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Smoking and Race Part II
1750 words
Almost two years ago, I wrote an article on smoking and race, discussing racial differences in smoking, the brains smoked, and biochemical differences brought on by different physiological differences when smoke is inhaled. In this article, I will look at how smoking can be prevented for all race/ethnies, the contribution of smoking to the black/white difference in mortality, and certain personality traits that may have one more likely to pick up the habit.
Tobacco and poverty are “inextricably linked“, with smoking contributing to more than 10 percent of household income among those in poverty. Tobacco has even been said to be a ‘social justice issue‘ since tobacco use is more prevalent in lower-income communities.
It is true that advertisements are concentrated around certain areas to target certain sociodemographic communities (Seidenberg et al, 2010). They looked at two communities in Boston, Massachusets, one high -income non-minority population, and the other a minority low-income population. They found that the low-income community was more likely to have stores which had larger advertisements, have more stores selling tobacco, promote menthol cigarettes (which low-income people are more likely to smoke, mainly blacks), and finally that advertisements for cigarettes would be more likely to be found 1000 feet away from a school zone in low-income communities compared to high-income communities.
They say that their study shows “evidence that features of tobacco advertising are manipulated to attract youth or racial minority sub-groups, and these features are disproportionately evident in low income, minority communities.” So, according to analysis, at least in the urban area of Dorchester, near Boston, if advertisements were to be lessened near schools, along with fewer overall advertisements, the percentage of minority smokers would decrease. (This same effect of low SES affecting the odds of smoking was also seen in a sample of Argentine children; Linetzky et al, 2012).
Higher SES communities have fewer tobacco advertisements than lower SES communities (Barbeau et al, 2005; Hillier et al, 2015). Big Tobacco (along with Big Food and Big Soda) advertise the most in lower-income communities, which then have deleterious health consequences in those populations, further increasing national health spending per year.
Among Americans, as income increases, smoking decreases:
Nationwide, the Gallup-Healthways Well-Being Index reveals that 21% of Americans say they smoke. As the accompanying graph illustrates, the likelihood of smoking generally increases as annual incomes decrease. One exception to this pattern occurs among those making less than $6,000 per year, an income bracket often skewed because many in that bracket are students. Among those making $6,000 to $11,999 per year, 34% say they smoke, while only 13% in the top two income brackets (those with incomes of at least $90,000 per year) say the same — a 21 percentage-point gap.
The Well-Being Index also confirms distinctions in U.S. smoking rates relating to gender and race. Among respondents, 23% of men and 19% of women say they smoke. Blacks are the most likely to smoke (23%) and Asians are least likely to smoke (12%). Hispanics and whites fall in between, at 17% and 20%, respectively.
Further, according to the CDC, the prevalence of smoking of people with a GED is at 40 percent, the highest amongst any SES group. The fact that tobacco companies attempt to advertise in low-income areas and to women is also well-studied. These factors combined to then cause higher rates of smoking in lower-income populations, and blacks are some of the most affected. There are also a slew of interesting physiological differences between blacks and whites regarding smoking.
Ho and Elo (2013) show that smoking differences between blacks and whites at age 50 accounted for 20 and 40 percent of the gap between 1980 and 2005, but not for women. Without adjusting for SES, smoking explains 20 percent of the excessive risk blacks have regarding all-cause mortality.
A study of 720 black smokers from Los Angeles, California showed that 57 percent only smoked menthols, 15 smoked regulars while 28 percent smoked both menthols and regulars (Unger et al, 2010). One of their main findings was that blacks who smoked menthol cigarettes thought that it was a ‘healthier alternative’ to regular cigarettes. Unger et al (2010: 405) also write:
This cross-sectional study identified correlates of menthol smoking, but it does not prove causality. It is possible that smoking menthol cigarettes causes changes in some of the psychological, attitudinal, social, and cultural variables. For example, people who smoke menthols may form beliefs about the positive medicinal benefits of menthols as a way of reducing their cognitive dissonance about smoking.
Figuring out the causation will be interesting, and I’m sure that advertisements outside of storefronts are causally related. Okuyemi et al (2004) also show that blacks who smoke menthol cigarettes are less likely to quit smoking than blacks who smoke regulars. Younger children were more likely to smoke cigarettes with a “longer rod length” (for instance, Newport 100s over Newport regulars). People smoke menthol cigarettes because they taste better, while menthol also “is a prominent design feature used by cigarette manufacturers to attract and retain new, younger smokers.” (Klausner, 2011). Klausner, though, advocates to ban menthol cigarettes, writing:
This evidence suggests that a ban on menthol in cigarettes would result in fewer people smoking cigarettes. Menthol is a prominent design feature used by cigarette manufacturers to attract and retain new, younger smokers. In addition, not only would some current smokers decide to quit rather than smoke non-mentholated cigarettes, but some young people would not make the transition from experimenting with cigarettes to becoming a confirmed smoker. The FDA should ban menthol in cigarettes which will help lower smoking rates particularly among African Americans and women.
Sterling et al (2016) also agree, but argue to ban little cigars and cigarillos (LCCs) writing “Our data add to the body of scientific evidence that supports the FDA’s ban of all characterising flavours in LCCs.” Numerous studies attest to the availability of menthol cigarettes and LCCs which then contributes to influence different demographics to begin smoking. Hersey et al (2006) also shows menthol cigarettes to be a ‘starter product for youth’, stating one reason that children begin smoking mentholated cigarettes is that they are more addictive than non-menthols. Menthol cigarettes are a ‘starter product’ because they taste better than regular cigarettes and, as shown above, seem like they are more ‘theraputic’ due to their taste and coolness compared to regular cigarettes.
Smokers are more likely to be extroverted, tense, anxious and impulsive, while showing more traits of neuroticism and psychoticism than ex- or non-smokers (Rondina, Gorayeb, and Botelho III, 2007). In a ten-year longitudinal study, Zvolensky et al (2015) showed that people who were more likely to be open to experience and be more neurotic would be more likely to smoke, whereas conscientiousness ‘protected’ against picking up the habit. Neuroticism is one of the most important factors of personality to study regarding the habit of smoking. Munafo, Zetteler, and Clark (2006) show in their meta-analysis on personality factors and smoking that neuroticism and increased extraversion were risk factors for being a smoker. I am aware of one study on the effects of different personality and smoking. Choi et al (2017) write:
The results emerging from this study indicate that neuroticism and conscientiousness are associated with the likelihood of being a current smoker, as well as level of ND. Furthermore, personality traits have a greater influence on smoking status and severity of ND in AAs relative to EAs. These relationships were particularly pronounced among smokers with reporting TTFC of ≤5 min.
…
… we found that higher neuroticism and lower conscientiousness were associated with higher likelihood of being a current smoker in the AA sample.
So black smokers were more likely to be conscientious, neurotic and open to experience whereas white smokers were more likely to be neurotic and conscientious.
Finally, racial differences in serum cotnine levels are seen, too. Black smokers have higher levels of cotnine than white smokers (Caraballo et al, 1998; Signorello et al, 2010). Perez-Stable et al (2006) show that higher levels of cotnine can be explained by slower clearance of cotnine along with a higher intake of nicotine per cigarette, because blacks take deeper pulls than whites (though they smoke fewer cigarettes than whites, taking deeper pulls off-sets this; Ho and Elo, 2013).
Smoking can be lessened in all populations—no matter the race/ethincity—with the right universal and intervention efforts (Kandel et al, 2004; Kahende et al, 2011). This can be achieved—especially in low-income areas—by lessening and eventually ridding storefronts of these advertisements for menthol cigarettes, which would then decrease the population of smokers in that area because most only smoke menthols. This would then close some of the black-white mortality gap since smoking causes a good amount of it.
Dauphinee et al (2013) even noted how 52 percent of students recognized Camel cigarettes, whereas 36 percent recognized Marlboro and 32 percent recognized Newports. Black students were three times more likely to recognize Newports than Marlboros (because, in my experience, blacks are way more likely to smoke Newports than Marlboros, which whites are more likely to smoke), while this effect held even after controlling for exposure to smoking by parents and peers. This is yet more proof of the ‘menthol effect’ in lower-income communities that partly drives the higher rates of smoking.
In conclusion, it seems that most of the disparity can be pinned down on Big Tobacco advertising mostly in low-income areas where they spend more than 10 percent of their income on cigarettes. Young children are more likely to know what menthol cigarettes are, what they look like and are more likely to know those type of brains of cigarettes, due mainly to how often and how much they are advertised in low-income areas in comparison to high-income areas. Blacks are also more likely than whites to have the personality traits found in smokers, so this, too, contributes to the how and why of black smoking in comparison to whites; they are more susceptible to it due to their personality along with being exposed to more advertisements since they are more likely to live in lower-income areas than whites.
I don’t believe in banning things, but the literature on this suggests that many people only smoke menthols and that if they were ever banned, most would just quit smoking. I don’t think that we need to go as far as banning menthol cigarettes—or cigarettes in general—we just need to educate people better and, of course, reel in Big Tobaccos reach in lower-income communities. Smoking also began to decline the same year that Joe Camel was ‘voluntarily’ discontinued by its parent company (Pampel and Aguilar, 2008), and so, that is good evidence that at least banning or reforming laws in low-income areas would change the number of smokers in a low-income area, and, along with it, close at least a small part of the black-white mortality gap.
Race/Ethnicity and the Microbiome
1800 words
The microbiome is the number and types of different microorganisms and viruses in the human body. Racial differences are seen everywhere, most notably in the phenotype and morphology. Though, of course, there are unseen racial differences that then effect bodily processes of different races and ethnic groups. The microbiome is one such difference, which is highly heritable (Goodrich et al, 2014; Beaumont et al, 2016; Hall, Tolonen, and Xavier, 2017) (though they use the highly flawed twin method, so heritabilities are most likely substantially lower). They also show that certain genetic variants predispose individuals to microbial dysbiosis. However, diet, antibiotics and birth mode can also influence the diversity of microbiota in your biome (Conlon and Bird, 2015; Bokulich et al, 2017; Singh et al, 2017) and so while the heritability of the microbiome is important (which is probably inflated due to the twin method), diet can and does change the diversity of the biome.
It used to be thought that our bodies contained 90 percent bacteria and only 10 percent human cells (Collen, 2014), however that has been recently debunked and the ratio is 1.3 to 1, human to microbe (Sender, Fuchs, and Milo, 2016). (Collen’s book is still an outstanding introduction to this subject despite the title of her book being incorrect.) Though the 10:1 microbe/human cell dogma is debunked, in no way does that lessen the importance of the microbiome regarding health, disease and longevity.
Lloyd-Price, Abu-Ali, and Huttenhower (2016) review definitions for the ‘healthy human microbiome’ writing “several population-scale studies have documented the ranges and diversity of both taxonomic compositions and functional potentials normally observed in the microbiomes of healthy populations, along with possible driving factors such as geography, diet, and lifestyle.” Studies comparing the biomes of North and South America, Europe and Africa, Korea and Japan, and urban and rural communities in Russia and China have identified numerous different associations that are related to differences in the microbiome between continents that include (but are not limited to) diet, genetics, lifestyle, geography, and early life exposures though none of these factors have been shown to be directly causal regarding geographic microbiome diversity.
Gupta, Paul, and Dutta (2017) question the case of a universal definition of a ‘healthy microbiome’ since it varies by geographic ancestry. Of course, ancestry and geographic location influence culture which influences diet which influences microbiome diversity between populations. This, of course, makes sense. why have a universal healthy microbiome with a reference man that doesn’t reflect the diversity of both the individual and group differences in the microbiome? This will better help different populations with different microbiomes lose weight and better manage diseases in certain populations.
The microbiome of athletes also differs, too. Athletes had enhanced microbiome diversity when compared to non-athletes (Clarke et al, 2016). In a further follow-up study, it was found that microbial diversity correlated with both protein consumption and creatine kinase levels in the body (Clarke et al, 2017) are proxies for exercise, and since they’re all associations, causality remains to be untangled. Nevertheless, these papers are good evidence that both lifestyle and diet leads to changes in the microbiome.
Fortenberry (2013: 165) notes that American racial and ethnic classifications are “social and political in origin and represent little meaningful biologic basis of between-group racial/ ethnic diversity“. It is also known that eating habits, differing lifestyles and metabolic levels also influence the diversity of the microbiome in the three ‘races’* studied (Chen et al, 2016), while deep sequencing of oral microbiota has the ability to classify “African Americans with a 100% sensitivity and 74% specificity and Caucasians with a 50% sensitivity and 91% specificity” (Mason et al, 2014). The infant microbiome, furthermore, is influenced by maternal diet and breastfeeding as well as the infant’s diet (Stearns et al, 2017). This is why differences in race/ethnicity call into question the term of ‘healthy human microbiota’ (Gupta, Paul, and Dutta, 2017). These differences in the microbiome also lead to increased risk for colorectal cancer in black Americans (Goyal et al, 2016; Kinross, 2017).
Further, the healthy vagina “contains one of the most remarkably structured microbial ecosystems, with at least five reproducible community types, or “community state types” (Lloyd-Price, Abu-Ali, and Huttenhower 2016). The diversity of the microbiome in the vagina also varies by race. It was found that 80 percent of Asian women and 90 percent of white women harbored a microbiota species named Lactobacillus, whereas only about 60 percent of ‘Hispanics’ and blacks harbored this species. The pH level, too, varied by race with blacks and ‘Hispanics’ averaging 4.7 and 5.0 and Asians and whites averaging 4.4 and 4.2. So, clearly, since Asians and whites have similar vaginal pH levels, then it is no surprise that they have similar levels of vaginal Lactobacillus, whereas blacks and ‘Hispanics’, with similar pH levels have similar vaginal levels of Lactobacillus.
White subjects also have more diverse species of microbiota than non-white subjects while also having a different microbiota structure (Chen et al, 2015). Caucasian ethnicity/race was also shown to have a lower overall microbiome diversity, but higher Bacteroidetes scores, while white babes also had lower scores of Proteobacteria than black Americans (Sordillo et al, 2017). This comes down to both diet and genetic factors (though causation remains to be untangled).
Differences in the skin microbiome also exist between the US population and South Americans (Blaser et al, 2013). They showed that Venezuelan Indians had a significantly different skin biome when compared to US populations from Colorado and New York, having more Propionibacterium than US residents. Regarding the skin microbiota in the Chinese, Leung, Wilkins, and Lee (2015) write “skin microbiomes within an individual is more similar than that of different co-habiting individuals, which is in turn more similar than individuals living in different households.” Skin microbiota also becomes similar in cohabitating couples (Ross, Doxey, and Neufeld, 2017) and even cohabitating family members and their dogs (Song et al, 2013; Cusco et al, 2017; Torres et al, 2017).
Differences between the East and West exist regarding chronic liver disease, which may come down to diet which may influence the microbiota and along with it, chronic liver disease. (Nakamoto and Schabl, 2016). The interplay between diet, the microbiome and disease is critical if we want to understand racial/ethnic differentials in disease acquisition/mortality, because the microbiome influences so many diseases (Cho and Blaser, 2012; Guinane and Cotter, 2013; Bull and Plummer, 2014; Shoemark and Allen, 2015; Zhang et al, 2015; Shreiner, Kao, and Young, 2016; Young, 2017).
The human microbiome has been called our ‘second genome’ (Zhu, Wang, and Li, 2010; Grice and Seger, 2012) with others calling it an ‘organ’ (Baquero and Nombela, 2012; Clarke et al, 2014; Brown and Hazen, 2015). This ‘organ’, our ‘second genome’ can also influence gene expression (Masotti, 2012; Maurice, Haiser, and Turnbaugh, 2013; Byrd and Seger, 2015) which could also have implications for racial differences in disease acquisition and mortality. This is why the study of the microbiome is so important; since the microbiome can up- and down-regulate gene expression—effectively, turning genes ‘on’ and ‘off’—then understanding the intricacies that influence the microbiome diversity along with the diet that one consumes will help us better understand racial differences in disease acquisition. Diet is a huge factor not only regarding obesity and diabetes differences within and between populations, but a ‘healthy microbiome’ also staves off obesity. This is important. The fact that the diversity of microbiota in our gut can effectively up- and down-regulate genes shows that we can, in effect, influence some of this ourselves by changing our diets, which would then, theoretically, lower disease acquisition and mortality once certain microbiome/diet/disease associations are untangled and shown to be causative.
Finally, the Hadza have some of the best-studied microbiota, and since they still largely live a hunter-gatherer lifestyle, this is an important look at what the diversity of microbiota may have looked like in our hunter-gatherer ancestors (Samuel et al, 2017). The fact that they noticed such diverse changes in the microbiome—some species effectively disappearing during the dry season and reappearing during the wet season—is good proof that what drives these changes in the diversity of the microbiota in the Hadza are seasonal changes in diet which are driven by the wet and dry seasons.
Gut microbiota may also influence our mood and behavior, and it would be interesting to see which types of microbiota differ between populations and how they would be associated with certain behaviors. The microbes are a part of the unconscious system which regulates behavior, which may have causal effects regarding cognition, behavioral patterns, and social interaction and stress management; this too makes up our ‘collective unconscious’ (Dinan et al, 2015). It is clear that the microbes in our gut influence our behavior, and it even may be possible to ‘shape our second genome’ (Foster, 2013). Endocrine and neurocrine pathways may also be involved in gut-microbiota-to-brain-signaling, which can then alter the composition of the microbiome and along with it behavior (Mayer, Tillisch, and Gupta, 2015). Gut microbiota also plays a role in the acquisition of eating disorders, and identifying the specific microbiotal profiles linked to eating disorders, why it occurs and what happens while the microbiome is out of whack is important in understanding our behavior, because the gut microbiome also influences our behavior to a great degree.
The debate on whether or not racial/ethnic differences in microbiome diversity differs due to ‘nature’ or ‘nurture’ (a false dichotomy in the view of developmental systems theory) remains to be settled (Gupta, Paul, and Dutta, 2017). However, like with all traits/variations in traits, it is due to a complex interaction of the developmental system in question along with how it interacts with its environment. Understanding these complex disease/gene/environment/microbiotal pathways will be a challenge, as will untangling direct causation and what role diet plays regarding the disease/microbiota/dysbiosis factor. As we better understand our ‘second genome’, our ‘other organ’, and individual differences in the genome and how those genomic differences interact with different environments, we will then be able to give better care to both races/ethnies along with individuals. Just like with race and medicine—although there is good correlative data—we should not jump to quick conclusions based on these studies on disease, diet, and microbiotal diversity.
The study of ethnic/racial/geographic/cultural/SES differences in the diversity of the microbiome and how it influences disease, behaviors and gene expression will be interesting to follow in the next couple of years. I think that there will be considerable ‘genetic’ (i.e., differences out of the womb; I am aware that untangling ‘genetic’ and ‘environmental’ in utero factors is hard, next to impossible) differences between populations regarding newborn children, and I am sure that even the microbiota will be found to influence our food choices in the seas of our obesogenic environments. The fact that our microbiota is changeable with diet means that, in effect, we can have small control over certain parts of our gene expression which may then have consequences for future generations of our offspring. Nevertheless, things such as that remain to be uncovered but I bet more interesting things never dreamed of will be found as we look into the hows and whys of both individual and populational differences in the microbiome.
IQ and Construct Validity
1550 words
The word ‘construct’ is defined as “an idea or theory containing various conceptual elements, typically one considered to be subjective and not based on empirical evidence.” Whereas the word ‘validity’ is defined as “the quality of being logically or factually sound; soundness or cogency.” Is there construct validity for IQ tests? Are IQ tests tested against an idea or theory containing various conceptual elements? No, they are not.
Cronbach and Meehl (1955) define construct validity, which they state is “involved whenever a test is to be interpreted as a measure of some attribute or quality which is not “operationally defined.”” Though, the construct validity for IQ tests has been fleeting to investigators. Why? Because there is no theory of individual IQ differences to test IQ tests on. It is even stated that “there is no accepted unit of measurement for constructs and even fairly well-known ones, such as IQ, are open to debate.” The ‘fairly well-known ones’ like IQ are ‘open to debate’ because no such validity exists. The only ‘validity’ that exists for IQ tests is correlations with other tests and attempted correlations with job performance, but I will show that that is not construct validity as is classicly defined.
Construct validity can be easily defined as the ability of a test to measure the concept or construct that it is intended to measure. We know two things about IQ tests: 1) they do not test ‘intelligence’ (but they supposedly do a ‘good enough job’ so that it does not matter) and 2) it does not even test the ‘construct’ that it is intended to measure. For example, the math problem ‘1+1’ is construct valid regarding one’s knowledge and application of that math problem. Construct validity can pretty much be summed up as the proof that it is measuring what the test intends…but where is this proof? It is non-existent.
Richardson (1998: 116) writes:
Psychometrists, in the absence of such theoretical description, simply reduce score differences, blindly to the hypothetical construct of ‘natural ability’. The absence of descriptive precision about those constructs has always made validity estimation difficult. Consequently the crucial construct validity is rarely mentioned in test manuals. Instead, test designers have sought other kinds of evidence about the valdity of their tests.
The validity of new tests is sometimes claimed when performances on them correlate with performances on other, previously accepted, and currently used, tests. This is usually called the criterion validity of tests. The Stanford-Binet and the WISC are often used as the ‘standards’ in this respect. Whereas it may be reassuring to know that the new test appears to be measuring the same thing as an old favourite, the assumption here is that (construct) validity has already been demonstrated in the criterion test.
Some may attempt to say that, for instance, biological construct validity for IQ tests may be ‘brain size’, since brain size is correlated with IQ at .4 (meaning 16 percent of the variance in IQ is explained by brain size). However, for this to be true, someone with a larger brain would always have to be ‘more intelligent’ (whatever that means; score higher on an IQ test) than someone with a smaller brain. This is not true, so therefore brain size is not and should not be used as a measure of construct validity. Nisbett et al (2012: 144) address this:
Overall brain size does not plausibly account for differences in aspects of intelligence because all areas of the brain are not equally important for cognitive functioning.
For example, breathalyzer tests are construct valid. There is a .93 correlation (test-retest) between 1 ml/kg bodyweight of ethanol in 20, healthy male subjects. Furthermore, obtaining BAC through gas chromatography of venous blood, the two readings were highly correlated at .94 and .95 (Landauer, 1972). Landauer (1972: 253) writes “the very high accuracy and validity of breath analysis as a correct estimate of the BAL is clearly shown.” Construct validity exists for ad-libitum taste tests of alcohol in the laboratory (Jones et al, 2016).
There is a casual connection between what one breathes into the breathalyzer and his BAC that comes out of the breathalyzer and how much he had to drink. For example, for a male at a bodyweight of 160 pounds, 4 drinks would have him at a BAC of .09, which would make him unfit to drive. (‘One drink’ being 12 oz of beer, 5 oz of wine, or 1.25 oz of 80 proof liquor.) He drinks more, his BAC reading goes up. Someone is more ‘intelligent’ (scores higher on an IQ test), then what? The correlations obtained from so-called ‘more intelligent people’, like glucose consumption, brain evoked potentials, reaction time, nerve conduction velocity, etc have never been shown to determine higher ‘ability’ to score higher on IQ tests. That, too, would not even be construct validation for IQ tests, since there needs to be a measure showing why person A scored higher than person B, which needs to hold one hundred percent of the time.
Another good example of the construct validity of an unseen construct is white blood cell count. White blood cell count was “associated with current smoking status and COPD severity, and a risk factor for poor lung function, and quality of life, especially in non-currently smoking COPD patients. The WBC count can be used, as an easily measurable COPD biomarker” (Koo et al, 2017). In fact, the PRISA II test has white blood cell count in it, which is a construct valid test. Even elevated white blood cell count strongly predicts all-cause and cardiovascular mortality (Johnson et al, 2005). It is also an independent risk factor for coronary artery disease (Twig et al, 2012).
A good example of tests supposedly testing one thing but testing another is found here:
As an example, think about a general knowledge test of basic algebra. If a test is designed to assess knowledge of facts concerning rate, time, distance, and their interrelationship with one another, but test questions are phrased in long and complex reading passages, then perhaps reading skills are inadvertently being measured instead of factual knowledge of basic algebra.
Numerous constructs have validity—but not IQ tests. It is assumed that they test ‘intelligence’ even though an operational definition of intelligence is hard to come by. This is important, as if there cannot be an agreement on what is being tested, how will there be construct validity for said construct in question?
Richardson (2002) writes that Detterman and Sternberg sent out a questionnaire to a group of theorists which was similar to another questionnaire sent out decades earlier to see if there was an agreement on what ‘intelligence’ is. Twenty-five attributes of intelligence were mentioned. Only 3 were mentioned by more than 25 percent of the respondents, with about half mentioning ‘higher level components’, one quarter mentioned ‘executive processes’ while 29 percent mentioned ‘that which is valued by culture’. About one-third of the attributes were mentioned by less than 10 percent of the respondents with 8 percent of them answering that intelligence is ‘the ability to learn’. So if there is hardly any consensus on what IQ tests measure or what ‘intelligence’ is, then construct validity for IQ seems to be very far in the distance, almost unseeable, because we cannot even define the word, nor actually test it with a test that’s not constructed to fit the constructors’ presupposed notions.
Now, explaining the non-existent validity of IQ tests is very simple: IQ tests are purported to measure ‘g’ (whatever that is) and individual differences in test scores supposedly reflect individual differences in ‘g’. However, we cannot say that it is differences in ‘g’ that cause differences in individual test scores since there is no agreed-upon model or description of ‘g’ (Richardson, 2017: 84). Richardson (2017: 84) writes:
In consequence, all claims about the validity of IQ tests have been based on the assumption that other criteria, such as social rank or educational or occupational acheivement, are also, in effect, measures of intelligence. So tests have been constructed to replicate such ranks, as we have seen. Unfortunately, the logic is then reversed to declare that IQ tests must be measures of intelligence, because they predict school acheivement or future occupational level. This is not proper scientific validation so much as a self-fulfilling ordinance.
Construct validity for IQ does not exist (Richardson and Norgate, 2015), unlike construct validity for breathalyzers (Landauer, 1972) or white blood cell count as a disease proxy (Wu et al, 2013; Shah et al, 2017). So, if construct validity is non-existent, then that means that there is no measure for how well IQ tests measure what it’s ‘purported to measure’, i.e., how ‘intelligent’ one is over another because 1) the definition of ‘intelligence’ is ill-defined and 2) IQ tests are not validated against agreed-upon biological models, though some attempts have been made, though the evidence is inconsistent (Richardson and Norgate, 2015). For there to be true validity, evidence cannot be inconsistent; it needs to measure what it purports to measure 100 percent of the time. IQ tests are not calibrated against biological models, but against correlations with other tests that ‘purport’ to measure ‘intelligence’.
(Note: No, I am not saying that everyone is equal in ‘intelligence’ (whatever that is), nor am I stating that everyone has the same exact capacity. As I pointed out last week, just because I point out flaws in tests, it does not mean that I think that people have ‘equal ability’, and my example of an ‘athletic abilities’ test last week is apt to show that pointing out flawed tests does not mean that I deny individual differences in a ‘thing’ (though athletic abilities tests are much better with no assumptions like IQ tests have.))
You Don’t Need Genes to Delineate Race
2100 words
Most race deniers say that race isn’t real because, as Lewontin (1972) and Rosenberg (2002) state, the within-group variation is larger than the between-group variation. Though, you can circumvent this claim by not even looking at genes/allele frequencies between races, you can show that race is real by looking at morphology, phenotype and geographic ancestry. This is one of Michael Hardimon’s race categories, the minimalist concept of race. This concept does not entail anything that we cannot physically ‘see’ with our eyes (e.g., mental and psychological traits are off the table). Using these concepts laid out by Hardimon can and does prove that race is real and useful without even arguing about any potential mental and psychological differences between human races.
Morphology
Morphology is one of the most simple tells for racial classification. Just by looking at average morphology between the races we can use attempt to use this data point as a premise in the argument that races exist.
East Asians are shorter with shorter limbs and have an endomorphic somatype. This is due to evolving in cold climate, as a smaller body and less surface area can be warmer much quicker than a larger body. This is a great example of Allen’s rule: that animals in colder climates will be smaller than animals in warmer climates. Using average morphology, of course, can show how the population in question evolved and where they evolved.
Regarding Europeans, they have an endomorphic somatype as well. This, again, is due to where they evolved. Morphology can tell us a lot about the evolution of a species. Though, East Asians and Europeans have similar morphologies due to evolving in similar climates. Like East Asians, Europeans have a wider pelvis in comparison to Africans, so this is yet another morphological variable we can use to show that race exists.
Finally, the largest group is ‘Africans’ who have the largest phenotypic and genetic diversity on earth. Generally, you can say that they’re tall, have long limbs and a short torso, which is due to evolving in the tropics. Furthermore, and perhaps most important, Africans have narrower pelves than East Asians and Europeans. This character is one of the most important regarding the reality of race because it’s one of the most noticeable, and we do notice in when it comes to sports competition because that certain type of morphology is conducive to athletic success. (Also read my recent article on strength and race and my article on somatype and race for more information on morphologic racial differences.)
Phenotype
Morphology is a part of the phenotype too, obviously, but there is a reason why it’s separated. As is true with morphology, different characters evolved due to cultural evolution (whether or not they adopted farming early) or evolution through natural selection, drift and mutation. Though, of course, favorable mutations in a certain environment will be passed on and eventually become a part of the characteristics of the population in question.
East Asians have the epicanthic fold, which probably evolved to protect the eye from the elements and UV rays on the Mongolian steppes. They also have softer features than Europeans and Africans, but this is not due to lower testosterone as is popularly stated. (Amusingly enough, there is a paper that stated that East Asians have Down Syndrome-like qualities due to their epicanthic folds to bring up one reason.) Even then, what some races find attractive or not can show how and why certain facial phenotypes evolved. To quote Gau et al (2018):
Compared with White women, East Asian women prefer a small, delicate and less robust face, lower position of double eyelid, more obtuse nasofrontal angle, rounder nose tip, smaller tip projection and slightly more protruded mandibular profile.
And they conclude:
The average faces are different from the attractive faces, while attractive faces differ according to race. In other words, the average facial and aesthetic criteria are different. We should use the attractive faces of a race to study that races aesthetic criteria.
We can use studies such as this to discern different facial phenotypes, which, again, proves that race exists.
The climate one’s ancestors evolved in dictates nose shape. In areas where it is extremely dry and also has a lot of heat, a larger mucous area is required to moisten inspired (inhaled) air, which is why a more flat and narrow nose is needed.
Zaidi et al (2017) write:
We find that width of the nares is correlated with temperature and absolute humidity, but not with relative humidity. We conclude that some aspects of nose shape may have indeed been driven by local adaptation to climate.
Though climate, of course, isn’t the only reason for differences in nose shape; sexual selection plays a part too, as seen in the above citation on facial preferences in East Asian and European women.
There are also differences in hirsutism between the races. Racial differences exist regarding upper lip hair, along with within-race differences (Javorsky et al, 2014). The self-reported races of African American, East Asian, Asian Indian, and ‘Hispanic’ predicted facial hair differences in women, but not how light their skin was. The women were from Los Angeles, USA; Rome, Italy; Akita, Japan; and London, England. Indian women had more hair than any other race, while European women had the least. Regarding within-race variation, Italian women had more hair on their upper lip than American and British women. Skin lightness was related to hair on the upper lip. (Also read my article The Evolution of Human Skin Variation for more information on racial differences in skin color.)
In 2012, an interesting study was carried out on hair greying on a sample population of a large number of the world’s ethnies titled Greying of the human hair: a worldwide survey, revisiting the ‘50’ rule of thumb. The objective of the study was to test the ’50-50-50′ rule; that at age 50, 50 percent of the population has at least 50 percent of their hair grey. Africans and Asians showed fewer grey hairs than whites who showed the most. The results imply that hair greyness varies by ethnicity/geographic origin, which is perfect for the argument laid out in this article. The global range for people over 50 with 50 percent or more of their hair grey was between 6 and 23 percent, far lower than what was originally hypothesized (Panhard, Lozano, and Loussouarn, 2012). They write on page 870:
With regard to the intensity of hair greying, the lowest values were found among African and Asian groups, especially Thai and Chinese, whereas the highest values were in subjects with the blondest hair (Polish, Scottish, Russian, Danish, CaucasianAustralian and French).
…
Altogether, these analyses clearly illustrate that the lowest incidences and intensities of grey hair are found in populations of the darkest hair whereas the highest intensities are found in populations with the lightest hair tones.
Actual hair diversity is much more concentrated in Europeans, however (Frost, 2005). (See Peter Frost’s article Why Do Europeans Have So Many Hair and Eye Colors?) It is largely due to sexual selection, with a few climatic factors thrown in. Dark hair, on the other hand, is a dominant trait, which is found all over the world.
Zhuang et al (2010) found significant differences in facial morphology between the races, writing:
African-Americans have statistically shorter, wider, and shallower noses than Caucasians. Hispanic workers have 14 facial features that are significantly larger than Caucasians, while their nose protrusion, height, and head length are significantly shorter. The other ethnic group was composed primarily of Asian subjects and has statistically different dimensions from Caucasians for 16 anthropometric values.
…
Statistically significant differences in facial anthropometric dimensions (P < 0.05) were noted between males and females, all racial/ethnic groups, and the subjects who were at least 45 years old when compared to workers between 18 and 29 years of age.
Blacks had statistically significant differences in lip and face length when compared to whites (whites had shorted lips than blacks who had longer lips than whites).
Brain size and cranial morphology, too, differs by geographic ancestry which is directly related to the climate where that population evolved (Beals, Smith, and Dodd, 1984). Most every trait that humans have—on average of course—differs by geographic location and the cause of this is evolution in these locations along with being a geographically isolated breeding population.
Geographic ancestry
The final piece to this argument is using where one’s recent ancestors came from. There are five major populations from a few geographic locales: Oceania, the Americas (‘Native Americans), Europe, Africa and East Asia. These geographic locales have peoples that evolved there and underwent different selective pressures due to their environment and their bodies evolved to better suit their environment, and so racial differences in morphology and phenotype occurred so the peoples could survive better in that location. No one part of this argument is more important than any other, though geographic ancestry is the final piece of the puzzle that brings everything together. Because race is correlated with morphology and phenotype, the geographic ancestry dictates what these characteristics look like.
Conclusion
Thus, this is the basic argument:
P1: Differing populations have differing phenotypes, including (but not limited to) facial structure, hair type/color, lip structure, skull size, brain size etc.
P2: Differing populations have differing morphology which, along with this population’s phenotype, evolved in response to climatic demands along with sexual selection.
P3: This population must originate from a distinct geographic location.
C: If all three of the above premises are true, then race—in the minimalist sense—exists and is biologically real.
This argument is extremely simple, and along with the papers cited above in support of the three premises and the ultimate conclusion, it will be extremely hard for race deniers to counter. We can say that P1 is logically sound because geographically isolated populations differ in the above-mentioned criteria. We can say that P2 is logically sound since differing populations have differing morphology (as I have discussed numerous times which leads to racial differences in sporting competition) such as differing trunk lengths, leg lengths, arm lengths and heights which are largely due to evolution in differing climates. We can say that P3 is logically sound because the populations that would satisfy P1 and P2 do come from geographically distinct locations; that is, they have a peculiar ancestry that they only share.
This concept of minimalist race from Michael Hardimon is (his) the racialist concept of race “stripped down to its barest bones” (Hardimon, 2017: 3). The minimalist concept of race, then, does not discuss any differences between populations that cannot be directly discerned with the naked eye. (Note: You can also use the above arguments/data laid out for the populationist concept of race, which, according to Hardimon (2017: 3) is: “A nonracialist (nonessentialist, nonhierarchical) candidate scientific concept that characterizes races as groups of populations belonging to biological lines of descent, distinguished by patterns of phenotypic differences, that trace back to geographically separated and extrinsically reproductively isolated founder populations.)
Minimalist race is biologically sound, grounded in genetics (though I have argued here that you don’t need genetics to define race), and is grounded in biology. Minimalist race is defined as characteristics of the group, not of the individual. Minimalist race are biologically real. Minimalist races exist because, as shown with the data presented in this article, phenotypic and morphologic traits are unevenly distributed throughout the world which then correlates with geographic ancestry. It cannot get any more simpler than that: race exists because differences in phenotype and morphology exist which then corresponds with geographic ancestry.
From Hardimon (2017: 177)\
No sane or logical person would deny the existence of race based on the criteria laid out in this article. We can also make another leap in logic and state that since minimalist races exist and are biologically real then geographic ancestry should be a guide when dealing with medicine and different minimalist races.
It is clear that race exists in the minimal sense; you do not need genes to show that race is real, nor that race has any utility in a medical context. This is important for race deniers to understand: genes are irrelevant when talking about the reality of race; you only need to just use your eyes and you’ll see that certain morphologies and phenotypes are distributed across geographic locations. It is also very easy to get someone to admit that races exist in this minimalist-biological sense. No one denies the existence of Africans, Europeans, ‘Native’ Indians, East Asians and Pacific Islanders. These populations differ in morphology and other physical characters which are unevenly distributed by geographic ancestry, so, therefore: minimialist races exist and are a biological reality.
Explaining African Running Success Through a Systems View
2100 words
Last year I bought The Genius in All of Us: New Insights Into Genetics, Talent, and IQ (Shenk, 2010) and while the book is interesting and I agree with a few things he says, he gets it horribly wrong on athleticism and ethnicity. Some of it I may be able to forgive since the book was written in 2010, but he does make some glaring errors. Chapter 6—pages 100-111—is titled Can White Men Jump? Ethnicity, Genes, Culture, and Success.
In the beginning of the chapter, Shenk writes that after the 2008 Beijing Summer Olympics, many articles were written about the Jamaican women who took the top three spots in the 100 and 200m races, with the emergence of Usain Bolt and his record-setting performance. Shenk (2010: 101) writes:
The powerful protein [alpha-actinin-3] is produced by a special gene variant called ACTN3, at least one copy of which is found in 98 percent of Jamaicans—far higher than in many other ethnic populations.
An impressive fact, but no one stopped to do the math. Eighty percent of Americans also had at least one copy of ACTN3—that amounts to 240 million people. Eighty-two percent of Europeans have it as well—that tacks on another 597 million potential sprinters. “There’s simply no clear relationship between the frequency of this variant in a population and its capacity to produce sprinting superstars,” concluded geneticist Daniel MacArthur.
I have written about MacArthur’s thoughts on the ACTN3 variant—that he helped discover, no less—in an article on Jamaicans, Kenyans, and Ethiopians and the explanatory factors in regard to their success in running competitions. Though, the article from MacArthur was written in 2008 and Shenk’s book was written in 2010, considerable advances have been made in this field. It was found that “combined effects of morphological and contractile properties of individual fast muscle fibers attribute to the enhanced performance observed in RR genotypes during explosive contractions” (Broos et al, 2016). Of course when talking about sprinting and morphology, you must think of the somatype. The somatype that is conducive to running success is a tall, lanky body with long limbs, as longer limbs can cover more distance. So European runners don’t have the right somatype, nor are the XX genotype for the ACTN3 variant high in Jamaicans (this genotype is present in ~2 percent of the Jamaican population; Scott et al, 2010). This—among other reasons I have laid out in the past—are why Jamaicans excel in sprinting competitions compared to other ethnic groups.
Shenk (2014: 10) further writes that sports success seem to come in ‘geographic clusters’, and the field of sports geography has been developed to understand it. “What they’ve discovered is that there’s never a single cause for a single cluster,” Shenk writes. “Rather, the success comes from many contributions of climate, media, demographics, politics, training, spirituality, education, economics and folklore. In short, athletic clusters are not genetic, but systemic.” Shenk then discusses the fact that these explanations are not good enough and that some ‘sports geographers’ have transformed themselves into ‘sports geneticists’ and then cites Jon Entine’s 2002 book Taboo: Why Black Athletes Dominate Sports and Why We’re Afraid to Talk About It where Shenk quotes Entine who quotes geneticist and physiologist Claude Bouchard who says that “these biological characteristics are not unique to West or East African blacks. These populations are seen in all populations, including whites” (Shenk, 2010: 102). Of course they’re not unique to one population and I don’t think that anyone has ever claimed that. Though the frequencies of these biological, morphological and physiological characteristics are not distributed evenly amongst populations and this explains how and why certain populations excel in certain sports when compared to others.
Shenk (2010: 102) also quotes Entine (2002), writing: “Entine also acknowledges that we haven’t actually found the actual genes he’s alluding to. “These genes will likely be identified early in the [twenty-first century],” he predicts.” We have ‘found some genes’ that aid in athletic performance, the ACTN3 genotype combined with type II fibers and the right morphology, as mentioned above for one. (Though a systems view—one of holism—makes much more sense here than a reducionist view. You must look at the whole system, not reduce things down, but that’s for another day.) That, in my opnion, is a large driver for ethnic differences in sports like this, because you need certain traits if you want to excel in these types of competitions.
He then discusses the success of the Kenyans in distance running—stating that 90 percent of Kenyan runners come from a small subset of Kenyans called the Kalenjin. He cites a few stories of some Kalenjin who talk about their experiences with no running water in their homes and that they had to “run to the river, to take your shower, run home, change, [run] to school . . . Everything is running” (Keino, a Kalenjin boy, quoted from Shenk, 2010: 104). Of course this is attributed to a multitude of factors, all of which have to work in concert to get the desired effect. For instance, sports psychologists have found that strong cultural achievement and the ability to work hard, compete, outdo others and seek new challenges drives their running dominance.
Shenk (2010: 106-107) then writes:
1.DESPITE APPEARANCES TO THE CONTRARY, RACIAL AND ETHNIC GROUPS ARE NOT GENETICALLY DISCRETE.
Skin color is a great deceiver; actual genetic differences between ethnic and geographic groups are very, very limited. All human beings are descended from the same African ancestors … [blah blah blah] … By no stretch of the imagination, then, does any ethnicity or region have an exclusive lock on a particular body type or secret high-performance gene. Body shapes, muscle fiber types, etc., are actually quite varied and scattered, and true athletic potential is widespread and plentiful.
Of course, I don’t think I have ever read anyone who denies this. However, as I’ve noted too many times to count, certain body types and muscle fiber distributions are more likely to be found in certain populations due to where their ancestors evolved recently, and so the fact that ‘actual genetic differences between ethnic and geographic groups are very, very, limited’ does not mean much when talking about dominance by a few populations in elite sporting competition. It just so happens to be the case that the somatypes and muscle fiber distributions that are conducive to running success are more likely to be found in populations of West and East African descent. This is an undeniable fact. (Also note how these ‘appearances to the contrary’ show how race is real.)
2.GENES DON’T DIRECTLY CAUSE TRAITS; THEY ONLY INFLUENCE THE SYSTEM.
Consistent with other lessons of GxE [Genes x Environment], the surprising finding of the $3 billion Human Genome Project is that only in rare instances do specific gene variants directly cause specific traits or diseases. …
As the search for athletic genes continues, therefore, the overwhelming evidence suggests that researchers will instead locate genes prone to certain types of interactions: gene variant A in combination with gene variant B, provoked into expression by X amount of training + Y altitude + Z will to win + a hundred other life variables (coaching, injuries, etc.), will produce some specific result R. What this means, of course, What this means, of course, is that we need to dispense rhetorically with thick firewall between biology (nature) and training (nurture). The reality of GxE assures that each persons genes interacts with his climate, altitude, culture, meals, language, customs and spirituality—everything—to produce unique lifestyle trajectories. Genes play a critical role, but as dynamic instruments, not a fixed blueprint. A seven- or fourteen- or twenty-eight-year-old is not that way merely because of genetic instruction. (Shenk, 2010: 107)
Nothing really wrong here. He is correct, which is why you need to look at the whole biological system, which also includes the culture, climate, environment and so on that the biological, developmental system finds itself in. However, Shenk then gets it wrong again writing that Jamaicans are a ‘quite heterogenous genetic group’ due to being a transport between North and South America. He states—correctly—that Jamaicans ancestry is about equal to that of African-Americans, but the individual variation in ancestry varies by “46.8 to 97.0 percent” (Shenk, 2010: 108).
Shenk gets a lot wrong here. For example. African-American and Jamaicans—despite both being descended from slave populations—have differing maternal ancestry which somehow influences athletic success. Deason (2017) found that 1) modern Jamaicans are descended from slaves and, who had considerable selective pressure on the population; 2) maternal ancestry could either influence sports success or be a false positive; 3) maternal lineages were different in Jamaicans and African-Americans, implying that the same maternal lineage is not distributed evenly between both sprinting populations; 4) some evidence exists that the genetic histories of Jamaicans and African-Americans are different based on their maternal haplotypes; 5) low SES and low access to healthcare—classic indicators of high African ancestry—were not directly linked to elite athletic success; 6) comparisons of the genomes of African-Americans and Jamaicans did not significantly differ since the estimated number of generations since admixture occurred, which implies that controls were not more likely to have more recent European ancestry than athletes; and 7) the regions of the genome that influence sprinting performance may be different in both populations. This is the best evidence to date against Shenk’s simplistic notions of the genetics between Jamaicans and African-Americans.
Differences in fast twitch fibers between Europeans and West Africans explain a large amount of the variance between Europeans and West African descendants in regard to sprinting success, while those with more symmetrical knees and ankles tend to run faster in the 100m dash (Trivers et al, 2014). This would also imply that Jamaicans have more symmetry in their knees and ankles than Europeans, though I am not aware of data that makes this comparison.
Shenk finally discusses the psycho-social-cultural aspects behind the phenomenon, stating that Roger Bannister, the first person to break the four minute mile, stated that while “biology sets limits to performance, it is the mind that plainly determines how close individuals come to those absolute limits” (Shenk, 2010: 110-111). Numerous psychological factors do, indeed, need to combine in order for the individual in question to excel in sports—along with the requisite anatomical/physiological/morphological traits too. Sasaki and Sekiya note that “changes in physiological arousal and movement velocuty induced by mild psychological pressure played a significant role in the sprint performance.” (See also Bali, 2015.)
Lippi, Favaloro, and Guidi, (2008) note how “An advantageous physical genotype is not enough to build a top-class athlete, a champion capable of breaking Olympic records, if endurance elite performances (maximal rate of oxygen uptake, economy of movement, lactate/ventilatory threshold and, potentially, oxygen uptake kinetics) (Williams & Folland, 2008) are not supported by a strong mental background.” I have argued this for months, even if the beneficial somatype is there in the athlete in question, if he/she does not have the will to win they will not succeed in their goals. Psychosocial factors, of course, matter just as much as the physical but all of these factors work in concert to get the outcomes that occur in these sports.
Attempting to pinpoint one or a few traits—while it may help us to understand better physilogic and anatomic processes—tells us nothing about the entire system. This is why, for instance, the whole athletes system needs to be looked at—call it the ‘systems view of the athlete’, where all of these aforementioned variables work in concert to express elite athletic performance, with no one variable being higher than another as an explanatory factor in sports success. Though Shenk gets a few things right (like his point on genes not causing traits on their own, they just influence the system, and I’d take it a step further to note that genes are passive in their relationship to the physiological system as a whole and are only activated by the system as needed, not being ’causes’ on their own; Noble, 2008), he’s largely misguided on how certain aspects of Jamaican ancestry and morphology help propel them to running success in comparison to other ethnies.
When explaining elite athletic performance in certain areas of sports, you must take a view of the whole system, with each known variable influencing the next in the chain, if you want to explain why certain ethnies or racial groups do better in a given sport than other groups. A systems view is the only view to take when comparing populations in different athletic competitions. So the influence of culture, psychology, social effects, morphology, ancestry, anatomy, physiology, muscle fibers, etc all work in concert to produce elite athletic phenotypes that then excel in these sports, and reducing this down to certain variables—while it may help us understand some of the inner mechanics—it does nothing to help advance the hows and whys of elite success in sports competition when comparing different populations.
Malaria, the Sickle Cell Trait, and Fast Twitch Muscle Fibers
2550 words
West Africans and their descendants have longer limbs and a shorter trunk than Europeans, on average—as I have extensively noted. Due to where they evolved, of course, they have a different morphology and physiology. Bergmann’s rule states that peoples with recent ancestry in the tropics will have slimmer pelvic bones and be narrower overall whereas Allen’s rule states that peoples with recent ancestry in the tropics will have long limbs, these traits being good for heat dissipation (Lieberman, 2015) and is one reason why West Africans and their descendants excel in these most sports in America.
The fact that a lot of African ethnic groups have different anatomic proportions and physiologic adaptations in comparison to people who have evolved in non-tropical climates is not contested. Morrison and Cooper’s (2006) hypothesis on sick cell anemia driving elite athletic performance in West Africans and their descendants is one of the most interesting explanations I’ve heard on the biochemical differences between the races. Sickle cell anemia is caused by a gene mutation. On amino acid 6, a single nucleotide substitution from A to T (As pair it Ts, Gs pair with Cs). This substitution changes a glutamic acid codon to valine codon which then causes sickling of the blood. Sickle cell anemia, of course, is not a ‘black disease’ as is popularly believed, but it, in fact, has to do with geography and the prevalence of malaria-carrying mosquitoes in that location. “This mutation“, Morrison and Cooper (2006) write “appears to have triggered a series of physiological adjustments, which have had favourable athletic consequences.”
Now, I’m aware that those who are already skeptical of this hypothesis may say ‘so does this mean that Italians, Greeks, MENA peoples etc have more type II fibers and would excel in these competitions?’, no it does not mean that because they don’t have the requisite morphology that West Africans have.
In the 1970s, a study was carried out on the physiological and anatomical proportions of Olympic athletes who competed in the 1968 Olympic games. Anatomic and physiologic measures were taken for each athlete. They used four racial classifications: Negroid, Caucasoid, Mongoloid, and mestizo (Indian/Spanish mix). The classifications were based on “were based on identification and somatotype photographs, as well as physical characteristics including skin color; general body shape; proportions of segments of the limbs; facial structure; form of eyes, lips, and nose; and colour and texture of hair” (Morrison and Cooper, 2006). This study, of course, also confirmed the anatomic differences between blacks and other races and how it leads to superior sports performance. Though, something peculiar was noted in the black athletes. Morrison and Cooper (2006) write: “Although the study failed to link athletic capability to a single gene system, the authors expressed “surprise” that “a sizeable number of Negroid Olympic athletes manifested the sickle-cell trait.””
One interesting study looked at the sickle cell trait (SCT) in French West Indian elite sprint athletes (Marlin et al, 2005). Using the French National Team for the year 2000, Marlin et al (2005) identified 3 sprinters (2 males and 1 female) who tested positive for the SCT. They also noticed a significantly higher presence of titles for people who tested positive for the SCT (38.6 percent for males and 50 percent for females. Marlin et al (2005: 624) conclude “that male SCT carriers are able to perform sprints and brief exercises at the highest levels” and “that brief and intensive exercise performance involving mainly alactic anaerobic metabolism may be enhanced by HbS in elite male sprinters.”
Blacks had narrower hips, longer arms and legs and a shorter trunk in comparison to other races. Of course, somatype is the variable that matters here but certain races are more likely to have certain anatomic characters that lead to superior spots performance on comparison to other races. The authors also attempted to link traits with single gene networks but were unsuccessful. However, they did notice that a large number of black athletes tested positive for the sickle cell trait. There is a conundrum here, however. People with the sickle cell gene might have a greater oxygen demand which causes more in vivo cell sickling. It was hypothesized that these individuals would be at a disadvantage since the 1968 Olympic games were held in Mexico city which is a high altitude area. They theorized that their blood would sickle more at the high altitude in comparison to low altitude but this was not seen.
Then another study was carried out which showed that not only do individuals with the sickle cell trait have lower hemoglobin levels, but all blacks do (Garn, Smith, and Clark, 1975). This is how and why they can perform at high altitudes despite having the sickle cell trait. Then, to test if this was mostly ‘environmental’ or ‘genetic’ they undertook a large study where they followed individuals throughout their whole lives and the difference persisted even later in life. Of course, according to other authors, some sort of compensatory mechanism should exist to counteract black’s lower hemoglobin levels, since this deficiency even exists in athletes (Morrison and Cooper, 2006).
As I’ve written about in the past, it was established that type I and type II fibers use different metabolic pathways and that type II fibers lead to improved athletic performance (along with the certain genotype for the ACTN3 gene). Morrison and Cooper (2006) also state that, of course, not all West Africans and descendants have this trait, and that these people came from a small area of West Africa.
A study looking at pulmonary differences between blacks and whites was conducted which found that blacks compensated for smaller lungs by breathing harder than whites while engaged in physical activity. In a study of 80 Asians and Europeans, Korotzer, Ong, and Hansen (2000) also showed that Asians had lower pulmonary functioning than Europeans. Even differences in chest size has been purported to explain differences in lung functioning, though this relationship did not hold (Whittaker, Sutton, and Beardsmore, 2005). Though, in his short review on race and the history of lung functioning, Braun (2015) writes that “At the very least, the idea that people labelled ‘white’ naturally have higher lung capacity than other races throughout the world should be approached with some skepticism.” because “Most commercially available spirometers internationally ‘correct’ or ‘adjust’ for race in one of two ways: by using a scaling factor for all people not considered to be ‘white’; or by applying population-specific norms. To enable the spirometer, the operator must select the race of an individual, as well as indicate their age, sex/gender and height. How race (or population) is determined varies, with most operators either asking patients to self-identify or ‘eyeballing it’. Interviews with users of the spirometer indicate that many operators are unaware that they are automatically activating race correction when they select a patient’s race (3). Because ‘correction’ is programmed into the spirometer by the manufacturer, it can be difficult to disable.”
Braun, Wolfgang, and Dickerson (2013) and Braun (2015) critiques pulmonary studies because in a large majority of cases, possible explanatory variables for lower lung functioning in black Americans could be related to SES. Harik-Khan, Muller, and Wise (2004) used participants from the Third National Health and Nutrition Examination Survey. They chose black and white children between the ages of 8 and 17 who did not smoke (n=1462, 623 whites and 839 blacks). Blacks were taller but had lower SES, had lower levels of vitamins A and C, along with lower levels of alpha carotene. They also had lower lung functioning. When they adjusted for confounds, sitting explained 42 to 53 percent of the racial difference, SES factors and antioxidant vitamin levels accounted for 7 to 10 percent of the difference. So they could only account for 50 to 63 percent of the difference. In 752 children aged 8 to 10 years of age, low birth weight accounted for 3 to 5 percent of the differences whereas maternal smoking had no effect (Harik-Khan, Muller, and Wise, 2004). So the remaining variation, obviously, will be accounted for by other SES variables, biology, or environmental factors.
Whitrow and Harding (2004) show that, at least for Caribbean blacks living in the UK, upper body differences explained most of the variation in lung functioning than did sitting height, with social correlates having a small but significant impact.
So because blacks have more type II fibers on average, they will convert glucose into energy more rapidly than whites. The energy for these muscle contractions comes from adenosine triphosphate (ATP). Blacks and whites both convert glucose into ATP for cellular functioning but in different ratios. These differences in muscular contractions driven by the metabolic pathway differences of the fibers are one large reason why blacks dominate sports.
Fibers are broken down into two types: fast and slow twitch. Slow twitch fibers use aerobic metabolism which is how they generate ATP and greater oxidative capacity due to higher levels for myoglobin. Oxygen bound to hemoglobin is carried to the red blood cells through capillaries that then influence muscular performance. Myoglobin is also essential for the transport of oxygen to the mitochondria where it is then consumed. Conversely, fast twitch fibers use anaerobic metabolism, have less oxidative capacity, less myoglobin and due to this, they are more dependent on anaerobic metabolism. Blacks also have “significantly higher levels of activity in their phosphagenic, glycolytic, and lactate dehydrogenase marbling pathways than their Caucasian counterparts” (Morrison and Cooper, 2006). This is where the production of ATP is regenerated,and so they have a huge advantage here. So higher faster production of ATP lead to more efficient ATP production, too. However when the ATP is depleted then it’s replaced by a reaction that depletes creatine phosphate. Skeletal muscle then converts “chemical energy into mechanical work” which only 30 to 50 percent is wasted as heat, so even small physiological differences can lead to large differences in performance (Morris and Cooper, 2006).
Though that’s not the only biochemical difference (faster ATP regeneration and production) between the blacks and whites that would explain sports performance. Morrison and Cooper (2006) write: “There is also considerably greater activity in the lactate dehydrogenase pathway of people of West African descent. A primary function of this pathway is to reduce muscle fatigue by converting lactic acid back to glucose and refeeding the muscles. This cyclic set of reactions, from muscles to liver and back to muscles, is known as the Cori cycle.”
Lactic acid production is that feeling in your muscles when during extended athletic activity whereas the postponement of muscle fatigue rests on the rate at which lactic acid is covered into glucose. The rate of this removal is further increased by the lactate dehydrogenase pathway describe above by Morrison and Cooper.
Clearly, the production of lactic acid causes problems during physical activity. The production of lactic acid into glucose to refers the muscles through the lactate dehydrogenase pathway is critical, for if glycogen reserves are depleted during extended physical activity then blood glucose would become the primary source of energy for the muscles, which could lead to lowered blood glucose levels and the nervous system may become compromised. During prolonged activity, however, if glucose isn’t available for energy then the body uses fat reserves which is less efficient than carbohydrates for energy and combustion.
Morrison and Cooper conclude: “Not the least of coincidence seems to be the influence of the compensatory sickle cell gene on oxygen transport and availability to the tissues. The reduced availability pulled with reduced oxygen myoglobin in the preponderant fast-twitch muscle fibres which are adapted for rapid anaerobic energy (ATP) regeneration, all give a new outcome of muscle anatomical and biochemical advantages which proffer a superior athleticism.”
Though, at the moment, as David Epstein states in his 2014 book The Sports Gene: Inside the Science of Extraordinary Athletic Performance, in a few studies done on mice genetically altered to have low hemoglobin levels, a there was a “shift of type IIa fast-twitch muscle fibers to type IIb “super fast twitch” muscle fibers in their lower legs” (Epstein, 2014: 179). This is also a developmental effect of mice in their lifetime, not a direct effect of evolution (Epstein, 2014: 179). No compensatory mechanism yet exists for humans, which I will attempt to untangle in future articles on the matter.
At the end of the chapter on this subject (Chapter 11, Malaria and Muscle Fibers, page 179), Epstein states that he asked physiologists their thoughts on the hypothesis. A few people approved of it, whereas one stated that he had evidence for physiological differences between blacks and whites that have not been studied before but he won’t release his results:
Several scientists I spoke to about the theory insisted they woud have no interest in investigating it because of the inevitably thorny issue of race involved. On of them told me that he actually has data on ethnic differences with respect to a particular physiological trait, but that he would never publish the data because of potential controversy. Another told me he would worry about following Cooper and Morrison’s line of inquiry because any suggestion of a physical advantage among a group of people could be equated to a corresponding lack of intellect, as if athleticism and intelligence were on some kind of biological teeter-totter. With that stigman in mind, perhaps the most important writing Cooper did in Black Superman [Cooper’s book] was his methodical eviseceration of any supposed inverse link between physical and mental prowess. “The concept that physical superiority could somehow be a symptomn of intellectual inferiority only developed when physical superiority became associated with African Americans,” Cooper wrote. “That association did not begin until about 1936.” The idea that athleticism was suddenly inversely proportional to intellect was never a cause of bigotry, but rather a result of it. And Cooper implied a more serious scientific inquiry into difficult issues, not less, is the appropriate path. (Epstein, 2014: 179) [Entine (2002) also spends a considerable amount of time debunking the myth of intelligence and athletic ability being negatively correlated in his 2002 book Taboo: Why Black Athletes Dominate Sports and Why We’re Afraid to Talk About It, which was kind of popularized by Rushton (1997) with his now debunked r/K selection theory.]
Things like this piss me off. These differences are actually measurable and lead to trait differences between the races, and know the mechanisms, pathways and whatnot and people are still. Scared to share their findings. One day, I hope, science will find a way to disregard people’s feelings in regard to people’s feelings on notable, testable and replicable differences between the races, most importantly between blacks and whites. I’ve noted how type II fibers lead to metabolic changes and small tears which then cause big problems. This is due to how fast the type II fibers fire in comparison to the slow twitch fibers.
This hypothesis is extremely interesting and now that I’ve laid out Morrison and Cooper’s (2006) hypothesis, I’m going to take a deep dive into this literature to see what I can prove about this hypothesis. Of course, the somatype along with the fiber distribution matters, as does having the XX genotype and not RR, which lends to superior athletic performance when coupled with type II muscle fibers (Broos et al, 2016). The pieces of this puzzle are, in my opinion, slowly being put together for someone to come along and integrate them into a coherent theory for the sickle cell trait and superior athletic performance through type II muscle fibers. It’s very interesting to note that elite sprinters were more likely to carry the SCT and that champion sprinters were more likely to have it too.
Is Diet An IQ Test?
1350 words
Dr. James Thompson is a big proponent of ‘diet being an IQ test‘ and has written quite a few articles on this matter. Though, the one he published today is perhaps the most misinformed.
He first shortly discusses the fact that 200 kcal drinks are being marketed as ‘cures’ for type II diabetes. People ‘beat’ the disease with only 200 kcal drinks. Sure, they lost weight, lost their disease. Now what? Continue drinking the drinks or now go back to old dietary habits? Type II diabetes is a lifestyle disease, and so can be ameliorated with lifestyle interventions. Though, Big Pharma wants you to believe that you can only overcome the disease with their medicines and ‘treatments’ along with the injection of insulin from your primary care doctor. Though, this would only exacerbate the disease, not cure it. The fact of the matter is this: these ‘treatments’ only ‘cure’ the proximate causes. The ULTIMATE CAUSES are left alone and this is why people fall back into habits.
When speaking about diabetes and obesity, this is a very important distinction to make. Most doctors, when treating diabetics, only treat the proximate causes (weight, symptoms that come with weight, etc) but they never get to the root of the problem. The root of the problem is, of course, insulin. The main root is never taken care of, only the proximate causes are ‘cured’ through interventions, however, the underlying cause of diabetes, and obesity as well is not taken care of because of doctors. This, then, leads to a neverending cycle of people losing a few pounds or whatnot and then they, expectedly, gain it back and they have to re-do the regimen all over again. The patient never gets cured, Big Pharma, hospitals et al get to make money off not curing a patients illness by only treating proximate and not ultimate causes.
Dr. Thompson then talks about a drink for anorexics, called ‘Complan“, and that he and another researcher gave this drink to anorexics, giving them about 3000 kcals per day of the drink, which was full of carbs, fat and vitamins and minerals (Bhanji and Thompson, 1974).
The total daily calorific intake was 2000-3000 calories, resulting in a mean weight gain of 12.39 kilos over 53 days, a daily gain of 234 grams, or 1.64 kilos (3.6 pounds) a week. That is in fact a reasonable estimate of the weight gains made by a totally sedentary person who eats a 3000 calorie diet. For a higher amount of calories, adjust upwards. Thermodynamics.
Thermodynamics? Take the first law. The first law of thermodynamics is irrelevant to human physiology (Taubes, 2007; Taubes, 2011; Fung, 2016). (Also watch Gary Taubes explain the laws of thermodynamics.) Now take the second law of thermodynamics which “states that the total entropy can never decrease over time for an isolated system, that is, a system in which neither energy nor matter can enter nor leave.” People may say that ‘a calorie is a calorie’ therefore it doesn’t matter whether all of your calories come from, say, sugar or a balanced high fat low carb diet, all weight gain or loss will be the same. Here’s the thing about that: it is fallacious. Stating that ‘a calorie is a calorie’ violates the second law of thermodynamics (Feinman and Fine, 2004). They write:
The second law of thermodynamics says that variation of efficiency for different metabolic pathways is to be expected. Thus, ironically the dictum that a “calorie is a calorie” violates the second law of thermodynamics, as a matter of principle.
So talk of thermodynamics when talking about the human physiological system does not make sense.
He then cites a new paper from Lean et al (2017) on weight management and type II diabetes. The authors write that “Type 2 diabetes is a chronic disorder that requires lifelong treatment. We aimed to assess whether intensive weight management within routine primary care would achieve remission of type 2 diabetes.” To which Dr. Thompson asks ‘How does one catch this illness?” and ‘Is there some vaccination against this “chronic disorder”?‘ The answer to how does one ‘catch this illness’ is simple: the overconsumption of processed carbohydrates, constantly spiking insulin which leads to insulin resistance which then leads to the production of more insulin since the body is resistant which then causes a vicious cycle and eventually insulin resistance occurs along with type II diabetes.
Dr. Thompson writes:
Patients had been put on Complan, or its equivalent, to break them from the bad habits of their habitual fattening diet. This is good news, and I am in favour of it. What irritates me is the evasion contained in this story, in that it does not mention that the “illness” of type 2 diabetes is merely a consequence of eating too much and becoming fat. What should the headline have been?
Trial shows that fat people who eat less become slimmer and healthier.
I hope this wonder treatment receives lots of publicity. If you wish to avoid hurting anyone’s feelings just don’t mention fatness. In extremis, you may talk about body fat around vital organs, but keep it brief, and generally evasive.
So you ‘break bad habits’ by introducing new bad habits? It’s not sustainable to drink these low kcal drinks and expect to be healthy. I hope this ‘wonder treatment’ does not receive a lot of publicity because it’s bullshit that will just line the pockets of Big Pharma et al, while making people sicker and, the ultimate goal, having them ‘need’ Big Pharma to care for their illness—when they can just as easily care for it themselves.
‘Trial shows that fat people who eat less become slimmer and healthier’. Or how about this? Fat people that eat well and exercise, up to 35 BMI, have no higher risk of early death then someone with a normal BMI who eats well and exercises (Barry et al, 2014). Neuroscientist Dr. Sandra Aamodt also compiles a wealth of solid information on this subject in her 2016 book “Why Diets Make Us Fat: The Unintended Consequences of Our Obsession with Weight Loss“.
Dr. Thompson writes:
I see little need to update the broad conclusion: if you want to lose weight you should eat less.
This is horrible advice. Most diets fail, and they fail because the ‘cures’ (eat less, move more; Caloric Reduction as Primary: CRaP) are garbage and don’t take human physiology into account. If you want to lose weight and put your diabetes into remission, then you must eat a low-carb (low carb or ketogenic, doesn’t matter) diet (Westman et al, 2008; Azar, Beydoun, and Albadri, 2016; Noakes and Windt, 2016; Saslow et al, 2017). Combine this with an intermittent fasting plan as pushed by Dr. Jason Fung, and you have a recipe to beat diabesity (diabetes and obesity) that does not involve lining the pockets of Big Pharma, nor does it involve one sacrificing their health for ‘quick-fix’ diet plans that never work.
In sum, diets are not ‘IQ tests’. Low kcal ‘drinks’ to ‘change habits’ of type II diabetics will eventually exacerbate the problem because when the body is in extended caloric restriction, the brain panics and releases hormones to stimulate appetite while stopping hormones that cause you to be sated and stop eating. This is reality; these studies that show that eating or drinking 800 kcal per day or whatnot are based on huge flaws: the fact that this could be sustainable for a large number of the population is not true. In fact, no matter how much ‘willpower’ you have, you will eventually give in because willpower is a finite resource (Mann, 2014).
There are easier ways to lose weight and combat diabetes, and it doesn’t involve handing money over to Big Pharma/Big Food. You only need to intermittently fast, you’ll lose weight and your diabetes will not be a problem, you’ll be able to lose weight and will not have problems with diabetes any longer (Fung, 2016). Most of these papers coming out recently on this disease are garbage. Real interventions exist, they’re easier and you don’t need to line the pockets of corporations to ‘get cured’ (which never happens, they don’t want to cure you!)
Athletic Ability and IQ
1150 words
Proponents of the usefulness of IQ tests may point to athletic competitions as an analogous test/competition that they believe may reinforce their belief that IQ tests ‘intelligence’ (whatever that is). Though, there are a few flaws in their attempted comparison. Some may say that “Lebron James and Usain Bolt have X morphology/biochemistry and therefore that’s why they excel! The same goes foe IQ tests!” People then go on to ask if I ‘deny human evolution’ because I deny the usefulness (that is built into the test by way of ‘item analysis; Jensen, 1980: 137) of IQ tests and point out flaws in their construction.
People who accept the usefulness of IQ tests and attempt to defend their flaws may attempt to make sports competition, like, say, a 100m sprint, an analogous argument. They may say that ‘X is better than Y, and the reason is ‘genetic’ in nature!’. Though, nature vs. nurture is a false dichotomy and irrelevant (Oyama, 1985, 2000; Oyama, 1999; Oyama, 2000; Moore, 2003). Behavior is neither ‘genetic’ nor ‘environmental’. with that out of the way, tests of athletic ability as mentioned above are completely different from IQ tests.
Tests of athletic ability do not have any arbitrary judgments as IQ tests do in their construction and analysis of the items to be put on the test. It’s a simple, cut-and-dry explanation: on this instance in this test, runner X was better than runner Y. We can then test runner X and see what kind of differences he has in his physiology and somatype, along with asking him what drives him to succeed. We can then do the same for the other athlete and discover that, as hypothesized, there are inherent differences in their physiology that make runner X be better than runner Y, say the ability to take deeper breaths, take longer strides per step due to longer legs, having thinner appendages as to be faster and so on. In regard to IQ, the tests are constructed on the prior basis of who is or is not intelligent. Basically, as is not the case with tests of athletic ability, the ‘winners and losers’, so to speak, are already chosen on the prior suppositions of who is or is not intelligent. Therefore, the comparison of athletic abilities tests and IQ tests are not good because athletic abilities tests are not constructed on the basis of who the constructors believe are athletic, like IQ tests are constructed on the basis of who the testers believe is ‘intelligent’ or not.
Some people are so far up the IQ-tests-test-intelligence idea that due to the critiques I cite on IQ tests, I actually get asked if I ‘deny human evolution’. That’s ridiculous and I will explain why.
Imagine an ‘athletic abilities’ test existed. Imagine that this test was constructed on the basis of who the test constructor believed who is or is not athletic. Imagine that he constructs the test to show that people who had previously low ability in past athletic abilities tests had ‘high athletic ability’ in this new test that he constructed. Then I discover the test. I read about it and I see how it is constructed and what the constructors did to get the results they wanted, because they believed that the lower-ability people in the previous tests had higher ability and therefore constructed an ‘athletic abilities’ test to show they were more ‘athletic’ than the former high performers. I then point out the huge flaws in the construction of such a test. The logic of people who claim that I deny human evolution because I blast the validity and construction of IQ tests would, logically, have to say that I’m denying athletic differences between groups and individuals, when in actuality I’m only pointing out huge flaws in the ‘athletic abilities’ test that was constructed. The athletic abilities example I’ve conjured up is analogous to the IQ test construction tirade I’ve been on recently. So, if a test of ‘athletic ability’ exists and I come and critique it, then no, I am not denying athletic differences between individuals I am only pointing out flawed tests.
The basic structure of my ‘athletic abilities’ argument is this: that test that would be constructed would not test true ‘athletic abilities’ just like IQ tests don’t test ‘intelligence’ (Richardson, 2002). Pointing out huge flaws in tests does not mean that you’re a ‘blank slatist’ (whatever that is; it’s a strawman for people who don’t bow down to the IQ alter). Pointing out flaws in IQ tests does not mean that you believe that everyone and every group is ‘equal’ in a psychological and mental sense. Pointing out the flaws in IQ tests does not mean that one is a left-wing egalitarian that believes that all humans—individuals and groups—are equal and that the only cause of their differences comes down to the environment (whether SES or the epigenetic environment, etc). Pointing out flaws in these tests is needed; lest people truly think that they do test, say, ability for complex cognition (they don’t). Indeed, it seems that everyday life is more complicated than the hardest Raven’s item. Richardson and Norgate (2014) write:
Indeed, typical IQ test items seem remarkably un-complex in their cognitive demands compared with, say, the cognitive demands of ordinary social life and other everyday activities that the vast majority of children and adults can meet. (pg 3)
On the other hand abundant cognitive research suggests that everyday, “real life”
problem solving, carried out by the vast majority of people, especially in social-cooperative situations, is a great deal more complex than that required by IQ test items, including those in the Raven. (pg 6)
Could it be possible that ‘real-life’ athletic ability, such as ‘walking’ or whatnot be more ‘complex’ than the analog of athletic ability? No, not at all. Because, as I previously noted, athletic abilities tests test who has the ‘better’ physiology or morphology for whichever competition they choose to compete in (and of course there will be considerable self-selection since people choose things they’re good at). It’s clear that there is absolutely no possibility of ‘real-life’ athletic ability possibly being more complex than tests of athletic ability.
In sum, no, I do not deny human evolution because I critique IQ tests. Just because I critique IQ tests doesn’t mean that I deny human evolution. My example of the ‘athletic test’ is a sound and logical analog to the IQ critiques that I cite. Just framing it in the way of a false test of athletic ability and then pointing out the flaws is enough to show that I don’t deny human evolution. Because if such an ‘athletic abilities’ test did exist and I pointed out its flaws, I would not be denying differences between groups or individuals due to evolution, I’d simply be critiquing a shitty test, which is what I do with IQ tests. Actual tests of athletic ability are not analogous to IQ tests because tests of athletic ability are not ‘constructed’ in the way that IQ tests are.
The Genomic Health of Our Ancestors: What Was It Like and Is It Relevant for Us Today?
1500 words
After I published my article Thoughts On Diseases of Civilization: Romanticizing the Hunter-Gatherer’s Diet the other day, someone dropped by and stated that we are ‘getting healthier and healthier’, citing an article by Berens, Cooper, and Lachance (2017) titled “The genomic health of ancient hominins” who show, through genetic analyses, that many of our ancestors had the ‘genes for’ diseases that plague us today in our societies. While this may be true, one important thing that the individual who left the paper did not say is that genotypic health does not equal phenotypic health.
The basic assumption of the paper is this: They genotyped the Altai Neanderthal, Denisovans, pastoralists and hunter-gatherers and computed their ‘GRS’ (genetic [disease] risk score). When focusing on the GRS, they found that the Altai Neanderthal had 97 percent worse ‘genomic health’ when compared to the genomes of people today, whereas Otzi man had a ‘genetic predisposition’ to cardiovascular and gastrointestinal disease.
Something important to keep in mind here is that GRS and sequencing the genomes of ancient hominins can only ‘predict’ what types of problems one would have based on their genomes; it cannot realiably state that this individual would have gotten/did get a certain disease because he had the alleles for it.
What they did was genotype ancient hominins and then compute their GRS and compare the ancient hominins GRS to that of a modern human and then match the set of “disease loci to generate standardized GRS percentiles“. The ancient samples they tested had a similar genetic risk when compared to modern samples, though the ancient samples may underestimate their genetic risk since there are numerous other alleles yet to be discovered that may cause or add to genetic disease risk.
Ancient hominins had lower risks for cancer, miscellaneous diseases and neurological/psychological diseases when compared to modern humans. According to their analysis, ancient hominins only had a higher risk for cardiovascular disease while “Risks of allergy/autoimmune, morphological/muscular, metabolism/weight, and dental/periodontal diseases were not significantly different between ancient and modern hominins” (Berens, Cooper, and Lachance, 2017). So ancient hominins seemed to have a reduced risk of cancer, neurological disease and other unclassified diseases.
The Altai Neanderthal was at high risk of immunological diseases, cancers, gastrointestinal problems, morphological and muscular problems, and other metabolic disorders. However, and this is important, this is only what his genome showed. This is only a risk assessment and DOES NOT state anything about phenotypic health. The Altai Neanderthal, however, did have a lower GRS for cardiovascular disease and average risk for dental diseases. This is in contrast with Otzi man, who had a genetic risk for cardiovascular disease. Otzi also had a high GRS score on immuno-related diseases, gastrointestinal diseases and other metabolic disorders—which I would assume would be similar to type II diabetes mellitus. However, Otzi had ‘normal’ risk for morphological and neurological disease.
I had to wait all paper to read this:
We note that genomic health does not necessarily equate to phenotypic health. Genetic risk scores are not deterministic, instead they merely indicate whether an individual has a predisposition to a particular disease. In addition, alleles that contribute to disease in modern environments may not have had the same effects in past environments.
This makes it an open and shut case. Just because you have the ‘predisposition’ for something doesn’t mean that it will occur to you. For example, if someone has a ‘genetic predisposition’ to become and alcoholic and he never drinks alcohol, will he become an alcoholic? If someone is extremely sensitive to carbohydrate intake and more susceptible to the allure of sugar and more likely to get addicted to it, but they never eat the carbs will they become obese and insulin resistant? The genes-as-destiny paradigm is wrong—especially in regard to human disease. Human disease is extremely complex and doctors are even having problems with GWAS and what it shows for the genetic basis for disease.
Further, in regard to disease, GWAS has a huge problem in detecting genetic variants: “many GWAS hits have no specific biological relevance to disease and wouldn’t serve as good drug targets. Rather, these ‘peripheral’ variants probably act through complex biochemical regulatory networks to influence the activity of a few ‘core’ genes that are more directly connected to an illness.” See also Boyle and Pritchard (2017): An Expanded View of Complex Traits: From Polygenic to Omnigenic. Disease-nomics will be much more complicated than identifying one or a few genes; gene networks interact with the environment—whether by what we eat or our immediate surroundings—and diseases arise through a complex interaction between genes and environment: GxE.
When our ancestors made the transition from a hunter-gatherer lifestyle to a more sedentary, agricultural one, this is what then started up the environmental mismatch between humans and our environments. Agriculturalists had the highest GRS for dental caries and other problems to do with dentition, though the number of alleles was small, it makes logical sense for the advent of agriculture to increase the incidence of dental caries and other problems with dentition, which would then be selected for due to the change of lifestyle from mobile hunter-gatherer to relatively sedentary agriculturalist. Hunter-gatherers have fewer dental caries than agriculturalists. It is also argued that when we began to eat fermentable plant foods, that this caused “changes in food processing caused an early shift toward a disease-associated oral microbiota in this population” (Humphrey et al, 2014). Adler et al (2013) also show that “Data from 34 early European skeletons indicate that the transition from hunter-gatherer to farming shifted the oral microbial community to a disease-associated configuration.” Clearly, the transition from the mobile hunter-gatherer lifestyle to the sedentary agriculturalist one was extremely bad for our health and dentition.
Though agriculture did increase the incidence of dental caries, evidence exists that, through dietary shifts in the Upper Paleolithic, dental caries appeared, probably due to the shift to more processed foods (keep in mind that processing food only has to mean, say, mashing food to make it easier to chew, not in the modern definition of ‘processing’). Nevertheless, the first toothpicks were discovered from the Late Upper Paleolithic, which implies that some human populations encountered some foods that then gave them dental caries to which our ancestors responded by making toothpicks (Oxilia et al, 2015). Hunter-gatherers had few—if any—dental caries which implies that their lifestyles did not give them the oral disease. It’s very peculiar that these have only been noticed, really, in populations that underwent the agricultural transformation. That’s yet another ‘disease of civilization’ that is low to nonexistent in those populations, which is attributed to their lifestyle and their diet.
Cultural evolution drives mismatch diseases as cultural evolution can greatly outstrip Darwinian evolution. This, especially in regard to our health, is bad for us since we did not have the time to biologically adapt to our new, novel diets. We still have yet to adapt genetically to the diets and lifestyle taken on by our ancestors 10kya, and I think it will be a long time—if ever—before we do adapt. I mean come on, can you really see whole groups of people adapting to constant insulin spikes brought on by highly processed carbohydrates and other foods? We are the running ape, so do you ever see us adapting to constantly sit? These are modern problems, which were brought on by our ancestors’ adoption of agriculture. I agree with Jared Diamond when he says that farming was ‘the worst mistake in the history of the human race‘, but, obviously not for the Marxist reasons he proposes. Clearly, hunter-gatherers had better phenotypic health while ours suffers.
In sum, the paper Berens, Cooper, and Lachance (2017) does not refute anything that I wrote in my previous article on diseases of civilization. If anything, most of what I wrote is strengthened, especially on the basis of genotypic health not equalling phenotypic health. This paper can be summed with three points:
1) genes aren’t destiny. 2) genes wouldn’t necessarily do the same things in different environments. 3) the GRS (genetic [disease] risk scores) are also not deterministic. This is the logical conclusion to draw. OK, so ancient hominins had a higher genetic risk for certain diseases. Here’s the catch: if they weren’t in the environments that would exacerbate the disease and cause it to express in the phenotype, does it really matter that they had ‘genetic predispositions’ for certain diseases? Of course it matters for us today due to our built food environments, but did it matter for them who did not have access to the novel environments that we do today?
This is a very interesting paper but my arguments on diseases of civilization still stand. Diseases of civilization will still plague our societies until we change the built food environment, but until then, we will have to live with the worst mistake we have made as a species: constructing obesogenic environments that then lead to a huge decrease in quality of life and life expectancy.
IQ Test Construction
1550 words
No one really discusses how IQ tests are constructed; people just accept the numbers that are spit out and think that it shows one’s intelligence level relative to others who took the test. However, there are huge methodological flaws in regard to IQ tests—one of the largest, in my opinion, being that they are constructed to fit a normal curve and based on the ‘prior knowledge’ of who is or is not intelligent.
What people don’t understand about test construction is that the behavior genetic (BG) method must assume a normal distribution. IQ tests have been constructed to display this normal distribution, so we cannot say whether or not it exists in nature, though few human traits fall on the normal distribution. The fact of the matter is this: The normal curve is achieved through keeping more items that people get right while keeping the smaller proportion of items that people get right and wrong. This forces the normal curve and all of the assumptions that come along with this so-called IQ bell curve.
Even then, the fact that the normal distribution is forced doesn’t mean as much as the assumptions and conclusions drawn from the forced curve. It is assumed that individual test score differences arise out of ‘biology’, however with how test questions are manipulated to get the results that the test constructors want, it is then assumed that the cause for individual test score differences are ‘biological’ in nature, however we don’t know if these distributions are ‘biological’ in nature due to how the tests are constructed.
The fact of the matter is, the tests are constructed based off of the prior knowledge of who is or is not intelligent. This means that we can ‘build the test’ to fit these preconceived notions. The problem of item selection was discussed by Richardson (1998) who discussed boys scoring a few points higher than girls, and wondering whether or not these differences should be ‘allowed to persist’ or not. Richardson (1998: 114) writes (12/26/17 Edit: I’ll also provide the quote that precedes this one):
“One who would construct a test for intellectual capacity has two possible methods of handling the problem of sex differences.
1 He may assume that all the sex differences yielded by his test items are about equally indicative of sex differences in native ability.
2 He may proceed on the hypothesis that large sex differences on items of the Binet type are likely to be factitious in the sense that they reflect sex differences in experience or training. To the extent that this assumption is valid, he will be justified in eliminating from his battery test items which yield large sex differences.
The authors of the New Revision have chosen the second of these alternatives and sought to avoid using test items showing large differences in percents passing.” (McNemar 1942:56)
This is, of course, a clear admission of the subjectivity of such assumptions: while ‘preferring’ to see sex differences as undesirable artefacts of test composition, other differences between groups or individuals, such as different social classes or, at various times, different ‘races’, are seen as ones ‘truly’ existing in nature. Yet these, too, could be eliminated or exaggerated by exactly the same process of assumption and manipulation of test composition.
And further writes on page 121:
Suffice it to say that investigators have simply made certain assumptions about‘what to expect’ in the patterns of scores, and adjusted their analytical equations accordingly: not surprisingly, that pattern emerges!
The only ‘assumption’ that the test constructors have is the biases they already have on who is or is not ‘intelligent’ and then they construct the test through item selection, excising items that don’t fit their desired distribution. Is that supposed to be scientific? You can ask a group of children a bunch of questions and then construct a test to get the conclusion you want based on item selection.
The BG method needs to assume that IQ test scores lie on a normal curve and that it is a quantitative trait that exhibits a normal distribution, though Micceri (1989) showed that normal distributions for measurable traits are the exception, rather than the rule, for numerous measurable traits. Richardson (1998: 113) further writes:
The same applies to many other ‘characteristics’ of IQ. For example, the ‘normal distribution, or bell-shaped curve, reflects (misleadingly as I have suggested in Chapters 1 to 3) key biological assumptions about the nature of cognitive abilities. It is also an assumption crucial to many statistical analyses done on test scores. But it is a property built into a test by the simple device of using relatively more items on which about half the testees pass, and relatively few items on which either many or only a few of them pass. Dangers arise, of course, when we try to pass this property off as something happening in nature instead of contrived by test constructors.
So with the knowledge of test construction, then there is something very obvious here: we can construct IQ tests that, say, show blacks scoring higher than whites and women scoring higher than men. We can then make the assumption that there are genes that are responsible for this distribution and then ‘find genes’ that supposedly cause these differences in test scores (which are constructed to show the differences!). What then? Let’s say that someone did do that, would the logical conclusion be that there are genes ‘driving’ the differences in IQ test scores?
Richardson (2017: 3) writes:
In summary, either directly or indirectly, IQ and related tests are calibrated against social class background, and score differences are inevitably consequences of that social stratification to some extent. Through that calibration, they will also correlate with any genetic cline within the social strata. Whether or not, and to what degree, the tests also measure “intelligence” remains debateable because test validity has been indirect and circular. … Such circularity is also reflected in correlations between IQ and adult occupational levels, income, wealth, and so on. As education largely determines the entry level to the job market, correlations between IQ and occupation are, again, at least partly, self-fullfilling. … CA [cognitive ability], as measured by IQ-type tests, is intrinsically inter-twined with social stratification, and its associated genetic background, by the very nature of the tests.
This, again, falls back on the non-existent construct validity that IQ tests have. Construct validity “defines how well a test or experiment measures up to its claims.” No such construct validity exists for IQ tests. If breathalyzers didn’t test someone’s fitness to drive, would they still be a good measure? If they had no construct validity, if there was no biological model to calibrate the breathalyzer against, would we still accept it as a realistic model to test people against and judge their fitness to drive? Still yet another definition of construct validity comes from Strauss and Smith (2009) who write that psychological constructs are “validated by testing whether they relate to measures of other constructs as specified by theory.” No such biological model exists for IQ; why expect some type of biological model like this when there are other perfectly well-reasoned response to how and why individuals differ in IQ test scores (Richardson, 2002)?
The normal distribution is forced, which IQ-ists claim to know. Richardson (1998) notes that Jensen “noted how ‘every item is carefully edited and selected on the basis of technical procedures known as “item analysis”, based on tryouts of the items on large samples and the test’s target population’ (1980:145).” These ‘tryouts’ are what force the normal curve, and no matter how ‘technical’ the procedures are, there are still huge biases, which then make people draw huge assumptions, again, based on who is or is not intelligent.
In sum, IQ tests are constructed to fit a normal curve on the basis of an assumption of a normal distribution, and on the presupposed basis of who is or is not ‘intelligent’ (whatever that means). The BG method needs to assume that IQ is a quantitative trait which exhibits a normal distribution. IQ is assumed to be like height, or weight, but which physiological process in the body does it mimick? I have argued that there is no physiological basis to ‘IQ’ or what they test and that they can be explained not by biology, but through test construction. I wonder what the distributions of IQ test scores would look like without forced normal distributions? Since it is assumed that IQ tests something directly measurable—like height and weight as is normally used—then they must fall on a normal distribution, which all other measurable psychological traits do not show (Micceri, 1989; Buzsaki and Mizseki, 2014).
Some may argue that ‘they know this’ (they being psychometricians). However, ‘they’ must know that most of their assumptions and conclusions about ‘good and bad genes’ lie on the huge assumption of the normal distribution. IQ test scores do not show a normal distribution, they were designed to create it. The fact that most psychological traits show a strong skew to one side and so that’s why a normal distribution is forced is meaningless. The fact of the matter is, just through how the tests are constructed means that we should be cautious as to what these tests test with the assumptions that we currently have about them.
Jean Baptiste Lamarck
Eva Jablonka
Charles Murray
Arthur Jensen
