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

• Published in: Nature communications Vol. 15; no. 1; p. 2817
• Main Authors: Nielsen, Rikke Linnemann, Monfeuga, Thomas, Kitchen, Robert R., Egerod, Line, Leal, Luis G., Schreyer, August Thomas Hjortshøj, Gade, Frederik Steensgaard, Sun, Carol, Helenius, Marianne, Simonsen, Lotte, Willert, Marianne, Tahrani, Abd A., McVey, Zahra, Gupta, Ramneek
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-04-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 38/39; 45; 631/114/1305; 631/114/2413; 692/499; Anti-inflammatory agents; Arthritis; Biobanks; Biomarkers; Collagen (type IX); Customization; Diagnosis; Growth differentiation factor 5; Health risks; Humanities and Social Sciences; Inflammation; Learning algorithms; Machine learning; multidisciplinary; Nonsteroidal anti-inflammatory drugs; Osteoarthritis; Pathogenesis; Risk assessment; Science; Science (multidisciplinary); Signal transduction; Subgroups; Transforming growth factor-b; United Kingdom > UK
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-46663-4

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.
Osteoarthritis can be caused by multiple biological mechanisms but the drivers of disease risk are not well understood. Here, the authors use data from UK Biobank in machine learning models to identify clinical and biological markers associated with development of osteoarthritis and identify sub-groups with different risk profiles.