Background Predicting response to treatment and long-term disability in multiple sclerosis (MS) remains challenging. In other complex diseases, combining genetic risk variants has enabled the detection of relevant clinical endophenotypes associated with important outcomes, but this strategy has never been applied to MS. Methods We applied unsupervised hierarchical clustering to genomic risk scores in a prospective Welsh MS cohort (n=1455) and replicated the findings in the postmortem Netherlands Brain Bank (NBB) MS (NBB-MS) cohort (n=272). Disease progression was assessed using survival analysis to determine the time to Expanded Disability Status Scale (EDSS) milestones. Results Three genomic clusters were identified, each with similar genetic profiles. Baseline demographics did not differ between clusters. Welsh patients in cluster 1 attained EDSS 6 and EDSS 8 significantly later than clusters 2 and 3 (by 6 years, p=3×10 −3 and 13 years, p=0.02, respectively). These findings were replicated in the NBB-MS cohort (6-year delay to EDSS 6 for cluster 1 vs 2, p=0.04). Genomic clustering independently predicted disease progression (HRs 1.3–2.0, all p<0.05), beyond established risk factors. Clusters 2 and 3 showed a greater annual increase in T2 lesion load on serial MR imaging (p=0.04). In cluster 2, patients receiving disease-modifying treatments had delayed progression to EDSS 6 (p=3×10 − ³), while no such benefit was observed in clusters 1 or 3. Cluster 2 patients also had earlier onset of symptoms, including dysphagia (p=0.02) and spasticity (p=8×10 − ⁴) in the NBB-MS cohort. Conclusions Genetic clustering reveals clinically meaningful MS subtypes with distinct prognoses and treatment responses, highlighting its potential role in precision medicine for MS management.
Kreft et al. (Fri,) studied this question.