Researchers used AI to analyze whole-body MRI scans of more than 66,000 participants to create the most detailed reference map to date showing how fat and muscle in the human body is distributed according to age, gender, and height. This research today RadiologyJournal of the Radiological Society of North America (RSNA). The results of this study show that visceral fat as well as skeletal muscle quality and quantity are strong predictors of diabetes, major cardiovascular events, and mortality.
Clinicians have long relied on body mass index (BMI) and body weight to estimate the relationship between cardiometabolic, or cardiovascular (heart/blood vessels) and metabolic (energy/nutrient processing) health, and overall health risk. However, BMI is a rough measure of body composition that relies only on height and weight and does not take into account muscle mass or fat distribution.
Many risk scores and treatment decisions still rely on BMI and waist circumference because they are easy to obtain. However, BMI does not reliably reflect a person’s actual body composition. ”
Jakob Weiss, MD, Ph.D., senior author, radiologist, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Germany
Dr. Weiss said the medical community also lacks reference standards for how the body composition of asymptomatic people changes with age and the differences between men and women.
“There is growing evidence that measurements of body composition are independent risk factors for cardiometabolic and oncological diseases and mortality,” said lead author Matthias Jung, MD, PhD, from the Department of Diagnostic and Interventional Radiology at the University Medical Center Freiburg. “However, these measures are influenced by height and gender, and change significantly with age.”
This retrospective study included a cohort of 66,608 people (mean age 57.7 years, 34,443 men, mean BMI: 26.2) who underwent whole-body MRI as participants in the UK Biobank and German National Cohort between April 2014 and May 2022.
The researchers used an open-source, fully automated deep learning framework to calculate age-, gender-, and height-normalized body composition metrics from MRI scans. Body composition indicators, such as subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, skeletal muscle fat fraction, and intramuscular adipose tissue, were expressed as Z-scores that indicate how much an individual deviates from the norm adjusted for age, sex, and height.
The researchers then used Z-score categories (low: z<-1、中: z=-1 ～ 1、高: z>We performed statistical analysis to evaluate the prognostic value of 1).
They found that high visceral fat was associated with a 2.26-fold increase in the risk of future diabetes, high intramuscular fat was associated with a 1.54-fold increase in the risk of future major cardiovascular events, and low skeletal muscle mass was associated with a 1.44-fold increase in all-cause mortality beyond cardiometabolic risk factors.
“It’s not just about how much muscle you have, it’s also about the quality of that muscle,” Jung says. “Knowing the amount of intramuscular fat can provide insight into muscle quality that cannot be easily obtained with other methods such as BMI, bioelectrical impedance analysis, or DEXA.”
The research team also created reference curves normalized by age, gender, and height for key body composition measurements.
“Adjusting for confounders is important to improve screening accuracy and tailor treatment decisions,” Dr. Weiss said. “This tool has the potential to identify whether an individual’s body composition puts them at increased risk for metabolic disease compared to their age-matched peers.”
Researchers have released an open-source, web-based, age-, sex-, and height-adjusted body composition Z-score calculator to support future research, accelerate clinical translation, and allow researchers and clinicians to normalize their own datasets to improve comparability and generalizability.
“This tool allows clinicians to use routine imaging opportunistically,” Dr. Weiss said. “A dedicated full-body MRI is not always necessary. If you already have regular CT or MRI body scans, that information can be extracted and benchmarked against baseline values.”
Dr. Weiss said this AI tool could also help improve risk stratification in oncology and distinguish between desirable fat loss and undesirable muscle loss in patients taking weight loss drugs such as GLP-1 agonists.
“We are already imaging patients every day,” Dr. Weiss said. “Every time we scan the abdomen or chest, we get information that we just don’t routinely measure or report. AI now allows us to tap into this hidden layer of data in a quantitative and reproducible way.”
The researchers’ next steps include validating the reference curve in clinical populations, specifically predicting treatment toxicity, survival and recurrence in cancer patients, and developing disease-specific reference values for other patient groups.
sauce:
Radiological Society of North America
Reference magazines:
Jung, M. Others. (2026). Body composition in the general population: A reference curve derived from whole-body MRI of over 66,000 people. Radiology. DOI: 10.1148/radiol.251939. https://pubs.rsna.org/doi/10.1148/radiol.251939
