Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using an interpretable machine learning framework: a UK biobank study

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, integrating 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. 14 sub-groups of OA risk profiles were identified, and validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients uniquely assigned to one of the sub-groups. 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 identified OA-specific biomarkers (e.g. CRTAC1 and COL9A1). In summary, this work identified opportunities for personalised OA prevention and insights into its underlying pathogenesis.