The atherogenic index of plasma (AIP) is a predictor for the severity of coronary artery disease - Li, Ya; Feng, Yujia; Li, Shu; Ma, Yulin; Lin, Jiesheng; Wan, Jing; Zhao, Min (Frontiers in Cardiovascular Medicine, 2023)
Objective Dyslipidemia is a key risk factor for coronary artery disease (CAD). This study aimed to investigate the correlation between the atherogenic index of plasma (AIP) and the severity of CAD. Methods 2,491 patients were enrolled in this study and analyzed retrospectively, including 665 non-CAD patients as the control group and 1,826 CAD patients. The CAD patients were classified into three subgroups according to tertiles of SYNTAX score (SS). Non-high-density lipoprotein cholesterol (Non-HDL-C) was defined as serum total cholesterol (TC) minus serum high-density lipoprotein cholesterol (Non-HDL-C), atherogenic index (AI) was defined as the ratio of non-HDL-C to HDL-C; AIP was defined as the logarithm of the ratio of the concentration of triglyceride (TG) to HDL-C; lipoprotein combine index (LCI) was defined as the ratio of TC∗TG∗ low-density lipoprotein cholesterol (LDL)to HDL-C; Castelli Risk Index I (CRI I) was defined as the ratio of TC to HDL-C; Castelli Risk Index II (CRI II) was defined as the ratio of LDL-C to HDL-C. Results The levels of AIP (P < 0.001), AI (P < 0.001), and LCI (P = 0.013) were higher in the CAD group compared with the non-CAD group. The Spearman correlation analysis showed that AIP (r = 0.075, P < 0.001), AI (r = 0.132, P < 0.001), and LCI (r = 0.072, P = 0.001) were positively correlated with SS. The multivariate logistic regression model showed CRI I (OR: 1.11, 95% CI: 1.03–1.19, P = 0.005), CRI II (OR: 1.26, 95% CI: 1.15–1.39, P < 0.001), AI (OR: 1.28, 95% CI: 1.17–1.40, P < 0.001), AIP (OR: 2.06, 95% CI: 1.38–3.07, P < 0.001), and LCI (OR: 1.01, 95% CI: 1.01–1.02, P < 0.001) were independent predictors of severity of CAD After adjusting various confounders. Conclusion CRI I, CRI II, AIP, AI, and LCI were independent predictors of the severity of CAD, which could be used as a biomarker for the evaluation of the severity of CAD.