AI-Enabled Opportunistic Measurement of Liver Steatosis in Coronary Artery Calcium Scans Predicts Cardiovascular Events and All-Cause Mortality: An AI-CVD Study Within the Multi-Ethnic Study of Atherosclerosis (MESA)

Details

• Journal Article:
  BMJ Open Diabetes Research & Care
• Personal Author:
  Naghavi, Morteza ; Atlas, Kyle ; Reeves, Anthony P. ; Zhang, Chenyu ; Wasserthal, Jakob ; Atlas, Thomas ; Henschke, Claudia I. ; Yankelevitz, David F. ; Zulueta, Javier J. ; Budoff, Matthew J. ; Branch, Andrea D. ; Ma, Ning ; Yip, Rowena ; Fan, Wenjun ; Roy, Sion K. ; Nasir, Khurram ; Molloi, Sabee ; Fayad, Zahi A. ; McConnell, Michael V. ; Kakadiaris, Ioannis A. ; Maron, David J. ; Narula, Jagat ; Williams, Kim A. ; Shah, Prediman K. ; Abela, George S. ; Vliegenthart, Rozemarijn ; Levy, Daniel ; Wong, Nathan D.
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• Description:
  Introduction: About one-third of adults in the USA have some grade of hepatic steatosis. Coronary artery calcium (CAC) scans contain more information than currently reported. We previously reported new artificial intelligence (AI) algorithms applied to CAC scans for opportunistic measurement of bone mineral density, cardiac chamber volumes, left ventricular mass, and other imaging biomarkers collectively referred to as AI-cardiovascular disease (CVD). In this study, we investigate a new AI-CVD algorithm for opportunistic measurement of liver steatosis. Methods: We applied AI-CVD to CAC scans from 5702 asymptomatic individuals (52% female, age 62 +/- 10 years) in the Multi-Ethnic Study of Atherosclerosis. Liver attenuation index (LAI) was measured using the percentage of voxels below 40 Hounsfield units. We used Cox proportional hazards regression to examine the association of LAI with incident CVD and mortality over 15 years, adjusted for CVD risk factors and the Agatston CAC score. Results: A total of 751 CVD and 1343 deaths accrued over 15 years. Mean +/- SD LAI in females and males was 38 +/- 15% and 43 +/- 13%, respectively. Participants in the highest versus lowest quartile of LAI had greater incidence of CVD over 15 years: 19% (95% CI 17% to 22%) vs 12% (10% to 14%), respectively, p<0.0001. Individuals in the highest quartile of LAI (Q4) had a higher risk of CVD (HR 1.43, 95% CI 1.08 to 1.89), stroke (HR 1.77, 95% CI 1.09 to 2.88), and all-cause mortality (HR 1.36, 95% CI 1.10 to 1.67) compared with those in the lowest quartile (Q1), independent of CVD risk factors. Conclusion: AI-enabled liver steatosis measurement in CAC scans provides opportunistic and actionable information for early detection of individuals at elevated risk of CVD events and mortality, without additional radiation. Description provided by NIOSH
• Subjects:
  • Artificial Intelligence
  • Cardiovascular System
  • Metabolism
  • Mortality
  • Occupational Health
  • Safety
• Keywords:
  • Imaging techniques; Artificial intelligence; Biomarkers; Radiology; Metabolic disorders; Muscle tissue; Cardiology; Cardiovascular disease; Mortality rates
• Source:
  BMJ Open Diabetes Res Care 2025 Mar; 13(2):e004760
• ISSN:
  2052-4897
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Birmingham Region OSHA ; California ; Chicago Region OSHA ; Dallas Region OSHA ; Kentucky ; Maryland ; Michigan ; New York ; New York City Region OSHA ; Philadelphia Region OSHA ; San Francisco Region OSHA ; Texas
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• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  11 pdf pages
• Volume:
  13
• NIOSHTIC Number:
  nn:20071633
• Email:
  mn@vp.org
• Federal Fiscal Year:
  2025
• Performing Organization:
  Icahn School of Medicine at Mount Sinai, New York
• Peer Reviewed:
  True
• Start Date:
  20230701
• End Date:
  20260630
• Download URL:
  https://stacks.cdc.gov/view/cdc/259408/cdc_259408_DS1.pdf
• File Type:
  [PDF - 1.88 MB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:28e594a9af1f0a4ee03e4dbc503b0179f4880a21049f5f2d0e30e2304ed87f442fdd6493540cfa1d3169802291c977f0c24be07c51856c44000c8e19ec1fc7ca