Residual Cholesterol Inflammatory Index: A Predictor of Cardiovascular-Kidney-Metabolic Syndrome; Insights from NHANES and CHARLS Cohorts.

BACKGROUND

Cardiovascular-kidney-metabolic syndrome (CKM) is a multi-organ metabolic disorder with an increased risk of morbidity and mortality among populations worldwide. Identifying effective biomarkers for early risk assessment is important. The residual cholesterol (RC) inflammatory index (RCII), which is derived from the combination of RC and C-reactive protein (CRP), represents a novel biomarker that captures the interplay between dyslipidemia and systemic inflammation. The interaction between RCII and CKM risk in the general populations of the US and China and evaluated its dose-response pattern, nonlinear relationship, and subgroup variations were evaluated.

METHODS

Data were retrieved by accessing 2 nationally representative cohorts: the US National Health and Nutrition Examination Survey (NHANES, n = 10,669) and the China Health and Retirement Longitudinal Study (CHARLS, n = 9,496). Multivariate logistic regression models (Model 1-3) were constructed to determine the relationship between RCII (as a continuous and quartile-categorized variable) and CKM. Progressive adjustments were made for demographic, socioeconomic, lifestyle, and anthropometric factors. In addition, a restricted cubic spline (RCS) analysis was conducted to examine nonlinearity and stratified analyses to assess effect modification across subgroups.

RESULTS

Increased RCII was significantly linked with increased CKM risk in both models. In fully adjusted models, each 1-unit increment in RCII was associated with a 4% increase in CKM risk in NHANES (odds ratio (OR) = 1.04, 95% CI: 1.03-1.06, P < .001) and a 3% increase in CHARLS (OR = 1.03, 95% CI: 1.02-1.05, P < .001). Quartile analysis revealed a strong dose-response pattern. In CHARLS, individuals in the highest quartile (Q4) exhibited a 9.60-fold higher CKM risk compared with that in Q1 (95% CI: 7.83-11.80). RCS models confirmed a nonlinear, upward-sloping relationship between RCII and CKM risk in both datasets. Subgroup analysis revealed robust associations across most strata, with a significant interaction by race in NHANES and by hypertension, diabetes, stroke, and smoking status in CHARLS.

CONCLUSION

RCII exhibited an independent and positive association with CKM risk in the US and Chinese populations, with consistent dose-response and nonlinear trends. This index may be a practical and integrative biomarker for identifying individuals with an increased CKM risk, particularly during the early stages of metabolic dysregulation. Prospective studies are warranted to validate their predictive performance and establish clinically relevant thresholds for risk stratification.