What question did this study set out to answer?

March 5, 2026Open Access

Remnant cholesterol inflammatory index as a novel predictor of cardiovascular disease: Cross-sectional, longitudinal, and cumulative exposure analyses

QLQiheng Li JSJiazhang SiBeijing Haidian Hospital QJQi JinBeijing Haidian Hospital

Key Points

Evaluate the predictive value of the remnant cholesterol inflammatory index (RCII) for cardiovascular disease (CVD).
Conducted secondary analyses of the China Health and Retirement Longitudinal Study (CHARLS).
Utilized cross-sectional, longitudinal, and cumulative exposure analyses.
Calculated RCII as remnant cholesterol × high-sensitivity C-reactive protein ÷ 10.
Applied logistic and Cox proportional hazards regression models for association quantification.
Highest RCII tertile showed 34% increased odds of prevalent CVD in cross-sectional analysis.
Longitudinal analysis indicated a 26% increased hazard for incident CVD over a 7-year follow-up.
Cumulative exposure analysis demonstrated a 32% increased hazard, the strongest association observed.
Associations were more pronounced for stroke compared to heart disease.

Abstract

Traditional cardiovascular risk assessment using individual biomarkers may inadequately capture the complex interactions between atherogenic lipids and systemic inflammation. The remnant cholesterol inflammatory index (RCII) represents a novel composite biomarker integrating these pathways, but comprehensive evaluation of its cardiovascular predictive value remains limited. We conducted secondary analyses of the China Health and Retirement Longitudinal Study (CHARLS), utilizing three complementary approaches: cross-sectional analysis (n = 10,459), longitudinal cohort analysis (n = 6,028), and cumulative exposure analysis (n = 3,515). RCII was calculated as remnant cholesterol × high-sensitivity C-reactive protein ÷ 10. Primary outcome was cardiovascular disease (CVD), defined as heart disease or stroke. Associations were quantified using logistic and Cox proportional hazards regression models with progressive adjustment strategies. RCII demonstrated consistent associations with CVD across all analytical approaches. In cross-sectional analysis, the highest RCII tertile showed 34% increased odds of prevalent CVD (OR: 1.34, 95% CI: 1.12–1.61). Longitudinal analysis revealed 26% increased hazard for incident CVD (HR: 1.26, 95% CI: 1.07–1.47) over 7-year follow-up. Cumulative exposure analysis demonstrated the strongest association, with 32% increased hazard (HR: 1.32, 95% CI: 1.04–1.67). Dose-response relationships were linear without threshold effects. Associations were particularly pronounced for stroke compared to heart disease. RCII represents a valuable integrated biomarker for cardiovascular risk assessment, with cumulative exposure providing superior predictive performance compared to single-point measurements. These findings support RCII’s potential clinical applicability for identifying high-risk individuals requiring intensified prevention strategies targeting both metabolic and inflammatory pathways.

Ask AI

Helpful

Bookmark

View Full Paper