The small dense LDL cholesterol-glucose index combined with visceral adiposity (sdLG-cVAI) as a novel predictor of incident cardiovascular disease: a prospective cohort study.

BACKGROUND

Dyslipidemia, impaired glucose metabolism, and obesity are established cardiovascular disease (CVD) risk factors. Composite markers like the triglyceride-glucose (TyG) index reflect lipid quantity but overlook qualitative features such as small dense low-density lipoprotein cholesterol (sdLDL-C), a highly atherogenic subfraction linked to dysglycemia. This study aimed to develop a novel sdLDL-C-glucose (sdLG) index, combine it with obesity indicators to enhance CVD risk stratification, and evaluate its predictive value for incident CVD.

METHODS

Adults aged ≥ 45 years without baseline CVD from the China Health and Retirement Longitudinal Study (CHARLS) were included (2011-2020). CVD (heart disease and stroke) events were identified during follow-up. The sdLG index, calculated from sdLDL-C and fasting glucose, was integrated with obesity indicators to form derived indices. The predictive performance of the indices was compared using time-dependent area under the receiver operating characteristic curve (AUC), Concordance index (C-index), and net reclassification improvement/integrated discrimination improvement (NRI/IDI) to select the optimal index. Dose-response relationships were examined via restricted cubic splines (RCS). Associations of the optimal index (baseline, cumulative exposure, and trajectory patterns) with incident CVD were evaluated using Cox proportional hazards models, supplemented by subgroup and sensitivity analyses.

RESULTS

Among 3,969 participants, 1,029 incident CVD events occurred. The sdLG index outperformed TyG index in long-term discrimination. Among derived indices, sdLG index combined with the Chinese visceral adiposity index (sdLG-cVAI) showed the best discrimination and reclassification performance. Dose-response relationships for baseline (P for overall < 0.001; P for nonlinearity = 0.105) and cumulative sdLG-cVAI (cusdLG-cVAI) (P for overall < 0.001; P for nonlinearity = 0.522) were linear. In fully adjusted models, the highest vs. lowest quartile was associated with increased CVD risk for both baseline sdLG-cVAI (HR 1.77, 95% CI 1.44-2.16, P < 0.001) and cumulative sdLG-cVAI (HR 1.87, 95% CI 1.53-2.29, P < 0.001). Participants in the persistently high trajectory cluster faced higher risk than those in the stable low cluster (HR 1.75, 95% CI 1.46-2.10, P < 0.001). Subgroup analyses revealed some heterogeneity. Sensitivity analyses yielded results consistent with the main regression analyses.

CONCLUSION

The sdLG index outperforms the traditional TyG index for long-term CVD risk prediction. The novel composite sdLG-cVAI independently predicts incident CVD and improves risk stratification in middle-aged and older adults.