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.

The impressive nutrition value of whole dried small fish

When I visited Japan a few years ago I noticed a variety of dried small fish for sale in grocery stores and supermarkets. They came in what seemed to be vacuum-packed flat plastic bags, often dried. The packing was a bit like that of beef jerky in the USA. Since I could not read the labels, I could not tell if preservatives or things like sugar were added. Beef jerky often has sugar added to it; at least the popular brands.

I have since incorporated dried or almost dried small fish, eaten whole, into my diet. My family eats it, but they don’t seem to like it as much as I do. The easiest small fish to find for sale where I live are smelts. A previous post has a recipe (). I can easily eat 200 g of smelts, about twice as much as on the plate below; not quite dried, but almost so. The veggies are a mix of lettuce and cabbage.

As you can see from the macronutrient composition below (from Nutritiondata.com, for a 100 g portion), 200 g of smelts have about 112 g of protein, and 36 g of fat. No carbohydrates; or a very small amount of them.

Unless you misguidedly think that they will “give you cholesterol”, the macronutrient to calorie ratio of a plate with 200 g of dried (or almost dried) smelts is very good. Let us take a look at the fat content, below (from Nutritiondata.com as well), which is for 100 g of dried smelts.

The “net” omega-3 content of 200 g of dried smelts, after subtracting the omega-6 content, is approximately 4.4 g. The concept of “net” omega-3 content was discussed in a previous post ().

So, the net omega-3 content of 200 g of dried smelts is the equivalent to the net omega-3 content of about 20 fish oil softgels. (Yes, you read it right!) And you would get a lot more omega-6 from the softgels.

Not to mention the fact that isolated omega-3 and omega-6 fats tend to become oxidized much more easily than when they come in “nature’s package”.

Below is the mineral content (also from Nutritiondata.com) of a 100 g portion. Dried smelts are clearly a very good source of selenium. The significant amount of calcium comes mostly from the bones, as with many varieties of small fish that are eaten whole. Combined with the above, we could say that, overall, the nutrient content is high up there next to beef liver as a super food; a natural multivitamin, if you will.

Smelts, like many small non-predatory fish, are not a significant source of toxic metals. Many people avoid seafood because of concerns about toxic metal contamination, particularly mercury. The infamous incident that led to a major scare in that respect – in Minamata, Japan – did involve consumption of small marine animals. But it also involved years of direct and indirect exposure to very high levels of methylmercury from untreated industrial waste.

Other cases have been reported among populations consuming large amounts of whale, shark, dogfish and other relatively large marine animals with tissues compromised via biomagnification. Generally speaking, large predatory fish and predatory aquatic mammals are best avoided as food. If they are consumed, they should be consumed very sporadically.

Many people would say that a plate like the one above, with smelts and veggies, is not very appetizing. But I can really devour it quickly and go for seconds. How come? I use a special spice that enhances the natural flavor or almost any combination of “natural” foods – foods that are not engineered by humans – making them taste delicious.

This special spice is “hunger”. This spice can be your best friend, or your worst enemy.

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 ().