes that the African sleep patterns is normal on Africa. Africans are more active during the cooler times of the day and sleep during the bitter periods. Frost says those who evolved in more northerly climes are particularly adapted to a certain sleep pattern with the same holding true for Africans.

However, these sleep patterns in first world countries have negative effects on metabolism and rates of obesity.

Here are some more studies showing that blacks sleep less than whites:

The sleep of African Americans: a comparative review: The researchers found that blacks take longer to fall asleep than whites, report poorer sleep quality, have more light and less deep sleep, and nap more often and longer. This is a huge recipe for risk factors for obesity, and it shows in their demographics.

Unfair Treatment is associated with Poor Sleep in African American and Caucasian Adults: Pittsburgh SleepSCORE Project: This is one of the studies spoken about above that show that discrimination leads to less sleep. Though, it holds for both black and white adults. The researchers conclude:

Taken together, the confluence of perceived unfair treatment as a chronic stressor and poor sleep and the interplay between the two may have critical roles in long-term health problems.

African Genetic Ancestry is Associated with Sleep Depth in Older African Americans: The researchers hypothesized that “racial differences in sleep phenotypes would show an association with objectively measured individual genetic ancestry in AAs.” They conclude that the slow wave sleep may have genetic underpinnings.

Mexican Americans sleep less than do Mexican immigrants. US-born Mexicans are 40 percent more likely to be short sleepers. This is attenuated by environmental factors such as smoking and stress, which shorten the duration of sleep (smoking decreases the Body Set Weight, whereas cortisol along with insulin in tandem increase it).

Also, in this study by Roane et al (2014) looked at the link between sleep disturbances and stress in Mexican Americans (average age 55) and non-‘Hispanic’ whites (average age 66). Mexicans reported higher levels of sleep disturbance (25 percent) compared to whites (17 percent). They conclude that disturbed sleep was positively correlated with depression.

So both blacks and Mexicans sleep less than whites. These differences in sleep between these three ethnies also affect the prevalence of obesity in these populations.

Obesity and Sleep

It’s long been known that poor sleep habits make people fat. This is due to the effects of insulin and cortisol. Increased insulin comes before increased cortisol–increased insulin is the cause for obesity. Sleeping less is linked to obesity. Since, as described above, the three ethnies differ in sleep patterns, the same also holds true for obesity rates (Ogden at al, 2014). The trends are as follows: 67.3% for whites, 75.6% for blacks, and 77.9% for Hispanics. Though, sleep is only one factor involved with obesity.

Getting adequate sleep is extremely important. Not doing so can lead to a myriad of negative health implications:

Sleep is an important modulator of neuroendocrine function and glucose metabolism and sleep loss has been shown to result in metabolic and endocrine alterations, including decreased glucose tolerance, decreased insulin sensitivity, increased evening concentrations of cortisol, increased levels of ghrelin, decreased levels of leptin, and increased hunger and appetite. Recent epidemiological and laboratory evidence confirm previous findings of an association between sleep loss and increased risk of obesity.

So a lack of sleep leads to an increase in ghrelin levels, decreased levels of leptin (the same effects as caloric restriction over time), increased appetite and hunger, increased evening cortisol (which insulin spikes then follow), decreased insulin sensitivity (the cortisol brings it back up and most people are insulin resistant independent of diet), decreased glucose tolerance, etc. We can see that these ethnic differences in sleep, which are partly genetic in nature, can and would have great effects on metabolism, contributing to the ethnic differences in obesity rates.

And from Harvard:

For example, in the Nurses’ Health Study, researchers followed roughly 60,000 women for 16 years, asking them about their weight, sleep habits, diet, and other aspects of their lifestyle. (2) At the start of the study, all of the women were healthy, and none were obese; 16 years later,women who slept 5 hours or less per night had a 15 percent higher risk of becoming obese, compared to women who slept 7 hours per night. Short sleepers also had 30 percent higher risk of gaining 30 pounds over the course of the study, compared to women who got 7 hours of sleep per night.

Damn!! This, pretty much, mirrors the black-white difference. I’d love to see a racial breakdown of this cohort and will keep an eye out for one, but in the meantime, those who were short sleepers had a 30 percent higher risk of gaining 30 pounds over the course of the study in comparison to women who got 7 hours of sleep per night. Blacks are the most likely group to be overweight and obese in the US, and this data from the Nurses Health Study (which tons of data can be drawn from this study) shows one reason why, however the driver is cortisol > insulin > processed carbs > increased insulin > insulin resistance > increased insulin > vicious cycle > obesity. These differences in sleep almost perfectly mirror the ethnic differences in obesity.

There are several possible ways that sleep deprivation could increase the chances of becoming obese. (1) Sleep-deprived people may be too tired to exercise, decreasing the “calories burned” side of the weight-change equation. Or people who don’t get enough sleep may take in more calories than those who do, simply because they are awake longer and have more opportunities to eat; lack of sleep also disrupts the balance of key hormones that control appetite, so sleep-deprived people may be hungrier than those who get enough rest each night.

Ah the old ‘exercise to increase the Calories Out part of the equation’. however, Calories Out does not stay constant. This also rebuts the ‘Eat Less and Move More’ CICO (Calories In/Calories Out) model of obesity, showing that because it doesn’t take insulin into account, it’s doomed to fail.

Speaking of insulin, it’s about time I focused on metabolic syndrome.

Metabolic Syndrome

As I discussed in a previous post, Race, Obesity, Poverty, and IQ, metabolic differences exist between race/ethnicity. ‘Hispanics’ metabolize carbohydrates differently, blacks have a lower fiber intake (increased fiber protects against obesity, another correlate) while whites have a more high fat diet. Contrary to popular belief, dietary fat doesn’t make you fat as it’s the macro that spikes your insulin the least.

Diaz et al (2005) showed that minority populations are more likely to be affected by diabetes mellitus which may be due to less healthy diets and/or genetic factors. Using the National Health and Nutrition Survey for 1999-2000, they analyzed overweight, healthy adults, calculating dietary intake variables and insulin sensitivity by ethnicity. They characterized insulin resistance with fasted insulin, as those who are more likely to become insulin resistant have higher fasted insulin levels (levels taken after waking, with the subject being told not to eat the night before as to get a better reading of fasted insulin levels). Non-‘Hispanic’ whites had higher energy and fat intake while ‘Hispanics’ had higher carb intake with blacks having lower fiber intake.  Blacks and ‘Hispanics’ were more likely to have lower insulin sensitivity. However, ‘Hispanics’ were more likely to have lower insulin sensitivity even after controlling for diet, showing that metabolic differences exist between ethnicities that affect carbohydrate metabolism which leads to higher rates of diabetes in those populations.

In ‘Hispanics’, several loci were discovered that play a role in hepatic (relating to the liver) fat content. Along with showing that ‘Hispanics’ have lower insulin (which due to low insulin, blood glucose builds up in the blood stream leading to diabetes) and showing that they metabolize glucose in the liver differently due to differing loci leading to more cases of fatty liver, this shows how and why ‘Hispanics’ have higher rates of Type II Diabetes Mellitus (TIIDM).

Since TIIDM affects Mexican Americans more, better measures to address their differences in carbohydrate metabolism need to be taken. Racial and ethnic differences in TIIDM are as follows:

7.6% of non-Hispanic whites

9.0% of Asian Americans

12.8% of Hispanics

13.2% of non-Hispanic blacks

15.9% of American Indians/Alaskan Natives

Whites eat a higher fat diet, which means a decrease in carbs. Asians eat white rice which spikes blood glucose eliciting a high insulin response leading to TIIDM, ‘Hispanics’, non-‘Hispanic’ blacks, and Indians and Alaskan Natives (I wish they separated Indians and Alaskan Natives as I’m almost positive that Alaskan natives have a lower rate) all eat high carb, low fat, low protein diets. Carbohydrates are a main staple, and since they spike insulin the most, they are the cause for obesity and TIIDM rates in these populations.

Turning my attention over to metabolic syndrome and blacks and whites, we can see that black women with PCOS have an increased risk for cardiovascular disease and metabolic syndrome in comparison to white women with PCOS. The researchers say that after controlling for age and body mass index (BMI) “black women with PCOS had a significantly increased prevalence of low high-density lipoprotein and high glucose. The general CVD risk was significantly increased in black adults with PCOS.” Though, a longitudinal study needs to be carried out to assess the independent impact of race and PCOS with CVD (Cardiovascular Disease).

Blacks have a higher chance to be diagnosed with metabolic syndrome since they are also at increased risk to have elevated blood pressure (hypertension), become obese, and be diabetic. This is due to their diet, which is due to their low IQ (obesity is correlated with intelligence), and different metabolism in comparison to whites.

There are also metabolic differences between race and sex. Fat oxidation is lower in black than white men and in African American men/women and white men/women, they have a lower metabolic rate!!! 24-hour energy expenditure is lower in black women in comparison to white women, whereas physical activity energy expenditure (PAEE) is the same as whites. Contrasted with women, black men had higher PAEE than white men. The authors conclude:

In conclusion, this comparative study of 24-h energy metabolism in African Americans and whites with use of a respiratory chamber not only confirms the previous findings from ventilated-hood studies of a lower resting metabolic rate, but also suggests a lower 24EE in African American women than in white women. Although only marginal ethnic differences in metabolic rate were found in men, African American men seem to have a lower rate of fat oxidation than do white men. The underlying mechanisms for these sex differences and the significance of these findings with respect to the development and maintenance of obesity remains to be investigated in longitudinal studies.

Metabolic Syndrome and Obesity

Seeing how the body acts when it has a lower metabolic rate due to the numerous confounders speaks for itself in regards to obesity. Metabolic syndrome does precede obesity a lot of the time. With insulin being one of the main drivers of metabolic syndrome, and with poor sleep being linked to metabolic syndrome, we can see how these factors combine to affect the health of the populations in question.
Conclusion
Sleep is a huge part of health, as it is important for brain activity among numerous other important factors. Not getting enough sleep causes the body to release hormones to make you eat more, hold more weight around your midsection (that’s one thing that cortisol does), have a decreased metabolism, and eventually leads to TIIDM. The fact that ethnies in America differ in metabolic syndrome and hours of sleep gotten per night shows that some of the obesity epidemic, both within and between race/ethnicity is genetic in origin due to carbohydrate metabolism and low insulin sensitivity independant of diet which raises insulin which then leads to obesity.

 

Race, Obesity, Poverty, and IQ

2100 words

America has a current and ongoing obesity epidemic. Some ethnicities are more likely to be obese or overweight than others due to lower intelligence which means a lack of ability to delay gratification, lack of ability to think into the future, lower funds which translates to eating more refined carbohydrates which means more blood glucose spikes which then leads to obesity as I will show. Insulin has a causal relationship with obesity so those who lack funds to buy healthier food then turn to refined foods high in carbohydrates as they are cheaper and more abundant in low-income neighborhoods.

Adult obesity rate by State (top 5) is: 1) Louisiana (36.2 percent), 2) Alabama (35.6), West Virginia (35.6), and Mississippi (35.6), and 5) Kentucky (34.6) with the 5 least obese States being 51) Colorado (20.2), 49) Hawaii (20.7), 48) Montana (23.6), 47) California (23.2), and 46) Massachusetts (24.3). Notice how the States with higher rates of obesity are in the South and the States with the lower rates are in the North, give or take. The average IQ for these States as follows: Lousiana: 95.3, Alabama: 95.7, West Virginia 98.7, Mississippi 94.2 (lowest IQ State in the country, largest black population at 37 percent), and Kentucky at 99.4. The average IQ for those States is 96.66. The average IQs for the States with the lowest obesity rates are: Colorado 101.6, Hawaii 95.6, Montana 103.4, California 95.5, and Massachusets 104.3 (highest IQ State). The average for these States being 100.08. So there is a 4 point IQ difference between the top 5 States with the highest and lowest percentage of obese people, which goes with the North/South gradient of higher IQ people living in the North and lower IQ people living in the South. Back in 2014, a California real estate group took 500,000 Tweets using a computer algorithm and estimated intelligence based on spelling, grammar, and word choice and found a difference in State by State intelligence. Notice how the further North you go the higher the average intelligence is, which is then correlated with the obesity levels in that State.

With poverty rates by State, we can see how the States in the South have less intelligent people in them which then correlates to the amount of obesity in the State. Though, there are some anomalies. West Virginia and Kentucky have a super majority of whites. This is easily explained by the fact that less intelligent whites live in those States, and since both the poverty rates and obesity rates are high, it follows that the State will be less intelligent than States that have more intelligent people and less obesity.

It is known that intelligence is correlated with obesity at around -.25 (Kanazawa, 2014). The negative correlation between intelligence and obesity means that they are inversely related so, on average, one with higher intelligence has less of a chance of being obese than one with lower intelligence. The States with the lowest IQ people having those with the highest BMIs corroborates this. In America, obesity rates by ethnicity are as follows: 67.3% for whites, 75.6% for blacks, and 77.9% for ‘Hispanics’.

Now that we know the average intelligence rates by State, the percentage of obese by State and the demographics by State, we can get into why obesity rates correlate with intelligence and race.

Diaz et al (2005) showed that minority populations are more likely to be affected by diabetes mellitus which may be due to less healthy diets and/or genetic factors. Using the National Health and Nutrition Survey for 1999-2000, they analyzed overweight, healthy adults, calculating dietary intake variables and insulin sensitivity by ethnicity. They characterized insulin resistance with fasted insulin, as those who are more likely to become insulin resistant have higher fasted insulin levels (levels taken after waking, with the subject being told not to eat the night before as to get a better reading of fasted insulin levels). Non-‘Hispanic’ whites had higher energy and fat intake while ‘Hispanics’ had higher carb intake with blacks having lower fiber intake.  Blacks and ‘Hispanics’ were more likely to have lower insulin sensitivity. However, ‘Hispanics’ were more likely to have lower insulin sensitivity even after controlling for diet, showing that metabolic differences exist between ethnicities that affect carbohydrate metabolism which leads to higher rates of diabetes in those populations.

Drewnowski and Specter (2004) showed that 1) the highest rates of obesity are found in populations with the lowest incomes and education (correlated with IQ), 2) an inverse relationship between energy density and energy cost, 3) sweets and fats have higher energy density and are more palatable (food scientists work feverishly in labs to find out different combinations of foods to make them more palatable so we will eat more of them), and 4) poverty and food insecurity are associated with lower food expenditures, lower fruit and vegetable intake, and lower-quality diet. All of these data points show that those who are poor are more likely to be obese due to more energy-dense food being cheaper and fats and sugars being more palatable.

Now that I’ve shown the relationship between race and IQ by state, obesity rates by state, insulin sensitivity by race, and that those in poverty are more likely to be obese, I can now talk about the actual CAUSE of obesity: insulin.

The conventional wisdom is that if you consume more kcal than you expend, you will gain weight, whereas if you consume less than your daily needs you will lose weight. This has been unchallenged for 50 years. Also known as Calories In and Calories Out (CICO), this mantra “eat less and move more!!!” has been bleated over and over with horrendous results. The CICO model only concerns itself with calories and not insulin which is a causal factor in obesity

In this study, participants in the basal insulin group which received the lowest average insulin dose gained the least average amount of weight at 4.2 pounds. Those on prandial insulin gained the most weight at 12.5 pounds. The intermediate group gained 10.3 pounds. More insulin, more weight gain. Moderate insulin, moderate weight gain. Low insulin, low weight gain.

Researchers compared a standard dose of insulin to tightly control blood sugars in type 1 diabetic patients. At the end of the 6 years, the study proved that intensive control of blood sugars resulted in fewer complications for those patients.

Though, in the high dose group, they gained on average 9.8 pounds more than those in the standard group.

More than 30 percent experienced major weight gain! Prior to the study, both groups were equal in weight. But the only difference was the amount of insulin administered. Were the ones given high levels of insulin all of a sudden more lazy? Were those who gained weight suddenly lacking in willpower? Were they lazier before the study? We’re they more gluttonous? No, no, and no!!

delprato_24

(source)

Finally, Henry et al (1993) took Type II diabetics and started them off with no insulin. They went from 0 units of insulin a day to 100 units at 6 months. As higher rates of insulin were administered, weight rose in the subjects. Insulin was given, people gained weight. A direct causal relationship (see figure above). However, what’s interesting about this study is that the researchers measured the amount of kcal ingested, the number of kcal ingested was reduced to 300 per day. Even as they took in less kcal, they gained 20 pounds! What’s going on here? Well, insulin is being administered and if you know anything about insulin it’s one of the hormones in the body that tells the body to either store fat or not burn it for energy. So what is occurring is the body is ramping down its metabolism in order for the subject to store more fat due to the exogenous insulin administered. Their TDEE dropped to about 1400 kcal, while they should have been losing weight on 1700 kcal! The CICO model predicts they should have lost weight, however, adaptive thermogenesis, better known as metabolic slow down, occurred which dropped the TDEE in order for the body to gain fat, as insulin directly causes obesity by signaling the body to store fat, so the body drops its metabolism in an attempt to do so. 

Putting this all together, blacks and ‘Hispanics’ are more likely to be in poverty, have lower intelligence, and have higher rates of obesity and diabetes. Furthermore, blacks are more likely to have metabolic diseases (adaptive thermogenesis aka metabolic slowdown is a metabolic disease) which are related with obesity due to their muscle fiber typing which leads to lower maximal aerobic capacity (less blood and oxygen get around the body). Type II skeletal muscle fibers’ metabolic profile contributes to lower average aerobic capacity in blacks. It also is related to cardiometabolic diseases, in my opinion because they don’t have the muscle fiber typing to run long distances, thus increasing their aerobic capacity and VO2 max.

Due to the diets they consume, which, due to being in poverty and having lower intelligence, they consume more carbohydrates than whites, which jacks their blood glucose levels up and the body then releases insulin to drive the levels glucose in the body down. As insulin levels are spiked, the body becomes insulin resistant due to the low-quality diet. Over time, even a change in diet won’t fix the insulin resistance in the body. This is because since the body is insulin resistant it created more insulin which causes insulin resistance, a vicious cycle.

Poverty, intelligence and race both correlate with obesity, with the main factor being lower intelligence. Since those with lower IQs have a lack of foresight into the future, as well as a lower ability to delay gratification which also correlates with obesity, they cannot resist low-quality, high-carb food the same way one with a higher IQ can. This is seen with the Diaz et al study I linked, showing that whites have higher levels of fat intake, which means lower levels of carbohydrate intake in comparison to blacks and ‘Hispanics’. As I’ve shown, those in poverty (code word for low intelligence) ingest more refined carbohydrates, they have higher levels of obesity due to the constant spiking of their insulin, as I have shown with the 3 aforementioned studies. Since blacks and ‘Hispanics’ have lower levels of intelligence, they have lower levels of income which they then can only afford cheap, refined carbs. This leads to insulin being constantly spiked, and with how Americans eat nowadays (6 times a day, 3 meals and snacks in between), insulin is being spiked constantly with it only dipping down as the body goes into the fasted state while sleeping. This is why these populations are more likely to be obese, because they spike their insulin more. The main factor here, of course, is intelligence.

Another non-CICO cause for obesity is exposure to BPA in the womb. Researchers carried out BPA testing in three differing subjects: 375 babies invitro, (3rd trimester) children aged 3 (n=408) and aged 5 (n=518) (Hoepner, et al, 2016). They measured the children’s bodies as well as measuring body fat levels with bioelectrical impedance scales.Prenatal urinary BPA was positively associated with waist circumference as well as fat mass index, which was sex-specific. When analyzed separately, it was found that there were no associated outcomes in body fat for boys (however it does have an effect on testosterone), but there was for girls (this has to do with early onset puberty as well). They found that after controlling for SES and other environmental factors there was a positive correlation with fat mass index – a measure of body fat mass adjusted for height, body fat percentage and waist circumference. The researchers say that since there was no correlation between BPA and increased obesity, that prenatal exposure to BPA indicates greater vulnerability in that period. The sample was of blacks and Dominicans from New York City. Whites drink less bottled water, which has higher levels of BPA. Blacks and ‘Hispanics’ consume more, and thus have higher levels of obesity.

In conclusion, blacks and ‘Hispanics’ are more likely to be in poverty, have lower intelligence, higher rates of obesity and lower incomes. Due to lower incomes, cheap, refined carbohydrates is what they can afford in bulk as that’s mostly what’s around poor neighborhoods. Ingesting refined carbohydrates more often consistently jacks up blood glucose which the body then releases insulin to lower the levels. Over time, insulin resistance occurs, which then leads to obesity. As I’ve shown, there is a direct causal relationship between the amount of insulin administered and weight gain. With the aforementioned factors with these two populations, we can see how the hormonal theory of obesity fits in perfectly with what we know about these ethnic groups and the obesity rates within them. Since people in poverty gravitate more towards cheap and refined carbohydrates, they’re constantly spiking their insulin which, over time, leads to insulin resistance and obesity.

Muscle Fiber Typing and Race: Redux

I recently blogged on Muscle Fiber Typing, HBD, and Sports. I showed that differences in which race wins at what competition comes down to ancestry, which then correlates with muscle fiber typing. I came across this paper, Black and White race differences in aerobic capacity, muscle fiber type, and their influence on metabolic processes, today which, of course, proved my point on muscle fiber typing.

The authors say that obesity is a known risk factor of cardiometabolic disease (though Blüher 2012 says that up to 30 percent of obese patients are metabolically healthy with insulin sensitivity on the same level as thin individuals) and that cardio can reduce excess adipose tissue (this isn’t true either), maintains weight (maybe) and reduces the risk of obesity (it doesn’t) and cardiometabolic disease (this is true). The two major determinants of aerobic capacity are muscle fiber typing and “the capacity of the cardiorespiratory system to deliver nutrient-rich content to the muscle”. As I said in my previous article on muscle fiber typing, depending on which fibers an individual has determines whether or not they are predisposed to being good at endurance sports (Type I fibers) or being good at explosive sports (Type II fibers). Recent research has shown that blacks fiber typing predisposes them to a lower overall VO2 max.

VO2 max comes down to a strong oxygen support system and the capacity to contract a large number of muscle fibers at once, both of which are largely genetic. Lactic acid makes us tired, the best way to train is to minimize lactic acid production and maximize lactic acid removal during exercise. High-Intensity Interval Training, or HIIT, achieves this. The more O2 consumed during exercise, the less of a reliance there will be on the anaerobic breakdown of CHO to lactic acid.

Along with inadequate exercise, these variables place blacks at an increased risk for obesity as well as other negative metabolic factors in comparison to other races/ethnic groups. The author’s purpose of the review was to show how skeletal muscle fiber typing contributes to obesity in non-“Hispanic” black populations.

The review indicates that the metabolic properties of Type II fibers (reduced oxidative capacity, capillary density, which is a physiological measure that takes a cross-section of muscle and counts the number of blood vessels within. The measurement can be considered an indicator of physical health and is also related to the ability to do strenuous activity) are related to various cardiometabolic diseases.

Since non-“Hispanic” blacks have more Type II fibers on average, they have a lower maximal aerobic capacity. Combined with low Resting Energy Expidenture (REE) and reduced hemoglobin concentration (hemoglobin is a protein in the red blood cells that shuttles oxygen to your tissues and organs and transports carbon dioxide from your organs and tissues back to your lungs), non-“Hispanic” blacks may be predisposed (they are when you look at what the differing skeletal muscle fibers do in the body and if you have a basic understanding of physiology) to a lower maximal aerobic capacity, which contributes to obesity and metabolic disease in the non-“Hispanic” black population.

I have written on ethnicity and obesity last year. In the two racial groups that were tested, American non-“Hispanic” whites and American non-“Hispanic” blacks, what the researchers say holds true.

On the other hand, Kenyans have an average BMI of 21.5. Since we know that a high VO2 max and low BMI are correlated, this is why Kenyans succeed in distance running (along with VO2 max training, which only enhances the genetic effects that are already there).

Moreover, I wrote an article on how Black American Men with More African Ancestry Less Likely to Be Obese. How do we reconcile this with the data I have just written about?

Simple. The population in the study I’m discussing in this article must have had more non-African ancestry than the population that was gathered showing that black American men with more African ancestry are less likely to be obese. The researchers in that study looked at  3,314 genetic markers. They then tested whether sex modifies the association of West African genetic ancestry and body mass index, waist circumference, and waist to hip ratio. Also, they adjusted for income and education as well as examined associations of ancestry with the phenotypes of males and females separately. They conclude that their results suggest that a combination of male gender and West African ancestry is correlated with protection against central obesity and suggests that a portion of the difference in obesity (13.2 percent difference) may be due, in part to genetic factors. The study also suggests that there are specific genetic and physiologic differences in African and European Americans (take that, race-denialists =^) ).

Since both black men and women in America share the same environment, some genetic factors are at play in the differences in obesity rates between the two sexes with more African ancestry for black American men being the main reason.

Finally, I wrote an article on BPA consumption and obesity. The sample was on blacks and Dominicans (they’re black as well) in NYC. It was discovered that babes who were exposed to BPA more in childhood and in the womb had higher chances of being obese. This goes with what the authors of the study I’m citing in this article say. There are numerous environmental factors that pertain to obesity that’s not kcal in/out (which the First Law of Thermodynamics is irrelevant to human physiology). BPA consumption is one of them (as well as a cause for the current and ongoing cucking of Europe). Whites at all age groups drink more tap water. Blacks and ‘Hispanics’ were pretty much even in consumption of bottled water. Bottled water has BPA in the plastic, and since they drink more bottled water, they run the risk of their children being more prone to obesity due to the negative effects of BPA in the human body.

In sum, blacks are more likely to be faster due to their fiber typing, but are also more likely to be obese (in this sample, anyway which I assume was a mix of men and women. I will update this article when I find and read the full paper). They also run a higher risk of having related diseases, most notably due to a lower REE (showing they don’t walk around as much, since too much sitting increases mortality EVEN WITH EXERCISE. So if you have a desk job and don’t do any other physical activity and enjoy living, do more LISS, low-intensity steady-state cardio). These factors also, in part, explain why blacks have higher rates of hypertension (with Sickle Cell Anemia being another cause since when the blood is sickle-shaped, they crowd in the blood vessels causing blockage in the veins which leads to strokes and other diseases). The more the genetic factors that predispose people to obesity are understood (let’s be real here, there ARE genetic correlates with obesity), the better we can help those who suffer from the condition.