Abstract
Determining effective means of preventing Multiple Sclerosis (MS) relies on testing preventive strategies in trial populations. However, because of the low incidence of MS, demonstrating that a preventive measure has benefit requires either very large trial populations or an enriched population with a higher disease incidence. Risk scores which incorporate genetic and environmental data could be used, in principle, to identify high-risk individuals for enrolment in preventive trials. Here we discuss the concepts of developing predictive scores for identifying individuals at high risk of MS. We discuss the empirical efforts to do so using real cohorts, and some of the challenges-both theoretical and practical-limiting this work. We argue that such scores could offer a means of risk stratification for preventive trial design, but are unlikely to ever constitute a clinically-helpful approach to predicting MS for an individual.
Highlights
Multiple Sclerosis (MS) is a prototypical complex autoimmune disease of the central nervous system
A variety of environmental influences are associated with increased susceptibility to MS; the most consistent and replicated risk factors are smoking, childhood obesity, infectious mononucleosis, and lower serum vitamin D [2]
The largest genome-wide association study (GWAS) of MS orchestrated by the International Multiple Sclerosis Genetics Consortium (IMSGC) discovered 233 genetic signals associated with MS, collectively explaining around 50% of MS heritability [3]
Summary
Multiple Sclerosis (MS) is a prototypical complex autoimmune disease of the central nervous system. Efforts to predict MS using risk scores comprising genetic and environmental risk factors have all failed to show meaningful predictive performance on an individual level As genotyping costs continue to fall and large biobank-scale GWAS become available for a number of common traits and diseases, it is conceivable that genotyping could become a routinely-available clinical test to help predict an individual’s risk of developing a complex disease [20].
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