Whole-body MRI scans assessed with a deep-learning (DL) algorithm have helped researchers create a "reference map" of how fat and muscle are distributed in the human body across age, sex and height, according to a study published May 5 in Radiology.
The map shows that the quality and amount of skeletal muscle, not just visceral fat, are strong predictors of diabetes, major cardiovascular events, and mortality, according to a senior author Jakob Weiss, MD, PhD, of the University Medical Center Freiburg in Germany.
“This tool has the potential to identify whether an individual’s body composition puts them at greater risk for metabolic disease compared to their age-matched peers," he said in a statement released by the RSNA.
Clinicians have used body mass index (BMI) and body weight to estimate cardiometabolic and overall health risk, the investigators noted. But BMI is a "crude measure" of body composition that only relies on height and weight and does not account for muscle mass or fat distribution.
“Many risk scores and treatment decisions still rely on BMI or waist circumference because they are simple to obtain,” Weiss explained. “But BMI does not reliably reflect a person’s actual body composition.”
Lead author Matthias Jung, MD, also of the University Medical Center Freiburg, and colleagues conducted a study that included 66,608 individuals (mean age 57.7 years, 34,443 males, mean BMI: 26.2) who underwent whole-body MRI as participants in the UK Biobank and the German National Cohort between April 2014 and May 2022. The investigators calculated age, sex, and height body composition metrics from the scans using an open-source deep-learning framework; body composition metrics (subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, skeletal muscle fat fraction, and intramuscular adipose tissue) were expressed as z‑scores, with the prognostic value of these scores to predict incidence of diabetes, major adverse cardiovascular events, and all-cause mortality coded in the following way: low: z < –1; middle: z = –1 to 1; high: z > 1.
The team reported these key findings:
- High visceral fat was associated with a 2.26-fold increased risk of future diabetes.
- High intramuscular fat was associated with a 1.54-fold increased risk of future major cardiovascular events.
- Low skeletal muscle was associated with a 1.44-fold higher all-cause mortality beyond cardiometabolic risk factors.
The study results highlight how AI can boost the already existing benefits of MRI, Weiss said.
“We’re already imaging patients every day,” he noted. “On every scan of the abdomen or chest, the information is there, we just don’t routinely measure or report it. AI now allows us to tap into this hidden layer of data in a quantitative, reproducible way.”
The group has released its open-source web-based age-, sex-, and height-adjusted body composition z-score calculator to support future research.
Access the full study here.
