Researchers at the University Medical Center Freiburg in Germany, say that detailed measures of body composition derived from whole-body MRI scans can predict diabetes, cardiovascular events, and mortality risk better than current methods that rely on body mass index (BMI). Using MRI imaging data from more than 66,000 people, the team has developed age-, sex-, and height-adjusted reference standards that show how fat and muscle are distributed across the body and how these patterns relate to health outcomes. Their findings, published in the journal Radiology, show analysis of both the quantity and quality of skeletal muscle, along with where fat is distributed in the body, can provide a more accurate way to determine risk as opposed to weight-based methods alone.
“Many risk scores and treatment decisions still rely on BMI or waist circumference because they are simple to obtain,” said senior author Jakob Weiss, MD, PhD, an interventional radiologist at University Medical Center Freiburg. “But BMI does not reliably reflect a person’s actual body composition.” This is one of the central findings of the study: that individuals with similar BMI values can have markedly different distributions of fat and muscle, which carry different levels of risk for cardiometabolic disease and mortality.
The team’s retrospective study analyzed whole-body MRI scans from 66,608 people using data from the UK Biobank and the German National Cohort collected between April 2014 and May 2022. The cohort had a mean age of 57.7 years and an average BMI of 26.2. Using a fully automated deep learning framework, the researchers quantified multiple body composition measures, including subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, skeletal muscle fat fraction, and intramuscular adipose tissue. These measures were normalized for age, sex, and height. A score is developed from these data to show how far individuals deviated from a population-adjusted reference.
“Whole-body MRI–derived BC (body composition) z-scores were used to identify at-risk individuals and predict cardiometabolic outcomes and mortality beyond traditional risk factors.” They then used the z-score categories to assess associations and clinical outcomes.
Their data showed that individuals with high visceral fat had a 2.26-fold increased risk of developing diabetes. High intramuscular fat was associated with a 1.54-fold increased risk of major adverse cardiovascular events, while low skeletal muscle was linked to a 1.44-fold increase in all-cause mortality.
The deep learning system used to develop the risk profiles was trained and evaluated against radiologist-defined reference standards, allowing it to extract volumetric measurements across the entire body rather than relying on single cross-sectional slices. This method allowed the researchers to capture meaningful variations in muscle quality and fat distribution that are not visible through other techniques.
“Manual BC measurement in large-scale imaging datasets is prohibitively time-consuming,” the researchers wrote. “However, recent advances in deep learning have enabled fully automated, accurate, and efficient quantification from cross-sectional imaging.” This capability allowed the team to construct reference curves reflective of how body composition changes with age and differs between men and women.
Importantly, the research shines a light on the limitations of using BMI to determine future risk. Because BMI is calculated using only two metrics, height and weight, it does not distinguish between fat and muscle or account for where fat is stored. Because of this, two people with the same BMI may have very different levels of visceral fat or muscle mass, factors that can lead to different to different health risks. The researchers showed that deviations in these specific components, captured via their MRI-based z-scores, were predictive of outcomes even after accounting for traditional risk factors.
“It’s not only how much muscle you have, but also it’s the quality of that muscle,” said first author Matthias Jung, MD, a radiologist at University Medical Center Freiburg. “Knowing the volume of intramuscular fat gives us a window into muscle quality that other methods like BMI, bioelectrical impedance analysis, or DEXA can’t easily provide.” This distinction is relevant because intramuscular fat is linked to metabolic dysfunction and cardiovascular risk.
The study also produced a web-based calculator that allows clinicians and researchers to compare individual patient data with population-based reference values. According to Weiss, this tool could be applied to existing imaging studies. “A dedicated whole-body MRI is not necessarily required. If a routine CT or MRI body scan already exists, the information can be extracted for benchmarking against the reference values,” he said.
The study has limitations, including a cohort of primarily White Western European adults, which may impact the generalizability of the findings. The researchers also pointed out that whole-body MRI is not routinely performed in clinical practice, although they provided reference values for commonly imaged regions such as the chest, abdomen, and pelvis to address this.
The team will continue their work by seeking to validate the reference curves in clinical populations and exploring their use in predicting treatment outcomes, including toxicity, survival, and recurrence in cancer patients. The team also plans to develop disease-specific reference values for broader patient groups to broaden the use of body composition analysis into clinical care.
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