AI-CVD Myosteatosis and Hepatosteatosis Findings Linked to Diabetes and Heart Failure Presented at Mount Sinai I-ELCAP Conference
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FOR IMMEDIATE RELEASE
New York, NY — October 10, 2025
Investigators from the AI-CVD™ consortium presented new results showing strong links between myosteatosis (fat infiltration in skeletal muscle) and hepatosteatosis (liver fat) with downstream risks of type 2 diabetes and heart failure at the I-ELCAP 48th International Conference on Screening for Lung Cancer in New York City. Presentations were delivered by Susan K. Fried and Andrea D. Branch, PhD.
Key theme: Opportunistic, AI-enabled analysis of routine noncontrast chest CTs (including lung-screening and CAC scans) can quantify muscle and liver fat to identify individuals at elevated cardiometabolic risk—without additional scan time, cost, or radiation—supporting earlier intervention and prevention.
Presenters
Susan K. Fried, PhD Professor of Medicine (Endocrinology), Icahn School of Medicine at Mount Sinai. Her research focuses on adipose tissue biology, obesity, and the endocrine and inflammatory pathways that drive cardiometabolic disease, including muscle and liver fat phenotypes. https://scholars.mssm.edu/en/persons/susan-fried
Andrea D. Branch, PhD Professor of Medicine (Liver Diseases), Icahn School of Medicine at Mount Sinai. Her work centers on viral and metabolic liver disease (including hepatitis C), liver inflammation and fibrosis, and the clinical implications of hepatosteatosis for cardiometabolic risk. https://scholars.mssm.edu/en/persons/andrea-branch
Related publications
AI-detected Myosteatosis Predicts Atrial Fibrillation and Heart Failure (MESA cohort).Using coronary artery calcium (CAC) scans, AI-CVD quantified thoracic skeletal-muscle attenuation and found that participants with myosteatosis had significantly higher 19-year risks of total CVD, atrial fibrillation (HR≈1.68), and heart failure (HR≈1.61) after multivariable adjustment; adding myosteatosis to CAC improved time-dependent AUC for all endpoints, indicating incremental predictive value beyond calcium scoring alone.
AI-derived Liver and Adiposity Metrics Predict Incident Type 2 Diabetes in People Without Obesity or Hyperglycemia. In normoglycemic, non-obese adults from MESA, AI-CVD measures from CAC scans showed that liver fat (liver attenuation index) was the strongest predictor of new-onset type 2 diabetes (HR≈3.13 highest vs. lowest quartile), outperforming other adiposity indices and enhancing discrimination when added to the ADA diabetes risk score—supporting opportunistic CT-based
About AI-CVDâ„¢
HeartLung Technologies' AutoChamberâ„¢ and AutoBMDâ„¢ are integral components of AI-CVDâ„¢, a suite of AI-powered tools designed to detect and prevent cardiovascular disease. AI-CVDâ„¢ leverages advanced algorithms to analyze CT scans, identifying hidden heart risks and enabling early intervention. This comprehensive approach underscores HeartLung's commitment to revolutionizing preventive healthcare through innovative AI technologies.
Conference
I-ELCAP 48th International Conference on Screening for Lung Cancer (& 16th Conference on Research for Early Lung Cancer Treatment) October 9–11, 2025 — Goldwurm Auditorium, Icahn School of Medicine at Mount Sinai
Conference site: https://www.ielcap.org/Â
