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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.))
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.
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.
Race and Medicine: Is Race a Useful Category?
2450 words
The New York Times published an article on December the 8th titled What Doctors Should Ignore: Science has revealed how arbitrary racial categories are. Perhaps medicine will abandon them, too. It is an interesting article and while I do not agree with all of it, I do agree with some.
It starts off by talking about sickle cell anemia (SCA) and how was once thought of as a ‘black disease’ because blacks were, it seemed, the only ones who were getting the disease. I recall back in high-school having a Sicilian friend who said he ‘was black’ because Sicilians can get SCA which is ‘a black disease’, and this indicates ‘black genes’. However, when I grew up and actually learned a bit about race I learned that it was much more nuanced than that and that whether or not a population has SCA is not based on race, but is based on the climate/environment of the area which would breed mosquitoes which carry malaria. SCA still, to this day, remains a selective factor in the evolution of humans; malaria selects for the sickle cell trait (Elguero et al, 2015).
This is a good point brought up by the article: the assumption that SCA was a ‘black disease’ had us look over numerous non-blacks who had the sickle cell trait and could get the help they needed, when they were overlooked due to their race with the assumption that they did not have this so-called ‘black disease’. Though it is understandable why it got labeled ‘a black disease’; malaria is more prevalent near to the equator and people whose ancestors evolved there are more likely to carry the trait. In regards to SCA, it should be known that blacks are more likely to get SCA, but just because someone is black does not automatically mean that it is a foregone conclusion that one has the disease.
The article then goes on to state that the push to excise race from medicine may undermine a ‘social justice concept’: that is, the want to rid the medical establishment of so-called ‘unconscious bias’ that doctors have when dealing with minorities. Of course, I will not discount that this doesn’t have some effect—however small—on racial health disparities but I do not believe that the scope of the matter is as large as it is claimed to be. This is now causing medical professionals to integrate ‘unconscious bias training’, in the hopes of ridding doctors of bias—whether conscious or not—in the hopes to ameliorate racial health disparities. Maybe it will work, maybe it will not, but what I do know is that if you know someone’s race, you can use it as a roadmap to what diseases they may or may not have, what they may or may not be susceptible to and so on. Of course, only relying on one’s race as a single data point when you’re assessing someone’s possible health risks makes no sense at all.
The author then goes on to write that the terms ‘Negroid, Caucasoid, and Mongoloid’ were revealed as ‘arbitrary’ by modern genetic science. I wouldn’t say that; I would say, though, that modern genetic science has shown us the true extent of human variation, while also showing that humans cluster into 5 distinct geographic categories, which we can call ‘race’ (Rosenberg et al, 2002; but see Wills, 2017 for alternative view that the clusters identified by Rosenberg et al, 2002 are not races. I will cover this in the future). The author then, of course, goes on to use the continuum fallacy stating that since “there are few sharp divides where one set of traits ends and another begins“. A basic rebuttal would be, can you point out where red and orange are distinct? How about violet and blue? Blue and Cyan? Yellow and orange? When people commit the continuum fallacy then the only logical conclusion is that if races don’t exist because there are “few sharp divides where one set of traits ends and another begins“, then, logically speaking, colors don’t exist either because there are ‘few [if any] sharp divides‘ where one color ends and another begins.
The author also cites geneticist Sarah Tishkoff who states that the human species is too young to have races as we define them. This is not true, as I have covered numerous times. The author then cites this study (Ng et al, 2008) in which Craig Venter’s genome was matched with the (in)famous [I love Watson] James Watson and focused on six genes that had to do with how people respond to antipsychotics, antidepressants, and other drugs. It was discovered that Venter had two of the ‘Caucasian’ variants whereas Watson carried variants more common in East Asians. Watson would have gotten the wrong medicine based on the assumption of his race and not on the predictive power of his own personal genome.
The author then talks about kidney disease and the fact that blacks are more likely to have it (Martins, Agodoa, and Norris, 2012). It was assumed that environmental factors caused the disparity of kidney disease in blacks when compared to whites, however then the APOL1 gene variant was discovered, which is related to worse kidney outcomes and is in higher frequencies in black Americans, even in blacks with well-controlled blood pressure (BP) (Parsa et al, 2013). The author then discusses that black kidneys were seen as ‘more prone to failure’ than white kidneys, but this is, so it’s said, due to that one specific gene variant and so, race shouldn’t be looked at in regards to kidney disease but individual genetic variation.
In one aspect of the medical community can using medicine based on one’s race help: prostate cancer. Black men are more likely to be afflicted with prostate cancer in comparison to whites (Odedina et al, 2009; Bhardwaj et al, 2017) with it even being proposed that black men should get separate prostate screenings to save more lives (Shenoy et al, 2016). Then he writes that we still don’t know the genes responsible, however, I have argued in the past that diet explains a large amount—if not all of the variance. (It’s not testosterone that causes it like Ross et al, 1986 believe).
The author then discusses another medical professional who argues that racial health disparities come down to the social environment. Things like BP could—most definitely—be driven by the social environment. It is assumed that the darker one’s skin is, the higher chance they have to have high BP—though this is not the case for Africans in Africa so this is clearly an American-only problem. I could conjure up one explanation: the darker the individual, the more likely he is to believe he is being ‘pre-judged’ which then affects his state of mind and has his BP rise. I discussed this shortly in my previous article Black-White Differences in Physiology. Williams (1992) reviewed evidence that social, not genetic, factors are responsible for BP differences between blacks and whites. He reviews one study showing that BP is higher in lower SES, darker-skinned blacks in comparison to higher SES blacks whereas for blacks with higher SES no effect was noticed (Klag et al, 1991). Sweet et al (2007) showed that for lighter-skinned blacks, as SES rose BP decreased while for darker-skinned blacks BP increased as SES did while implicating factors like ‘racism’ as the ultimate causes.
There is evidence for the effect of psychosocial factors and BP (Marmot, 1985). In a 2014 review of the literature, Cuffee et al (2014) identify less sleep—along with other psychosocial factors—as another cause of higher BP. It just so happens that blacks average about one hour of sleep less than whites. This could cause a lot of the variation in BP differences between the races, so clearly in the case of this variable, it is useful to know one’s race, along with their SES. Keep in mind that any actual ‘racism’ doesn’t have to occur; the person only ‘needs to perceive it’, and their blood BP will rise in response to the perceived ‘racism’ (Krieger and Sidney, 1996). Harburg et al (1978) write in regards to Detroit blacks:
For 35 blacks whose fathers were from the West Indies, pressures were higher than those with American-born fathers. These findings suggest that varied gene mixtures may be related to blood pressure levels and that skin color, an indicator of possible metabolic significance, combines with socially induced stress to induce higher blood pressures in lower class American blacks.
Langford (1981) shows that when SES differences are taken into account that the black-white BP disparity vanishes. So there seems to be good evidence for the hypothesis that psychosocial factors, sleep deprivation, diet and ‘perceived discrimination’ (whether real or imagined) can explain a lot of this gap so race and SES need to be looked at when BP is taken into account. These things are easily changeable; educate people on good diets, teach people that, in most cases, no, people are not being ‘racist’ against you. That’s really what it is. This effect holds more for darker-skinned, lower-class blacks. And while I don’t deny a small part of this could be due to genetic factors, the physiology of the heart and how BP is regulated by even perceptions is pretty powerful and could have a lot of explanatory power for numerous physiological differences between races and ethnic groups.
Krieger (1990) states that in black women—not in white women—“internalized response to unfair treatment, plus non-reporting of race and gender discrimination, may constitute risk factors for high blood pressure among black women“. This could come into play in regards to black-white female differences in BP. Thomson and Lip (2005) show that “environmental influence and psychosocial factors may play a more important role than is widely accepted” in hypertension but “There remain many uncertainties to the relative importance and contribution of environmental versus genetic influences on the development of blood pressure – there is more than likely an influence from both. However, there is now evidence to necessitate increased attention in examining the non-genetic influences on blood pressure …” With how our physiology evolved to respond to environmental stimuli and respond in real time to perceived threats, it is no wonder that these types of ‘perceived discrimination’ causes higher BP in certain groups with lower SES.
Wilson (1988) implicates salt as the reason why blacks have higher BP than whites. High salt intake could affect the body’s metabolism by causing salt retention which influences blood plasma volume, cardiac output. However, whites have a higher salt intake than blacks, but blacks still ate twice the recommended amounts from the dietary guidelines (all ethnic subgroups they analyzed from America over-consumed salt as well) (Fulgoni et al, 2014). Blacks are also more ‘salt-sensitive’ than whites (Sowers et al 1988; Schmidlin et al, 2009; Sanada, Jones, and Jose, 2014) which is also heritable in blacks (Svetke, McKeown, and Wilson, 1996). A slavery hypothesis does exist to explain higher rates of hypertension in blacks, citing salt deficiency in the parts of Africa that supplied the slaves to the Americas, to the trauma of the slave trade and slavery in America. However, historical evidence does not show this to be the case because “There is no evidence that diet or the resulting patterns of disease and demography among slaves in the American South were significantly different from those of other poor southerners” (Curtin, 1992) whereas Campese (1996) hypothesizes that blacks are more likely to get hypertension because they evolved in an area with low salt.
The NYT article concludes:
Science seeks to categorize nature, to sort it into discrete groupings to better understand it. That is one way to comprehend the race concept: as an honest scientific attempt to understand human variation. The problem is, the concept is imprecise. It has repeatedly slid toward pseudoscience and has become a major divider of humanity. Now, at a time when we desperately need ways to come together, there are scientists — intellectual descendants of the very people who helped give us the race concept — who want to retire it.
Race is a useful concept. Whether in medicine, population genetics, psychology, evolution, physiology, etc it can elucidate a lot of causes for differences between races and ethnic groups—whether or not they are genetic or psychosocial in nature. That just attests to both the power of suggestion along with psychosocial factors in regards to racial differences in physiological factors.
Finally let’s see what the literature says about race in medicine. Bonham et al (2009) showed that both black and white doctors concluded that race is medically relevant but couldn’t decide why however they did state that genetics did not explain most of the disparity in relation to race and disease aside from the obvious disorders like Tay Sachs and sickle cell anemia. Philosophers accept the usefulness of race in the biomedical sciences (Andreason, 2009; Efstathiou, 2012; Hardimon, 2013; Winther, Millstein, and Nielsen, 2015; Hardimon, 2017) whereas Risch et al (2002) and Tang et al (2002) concur that race is useful in the biomedical sciences. (See also Dorothy Roberts’ Ted Talk The problem with race-based medicine which I will cover in the future). Richard Lewontin, naturally, has hang-ups here but his contentions are taken care of above. Even if race were a ‘social construct‘, as Lewontin says, it would still be useful in a biomedical sense; but since there are differences between races/ethnic groups then they most definitely are useful in a biomedical sense, even if at the end of the day individual variation matters more than racial variation. Just knowing someone’s race and SES, for instance, can tell you a lot about possible maladies they may have, even if, utltimately, individual differences in physiology and anatomy matter more in regards to the biomedical context.
In conclusion, race is most definitely a useful concept in medicine, whether race is a ‘social construct’ or not. Just using Michael Hardimon’s race concepts, for instance, shows that race is extremely useful in the biomedical context, despite what naysayers may say. Yes, individual differences in anatomy and physiology trump racial differences, but just knowing a few things like race and SES can tell a lot about a particular person, for instance with blood pressure, resting metabolic rate, and so on. Denying that race is a useful concept in the biomedical sciences will lead to more—not less—racial health disparities, which is ironic because that’s exactly what race-deniers do not want. They will have to accept a race concept, and they would accept Hardimon’s socialrace concept because that still allows it to be a ‘social construct’ while acknowledging that race and psychosocial factors interact to cause higher physiological variables. Race is a useful concept in medicine, and if the medical establishment wants to save more lives and actually end the racial disparities in health then they should acknowledge the reality of race.
Musings on Testosterone and Race
1500 words
People don’t understand the relationship between testosterone, aggression, and crime. People hear the sensational media stating that testosterone causes crime, aggression, and anger. However, I have written numerous articles on this blog on the true nature of testosterone, what it’s really needed for and why we need it in high amounts. I’ve mused a lot on this hormone, which is one of my favorites to discuss due to the numerous misconceptions surrounding it.
Which way does causation run in regard to prisoners and their testosterone level?: heightened testosterone > aggression > violence or aggression > heightened testosterone > dominance > possibility (not necessarily, as I have written myself in the past) of violence.
People may use animal studies in support of their contention that testosterone causes aggressive behavior. However, for reasons I have discussed in the past, animal models only show avenues for future research and do not necessarily mean that this is the case for humans (as Mazur, 2006 point out). I don’t use animal studies. They’re good for future research, but to use them as evidence for causation in humans doesn’t make sense.
People may cite Dabbs et al showing that the more violent prisoners had higher levels of testosterone and therefore conclude that higher levels of testosterone drive the violent crime that they commit, however it is much more nuanced than that.
Does being a violent criminal raise testosterone or are violent people more likely to have high testosterone? Dabbs never untangles this; they just showed a correlation, which is small as evidenced by my other citations.
Testosterone is, as evidenced by numerous studies, related to dominance and dominance contests, however, during these dominance contests “a killing is rarely the outcome of a violent dominance contest” (Mazur, 2006: 25). Therefore, this throws a wrench in the testosterone-causes-crime hypothesis.
Some individuals may state that these dominance contests then lead to violence, however, as Mazur (2006) puts it: “Heightened testosterone is not a direct cause of male violence.”
Other animals assert dominance violently but we, necessarily, do not.
Mazur (2006) states that dominance contests rarely escalate to murder. Mazur also states that dominance contests also lead to increased T for the winners and decreased T for the losers, and these contests also don’t necessarily lead to murder/violent behavior. There is a feedback loop with high T causing behavior and behavior causing high T (Mazur, 2006) while this feedback loop may lead to “lethal effects” (Mazur and Booth, 2008).
It’s worth noting that Mazur seems to advocate for ‘testosterone-depressing drugs’. He concludes:
There are strong linkages between macro-level culture and the physiology of
individuals. We may find solutions to some of our social problems by altering these linkage.
Macro-level culture being white culture, black culture, Asian culture, etc.
The physiological differences are due to the preparation for dominance contests. So, his hypothesis goes, the culture of dominance among young black men with no education is why their T is so high. That low education was also associated with low education lends credence to the claim that this is changeable.
However, in his newer article on education, low testosterone and blacks he advocates for more sensible solutions (attempting an environmental change). I don’t know about you but I have big problems with using FDA/Big Pharma drugs to ‘reduce societal problems’, and it seems that Mazur has changed his view there. Mazur (2016) writes:
If high T does facilitate the high violence rate among young black men, there would be a troubling policy question of what, if anything, to do about it. Any notion of a medical or pharmaceutical fix, rather like prescribing Ritalin for hyperactivity, would reek of race-based chemical castration and should be regarded as outside the pale. However, social interventions might be workable and ethically acceptable.
I have railed against measures like this in the past, since proposing measures to attempt to ‘decrease crime through supposedly decreasing one of the main “causes”‘ is very Brave New World-ish, and I am highly against those measures. Social interventions are, in my view, the more sensible measures to undertake.
In regard to low education and testosterone, this same relationship was noticed by Assari, Caldwell, and Zimmerman (2014) where they note that testosterone was not associated with aggression in men, but low education was, which Mazur (2016) replicates, showing that blacks of the same age group with more education had lower levels of testosterone when compared to age-matched blacks. Mazur (2016) cites one study in support for his contention that education can decrease aggressive behavior (Carre et al, 2014)
The correlation is there, I agree. let’s take the middle value of .11 between Archer, Graham-Kevan, and Davies, 2005 at .08; and Book, Starzyk, and Quinsey, 2001 .14. So testosterone explains 3 percent of the the relationship with aggression. Not high at all.
Great evidence against the testosterone-causes-aggressive-behavior hypothesis are data on the Yanomami. About 50 percent of Yanomami men meet their deaths by other Yanomami men. So the Yanomami must have testosterone levels through the roof, right? Wrong. De Lima et al (2015) write:
We observed that Yanomamis present lower levels of testosterone (414 ng/dL) in relation to other ethnic groups (502/512 ng/dL), but still within normal limits (350-1000 ng/dL).
(Note that these values for “normal limits” changed, going into effect at the end of July.)
The Yanomami with an extremely high murder rate with nowhere near a modern society have T levels on the lower end of our range. So….. The Yanomami example is direct evidence against the assertion of testosterone directly causing crime, as some people assert (it is even evidence against an indirect cause). The evidence of the Yanomami having testosterone levels near our lower range is direct evidence against the testosterone/crime hypothesis. Clearly, other variables drive the high violence rate in this society that are not testosterone. More interestingly, these people have had little contact with Western societies, and their T levels are still low compared to ours despite constantly being vigilant for threats from other Yanomami.
Most dominance contests do not end violently in the first-world, there is numerous evidence to attest to this fact. So with the low correlation between testosterone and aggression (Book, Starzyk, and Quinsey, 2001; Archer, Graham-Kevan and Davies, 2005; Book and Quinsey, 2005), along with dominance contests rarely ending in murder/violent crime, then there are way more factors influencing these phenomena.
So the feedback loop goes: Testosterone rises in expectation of a challenge which then, after the dominance contest (which doesn’t always necessarily lead to violence), it affects both individuals differently depending on whether or not they won or lost that dominance contest and these values then persist over time if the dominance contests continuously end up the same.
Let’s say, for argument’s sake, that testosterone is a large cause for aggressive behavior in lower-educated blacks, what should be done about it? Mazur cites evidence that behavioral interventions seem to work to decrease violent behavior during certain circumstances (Carre et al, 2014), so that is a good way to lower violence in populations that have low education.
So heightened testosterone does lead to dominance which then facilitates a dominance contest between two individuals which does not necessarily lead to crime and aggressive, violent behavior (this outcome is rare in dominance contests among “higher primates” [Mazur’s words]) so, therefore, while testosterone does facilitate dominance contests, it rarely leads to violence in our species. Therefore, testosterone does not cause aggressive behavior and crime, but it does cause dominance which, for the most part, do not always result in violent, aggressive, murderous behavior.
I’ve shown that Mazur replicated other analyses that show that testosterone and aggressive behavior are related to lower education. Testosterone wasn’t associated with aggressive behavior in Assari, Caldwell, and Zimmerman’s (2014) study, and, as Mazur (2016) replicates, low education was. So one way to end this relationship is to educate people, as shown by Carre et al (2014), and with this education, crime will begin to fall. Heightened testosterone is not a direct cause of male violence.
(Note: I also believe that other factors such as sleep and depressed nutrition play a factor in crime, as well as racial differences in it. See Birch, 1972; Liu et al, 2003; Liu et al, 2004; Walker et al, 2007; Galler et al, 2011, 2012a, 2012b; Spratt et al, 2012; Gesch, 2013; Kuratko et al, 2013; Waber et al, 2014; Raine et al, 2015; Thompson et al, 2017 for more information, I will cover this in the future. I’m of course not daft enough to believe that no genetic differences between individuals/populations are the cause of a lot of crime between them, however, as I have laid out the case in regard to testosterone and MAOA numerous times, these two explanations for both individual differences in crime as well as racial differences in crime leave a lot to be desired. Other genetic factors, of course, influence these differences, however, I am only worried about refuting the popular notions of ‘testosterone and MAOA, the ‘warrior gene” cause crime. The relationship is a lot more nuanced as I have provided mountains of evidence for.)
IQ Test Construction, IQ Test Validity, and Raven’s Progressive Matrices Biases
2050 words
There are a lot of conceptual problems with IQ tests that I never see talked about. The main ones are how the tests are constructed (to fit a normal curve, no less); to the fact that there is no construct validity to the tests (IQ tests aren’t calibrated against a biological model like breathalyzers are calibrated against a model of blood in the blood stream); and how the Raven’s Progressive Matrices test is actually biased despite being touted as the most culture-free test since all you’re doing is rotating abstract symbols to see what comes next in the sequence. These three assumptions have important implications for the ‘power’ of the IQ tests, the most important being the test construction and validity.
I) IQ test construction
IQ tests are constructed with the assumption that we know what IQ tests test (we don’t) and with the prior ‘knowledge’ of who is or is not intelligent. Test constructors construct the tests to reveal presumed differences between individuals.
It is assumed that 1) IQ scores lie on a normal distribution (they don’t) and 2) few natural bio functions conform to this curve. Another problem with IQ test construction is the assumption that it increases with age and levels off after puberty. Though this, like the other things, has been built into the test by choosing items that an increasing proportion of children pass. You can, of course, reverse this effect by choosing items that older people do well on and younger people don’t.
Further, they keep 50 percent of items that children get right while keeping a smaller proportion of items that children get right, which, in effect, presupposes who is or is not intelligent.
Though, you never see those who believe that IQ is a ‘good enough’ proxy for intelligence ever being this up. Why? This is very important for the validity of these tests. Because if how the tests are constructed is wrong and test scores are not to fit a normal distribution when no normal distribution actually exists for most human mental (including IQ scores) and physiological traits, then the assumptions and conclusions drawn from them are wrong. IQ tests are constructed with the prior idea of who is or is not ‘intelligent’ and this is done by how the items are chosen—50 percent of the items that people get right are kept while the smaller proportion of items people get right or wrong are kept. This is how this so-called ‘normal curve’ appears in IQ tests and is why the book The Bell Curve has the name it has. But bell curve don’t exist for a modicum of traits including IQ!!
Simon (1997: 204) writes (emphasis mine):
There is another, and completely irrefutable, reason why the bell-shaped curve proves nothing at all in the context of H-M’s book: The makers of IQ tests consciously force the test into such a form that it produces this curve, for ease of statistical analysis. The first versions of such tests invariably produce odd-shaped distributions. The test-makers then subtract and add questions to find those that discriminate well between more-successful and less-successful test-takers. For this reason alone the bell-shaped IQ curve must be considered an artifact rather than a fact, and therefore tells us nothing about human nature or human society.
The analysis and selection of items that go on the tests are biased since there is no cognitive theory on which the analysis and selection of items are based. Carpenter, Just and Shell (1990: 408) note how John Raven, the creator of the Raven’s Progressive Matrices, even discussed this in his personal notes, writing “He used his intuition and clinical experience to rank order the difficulty of the six problem types. Many years later, normative data from Forbes (1964), shown in Figure 3, became the basis for selecting problems for retention in newer versions of the test and for arranging the problems in order of increasing difficulty, without regard to any underlying processing theory.”
II) IQ test validity
Another problem with IQ tests are its validity. People attempt to ‘prove’ its validity with correlating job performance success with IQ scores, though there are huge flaws in the studies purporting to show a .5 correlation between IQ and job performance (Richardson, 2002; Richardson and Norgate, 2015). IQ tests are not like, say, breathalyzers (which are calibrated against a model of blood alcohol) or white blood cell count (which is a proxy for disease in the body). Those two measures have a solid theoretical basis and underpinning; as blood alcohol rises, the individual had increased alcohol consumption. The same is true for white blood cell count. The same is not true for IQ tests.
One of the biggest measures used in regards to job performance and IQ testing (people attempt to use job performance to attempt to validate IQ tests) is supervisor rating. However, supervisory ratings are hugely subjective and a lot of factors that would have a supervisor be said to be a ‘good worker’ are not variables that entail just that job.
The only ‘validity’ that IQ test have is correlations with other IQ tests and tests like the SAT. This is not validity. Say the breathalyzer wasn’t calibrated against a model of blood alcohol in the body, would breathalyzers still be a valid tool to test people’s blood/alcohol level? On that same note let’s say that white blood cells wasn’t construct valid. Would we be able to reliably use white blood cell count as a valid measure for disease in the body? These very same problems plague IQ tests and people accept them as ‘proxies’ for intelligence, they test ‘enough of intelligence’ to be able to say that one is smarter than another because they scored higher in a test and therefore tap into this mystical ‘g’ that they have more of which is like a ‘power’ or ‘energy’.
These tests, therefore, are constructed with the idea of who is or is not intelligent and you can see that by looking at how the items are chosen for the test. That’s not scientific. So a true test of ‘intelligence’ may not even exist since these tests have this type of construct bias already in them.
IQ tests have no validity like breathalyzers and white blood cell count, and the so-called ‘culture-free’ IQ test Raven’s Progressive Matrices is anything but.
III) Raven’s and culture bias
I specifically asked Dr. James Thompson about Raven’s being culture-fair. I said that I recall Linda Gottfredson saying that people say that Ravens is culture-fair only because Jensen said it:
Yes, Gottfredson made that remark, and I remember her doing it at an ISIR conference.
So that’s one thing about Ravens that crumbles. A quote from Ken Richardson’s book Genes, Brains, and Human Potential: The Science and Ideology of Intelligence:
It is well known that families and subcultures vary in their exposure to, and usage of, the tools of literacy, numeracy, and associated ways of thinking. Children will vary in these because of accidents of background. …that background experience with specific cultural tools like literacy and numeracy is reflected in changes in brain networks. This explains the importance of social class context to cognitive demands, but is says nothing about individual potential.
(This argument on social class is much more complex than ‘poor people are genetically predisposed to be dumb and poor’.
Consider a recent GCTA study by Plomin et al., who reported a SNP-based heritability estimate of 35% for “general cognitive ability” among UK 12 year olds (as compared to a twin heritability estimate of 46%) [8]. According to the Wellcome Trust “genetic map of Britain,” striking patterns of genetic clustering (i.e. population stratification) exist within different geographic regions of the UK, including distinct genetic clusterings comprised of the residents of the South, South-East and Midlands of England; Cumbria, Northumberland and the Scottish borders; Lancashire and Yorkshire; Cornwall; Devon; South Wales; the Welsh borders; Anglesey in North Wales; Scotland and Ireland; and the Orkney Islands [8]. Now consider the title of a study from the University and College Union: “Location, Location, Location – the widening education gap in Britain and how where you live determines your chances” [9]. This state of affairs (not at all unique to the UK), combined with widespread geographic population stratification, is fertile ground for spurious heritability estimates.
Still Chasing Ghosts: A New Genetic Methodology Will Not Find the “Missing Heritability”
I think this argument is interesting, and it throws a wrench into a lot of things, but more on that another day.)
Richardson continues:
In other words, items like those in the Raven contain hidden structure which makes them more, not less, culturally steeped than any other kind of intelligence testing items, like the Raven, as somehow not knowledge-based, when all are clearly learning dependent. Ironically, such cultural-dependency testing is sometimes tacitly admitted by test users. For example, when testing children in Kuwait on the Raven in 2006, Ahmed Abdel-Khalek and John Raven transposed the items “to read from left to right following the custom of Arabic writings.” (Richardson, 2017: 99)
Finally, we have this dissertation which shows that urban peoples score better than hunter-gatherers (relevant to this present article):
Reading was the greatest predictor of performance Raven’s, despite controlling for age and sex. Attendance was also strongly correlated with Raven’s performance. These findings suggest that reading, or pattern recognition, could be fundamentally affecting the way an individual problem solves or learns to learn, and is somehow tapping into ‘g’. Presumably the only way to learn to read is through schooling. It is, therefore, essential that children are exposed to formal education, have the motivation to go/stay in school, and are exposed to consistent, quality training in order to develop the skills associated with improved performance. (pg. 83)
This is telling: This means that there is no such thing as a ‘culture-free’ IQ test and there will always be something involved that makes it culture un-fair.
People may say ‘It’s only rotating pictures and shapes to get the final answer, how much schooling could you need??’, well as seen above with the Tsimane, schooling is very important to IQ tests since they test learned skills. I’ve seen some people claim that IQ tests don’t test learned ability and that it’s all native, unlearned ability. That’s a very incorrect statement.
So although the symbols in a test like the RPM are experience-free, the rules governing their changes across the matrix are certainly not, and they are more likely to be already represented in the minds of children from middle-class homes, less so in others. Performance on the Raven’s test, in other words, is a question not of inducing ‘rules’ from meaningless symbols, in a totally abstract fashion, but of recruiting ones that are already rooted in the activites of some cultures rather than others. Like so many problems in life, including fields as diverse as chess, science and mathematics (e.g. Chi & Glaser, 1985), each item on the Raven’s test is a recognition problem (matching the covariation structure in a stimulus array to ones in background knowledge) before it is a reasoning problem. The latter is rendered easy when the former has been achieved. Similar arguments can be made about other so-called ‘culture-free’ items like analogies and classifications (Richardson & Webster, 1996). (Richardson, 2002: pg 292-292)
Everyday life is also more complex than the hardest items on Raven’s Matrices, while the test is not complex in its demands compared to tasks undertaken in everyday life (Carpenter, Just, and Shell, 1990). They conclude that the cause is differences in working memory, but that is an ill-defined concept in psychology. They do say, though, that “The processes that distinguish among individuals are primarily the ability to induce abstract relations and the ability to dynamically manage a large set of problem-solving goals in working memory.” So item complexity doesn’t make Raven’s items more difficult for others, since everyday life is more complex.
I’ll end with a bit of physiology. What physiological process is does IQ mimic in the body? If it is a physiological process, surely you’re aware that physiological processes *are not* static. IQ is said to be stable at adulthood, what a strange physiological process. Let’s say for arguments’ sake that IQ really does test some intrinsic, biological process. Does it seem weird to you that a supposed real, stable, biological, bodily function of an individual would be different at different times?
Conclusion
There are a lot of assumptions about IQ tests that are never talked about. The most important being how the tests are constructed to fit a normal curve when most traits important for survival aren’t normally distributed. IQ tests are constructed with the assumption of who is or isn’t intelligent just on the knowledge of how the items are prepared for the test. When you look at how the tests are constructed you can see how they are constructed to fit the normal curve because most of their assumptions and conclusions rest on the reality of the normal curve. There is no construct validity to IQ tests, they’re not like breathalyzers for instance which are calibrated against a model of blood alcohol or white blood cell count as a proxy for disease in the body. Raven’s—despite what is commonly stated about the test—is not unbiased, it perhaps is the most biased IQ test of them all. This highlights the problems with IQ tests that are rarely ever spoken about, and should have you call into question the ‘power’ of the IQ test which assumes who is or isn’t intelligent ahead of time.
NotPoliticallyCorrect 