Latitude and cancer rates in US states: Aaron Blaisdell’s intuition confirmed

In the comments section of my previous post on cancer rates in the US states () my friend Aaron Blaisdell noted that: …comparing states that are roughly comparable in terms of number of seniors per 1000 individuals, latitude appears to have the largest effect on rates of cancer.

Good point, so I collected data on the latitudes of US states, built a more complex model (with several multivariate controls), and analyzed it with WarpPLS 4.0 ().

The coefficient of association for the effect of latitude on cancer rates (path coefficient) turned out to be 0.35. Its P value was lower than 0.001, meaning that the probability that this is a false positive is less than a tenth of a percent, or that we can be 99.9 percent confident that this is not a false positive.

This was calculated controlling for the: (a) proportion of seniors in the population (population age); (b) proportion of obese individuals in the population (obesity rates); and (c) the possible moderating effect of latitude on the effect of population age on cancer rates. The graph below shows this multivariate-adjusted association.



What is cool about a multivariate analysis is that you can control for certain effects. For example, since we are controlling for proportion of seniors in the population (population age), the fact that we have a state with a very low proportion of seniors (Alaska) does not tilt the effect toward that outlier as much as it would if we had not controlled for the proportion of seniors. This is a mathematical property that is difficult to grasp, but that makes multivariate adjustment such a powerful technique.

I should note that the 99.9 percent confidence mentioned above refers to the coefficient of association. That is, we are quite confident that the coefficient of association is not zero; that is it. The P value does not support the hypothesized direction of causality (latitude -> cancer) or exclude the possibility of a major confounder causing the effect.

Nonetheless, among the newest features of WarpPLS 4.0 (still a beta version) are several causality assessment coefficients: path-correlation signs, R-squared contributions, path-correlation ratios, path-correlation differences, Warp2 bivariate causal direction ratios, Warp2 bivariate causal direction differences, Warp3 bivariate causal direction ratios, and Warp3 bivariate causal direction differences. Without going into a lot of technical detail, which you can get from the User Manual () without even having to install the software, I can tell you that all of these causality assessment coefficients support the hypothesized direction of causality.

Also, while we cannot exclude the possibility of a major confounder causing the effect, we included two possible confounders in the analysis and controlled for their effects. They were the proportion of seniors in the population (population age) and the proportion of obese individuals in the population (obesity rates).

Having said all of the above, I should also say that the effect is similar in magnitude to the effect of population age on cancer rates, which I discussed in the previous post linked above. That is, it is not the type of effect that would be clearly noticeable in a person’s normal life.

Sunlight exposure? Maybe.

We do know that our body naturally produces as much as 10,000 IU of vitamin D based on a few minutes of sun exposure when the sun is high (). Getting that much vitamin D from dietary sources is very difficult, even after “fortification”.

Trip to South Korea: Hidden reasons for the leanness of its people

In September last year (2012) I went to South Korea to speak about nonlinear data analysis with WarpPLS (), initially for business and engineering faculty and students at Korea University in Seoul, and then as a keynote speaker at the HumanCom 2012 Conference () in Gwangju. Since Seoul is in the north part of the country, and Gwangju in the south, I had the opportunity to see quite a lot of the land and the people in this beautiful country.


(Korea University’s main entrance, Anam campus)


(In front of Korea University’s main Business School building)

Korea University is one of the most prestigious universities in South Korea. In the fields of business and engineering, it is arguably the most prestigious. It also has a solid international reputation, attracting a large number of highly qualified foreign students.

I wanted to take this opportunity and try to understand why obesity prevalence is so low in South Korea, which is a common characteristic among Southeast Asian countries, even though the caloric intake of South Koreans seems to be relatively high. Foods that are rich in carbohydrates, such as rice, are also high-calorie foods. At 4 calories per gram, carbohydrates are not as calorie-dense as fats (9 calories per gram), but they sure add up and can make one obese.

Based on my observations, explanations for the leanness that are too obvious or that focus on a particular dietary item (e.g., kimchi, green tea etc.) tend to miss the point.

Let us take for example a typical South Korean meal, like the one depicted in the photos below, which we had at a restaurant in Seoul. If you are a foreigner, this type of meal would be difficult to have without a local accompanying you, because it is not easy to make yourself understood in a traditional restaurant in South Korea speaking anything other than Korean.


(Main items of a traditional South Korean meal)


(You cook your own meal)

The meal started with thin-sliced meat (with some fat, but not much) and vegetables, with the obligatory side dishes, notably kimchi (). This part of the meal was low in calories and high in nutrients. Then we had two high-calorie low-nutrient items: noodles and rice. The rice was used in the end to soak up the broth left in the pot, so it ended adding to the nutrition value of the meal.

Because we started the meal with the low-calorie high-nutrient items, the meat and vegetables, our consumption of noodles and rice was not as high as if we had started the meal with those items. In a meal like this, a good chunk of calories would come from the carbohydrate-rich items. Still, it seems to me that we ingested plenty of calories, enough to make one fat over the long run, eating these types of meals regularly.

A side note. As I said here before, the caloric value of protein is less than the commonly listed 4 calories per gram, essentially because protein is a multi-purpose macronutrient.

In our meal, the way in which at least one of the carbohydrate-rich items was prepared possibly decreased its digestible carbohydrate content, and thus its calorie content, in a significant way. I am referring to the rice, which had been boiled, cooled and stored, way before it was re-heated and served. This likely turned some of its starch content into resistant starch (). Resistant starch is essentially treated by our digestive system as fiber.

Another factor to consider is the reduction in the glycemic load (not to be confused with glycemic index) of the rice. As I noted, the rice was used to soak up the broth from the pot. This soaking up process significantly reduces the rice’s glycemic load, because of a unique property of rice. It has an amazing capacity of absorbing liquid and swelling in the process.

This was one of several traditional Korean meals I had, and all of them followed a similar pattern in terms of the order in which the food items were consumed, and the way in which the carbohydrate-rich items were prepared. The order in which you eat foods affects your calorie intake because if you eat high nutrient-to-calorie ratio foods before, and leave the low nutrient-to-calorie ones for later, my experience is that you will eat less of the latter.

Another possible hidden reason for the low rate of obesity in South Korea is what seems to be a cultural resistance to industrialized foods, particularly among older generations; a sort of protective cultural inertia, if you will. Those foods are slowly being adopted – my visit left me with that impression – by not as quickly as in other countries. And there is overwhelming evidence that consumption of highly industrialized foods, especially those rich in refined carbohydrates and sugars, is a major cause obesity and a host of other problems.

Cultural resistance to, or cultural inertia against the adoption of, highly industrialized foods among pregnant mothers limits one’s exposure to those foods at a particularly critical time in one’s life – the 9-month gestation period in the mother’s womb. This could have a major impact on a person’s propensity to become obese or have other metabolic derangements later on in life. Some refer to this phenomenon as a classic example of modern epigenetics, whereby acquired traits appear to induce innate traits across generations.

Another reason I was excited about this trip to South Korea was my interest in table tennis. I wanted to know more about their table tennis “culture”, and how it was influenced by their general culture. China dominates modern table tennis, with such prodigies as Ma Lin, Ma Long, Wang Hao, Wang Liqin, and Zhang Jike. South Korea is not far behind; two of my all-time favorite South Korean players are Kim Taek-Soo and former Olympic champion Ryu Seung-Min.

Another side note. The best table tennis player of all time is arguably Jan-Ove Waldner (), from Sweden. I talked about him in my book on compensatory adaptation (). Waldner has been one of the few players outside China to be able to consistently beat the best Chinese players at times when they were at the top of the games, including Ma Lin ().

But, as I soon learned, as far as sports are concerned, it is not table tennis that most South Koreans are interested in these days. It is soccer.

A nice surprise during this trip was a tour in Gwangju in which we visited a studio that converted standard movies to stereoscopic three-dimensional ones (photo below). These folks were getting a lot of business, particularly from the USA, in a market that is very competitive.


(A standard-to-3D movie conversion studio in Gwangju)

Let’s get back to the health angle of the post. So there you have it, two possible “hidden” reasons for the low prevalence of obesity in South Korea, and maybe in other Southeast Asian countries. One is the way in which foods are prepared and consumed, and the other is cultural inertia. These are not very widely discussed, but future research may change that.

Drs. Francisco Cervantes and Marivic Torregosa, and the 2013 Ancestral Health Symposium

Last year I traveled to South Korea to give presentations on nonlinear structural equation modeling and WarpPLS (). These are an advanced statistical analysis technique and related software tool, respectively, which have been used extensively in this blog to analyze health data, notably data related to the China Study.

I gave a couple of presentations at Korea University, which is in Seoul, and a keynote address at a conference in Gwangju, in the south part of the country. So I ended up seeing quite a lot of this beautiful country, and meeting many people. Some of my impressions regarding health and lifestyle issues need separate blog posts, which are forthcoming.

One issue that kept me thinking, as it did when I visited Japan a few years ago as well, was the obvious leanness of the South Koreans, compared with Americans, even though you don’t see a lot of emphasis on dieting there. Interestingly, this phenomenon also poses a challenge to many dietary schools of thought. For example, consumption of high-glycemic-index carbohydrates seems to be relatively high in South Korea.

The relative leanness of South Koreans is probably due to a combination of factors. A major one, it seems, is often forgotten. It is related to epigenetics. This term, “epigenetics”, is often assigned different meanings depending on the context in which it is used. Here it is used to refer to innate predispositions that don’t have a primarily genetic basis.

Epigenetic phenomena often give the impression that acquired characteristics can be inherited, and are frequently, and misguidedly, used as examples in support of a theory often associated with Jean-Baptiste Pierre Antoine de Monet, better known as Lamarck.

A classic example of epigenetics, in this context, is that of a mother with type II diabetes giving birth to a child that will develop type II diabetes at a young age. Typically type II diabetes develops in adults, but its incidence in children has been increasing lately, particularly in certain areas. And I think that this classic example is in part related to the general leanness of South Koreans and of people in other cultures where adoption of highly industrialized foods has been relatively slow.

In other words, I think that it is possible that a major protection in South Korea, as well as in Japan and other countries, is the cultural resistance, particularly among older generations, against adopting modern diets and lifestyles that deviate from their traditional ones.

This brings me to Drs. Francisco Cervantes and Marivic Torregosa (pictured below). Dr. Cervantes is the Chief Director of Laredo Pediatrics and Neonatology, a pediatrician who studied and practiced in a variety of places, including Mexico, New Jersey, and Texas. Dr. Torregosa is a colleague of mine, a college professor and nurse practitioner in Laredo, with a Ph.D. in nursing and a research interest in child obesity.



As it turns out, Laredo, a city in Southwestern Texas near the border with Mexico, seems like the opposite of South Korea in terms of health, and this may well be related to epigenetics. This presents an enormous opportunity for research, and for helping people who really need help.

In Laredo, as well as in other areas where insulin resistance and type II diabetes are rampant, there is a great deal of variation in health. There are very healthy folks in Laredo, and very sick ones. This great deal of variation is very useful in the identification of causative factors through advanced statistical analyses. Lack of variation tends to have the opposite effect, often “hiding” causative effects.

Drs. Cervantes, Torregosa, and I had a presentation accepted for the 2013 Ancestral Health Symposium (). It is titled “Gallbladder Disease in Children: Separating Myths from Facts”. It is entirely based on data collected and analyzed by Dr. Cervantes, who is very knowledgeable about statistics. Below is the abstract.

Cholesterol’s main role in the body is to serve as raw material for bile acids; the conversion of cholesterol to bile acids by the liver accounts for approximately 70 percent of the daily disposal of cholesterol. Bile acids are then stored in the gallbladder and secreted to aid in the digestion of dietary fat. It is often believed that high cholesterol levels cause gallbladder disease. In this presentation, we will discuss various aspects of gallbladder disease, with a focus on children. The presentation will be based on data from 2116 patients of the Laredo Pediatrics & Neonatology. The patients, 1041 boys and 1075 girls, are largely first generation American-born children of Hispanic descent; a group at very high risk of developing gallbladder disease. This presentation will dispel several myths, and lay out a case for a strong association between gallbladder disease and abnormally high body fat levels. Gallbladder disease appears to be largely preventable in children through diet and lifestyle modifications, some of which will be discussed during the presentation.

Many people seem to be unaware of the fact that cholesterol production and disposal are strongly associated with secretion of bile acids. Most of the body's cholesterol is used to produce bile acids, which are reabsorbed from the gut, in a cyclical process. This is the reason behind the use of "bile acid sequestrants" to reduce cholesterol levels.

The focus on gallbladder disease in the presentation comes from an interest by Dr. Cervantes, based on his many years of clinical experience, in using gallbladder disease markers to identify and prevent other conditions, including several conditions associated with what we refer to as diseases of affluence or civilization.

Dr. Cervantes is unique among clinical practitioners in that he spends a lot of time analyzing data from his patients. His knowledge of data analyses techniques rivals that of many professional researchers I know. And he does that at his own expense, something that most clinical practitioners are unwilling to do. Dr. Cervantes and I will be co-authoring blog posts here in the future.

The 2013 PLoS ONE sugar and diabetes study: Sugar from fruits is harmless

A new study linking sugar consumption with diabetes prevalence has gained significant media attention recently. The study was published in February 2013 in the journal PLoS ONE (). The authors are Sanjay Basu, Paula Yoffe, Nancy Hills and Robert H. Lustig.

Among the claims made by the media is that “… sugar consumption — independent of obesity — is a major factor behind the recent global pandemic of type 2 diabetes” (). As it turns out, the effects revealed by the study seem to be very small, which may actually be a side effect of data aggregation; I will discuss this further below.

Fruits are exonerated

Let me start by saying that this study also included in the analysis the main natural source of sugar, fruit, as a competing variable (competing with the effects of sugar itself), and found it to be unrelated to diabetes. As the authors note: “None of the other food categories — including fiber-containing foods (pulses, nuts, vegetables, roots, tubers), fruits, meats, cereals, and oils — had a significant association with diabetes prevalence rates”.

This should not surprise anyone who has actually met and talked with Dr. Lustig, the senior author of the study and a very accessible man who has been reaching out to the public in a way that few in his position do. He is a clinician and senior researcher affiliated with a major university; public outreach, in the highly visible way that he does it, is probably something that he does primarily (if not solely) to help people. Dr. Lustig was at the 2012 Ancestral Health Symposium, and he told me, and anyone who asked him, that sugar in industrialized foods was his target, not sugar in fruits.

As I noted here before, the sugar combination of fruits, in their natural package, may in fact be health-promoting (). The natural package probably promotes enough satiety to prevent overconsumption.

Both (unnatural) sugar and obesity have effects, but they are tiny in this study

The Diabetes Report Card 2012 () provides a wealth of information that can be useful as a background for our discussion here.

In the USA, general diabetes prevalence varies depending on state, with some states having higher prevalence than others. The vast majority of diabetes cases are of type 2 diabetes, which is widely believed to be strongly associated with obesity.

In 2012, the diabetes prevalence among adults (aged 20 years or older) in Texas was 9.8 percent. This rate is relatively high compared to other states, although lower than in some. So, among a random group of 1,000 adult Texans, you would find approximately 98 with diabetes.

Prevalence increases with age. Among USA adults in general, prevalence of diabetes is 2.6 percent within ages 20–44, 11.7 percent within ages 45–64, and 18.9 percent at age 64 or older. So the numbers above for Texas, and prevalence in almost any population, are also a reflection of age distribution in the population.

According to the 2013 study published in PLoS ONE, a 1 percent increase in obesity prevalence is associated with a 0.081 percent increase in diabetes prevalence. This comes directly from the table below, fifth column on the right. That is the column for the model that includes all of the variables listed on the left.



We can translate the findings above in more meaningful terms by referring to hypothetical groups of 1,000 people. Let us say we have two groups of 1,000 people. In one of them we have 200 obese people (20 percent); and no obese person in the other. We would find only between 1 and 2 people with diabetes in the group with 200 obese people.

The authors also considered overweight prevalence as a cause of diabetes prevalence. A section of the table with the corresponding results in included below. They also found a significant effect, of smaller size than for obesity – which itself is a small effect.



The study also suggests that consumption of the sugar equivalent of a 12 oz. can of regular soft drink per person per day was associated with a 1.1 percent rise in diabetes prevalence. The effect here is about the same as that of a 1 percent increase in obesity.

That is, let us say we have two groups of 1,000 people. In one of them we have 200 people (20 percent) consuming one 12 oz. can of soft drink per day; and no one consuming sugar in the other. (Sugar from fruits is not considered here.) We would find only about 2 people with diabetes in the group with 200 sugary soda drinkers.

In other words, the effects revealed by this study are very small. They are so small that their corresponding effect sizes make them borderline irrelevant for predictions at the individual level. Based on this study, obesity and sugar consumption combined would account for no more than 5 out of each 100 cases of diabetes (a generous estimate, based on the results discussed above).

Even being weak, the effects revealed by this study are not irrelevant for policy-making, because policies tend to influence the behavior of very large numbers of people. For example, if the number of people that could be influenced by policies to curb consumption of refined sugar were 100 million, the number of cases of diabetes that could be prevented would be 200 thousand, notwithstanding the weak effects revealed by this study.

Why are the effects so small?

The effects in this study are based on data aggregated by country. When data is aggregated by population, the level of variation in the data is reduced; sometimes dramatically, a problem that is proportional to the level of aggregation (e.g., the problem is greater for country aggregation than for city aggregation).

Because there can be no association without correlation, and no correlation without variation, coefficients of association tend to be reduced when data aggregation occurs. This is, in my view, the real problem behind what statisticians often refer to, in “statospeech”, as “ecological fallacy”. The effects in aggregated data are weaker than the effects one would get without aggregation.

So, I suspect that the effects in this study, which are fairly weak at the level of aggregation used (the country level), reflect much stronger effects at the individual level of analysis.

Bottom line

Should you avoid getting obese? Should you avoid consuming industrialized products with added sugar? I think so, and I would still have recommended these without this study. There seems to be no problem with natural foods containing sugar, such as fruits.

This study shows evidence that sugar in industrialized foods is associated with diabetes, independently from obesity, but it does not provide evidence that obesity doesn’t matter. It shows that both matter, independently of one another, which is an interesting finding that backs up Dr. Lustig’s calls for policies to specifically curb refined sugar consumption.

Again, what the study refers to as sugar, as availability but implying consumption, seems to refer mostly to industrialized foods where sugar was added to make them more enticing. Fruit consumption was also included in the study, and found to have no significant effect on diabetes prevalence.

Here is a more interesting question. If a group of people have a predisposition toward developing diabetes, due to any reason (genetic, epigenetic, environmental), what would be the probability that they would develop diabetes if they became obese and/or consumed unnatural sugar-added foods?

This type of question can be answered with a moderating effects analysis, but as I noted here before (), moderating effects analyses are not conducted in health research.

The man who ate 25 eggs per day: What does this case really tell us?

Many readers of this blog have probably heard about the case of the man who ate approximately 25 eggs (20 to 30) per day for over 15 years (probably well over), was almost 90 years old (88) when the case was published in the prestigious The New England Journal of Medicine, and was in surprisingly good health ().

The case was authored by the late Dr. Fred Kern, Jr., a widely published lipid researcher after whom the Kern Lipid Conference is named (). One of Kern’s research interests was bile, a bitter-tasting fluid produced by the liver (and stored in the gallbladder) that helps with the digestion of lipids in the small intestine. He frames the man’s case in terms of a compensatory adaptation tied to bile secretion, arguing that this man was rather unique in his ability to deal with a lethal daily dose of dietary cholesterol.

Kern seemed to believe that dietary cholesterol was harmful, but that this man was somehow “immune” to it. This is ironic, because often this case is presented as evidence against the hypothesis that dietary cholesterol can be harmful. The table below shows the general nutrient content of the man’s daily diet of eggs. The numbers in this and other tables are based on data from Nutritiondata.com (), in some cases triangulated with other data. The 5.3 g of cholesterol in the table (i.e., 5,300 mg) is 1,775 percent the daily value recommended by the Institute of Medicine of the U.S. National Academy of Sciences ().

As you can see, the man was on a very low carbohydrate diet with a high daily intake of fat and protein. The man is described as an: “… 88-year-old man who lived in a retirement community [and] complained only of loneliness since his wife's death. He was an articulate, well-educated elderly man, healthy except for an extremely poor memory without other specific neurologic deficits … His general health had been excellent, without notable symptoms. He had mild constipation.”

The description does not suggest inherited high longevity: “His weight had been constant at 82 to 86 kg (height, 1.87 m). He had no history (according to the patient and his personal physician of 15 years) of heart disease, stroke, or kidney disease … The patient had never smoked and never drank excessively. His father died of unknown causes at the age of 40, and his mother died at 76 … He kept a careful record, egg by egg, of the number ingested each day …”

The table below shows the fat content of the man’s daily diet of eggs. With over 14 g of omega-6 fat intake every day, this man was probably close to or in “industrial seed oils territory” (), as far as daily omega-6 fat intake is concerned. And the intake of omega-3 fats, at less than 1 g, was not nearly enough to balance it. However, here is a relevant fact – this man was not consuming any industrial seed oils. He liked his eggs soft-boiled, which is why the numbers in this post refer to boiled eggs.

This man weighed between 82 to 86 kg, which is about 180 to 190 lbs. His height was 1.87 m, or about 6 ft 1 in. Therefore his body mass index varied between approximately 23 and 25, which is in the normal range. In other words, this person was not even close to obese during the many years he consumed 25 eggs or so per day. In the comments section of a previous post, on the sharp increase in obesity since the 1980s (), several readers argued that the sharp increase in obesity was very likely caused by an increase in omega-6 fat consumption.

I am open to the idea that industrialized omega-6 fats played a role in the sharp increase in obesity observed since the 1980s. When it comes to omega-6 fat consumption in general, including that in “more natural” foods (e.g., poultry and eggs), I am more skeptical. Still, it is quite possible that a diet high in omega-6 fats in general is unhealthy primarily if it is devoid of other nutrients. This man’s overall diet might have been protective not because of what he was not eating, but because of what he was eating.

The current debates pitting one diet against another often revolve around the ability of one diet or another to eliminate or reduce the intake of a “bad thing” (e.g., cholesterol, saturated fat, carbohydrates). Perhaps the discussion should be more focused on, or at least not completely ignore, what one diet or another include as protective factors. This would help better explain “odd findings”, such as the lowest-mortality body mass index of 26 in urban populations (). It would also help better explain “surprising cases”; such as this 25-eggs-a-day man’s, vegetarian-vegan “ageless woman” Annette Larkins’s (), and the decidedly carnivore De Vany couple’s ().

The table below shows the vitamin content of the man’s daily diet of eggs. The vitamin K2 content provided by Nutritiondata.com was incorrect; I had to get what seems to be the right number by triangulating values taken from various publications. And here we see something interesting. This man was consuming approximately the equivalent in vitamin K2 that one would get by eating 4 ounces of foie gras () every day. Foie gras, the fatty liver of overfed geese, is the richest known animal source of vitamin K2. This man’s diet was also high in vitamin A, which is believed to act synergistically with vitamin K2 – see Chris Masterjohn’s article on Weston Price’s “activator X” ().

Kern argued that the very high intake of dietary cholesterol led to a sharp increase in bile secretion, as the body tried to “get rid” of cholesterol (which is used in the synthesis of bile). However, the increased bile secretion might have been also been due to the high fat content of this man’s diet, since one of the main functions of bile is digestion of fats. Whatever the case may be, increased bile secretion leads to increased absorption of fat-soluble vitamins, and vitamins K2 and A are fat-soluble vitamins that seem to be protective against cardiovascular disease, cancer and other degenerative diseases.

Finally, the table below shows the mineral content of the man’s daily diet of eggs. As you can see, this man consumed 550 percent the officially recommended daily intake of selenium. This intake was slightly lower than the 400 micrograms per day purported to cause selenosis in adults (). Similarly to vitamins K2 and A, selenium seems to be protective against cardiovascular disease, cancer and other degenerative diseases. This man’s diet was also rich in phosphorus, needed for healthy teeth and bones.

Not too many people live to be 88 years of age; many fewer reach that age in fairly good health. The country with the highest average life expectancy in the world at the time of this writing is Japan, with a life expectancy of about 82 years (79 for men, and 86 for women). Those who think that they need a high HDL cholesterol and a low LDL cholesterol to be in good health, and thus live long lives, may be surprised at this man’s lipid profile: “The patient's plasma lipid levels were normal: total cholesterol, 5.18 mmol per liter (200 mg per deciliter); LDL, 3.68 mmol per liter (142 mg per deciliter); and HDL, 1.17 mmol per liter (45 mg per deciliter). The ratio of LDL to HDL cholesterol was 3.15.”

If we assume that this man is at least somewhat representative of the human species, and not a major exception as Kern argued, this case tells us that a diet of 25 eggs per day followed by over 15 years may actually be healthy for humans. Such diet has the following features:

- It is very high in dietary cholesterol.

- It involves a high intake of omega-6 fats from animal sources, with none coming from industrial seed oils.

- It involves a high overall intake of fats, including saturated fats.

- It is fairly high in protein, all of which from animal sources.

- It is a very low carbohydrate diet, with no sugar in it.

- It is a nutritious diet, rich in vitamins K2 and A, as well as in selenium and phosphorus.

This man ate 25 eggs per day apparently due to an obsession tied to mental problems. Repeated attempts at changing his behavior were unsuccessful. He said: “Eating these eggs ruins my life, but I can't help it.”

The steep obesity increase in the USA in the 1980s: In a sense, it reflects a major success story

Obesity rates have increased in the USA over the years, but the steep increase starting around the 1980s is unusual. Wang and Beydoun do a good job at discussing this puzzling phenomenon (), and a blog post by Discover Magazine provides a graph (see below) that clear illustrates it ().

What is the reason for this?

You may be tempted to point at increases in calorie intake and/or changes in macronutrient composition, but neither can explain this sharp increase in obesity in the 1980s. The differences in calorie intake and macronutrient composition are simply not large enough to fully account for such a steep increase. And the data is actually full of oddities.

For example, an article by Austin and colleagues (which ironically blames calorie consumption for the obesity epidemic) suggests that obese men in a NHANES (2005–2006) sample consumed only 2.2 percent more calories per day on average than normal weight men in a NHANES I (1971–1975) sample ().

So, what could be the main reason for the steep increase in obesity prevalence since the 1980s?

The first clue comes from an interesting observation. If you age-adjust obesity trends (by controlling for age), you end up with a much less steep increase. The steep increase in the graph above is based on raw, unadjusted numbers. There is a higher prevalence of obesity among older people (no surprise here). And older people are people that have survived longer than younger people. (Don’t be too quick to say “duh” just yet.)

This age-obesity connection also reflects an interesting difference between humans living “in the wild” and those who do not, which becomes more striking when we compare hunter-gatherers with modern urbanites. Adult hunter-gatherers, unlike modern urbanites, do not gain weight as they age; they actually lose weight (, ).

Modern urbanites gain a significant amount of weight, usually as body fat, particularly after age 40. The table below, from an article by Flegal and colleagues, illustrates this pattern quite clearly (). Obesity prevalence tends to be highest between ages 40-59 in men; and this has been happening since the 1960s, with the exception of the most recent period listed (1999-2000).

In the 1999-2000 period obesity prevalence in men peaked in the 60-74 age range. Why? With progress in medicine, it is likely that more obese people in that age range survived (however miserably) in the 1999-2000 period. Obesity prevalence overall tends to be highest between ages 40-74 in women, which is a wider range than in men. Keep in mind that women tend to also live longer than men.

Because age seems to be associated with obesity prevalence among urbanites, it would be reasonable to look for a factor that significantly increased survival rates as one of the main reasons for the steep increase in the prevalence of obesity in the USA in the 1980s. If significantly more people were surviving beyond age 40 in the 1980s and beyond, this would help explain the steep increase in obesity prevalence. People don’t die immediately after they become obese; obesity is a “disease” that first and foremost impairs quality of life for many years before it kills.

Now look at the graph below, from an article by Armstrong and colleagues (). It shows a significant decrease in mortality from infectious diseases in the USA since 1900, reaching a minimum point between 1950 and 1960 (possibly 1955), and remaining low afterwards. (The spike in 1918 is due to the influenza pandemic.) At the same time, mortality from non-infectious diseases remains relatively stable over the same period, leading to a similar decrease in overall mortality.

When proper treatment options are not available, infectious diseases kill disproportionately at ages 15 and under (). Someone who was 15 years old in the USA in 1955 would have been 40 years old in 1980, if he or she survived. Had this person been obese, this would have been just in time to contribute to the steep increase in obesity trends in the USA. This increase would be cumulative; if this person were to live to the age of 70, he or she would be contributing to the obesity statistics up to 2010.

Americans are clearly eating more, particularly highly palatable industrialized foods whose calorie-to-nutrient ratio is high. Americans are also less physically active. But one of the fundamental reasons for the sharp increase in obesity rates in the USA since the early 1980s is that Americans have been surviving beyond age 40 in significantly greater numbers.

This is due to the success of modern medicine and public health initiatives in dealing with infectious diseases.

PS: It is important to point out that this post is not about the increase in American obesity in general over the years, but rather about the sharp increase in obesity since the early 1980s. A few alternative hypotheses have been proposed in the comments section, of which one seems to have been favored by various readers: a significant increase in consumption of linoleic acid (not to be confused with linolenic acid) since the early 1980s.

Good insulin, bad insulin: Its role in obesity?

Gary Taubes makes insulin out to be a bad guy. In his latest article in Newsweek Magazine commenting on HBO's Weight of the Nation documentary, he once again challenges energy balance (energy intake versus energy expended) as a paradigm for understanding obesity. The author of Good Calories, Bad Calories offers an alternative theory: refined sugars and grains trigger insulin, which leads to fat accumulation. He also doesn't think much of physical activity as playing a "meaningful role in keeping off the pounds."

Is Taubes right? Not according to Jim Hill, Ph.D., a professor of pediatrics and medicine at the University of Colorado School of Medicine, Denver. Hill is the cofounder of the National Weight Control Registry, a registry of individuals who've succeeded in maintaining weight loss over time. He is also the co-founder of America on the Move, a national weight-gain prevention initiative.

At a session at Experimental Biology, Hill said that the the "energy-in energy-out" framework continues to dominate as correct in current scientific literature on obesity. When asked whether or not the rise of obesity epidemic is related to diet or physical activity, Hill simply responds, "Yes." That is because studies have shown that either restriction of calories or greater physical activity can lead to weight loss.

Then, what's wrong with Taubes's insulin hypothesis? First, it's important to point out that insulin is also a good guy. As kinesiologist John Ivy, Ph.D., of the University of Texas at Austin, pointed out to me a few years ago, insulin is too often misunderstood. The unfortunate consequence can be a detriment of muscle and strength. Ivy's own research is on muscle insulin resistance and how it is reduced with exercise.

Insulin's role is more clearly explained in Ivy's book The Future of Sports Nutrition: Nutrient Timing. He writes that, yes, insulin is a promoter of fat synthesis. But it is also a crucial hormone for promoting protein synthesis, reducing protein degradation (including suppressing cortisol, which can be catabolic in nature), and promoting glucose uptake and glycogen storage in muscle. Insulin, notably, also suppresses appetite.

According to Ivy, the most important factor involved in whether or not insulin promotes fat storage, carbohydrate storage, or protein synthesis is the "individual's body state." For example, under conditions where insulin sensitivity heightened in fat cells (a sedentary lifestyle), there will be more promotion of fat storage. On the other hand, after physical activity, when muscle cells are more insulin sensitive, insulin will promote glycogen and protein synthesis.

Perhaps where Taubes goes wrong is in failing to realize the role of muscle in body metabolism. It wouldn't be the first time. As I've discussed before in a post about the work of another kinesiologist, Stuart Phillips, Ph.D., of McMaster University, skeletal muscle is often forgotten in discussions of obesity. However, as Phillips affirms, skeletal muscle is a highly metabolically active tissue, consuming a great deal of energy as a primary site for glycogen storage and the largest site for fat burning. Skeletal muscle mass also helps determine metabolic rate.

Taubes, in this latest article, also fails to mention that carbohydrate is not the only macronutrient that stimulates insulin. Protein stimulates insulin too; in fact, it's the branched-chain amino acids (leucine, isoleucine, valine) that trigger the insulin release -- these same amino acids are also the key players in triggering protein synthesis, which is explained in part by their effects on insulin.

Ivy explains that insulin has earned the title "anabolic regulator of muscle," meaning it's the most important hormone to increase muscle and strength. Yet, by Taubes's judgment, insulin release should be avoided as much as possible. Taken to its logical conclusion, Taubes's mindset means that one should eat less carbohydrate and protein per day, and eat plenty more fat -- along with the dismissal of exercise as being important, that's the perfect recipe for gradual muscle degradation and (what?) insulin resistance, hyperglycemia, and hyperinsulinemia!

Is there something really wrong with "eat less, move more"? After all, this "tired advice" as Taubes calls it has largely failed in producing results in the United States. There still exists an obesity epidemic and it's getting worse. Is there another alternative theory to energy balance? Hill says energy balance still stands, although he acknowledges "eat less, move more" is too simplistic as advice. He offers his own new paradigm, which largely represents what other nutrition scientists have concluded including the American Society for Nutrition (see my report here). It's that "diet and physical activity interact." And how they interact may explain how the body regulates -- with a sort of "settling point," according to Hill -- balance of energy, energy stores, glucose, and temperature.

Looking at the problem from historical standpoint, Hill reminds, we no longer have to hunt or travel long distances to gather food anymore. We no longer have to farm to produce our food. Now, it's all about heading to the supermarket, filling our carts, and sitting in some form or another for the rest of the day. Our environment has changed. What's the solution to an obesity epidemic? Hill suggests in taking "small steps" for changing our environment back; this means continuing with "eat less, move more," and finding any opportunity to bring reduced-calorie eating, walking, and other physical activities back into lifestyles.

Another recommendation comes from Ivy and Phillips, which is to make greater use of the "anabolic regulator of muscle" and focus on muscle maintenance and growth through regular physical activity. They also encourage balanced eating with healthy portions of quality protein, carbohydrates, and fats. Yes, carbohydrate is important for endurance and maximal recovery of glycogen stores.

Resistance training is primary for muscle building; aerobic exercise also helps in depletion of glycogen stores. Both forms of physical activity make muscles more insulin sensitive, cause greater uptake of glucose into muscles, and they also help keep extra calories from heading toward fat stores. Far from Taubes's advice that physical activity is meaningless, these kinesiologists suggest some form of exercise should happen every single day.

To greater understand the role of "nutrient timing" and how carbohydrate and protein relate to exercise, read Ivy's book and see this 2008 position statement from the International Society of Sports Nutrition where Ivy serves as part of the editorial board.

Update: Those of you who've read Good Calories, Bad Calories or Why We Get Fat may also be interested in Yoni Freedhoff's review of the latter over on his "Weighty Matters" blog. I have only read the first book.

BMI puts young Asian-American women at risk of being "skinny fat"

As if we needed any more reason to pick on Body Mass Index (BMI): new research finds that this most accepted approach for assessing overweight and obesity based on height and weight could lead to misclassification of young Asian-American women as healthy when they're really "skinny fat," which could put them at greater risk for type 2 diabetes and cardiovascular disease.

In my last post, I discussed the scary, growing problem of sarcopenic obesity (aka "skinny fat") in older adults, described as age-related muscle loss in combination with the accumulation of body fat. One common result of sarcopenic obesity is a misclassification using BMI as "normal-weight" in these aged individuals. Misclassification in older adults with sarcopenic obesity is just one reason why BMI is loathed by those interested in public health.

Sadly, I learned at Experimental Biology, misclassification also occurs frequently across all ages depending on ethnicity. Asian Americans with normal weight sarcopenic obesity, for example, are often misclassified even in a college-aged population, according to nutritional epidemiologist Catherine Carpenter, an associate professor from University of California, Los Angeles.

In a symposium organized by the American Society for Nutrition to discuss the topic of obesity and BMI classification, Carpenter presented findings of a yet-to-be-published cross-sectional study that evaluated BMI and percent body fat measured by biolectrical impedance analysis among on the multi-ethnic college students on campus. The study's findings were that college-aged Asian American women were most likely to be misclassified by BMI.

Kindly, Carpenter provided me with further details about the study after the event. The study included 940 college students recruited over four consecutive years. The average participant was 21.4 years old with an average normal weight BMI of 22.9 kilogram over meter squared (kg/m2). The average percent body fat was 24.8 percent. The ethnic samples consisted of four ethnic groups: 49 percent Asian, 23 percent Caucasian, 7 percent Hispanic, and 21 percent as Other.

Among the college-age students, Asian-American women had the lowest BMI (21.5 kg/m2), but the second highest percent body fat (27.8 percent). In comparison, Hispanic women had the highest percent body fat (29.9 percent) and the highest BMI (23.5 kg/m2).

These findings suggest Asian-American women are most likely to be misclassified by BMI, which could mean greater likelihood risk of normal-weight obesity gone unnoticed. Ultimately, greater risk of obesity-related chronic disease such as type 2 diabetes and cardiovascular disease is the result.

No question, studies like these will continue to reveal the limitations of BMI. Once again, I'll repeat that it may be better to focus physical activity and amount of skeletal muscle mass (along with or maybe more than dietary calories or macronutrients like sugar) for prevention of obesity and chronic disease.

Skeletal muscle is critical as a large site for fat burning, glucose disposal, and metabolic rate. Sarcopenia in college-age adults should be most disturbing! It's much easier to hold on to muscle than it is to gain it back. Prevention, as usual, is key; and, prevention can be achieved with daily physical activity and a balanced diet -- with sufficient daily intake of quality protein, especially after exercise and throughout the day.

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How to fight "job-esity"

Workplace programs are an effective and worthwhile way for employers to help improve the health of their employees and reduce medical costs, scientists said Tuesday at Experimental Biology 2012 in San Diego at a session organized by the American Society for Nutrition. 

The medical expenses for employees who are obese are estimated at about 42 percent higher than for those with a healthy weight, according to the Centers for Disease Control and Prevention. Yet for the approximately 60 percent of Americans who are employed, it may be the workplace itself that is at the root of weight gain in the first place.

Shirley Beresford, Ph.D., professor of epidemiology of University of Washington, expressed optimism that workplace obesity programs could help reduce obesity based on a history of research. She explained that programs offered a way for employers to partner in intervention; however, she noted that the enthusiasm of employees for behavioral change may fall short.

The trouble with the workplace of Americans is that too often the mix of job insecurity, stress, sedentary behavior, and social eating present an "obesogenic environment," said Sai Krupa Das, Ph.D., assistant professor at the Friedman School of Nutrition Science and Policy, Tufts University.

Preliminary findings of the Tufts Healthy Weight Study offered a more promising picture for workplace intervention, Das said. The study resulted in substantial weight loss at six months that was sustained for at least a year.

Among the reasons why the intervention worked, Das said, are multiple components: encouraging physical activity combined with diet instead of either alone, behavioral counseling versus educational approaches, a high intensity versus a moderate intensity support group, and a structured maintenance protocol versus an unstructured self-directed program.

"These promising practices provide a broad framework for going forward," Das said. "We had a very robust intervention."

A workplace intervention program is likely to be cost effective for companies, according to economist Chad Meyerhoefer, Ph.D., an assistant professor of Lehigh University. In his presentation on cost-benefit projections for healthcare savings for employers, he gave the estimate that for every 5 pounds of weight loss the savings could range from $30 to $80 per person annually.  

William Dietz, Ph.D., director of the division of nutrition, physical, and obesity at the CDC, discussed a toolkit named LEAN Works! that was developed to promote worksite interventions. To set the example "at home," Dietz said, The toolkit was adopted on CDC campus. 

"We focused on changes on the environment," he said. For example, the campus featured music in stairwells to encourage their use, conducted a walkability audit, redesigned the campus to be more walkable, and installed lactation rooms. In addition, the CDC introduced fruit-and-vegetable vendors on campus so that employees could buy fresh produce once a week, made campuses smoke-free, and installed "lifestyle centers" (gyms).

Beyond calories in, calories out -- look to the Amish

What is wrong with "eat less, move more"? Most of us are familiar with this mantra as weight-loss advice. However, a new consensus statement from the American Society for Nutrition (ASN) and the International Life Sciences Institute (ILSI) contends that this energy-in-energy-out framework isn't really so simple.

The problem lies in that consuming fewer calories and burning more through physical activity doesn’t always translate well to weight management. That is not to say that the framework of energy balance—negative energy balance for weight loss; positive energy balance for weight—is wrong. At some level, it’s right; however, several factors come into the equation.

During a Saturday morning session of Experimental Biology (#EB2012) in San Diego, Calif., researchers discussed the topic of this complexity and promoting a new paradigm on energy balance.

Energy balance is not just about addition of diet and exercise; each affects the other, so that changing one changes the others, explained Jim Hill, professor of pediatrics and medicine at the University of Colorado School of Medicine, Denver, and new recipient of the W.O. Atwater Lectureship awarded by the American Society for Nutrition.

What must be appreciated is the body's system of active regulation, he said.

"It's not just a little man in your head that," he said, adding that the body may rely on a 'set point' or 'settling point' type of system that attempts to balance energy, energy stores, glucose, glycogen, fat stores, and temperature.

"You might argue that up to the 1970s, the system worked pretty well," Hill said. "This whole system is based on inherited factors. If you look at what’s really changed since the 70s, it's really the environmental factors. We don't have to hunt and kill our food anymore. We go to the supermarket and fill our carts. We also sit in some form of fashion every day."

Obesity, a problem of overwhelmed "active regulation"

Most of these environmental influences are designed to increase energy intake. It becomes more difficult to avoid overeating and underactivity. These influences, summed up, overwhelm active regulation and the body’s energy balance system. Then the body's physiology adjusts.

Are there things in the food supply that cause to eat more? Are they influences that affect our brains and nervous system? Hill said that however they may affect us, it is still through energy balance. He said he "laughs every time" he sees studies questioning whether the rise of obesity epidemic is related to energy intake or expenditure.

"Diet and physical activity interact," he said. "Please don't ask if it's diet or physical activity. The answer is 'yes'."

What wrong with diets? No matter which diet, Hill said, the results are always the same: Body weight may increase, may not change, or may decrease. The inconsistent outcomes may be because of poor compliance, physical activity, metabolic rate differences, or food intake adjustments. Eventually, what goes wrong is that environment affects behavior so strongly that over time that people are gaining a little weight. They don’t gain a lot, but they gain about a pound or two a year with some push back from their active regulation systems.

One way to combat the obesity epidemic is by encouraging an environment more like that of a half a century ago, Hill said. How the old order Amish live today is a good example.

"The Amish walk 18 thousand steps a day. They don’t have spandex or a gym membership. That explains about 400 to 600 calories a day. A typical person in the U.S. walks only five to six thousand steps a day," Hill said.

Hill suggests preventing weight gain with small steps—or, more specifically, 2 thousand steps daily.

"We have a poor ability to maintain weight loss, but we have a better ability to prevent weight loss,” he said. “This is why we started a movement to move. Two-thousand steps a day is not going to create weight loss, but might prevent weight gain."

What role can small changes play in an environment where we need bigger systemic changes? Hill argues that it will "set the table" for bigger change. Another factor may be that it may help to improve performance or "reset" active regulation.

"Our biology works best at high levels of physical activity. Obesity is in the 'unregulated zone.' When physical activity increases, you enter 'regulated zone'. Physical activity may help the 'regulatory system' work better."

Stealth health

Until bigger changes can be created in the environment, Hill is a fan of "stealth health" strategies for reducing obesity. That is, finding ways of encouraging better energy in, energy out balance without the public noticing.

For example, Hill explains, at Disney parks and at Starbucks, drinks are now made with low-fat milk. Does anyone notice the difference? What about portion sizes? Would anyone notice a 5 percent reduction at restaurants?

Another idea Hill proposed was that of regulation of school drop offs—so that they would be 500 steps away from the school. Would it be so far that the children would notice a difference?

The implementation of these "stealth health" strategies would increase physical activity and reduce overeating. Combined with teaching children energy balance skills, it could reduce childhood and overall obesity.

What also may be important is to combine both diet and physical activity guidelines for society? But how can this be achieved when studies show that energy balance is such a dynamic versus static process?

Learn more about the questions raised about energy balance at Experimental Biology from the American Society for Nutrition’s cover page story in Nutrition Notes Daily. 

The “pork paradox”? National pork consumption and obesity

In my previous post () I discussed some country data linking pork consumption and health, analyzed with WarpPLS (). One of the datasets used, the most complete, contained data from Nationmaster.com () for the following countries: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. That previous post also addressed a study by Bridges (), based on country-level data, suggesting that pork consumption may cause liver disease.

In this post we continue that analysis, but with a much more complex model containing the following country variables: wealth (PPP-adjusted GNP/person), pork consumption (lbs/person/year), alcohol consumption (liters/person/year), obesity (% of population), and life expectancy (years). The model and results, generated by WarpPLS, are shown on the figure below. (See notes at the end of this post.) These results are only for direct effects.

WarpPLS also calculates total effects, which are the effects of each variable on any other variable to which it is linked directly and/or indirectly. Two variables may be linked indirectly, through various paths, even if they are not linked directly (i.e., have an arrow directly connecting them). Another set of outputs generated by the software are effect sizes, which are calculated as Cohen’s f-squared coefficients. The figure below shows the total effects table. The values underlined in red are for total effects that are both statistically significant and also above the effect size threshold recommended by Cohen to be considered relevant (f-squared > 0.02).

As I predicted in my previous post, wealth is positively associated with pork consumption. So is alcohol consumption, and more strongly than wealth; which is consistent with a study by Jeanneret and colleagues showing a strong association between alcohol consumption and protein rich diets (). The inclusion of wealth in the model, compared with the model without wealth in the previous post, renders the direct and total effects of alcohol and pork consumption on life expectancy statistically indistinguishable from zero. (This often happens when a confounder is added to a model.)

Pork consumption is negatively associated with obesity, which is interesting. So is alcohol consumption, but much less strongly than pork consumption. This does not mean that if you eat 20 doughnuts every day, together with 1 lb of pork, you are not going to become obese. What this does suggest is that maybe countries where pork is consumed more heavily are somewhat more resistant to obesity. Here it should be noted that pork is very popular in Asian countries, which are becoming increasingly wealthy, but without the widespread obesity that we see in the USA.

But it is not the inclusion of Asian countries in the dataset that paints such a positive picture for pork consumption vis-à-vis obesity, and even weakens the association between wealth and obesity so much as to make it statistically non-significant. Denmark is a wealthy country that has very low levels of obesity. And it happens to have the highest level of pork consumption in the whole dataset: 142.6 lbs/person/year. So we are not talking about an “Asian paradox” here.

More like a “pork paradox”.

Finally, as far as life expectancy is concerned, the key factors seem to be wealth and obesity. Wealth has a major positive effect on life expectancy, while obesity has a much weaker negative effect. Well, access to sanitation, medical services, and other amenities of civilization, still trumps obesity in terms of prolonging life; however miserable life may turn out to be. The competing effects of these two variables (i.e., wealth and obesity) were taken into consideration, or controlled for, in the calculation of total effects and effect sizes.

The fact that pork consumption is negatively associated with obesity goes somewhat against the idea that pork is inherently unhealthy; even though pork certainly can cause disease if not properly prepared and/or cooked, which is true for many other plant and animal foods. The possible connection with liver problems, alluded to in the previous post, is particularly suspicious in light of these results. Liver diseases often impair that organ’s ability to make glycogen based on carbohydrates and protein; that is, liver diseases frequently lead to liver insulin resistance. And obesity frequently follows from liver insulin resistance.

Given that pork consumption appears to be negatively associated with obesity, it would be surprising if it was causing widespread liver disease, unless its relationship with liver disease was found to be nonlinear. (Alcohol consumption seems to be nonlinearly associated with liver disease.) Still, most studies that suggest the existence of a causal link between pork consumption and liver disease, like Bridges’s (), hint at a linear and dose-dependent relationship.

Notes

- Country-level data is inherently problematic, particularly when simple models are used (e.g., a model with only two variables). There are just too many possible confounders that may lead to the appearance of causal associations.

- More complex models ameliorate the above situation somewhat, but bump into another problem associated with country-level data – small sample sizes. We used data from 18 countries in this analysis, which is more than in the Bridges study. Still, the effective sample size here (N=18) is awfully small.

- There were some missing values in this dataset, which were handled by WarpPLS employing the most widely used approach in these cases – i.e., by replacing the missing values with the mean of each column. The percentages of missing values per variable (i.e., column) were: alcohol consumption: 27.78%; life expectancy: 5.56%; and obesity: 33.33%.

Does pork consumption cause cirrhosis? Perhaps, if people become obese from eating pork

The idea that pork consumption may cause cirrhosis has been around for a while. A fairly widely cited 1985 study by Nanji and French () provides one of the strongest indictments of pork: “In countries with low alcohol consumption, no correlation was obtained between alcohol consumption and cirrhosis. However, a significant correlation was obtained between cirrhosis and pork.”

Recently Paul Jaminet wrote a blog post on the possible link between pork consumption and cirrhosis (). Paul should be commended for bringing this topic to the fore, as the implications are far-reaching and very serious. One of the key studies mentioned in Paul’s post is a 2009 article by Bridges (), from which the graphs below were taken.

The graphs above show a correlation between cirrhosis and alcohol consumption of 0.71, and a correlation between cirrhosis and pork consumption of 0.83. That is, the correlation between cirrhosis and pork consumption is the stronger of the two! Combining this with the Nanji and French study, we have evidence that: (a) in countries with low alcohol consumption we can find a significant correlation between cirrhosis and pork consumption; and (b) in countries where both alcohol and pork are consumed, pork consumption has the strongest correlation with cirrhosis.

Do we need anything else to ban pork from our diets? Yes, we do, as there is more to this story.

Clearly alcohol and pork consumption are correlated as well, as we can see from the graphs above. That is, countries where alcohol is consumed more heavily also tend to have higher levels of pork consumption. If alcohol and pork consumption are correlated, then a multivariate analysis of their effects should be conducted, as one of the hypothesized effects (of alcohol or pork) on cirrhosis may even disappear after controlling for the other effect.

I created a dataset, as best as I could, based on the graphs from the Bridges article. (I could not get the data online.) I then entered it into WarpPLS (). I wanted to run a moderating effect analysis, which is a form of nonlinear multivariate analysis. This is important, because the association between alcohol consumption and disease in general is well known to be nonlinear.

In fact, the relationship between alcohol consumption and disease is often used as a classic example of hormesis (), and its characteristic J-curve shape. Since correlation is a measure of linear association, the lower correlation between alcohol consumption and cirrhosis, when compared with pork consumption, may be just a “mirage of linearity”. In multivariate analyses, this mirage of linearity may lead to what are known as type I and II errors, at the same time ().

I should note that the Bridges study did something akin to a moderating effect analysis; through an analysis of the interaction between alcohol and pork consumption. However, in that analysis the values of the variables that were multiplied to create a “dummy” interaction variable were on their original scales, which can be a major source of bias. A more advisable way to conduct an interaction effect analysis is to first make the variables dimensionless, by standardizing them, and then creating a dummy interaction variable as a product of the two variables. That is what WarpPLS does for moderating effects’ estimation.

One more detour, leading to an important implication, and then we will get to the results. In a 1988 article, Jeanneret and colleagues show evidence of a strong and possibly causal association between alcohol consumption and protein-rich diets (). One possible implication of this is that in countries where pork is a dietary staple, like Denmark and Germany, alcohol consumption should be strongly and causally associated with pork consumption. (I guess Anthony Bordain would agree with this eh?)

Below are the results of a multivariate analysis on a model that incorporates the above implication, by including a link between alcohol and pork consumption. The model also explores the role of pork consumption as a moderator of the relationship between alcohol and cirrhosis, as well as the direct effect of pork consumption on cirrhosis. Finally, the total effects of alcohol and pork consumption on cirrhosis are also investigated; they are shown on the left.

The total effects are both statistically significant, with the total effect of alcohol consumption being 94 percent stronger than the total effect of pork consumption on cirrhosis. Looking at the model, alcohol consumption is strongly associated with pork consumption (which is consistent with Jeanneret and colleagues’s study). Alcohol consumption is also strongly associated with cirrhosis, through a direct effect; much more so than pork. Finally, pork consumption seems to strengthen the relationship between alcohol consumption and cirrhosis (the moderating effect).

As we can see the relationship between pork consumption and cirrhosis is still there, in moderating and direct effects, even though it seems to be a lot weaker than that between alcohol consumption and cirrhosis. Why does pork seem to influence cirrhosis at all in this dataset?

Well, there is another factor that is strongly associated with cirrhosis, and that is obesity (). In fact, obesity is associated with just about any major disease, including various types of cancer ().

And in countries where pork is a dietary staple, isn’t it reasonable to assume that pork consumption will play a role in obesity? Often folks who consume a lot of addictive industrial foods (e.g., bread, candy, regular sodas) also eat plenty of foods with saturated fat; and the latter end up showing up in disease statistics, misleadingly supporting the lipid hypothesis. The phenomenon involving pork and cirrhosis may well be similar.

But you may find the above results and argument not convincing enough. Maybe you want to see some evidence that pork is actually good for one’s health. The results above suggest that it may not be bad at all, if you buy into the obesity angle, but not that it can be good.

So I downloaded the most recent data from Nationmaster.com () on the following variables: pork consumption, alcohol consumption, and life expectancy. The list of countries was a bit larger than and different from that in the Bridges study; the following countries were included: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. Below are the results of a simple multivariate analysis with WarpPLS.

As with the Bridges dataset, there is a strong multivariate association between alcohol and pork consumption (0.43). The multivariate association between alcohol consumption and life expectancy is negative (-0.14). The multivariate association between pork consumption and life expectancy is positive (0.36). Neither association is statistically significant, although the association involving pork consumption gets close to significance with a P=0.11 (a confidence level of 89 percent; calculated through jackknifing, a nonparametric technique). The graphs show the plots for the associations and the best-fitting lines; the blue dashed arrows indicate the multivariate associations to which the graphs refer. So, in this second dataset from Nationmaster.com, the more pork is consumed in a country, the longer is the life expectancy in that country.

In other words, for each 1 standard deviation variation in pork consumption, there is a 0.36 standard deviation variation in life expectancy, after we control for alcohol consumption. The standard deviation for pork consumption is 36.281 lbs/person/year, or 45.087 g/person/day; for life expectancy, it is 4.677 years. Working the numbers a bit more, the results above suggest that each extra gram of pork consumed per person per day is associated with approximately 13 additional days of overall life expectancy in a country! This is calculated as: 4.677/45.087*0.36*365 = 13.630.

Does this prove that eating pork will make you live longer? No single study will “prove” something like that. Pork consumption is also likely a marker for wealth in a country; and wealth is strongly and positively associated with life expectancy at the country level. Moreover, when you aggregate dietary and disease incidence data by country, often the statistical effects are caused by those people in the dietary extremes (e.g., alcohol abuse, not moderate consumption). Finally, if people avoid death from certain diseases, they will die in higher quantities from other diseases, which may bias statistical results toward what may look like a higher incidence of those other diseases.

What the results summarized in this post do suggest is that pork consumption may not be a problem at all, unless you become obese from eating it. How do you get obese from eating pork? Eating it together with industrial foods that are addictive would probably help.

Kleiber's law and its possible implications for obesity

Kleiber's law () is one of those “laws” of nature that is both derived from, and seems to fit quite well with, empirical data. It applies to most animals, including humans. The law is roughly summarized through the equation below, where E = energy expenditure at rest per day, and M = body weight in kilograms.

Because of various assumptions made in the original formulation of the law, the values of E do not translate very well to calories as measured today. What is important is the exponent, and what it means in terms of relative increases in weight. Since the exponent in the equation is 3/4, which is lower than 1, the law essentially states that as body weight increases animals become more efficient from an energy expenditure perspective. For example, the energy expenditure at rest of an elephant, per unit of body weight, is significantly lower than that of a mouse.

The difference in weight does not have to be as large as that between an elephant and a mouse for a clear difference in energy expenditure to be noticed. Moreover, the increase in energy efficiency predicted by the law is independent of what makes up the weight; whether it is more or less lean body mass, for example. And the law is very generic, also applying to different animals of the same species, and even the same animal at different developmental stages.

Extrapolating the law to humans is quite interesting. Let us consider a person weighing 68 kg (about 150 lbs). According to Kleiber's law, and using a constant multiplied to M to make it consistent with current calorie measurement assumptions (see Notes at the end of this post), this person’s energy expenditure at rest per day would be about 1,847 calories.

A person weighing 95 kg (about 210 lbs) would spend 2,374 calories at rest per day according to Kleiber's law. However, if we were to assume a linear increase based on the daily calorie expenditure at a weight of 68 kg, this person weighing 95 kg would spend 2,508 calories per day at rest. The difference of approximately 206 calories per day is a reflection of Kleiber's law.

This difference of 206 calories per day would translate into about 23 g of extra body fat being stored per day. Per month this would be about 688 g, a little more than 1.5 lbs. Not a negligible amount. So, as you become obese, your body becomes even more efficient on a weight-adjusted basis, from an energy expenditure perspective.

One more roadblock to go from obese to lean.

Now, here is the interesting part. It is unreasonable to assume that the extra mass itself has a significantly lower metabolic rate, with this fully accounting for the relative increase in efficiency. It makes more sense to think that the extra mass leads to systemic adaptations, which in turn lead to whole-body economies of scale (). In existing bodies, these adaptations should happen over time, as long-term compensatory adaptations ().

The implications are fascinating. One implication is that, if the compensatory adaptations that lead to a lower metabolic rate are long term, they should also take some time to undo. This is what some call having a “broken metabolism”; which may turn out not to be “broken”, but having some inertia to overcome before it comes back to a former state. Thus, lower metabolic rates should generally be observed in the formerly obese, with reductions compatible with Kleiber's law. Those reductions themselves should be positively correlated with the ratio of time spent in the obese and lean states.

Someone who was obese at 95 kg should have a metabolic rate approximately 5.6 percent lower than a never obese person, soon after reaching a weight of 68 kg (5.6 percent = [2,508 – 2,374] / 2,374). If the compensatory adaptation can be reversed, as I believe it can, we should see slightly lower percentage reductions in studies including formerly obese participants who had been lean for a while. This expectation is consistent with empirical evidence. For example, a study by Astrup and colleagues () concluded that: “Formerly obese subjects had a 3–5% lower mean relative RMR than control subjects”.

Another implication, which is related to the one above, is that someone who becomes obese and goes right back to lean should not see that kind of inertia. That is, that person should go right back to his or her lean resting metabolic rate. Perhaps Drew Manning’s Fit-2-Fat-2-Fit experiment () will shed some light on this possible implication.

A person becoming obese and going right back to lean is not a very common occurrence. Sometimes this is done on purpose, for professional reasons, such as before and after photos for diet products. Believed it or not, there is a market for this!

Notes

- Calorie expenditure estimation varies a lot depending on the equation used. The multiplier used here was 78,  based on Cunningham’s equation, and assuming 10 percent body fat. The calorie expenditure for the same 68 kg person using Katch-McArdle’s equation, also assuming 10 percent body fat, would be about 1,692 calories. That would lead to a different multiplier.

- The really important thing to keep in mind, for the purposes of the discussion presented here, is the relative decrease in energy expenditure at rest, per unit of weight, as weight goes up. So we stuck with the 78 multiplier for illustration purposes.

- There is a lot of variation across individuals in energy expenditure at rest due to other factors such as nonexercise activity thermogenesis ().