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. 2025 Apr:81:103588.
doi: 10.1016/j.redox.2025.103588. Epub 2025 Mar 7.

Role of oxidative balance score in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: Insights from traditional and machine learning approaches

Yang Chen  1 , Shuang Wu  2 , Hongyu Liu  3 , Ziyi Zhong  4 , Tommaso Bucci  5 , Yimeng Wang  2 , Manlin Zhao  6 , Yang Liu  7 , Zhengkun Yang  8 , Ying Gue  9 , Garry McDowell  10 , Bi Huang  11 , Gregory Y H Lip  12
Affiliations

Role of oxidative balance score in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: Insights from traditional and machine learning approaches

Yang Chen et al. Redox Biol. 2025 Apr.

Abstract

Objectives: To evaluate the roles of oxidative balance score (OBS) in staging and mortality risk of cardiovascular-kidney-metabolic syndrome (CKM).

Methods: Data of this study were from the National Health and Nutrition Examination Survey 1999-2018. We performed cross-sectional analyses using multinomial logistic regression to investigate the relationship between OBS and CKM staging. Cox proportional hazards models were used to assess the impact of OBS on mortality outcomes in CKM patients. Additionally, mediation analyses were performed to explore whether OBS mediated the relationships between specific predictors (Life's Simple 7 score [LS7], systemic immune-inflammation index [SII], frailty score) and mortality outcomes. Then, machine learning models were developed to classify CKM stages 3/4 and predict all-cause mortality, with SHapley Additive exPlanations values used to interpret the contribution of OBS components.

Results: 21,609 participants were included (20,319 CKM, median [IQR] age: 52.0 [38.0-65.0] years, 54.3% male, median [IQR] follow-up: 9.4 [5.3-14.1] years). Lower OBS quartiles were associated with advanced CKM staging. Moreover, lower OBS quartiles were related to increased mortality risk, compared to Q4 of OBS (all-cause mortality: Q1: HR 1.31, 95% CI 1.18-1.46, Q2: HR 1.27, 95% CI 1.14-1.42, Q3: HR 1.18, 95% CI 1.06-1.32; cardiovascular mortality: Q1: HR 1.44, 95% CI 1.16-1.79, Q2: HR 1.39, 95% CI 1.11-1.74, Q3: HR 1.26, 95% CI 1.01-1.57; non-cardiovascular mortality, Q1: HR 1.27, 95% CI 1.12-1.44, Q2: HR 1.23, 95% CI 1.08-1.40, Q3: HR 1.16, 95% CI 1.02-1.31), with optimal risk stratification threshold for OBS was 22. Additionally, OBS mediated (ranging 4.25%-32.85 %) effects of SII, LS7, frailty scores on mortality outcomes. Moreover, light gradient boosting machine achieved the highest performance for predicting advanced CKM staging (area under curve: 0.905) and all-cause mortality (area under curve: 0.875). Cotinine increased risk, while magnesium, vitamin B6, physical activity were protective.

Conclusions: This study highlights OBS as a risk stratification tool for CKM, emphasizing oxidative stress's role in CKM staging and mortality risk management.

Keywords: Cardiovascular-kidney-metabolic syndrome; Mortality; Oxidative balance score; Oxidative stress; Risk stratification.

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Conflict of interest statement

Declaration of competing interest The authors declare that there are no conflicts of interest associated with this manuscript. All authors have no financial, personal, or professional relationships that could inappropriately influence or bias the content of this work.

Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
Association Between OBS Quartiles and CKM Staging (Stages 0–4). Q1: OBS<15; Q2: 15≤ OBS <20; Q3: 20≤ OBS <26; Q4: OBS ≥26. P values from multinomial logistic regression models adjusted for age, sex, race and ethnicity, education level, poverty income ratio, smoking status, alcohol consumption, physical activity. CI, confidence interval; CKM, cardiovascular-kidney-metabolic syndrome; OBS, oxidative balance score; OR, odds ratio.
Fig. 2
Fig. 2
Associations Between OBS and Mortality Outcomes in CKM Patients. P values from multivariable Cox proportional hazards models adjusted for age, sex, race and ethnicity, education level, poverty income ratio, smoking status, alcohol consumption, physical activity. CI, confidence interval; CKM, cardiovascular-kidney-metabolic syndrome; HR, hazard ratio; OBS, oxidative balance score.
Fig. 3
Fig. 3
Mediation Analysis of OBS in the Associations Between SII, LS7, Frailty Scores, and Mortality Outcomes in CKM Patients. P values from the bootstrap sampling distribution or the normality assumption for statistical testing. CI, confidence interval; CKM, cardiovascular-kidney-metabolic syndrome; OBS, oxidative balance score.
Fig. 4
Fig. 4
ROC curves and SHAP-based feature importance of machine learning models in predicting advanced CKM staging and all-cause mortality. (a) ROC curves for model in predicting advanced CKM staging, (b) ROC curves for model in predicting all-cause mortality in CKM patients, (c) SHAP summary plot for model in predicting advanced CKM staging, (d) SHAP summary plot for model in predicting all-cause mortality in CKM patients. AUC, area under the curve; CKM, cardiovascular-kidney-metabolic syndrome; LightGBM, light gradient boosting machine; LR, logistic regression; MLP, multi-layer perceptron; RF, random forest; ROC, receiver operating characteristic; SHAP, SHapley Additive exPlanations; SVM, support vector machine.
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