How much alcohol is optimal? Maybe less than you think

I have been regularly recommending to users of the software HCE () to include a column in their health data reflecting their alcohol consumption. Why? Because I suspect that alcohol consumption is behind many of what we call the “diseases of affluence”.

A while ago I recall watching an interview with a centenarian, a very lucid woman. When asked about her “secret” to live a long life, she said that she added a little bit of whiskey to her coffee every morning. It was something like a tablespoon of whiskey, or about 15 g, which amounted to approximately 6 g of ethanol every single day.

Well, she might have been drinking very close to the optimal amount of alcohol per day for the average person, if the study reviewed in this post is correct.

Studies of the effect of alcohol consumption on health generally show results in terms of averages within fixed ranges of consumption. For example, they will show average mortality risks for people consuming 1, 2, 3 etc. drinks per day. These studies suggest that there is a J-curve relationship between alcohol consumption and health (). That is, drinking a little is better than not drinking; and drinking a lot is worse than drinking a little.

However, using “rough” ranges of 1, 2, 3 etc. drinks per day prevents those studies from getting to a more fine-grained picture of the beneficial effects of alcohol consumption.

Contrary to popular belief, the positive health effects of moderate alcohol consumption have little, if anything, to do with polyphenols such as resveratrol. Resveratrol, once believed to be the fountain of youth, is found in the skin of red grapes.

It is in fact the alcohol content that has positive effects, apparently reducing the incidence of coronary heart disease, diabetes, hypertension, congestive heart failure, stroke, dementia, Raynaud’s phenomenon, and all-cause mortality. Raynaud's phenomenon is associated with poor circulation in the extremities (e.g., toes, fingers), which in some cases can progress to gangrene.

In most studies of the effects of alcohol consumption on health, the J-curves emerge from visual inspection of the plots of averages across ranges of consumption. Rarely you find studies where nonlinear relationships are “discovered” by software tools such as WarpPLS (), with effects being adjusted according.

You do find, however, some studies that fit reasonably justified functions to the data. Di Castelnuovo and colleagues’ study, published in JAMA Internal Medicine in 2006 (), is probably the most widely cited among these studies. This study is a meta-analysis; i.e., a study that builds on various other empirical studies.

I think that the journal in which this study appeared was formerly known as Archives of Internal Medicine, a fairly selective and prestigious journal, even though this did not seem to be reflected in its Wikipedia article at the time of this writing ().

What Di Castelnuovo and colleagues found is interesting. They fitted a bunch of nonlinear functions to the data, all with J-curve shapes. The results suggest a lot of variation in the maximum amount one can drink before mortality becomes higher than not drinking at all; that maximum amount ranges from about 4 to 6 drinks per day.

But there is little variation in one respect. The optimal amount of alcohol is somewhere around 5 and 7 g/d, which translates into about the following every day: half a can of beer, half a glass of wine, or half a “shot” of spirit. This is clearly a common trait of all of the nonlinear functions that they generated. This is illustrated in the figure below, from the article.

As you can seen from the curves above, a little bit of alcohol every day seems to have an acute effect on mortality reduction. And it seems that taking little doses every day is much better than taking the equivalent dose over a larger period of time; for instance, the equivalent per week, taken once a week. This is suggested by other studies as well ().

The curves above do not clearly reflect a couple of problems with alcohol consumption. One is that alcohol seems to be treated by the body as a toxin, which causes some harm and some good at the same time, the good being often ascribed to hormesis (). Someone who is more sensitive to alcohol’s harmful effects, on the liver for example, may not benefit as much from its positive effects.

The curves are averages that pass through points, after which the points are forgotten; even though they are real people.

The other problem with alcohol is that most people who are introduced to it in highly urbanized areas (where most people live) tend to drink it because of its mood-altering effects. This leads to a major danger of addiction and abuse. And drinking a lot of alcohol is much worse than not drinking at all.

Interestingly, in traditional Mediterranean Cultures where wine is consumed regularly, people tend to generally frown upon drunkenness ().

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.

Alcohol intake increases LDL cholesterol, in some people

Occasionally I get emails from people experiencing odd fluctuations in health markers, and trying to figure out what is causing those fluctuations. Spikes in LDL cholesterol without any change in diet seem to be a common occurrence, especially in men.

LDL cholesterol is a reflection of many things. It is one of the least useful measures in standard lipid profiles, as a predictor of future health problems. Nevertheless, if one’s diet is not changing, whether it is high or low in fat, significant fluctuations in LDL cholesterol may signal a change in inflammatory status. Generally speaking, the more systemic inflammation, the higher is the measured LDL cholesterol.

Corella and colleagues (2001) looked into alcohol consumption and its effect on LDL cholesterol, as part of the Framingham Offspring Study. They split the data into three genotypes, which are allele combinations. Alleles are genes variations; that is, they are variations in the sections of DNA that have been identified as coding for observable traits. The table below summarizes what they have found. Take a look at the last two columns on the right.

As you can see, for men with the E2 genotype, alcohol consumption significantly decreases LDL cholesterol. For men with the E4 genotype, alcohol consumption significantly increases LDL cholesterol. No significant effects were observed in women. The figure below illustrates the magnitude of the effects observed in men.

On average, alcohol consumption was moderate, around 15 g per day, and did not vary significantly based on genotype. This is important. Otherwise one could argue that a particular genotype predisposed individuals to drink more, which would be a major confounder in this study. Other confounders were also ruled out through multivariate controls - e.g., fat and calorie intake, and smoking.

Alcohol consumption in moderation seems, on average, to be beneficial. But for some individuals, particularly men with a certain genotype, it may be advisable to completely abstain from alcohol consumption. Who are those folks? They are the ones for whom LDL cholesterol goes up significantly following moderate alcohol consumption.

Alcohol consumption, gender, and type 2 diabetes: Strange … but true

Let me start this post with a warning about spirits (hard liquor). Taken on an empty stomach, they cause an acute suppression of liver glycogenesis. In other words, your liver becomes acutely insulin resistant for a while. How long? It depends on how much you drink; possibly as long as a few hours. So it is not a very good idea to consume them immediately before eating carbohydrate-rich foods, natural or not, or as part of sweet drinks. You may end up with near diabetic blood sugar levels, even if your liver is insulin sensitive under normal circumstances.

The other day I was thinking about this, and the title of this article caught my attention: Alcohol Consumption and the Risk of Type 2 Diabetes Mellitus. This article is available here in full text. In it, Kao and colleagues show us a very interesting table (Table 4), relating alcohol consumption in men and women with incidence of type 2 diabetes. I charted the data from Model 3 in that table, and here is what I got:

I used the data from Model 3 because it adjusted for a lot of things: age, race, education, family history of diabetes, body mass index, waist/hip ratio, physical activity, total energy intake, smoking history, history of hypertension, fasting serum insulin, and fasting serum glucose. Whoa! As you can see, Model 3 even adjusted for preexisting insulin resistance and impaired glucose metabolism.

So, according to the charts, the more women drink, the lower is the risk of developing type 2 diabetes, even if they drink more than 21 drinks per week. For men, the sweet spot is 7-14 drinks per week; after 21 drinks per week the risk goes up significantly.

A drink is defined as: a 4-ounce glass of wine, a 12-ounce bottle or can of beer, or a 1.5-ounce shot of hard liquor. The amounts of ethanol vary, with more in hard liquor: 4 ounces of wine = 10.8 g of ethanol, 12 ounces of beer = 13.2 g of ethanol, and 1.5 ounces of spirits = 15.1 g of ethanol.

Initially I thought that these results were due to measurement error, particularly because the study relies on questionnaires. But I did some digging and checking, and now think they are not. In fact, there are plausible explanations for them. Here is what I think, and it has to do with a fundamental difference between men and women – sex hormones.

In men, alcohol consumption, particularly in large quantities, suppresses testosterone production. And testosterone levels are inversely associated with diabetes in men. Heavy alcohol consumption also increases estrogen production in men, which is not good news either.

In women, alcohol consumption, particularly in large quantities, increases estrogen production. And estrogen levels are (you guessed it) inversely associated with diabetes in women. Unnatural suppression of testosterone levels in women is not good either, as this hormone also plays important roles in women; e.g., it influences mood and bone density.

What if we were to disregard the possible negative health effects of suppressing testosterone production in women; should women start downing 21 drinks or more per week? The answer is “no”, because alcohol consumption, particularly in large quantities, increases the risk of breast cancer in women. So, for women, alcohol consumption in moderation may also provide overall health benefits, as it does for men; but for different reasons.

How much dietary protein can you store in muscle? About 15 g/d if you are a gifted bodybuilder

Let us say you are one of the gifted few who are able to put on 1 lb of pure muscle per month, or 12 lbs per year, by combining strength training with a reasonable protein intake. Let us go even further and assume that the 1 lb of muscle that we are talking about is due to muscle protein gain, not glycogen or water. This is very uncommon; one has to really be genetically gifted to achieve that.

And you do that by eating a measly 80 g of protein per day. That is little more than 0.5 g of protein per lb of body weight if you weigh 155 lbs; or 0.4 per lb if you weigh 200 lbs. At the end of the year you are much more muscular. People even think that you’ve been taking steroids; but that just came naturally. The figure below shows what happened with the 80 g of protein you consumed every day. About 15 g became muscle (that is 1 lb divided by 30) … and 65 g “disappeared”!

Is that an amazing feat? Yes, it is an amazing feat of waste, if you think that the primary role of protein is to build muscle. More than 80 percent of the protein consumed was used for something else, notably to keep your metabolic engine running.

A significant proportion of dietary protein also goes into the synthesis of albumin, to which free fatty acids bind in the blood. (Albumin is necessary for the proper use of fat as fuel.) Dietary protein is also used in the synthesis of various body tissues and hormones.

Dietary protein does not normally become body fat, but can be used in place of fat as fuel and thus allow more dietary fat to be stored. It leads to an insulin response, which causes less body fat to be released. In this sense, dietary protein has a fat-sparing effect, preventing it from being used to supply the energy needs of the body.

Nevertheless, the fat-sparing effect of protein is lower than that of another "macronutrient" – alcohol. That is, alcohol takes precedence over carbohydrates for use as fuel. However, protein takes precedence over carbohydrates. Neither alcohol nor protein typically becomes body fat. Carbohydrates can become body fat, but only when glycogen stores are full.

What does this mean?

As it turns out, a reasonably high protein intake seems to be quite healthy, and there is nothing wrong with the body using protein to feed its metabolism.

Having said that, one does not need enormous amounts of protein to keep or even build muscle if one is getting enough calories from other sources.

In my next post I’ll talk a little bit more about that.