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Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms

Author

Listed:
  • 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)

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

Suggested Citation

  • Peter Kosa & Christopher Barbour & Mihael Varosanec & Alison Wichman & Mary Sandford & Mark Greenwood & Bibiana Bielekova, 2022. "Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35357-4
    DOI: 10.1038/s41467-022-35357-4
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Benjamin B. Sun & Joseph C. Maranville & James E. Peters & David Stacey & James R. Staley & James Blackshaw & Stephen Burgess & Tao Jiang & Ellie Paige & Praveen Surendran & Clare Oliver-Williams & Mi, 2018. "Genomic atlas of the human plasma proteome," Nature, Nature, vol. 558(7708), pages 73-79, June.
    3. Shane A. Liddelow & Kevin A. Guttenplan & Laura E. Clarke & Frederick C. Bennett & Christopher J. Bohlen & Lucas Schirmer & Mariko L. Bennett & Alexandra E. Münch & Won-Suk Chung & Todd C. Peterson & , 2017. "Neurotoxic reactive astrocytes are induced by activated microglia," Nature, Nature, vol. 541(7638), pages 481-487, January.
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    1. Julia Åkesson & Sara Hojjati & Sandra Hellberg & Johanna Raffetseder & Mohsen Khademi & Robert Rynkowski & Ingrid Kockum & Claudio Altafini & Zelmina Lubovac-Pilav & Johan Mellergård & Maria C. Jenmal, 2023. "Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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