Abstract Multiple sclerosis (MS) affects 2.9 million people. Traditional classification of MS into distinct subtypes poorly reflects its pathobiology and has limited value for prognosticating disease evolution and treatment response, thereby hampering drug discovery. Here we report a data-driven classification of MS disease evolution by analyzing a large clinical trial database (approximately 8,000 patients, 118,000 patient visits and more than 35,000 magnetic resonance imaging scans) using probabilistic machine learning. Four dimensions define MS disease states: physical disability, brain damage, relapse and subclinical disease activity. Early/mild/evolving (EME) MS and advanced MS represent two poles of a disease severity spectrum. Patients with EME MS show limited clinical impairment and minor brain damage. Transitions to advanced MS occur via brain damage accumulation through inflammatory states, with or without accompanying symptoms. Advanced MS is characterized by moderate to high disability levels, radiological disease burden and risk of disease progression independent of relapses, with little probability of returning to earlier MS states. We validated these results in an independent clinical trial database and a real-world cohort, totaling more than 4,000 patients with MS. Our findings support viewing MS as a disease continuum. We propose a streamlined disease classification to offer a unifying understanding of the disease, improve patient management and enhance drug discovery efficiency and precision.
Ganjgahi et al. (Wed,) studied this question.