AI-Based Body Composition Analysis Enhances Prediction of Cardiometabolic Risks

Adiposity, or excess fat accumulation in the body, is a key factor linked to a range of serious health conditions including heart disease, stroke, type 2 diabetes, and kidney disease. However, assessing an individual's risk profile accurately remains challenging. Conventional measures like body mass index (BMI) often fall short because they do not distinguish between fat and muscle mass, nor do they specify fat distribution within the body.

Recent advancements have shown that artificial intelligence (AI) can significantly improve the assessment of body composition. A groundbreaking study by researchers at Mass General Brigham utilized an AI tool capable of analyzing body composition based on routine imaging scans. This technology can deliver detailed insights in just three minutes from body scans, enabling a more precise evaluation of fat and muscle distribution.

The study, published in Annals of Internal Medicine, analyzed data from over 33,000 adults in the UK Biobank, with no prior history of diabetes or cardiovascular events, followed over a median of 4.2 years. The findings revealed that visceral adipose tissue (fat surrounding abdominal organs) and fat deposits in muscles, as measured by AI, were strongly associated with future risk of diabetes and cardiovascular diseases beyond traditional obesity metrics such as BMI and waist circumference.

Interestingly, in men, lower skeletal muscle volume also correlated with increased risk. These insights demonstrate that not all fat poses the same health threat and that the distribution of fat plays a crucial role in disease development.

The researchers emphasize the potential of leveraging existing routine scans, like MRI and CT images, to perform opportunistic screening for high-risk body composition profiles. This approach could identify individuals at elevated risk who might otherwise go unnoticed, paving the way for earlier intervention and personalized prevention strategies.

While promising, further studies are necessary to confirm the generalizability of these findings and to establish the reliability of AI measurements from clinical scans. Ultimately, integrating AI-driven body composition analysis into clinical workflows could transform preventive care and significantly improve cardiometabolic health outcomes.