Tuesday, July 17, 2012

Is Red Meat Risky? Complexity and Context in Human Health and Diets

Note: I wrote this one up in immediate reaction last March to Harvard's "red meat is risky" study. I bring up a number of issues in this post that have interested me for some time.

The recent "Harvard Meat Study" purports to show that red meat consumption is risky. By this, the researchers mean that a broad questionaire-based assessment of two study populations-- the Health Professionals Follow-Up Study and the Nurses Health Study-- showed a higher all-cause mortality for those including red meat in their diets (interestingly, the risk was even higher for those consuming processed red meats). Here’s the meat of the study (pun intended):

A combined 23,926 deaths were documented in the two studies, of which 5,910 were from CVD and 9,464 from cancer. Regular consumption of red meat, particularly processed red meat, was associated with increased mortality risk. One daily serving of unprocessed red meat (about the size of a deck of cards) was associated with a 13% increased risk of mortality, and one daily serving of processed red meat (one hot dog or two slices of bacon) was associated with a 20% increased risk.
Among specific causes, the corresponding increases in risk were 18% and 21% for cardiovascular mortality, and 10% and 16% for cancer mortality. These analyses took into account chronic disease risk factors such as age, body mass index, physical activity, family history of heart disease, or major cancers.”

Correlation is not causation. This is a mantra that is well worth repeating. Although it may be overdone in the blogosphere, it really is worth thinking through carefully and repeatedly. What studies like this do is establish a statistical correlation (including an effect size and some level of statistical significance). This is not a demonstration of biological causation. In fact, there is no clear causation here at all. This is a trade-off in large scale epidemiology- mechanisms and pathways are almost necessarily obscure1.

The task is to decide what these statistics mean. In this case, we are talking about a large class of food. We have therefore to deal with the complexity of the food itself, its role in human nutrition, and the whole complex of behaviors that go into people eating. The researchers hope they have accounted for a sufficient array of these factors in order to isolate the effect of the variable of interest, in this case red meat consumption per se. 

I have not seen the data, and am not familiar with the particular methods that are used in epidemiological studies of this sort. So I cannot comment on how well they performed this aspect of analysis. Being a large-ish team from Harvard, I’m sure they got the statistics technically correct. For instance, they had to “correct” for the fact that the red meat group was more obese and smoked more than the rest. If there is a non-linearity at work in the system dynamics, then the very premise of correcting for such “competing” risk factors to isolate the risk associated with red meat per se, is flawed2.

In epidemiology there are other strategies as well. The gold standard in medicine is a randomized double-blind intervention trial that follows a treatment and a control group for some period of time and assesses outcomes. This is the only way to be sure that we are avoiding the pitfalls of the illusory world of “data-mining”. If the treatment groups are well chosen and the intervention specific enough, we can be relatively certain in attributing a causal connection between intervention and outcome.

In contrast, this study is an observational study, albeit a prospective cohort which is definitely stronger than, say a retrospective cohort. The mortality effect described in this study would be a lot more convincing if this were an intervention trial that eliminated red meat in one group, allowed a certain consumption in another, and held the rest of the diets constant, or at least had a treatment and control group with a broadly representative (and normally distributed) range of baseline diets.

The problem is that in a complex system, there may be pathways of causality that are non-linear, and/or may be distributed statistically in unexpected ways. For instance, there may be a sub-set of people who are “hyper-responders” to red meat, and experience a dramatic increase in mortality, while everyone else is fine, or even has a lower mortality3. The net result, averaged over the whole population, may show up as a slight increase in risk, such as this study described. Also, a confluence of two or more synergistic, negative, factors may be necessary for the ill effect of red meat to manifest-- for instance obesity and smoking. I’m sure the researchers must have looked for such “interaction effects”, but when the number of variables and possible pathways is high, it may not be possible with ordinary methods.

Another problem is that this study did not bother to distinguish factory-farmed, conventional-grain-finished red meat, from grass-fed meat. We already know that the two kinds of foods differ significantly as to composition and the balance of health-promoting factors. The “null hypothesis” has got to be that they have different effects in human nutrition. In the end, this study may be simply a referendum on conventional feedlot beef.

Similarly, cooking and preparation were not assessed. For instance, people often eat red meat off the grill, replete with char and smoke carcinogens. Now, I love me some tasty grilled beef, but I’m not going to suggest that it’s healthy!

The effects of a given food are complex in themselves, and also mediated by dietary context4. So it may be that an even more stringent interpretation is necessary: in the context of a SAD (standard American diet--which is hypercaloric, hyper-processed, high in artificials and low in protective nutrients), the consumption of conventional feedlot beef and its processed derivatives, is associated with a slightly increased risk in mortality. However, the pathways and mechanisms of this potential toxicity are unknown, and until a suitable intervention trial is completed, we won’t really know if this effect is real. Extrapolations to naturally grass-finished beef and a non-SAD dietary context are, at this stage, flights in fancy.

1 My point is not to trash talk epidemiology. For one thing, it’s a hugely useful field of study and we need it. For another, I’m not expert in it. My main trope is the need, in thinking about human health and nutrition, to weigh these kinds of studies in the context of other high-quality sources of information; things that may be true in the statistical aggregate, may not be useful guidelines for any particular person or definite group.
2 Depending on the nature of the non-linearity, actually. The question is whether the researchers can correctly identify the form of non-linearity and parametrize it in their statistical model. In a system of high complexity, this is unlikely.
3 Dr. Ronald Krauss is actually working on the angle of iron levels. It may be that excess heme-iron, and iron storage overload, underlie most of the negative effects. Like many other things, red meat may be good in moderation, and problematic in excess- and what defines moderation may be very individual.
4 Context is super important in assessing diets because the addition of one food generally has to come at the exclusion of another. So, in choosing to eat one thing in particular, you are choosing *not to eat* a whole bunch of other alternatives. Also, the nutritive quality of foods in part depends on what other foods are eaten, and partly also the organism’s needs at any given time. Finally, if we consider foods as a complex mixture of nutritive, anti-nutritive and toxic factors, the overall dietary composition is irreducibly important. The usefulness of a food in human nutrition has to be assessed in this context, and cannot be predicted from its own constituents alone. An example, tannins from coffee, tea, and other plant foods may reduce iron absorption- mitigating risk of overload in those who are susceptible.

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