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Association between oxidative balance score and cardiometabolic multimorbidity: differential mortality, mediation mechanisms, and machine learning insights

调解 医学 逻辑回归 胰岛素抵抗 优势比 可能性 机器学习 内科学 糖尿病 维生素D与神经学 老年学 平衡(能力) 体质指数 联想(心理学) 全国健康与营养检查调查 胰岛素 比例危险模型 低风险 年轻人
作者
Siyuan Tan,Gaoming Zeng,Ting Huang,Zixi Zhang,J Zhou,Jiayi Zhu,Fanqi Li,Hao Chen,Chan Liu,Yichao Xiao,Liu Z,Qiuzhen Lin,Tao Tu,Qiming Liu
出处
期刊:Journal of Translational Medicine [BioMed Central]
标识
DOI:10.1186/s12967-026-08291-y
摘要

BACKGROUND: Cardiometabolic multimorbidity (CMM) is a major global health burden associated with increased morbidity and mortality. The oxidative balance score (OBS), a composite measure of dietary and lifestyle factors related to oxidative balance, has not been systematically evaluated in relation to CMM and mortality. METHODS: We analyzed 11,365 NHANES participants. Logistic regression, Cox proportional hazards models, and restricted cubic splines were used to evaluate associations of OBS with CMM and mortality. Mediation analysis further evaluated inflammatory and insulin resistance indicators as potential mediators among non-CMM participants. Machine learning identified key OBS components for predicting outcomes. RESULTS: Among 11,365 participants (median age, 45 years; 52.55% male), higher OBS was associated with more favorable sociodemographic characteristics and a lower prevalence of chronic conditions. OBS was lower in participants with CMM and decreased further with a greater number of comorbidities. CMM was associated with higher mortality, whereas each 5-point increase in OBS was associated with lower odds of CMM (OR = 0.96) and reduced risks of all-cause mortality (HR = 0.85), with similar inverse associations observed for cardiovascular and non-cardiovascular mortality. These associations remained robust among non-CMM individuals but were attenuated among those with CMM. Mediation analysis suggested partial mediation of the association between OBS and mortality through inflammatory and insulin resistance pathways. In machine learning analysis, LightGBM achieved the best predictive performance (AUC: 0.817 and 0.849), with body mass index, physical activity, and vitamin intake identified as key predictors. CONCLUSION: Higher OBS was associated with lower CMM prevalence and reduced mortality risk among non-CMM participants, partially through inflammatory and insulin resistance pathways, underscoring its potential relevance in cardiometabolic risk assessment.
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