Cargando…
Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study
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...
Autores principales: | , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7847161/ https://www.ncbi.nlm.nih.gov/pubmed/33514365 http://dx.doi.org/10.1186/s12933-021-01220-x |
_version_ | 1783644875912118272 |
---|---|
author | Lin, Andrew Wong, Nathan D. Razipour, Aryabod McElhinney, Priscilla A. Commandeur, Frederic Cadet, Sebastien J. Gransar, Heidi Chen, Xi Cantu, Stephanie Miller, Robert J. H. Nerlekar, Nitesh Wong, Dennis T. L. Slomka, Piotr J. Rozanski, Alan Tamarappoo, Balaji K. Berman, Daniel S. Dey, Damini |
author_facet | Lin, Andrew Wong, Nathan D. Razipour, Aryabod McElhinney, Priscilla A. Commandeur, Frederic Cadet, Sebastien J. Gransar, Heidi Chen, Xi Cantu, Stephanie Miller, Robert J. H. Nerlekar, Nitesh Wong, Dennis T. L. Slomka, Piotr J. Rozanski, Alan Tamarappoo, Balaji K. Berman, Daniel S. Dey, Damini |
author_sort | Lin, Andrew |
collection | PubMed |
description | 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 (cm(3)) 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 cm(3) vs. 73.7 cm(3)), 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. |
format | Online Article Text |
id | pubmed-7847161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-78471612021-02-01 Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study Lin, Andrew Wong, Nathan D. Razipour, Aryabod McElhinney, Priscilla A. Commandeur, Frederic Cadet, Sebastien J. Gransar, Heidi Chen, Xi Cantu, Stephanie Miller, Robert J. H. Nerlekar, Nitesh Wong, Dennis T. L. Slomka, Piotr J. Rozanski, Alan Tamarappoo, Balaji K. Berman, Daniel S. Dey, Damini Cardiovasc Diabetol Original Investigation 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 (cm(3)) 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 cm(3) vs. 73.7 cm(3)), 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. BioMed Central 2021-01-29 /pmc/articles/PMC7847161/ /pubmed/33514365 http://dx.doi.org/10.1186/s12933-021-01220-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Original Investigation Lin, Andrew Wong, Nathan D. Razipour, Aryabod McElhinney, Priscilla A. Commandeur, Frederic Cadet, Sebastien J. Gransar, Heidi Chen, Xi Cantu, Stephanie Miller, Robert J. H. Nerlekar, Nitesh Wong, Dennis T. L. Slomka, Piotr J. Rozanski, Alan Tamarappoo, Balaji K. Berman, Daniel S. Dey, Damini Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study |
title | Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study |
title_full | Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study |
title_fullStr | Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study |
title_full_unstemmed | Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study |
title_short | Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study |
title_sort | metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study |
topic | Original Investigation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7847161/ https://www.ncbi.nlm.nih.gov/pubmed/33514365 http://dx.doi.org/10.1186/s12933-021-01220-x |
work_keys_str_mv | AT linandrew metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT wongnathand metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT razipouraryabod metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT mcelhinneypriscillaa metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT commandeurfrederic metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT cadetsebastienj metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT gransarheidi metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT chenxi metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT cantustephanie metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT millerrobertjh metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT nerlekarnitesh metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT wongdennistl metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT slomkapiotrj metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT rozanskialan metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT tamarappoobalajik metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT bermandaniels metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy AT deydamini metabolicsyndromefattyliverandartificialintelligencebasedepicardialadiposetissuemeasurespredictlongtermriskofcardiaceventsaprospectivestudy |