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.

Ferroptosis is a novel form of programmed iron-dependent cell death. The ferroptosis-related genes (FRGs) have been recognized as biomarkers for cancers. Increasing evidence has indicated that ferroptosis is involved in the process of atherosclerosis. However, the potential FRGs used for the diagnosis, prognosis and therapy for atherosclerosis are still unclear. We aimed to identify the ferroptosis-related differentially expressed genes (DEGs) of atherosclerosis. We downloaded the mRNA-sequencing data of patients with atherosclerosis from the Gene Expression Omnibus (GEO) database. HMOX1 was identified as an essential ferroptosis-related DEG by bioinformatic analysis of the GSE28829 and GSE43292 datasets. The pro-ferroptotic effect of HMOX1 was validated through cell experiments. Then we conducted a single-gene analysis of HMOX1 and found that high-expression of HMOX1 in atherosclerotic plaques was accompanied by matrix metalloproteinases (MMPs) producing and M0 macrophages infiltration. Taken together, our present study suggested HMOX1 as a potential diagnostic biomarker for atherosclerosis and provided more evidence about the vital role of ferroptosis in atherosclerosis progression.

Objective The present study investigated the potential correlation between non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio (non-HDL-C/HDL) and the formation of coronary collateral circulation (CCC) in coronary artery disease cases with chronic total occlusive (CTO) lesions. Methods Two experienced cardiologists identified and selected patients with CTO lesions for retrospective analysis. The 353 patients were divided into a CCC poor formation group (Rentrop 0–1 grade, n = 209) and a CCC good formation group (Rentrop 2–3 grade, n = 144) based on the Cohen-Rentrop standard. A comparison of non-HDL-C/HDL ratios between the two groups was performed. The Spearman test was used to obtain the correlation between the cholesterol ratio and Rentrop grade. Independent predictors of CCC were analyzed using logistic regression. Receiver operating characteristic (ROC) curve analysis was also performed to quantify the predictive value of research indicator. Results The non-HDL-C/HDL ratio in the CCC poor formation group was elevated markedly compared to the CCC good formation group [( 3.86 ± 1.40) vs ( 3.31 ± 1.22), P = 0.000]. The Spearman test results indicated that non-HDL-C/HDL negatively correlated with Rentrop grade (r = − 0.115, P = 0.030). Multivariate logistic regression analysis showed that non-HDL-C/HDL ratio was an independent predictor of CCC formation (OR = 1.195, 95%CI = 1.020–1.400, P = 0.027). The area under the curve of ROC for detecting CCC poor formation was 0.611 (95% CI: 0.551–0.671, P = 0.000) with an optimal cut-off value of 2.77. Conclusion Non-HDL-C/HDL negatively correlated with the formation of CCC and served as an independent predictor of CCC formation, which may be used as a biomarker for the evaluation of CCC.

Objective: The present study aimed to investigate the association of triglyceride-glucose (TyG) index and neutrophil-to-lymphocyte ratio (NLR) with coronary artery disease (CAD), and evaluate the cumulative value of TyG index and NLR in identifying CAD, as well as the severity of CAD. Methods: This retrospective study enrolled 2867 patients who underwent coronary angiography (CAG) for the first time between January 2013 and June 2022 in Zhongnan Hospital of Wuhan University. There were 2109 patients with CAD and 758 patients without CAD. The CAD patients were divided into two groups based on the median of Gensini score (mild stenosis CAD group: Gensini score < 26 points; severe stenosis CAD group: Gensini score ≥ 26 points). To further evaluate the cumulative value of TyG index and NLR in identifying CAD and CAD severity, all patients were classified into four groups based on median of TyG index and NLR: (1) the control group: patients with low-TyG and low-NLR; (2) isolated high-NLR group: patients with low-TyG and high- NLR; (3) isolated high- TyG group: patients with high-TyG and low- NLR; (4) high-TyG combined with high-NLR group: patients with high-TyG and high- NLR. Results: Multivariate logistic regression analysis showed that both the TyG index and NLR were independent risk factors for CAD, and they were also independent risk factors for severe stenosis in CAD (P < 0.05). Compared with the low-TyG and low- NLR group, patients in high-TyG and high- NLR group had a 1.418 times higher odds ratio (OR) of having CAD and a 1.692 times higher OR of having severe stenosis in CAD in the multivariable logistic regression model. It is worth noting that the OR values of the high-TyG and high- NLR group were higher than those of the isolated high-NLR group and the isolated high- TyG group. The ROC analysis showed that the combination of the TyG index and NLR was superior to TyG index or NLR in predicting CAD and CAD severity. Conclusion: Compared to TyG index or NLR, the combination of the TyG index and NLR is beneficial to improve the diagnostic accuracy of CAD and CAD severity.