Residual Cholesterol Inflammation Index as a Predictive Biomarker for Stroke Risk in Middle-Aged and Elderly Individuals: Insights from the China Health and Retirement Longitudinal Study

Stroke, a major global health concern characterized by high incidence, disability, and mortality rates, imposes significant societal and economic burdens, particularly in China, where the disease is increasingly affecting younger populations. While interventions targeting traditional risk factors have shown some efficacy, a substantial proportion of stroke events remain unpredictable, underscoring the urgent need to identify novel risk factors and predictive markers. This study investigates the Residual Cholesterol Inflammation Index (RCII), a novel biomarker integrating residual cholesterol (RC) and high-sensitivity C-reactive protein (hs-CRP), to elucidate its association with stroke risk and explore its potential as a predictive tool among middle-aged and older adults in China. Utilizing a prospective cohort design from the China Health and Retirement Longitudinal Study (CHARLS), the research employs robust statistical methodologies, including Cox regression and dose-response analysis, to evaluate the relationship between RCII and stroke incidence across varying glycemic statuses. Findings reveal a significant positive correlation between baseline RCII and stroke risk, with an 11% increase in risk per unit increase of RCII, particularly pronounced in individuals with impaired glucose regulation. This study not only highlights the utility of RCII in identifying high-risk subgroups but also emphasizes its independent predictive value beyond conventional metabolic factors. By enriching the understanding of stroke risk factors, this research offers pivotal insights for targeted prevention strategies, ultimately contributing to enhanced clinical management and individualized interventions for stroke. The innovative identification of RCII as a significant risk marker for stroke may pave the way for future studies aimed at refining risk stratification and optimizing preventive measures in clinical practice.