Remnant cholesterol inflammatory index and chronic lung disease in middle-aged and older Chinese adults: A cross-sectional analysis of the China Health and Retirement Longitudinal Study

Chronic lung diseases are common among middle-aged and older adults in China. Because remnant cholesterol (RC) and systemic inflammation may jointly contribute to respiratory disease risk, simple blood-based composite markers may help identify individuals at higher risk. We conducted a cross-sectional analysis of the China Health and Retirement Longitudinal Study using biomarker data collected in 2011 and 2015. The study included 7694 adults aged ≥ 45 years with complete lipid and inflammation measurements at both time points. We calculated the RC inflammatory index (RCII), which integrates RC and high-sensitivity C-reactive protein, and constructed a time-weighted cumulative RCII between 2011 and 2015. Chronic lung disease was defined as a self-reported physician diagnosis of chronic bronchitis, emphysema, or chronic pulmonary heart disease. Multivariable logistic regression models were used to estimate associations. Overall, 968 participants (12.6%) reported chronic lung disease (CLD). Higher baseline RCII was associated with higher odds of CLD, with a dose-response pattern across quartiles. In the fully adjusted model, the odds ratios (ORs) across increasing baseline RCII quartiles were 1.18 (95% confidence interval [CI]: 0.83-1.50), 1.32 (95% CI: 1.11-1.53), and 1.47 (95% CI: 1.15-1.79), with the lowest quartile as the reference. Higher cumulative RCII was also associated with CLD, with corresponding ORs of 1.24 (95% CI: 1.07-1.44), 1.52 (95% CI: 1.31-1.76), and 1.72 (95% CI: 1.45-2.05). Higher RCII was independently associated with CLD in middle-aged and older Chinese adults. Although the observed associations were modest in magnitude and the outcome relied on self-reported physician diagnoses, RCII may represent an accessible composite epidemiologic marker warranting further investigation; its value for individual-level risk stratification requires validation through studies incorporating calibration, reclassification, and decision-analytic assessment.