Tu Ling, Wei Fuling, Song Yuqing, Huang Haitao, Qing Ligang, Luo Xi, Liu Ying, Chen Hong
Department of Nursing, West China School of Nursing, West China Hospital, Sichuan University, Chengdu, 610064, China.
School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Sci Rep. 2025 May 13;15(1):16620. doi: 10.1038/s41598-025-00816-7.
This study measured sociodemographic characteristics, dietary habits, lifestyle habits, genetics, and other factors that may contribute to the development of Rheumatoid Arthritis (RA). Independent risk factors for RA were identified by logistic regression analysis, and a prediction model was constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the prediction accuracy of the model, and the calibration of the model was evaluated by the Hosmer-Lemeshow test. A total of 432 participants, comprising 216 healthy individuals and 216 patients diagnosed with RA at two hospitals in Sichuan, China, from March 2022 to January 2023 were included in this study. Logistic regression analysis revealed that occupation type, place of residence, history of mumps, dietary combination, sweet, damp dwelling, fish, vaccine history, and rs805297 were significantly associated with the pathogenesis of RA. The model constructed in this study showed good prediction, with an AUC of 0.912. The Youden index was 0.699, the sensitivity was 0.847 and the specificity was 0.852. The Hosmer-Lemeshow test results (χ = 8.441, P = 0.392) indicated that the model had good diagnostic value. The internal validation AUC was 0.942. We propose a new promising model for identifying individuals at risk of developing RA.
本研究测量了社会人口学特征、饮食习惯、生活方式习惯、遗传学以及其他可能导致类风湿关节炎（RA）发病的因素。通过逻辑回归分析确定RA的独立危险因素，并构建预测模型。采用受试者工作特征曲线下面积（AUC）评估模型的预测准确性，通过Hosmer-Lemeshow检验评估模型的校准情况。本研究纳入了2022年3月至2023年1月在中国四川两家医院就诊的432名参与者，其中包括216名健康个体和216名被诊断为RA的患者。逻辑回归分析显示，职业类型、居住地点、腮腺炎病史、饮食组合、甜食、居住环境潮湿、鱼类、疫苗接种史以及rs805297与RA的发病机制显著相关。本研究构建的模型显示出良好的预测效果，AUC为0.912。约登指数为0.699，灵敏度为0.847，特异度为0.852。Hosmer-Lemeshow检验结果（χ = 8.441，P = 0.392）表明该模型具有良好的诊断价值。内部验证AUC为0.942。我们提出了一个有前景的新模型，用于识别有患RA风险的个体。
