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Evaluation of Epicardial Adipose Tissue by Coronary Multi-Detector Computed Tomography: An Independent Predictor of Obstructive Coronary Artery Disease Publisher



Moradi M1 ; Talebi V1
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Authors Affiliations
  1. 1. Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8176473461, Iran

Source: Egyptian Journal of Radiology and Nuclear Medicine Published:2023


Abstract

Background: The aim of this retrospective cross-sectional study was to investigate the relationship between epicardial adipose tissue and coronary artery disease and its severity. Methods: A total of 344 subjects with clinically suspected coronary artery disease underwent 256-slice multi-detector CT coronary angiography and were categorized as follows: no CAD, non-obstructive CAD (luminal narrowing < 50%) and obstructive CAD (luminal narrowing ≥ 50%). Calcium score was quantified using the Agatston method. Mean Epicardial adipose tissue (EAT) and mean pericoronary fat thickness (PCFT) were measured and utilized for data analysis. Results: There was a significant association between EAT thickness and PCFT with clinical risk factors of CAD including HTN, DM and obesity (P-value: < 0.001) and dyslipidemia (P-value: 0.003, P-value: 0.008 for EAT and PCFT, respectively). EAT thickness and PCFT were significantly higher in obstructive CAD compared to non-obstructive CAD and no CAD categories (P-value: < 0.001). Also, there is positive strong correlation between EAT thickness, PCFT and Calcium score (P-value: < 0.001). Optimal cut-off point of PCFT for estimating obstructive CAD was > 12.87 mm (sensitivity: 77.06%, specificity: 66.06% and AUC:0.76; 95%CI:0.69–0.81) via receiver operating characteristic curve. On multivariate logistic analysis which included conventional risk factors of CAD, PCFT was an independent predictor of obstructive CAD (Odds ratio:1.55; P-value: < 0.001). Conclusions: EAT thickness and PCFT were significantly increased in coronary artery disease. PCFT is a simple accessible marker for predicting obstructive CAD with acceptable diagnostic performance. © 2023, The Author(s).
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