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 < 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.

Keywords: CAC score; Cardiac computed tomography; Deep learning; Epicardial adipose tissue; Type 2 diabetes.

Fig. 1

Schematic representation of the scientific approach to the development of the deep-learning model Automated Segmentation of Epicardial adipose tissue within the Pericardium Schematic representation of the scientific approach to the development of the deep-learning model from LDCCT, to CCT used for CAC scoring. The development of the deep learning network model was performed through internal and external subgroups of the Angiosafe DT2 cohort, to validate a reliable application for the automated epicardial adipose tissue (EAT) volume quantification LDCCT low dose chest computed tomography CCT cardiac computed tomography

Fig. 2

A Association deep-learning model Automated Segmentation of EAT volume with cardiovascular risk factors. W: women M: men ***p < 0.0001. B EAT volume relationship with CAC score represented by tertiles and diabetic retinopathy status assessed by retinophotography. DR diabetic retinopathy; NPDR non proliferative diabetic retinopathy; PDR proliferative diabetic retinopathy