부산대학교 도서관

Introduction: Metabolic risk factors for CVD tend to cluster. However, the association between metabolic clusters considering lipoprotein subclasses and subclinical atherosclerosis is not well understood. This study aimed to characterize metabolic clusters and determine their association with CAC progression.Methods: We included individuals with available lipid profiles assessed by NMR spectroscopy and excluded those taking lipid-lowering medications. Hierarchical clustering was performed using BMI, fasting glycemia, HbA1c, HOMA-IR, LDL-c, HDL-c, TG, and subclasses of LDLs, HDLs, and TG-rich lipoproteins (TRLs). CAC was evaluated in a subset of participants with two cardiac CT measurements and no prior history of ASCVD. The association between clusters and CAC progression (evaluated by the Berry method) was assessed using logistic regression.Results: The study included 4328 participants (55% females, mean age 50.6 ± 8.7 years), among whom 4 metabolic clusters were identified (Table). HOMA-IR, BMI, glycemia, HbA1c, TG, and hsCRP increased from Cluster 1 to Cluster 4. Compared to Cluster 1, Clusters 3 and 4 were significantly associated with CAC progression (OR 1.59 [95% CI 1.21; 2.09], p <0.001 and OR 2.79 [95% CI 1.43; 5.45], p=0.003, respectively). The Figure demonstrates that individuals with lower calculated CVD risk but more pronounced metabolic abnormalities may have a probability of CAC progression as high as those with higher calculated CVD risk but fewer metabolic alterations.Conclusions: Metabolic clustering around insulin resistance can help identify individuals at higher risk of CAC progression, regardless of calculated CVD risk and baseline CAC.