Abstract
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.