The prevalence of knee osteoarthritis (OA) is widespread and the heterogeneous patient factors and clinical symptoms in OA patients impede developing personalized treatments for OA patients. In this study, we used unsupervised and supervised machine learning to organize the heterogeneity in knee OA patients and predict disease progression in individuals from the Osteoarthritis Initiative (OAI) dataset. We identified four distinct knee OA phenotypes using unsupervised learning that were defined by nutrition, disability, stiffness, and pain (knee and back) and were strongly related to disease fate. Interestingly, the absence of supplemental vitamins from an individuals diet was protective from disease progression. Moreover, we established a phenotyping tool and prognostic model from 5 variables (WOMAC disability score of the right knee, WOMAC total score of the right knee, WOMAC total score of the left knee, supplemental vitamins and minerals frequency, and antioxidant combination multivitamins frequency) that can be utilized in clinical practice to determine the risk of knee OA progression in individual patients. We also developed a prognostic model to estimate the risk for total knee replacement and provide suggestions for modifiable variables to improve long-term knee health. This combination of unsupervised and supervised data-driven tools provides a framework to identify knee OA phenotype in a clinical scenario and personalize treatment strategies.

The authors have declared no competing interest.

https://github.com/weihuaguo/cluster_oai.

Dr. Zeyu Huang wishes to acknowledge funding from the National Natural Science Foundation of China (NSFC: 92049101; 81972097; 81702185) and Sichuan Science and Technology Programs (No. 2022YFH0101, 2018HH0071). Dr. John T. Martin wishes to acknowledge funding from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (K99 AR077685, R00 AR077685).

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This study utilizes publicly available de-identified human data from the Osteoarthritis Initiative.

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