Journal of translational medicine · 2026
Association between oxidative balance score and cardiometabolic multimorbidity: differential mortality, mediation mechanisms, and machine learning insights.
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Tan Siyuan, Zeng Gaoming, Huang Ting, Zhang Zixi, Zhou Jiabao, Zhu Jiayi, Li Fanqi, Chen Hao, Liu Chan, Xiao Yichao, Liu Zhenjiang, Lin Qiuzhen, Tu Tao, Liu Qiming
Аннотация
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.
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.