Objective:To investigate the correlation between gamma-glutamyl transferase/high-density lipoprotein cholesterol ratio (GHR), neutrophil/lymphocyte ratio (NLR) and coronary heart disease (CHD), and evaluated its pathogenic risk and predictive value for CHD.Methods:A total of 694 patients admitted to our hospital from December 2017 to December 2018 for suspected CHD and coronary angiography were selected. According to the results of coronary angiography,the patients were divided into CHD group ( n=527) and non-CHD group ( n=167). The clinical data of all patients were recorded. Gamma-glutamyl transferase (GGT), high-density lipoprotein cholesterol (HDL-C) and other biochemical indicators were recorded. Neutrophils, lymphocyte count and other hematological indicators were recorded. GHR, NLR and Gensini scores of the patients were calculated. Clinical data and GHR, NLR and other indicators were compared between the two groups. Receiver operating characteristic curve (ROC) was used to evaluate the predictive value of GHR, NLR in CHD, and to determine the optimal cut-off value; Logstic regression analysis was used to investigate the risk factors of CHD.Spearman correlation analysis was used to analyze the correlation between serum OPN, OPG and Gensini score in patients with CHD. Results:The GHR and NLR were 32.59(21.05, 48.24) and 3.53(2.18, 8.46) significantly higher in the CHD group than in the non-CHD group 16.56(10.07, 25.21) and 2.20(1.45, 3.28) respectively, with statistically significant differences ( Z=11.094, 9.055, P<0.05). ROC curve analysis showed that the AUC of NLR and MLR in diagnosing CHD was 0.785 and 0.732( P<0.05). When the critical values of GHR and NLR respectively were 19.805 and 2.678, respectively, the diagnostic efficiency of CHD was the highest, and the sensitivity and specificity were 79.30%, 62.90% and 63.80%, 68.30%, and the AUC of GGT in diagnosing CHD was 0.628. When the critical value was 19.500, the sensitivity and specificity were 80.50% and 39.50%, respectively,the AUC of GHR was greater than that of GGT ( Z=12.973, P<0.05). Multivariate Logistic regression analysis showed that Smoking ( OR=2.887, 95% CI:1.850-4.505, P<0.05), hypertension ( OR=2.009, 95% CI: 1.311-3.080, P<0.05), fasting plasma glucose ( OR=1.109, 95% CI:1.034-1.189, P<0.05), age ≥60 years ( OR=1.567, 95% CI:1.179-2.415, P<0.05), NLR ≥2.687 ( OR=3.152, 95% CI:2.066-4.808, P<0.05) and GHR ≥19.805 ( OR=4.768, 95% CI:3.131-7.262, P<0.05) was an independent risk factor for CHD. After gradually adjustment for risk factors such as smoking, hypertensive, fasting plasma glucose, age ≥60 years and NLR ≥2.687, GHR ≥19.805 was still an independent risk factor for coronary heart disease(OR and 95% CI were 4.620 (3.049-7.000), 4.768 (3.131-7.262), 6.567 (4.408-9.810), 4.768 (3.131-7.262), 4.768 (3.131-7.262), respectively; all P<0.001). Spearman correlation analysis showed that GHR and NLR were positively correlated with Gensini score ( r=0.312, 0.394; all P<0.05). Conclusion:GHR and NLR were positively correlated with the severity of coronary artery disease, which is of significance in the diagnosis of coronary heart disease. NLR ≥2.687 and GHR ≥19.805 were independent risk factors for CHD. GHR was superior to GGT and HDL-C alone in the diagnosis of CHD,and has certain clinical application value

Objective:To study the relationship between serum osteopontin and osteopontin and type 2 diabetes mellitus (T2DM) complicated with coronary heart disease, and to evaluate the correlation between the levels of serum osteopontin and osteopontin with the severity of coronary artery lesions in T2DM patients.Methods:A total of 100 T2DM patients who were suspected to have stable coronary heart disease and underwent coronary angiography from November 2019 to December 2020 were selected from the Affiliated Hospital of Chengde Medical College, according to coronary angiography results, 60 patients with confirmed coronary heart disease were classified as the case group and 40 patients with non-coronary heart disease were classified as the control group for retrospective analysis. The clinical data and biochemical indicators of all patients were recorded, and Gensini score was calculated. The concentration of osteopontin and osteopontin in serum was quantitatively determined by double-antibody enzyme linked immunosorbent assay method. Independent sample t-test was used to compare the mean of normal distribution measurement data between the two groups. The non normal distribution data are represented by M ( Q1, Q3), and Mann Whitney U test is used for comparison between groups. Composition comparison between count data groups χ 2 inspection. Spearman correlation analysis was used to analyze the correlation between serum osteopontin and osteopontin and Gensini score in patients with T2DM. Results:Univariate analysis showed that serum osteopontin and osteopontin were (13.076(8.433, 23.552) μg/L) and (0.437(0.300, 0.630) μg/L) significantly higher in the case group than in the control group (6.367(4.605, 9.048) μg/L) and (0.299(0.196, 0.399) μg/L) respectively, with statistically significant differences ( Z=5.12, 3.28, all P<0.001). Multi-factor logistic regression analysis showed that osteoprotegerin ( OR=2.887, 95% CI:1.850-8.515, P=0.024) and osteopontin ( OR=13.109, 95%CI: 2.557-67.204, P=0.002) were associated with T2DM combined with coronary heart disease, and the risk of T2DM combined with coronary heart disease increased with higher levels of osteoprotegerin and osteopontin. Spearman correlation analysis showed that serum osteopontin and osteoprotegerin were positively correlated with Gensini score in T2DM patients ( r=0.591, 0.467; all P<0.05). Conclusion:Serum osteopontin and osteoprotegerin are associated with T2DM combined with coronary heart disease, and high serum osteopontin and osteoprotegerin are risk factors for T2DM combined with coronary heart disease; serum osteopontin and osteoprotegerin are positively correlated with the degree of coronary artery disease in T2DM patients.