Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms

• Published in: Nature communications Vol. 13; no. 1; pp. 7670 - 16
• Main Authors: Kosa, Peter, Barbour, Christopher, Varosanec, Mihael, Wichman, Alison, Sandford, Mary, Greenwood, Mark, Bielekova, Bibiana
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 38/79; 59/57; 631/154/53; 631/553/2695; 692/617/375/1666; Abnormalities; Aging; Aging (natural); Autoimmune diseases; Autopsies; Autopsy; Biomarkers; Biomarkers - cerebrospinal fluid; Central nervous system; Cerebrospinal fluid; Clinical trials; Drug development; Drug screening; Heterogeneity; Humanities and Social Sciences; Humans; Life span; Machine learning; Models, Molecular; Molecular modelling; multidisciplinary; Multiple sclerosis; Multiple Sclerosis - cerebrospinal fluid; Nervous System Diseases; Neurological diseases; Pharmacodynamics; Phenotypes; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-35357-4

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 < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful. Multiple sclerosis (MS) changes the composition of the CSF. Here the authors use patient samples and aggregate CSF biomarkers into models that predict disability across all MS phenotypes, and identify potentially causal mechanisms and molecular disease heterogeneity.