Estimation of small dense low-density lipoprotein cholesterol and cardiometabolic multimorbidity risk in middle-aged and elderly Chinese: findings from a nationwide prospective cohort

Abstract Background Cardiometabolic multimorbidity (CMM) poses a growing burden on aging populations. Small dense low-density lipoprotein cholesterol (sdLDL-C) is a key atherogenic particle, practically estimated by the Sampson equation. Prospective evidence on estimated sdLDL-C (EsdLDL-C) and CMM risk is lacking. We aimed to investigate this association in middle-aged and elderly Chinese. Methods This study analyzed 10,187 adults ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018). Kaplan–Meier curves, Cox regression analyses, and restricted cubic spline (RCS) analysis were applied to assess the relationship between EsdLDL-C and CMM. Subgroup analyses, interaction tests, and sensitivity analyses were also conducted to evaluate the stability of these results. Receiver operating characteristic analysis was used to test the predictive value of EsdLDL-C for CMM. Results Kaplan-Meier analysis confirmed that elevated EsdLDL-C significantly increased the cumulative incidence of CMM (log-rank p<0.001). Each 1-unit increment in EsdLDL-C was linked to 47% higher CMM risk (HR 1.47; 95% CI: 1.17-1.85) and participants in the highest EsdLDL-C quartile exhibited a 2.10-fold increase in CMM risk than those in the lowest quartile (HR 2.10; 95% CI 1.41-3.14; p<0.001). RCS analysis showed a significant linear relationship between the EsdLDL-C and CMM risk(p for non-linear= 0.063). Notably, a significant interaction emerged between location, EsdLDL-C, and CMM risk. (p for interaction = 0.042). Sensitivity analyses identified these results were stable and reliable. The EsdLDL-C-incorporated model showed superior predictive performance (AUC=0.637) versus the baseline model (AUC=0.587). Conclusion Elevated EsdLDL-C independently predicts increased CMM risk in middle-aged and elderly Chinese, particularly in rural populations, supporting its utility for early identification and risk stratification.