AI body composition measurements have the potential to predict cardiometabolic risk

A study by researchers at Mass General Brigham and their colleagues, has found that an artificial intelligence (AI) tool designed to measure body composition, could accurately capture details in just three minutes from a body scan. Their results show that not all fat is equally harmful and highlight the potential of using AI to repurpose data from routine scans.

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"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, Dr Vineet K Raghu, a computational scientist with the Mass General Brigham Heart and Vascular Institute.

Raghu and colleagues conducted a prospective cohort study using data from the UK 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.

An AI tool was applied to MRI to derive 3-dimensional (3D) BC measures, including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle (SM), and SM fat fraction (SMFF), and then calculate their relative distribution. Sex-stratified associations of these relative compartments with incident diabetes mellitus (DM) and major adverse cardiovascular events (MACE) were assessed using restricted cubic splines.

The outcomes revealed that adipose tissue compartments and SMFF increased and SM decreased with age. After adjustment for age, smoking, and hypertension, greater adiposity and lower SM proportion were associated with higher incidence of DM and MACE after a median follow-up of 4.2 years in sex-stratified analyses.

However, after additional adjustment for BMI and waist circumference (WC), only elevated VAT proportions and high SMFF (top fifth percentile in the cohort for each) were associated with increased risk for DM (respective adjusted hazard ratios [aHRs], 2.16 [95% CI, 1.59 to 2.94] and 1.27 [CI, 0.89 to 1.80] in females and 1.84 [CI, 1.48 to 2.27] and 1.84 [CI, 1.43 to 2.37] in males) and MACE (1.37 [CI, 1.00 to 1.88] and 1.72 [CI, 1.23 to 2.41] in females and 1.22 [CI, 0.99 to 1.50] and 1.25 [CI, 0.98 to 1.60] in males). In addition, in males only, those in the bottom fifth percentile of SM proportion had increased risk for DM (aHR for the bottom fifth percentile of the cohort, 1.96 [CI, 1.45 to 2.65]) and MACE (aHR, 1.55 [CI, 1.15 to 2.09]).

The authors note that future studies are needed to determine if their findings are generalisable 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.

The findings were reported in the paper, ‘Association Between Body Composition and Cardiometabolic Outcomes’, published in the Annals of Internal Medicine. To access this paper, please click here