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

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 (< 30 s per case). NAFLD was defined as liver-to-spleen attenuation ratio < 1.0 and/or average liver attenuation < 40 HU.

RESULTS:

In the final population of 2068 participants (59% males, 56 ± 9 years), those with MetS (n = 280;13.5%) had a greater prevalence of NAFLD (26.0% vs. 9.9%), higher EAT volume (114.1 cm3 vs. 73.7 cm3), and lower EAT attenuation (-76.9 HU vs. -73.4 HU; all p < 0.001) compared to those without MetS. At 14 ± 3 years, MACE occurred in 223 (10.8%) participants. In multivariable Cox regression, MetS was associated with increased risk of MACE (HR 1.58 [95% CI 1.10-2.27], p = 0.01) independently of CAC score; however, not after adjustment for EAT measures (p = 0.27). In a separate Cox analysis, NAFLD predicted MACE (HR 1.78 [95% CI 1.21-2.61], p = 0.003) independently of MetS, CAC score, and EAT measures. Addition of EAT volume to current risk assessment tools resulted in significant net reclassification improvement for MACE (22% over ASCVD risk score; 17% over ASCVD risk score plus CAC score).

CONCLUSIONS:

MetS, NAFLD, and artificial intelligence-based EAT measures predict long-term MACE risk in asymptomatic individuals. Imaging biomarkers of cardiometabolic disease have the potential for integration into routine reporting of CAC scoring CT to enhance cardiovascular risk stratification. Trial registration NCT00927693.

• Authors: 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
• Journal: Cardiovascular diabetology (Cardiovasc Diabetol) Vol. 20 Issue 1 Pg. 27 (01 29 2021) ISSN: 1475-2840 [Electronic] England
• PMID: 33514365 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Topics

• Adipose Tissue (diagnostic imaging, physiopathology)
• Adiposity
• Aged
• Aged, 80 and over
• Cardiometabolic Risk Factors
• Deep Learning
• Female
• Heart Diseases (diagnostic imaging, epidemiology)
• Humans
• Los Angeles (epidemiology)
• Male
• Metabolic Syndrome (diagnostic imaging, epidemiology, physiopathology)
• Middle Aged
• Non-alcoholic Fatty Liver Disease (diagnostic imaging, epidemiology, physiopathology)
• Pericardium
• Predictive Value of Tests
• Prevalence
• Prognosis
• Prospective Studies
• Radiographic Image Interpretation, Computer-Assisted
• Registries
• Risk Assessment
• Time Factors
• Tomography, X-Ray Computed