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Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Author

Listed:
  • Rikke Linnemann Nielsen

    (Novo Nordisk Research Centre Oxford)

  • Thomas Monfeuga

    (Novo Nordisk Research Centre Oxford)

  • Robert R. Kitchen

    (Novo Nordisk Research Centre Oxford)

  • Line Egerod

    (Novo Nordisk Research Centre Oxford)

  • Luis G. Leal

    (Novo Nordisk Research Centre Oxford)

  • August Thomas Hjortshøj Schreyer

    (Novo Nordisk Research Centre Oxford)

  • Frederik Steensgaard Gade

    (Novo Nordisk A/S)

  • Carol Sun

    (Novo Nordisk Research Centre Oxford)

  • Marianne Helenius

    (Technical University of Denmark)

  • Lotte Simonsen

    (Novo Nordisk A/S)

  • Marianne Willert

    (Novo Nordisk A/S)

  • Abd A. Tahrani

    (Novo Nordisk A/S)

  • Zahra McVey

    (Novo Nordisk Research Centre Oxford)

  • Ramneek Gupta

    (Novo Nordisk Research Centre Oxford)

Abstract

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients’ lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71–0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

Suggested Citation

  • Rikke Linnemann Nielsen & Thomas Monfeuga & Robert R. Kitchen & Line Egerod & Luis G. Leal & August Thomas Hjortshøj Schreyer & Frederik Steensgaard Gade & Carol Sun & Marianne Helenius & Lotte Simons, 2024. "Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46663-4
    DOI: 10.1038/s41467-024-46663-4
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    References listed on IDEAS

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    1. Heli Julkunen & Anna Cichońska & Mika Tiainen & Harri Koskela & Kristian Nybo & Valtteri Mäkelä & Jussi Nokso-Koivisto & Kati Kristiansson & Markus Perola & Veikko Salomaa & Pekka Jousilahti & Annamar, 2023. "Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Benjamin B. Sun & Joshua Chiou & Matthew Traylor & Christian Benner & Yi-Hsiang Hsu & Tom G. Richardson & Praveen Surendran & Anubha Mahajan & Chloe Robins & Steven G. Vasquez-Grinnell & Liping Hou & , 2023. "Plasma proteomic associations with genetics and health in the UK Biobank," Nature, Nature, vol. 622(7982), pages 329-338, October.
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