Zeyu Huang, Mary A. Bucklin, Weihua Guo, John T. Martin
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引用次数: 0
摘要
膝关节骨关节炎(OA)的发病率很高,OA患者的异质性因素和临床症状阻碍了为OA患者开发个性化治疗方法。在这项研究中,我们使用无监督和有监督机器学习来组织膝关节 OA 患者的异质性,并从骨关节炎倡议(OAI)数据集中预测个体的疾病进展。我们利用无监督学习确定了四种不同的膝关节 OA 表型,它们由营养、残疾、僵硬和疼痛(膝关节和背部)定义,并与疾病的命运密切相关。有趣的是,个人饮食中缺乏补充维生素对疾病进展具有保护作用。此外,我们还通过五个变量(右膝WOMAC残疾评分、右膝WOMAC总评分、左膝WOMAC总评分、补充维生素和矿物质的频率、抗氧化复合维生素的频率)建立了表型工具和预后模型,可用于临床实践,以确定个体患者的膝关节OA进展风险。我们还开发了一个预后模型,用于估算全膝关节置换术的风险,并为改善膝关节长期健康的可调节变量提供建议。这种将无监督和有监督数据驱动工具相结合的方法提供了一个框架,可用于在临床场景中识别膝关节 OA 表型并制定个性化治疗策略。
Disease progression and clinical outcomes in latent osteoarthritis phenotypes: Data from the Osteoarthritis Initiative
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 individual’s 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.