Epicardial Adipose Tissue Thickness Is Related to Plaque Composition in Coronary Artery Disease.
Soon Sang ParkJisung JungGary S MintzUram JinJin-Sun ParkBumhee ParkHan-Bit ShinKyoung Woo SeoHyoung-Mo YangHong-Seok LimByoung-Joo ChoiMyeong-Ho YoonJoon-Han ShinSeung-Jea TahkSo-Yeon ChoiPublished in: Diagnostics (Basel, Switzerland) (2022)
(1) Background: Currently, limited data are available regarding the relationship between epicardial fat and plaque composition. The aim of this study was to assess the relationship between visceral fat surrounding the heart and the lipid core burden in patients with coronary artery diseases; (2) Methods: Overall, 331 patients undergoing coronary angiography with combined near-infrared spectroscopy and intravascular ultrasound imaging were evaluated for epicardial adipose tissue (EAT) thickness using transthoracic echocardiography. Patients were divided into thick EAT and thin EAT groups according to the median value; (3) Results: There was a positive correlation between EAT thickness and max LCBI 4mm , and max LCBI 4mm was significantly higher in the thick EAT group compared to the thin EAT group (437 vs. 293, p < 0.001). EAT thickness was an independent predictor of max LCBI 4mm ≥ 400 along with age, low-density lipoprotein-cholesterol level, acute coronary syndrome presentation, and plaque burden in a multiple linear regression model. Receiver operating characteristic curve analysis showed that EAT thickness was a predictor for max LCBI 4mm ≥ 400; (4) Conclusions: In the present study, EAT thickness is related to the lipid core burden assessed by NIRS-IVUS in patients with CAD which suggests that EAT may affect the stability of the plaques in coronary arteries.
Keyphrases
- coronary artery disease
- adipose tissue
- coronary artery
- optical coherence tomography
- acute coronary syndrome
- patients undergoing
- end stage renal disease
- heart failure
- percutaneous coronary intervention
- high fat diet
- chronic kidney disease
- cardiovascular events
- computed tomography
- pulmonary hypertension
- machine learning
- atrial fibrillation
- electronic health record
- big data
- deep learning
- pulmonary arterial hypertension
- type diabetes
- artificial intelligence