Association between the oxidative balance score and all-cause mortality in patients with cardiovascular disease-cancer comorbidity

PURPOSE: The Oxidative Balance Score (OBS) is a composite measure of systemic oxidative stress. This study aims to evaluate the impact of OBS on all-cause mortality in patients with cardiovascular disease-cancer comorbidity and to use machine learning to identify related factors. METHODS: We analyzed data from the 2007-2018 US National Health and Nutrition Examination Survey (NHANES). Cox regression, Kaplan-Meier analysis, restricted cubic splines (RCS), and subgroup analysis were used to explore the association between OBS and CVD-cancer comorbidity. Five machine learning models were constructed and compared to identify the optimal CVD-cancer comorbidity risk prediction model, and feature importance was assessed. RESULTS: Among the study participants, compared to participants in the lowest tertile of the OBS score, those in the highest tertile exhibited a lower risk of all-cause mortality (HR = 0.78, 95% CI: 0.64-0.95, p = 0.016). RCS showed that OBS had a no nonlinear evidences with CVD-cancer comorbidity. In subgroup analyses, the association remained consistent across all subgroups, with no statistically significant interaction observed (all P for interaction > 0.05). The random forest algorithm was identified as the optimal predictive model through machine learning evaluation. Decision curve analysis (DCA) and calibration curves further supported the internal validity of the model. SHAP analysis revealed that age, smoking intensity, niacin intake, and selenium levels were the most influential predictive factors. CONCLUSIONS: This study demonstrates a significant inverse association between higher OBS and all-cause mortality in patients with cardiovascular disease-cancer comorbidity and provides an interpretable machine learning model to predict this comorbidity.