Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study

Abstract Background The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D). Methods EPIDIAB is a post hoc analysis from the AngioSafe T2D study, which is a multicentric study aimed at determining the safety of antihyperglycemic drugs on retina and including patients with T2D screened for diabetic retinopathy (DR) (n = 7200) and deeply phenotyped for MVC. Patients included who had undergone cardiac CT for CAC (Coronary Artery Calcium) scoring after inclusion (n = 1253) were tested with a validated deep learning segmentation pipeline for EAT volume quantification. Results Median age of the study population was 61 [54;67], with a majority of men (57%) a median duration of the disease 11 years [5;18] and a mean HbA1c of7.8 ± 1.4%. EAT was significantly associated with all traditional CV risk factors. EAT volume significantly increased with chronic kidney disease (CKD vs no CKD: 87.8 [63.5;118.6] vs 82.7 mL [58.8;110.8], p = 0.008), coronary artery disease (CAD vs no CAD: 112.2 [82.7;133.3] vs 83.8 mL [59.4;112.1], p = 0.0004, peripheral arterial disease (PAD vs no PAD: 107 [76.2;141] vs 84.6 mL[59.2; 114], p = 0.0005 and elevated CAC score (> 100 vs