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.

Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: Results from the EISNER study

“…Subjects were followed up through clinical visits, phone calls and mail. Any adverse events were verified by comprehensive medical, hospital and death records by two independent cardiologists [17] .

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“Big Data” Approaches for Prevention of the Metabolic Syndrome

“…For the same purpose, different models could exhibit different efficacy, such as how in supervised machine learning, the sensitivity and specificity vary among different models and detailed studies are needed to designate the most suitable model. For MetS, various algorithms have been tested, and many highlighted the “random forest” as the most appropriate model ( Xia et al, 2021 ; Yu et al, 2021 ), and studies have also used deep learning tools to analyze data from medical images to further contribute to the prediction of MetS occurrence and outcomes, contributing to secondary and tertiary prevention strategies ( Lin et al, 2021a ; Pickhardt et al, 2021 ).…”

“…CT and MRI or ultrasound are commonly administered screening modalities for various abdominal diseases both emergent or chronic ( Tanaka et al, 2006 ). This location of imaging harbors indicators including visceral fat, hepatic steatosis, and skeleton muscle, which are closely related to MetS ( Lin et al, 2021a ; Chou et al, 2021 ; Pickhardt et al, 2021 ; Wu and Park, 2021 ). Machine learning, especially deep learning methods, has been studied for fast automated quantification of different fat compartments, level of steatosis, and fat distribution ( Sun et al, 2020 ; Bhanu et al, 2021 ; Rhyou and Yoo, 2021 ).…”

Tejido adiposo epicárdico como predictor de enfermedad coronaria. ¿Un nuevo parámetro para la estratificación del riesgo cardiovascular?

“…La adición de volumen del TAE a la evaluación del riesgo cardiovascular implicó una mejoría neta en el índice de reclasificación (0,218, IC 95% 0,079-0,357, p = 0,002). (33) Sin embargo, a pesar de la gran cantidad de evidencia que sostiene la asociación entre el TAE y la enfermedad coronaria, algunos estudios reportaron resultados conflictivos. (34)(35)(36) De hecho, algunos autores plantearon la posibilidad de que el cambio metabólico y anatómico del TAE podría ser secundario a las lesiones ateroscleróticas coronarias y no un factor predisponente.…”