The cause of MS is still not known. Genetic studies have shown that over 200 sites in the genome determine susceptibility to MS, but altogether this only explains a fraction of MS risk. It’s also clear that ‘environmental factors’ – by which I mean anything not directly to do with genes – have an important role in determining who gets MS. Environmental factors that seem to increase the risk of MS include having lower vitamin D levels, smoking, obesity, glandular fever, being exposed to solvents, living at higher latitudes, head injury in early life, and possibly some other things like infection with a common virus, HHV6. All of these factors individually confer a very small additional risk of getting MS. This suggests a bit of a paradox – sometimes called ‘missing risk’ – MS is reasonably common (~1/1000 people in the UK), but individual genetic and environmental risk factors have such weak effects that they can’t account for the amount of MS in the population.
We and others think that interactions between genetic and environmental risk factors – where the combined effect of risk factors is greater than the sum of the individual risks – will help to explain a lot of this ‘missing risk’. If we can explain why certain people exposed to certain cocktails of genetic and environmental risk develop MS, we’ll be one step closer to preventing it, which is our ultimate goal.
Attempts to understand which environmental factors are associated with MS have generally been the preserve of classical observational study designs: case-control and cohort studies. In these study types, researchers collect data on who is ‘exposed’ to a risk factor (e.g. who smokes, has low vitamin D, has high body mass index – BMI), data on who has/gets MS, and data on lots of possible ‘confounding’ factors, i.e. factors which might obscure or generate spurious associations. Even the most rigorously designed observational study can only tell us about association (i.e. correlation), not causation. Moving from understanding that a particular risk factor is associated with MS to showing that it causes MS is very challenging. There are two main reasons for this:
- Confounding: another factor explains why someone is exposed to the risk factor and why they get MS. E.g. socio-economic status might be associated with higher levels of glandular fever and higher levels of MS, which would create an association between glandular fever and MS, but doesn’t mean glandular fever causes MS.
- Reverse causation: having the disease (even in very early stages, before diagnosis) might cause you to be exposed to a risk factor. E.g. early MS might make you slow down, be less able to do exercise, and gain weight. This would create an association between high body weight and MS, but doesn’t mean that obesity causes MS.
A clever technique for overcoming these problems is called Mendelian Randomisation. This approach uses genetic variants – which are randomly assigned at conception and don’t change throughout life – as proxies for a risk factor of interest. For instance, if particular genetic variants (also called Single Nucleotide Polymorphisms; SNPs) are strongly associated with levels of vitamin D, those variants can be used as a rough estimate of what an individual’s average level of vitamin D might be over a lifetime. Using data from large genetic studies of MS, you can then see what effect these same variants have on MS risk. If a variant is associated with lower vitamin D levels, and the same variant is also associated with a higher risk of MS, you can infer that lifetime exposure to lower vitamin D may cause MS. Because these variants are randomly assigned and don’t change, they overcome the problems of confounding and reverse causation:
It was already suspected from well-designed observational studies that obesity, particularly during childhood, and low vitamin D were strongly associated with MS risk. Mendelian randomisation studies had also shown that the effects of BMI in adulthood and of low vitamin D on MS risk were likely to be causal.
We extended this approach using newly-available large datasets to test whether childhood obesity was also causally associated with MS risk. We found evidence, using mendelian randomisation, that having higher BMI during childhood causes MS.
Higher BMI also lowers vitamin D levels, so it’s plausible that BMI only affects MS risk via its effects on vitamin D, and not directly. Using mendelian randomisation, you can control for the effects of confounders – i.e. if a SNP which raises BMI also lowers vitamin D. We did this and found that the effect of childhood BMI on MS risk is at least partly independent of its effects on vitamin D.
There are lots of limitations to this approach which you can read more about in the paper.
For me this study has two main implications:
Firstly, it suggests that targeting childhood obesity and low vitamin D may help to prevent MS. The effects we see are very small, and so in the general population these two measures would not prevent much MS. However, for people at higher risk, such as those who have family members with MS, those at high latitude, those who’ve had glandular fever – we anticipate that the effects of targeting these two risk factors would be greater. Of course, as has been pointed out by people on this blog – this is just sensible health advice in general, and there are many good reasons to prevent childhood obesity, which is associated with far more common diseases like cancer and diabetes.
Secondly, these findings confirm, for me, that obesity during childhood is a causal risk factor, and this is important because of what it says about the pathways which cause MS. Clearly, obesity during this critical period of development does something to the immune system to trigger an autoimmune attack on the Central Nervous System. Understanding how childhood obesity leads to MS may hopefully yield fundamental insights into what causes the disease, which I hope will lead to effective strategies for preventing it or halting it in its earliest stages.
The paper is free to read here: https://nn.neurology.org/content/7/2/e662
Objective To update the causal estimates for the effects of adult body mass index (BMI), childhood BMI, and vitamin D status on multiple sclerosis (MS) risk.
Methods We used 2-sample Mendelian randomization to determine causal estimates. Summary statistics for SNP associations with traits of interest were obtained from the relevant consortia. Primary analyses consisted of random-effects inverse-variance-weighted meta-analysis, followed by secondary sensitivity analyses.
Results Genetically determined increased childhood BMI (ORMS 1.24, 95% CI 1.05–1.45, p = 0.011) and adult BMI (ORMS 1.14, 95% CI 1.01–1.30, p = 0.042) were associated with increased MS risk. The effect of genetically determined adult BMI on MS risk lessened after exclusion of 16 variants associated with childhood BMI (ORMS 1.11, 95% CI 0.97–1.28, p = 0.121). Correcting for effects of serum vitamin D in a multivariate analysis did not alter the direction or significance of these estimates. Each genetically determined unit increase in the natural-log-transformed vitamin D level was associated with a 43% decrease in the odds of MS (OR 0.57, 95% CI 0.41–0.81, p = 0.001).
Conclusions We provide novel evidence that BMI before the age of 10 is an independent causal risk factor for MS and strengthen evidence for the causal role of vitamin D in the pathogenesis of MS.