Cardiovascular MRI shows that abdominal obesity is linked to harmful changes in heart structure, especially in men, according to a German study presented on 1 December at the RSNA meeting in Chicago.
The finding is from an analysis of 2,173 subjects in whom researchers explored the effects of obesity on the heart based on waist-to-hip ratio (WHR) and body mass index, noted lead author Dr. Jennifer Erley, of the University Medical Center Hamburg-Eppendorf, in an RSNA press release.
“Abdominal obesity according to WHR is associated to concentric remodeling, while a higher body mass is associated with ventricular dilatation,” she said.
While body mass index (BMI) is a measure of general obesity calculated from a person’s weight and height, waist-to-hip ratio (WHR) is a measure of abdominal obesity, the accumulation of visceral fat deep around internal organs. In this study, Erley and colleagues analyzed the effects of these two measures on the heart, based on sex.
Out of the 2,173 subjects (43% female, mean age 64 years old), 80% of whom were obese according to the WHR (≥ 0.85 in females, ≥ 0.90 in males), and 20% were obese based on BMI (BMI ≥ 30). Participants had no known cardiovascular disease.
According to the results, increases in WHRs were associated with a higher left ventricular (LV) mass and lower ventricular volumes, and their association with right ventricular volumes was weaker in females than in males.
Pictogram showing the results of the research. An increase in waist-to-hip ratio (WHR) is associated with a higher left ventricular (LV) mass and lower ventricular volumes. Its association with right ventricular (RV) volumes is weaker in women than in men. An increase in body mass index (BMI) is associated with ventricular dilatation and a higher LV mass, although this relationship is also weaker in women.RSNA
“[Abdominal obesity] appears to lead to a potentially pathological form of cardiac remodeling, concentric hypertrophy, where the heart muscle thickens, but the overall size of the heart doesn’t increase, leading to smaller cardiac volumes. In fact, the inner chambers become smaller, so the heart holds and pumps less blood. This pattern impairs the heart’s ability to relax properly, which eventually can lead to heart failure,” she said.
Regarding sex-specific differences, Erley suggested that male patients may be more vulnerable to the structural effects of obesity on the heart, which is a finding not widely reported in earlier studies. She said that rather than focusing on reducing overall weight, middle-aged adults should focus on preventing abdominal fat accumulation through regular exercise, a balanced diet, and timely medical intervention, if necessary.
From a radiologist's perspective, she added that clinicians typically think of cardiomyopathy, hypertensive heart disease, or some other form of disease when they see this cardiac remodeling pattern, rather than connecting it to obesity in reports.
“This study should alert radiologists and cardiologists to be more aware that this remodeling could be attributed independently to obesity,” Erley said.
For full coverage of RSNA 2025, visit the AuntMinnie.com RADCast.




![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)






![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








