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Muscle Fibers, Obesity, Cardiometabolic Disorders, and Race

2650 words

The association between muscle fiber typing obesity and race is striking. It is well-established that blacks have a higher proportion of type II skeletal muscle fibers than whites and these higher proportions of these specific types of muscle fibers lead to physiological differences between the two races which then lead to differing health outcomes between them—along with differences in athletic competition. Racial differences in health are no doubt complex, but there are certain differences between the races that we can look at and say that there is a relationship here that warrants further scrutiny.

Why is there an association between negative health outcomes and muscle phsyiology? The answer is very simple if one knows the basics of muscle physiology and how and why muscles contract (it is worth noting that out of a slew of anatomic and phsyiologic factors, movement is the only thing we can consciously control, compare to menstration and other similar physiologic processes which are beyond our control). In this article, I will describe what muscles do, how they are controlled, muscle physiology, the differences in fiber typing between the races and what it means for health outcomes between them.

Muscle anatomy and physiology

Muscle fiber number is determined by the second trimester. Bell (1980) noted that skeletal muscle fiber in 6 year olds is not different from normal adult tissue, and so, we can say that between the time in the womb and age 6, muscle fiber type is set and cannot be changed (though training can change how certain fibers respond, see below).

Muscle anatomy and physiology is interesting because it shows us how and why we move the way we do. Tendons attach muscle to bone. Attached to the tendon is the muscle belly. The muscle belly is made up of facsicles and the fascicles are made up of muscle fibers. Muscle fibers are made up of myofibrils and myofibrils are made up of myofilaments. Finally, myofilaments are made up of proteins—specifically actin and myosin, this is what makes up our muscles.

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(Image from here.)

Muscle fibers are encased by sarcolemma which contains cell components such as sarcoplasm, nuclei, and mitochondria. They also have other cells called myofibrils which contain myofilaments which are then made up of actin (thin filaments) and mysoin (thick filaments). These two types of filaments form numerous repeating sections within a myofibril and each repeating section is known as a sarcomere. Sarcomeres are the “functional” unit of the muscle, like the neuron is for the nervous system. Each ‘z-line’ denotes another sarcomere across a myofibril (Franzini-Armstrong, 1973; Luther, 2009).

Other than actin and myosin, there are two more proteins important for muscle contraction: tropomyosin and troponin. Tropomyosin is found on the actin filament and it blocks myosin binding sites which are located on the actin filament, and so it keeps myosin from attaching to muscle while it is in a relaxed state. On the other hand, troponin is also located on the actin filament but troponin’s job is to provide binding sites for calcium and tropomyosin when a muscle needs to contract.

So the structure of skeletal muscle can be broken down like so: epymyseum > muscle belly > perimyseum > fascicle > endomyseum > muscle fibers > myofibrils > myofilaments > myosin and actin. Note diagram (C) from above; the sarcomere is the smallest contractile unit in the myofibril. According to sliding filament theory (see Cook, 2004 for a review), a sarcomere shortens as a result of the ‘z-lines’ moving closer together. The reason these ‘z-lines’ converge is because myosin heads attach to the actin filament which asynchronistically pulls the actin filament across the myosin, which then results in the shortening of the muscle fiber. Sarcomeres are the basic unit controlling changes in muscle length, so the faster or slower they fire depends on the majority type of fiber in that specific area.

But the skeletal muscle will not contract unless the skeletal muscles are stimulated. The nervous system and the muscular system communicate, which is called neural activiation—defined as the contraction of muscle generated by neural stimulation. We have what are called “motor neurons”—neurons located in the CNS (central nervous system) which can send impulses to muscles to move them. This is done through a special synapse called the neuromuscular junction. A motor neuron that connects with muscle fibers is called a motor unit and the point where the muscle fiber and motor unit meet is callled the neuromuscular junction. It is a small gap between the nerve and muscle fiber called a synapse. Action potentials (electrical impulses) are sent down the axon of the motor neuron from the CNS and when the action potential reaches the end of the axon, hormones called neurotransmitters are then released. Neurotransmitters transport the electrical signal from the nerve to the muscle.

Muscle fiber types

The two main categories of muscle fiber are type I and type II—‘slow’ and ‘fast’ twitch, respectively. Type I fibers contain more blood cappilaries, higher levels of mitochondria (which transforms food into ATP) and myoglobin which allows for an improved delivery of oxygen. Since myoglobin is similar to hemoglobin (the red pigment which is found in red blood cells), type I fibers are also known as ‘red fibers.’ Type I fibers are also smaller in diameter and slower to produce maximal tension, but are also the most fatigue-resistant type of fiber.

Type II fibers have two subdivisions—IIa and IIx—based on their mechanical and chemical properties. Type II fibers are in many ways the opposite of type I fibers—they  contain far fewer blood cappilaries, mitochondria and myoglobin. Since they have less myoglobin, they are not red, but white, which is why they are known as ‘white fibers.’ IIx fibers have a lower oxidative capacity and thusly tire out quicker. IIa, on the other hand, have a higher oxidative capacity and fatigue slower than IIx fibers (Herbison, Jaweed, and Ditunno, 1982; Tellis et al, 2012). IIa fibers are also known as intermediate fast twitch fibers since they can use both anarobic and aerobic metabolism equally to produce energy. So IIx fibers are a combo of I and II fibers. Type II fibers are bigger, quicker to produce maximal tension, and tire out quicker.

Now, when it comes to fiber typing between the races, blacks have a higher proportion of type II fibers compared to whites who have a higher proportion of type I fibers (Ama et al, 1986; Ceaser and Hunter, 2015; see Entine, 2000 and Epstein, 2014 for reviews). Higher proportions of type I fibers are associated with lower chance of cardiovascular events, whereas type II fibers are associated with a higher risk. Thus, “Skeletal muscle fibre composition may be a mediator of the protective effects of exercise against cardiovascular disease” (Andersen et al, 2015).

Now that the basics of muscle anatomy and physiology are apparent, hopefully the hows and whys of muscle contraction and what different muscle fibers do are becoming clear, because these different fibers are distributed between the races in uneven frequencies, which then leads to differences in sporting performance but also differents in health outcomes.

Muscle fibers and health outcomes

We now know the physiology and anatomy of muscle and muscle fiber typing. We also know the differences between each type of skeletal muscle fiber. Since the two races do indeed differ in the percentage of skeletal muscle fiber possessed on average, we then should find stark differences in health outcomes, part of the reason being these differences in muscle fiber typing.

While blacks on average have a higher proportion of type II muscle fibers, whites have a higher proportion of type I muscle fibers. Noting what I wrote above about the differences between the fiber types, and knowing what we know about racial differences in disease outcomes, we can draw some inferences on how differences in muscle fiber typing between races/individuals can then affect disease seriousness/acquisition.

In their review of black-white differences in muscle fiber typing, Ceaser and Hunter (2015) write that “The longitudinal data regarding the rise in obesity indicates obesity rates have been highest among non-Hispanic Black women and Hispanic women.” And so, knowing what we know about fiber type differences between races and how these fibers act when they fire, we can see how muscle fiber typing would contribute to differences in disease acquisition between groups.

Tanner et al (2001) studied 53 women (n=28, lean women; and n=25, obese women) who were undergoing an elective abdominal surgery (either a hysterectomy or gastric bypass). Their physiologic/anatomic measures were taken and they were divided into races: blacks and whites, along with their obesity status. Tanner et al found that the lean subjects had a higher proportion of type I fibers and a lower proportion of type IIx fibers whereas those who were obese were more likely to have a higher proportion of type IIb muscle fibers.

Like other analyses on this matter, Tanner et al (2001) showed that the black subjects had a higher proportion of type II fibers in comparison to whites who had a higher proportion of type I fibers (adiposity was not taken into account). Fifty-one percent of the fiber typing from whites was type I whereas for blacks it was 43.7 pervent. Blacks had a higher proportion of type IIx fibers than whites (16.3 percent for whites and 23.4 for blacks). Lean blacks and lean whites, though, had a similar percentage of type IIx fibers (13.8 percent for whites and 15 percent for blacks). It is interesting to note that there was no difference in type I fibers between lean whites and blacks (55.1 percent for whites and 54.1 percent for blacks), though muscle fibers from obese blacks contained far fewer type I fibers compared to their white counterparts (48.6 percent for whites and 34.5 for blacks). Obese blacks’ muscle fiber had a higher proportion of type IIx fibers than obese whites’ fiber typing (19.2 percent for whites and 31 percent for blacks). Lean blacks and lean whites had a higher proportion of type I fibers than obese blacks and obese whites. Obese whites and obese blacks had more type IIx fibers than lean whites and lean blacks.

So, since type II fibers are insulin resistant (Jensen et al, 2007), then they should be related to glucose intloerance—type II diabetes—and blacks with ancestry from West Africa should be most affected. Fung (2016, 2018) shows that obesity is a disease of insulin resistance, and so, we can bring that same rationale to racial differences in obesity. Indeed, Nielsen and Christensen (2011) hypothesize that the higher prevalence of glucose intolerance in blacks is related to their lower percentage of type I fibers and their higher percentage of type II fibers.

Nielsen and Christensen (2011) hypothesize that since blacks have a lower percentage of type I fibers (the oxidative type), this explains the lower fat oxidation along with lower resting metabolic rate, sleeping metabolic rate, resting energy expenditure and Vo2 max in comparison to whites. Since type I fibers are more oxidative over the glycolitic type II fibers, the lower oxidative capacity in these fibers “may cause a higher fat storage at lower levels of energy intake than in individuals with a higher oxidative capacity” (Nielsen and Christensen, 2011: 611). Though the ratio of IIx and IIa fibers are extremely plastic and affected by lifestyle, Nielsen and Christensen do note that individuals with different fiber typings had similar oxidative capacity if they engaged in physical activity. Recall back to Caesar and Hunter (2015) who note that blacks have a lower maximal aerobic capacity and higher proportion of type II fibers. They note that lack of physical activity exacerbates the negative effects that a majority type II fibers has over majority type I. And so, some of these differences can be ameliorated between these two racial groups.

The point is, individuals/groups with a higher percentage of type II fibers who do not engage in physical activity have an even higher risk of lower oxidative capacity. Furthermore, a higher proportion of type II fibers implies a higher percentage of IIx fibers, “which are the least oxidative fibres and are positively associated with T2D and obesity” (Nielsen and Christensen, 2011: 612). They also note that this may explain the rural-urban difference in diabetes prevalance, with urban populations having a higher proportion of type II diabetics. They also note that this may explain the difference in type II diabetes in US blacks and West African natives—but the reverse is true for West Africans in the US. There is a higher rate of modernization and, with that, a higher chance to be less physically active and if the individual in question is less physically active and has a higher proportion of type II fibers then they will have a higher chance of acquiring metabolic diseases (obesity is also a metabolic disease). Since whites have a higher proportion of type I fibers, they can increase their fat intake—and with it, their fat oxidation—but this does not hold for blacks who “may not adjust well to changes in fat intake” (Nielsen and Christensen, 2011: 612).

Nielsen and Christensen end their paper writing:

Thus, Blacks of West African ancestry might be genetically predisposed to T2D because of an inherited lower amount of skeletal muscle fibre type I, whereby the oxidative capacity and fat oxidation is reduced, causing increased muscular tissue fat accumulation. This might induce skeletal muscle insulin resistance followed by an induced stress on the insulin-producing beta cells. Together with higher beta-cell dysfunction in the West African Diaspora compared to Whites, this will eventually lead to T2D (an overview of the ‘skeletal muscle distribution hypothesis’ can be seen in Figure 2).

muscleefiber

Lambernd et al (2012) show that muscle contractions eliminated insuin resistance by blocking pro-inflammatory signalling pathways: this is the mechanism by which physical activity decreases glucose intolerance and thusly improves health outcomes—especially for those with a higher proportion of type II fibers. Thus, it is important for individuals with type II fibers to exercise, since sedentariness is associated with an age-related insulin resistance due to impaired GLUT4 utilization (Bunprajun et al, 2013).

(Also see Morrison and Cooper’s (2006) hypothesis that “reduced oxygen-carrying capacity induced a shift to more explosive muscle properties” (Epstein, 2014: 179). Epstein notes that the only science there is on this hypothesis is one mouse and rat study showing that low hemoglobin can “induce a switch to more explosive muscle fibers” (Epstein, 2014: 178), but this has not been tested on humans to see if it would hold. If this is tested on humans and if it does hold, then that would lend credence to Morrison’s and Cooper’s (2006) hypothesis.)

Conclusion

Knowing what we know about muscle anatomy and physiology and how muscles act we can understand the influence the different muscle types have on disease and how they contribute to disease variation between race, sex and the individual level. Especially knowing how type II fibers act when the individual in question is insulin resistant is extremely important—though it has been noted that individuals who participate in aerobic exercise decrease their risk for cardiometabolic diseases and can change the fiber distribution difference between IIx and IIa fibers, lowering their risk for acquiring cardiometabolic diseases (Ceaser and Hunter, 2015).

Thinking back to sarcomeres (the smallest contractile unit in the muscle) and how they would act in type II fibers: they would obviously contract much faster in type II muscles over type I muscles; they would then obviously tear faster than type I muscles; since type II muscles are more likely to be insulin resistant, then those with a higher proportion of type II fibers need to focus more on aerobic activity to “balance out” type IIx and IIa fibers and decrease the risk of cardiometabolic disease due to more muscle contractions (Lambernd et al, 2012). Since blacks have a higher proportion of type II fibers and are more likely to be sedentary than whites, and since those who have a higher proportion of type II fibers are more likely to be obese, then it is clear that exercise can and will ameliorate some of the disparity in cardiometabolic diseases between blacks and whites.

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Is Obesity Caused by a Virus?

2150 words

I’ve recently taken a large interest in the human microbiome and parasites and their relationship with how we behave. There are certain parasites that can and do have an effect on human behavior, and they also reduce or increase certain microbes, some of which are important for normal functioning. What I’m going to write may seem weird and counter-intuitive to the CI/CO (calories in/calories out) model, but once you understand how the diversity in the human mirobiome matters for energy acquisiton, then you’ll begin to understand how the microbiome contributes to the exploding obesity rate in the first world.

One of the books I’ve been reading about the human microbiome is 10% Human: How Your Body’s Microbes Hold the Key to Health and Happiness. P.h.D. in evolutionary biology Alanna Collen outlines how the microbiome has an effect on our health and how we behave. Though one of the most intriquing things I’ve read in the book so far is how there is a relationship with microbiome diversity, obesity and a virus.

Collen (2014: 69) writes:

But before we get too excited about the potential for a cure for obesity, we need to know how it all works. What are these microbes doing that make us fat? Just as before, the microbiotas in Turnbaugh’s obese mice contained more Firmicutes and fewer Bacteroidetes, and they somehow seemed to enable the mice to extract more energy from their food. This detail undermines one of the core tenets of the obesity equation. Counting ‘calories-in’ is not as simple as keeping track of what a person eats. More accurately, it is the energy content of what a person absorbs. Turnbaugh calculated that the mice with the obese microbiota were collecting 2 per cent more calories from their food. For every 100 calories the lean mice extracted, the obese mice squeezed out 102.

Not much, perhaps, but over the course of a year or more, it adds up. Let’s take a woman of average height. 5 foot 4 inches, who weights 62 kg (9st 11 lb) and a healthy Body Mass Index (BMI: weight (kg) /(height (m)^2) of 23.5. She consumes 2000 calories per day, but with an ‘obese’ microbiota, her extra 2 per cent calorie extraction adds 40 more calories each day. Without expending extra energy, those further 40 calories per day should translate, in theory at least, to a 1.9 kg weight gain over a year. In ten years, that’s 19 kg, taking her weight to 81 kg (12 st 11 lb) and her BMI to an obese 30.7. All because of just 2 percent extra calories extracted from her food by her gut bacteria.

Turnbaugh et al (2006) showed that differing microbiota contributes to differing amounts of weight gain. The obese microbiome does have a greater capacity to extract more energy out of the same amount of food in comparison to the lean microbiome. This implies that obese people would extract more energy eating the same food as a lean person—even if the so-called true caloric value on the package from a caloriometer says otherwise. How much energy we absorb from the food we consume comes down to genes, but not the genes you get from your parents; it matters which genes are turned on or off. Our microbes also control some of our genes to suit their own needs—driving us to do things that would benefit them.

Gut microbiota does influence gene expression (Krautkramer et al, 2016). This is something that behavioral geneticists and psychologists need to look into when attempting to explain human behavior, but that’s for another day. Fact of the matter is, where the energy that’s broken down from the food by the microbiome goes is dictated by genes; the expression of which is controlled by the microbiome. Certain microbiota have the ability to turn up production in certain genes that encourage more energy to be stored inside of the adipocite (Collen, 2014: 72). So the ‘obese’ microbiota, mentioned previously, has the ability to upregulate genes that control fat storage, forcing the body to extract more energy out of what is eaten.

Indian doctor Nikhil Dhurandhar set out to find out why he couldn’t cure his patients of obesity, they kept coming back to him again and again uncured. At the time, an infectious virus was wiping out chickens in India. Dhurandhar had family and friends who were veteraniarians who told him that the infected chickens were fat—with enlarged livers, shrunken thymus glands and a lot of fat. Dhurandhar then took chickens and injected them with the virus that supposedly induced the weight gain in the infected chickens, and discovered that the chickens injected with the virus were fatter than the chickens who were not injected with it (Collen, 2014: 56).

Dhurandhar, though, couldn’t continue his research into other causes for obesity in India, so he decided to relocate his family to America, as well as studing the underlying science behinnd obesity. He couldn’t find work in any labs in order to test his hypothesis that a virus was responsible for obesity, but right before he was about to give up and go back home, nutrional scientist Richard Atkinson offered him a job in his lab. Though, of course, they were not allowed to ship the chicken virus to America “since it might cause obesity after all” (Collen, 2014: 75), so they had to experiment with another virus, and that virus was called adenovirus 36—Ad-36 (Dhurandhar et al, 1997Atkinson et al, 2005; Pasarica et al, 2006;  Gabbert et al, 2010Vander Wal et al, 2013;  Berger et al, 2014; Pontiero and Gnessi, 2015; Zamrazilova et al. 2015).

Atkinson and Dhurandhar injected one group of chickens with the virus and had one control group. The infected chickens did indeed grow fatter than the ones who were not infected. However, there was a problem. Atkinson and Dhurandhar could not outright infect humans with Ad-36 and test them, so they did the next best thing: they tested their blood for Ad-36 antibodies. 30 percent of obese testees ended up having Ad-36 antibodies whereas only 11 percent of the lean testees had it (Collen, 2014: 77).

So, clearly, Ad-36 meddles with the body’s energy storage system. But we currently don’t know how much this virus contributes to the epidemic. This throws the CI/CO theory of obesity into dissarray, proving that stating that obesity is a ‘lifestyle disease’ is extremely reductionist and that other factors strongly influence the disease.

On the mechanisms of exactly how Ad-36 influences obesity:

The mechanism in which Ad-36 induces obesity is understood to be due to the viral gene, E4orf1, which infects the nucleus of host cells. E4orf1 turns on lipogenic (fat producing) enzymes and differentiation factors that cause increased triglyceride storage and differentiation of new adipocytes (fat cells) from pre-existing stem cells in fat tissue.

We can see that there is a large variation in how much energy is absorbed by looking at one overfeeding study. Bouchard et al (1990) fed 12 pairs of identical twins 1000 kcal a day over their TDEE, 6 days per week for 100 days. Each man ate about 84,000 kcal more than their bodies needed to maintain their previous weight. This should have translated over to exactly 24 pounds for each individual man in the study, but this did not turn out to be the case. Quoting Collen (2014: 78):

For starters, even the average amount the men gained was far less than maths dictates that it should have been, at 18 lb. But the individual gains betray the real failings of applying a mathematical rule to weight loss. The man who gained the least managed only 9 lb — just over a third of the predicted amount. And the twin who gained the most put on 29 lb — even more than expected. These values aren’t ’24 lb, more or less’, they are so far wide of the mark that using it even as a guide is purposeless.

This shows that, obviously, the composition of the individual microbiome contributes to how much energy is broken down in the food after it is consumed.

One of the most prominent microbes that shows a lean/obese difference is one called Akkermansia micinphilia. The less Akkermensia one has, the more likely they are to be obese. Akkermansia comprise about 4 percent of the whole microbiome in lean people, but they’re almost no where to be found in obese people. Akkermansia lives on the mucus lining of the stomach, which prevents the Akkermansia from crossing over into the blood. Further, people with a low amount of this bacterium are also more likely to have a thinner mucus layer in the gut and more lipopolysaccharides in the blood (Schneeberger et al, 2015). This one species of microbiota is responsible for dialing up gene activity which prevents LPS from crossing into the blood along with more mucus to live on. This is one example of the trillions of the bacteria in our microbiome’s ability to upregulate the expression of genes for their own benefit.

Everard et al (2013) showed that by supplementing the diets of a group of mice with Akkermensia, LPS levels dropped, their fat cells began creating new cells and their weight dropped. They conclude that the cause of the weight gain in the mice was due to increased LPS production which forced the fat cell to intake more energy and not use it.

There is evidence that obesity spreads in the same way that an epidemic does. Christakis and Fowler (2007) followed over 12000 people from 1971 to 2003. Their main conclusion was that the main predictor of weight gain for an individual was whether or not their closest loved one had become obese. One’s chance of becoming obese increased by a staggering 171 percent if they had a close friend who had become obese in the 32 year time period, whereas among twins, if one twin became obese there was a 40 percent chance that the co-twin would become obese and if one spouse became obese, the chance the other would become obese was 37 percent. This effect also did not hold for neighbors, so something else must be going in (i.e., it’s not the quality of the food in the neighborhood). Of course when obesogenic environments are spoken of, the main culprits are the spread of fast food restaurants and the like. But in regards to this study, that doesn’t seem to explain the shockingly high chance that people have to become obese if their closest loved ones did. What does?

There are, of course, the same old explanations such as sharing food, but by looking at it from a microbiome point of view, it can be seen that the microbiome can and does contribute to adult obesity—due in part to the effect on different viruses’ effects on our energy storage system, as described above. But I believe that introducing the hypothesis that we share microbes with eachother, which also drive obesity, should be an alternate or complimentary explanation.

As you can see, the closer one is with another person who becomes obese, the higher chance they have of also becoming obese. Close friends (and obviously couples) spend a lot of time around each other, in the same house, eating the same foods, using the same bathrooms, etc. Is it really an ‘out there’ to suggest that something like this may also contribute to the obesity epidemic? When taking into account some of the evidence reviewed here, I don’t think that such a hypothesis should be so easily discarded.

In sum, reducing obesity just to CI/CO is clearly erroneous, as it leaves out a whole slew of other explanatory theories/factors. Clearly, our microbiome has an effect on how much energy we extract from our food after we consume it. Certain viruses—such as Ad-36, an avian virus—influence the body’s energy storage, forcing the body to create no new fat cells as well as overcrowding the fat cells currently in the body with fat. That viruses and our diet can influence our microbiome—along with our microbiome influencing our diet—definitely needs to be studied more.

One good correlate of the microbiomes’/virsuses’ role in human obesity is that the closer one is to one who becomes obese, the more likely it is that the other person in the relationship will become obese. And since the chance increases the closer one is to who became obese, the explanation of gut microbes and how they break down our food and store energy becomes even more relevant. The trillions of bacteria in our guts may control our appetites (Norris, Molina, and Gewirtz, 2013; Alcock, Maley, and Atkipis, 2014), and do control our social behaviors (Foster, 2013; Galland, 2014).

So, clearly, to understand human behavior we must understand the gut microbiome and how it interacts with the brain and out behaviors and how and why it leads to obesity. Ad-36 is a great start with quite a bit of research into it; I await more research into how our microbiome and parasites/viruses control our behavior because the study of human behavior should now include the microbiome and parasites/viruses, since they  have such a huge effect on eachother and us—their hosts—as a whole.

Black-White Differences in Muscle Fiber and Its Role In Disease and Obesity

1700 words

How do whites and blacks differ by muscle fiber and what does it mean for certain health outcomes? This is something I’ve touched on in the past, albeit briefly, and decided to go in depth on it today. The characteristics of skeletal muscle fibers dictate whether one has a higher or lower chance of being affected by cardiometabolic disease/cancer. Those with more type I fibers have less of a chance of acquiring diabetes while those with type II fibers have a higher chance of acquiring debilitating diseases. This has direct implications for health disparities between the two races.

Muscle fiber typing by race

Racial differences in muscle fiber typing explain differences in strength and mortality. I have, without a shadow of a doubt, proven this. So since blacks have higher rates of type II fibers while whites have higher rates of type I fibers (41 percent type I for white Americans, 33 percent type I for black Americans, Ama et al, 1985) while West Africans have 75 percent fast twitch and East Africans have 25 percent fast twitch (Hobchachka, 1988). Further, East and West Africans differ in typing composition, 75 percent fast for WAs and 25 percent fast for EAs, which has to do with what type of environment they evolved in (Hochhachka, 1998). What Hochhachka (1998) also shows is that high latitude populations (Quechua, Aymara, Sherpa, Tibetan and Kenyan) “show numerous similarities in physiological hypoxia defence mechanisms.” Clearly, slow-twitch fibers co-evolved here.

Clearly, slow-twitch fibers co-evolved with hypoxia. Since hypoxia is the deficiency in the amount of oxygen that reaches the tissues, populations in higher elevations will evolve hypoxia defense mechanisms, and with it, the ability to use the oxygen they do get more efficiently. This plays a critical role in the fiber typing of these populations. Since they can use oxygen more efficiently, they then can become more efficient runners. Of course, these populations have evolved to be great distance runners and their morphology followed suit.

Caesar and Henry (2015) also show that whites have more type I fibers than blacks who have more type II fibers. When coupled with physical inactivity, this causes higher rates of cancer and cardiometabolic disease. Indeed, blacks have higher rates of cancer and mortality than whites (American Cancer Society, 2016), both of which are due, in part, to muscle fiber typing. This could explain a lot of the variation in disease acquisition in America between blacks and whites. Physiologic differences between the races clearly need to be better studied. But we first must acknowledge physical differences between the races.

Disease and muscle fiber typing

Now that we know the distribution of fiber types by race, we need to see what type of evidence there is that differing muscle fiber typing causes differences in disease acquisition.

Those with fast twitch fibers are more likely to acquire type II diabetes and COPD (Hagiwara, 2013); cardiometabolic disease and cancer (Caesar and Henry, 2015); a higher risk of cardiovascular events (Andersen et al, 2015, Hernelahti et al, 2006); high blood pressure, high heart rate, and unfavorable left ventricle geometry leading to higher heart disease rates and obesity (Karjalainen et al, 2006) etc. Knowing what we know about muscle fiber typing and its role in disease, it makes sense that we should take this knowledge and acknowledge physical racial differences. However, once that is done then we would need to acknowledge more uncomfortable truths, such as the black-white IQ gap.

One hypothesis for why fast twitch fibers are correlated with higher disease acquisition is as follows: fast twitch fibers fire faster, so due to mechanical stress from rapid and forceful contraction, this leads the fibers to be more susceptible to damage and thus the individual will have higher rates of disease. Once this simple physiologic fact is acknowledged by the general public, better measures can be taken for disease prevention.

Due to differences in fiber typing, both whites and blacks must do differing types of cardio to stay healthy. Due to whites’ abundance of slow twitch fibers, aerobic training is best (not too intense). However, on the other hand, due to blacks’ abundance of fast twitch fibers, they should do more anaerobic type exercises to attempt to mitigate the diseases that they are more susceptible due to their fiber typing.

Black men with more type II fibers and less type I fibers are more likely to be obese than ‘Caucasian‘ men are to be obese (Tanner et al, 2001). More amazingly, Tanner et al showed that there was a positive correlation (.72) between weight loss and percentage of type I fibers in obese patients. This has important implications for African-American obesity rates, as they are the most obese ethny in America (Ogden et al, 2016) and have higher rates of metabolic syndrome (a lot of the variation in obesity does come down food insecurity, however). Leaner subjects had higher proportions of type I fibers compared to type II. Blacks have a lower amount of type I fibers compared to whites without adiposity even being taken into account. Not surprisingly, when the amount of type I fibers was compared by ethnicity, there was a “significant interaction” with ethnicity and obesity status when type I fibers were compared (Tanner et al, 2001). Since we know that blacks have a lower amount of type I fibers, they are more likely to be obese.

In Tanner et al’s sample, both lean blacks and whites had a similar amount of type I fibers, whereas the lean blacks possessed more type I fibers than the obese black sample. Just like there was a “significant interaction” between ethnicity, obesity, and type I fibers, the same was found for type IIb fibers (which, as I’ve covered, black Americans have more of these fibers). There was, again, no difference between lean black and whites in terms of type I fibers. However, there was a difference in type IIb fibers when obese blacks and lean blacks were compared, with obese blacks having more IIb fibers. Obese whites also had more type IIb fibers than lean whites. Put simply (and I know people here don’t want to hear this), it is easier for people with type I fibers to lose weight than those with type II fibers. This data is some of the best out there showing the relationship between muscle fiber typing and obesity—and it also has great explanatory power for black American obesity rates.

Conclusion

Muscle fiber differences between blacks and whites explain disease acquisition rates, mortality rates (Araujo et al, 2010), and differences in elite sporting competition between the races. I’ve proven that whites are stronger than blacks based on the available scientific data/strength competitions (click here for an in-depth discussion). One of the most surprising things that muscle fibers dictate is weight loss/obesity acquisition. Clearly, we need to acknowledge these differences and have differing physical activity protocols for each racial group based on their muscle fiber typing. However, I can’t help but think about the correlation between strength and mortality now. This obesity/fiber type study puts it into a whole new perspective. Those with type I fibers are more likely to be physically stronger, which is a cardioprotectant, which then protects against all-cause mortality in men (Ruiz et al, 2008; Volaklis, Halle, and Meisenger, 2015). So the fact that black Americans have a lower life expectancy as well as lower physical strength and more tpe II fibers than type I fibers shows why blacks are more obese, why blacks are not represented in strength competitions, and why blacks have higher rates of disease than other populations.The study by Tanner et al (2001) shows that there obese people are more likely to have type II fibers, no matter the race. Since we know that blacks have more type II fibers on average, this explains a part of the variance in the black American obesity rates and further disease acquisition/mortality.

The study by Tanner et al (2001) shows that there obese people are more likely to have type II fibers, no matter the race. Since we know that blacks have more type II fibers on average, this explains a part of the variance in the black American obesity rates and further disease acquisition/mortality.

Differences in muscle fiber typing do not explain all of the variance in disease acquisition/strength differences, however, understanding what the differing fiber typings do, metabolically speaking, along with how they affect disease acquisition will only lead to higher qualities of life for everyone involved.

References

Araujo, A. B., Chiu, G. R., Kupelian, V., Hall, S. A., Williams, R. E., Clark, R. V., & Mckinlay, J. B. (2010). Lean mass, muscle strength, and physical function in a diverse population of men: a population-based cross-sectional study. BMC Public Health,10(1). doi:10.1186/1471-2458-10-508

Andersen K, Lind L, Ingelsson E, Amlov J, Byberg L, Miachelsson K, Sundstrom J. Skeletal muscle morphology and risk of cardiovascular disease in elderly men. Eur J Prev Cardiol 2013.

Ama PFM, Simoneau JA, Boulay MR, Serresse Q Thériault G, Bouchard C. Skeletal muscle characteristics in sedentary Black and Caucasian males. J Appl Physiol 1986: 6l:1758-1761.

American Cancer Society. Cancer Facts & Figures for African Americans 2016-2018. Atlanta: American Cancer Society, 2016.

Ceaser, T., & Hunter, G. (2015). Black and White Race Differences in Aerobic Capacity, Muscle Fiber Type, and Their Influence on Metabolic Processes. Sports Medicine,45(5), 615-623. doi:10.1007/s40279-015-0318-7

Hagiwara N. Muscle fibre types: their role in health, disease and as therapeutic targets. OA Biology 2013 Nov 01;1(1):2.

Hernelahti, M., Tikkanen, H. O., Karjalainen, J., & Kujala, U. M. (2005). Muscle Fiber-Type Distribution as a Predictor of Blood Pressure: A 19-Year Follow-Up Study. Hypertension,45(5), 1019-1023. doi:10.1161/01.hyp.0000165023.09921.34

Hochachka, P.W. (1998) Mechanism and evolution of hypoxia-tolerance in humans. J. Exp. Biol. 201, 1243–1254

Karjalainen, J., Tikkanen, H., Hernelahti, M., & Kujala, U. M. (2006). Muscle fiber-type distribution predicts weight gain and unfavorable left ventricular geometry: a 19 year follow-up study. BMC Cardiovascular Disorders,6(1). doi:10.1186/1471-2261-6-2

Ogden C. L., Carroll, M. D., Lawman, H. G., Fryar, C. D., Kruszon-Moran, D., Kit, B.K., & Flegal K. M. (2016). Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014. JAMA, 315(21), 2292-2299.

Ruiz, J. R., Sui, X., Lobelo, F., Morrow, J. R., Jackson, A. W., Sjostrom, M., & Blair, S. N. (2008). Association between muscular strength and mortality in men: prospective cohort study. Bmj,337(Jul01 2). doi:10.1136/bmj.a439

Tanner, C. J., Barakat, H. A., Dohm, G. L., Pories, W. J., Macdonald, K. G., Cunningham, P. R., . . . Houmard, J. A. (2001). Muscle fiber type is associated with obesity and weight loss. American Journal of Physiology – Endocrinology And Metabolism,282(6). doi:10.1152/ajpendo.00416.2001

Volaklis, K. A., Halle, M., & Meisinger, C. (2015). Muscular strength as a strong predictor of mortality: A narrative review. European Journal of Internal Medicine,26(5), 303-310. doi:10.1016/j.ejim.2015.04.013

Between-group Differences in Obesity Rates

By Scott Jameson

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

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

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

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

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

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

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

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

If only such research existed!

Is Obesity Genetic? A Reply to PumpkinPerson and Robert Lindsay

1200 words

I come across a lot of ridiculous articles from PumpkinPerson, but this has to be one of the most ridiculous. He writes:

Identical twin studies show that obesity has a heritability of almost 80%. Although I generally lean towards nature in most nature-nurture debates, I’ve always had a problem with the idea that obesity is highly genetic, and thus enjoyed this epic rant by blogger Robert Lindsay:

It is 80% genetic[?]

That is why you have whole tribes in South America where not one person has ever been fat.

That is why you have whole towns in Melanesia with 1000’s of people where not one person is fat.

There are fat people in the cities of Solomon Islands. In the study I read, the only man who was fat was one who had gone off to the city for a while and ate salt and processed, packaged food. Do you realize that if you did a genetic study of the fatties in Melanesia, you would find that wonderful 80% “genetic” link you guys are shouting about?

That is why the fatness and obesity rate has exploded in the US and much of the rest of the world. Because it’s 80% genetic!

I do not believe that fatsos act just like the rest of us. Ever known a blimp who ate like a bird? Me either.

I dunno about you, but I have never seen a fat person who wasn’t stuffing their face all the time with lousy food. They are always in restaurants. Always going out to eat. If you go to a restaurant, look around at all the fat people. Those people are fat because fat people like to eat out all the time and restaurant food is fattening. Fat people love to eat. Have you ever noticed that?

It’s 80 percent heritable in first world countries. Obviously the heritability will be lower in the third world. Clearly in first-world countries we have an overabundance of food. We don’t know what to do with it. So instead of having the opposite problem (not enough food) we now have too much food and this is what caused weight to increase (along with added sugars processed carbs).

Look at Melanesia—they still eat an ancestral diet. I can’t tell if Lindsay is being serious or not. He’s comparing people who still eat their ancestral diet to people who live in first-world countries and eat a Western diet. There’s no comparison there. If you want to see why people aren’t fat nor have the same diseases at the same rates (they are low to nonexistent in places like that) read Agriculture and Diseases of Civilization

This is the study that’s being referred to Elks et al 2012. The heritability of BMI is between .75 and .82. Again: this is in first-world countries.

PP then says:

In fact just the other day, I was at the home of someone who was so incredibly fat I thought “it must be genetic.” And then just as I was leaving his house, I noticed a huge empty box of pizza in the kitchen.

Binge eating and obesity both have a heritable component (Bulk, Sullivan and Kendler, 2003). Further, to quote Gary Taubes from Why We Get Fat and What to Do About It:

“So maybe the answers to be found are in the integration of factors – starting with the physiological, metabolic, and genetic ones and letting them lead us to the environmental triggers. Because the one thing we know for sure is that the laws of thermodynamics, true as they always are, tell us nothing about why we get fat or why we take in more calories than we expend while it’s happening. (emphasis mine) (Taubes, 2011: pg 74, excerpt from Why We Get Fat and What to Do About It)

PP says:

The fatness itself or the tendency to engage in behaviors that cause fatness such as ordering large pizzas? So while obesity might technically be nearly 80% genetic, the statistic is misleading because it’s not directly genetic in the same was as height is.

If you don’t eat enough, nor get the right nutrients, you don’t hit your genetic height. If you don’t eat enough you don’t hit your genetic weight.

I don’t get why studies like this get generalized to the whole population. This study was done in first-world countries and so this only applies to first-world countries. You’d think that people who think they know science would know that studies are only applicable for the cohort and people they are done on. Guess not.

Of course I don’t deny obesity has some direct genetic component. Some people gain weight a lot easier than others and for some people, it’s virtually impossible to lose weight no matter how well they eat, though this is rare.

Of course some people gain weight easier than others. Some people lose weight easier than others. Much of the biological opposition to sustained weight loss is due to the hormone leptin (Rosenbaum et al, 2010). The more fat you have in your body, the more leptin you have. Moreover, the longer you are at a certain weight, the more likely it is that is your bodyweight set-point and thus you can only move up or down at around a range of 10 to 15 pounds. Also see this quote from neuroscientist Sandra Aamodt’s book Why Diets Make Us Fat (see her Ted Talk here):

Like nearsightedness, environmental influences on weight also mostly affect the genetically vulnerable, although we understand the details of the process in only rare cases. Fitness gains on a standardized exercise program vary from one person to another largely because of differences in their genes. When identical twins, men in their early twenties, were fed an extra thousand calories per day for about three months, each pair showed similar weight gains . In contrast, the gain varied across twin pairs, ranging from nine to twenty-nine pounds, even though the caloric imbalance was the same for everyone. An individual’s genes also influence weight loss. When another group of identical twins burned a thousand more calories per day through exercise while maintaining a stable food intake in an in-patient facility, their losses ranged from two to eighteen pounds and were even more similar within twin pairs than weight gain. (Aamodt, 2016 pg. 138)

The cold, hard truth is that dieting doesn’t have a good track record. See Mann et al (2007) here. People don’t understand the bodies’ biological processes and assume something is easy while being ignorant to how the body reacts under caloric deprivation. This wouldn’t happen if people actually had some knowledge of human physiology. Something that PP and RL lack. They are speaking about a complex problem than they’re too ignorant to really know about.  

PP then says:

“Now I have no doubt that if that person has an identical twin raised apart, he too is extremely fat, and thus fatness technically has a high heritability, but what exactly is genetic here?”

Would the identical twin be raised in an obesogenic environment? If so, there’s a high chance that, yes he’d be fat too.

It’s also true that most people who lose weight end up gaining it back, but that’s because they end up returning to their compulsive eating habits.

Most people do end up gaining it back but it has to do with biological and physiological processes; obesity has nothing to do with willpower. You can’t willpower your way to extra weight loss.

People should read a few papers and books to see some data and facts before they write what “sounds good” in their head. These two clearly have no idea what they’re talking about and clearly talking from emotion and what sounds good.

Also read Are There Genetic Causes for Obesity?

An Evolutionary Look At Obesity

2050 words

Diet is the main driver of our evolution. Without adequate energy, we wouldn’t be able to able to have a brain as large as we do that has the number of neurons we have due to how calorically expensive each neuron is (6 kcal per billion neurons). However, as I’m sure everyone can see, our current diets and environment has caused the current obesity crisis in the world. What is the cause of this? Our genomes are adapted for a paleolithic diet and not our modern environment with processed foodstuffs along with an overabundance of energy. With an overabundance of novel food items and situations due to our obesogenic environments, it is easier for a higher IQ person to stay thinner than it is for a lower IQ person. Tonight I will talk about the causes for this, how and what we evolved to eat and, of course, how to reverse this phenomenon.

“Gourmet Sapiens” arose around 1-1.5 mya with the advent of cooking by Homo erectus. Even before then, when we became bipedal our hands were freed which then allowed us to make tools. With these tools, we could mash and cut food which was a sort of pre-digestion outside the body (exactly what cooking is). Over time, our guts shrank (Aiello, 1997) and we became adapted for a certain diet (Eaton, 2006). Over time, we evolved to eat a certain way—that is, we had times of feast and famine. Due to this, eating three meals a day is abnormal from an evolutionary perspective (Mattson et al, 2014). This sets the stage for the acquisition of diseases of civilization along with the explosion of obesity rates.

When looking for the causes—and not symptoms—of the rise of obesity rates, the first thing we should do is look at our current environment. How is it constructed? What type of foodstuffs are in it? What kinds of foods get advertised to us and how does this have an effect on our psyche and what we eventually buy? All three of these questions are extremely important to think of when talking about why we are so obese as a society. First-world environments are obesogenic (Galgani and Ravussin, 2008) due to being evolutionarily novel. Our genomes are adapted to a paleolithic diet, and so the introduction of the neolithic diet and agriculture reduced our quality of life, with a marked decrease in the quality of skeletal remains discovered after the advent of agriculture. However, agriculture is obviously responsible for the population boom that allowed us to become the apes the took over the world, cause being the population boom that followed the agricultural revolution (Richards, 2002).

Evolutionary mismatches occur when the rate of cultural or technological change is far faster than the genome can change to adapt to the new pressure. These dietary mismatches occur when cultural and technological change which can vastly outstrip biological evolution. The two big events that occurred in human history that have vastly outstripped biological evolution are the agricultural and Industrial Revolution. Contrary to Ryan Faulk’s belief, East Asians are not ‘less sensitive to carbohydrates’ and he did not “solve Gary Taubes’ race problem” in regards to diabesity rates. The rate of cultural and technological change has had large deleterious effects on our quality of life, and our increasing obesity rates have a lot to do with it.

Cofnas (2016) showed that mice taken off of their ancestral diet lead to worse healthy outcomes. The results of Lamont et al (2016) show that we, as animals, are adapted for ancestral diets, not the diets of the environment we have currently made for ourselves. This is a big point to take home from this. All organisms are adapted/evolved for what occurred in the ancestral past, not any possible future events. Therefore, to be as healthy as possible, it stands to reason you should eat a diet that’s closer to the ones your ancestors ate, especially since it can reverse type II diabetes and reverse bad blood markers (Klonoff, 2009). Even a short-term switch to a paleo diet “improves BP and glucose tolerance, decreases insulin secretion, increases insulin sensitivity and improves lipid profiles without weight loss in healthy sedentary humans.” (Frassetto et al, 2009) Since we evolved for a past environment and not any possible future ones, then eating a diet that’s as close as possible to our paleolithic ancestors looks like a smart way to beat the evolutionary mismatch in terms of our new, current obesogenic environment.

In one extremely interesting study, O’dea (1984) took ten middle-aged Australian Aborigines with type 2 diabetes and had them return to their ancestral hunter-gatherer lifestyle. With seven weeks of an ancestral diet and exercise, the diabetes had almost completely reversed! Clearly, when the Aborigines were taken off of our Western diet and put back in their ancestral environment with their ancestral diet, their diabetes disappeared. If we went back to a more ancestral eating pattern, the same would happen with us. This one small study lends credence to my claim that we need to eat a diet that’s more ancestral to us for us to ameliorate diseases of civilization (Eaton, 2006).

Further, looking at obesity from an evolutionary perspective can and will help us understand the disease of obesity (Ofei, 2005) better. Speakman (2009) reviewed three different explanations of the current obesity epidemic and assessed their usefulness in explaining the epidemic. The thrifty gene hypothesis states that obesity is an adaptive trait, that people who carry so-called ‘thrifty genes’ would be at an adaptive advantage. And since we have an explosion of obesity today from the 70s to today, this must explain a large part of the variance, right? There is evidence pointing in this direction, however (Southam et al, 2009). The second cause that Speakman looks at is the adaptive viewpoint—that obesity may have never been advantageous in our history, but genes that ultimately predispose us to obesity become “selected as a by-product of selection on some other trait that is advantageous.” (Speakman, 2009) The third and final perspective he proposes is that it’s due to random genetic drift, called ‘drifty genes’, predisposing some—and not others—to obesity. Whatever the case may be, there is some truth to their being genetic factors involved in the acquisition of fat storage. Though drifty genes and the adaptive viewpoint on obesity make more sense than any thrifty gene hypothesis.

For there to be any changes in the rate of obesity in the world, we need to begin to change our obesogenic environments to environments that are more like our ancestral one in terms of what foods are available. Once we alter our obesogenic environment into one that is more ancestrally ‘normal’ (since we are adapted for our past environments and not any possible future ones) then and only then will we see a reduction in obesity around the world. We are surrounded and bombarded with ads since we are children, which then effects our choices later in life; children consume 45 percent more when exposed to advertising (Harris et al, 2009). Clearly, advertisements can have one eat more, and the whole environmental mismatch in regards to being surrounded by foodstuffs not ancestral to us causes the rate of obesity to rise.

Finally, one thing we need to look at is the n-3 to n-6 ratio of our diets. As I covered last month, the n-6/n-3 is directly related to cognitive ability (Lassek and Gaulin, 2011). Our obesogenic environments cause our n-3/n-6 levels to be thrown out of whack. Our hunter-gatherer ancestors had a 1:1 level of n-3 and n-6 (Kris-Etherton, 2000). However, today, our diets contain 14 to 25 times more n-6 than n-3!! Still wondering why we are getting stupider and fatter? Further,  Western-like diets (high in linolic acid; an n-6 fatty acid) induces a general fat mass enhancement, which is in line with the observation of increasing obesity in humans (Massiera et al, 2010). There is extreme relevance to the n-3/n-6 ratio on human health (Griffin, 2008), so to curb obesity and illness rates, we need to construct environments that promote a healthy n-3/n-6 ratio, as that will at least curb the intergenerational transmission of obesity. Lands (2015) has good advice: “A useful concept for preventive nutrition is to NIX the 6 while you EAT the 3.” Here is a good list to help balance n-6 to n-3 levels.

In sum, obesity rates are a direct product of obesogenic environments. These environments cause obesity since we are surrounded by evolutionary novel situations and food. The two events in human history that contribute to this is the agricultural and Industrial Revolution. We have paleolithic genomes in a modern-day world, which causes a mismatch between our genomes and environment. This mismatch can be ameliorated if we construct differing environments—ones that are less obesogenic with less advertisement of garbage food—and we should see rates of obesity begin to decline as our environment becomes more and more similar to our ancestral one (Genné-Bacon, 2014).

The study on mice showed that for them to be healthy they need to eat a diet that is ancestral to them. We humans are no different.The evidence from the study on Australian Aborigines and the positive things that occur after going on a paleo diet for humans—even for sedentary people—shows that for us to be as healthy as possible in these obesogenic environments that we’ve made for ourselves, we need to eat a diet that matches with our paleolithic genome. This is how we can begin to fight these diseases of civilization and heighten our quality of life.

Note: Diet and exercise only under Doctor’s supervision, of course

References

Aiello, L. C. (1997). Brains and guts in human evolution: The Expensive Tissue Hypothesis. Brazilian Journal of Genetics,20(1). doi:10.1590/s0100-84551997000100023

Cofnas, N. (2016). Methodological problems with the test of the Paleo diet by Lamont et al. (2016). Nutrition & Diabetes,6(6). doi:10.1038/nutd.2016.22

Eaton, S. B. (2006). The ancestral human diet: what was it and should it be a paradigm for contemporary nutrition? Proceedings of the Nutrition Society,65(01), 1-6. doi:10.1079/pns2005471

Frassetto, L. A., Schloetter, M., Mietus-Synder, M., Morris, R. C., & Sebastian, A. (2009). Metabolic and physiologic improvements from consuming a paleolithic, hunter-gatherer type diet. European Journal of Clinical Nutrition,63(8), 947-955. doi:10.1038/ejcn.2009.4

Galgani, J., & Ravussin, E. (2008). Energy metabolism, fuel selection and body weight regulation. International Journal of Obesity,32. doi:10.1038/ijo.2008.246

Genné-Bacon EA, Thinking evolutionarily about obesity. Yale J Biol Med 87: 99112, 2014

Griffin, B. A. (2008). How relevant is the ratio of dietary n-6 to n-3 polyunsaturated fatty acids to cardiovascular disease risk? Evidence from the OPTILIP study. Current Opinion in Lipidology,19(1), 57-62. doi:10.1097/mol.0b013e3282f2e2a8

Harris, J. L., Bargh, J. A., & Brownell, K. D. (2009). Priming effects of television food advertising on eating behavior. Health Psychology,28(4), 404-413. doi:10.1037/a0014399

Klonoff, D. C. (2009). The Beneficial Effects of a Paleolithic Diet on Type 2 Diabetes and other Risk Factors for Cardiovascular Disease. Journal of Diabetes Science and Technology,3(6), 1229-1232. doi:10.1177/193229680900300601

Kris-Etherton PM, Taylor DS,  Yu-Poth S, et al. Polyunsaturated fatty acids in the food chain in the United States.  Am J Clin Nutr, 2000, vol. 71 suppl(pg. 179S-88S)

Lamont, B. J., Waters, M. F., & Andrikopoulos, S. (2016). A low-carbohydrate high-fat diet increases weight gain and does not improve glucose tolerance, insulin secretion or β-cell mass in NZO mice. Nutrition & Diabetes,6(2). doi:10.1038/nutd.2016.

Lands, B. (2015). Choosing foods to balance competing n-3 and n-6 HUFA and their actions. Ocl,23(1). doi:10.1051/ocl/2015017

Lassek, W. D., & Gaulin, S. J. (2011). Sex Differences in the Relationship of Dietary Fatty Acids to Cognitive Measures in American Children. Frontiers in Evolutionary Neuroscience,3. doi:10.3389/fnevo.2011.00005

Massiera, F., Barbry, P., Guesnet, P., Joly, A., Luquet, S., Moreilhon-Brest, C., . . . Ailhaud, G. (2010). A Western-like fat diet is sufficient to induce a gradual enhancement in fat mass over generations. The Journal of Lipid Research,51(8), 2352-2361. doi:10.1194/jlr.m006866

Mattson, M. P., Allison, D. B., Fontana, L., Harvie, M., Longo, V. D., Malaisse, W. J., . . . Panda, S. (2014). Meal frequency and timing in health and disease. Proceedings of the National Academy of Sciences,111(47), 16647-16653. doi:10.1073/pnas.1413965111

O’dea, K. (1984). Marked improvement in carbohydrate and lipid metabolism in diabetic Australian aborigines after temporary reversion to traditional lifestyle. Diabetes,33(6), 596-603. doi:10.2337/diabetes.33.6.596

Ofei F. Obesity- a preventable disease. Ghana Med J 2005;39: 98-101

Richards, M. P. (2002). A brief review of the archaeological evidence for Palaeolithic and Neolithic subsistence. European Journal of Clinical Nutrition,56(12), 1270-1278. doi:10.1038/sj.ejcn.1601646

Southam, L., Soranzo, N., Montgomery, S. B., Frayling, T. M., Mccarthy, M. I., Barroso, I., & Zeggini, E. (2009). Is the thrifty genotype hypothesis supported by evidence based on confirmed type 2 diabetes- and obesity-susceptibility variants? Diabetologia,52(9), 1846-1851. doi:10.1007/s00125-009-1419-3

Speakman, J. R. (2013). Evolutionary Perspectives on the Obesity Epidemic: Adaptive, Maladaptive, and Neutral Viewpoints. Annual Review of Nutrition,33(1), 289-317. doi:10.1146/annurev-nutr-071811-150711

Ethnic Differences in Sleep, Obesity, and Metabolic Syndromes

2300 words

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

Sleep Differences

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

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

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

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

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

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

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

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

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

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

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

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

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

Obesity and Sleep

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

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

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

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

And from Harvard:

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

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

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

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

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

Metabolic Syndrome

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

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

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

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

7.6% of non-Hispanic whites

9.0% of Asian Americans

12.8% of Hispanics

13.2% of non-Hispanic blacks

15.9% of American Indians/Alaskan Natives

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

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

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

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

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

Metabolic Syndrome and Obesity

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

 

Race, Obesity, Poverty, and IQ

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

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

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

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

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

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

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

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

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

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

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

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

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

delprato_24

(source)

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

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

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

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

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

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

Dysgenic Fertility and America’s Obesity Crisis

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The dysgenic trend currently occurring in America has implications for obesity as well. Since intelligence is negatively correlated with obesity, as America’s average IQ decreases, the rates of obesity in our country will increase. This is due to the high correlation between intelligence and obesity. As we continue to allow unfettered immigration into America, the average IQ of the country will decrease, while the amount of people that are overweight and obese will increase.

The ethnic differences in obesity rates lead more credence to what I am saying. As the demographics shift, more people will be overweight or obese due to having a lower IQ. Whites, too, are experiencing this dysgenic effect, as intelligent people of all ethnicities are not reproducing. As more and more genetically less fit individuals continue to have a higher rate of reproduction in comparison to intelligent individuals, this crisis will continue to persist.

Those with lower intelligence have less of an ability to delay gratification, which has a strong genetic component. As more people breed who cannot delay their gratification, the rates of obesity will increase in the country. Of course, the lack of ability to delay gratification comes with a lowered IQ. This is what we see in regards to sex. Those with higher IQs lose their virginities at a later age in comparison to those with lower IQs. Along with the data from Kanazawa that shows that more intelligent people have a lower BMI than those with lower intelligence, this study gives more credence to the theory that those with higher levels of intelligence can better delay their gratification.

JayMan says that there is evidence for an increased genetic load for those with lower IQs, which we can then reason that this also leads to a higher prevalence for obesity in low IQ populations. JayMan then says that many of the genes found to influence obesity seem to operate in the brain and that they have a pleiotropic effect, meaning that multiple genes affect one or more traits. With the increased genetic load comes with an increased chance to have a lower IQ and become obese, as these two things correlate with the lack of ability to delay gratification.

Of course, these problems persist due to modern medicine. With the advent of better medicine, it allowed us to beat diseases that formerly would have been devastating to the population at large. This led to an increase of alleles with negative effects in the population that continue to pass down through the generations. Along with these advances in medical technology, welfare and other government-funded programs also enable those that are less genetically fit. Since intelligence is correlated with ability to care for offspring, as well as r- and K-selected traits, those with lower intelligence exhibit more r-selected traits. This is why America is facing a dysgenic fertility crisis. Welfare props up those with less intelligence, giving them more incentives to breed. They then breed more low IQ children who then will live off of the government. This vicious cycle then continues unfettered due to how America’s dysgenic welfare structure is implemented.

Before the advent of modern technology, those who were less genetically fit didn’t survive to pass on their genes. But, in the modern day with all of our superior technology, this allows the less intelligent to breed when in the past they would have been selected out of the gene pool due to being less biologically fit.

Another variable that is involved with the dysgenic fertility of America is Mexican immigration. With the influx of illegal (and legal) peoples from the South of the Border, this is having both dysgenic effect on both the average intelligence of our country along with the average BMI. The average BMI for the average American male is 28.6In the 1950s, 10 percent of American adults were obese compared to 35 percent of American adults today. Now, this has to do with ability to access food, as well as the effect of the media on children has a huge effect on obesity, due in part to not getting a full nights sleep, as that is correlated with obesity. However, an increase in genetic load, which also comes with a decrease in intelligence, has a lot to do with this as well. The increase in the BMI of the average American has to do with immigration as well. The rates of obesity for different ethnicities in America are as follows: 67.3% for whites, 75.6% for blacks, and 77.9% for ‘Hispanics’. So of course, with more immigration from the South of the Border, the average IQ for America is decreasing while obesity rates are increasing, due mostly to this illegal immigration.

Height and intelligence are both correlated. Ever since the advent of the industrial revolution, we have had an excess surplus of food. As Gina Kolata says in her book Rethinking Thin, an increase in obesity is inevitable. She says this since the increase in genetic height and IQ has occurred, so the increase in obesity follows with it. We need to influence those with higher IQs to have more children. Further, we also need to restrict immigration to only high-skilled immigrants (only when necessary) to reverse this trend that has been occurring since the 1960s. Though, with higher levels of intelligence one can forgo their urges and live a healthier lifestyle due to having higher cognition which leads to a better ability to delay gratification than one with lower intelligence.

Those with higher IQs make better choices on what to eat than those with lower IQs. This is shown in the BMIs of the intelligent and non-intelligent population. As more and more people with lower genotypic IQ come into the country, the quality of life will decrease as will the average intelligence of the country. In turn, the BMI of the average American will increase along with the decrease of our country’s average intelligence. To ameliorate this, we need to have extremely stringent criteria on who we allow into the country. An IQ test, to start, would be a good idea. As those with higher intelligence have less of a genetic load and have less of a chance of becoming obese than one with a lower IQ, the current dysgenic effect that this unfettered immigration is having on America can be lessened.

Obesity and Intelligence

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[Edit: My view here has changed, read my recent article Is Diet an IQ Test?  It isn’t and it is, of course, much more nuanced than ‘IQ’ (which is a proxy for social class’ leading to obesity which would imply lack of funds and education on what and when to eat. Obesity is much more complex than ‘IQ’, numerous other variables come into play and since ‘IQ’ (which is just a proxy for general knowledge ‘is low then the individual in question won’t know what and when to eat and since this occurs in low income families more often than not who have low IQs then this effects them the most.] 

The relationship between intelligence and obesity is often misinterpreted. Numerous studies have concluded that becoming obese leads to a drop in IQ. This mistake happens due to improper interpretation of cross-sectional studies. However, analyses of population-based, longitudinal data show that low intelligence from birth causes obesity. No credible evidence exists for obesity lowering intelligence. There are, however, mountains of evidence showing that low intelligence from childhood leads to obesity (Kanazawa, 2014).

Kanazawa (2014), reviewed the data on the research between obesity and IQ. What he found was that those studies that concluded that obesity causes lowered intelligence only observed cross-sectional studies. Longitudinal studies that looked into the link between obesity and intelligence found that those who had low IQs since childhood then became obese later in life and that obesity does not lead to low IQ. Those with IQs below 74 gained 5.19 BMI points, whereas those with IQs over above 126 gained 3.73 BMI points in 22 years, which is a statistically significant difference. Also noted, was that those at age 7 who had IQs above 125 had a 13.5 percent chance of being obese at age 51, whereas those with IQs below 74 at age 7 had a 31.9 percent chance of being obese. This data makes it clear: low IQ is correlated with obesity, so we, therefore, need to find sufficient measures to help those with lower IQs to learn how to manage their weight.  Moreover, the lack of ability to delay gratification is also correlated with low IQ (Mischel, Ebbeson, and Zeiss, 1972).

Less intelligent individuals are more likely to become obese than those who are more intelligent. With what we know about low IQ people and how there is a strong relationship between low intelligence and lack of ability to delay gratification, we can see how this lack of thought for future problems for their actions in the present can manifest itself in obesity.

This study claims that there is a link between morbid obesity and a drop in IQ. The researchers compared 24 children who weighed 150 percent of their bodyweight before age 4 with 19 children and adults with Prader Willi’s Syndrome, using 24 siblings as controls as “they share the same socioeconomic environment and genetics”. Prader Willi’s Syndrome (PWS) is a chromosomal disorder in which chromosome 15 is deleted. They have an almost insatiable desire to eat,which can cause one suffering from PWS to eat themselves to death. Those with PWS were found to have an IQ of 63, while those who became obese were found to have an IQ of 78 with the control siblings having an IQ of 106. The researchers were surprised to see such a difference in IQ between siblings. They then state that this could be one facet of obesity that could be irreversible. MRI scans of the cohort discovered white matter lesions on the subjects with PWS and early-onset obesity. The researcher says that these lesions could affect food seeking centers in the brain leading to a want to gorge on food. Seeing how those with PWS eat when unsupervised, this is an interesting hypothesis.

This study compared 49 teens with metabolic syndrome and 62 peers without the disorder, while controlling for socioeconomics status. They found significantly lower scores in arithmetic, attention and attention span, spelling, mental flexibility and regions of the brain with lower volumes of matter in the hippocampus and white matter integrity.

There are a few problems with these two studies. In a population-representative birth cohort study of 1037 children, it was found that cohort members who became obese had a low IQ, as expected. But, contrary to what your study said, cohort members didn’t exhibit a decline in IQ from becoming obese, they instead had a lower IQ since childhood. There is no evidence of obesity contributing to a decline in IQ, even in obese individuals and those on the verge of metabolic syndrome. Another problem is that they wrongly conclude that obesity leads to lowered intelligence, completely misinterpreting the extremely strong negative correlation between obesity and intelligence.

This study shows how obese mothers give birth to less intelligent children. In an observational study (already garbage), the researchers took 3412 participants and found a strong relationship with pre-pregnancy obesity and math and reading scores in children. For math, a 3 percent reduction was observed. There was a 3-point drop in reading scores with math scores showing a decline of 2 points. These differences are within the normal variation between tests, so it’s nothing to take note of. Also, this is an observational study. I have shown above that longitudinal studies are superior for this, as well as researchers misinterpreting the results found from their studies.

There is a strong relationship between parental years of education and childhood obesity. Since the mother’s IQ is the most important predictor of a child’s IQ *, that passes on to the child as well. (BMI is also 80 percent heritable). **.

So because of those factors involving the mother and child, that is what accounts for it. Not the environmental factors brought up.

This study claims that overweight parents are more likely to fail. This is all due to the fact that low IQ people are more likely to be obese or overweight, with heritability of BMI being .8, you can see how low IQ is the cause of both of those variables. 

This shows that binge eating is linked to memory loss. I heard about a study a few months ago actually like this. Rats were fed high fat diets and they noticed that the brain microglia actually started to eat neuronal pathways actually leading to a decrease in cognitive ability. But they said that returning to a new diet will stop its effects. Researchers say the negative cognitive effects are reversible, but I already gave the citstion about obesity not being linked to decreased IQ. I should also note that this study was carried out on rats and while this may be a factor for humans as well, a few studies need to be done.

Binge eating, however, actually has a genetic component. Though this was only observed in girls. One reason I can think of for this is that women need higher body fat for a leptin release so puberty can begin so they can bear children.

This article purports to show 5 ways obesity affects the brain. Obesity does cause food addiction, however, those who lack the ability to delay gratification are more likely to not be able to control their impulse to overeat. I always link to the MRI scan showing the control, obese and cocaine user’s brain. Interesting to see that sugar is just as addictive as cocaine. Obesity doesn’t make us more impulsive. Check out the Marshmallow Experiment, as well as its follow-up studies. Those who are more impulsive are more likely to be obese, as well as have lower SAT scores.

Satoshi Kanazawa also noted that childhood IQ predicted whether or not one would become obese at the age of 51. General intelligence in childhood has a direct effect on weight gain, BMI, and obesity, net of parents education and SES, parents BMI, the child’s social class, and sex. More intelligent children grew up to make healthier choices, and therefore stayed leaner than those children who were less bright. The link between childhood obesity and intelligence also shows that the effect between childhood is unmediated by education of income. Meaning, those with lower IQs in a higher socioeconomic bracket STILL have the same chance of becoming obese as those in the lower socioeconomic bracket. Finally, parental BMI itself is a consequence of parental general intelligence, which the parents pass on to their children. This shows the extremely high heritability of obesity as well as showing how intelligence plays a factor in the causes of obesity.

The known differences in ethnic obesity rates generally mirror the intelligence of those populations. All populations are showing a sharp dysgenic decline, which coincides with a more obese population as well. Sociologists and the like may say that those who are poor cannot afford the same types of food that those who have more wealth can. However, this is a false statement. Whole foods are not more expensive. The conclusion that was (obviously) reached is that there is expensive and non-expensive junk food as well as whole foods. Natural diets will not cost more, all things being equal. If you know how to eat and how to buy food, you will avoid spending too much money. This goes back to intelligence. One with a higher IQ will be able to think of what his present actions will lead to in the future while those with a lower IQ live in the now without a care for the future, which then manifests itself in their obesity.

There are numerous articles showing that the causality for low intelligence is not becoming obese, but that those who become obese have a lower IQ since childhood. Longitudinal studies show the relationship, while observational studies show that obesity drops intelligence. Clearly, observational studies are inferior for seeing the relationship between IQ and obesity. This then leads to researchers misinterpreting the data and drawing wrong conclusions.

The IQ of the mother is the most important factor in determining the future intelligence of the child.

** This is a great one. In a meta-analysis of twin and family studies, including mono and dizygotic twin studies, with a sample of 140,525 people, heritability of BMI was found to be between .75 and .82. Both extremely high correlations. Since the heritability of intelligence as well as height (another good predictor of intelligence), there is good evidence for the claim that becoming obese is due to lower childhood IQ, which is genetic in nature.