AI body composition measurements can predict cardiometabolic risk
Mass General Brigham researchers found that information gleaned from MRI scans were associated with cardiometabolic risk beyond BMI and other traditional risk factors
2025-09-29
(Press-News.org) Adiposity—or the accumulation of excess fat in the body—is a known driver of cardiometabolic diseases such as heart disease, stroke, type 2 diabetes, and kidney disease. But getting the full picture of a person’s risk is harder than it may seem. Traditional measures such as body mass index (BMI) are imperfect, conflating fat and muscle mass and not capturing where in the body fat is located. A new study from researchers at Mass General Brigham and their colleagues found that an AI tool designed to measure body composition could accurately capture details in just three minutes from a body scan. Their results, published in Annals of Internal Medicine, show that not all fat is equally harmful and highlight the potential of using AI to repurpose data from routine scans.
“We are hoping that these findings could be used to develop an ‘opportunistic screening’ tool to repurpose existing MRI and CT scans taken at the hospital to find patients with high-risk body composition who may be flying under the radar and could benefit from targeted diabetes and cardiovascular disease prevention,” said co-senior author Vineet K. Raghu, PhD, a computational scientist with the Mass General Brigham Heart and Vascular Institute.
Raghu and colleagues conducted a prospective cohort study using data from the U.K. Biobank. The researchers used whole-body MRIs from more than 33,000 adults with no prior history of diabetes or cardiovascular events who were followed for a median of 4.2 years.
The team found that in both men and women, AI-derived visceral adipose tissue volume (fat surrounding the abdominal organs) and fat deposits in muscle were strongly associated with diabetes and cardiovascular disease risk beyond standard measures of obesity like BMI and waist circumference. In men only, lower skeletal muscle volume was strongly associated with risk.
The authors note that future studies are needed to determine if their findings are generalizable and if AI can reliably measure these body composition metrics from routine scans. With further validation, an AI-driven approach could help leverage routine imaging to identify patients at high risk.
Authorship: Mass General Brigham authors include Matthias Jung, Michael T. Lu and Vineet K. Raghu. Additional authors include Marco Reisert, Hanna Rieder, Susanne Rospleszcz, Fabian Bamberg, Jakob Weiss.
Disclosures: Disclosure forms are available with the article online.
Funding: Jung was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) (518480401). Dr. Raghu was supported by Norn Group Longevity Impetus Grant NHLBI K01HL168231 and AHA Career Development Award 935176.
Paper cited: Jung M et al. “Association Between Body Composition and Cardiometabolic Outcomes” Annals of Internal Medicine DOI: 10.7326/ANNALS-24-01863
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About Mass General Brigham
Mass General Brigham is an integrated academic health care system, uniting great minds to solve the hardest problems in medicine for our communities and the world. Mass General Brigham connects a full continuum of care across a system of academic medical centers, community and specialty hospitals, a health insurance plan, physician networks, community health centers, home care, and long-term care services. Mass General Brigham is a nonprofit organization committed to patient care, research, teaching, and service to the community. In addition, Mass General Brigham is one of the nation’s leading biomedical research organizations with several Harvard Medical School teaching hospitals. For more information, please visit massgeneralbrigham.org.
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[Press-News.org] AI body composition measurements can predict cardiometabolic risk
Mass General Brigham researchers found that information gleaned from MRI scans were associated with cardiometabolic risk beyond BMI and other traditional risk factors
