Genetic profiles predict risk of multiple sclerosis response to treatment and severity

<h3>Objective</h3> The first genetic loci associated with multiple sclerosis (MS) disease severity have recently been identified. Two pivotal genome wide association studies identified independent associations. In metabolic disorders, clustering of patients based on multiple genetic loci is associated with relevant clinical outcomes, however this approach has never been applied to MS. <h3>Methods</h3> We used detailed genomic data from 1,455 MS patients to assess whether genetic unsupervised hierarchical clustering analysis can predict relevant disease outcomes (time to EDSS milestone, MRI outcomes etc). <h3>Results</h3> We identified three clusters, each of which had patients who shared similar genetic profiles. Patients in genetic cluster 1 had a significantly lower risk to develop early sustained disability (time to use of a walking aid more than 6 years later compared to patients in cluster 2 and 3, p=0.003, and they became wheelchair dependent almost 13 years later than people in cluster 2, p=0.02). Patients in cluster 2 responded well to MS disease modifying treatments (p=0.003), whereas no treatment effect was observed in those who did vs did not receive DMT in either cluster 1 and cluster 3. <h3>Interpretation</h3> This is the first study showing that unsupervised clustering based on genotype is able to predict important disease outcomes in MS.