The alcohol J-curve is the common empirical finding that moderate drinkers have better health than abstainers, and better health than heavy drinkers (see this post, for example). If you plot the relationship between the amount a person drinks and negative measures of health, the resulting curve is shaped like the letter J (as in that earlier post). However, few of the studies that establish a J-curve relationship demonstrate a causal relationship. That's because it is difficult to randomise people into a level of drinking.
However, a relatively new development in epidemiology is the idea of Mendelian randomisation. People are randomly assigned genes at birth. Some of those genes are associated with alcohol consumption. So, alcohol consumption is (partially) randomly assigned by the assignment of genes. Studies can then use instrumental variables regression (a relatively common technique in economics) to estimate the causal effect of alcohol consumption on a range of health (and other) outcomes.
What happens to the J-curve in these Mendelian randomisation studies? This editorial published in the journal Addiction in 2015 (open access), by Tanya Chikritzhs (Curtin University) and co-authors, summarises the state of knowledge up to that point. They note that there is no J-curve relationship observed in Mendelian randomisation studies, or at least that the results are much more equivocal about its existence, and conclude that:
The foundations of the hypothesis for protective effects of low-dose alcohol have now been so undermined that in our opinion the field is due for a major repositioning of the status of moderate alcohol consumption as protective.
I was recently referred to this editorial during a discussion on the J-curve. Having read a bit more about Mendelian randomisation though, I am not entirely convinced. The problem is that in these Mendelian randomisation studies, the two main assumptions of instrumental variables analysis must be met. First, the instrument (having the gene, or not) must be associated with the endogenous variable (alcohol consumption). That assumption should be relatively easy to meet. A researcher simply searches the literature on genome-wide association studies for some gene that is associated with alcohol consumption. Second, the instrument must only affect the dependent variable (health outcomes) through its effect on the endogenous variable (alcohol consumption), and not directly or through any other variable. This is known in economics as the exclusion restriction.
The exclusion restriction is a difficult to satisfy, in part because it is impossible to test statistically. Instead, most researchers settle for being able to identify an instrument that is 'plausibly exogenous'. That is, they find an instrument that is extremely unlikely to affect the outcome variable directly, or indirectly through any other mechanism than through the exogenous variable. In this case, that would mean identifying a gene that could only possibly affect health outcomes through its effect on alcohol consumption.
And that is the problem here. There is no gene for alcohol consumption. All that genome-wide association studies will identify is genes that are associated with alcohol consumption. Those genes all have some other purpose, and that other purpose may be linked to health outcomes in a way that doesn't involve alcohol consumption. As far as I am aware, the Mendelian randomisation studies to date haven't been able to establish that the gene in question has no other effects on health outcomes. That makes their claims to causality no better than those of the correlational studies that they are supposed to improve on.
Mendelian randomisation is good in theory, but not always in practice. For now, the J-curve lives.
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