AI-driven reclassification of multiple sclerosis progression.

Journal: Nature medicine
Published Date: Aug 20, 2025

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.

Authors

  • Habib Ganjgahi
    Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, United Kingdom.
  • Dieter A Häring
    Novartis Pharma AG, Basel, Switzerland.
  • Piet Aarden
    Novartis Pharma AG, Basel, Switzerland.
  • Gordon Graham
    Novartis Pharma AG, Basel, Switzerland.
  • Yang Sun
    Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Stephen Gardiner
    Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Wendy Su
    Novartis Pharma AG, Basel, Switzerland.
  • Claude Berge
    Roche, Basel, Switzerland.
  • Antje Bischof
    Clinic and Polyclinic for Neurology, University Hospital Münster, Münster, Germany.
  • Elizabeth Fisher
    Novartis Biomedical Research, Cambridge, MA, USA.
  • Laura Gaetano
    Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland.
  • Stefan P Thoma
    Roche, Basel, Switzerland.
  • Bernd C Kieseier
    Novartis Pharma AG, Basel, Switzerland.
  • Thomas E Nichols
    Department of Statistics, University of Warwick, Coventry, UK.
  • Alan J Thompson
    Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
  • Xavier Montalbán
    Hospital Universitari Vall d'Hebron, Barcelona, Spain.
  • Fred D Lublin
    Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Ludwig Kappos
    Department of Neurology, University Hospital Basel, Basel, Switzerland.
  • Douglas L Arnold
    Montreal Neurological Institute, McGill University, Montréal, Canada; NeuroRx Research, Montréal, Canada.
  • Robert A Bermel
    Department of Neurology, Mellen MS Center, Cleveland Clinic, Cleveland, OH, USA.
  • Heinz Wiendl
    German Competence Network Multiple Sclerosis (KKNMS), Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
  • Chris C Holmes
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LF, UK.

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