Related Experiment Video
Updated: May 7, 2026
Determining Glucose Metabolism Kinetics Using 18F-FDG Micro-PET/CT
Published on: May 2, 2017
Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping
Alexander Rau1, Lea Michel2, Marco Reisert2
1Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany (A.R., L.M., M.R., S.R., M.F.R., F.B., C.L.S., J.W., J.T.); Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany (A.R.).
Rationale And Objectives:
Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging (MRI) and to investigate the association between aortic features and impaired glucose metabolism beyond traditional cardiovascular (CV) risk factors.
Materials And Methods:
This study used data from the prospective Cooperative Health Research in the Region of Augsburg (KORA) study to develop a deep learning framework for automatic quantification of aortic features (maximum aortic diameter, total volume, length, and width of the aortic arch) derived from MRI. Aortic features were compared between different states of glucose metabolism and tested for associations with impaired glucose metabolism adjusted for traditional CV risk factors (age, sex, height, weight, hypertension, smoking, and lipid panel).
Results:
The deep learning framework yielded a high performance for aortic feature quantification with a Dice coefficient of 91.1±0.02. Of 381 participants (58% male, mean age 56 years), 231 (60.6%) had normal blood glucose, 97 (25.5%) had prediabetes, and 53 (13.9%) had diabetes. All aortic features showed a significant increase between different groups of glucose metabolism (p≤0.04). Total aortic length and total aortic volume were associated with impaired glucose metabolism (OR 0.85, 95%CI 0.74-0.96; p=0.01, and OR 0.99, 95%CI 0.98-0.99; p=0.02) independent of CV risk factors.
Conclusion:
Aortic features showed a glucose level dependent increase from normoglycemic individuals to those with prediabetes and diabetes. Total aortic length and volume were independently and inversely associated with impaired glucose metabolism beyond traditional CV risk factors.
