Cluster-Based Analysis of Lipid Profiles and Inflammation in Association With Cardiovascular Disease Incidence and Mortality: A 17.5-Year Longitudinal Study

Abstract

Cardiovascular mortality is a leading cause of global deaths, with aging, dyslipidemia, and inflammation recognized as key risk factors. This study aimed to identify distinct cardiovascular risk profiles using cluster analysis based on lipid profiles and inflammatory markers in a large cohort of middle-aged Korean adults. Our analysis included 8115 participants without cardiovascular disease (CVD) at baseline from the Korean Genome and Epidemiology Study. We applied the K-means clustering algorithm to conduct a cluster analysis of six normalized variables: age, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, and CRP. Multivariable Cox proportional-hazard regression analysis was performed to assess the hazard ratio with 95% confidence interval for CVD incidence, CVD mortality, major adverse cardiac event (MACE) mortality, and all-cause mortality. Four clusters were identified based on age, lipids (TC, TG, HDL-C, non-HDL-C), and CRP. Cluster 1 (older age, high CRP) and cluster 2 (high TC, non-HDL-C, insulin resistance) had the highest risks for new-onset CVD, while cluster 1 had the highest risks for all-cause and cardiovascular mortality. Cluster 3 (high HDL-C) showed a lower CVD risk, while cluster 4 (younger age, favorable lipid profile) had the lowest risk across all outcomes. This study highlighted the combined impact of aging, dyslipidemia, and inflammation on CVD risk. The clusters with older age and high inflammation or dyslipidemia had the highest cardiovascular risks, emphasizing the importance of managing these factors in high-risk populations.