This was a retrospective study consisting of 404 consecutive patients (174 males, 43.1%), who had undergone coronary angiography at the Sichuan Provincial People's Hospital from June 2018 to January 2020 due to CAD-related symptoms such as typical chest pain. All patients with coronary stent implantation, acute coronary Syndrome (ACS), coronary artery bypass surgery, liver and kidney disease, inflammatory disease, heart failure, blood diseases, rheumatic diseases, malignant tumors, alcohol use and antioxidant drugs (including statins, beta blockers, and aspirin) were excluded. Based on the results of coronary angiography, patients in CAD⁺ group (n = 155) were defined as those having angiographic coronary stenosis of at least 50% lumen reduction in at least one major coronary artery (including left anterior descending artery, left circumflex artery, left main coronary artery, right coronary artery).Patients with luminal stenosis but no more than 50% were defined as CAD⁻ group (n = 49), and patients without luminal stenosis (n = 200) were regarded as control group. Specifically, we did not include patients with diffuse coronary artery involvement but no stenosis. At the same time, when measuring the degree of obstuction on lumenogram, two experienced interventional cardiologists made a comprehensive judgment. The current study was in line with the principles of the Declaration of Helsinki.

Height and weight were measured to calculate body mass index (BMI), which was counted as weight divided by height squared, weight in kilograms and height in meters. Collection of serum biomarkers such as uric acid, creatinine, triglycerides, bilirubin, total cholesterol, high-density lipoprotein cholesterol (HDL-C), albumin, low-density lipoprotein cholesterol (LDL-C), neutrophils, and eosinophils. Serum total antioxidant status of was calculated as: (0.63 × albumin concentration) + (1.53 × bilirubin concentration) + (1.02 × uric acid concentration), where the concentrations of uric acid, bilirubin and albumin were expressed in mmol/L. Neutrophil and HDL-C ratios were calculated. Hypertension was defined as blood pressure 140/90 mmHg or higher, or they were being given antihypertensive drugs. Those who had been diagnosed with hypertension in the past were all classified as having a history of hypertension. Patients were considered to have diabetes if they had a previous history of diabetes, or plasma glucose-lowering drugs use, including oral tablets and insulin. Current smokers were defined as those who smoked at least one cigarette a day for more than one year in a row; Long-term smokers who have quit for less than 6 months were still considered to have a smoking history.

The modified Gensini score (GS) system was used to measure CAD severity. These scores were expressed as the sum of the positional score of each lesion multiplied by the obstruction severity score, which emphasizes the severity of the disease. The calculation of the modified Gensini score system is shown in Table 1.

All statistical analyses were performed using SPSS 25.0 for Mac. Kolmogorov–Smirnov test was used to verify that the continuous variable was normally distributed. The continuous variables of normal distribution were indicated as the mean ± standard deviation. Nonnormally distributed variables were represented as medians. Categorical variables were expressed in terms of frequency percentages and absolute numbers. Homogeneity of variance was evaluated by Levene’s test. If the data for continuous variables satisfied normal distribution and homogeneity of variance, a one-way analysis of variance was used, and the comparison among groups were performed with LSD method; Otherwise, the Kruskal–Wallis test was used to compare the differences of the non-conforming measurement data. Categorical variables were compared using the chi-squared. Correlation of Gensini score with serum biomarkers was analyzed by Pearson or Spearman’s correlation coefficient, as appropriate. Before further statistical analysis, Spearman’s correlation coefficients were first calculated to eliminate multicollinearity between or among variables. To determine the risk factors associated with CAD, multiple unconditional logistic regression models were performed as the primary analysis for the independent effects of each variable. Univariate logistic regression models were first performed to explore the crude association between the severity of coronary stenosis and each factor, including the ratio of neutrophils to high-density lipoprotein (NHR), sex, age, smoking, hypertension, albumin, creatinine, uric acid, HDL cholesterol, LDL cholesterol, and neutrophils individually. Factors that were significant in the univariate model at the p < 0.10 level were contained in the multivariate logistic regression model, so as to identify factors independently associated with the presence of coronary artery stenosis. Using the backward step-by-step approach, All statistically significant factors, i.e. P < 0.05 level, were contained in the final prediction model. A receiver operating characteristic (ROC) curve was presented. Regression coefficients and 95% confidence intervals (CIs) for risk factors significantly associated with CAD were calculated. All statistical tests were double-sided, and P value < 0.05 was considered statistically significant.

A total of 404 patients were included in our analysis. Demographic and clinical patient characteristics are presented in Table 2. The smoking incidence, hypertension incidence, creatinine, and neutrophil levels in CAD⁺ group were apparently higher than that in CAD⁻ group and control group (P < 0.05, respectively). Compared to the CAD⁻ group, the CAD⁺ group showed a significantly higher Gensini score (P < 0.05). Nevertheless, the levels of HDL-C and albumin in the CAD⁺ group were lower than those in the CAD⁻ group (P < 0.05). There were statistically significant differences in age and gender among the three groups (P < 0.05), and the average age of patients in the CAD⁺ group was higher than that in the CAD⁻ group (P < 0.001). The proportion of males in the CAD⁺ group was apparently higher than that in the CAD⁻ group and control group (P < 0.05, respectively). There were no significant differences in BMI, diabetes mellitus,cTAS,eGFR,bilirubin,total cholesterol, triglycerides,uric acid, and LDL-C, eosinophils among the three groups (P > 0.05, respectively).

To clarify the relationship between various risk factors and the modified Gensini score in patients with coronary atherosclerotic heart disease, Spearman rank correlation analysis was conducted. As shown in Table 3. The results showed that the Gensini score was positively correlated with NHR,neutrophils, creatinine, LDL-C, sex, age, cigarette smoking, hypertension, there was a negative correlation between Gensini score and HDL-C, as well as albumin. while Gensini score was not correlated with cTAS, eosinophil, uric acid, bilirubin, total cholesterol, triglyceride, and diabetes mellitus. The scatter diagram is shown in Fig. 1.

To further explore the possible risk factors for coronary artery stenosis, univariate and multivariate logistic regression analyses were applied (Table 4). Univariate Logistic regression analyses showed that risk factors associated with CAD included NHR, smoking, hypertension, sex, age, creatinine, albumin, LDL-C, and central granulocyte (p < 0.05). Additionally, after adjusting for all covariates, multiple regression analyses showed that NHR, age, sex, albumin, and LDL-C were significantly correlated with CAD. Consequently, NHR was an independent predictor of CAD.The ROC analysis displayed that the critical value of NHR was 1.51, 94.8% of sensitivity and 0.024 of Yoden index could predict CAD, and the area under the ROC curve was 0.617 (95% CI 0.560–0.675, P < 0.001, Fig. 2).