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Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis

Author

Listed:
  • Julia Åkesson

    (Linköping University
    University of Skövde)

  • Sara Hojjati

    (Linköping University)

  • Sandra Hellberg

    (Linköping University
    Linköping University)

  • Johanna Raffetseder

    (Linköping University)

  • Mohsen Khademi

    (Karolinska University Hospital, Karolinska Institute)

  • Robert Rynkowski

    (Linköping University)

  • Ingrid Kockum

    (Karolinska University Hospital, Karolinska Institute)

  • Claudio Altafini

    (Linköping University)

  • Zelmina Lubovac-Pilav

    (University of Skövde)

  • Johan Mellergård

    (Linköping University)

  • Maria C. Jenmalm

    (Linköping University)

  • Fredrik Piehl

    (Karolinska University Hospital, Karolinska Institute)

  • Tomas Olsson

    (Karolinska University Hospital, Karolinska Institute)

  • Jan Ernerudh

    (Linköping University)

  • Mika Gustafsson

    (Linköping University)

Abstract

Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42682-9
    DOI: 10.1038/s41467-023-42682-9
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    References listed on IDEAS

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    1. 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.
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