Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms

Abstract

The inability to measure putative pathogenic processes in the central nervous system (CNS) of living subjects precludes the determination of their temporal distribution, intra-individual heterogeneity, and their ability to predict disease course. Using multiple sclerosis (MS) as an example of a complex neurological disorder, we sought to determine if cerebrospinal fluid (CSF) biomarkers can be aggregated to predict future rates of MS progression and provide molecular insight into mechanisms of CNS destruction. 1,305 CSF biomarkers were analyzed blindly in the longitudinal training dataset (N=129) of untreated MS patients, using DNA-aptamer assay. Random forest models, validated in an independent longitudinal cohort (N=64), uncovered signatures of MS severity, measured by clinical scales and volumetric brain imaging. Cluster analysis revealed intra-individual molecular heterogeneity of disease mechanisms that include both CNS- and immune-related pathways and may represent novel targets for inhibiting MS progression.