Lin Yan, Qiu Bin-Wei, Xu Kai-Li, Lin Jin-Liang
Pediatrics, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China.
Medicine (Baltimore). 2025 Apr 25;104(17):e42262. doi: 10.1097/MD.0000000000042262.
Asthma is a common chronic respiratory disease related to oxidative stress. Oxidative balance score (OBS) could assess systemic oxidative stress status. Thus, we tried to explore the prediction value of OBS in asthma and the disease course. The data were obtained from the National Health and Nutrition Examination Survey database. Asthma and the disease course were determined by the Patient Health Questionnaire. OBS was scored by 20 dietary and lifestyle components. The receiver operating characteristic and decision curve analysis were used to assess the prediction value of OBS. Logistic regression, XG Boost, and Random Forest methods were used to obtain an optimal OBS-based model and rank the importance of OBS components. Mediation analysis was used to explore the possible interplay of OBS components on the disease course of asthma. From 2011 to 2018, 7348 participants including 6597 participants without asthma and 751 participants with asthma were enrolled. Receiver operating characteristic and decision curve analysis curves exhibited that the OBS-based model showed an improved prediction value than the OBS for the disease course of asthma. Machine learning techniques results showed that the body mass index, niacin, and selenium were the key components of OBS. Besides, niacin had a direct relation with the disease course and could also regulate the course of asthma by regulating body mass index. OBS could predict the disease course of asthma, and niacin may be the most important component of OBS in the development of asthma.
哮喘是一种与氧化应激相关的常见慢性呼吸道疾病。氧化平衡评分（OBS）可评估全身氧化应激状态。因此，我们试图探讨OBS在哮喘及其病程中的预测价值。数据来自国家健康与营养检查调查数据库。哮喘及其病程由患者健康问卷确定。OBS由20种饮食和生活方式因素评分。采用受试者工作特征曲线和决策曲线分析来评估OBS的预测价值。使用逻辑回归、XG Boost和随机森林方法来获得基于OBS的最优模型，并对OBS各因素的重要性进行排名。采用中介分析来探讨OBS各因素在哮喘病程中可能的相互作用。2011年至2018年，共纳入7348名参与者，其中6597名无哮喘参与者和751名哮喘参与者。受试者工作特征曲线和决策曲线分析表明，基于OBS的模型在哮喘病程预测价值方面优于OBS。机器学习技术结果显示，体重指数、烟酸和硒是OBS的关键因素。此外，烟酸与病程直接相关，还可通过调节体重指数来调节哮喘病程。OBS可预测哮喘病程，烟酸可能是OBS在哮喘发生发展中最重要的因素。
