Dataset Information

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

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.

SUBMITTER: Nielsen RL

PROVIDER: S-EPMC10985086 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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

Nielsen Rikke Linnemann RL Monfeuga Thomas T Kitchen Robert R RR Egerod Line L Leal Luis G LG Schreyer August Thomas Hjortshøj ATH Gade Frederik Steensgaard FS Sun Carol C Helenius Marianne M Simonsen Lotte L Willert Marianne M Tahrani Abd A AA McVey Zahra Z Gupta Ramneek R

Nature communications 20240401 1

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 predicti ...[more]

PMID: 38561399

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Dataset Information

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

Publications

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

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