Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms

Peter Kosa, Christopher Barbour, Mihael Varosanec, Alison Wichman, Mary Sandford, Mark Greenwood and Bibiana Bielekova ()
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Peter Kosa: National Institutes of Health
Christopher Barbour: National Institutes of Health
Mihael Varosanec: National Institutes of Health
Alison Wichman: National Institutes of Health
Mary Sandford: National Institutes of Health
Mark Greenwood: Montana State University
Bibiana Bielekova: National Institutes of Health

Nature Communications, 2022, vol. 13, issue 1, 1-16

Abstract: Abstract While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p

Date: 2022
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DOI: 10.1038/s41467-022-35357-4

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