An interesting new paper from a British/Danish collaboration uses a clever trick based on genetics to untangle the messy correlation between obesity and mental health.
They had a huge (53,221) sample of people from Copenhagen, Denmark. It measured people’s height and weight to calculate their BMI, and asked them some simple questions about their mood, such as ”Do you often feel nervous or stressed?”
Many previous studies have found that being overweight is correlated with poor mental health, or at least with unhappiness (“psychological distress”). And this was exactly what the authors found in this study, as well.
Being very underweight was also correlated with distress; perhaps these were people with eating disorders or serious medical illnesses. But if you set those small number of people aside, there was a nice linear correlation between BMI and unhappiness. When they controlled for various other variables like income, age, and smoking, the effect of BMI became smaller but it was still significant.
But that’s just a correlation, and as we all know, “correlation doesn’t imply causation”. Actually, it does; something must be causing the correlation, it didn’t just magically appear out of nowhere. The point is that shouldn’t make simplistic assumptions about what the causal direction is.
It would be easy to make these assumptions. Maybe being miserable makes you fat, due to comfort eating. Or maybe being fat makes you miserable, because overweight is considered bad in our society. Or both. Or neither. We don’t know.
Finding this kind of correlation and then speculating about it is where a lot of papers finish, but for these authors, it was just the start. They genotyped everyone for two different genetic variants known, from lots of earlier work, to consistently affect body weight (FTO rs9939609 and MC4R rs17782313).
They confirmed that they were indeed associated with BMI; no surprise there. But here’s the surprising bit: the “fat” variants of each gene were associated with lesspsychological distress. The effects were very modest, but then again, their effects on weight are small too (see the graph above; the effects are in terms of z scores and anything below 0.3 is considered “small”.)
The picture was very similar for the other gene.
This allows us to narrow down the possibilities about causation. Being depressed clearly can’t change your genotype. Nothing short of falling into a nuclear reactor can change your genotype. It also seems unlikely that genotype was correlated with something else which protects against depression. That’s not impossible; it’s theproblem of population stratification, and it’s a serious issue with multi-ethnic samples, but this paper only included white Danish people.
So the author’s conclusion is that being slightly heavier causes you to be slightly happier, even though overall, weight is strongly correlated with being less happy. This seems paradoxical, but that’s what the data show.
That conclusion would fall apart, though, if these genes directly effect mood, and also, separately, make you fatter. The authors argue that this is unlikely, but I wonder. Both FTO and MC4R are active in the brain: they influence weight by making you eat more. If they can affect appetite, they might also affect mood. A quick PubMed search only turns up a couple of ratherspeculative papers about MC4R and its possible links to mood, so there’s no direct evidence for this, but we can’t rule it out.
But this paper is still an innovative and interesting attempt to use genetics to help get beneath the surface of complex correlations. It doesn’t explain the observed correlation between BMI and unhappiness – it actually makes it more mysterious. But that’s a whole lot better than just speculating about it.
Lawlor DA, Harbord RM, Tybjaerg-Hansen A, Palmer TM, Zacho J, Benn M, Timpson NJ, Smith GD, & Nordestgaard BG (2011). Using genetic loci to understand the relationship between adiposity and psychological distress: a Mendelian Randomization study in the Copenhagen General Population Study of 53,221 adults. Journal of internal medicine PMID: 21210875