Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study

Autor:	Andrew Lin, Nathan D. Wong, Aryabod Razipour, Priscilla A. McElhinney, Frederic Commandeur, Sebastien J. Cadet, Heidi Gransar, Xi Chen, Stephanie Cantu, Robert J. H. Miller, Nitesh Nerlekar, Dennis T. L. Wong, Piotr J. Slomka, Alan Rozanski, Balaji K. Tamarappoo, Daniel S. Berman, Damini Dey
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Metabolic syndrome Non-alcoholic fatty liver disease Epicardial adipose tissue Artificial intelligence Cardiovascular risk Diseases of the circulatory (Cardiovascular) system RC666-701
Zdroj:	Cardiovascular Diabetology, Vol 20, Iss 1, Pp 1-11 (2021)
Druh dokumentu:	article
ISSN:	1475-2840
DOI:	10.1186/s12933-021-01220-x
Popis:	Abstract Background We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals. Methods This was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm3) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (
Databáze:	Directory of Open Access Journals
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