Study population and grouping
The study protocol was approved by the Ruijin Hospital and Shanghai Jiao Tong University School of Medicine Ethics Committee, and written informed consent was obtained from all participants.
A total of 615 T2DM patients with stable angina and at least one lesion with coronary angiographic total occlusion were enrolled between January 2012 and December 2019. This inclusion criterion was based on long-standing knowledge that a severe coronary artery obstruction was a prerequisite for spontaneous collateral recruitment . Stable angina was diagnosed according to the criteria recommended by the American College of Cardiology/American Heart Association . For the purpose of this research, we excluded patients with chronic heart failure (n=69), pulmonary heart disease (n=25), malignant tumors or immune system disorders (n=71), renal failure requiring hemodialysis (n=34) as well as patients who had a history of coronary artery bypass grafting (n=79) or received percutaneous coronary intervention within the prior 3 months (n=95). The remaining 242 diabetic patients with stable angina and CTO (>3 months) were eligible and categorized in this study (Figure 1). The diagnosis of T2DM and hyperlipidemia were made according to the 2016 guideline of ESC  and 2017 update of ESC/EAS on PCSK 9 inhibition . Type 1 diabetes was excluded by measurement of C-peptide levels. Detailed information regarding demographics, clinical manifestation and medications used was obtained.
Coronary angiography was performed through the femoral or radial approach. All angiograms were reviewed by two experienced interventional cardiologists, according to lesion classification scheme of the American College of Cardiology/American Heart Association . Both of them were blinded to the study protocol and clinical data. Any differences in interpretation were resolved by a third reviewer.
The condition of CCV was determined using Rentrop score as in previous studies [18-20], as follows: grade 0=no collaterals, grade 1=side branch filling of the recipient artery without visualization of the epicardial artery, grade 2=partial filling of the main epicardial coronary artery, grade 3=complete filling of the main epicardial coronary artery . Patient with Rentrop 0 or 1 were categorized as poor CCV group and those with Rentrop 2 or 3 were belong to good CCV group.
Thus, the present study contained 242 patients altogether: Rentrop 0 (n=46), Rentrop 1 (n=61), Rentrop 2 (n=66), Rentrop 3 (n=69). Poor CCV (Rentrop 0 or 1) group had 107 patients and good CCV (Rentrop 2 or 3) group had 135 patients.
Sample acquisition and biochemical measurement
Blood samples were obtained from patients undergoing angiography after 12h fasting. Samples were collected by centrifugation at the speed of 3000 rpm for 10 min. All serum samples were stored at −80 °C until analysis. Serum glucose, glycosylated hemoglobin A1c (HbA1c), blood urea nitrogen, creatinine, uric acid, and lipid profiles were measured with standard laboratory techniques on a Hitachi 912 Analyzer (Roche Diagnostics, Germany). Modified estimated glomerular filtration rate (eGFR) was calculated.
Serum CML levels were measured with Cell BioLabs CML Competitive ELISA kit (STA-816) according to the manufacturer’s instructions. The CML ELISA kit used a colorimetric immunoassay method and CML levels of samples were determined by comparing samples OD values with a standard curve of gradient dilution of CML-modified BSA, in which higher CML modification correlates with lower OD signal. The final CML levels were shown with ng/ml unit by calculation of CML-modified BSA/CML. The inter-assay variation was controlled in acceptable range.
Continuous variables are presented as mean ± standard deviation (SD), and categorical data are summarized as frequency (percentage). For categorical clinical variables, differences between groups were evaluated by the chi-square test followed by Bonferroni’s correction. For continuous variables, normal distribution was evaluated with the Kolmogorov–Smirnov test. Differences among groups were analyzed by one-way analysis of variance (ANOVA) followed by post-hoc analysis (Bonferroni’s correction). Receiver operating characteristic (ROC) curves were plotted to assess the power of CML for detecting poor collateralization and to compare its power with HbA1c and with combined risk factors. Area under the curve (AUC) was compared using the DeLong method. Risk factors for CAD including gender, age, body mass index (BMI), hypertension, smoke, HbA1c, eGFR and high-sensitivity C reactive protein (hsCRP) were recruited into multivariable logistic regression analyses with or without CML measurements to assess determinants for poor CCV. All analyses used 2-sided tests with alpha value set at 0.05. All statistical analyses were performed with IBM SPSS Version 26 for Mac (IBM SPSS Inc, Chicago, IL, USA) and Prism 9 for macOS (1994 - 2021 GraphPad Software, LLC).