There have been many academic articles in the past linking red meat intake with increased mortality, and there will be more in the future. I discussed one such article before here (, ). The findings in this article, which received an enormous amount of media attention, are the basis for my discussion in this post. I am interested in answering the question: Why red meat consumption may appear unhealthy in scientific studies?
This question leads to other questions, which are also addressed in this post. Can red meat intake be associated with increases and decreases in mortality, in the same study? Can red meat intake possibly cause increased mortality, at least for a percentage of the population?
All of the analyses discussed below have been conducted with the software WarpPLS (). This software supports multivariate analyses where relationships can be modeled as linear or nonlinear, with or without moderating effects included.
The ubiquitous J curve
The graph below shows how mortality varies with red meat intake. As you can see, the relationship is overall flat, meaning that red meat intake is overall unrelated with mortality. However, when we look at the two sets of points above and below the relationship line, for males and females, we see a different pattern. It appears that red meat intake and mortality are indeed significantly associated with one another, but in a J-curve pattern. That is, red meat intake is associated with increases and decreases in mortality, in the same study.
Each serving of red meat corresponds to approximately 84 g. Therefore, we could say, based on the graph above, that mortality would be minimized with consumption of a little less than 67 g/d of red meat (0.80*84) for males, and a little more than 115 g/d (1.37*84) for females. Not zero consumption, simply not a lot.
Now, one may say that this is very reasonable: a little bit of red meat is fine, but not too much. Generally females lose blood periodically, so they need a bit more than males. However, based on a number of other studies, it seems that the optimal intake amounts that we are seeing here are unusually low. If this is the case, what could be biasing the results?
Multivariate associations
Multivariate associations can distort results quite a lot. Such associations arise from correlations among multiple variables; correlations that should not per se be taken as strong indications of causality. Below are the correlations between “Red meat intake (servings/d)” and other relevant variables in the dataset taken from the study being considered here.
- Physical activity (MET-h/wk): -0.696. That is, increases in red meat intake are very strongly associated with decreases in physical activity in this study. One MET unit is equal to the energy produced per unit surface area of an average person seated at rest.
- Diabetes (%): 0.781. Increases in red meat intake are very strongly associated with increases in the percentages of individuals with diabetes.
- Food intake (cal/d): 0.604. Increases in red meat intake are strongly associated with increases in food intake in general.
- Current smoker (%): 0.519. Increases in red meat intake are strongly associated with increases in the percentages of smokers.
Let us take the physical activity variable, for example. It is inversely correlated with red meat intake, with a strong correlation coefficient, and it is unlikely that this correlation is due to direct causation - one way or the other. Below is the same graph as above, but now with labels indicating physical activity levels.
You can see that physical activity levels tend to be lower among females, which is in part due to them being on average smaller than males and thus burning fewer calories. Here you can see that physical activity is associated with mortality in a pattern that is pretty much the reverse of red meat intake. The reason for this is the strong inverse correlation between physical activity and red meat intake.
The highest mortality is associated with the lowest physical activity at the highest red meat intake. Interestingly, mortality goes up as one reaches the point at which physical activity is the highest at the lowest red meat intake.
Now take a look at the two graphs below. Both show the relationship between diabetes incidence and mortality. The first has biological sex indicated through legends. The second has physical activity levels indicated through labels.
One way to untangle the messy nature of the relationships above is to try to look for possible moderating effects, based on reasonable causal assumptions. One such assumption is that physical activity moderates the relationship between red meat intake and mortality.
The moderating effect of physical activity
The two graphs below show the relationships between red meat intake and mortality with (first graph) and without (second graph) the moderating effect of physical activity. Basically and with minimum statistical jargon, the numbers next to the arrows indicate the strengths of the associations (betas) and the probabilities that the associations are not real (Ps). By convention, a P value lower than 0.05 is normally seen as an indication that the association is strong enough to be considered real – i.e., not due to chance.
What the graphs above suggest is that increases in physical activity tend to make the relationship between red meat intake and mortality go from flat (or nonexistent) to negative. This is the meaning of the negative moderating coefficient next to the dashed arrow. In other words, as physical activity levels go up, more red meat intake is associated with less mortality.
The role of genetics
While being male or female means having different genetic profiles, with a full chromosome difference, the effect of biological sex on mortality appears to be confounded by the effect of physical activity. That is, physical activity, as measured in this study (using METs), is strongly correlated with biological sex, and also with mortality. As noted earlier, physical activity levels tend to be lower among females, which is in part due to them being on average smaller than males and thus burning fewer calories.
But another genetic factor that may influence the results and that is not included in this analysis is HFE hereditary haemochromatosis, a hereditary disease that leads to excessive intestinal absorption of dietary iron, resulting in iron overload. This genetic condition is relatively common in northern Europeans and their descendants, with a prevalence of 1 in 200 in this group. Factoid: it is quite common in Australia.
This level of prevalence matters when you are looking at mortality levels that vary along only approximately 8 in 1,000, as in this study. That translates to 0.4 in 200; much less than the prevalence of HFE hereditary haemochromatosis in northern Europeans and their descendants. That is, HFE hereditary haemochromatosis may be a major confounder in our analyses above, one that has not been controlled for. The study included 37,698 men from the Health Professionals Follow-up Study (1986-2008) and 83,644 women from the Nurses' Health Study (1980-2008). There must have been many individuals with HFE hereditary haemochromatosis in the sample.
In summary …
Based on all of the above, I think it is quite possible that for those who suffer from HFE hereditary haemochromatosis, both biological sex and physical activity affect the relationship between red meat intake and mortality.
Past menopause, women who suffer from HFE hereditary haemochromatosis should consider reducing their red meat intake, as well as intake of iron from other sources (particularly from pills). The same goes for men with the condition. Male and post-menopausal female sufferers should consider regularly donating blood.
Both men and women who suffer from HFE hereditary haemochromatosis should consider significantly increasing their level of physical activity to reduce the likelihood of iron overload. (This would be good for anyone.)
Why physical activity? Because iron is used to transport oxygen and in biological redox reactions, both of which are significantly increased during and after physical activity. In those who tend to accumulate iron in tissues, physical activity creates an increase in demand for iron that can balance the increased supply from iron-rich sources.
Our bodies evolved in the context of physical activity, often intense physical activity, and are thus maladapted for sedentary behavior.
