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

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 Simonsen, Marianne Willert, Abd A. Tahrani, Zahra McVey and Ramneek Gupta (rmgp@novonordisk.com)
Additional contact information
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

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46663-4

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