ArticleCardiovascular diabetology2024
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.
Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Association of epicardial fat volume with early outcomes in patients undergoing cryoablation for atrial fibrillation.BMC cardiovascular disorders · 2026Article
- Fine-grained epicardial adipose tissue segmentation in cardiac CT images with position priors and edge enhancement.Biomedical engineering letters · 2026Article
- Epicardial adipose tissue volume and arterial stiffness in people living with diabetes: the METAB-CV-PWV study.Cardiovascular diabetology · 2025Article
- Effect of Semaglutide on Epicardial Adipogenesis and hiPSC-Atrial Cardiomyocytes New Hope in Targeting Epicardial Adipose Tissue.JACC. Basic to translational science · 2025Article
- Pericoronary adipose tissue: potential for pathological diagnosis and therapeutic applications.Cardiovascular intervention and therapeutics · 2025Review
- Molecular crosstalk in perivascular adipose tissue: mechanisms of inflammation, metabolic dysregulation, and therapeutic opportunities in cardiovascular disease.Frontiers in cardiovascular medicine · 2025Review
- Roles of perivascular adipose tissue in the pathogenesis of atherosclerosis - an update on recent findings.Frontiers in physiology · 2024Review
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21 authors.
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Abstract
backgroundThe 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).
methodsEPIDIAB 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.
resultsMedian 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.
conclusionsFully-automated EAT volume quantification could provide useful information about the risk of both renal and macrovascular complications in T2D patients.
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