SPSS version 23.0 (IBM statistics) was used for all statistical analyses. Categorical variables were presented as frequencies (percentages) and continuous variables as mean ± SD. For the general population of diabetics and non diabetics we calculated a sample size using a power of 80% and confidence of 95%. For comparison among diabetic never-incretin-users and diabetic current-incretin-users, a propensity score matching (PSM) was developed from the predicted probabilities of mortality and MACE by a multivariable logistic regression model. Diabetic never-incretin-users were matched to diabetic current-incretin-users on the basis of PSM. In all matched patients, the balancing property was satisfied. Overall survival and event-free survival were presented using Kaplan–Meier survival curves and compared using the log-rank test. Univariable Cox models were then used to compare event risks. Within all the diabetic and non-diabetic groups, all cause of deaths, cardiac deaths, and MACE were assessed by using multivariable Cox models with adjustment for statistically different variables at baseline and follow-up: hypertension, dyslipidemia, current smoking, ace-inhibitors, calcium inhibitors, thiazide diuretics, aspirin, statin, BMI, heart rate, HDL-cholesterol, LDL-cholesterol, triglycerides levels, hs-CRP, M1/M2, and GLP-1 levels. The resulting hazard ratios (HRs) and 95% confidence intervals (CIs) were reported. To investigate the effects of GLP1 levels on cardiovascular endpoints, we evaluated STEMI outcomes at 1-year follow-up stratified by GLP-1 quartiles. A 2-tailed p value < 0.05 was considered statistically significant.
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