Abstract #0836

Age dependency of abdominal fat depot volumes and proton density fat fractions in people with obesity

Mingming Wu¹, Arun Somasundaram¹, Selina Rupp¹, Jessie Han¹, Daniela Junker¹, Anna Reik², Stella Naebauer¹, Johannes Raspe¹, Lisa Patzelt¹, Meike Wiechert², Daniel Rueckert^3,4, Hans Hauner^2,5, Christina Holzapfel^2,6, and Dimitrios Karampinos^1,7,8

¹Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany, ²Institute of Nutritional Medicine, Technical University of Munich, Munich, Germany, ³TUM School of Computation, Information, and Technology, Technical University of Munich, Munich, Germany, ⁴Department of Computing, Imperial College London, London, United Kingdom, ⁵Else Kroener Fresenius Center for Nutritional Medicine, Technical University of Munich, Munich, Germany, ⁶Department of Nutritional, Food and Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany, ⁷Munich Institute of Biomedical Engineering, Technical University of Munich, Garching, Germany, ⁸Munich Data Science Institute, Technical University of Munich, Garching, Germany

Synopsis

Keywords: Endocrine, Aging, Obesity

Motivation: As cardiometabolic risk in obesity is associated with specific body composition types, we aim at deciphering age-related body composition changes in people with obesity.

Goal(s): To assess age-specific abdominal organ volume and proton density fat fraction (PDFF) in people with obesity and predict chronological age.

Approach: An nnU-Net-based automatic pipeline was used to segment abdominal organs. Machine-learning-based methods were applied to predict chronological age based on the organs' volumes and PDFF in chemical-shift encoding-based MRI.

Results: The best predictors for chronological age were increased visceral adipose tissue volume and elevated ectopic fat deposition in the paraspinal muscle, measured via proton density fat fraction.

Impact: Age-specific differences in volumes and PDFF of abdominopelvic fat depots, and ectopic fat in liver and two muscles were found in people with obesity using automated segmentation on quantitative chemical-shift encoding-based MRI scans.

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