Association of deep learning-derived epicardial fat volume with target organ damage in subjects with nonobstructive coronary artery disease.

backgroundEpicardial fat exerts both protective and deleterious effects on organs through diverse cytokine-mediated pathways. This study aimed to investigate computed tomography (CT)-based indexed epicardial fat volume (EFVi) in association with target organ damage parameters.

methodsThe prospectively enrolled cohort of 75 patients with nonobstructive coronary artery disease underwent electrocardiogram-gated CT and was evaluated for target organ damage parameters: estimated glomerular filtration rate, proteinuria, echocardiographic septal e' velocity, E/e' and tricuspid regurgitation velocity, brachial-ankle pulse wave velocity, and ankle-brachial index. EFVi was measured from semiautomated 3D segmentation of electrocardiogram-gated CT. Partial correlation, multiple linear regression, and receiver operating characteristic (ROC) analyses were conducted.

resultsAge and EFVi showed moderate positive linear correlation (r = 0.567, P < 0.001). After adjusting for age, EFVi was significantly correlated with the septal e' velocity (r = - 0.489, P < 0.001) and E/e' (r = 0.256, P = 0.034), but not with other target organ damage parameters (P > 0.05). Multiple linear regression analysis showed that the correlations of the EFVi with the septal e' velocity (β = -0.0003, P = 0.007) and E/e' (β = 0.0606, P = 0.024) remained significant after adjusting for potential confounders. ROC analysis identified optimal EFVi thresholds: 95.78 cm

conclusionsEFVi was related to left ventricular diastolic function more than other target organ damage parameters, including renal function and arterial stiffness, which suggests that the epicardial fat may have a role in the pathogenesis of left ventricular diastolic dysfunction.