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.

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 < 100 AU: 96.8 mL [69.1;130] vs 77.9 mL [53.8;107.7], p < 0.0001). By contrast, EAT volume was neither associated with DR, nor with peripheral neuropathy. We further evidenced a subgroup of patients with high EAT volume and a null CAC score. Interestingly, this group were more likely to be composed of young women with a high BMI, a lower duration of T2D, a lower prevalence of microvascular complications, and a higher inflammatory profile. Conclusions: Fully-automated EAT volume quantification could provide useful information about the risk of both renal and macrovascular complications in T2D patients. Article Highlights: Why did we undertake this study? What is the specific question(s) we wanted to answer? This study addresses the unmet need to assess epicardial fat volume quantification in high-risk people living with type 2 diabetes using a fully-automated deep learning AI tool. What did we find? Fully automated epicardial fat volume quantification with cardiac CT performed for CAC scoring is possible and reliable in T2D. Epicardial fat volume was associated with all cardiovascular risk factors, CKD and macrovascular complications but not with diabetic retinopathy or peripheral neuropathy. We identified a subgroup of T2D patients with a null CAC score and high EAT volume which was characterized by a higher systemic proinflammatory profile. What are the implications of our findings? This study provides new insights for non-invasive deep phenotyping of patients living with type 2 diabetes with epicardial fat volume quantification using cardiac CT performed for CAC scoring, that could be used in clinical practice. These findings set the stage for personalized medicine and prospective randomized trials testing new antihyperglycemic drugs that target inflammation. [ABSTRACT FROM AUTHOR]