ArticleJournal of medical imaging (Bellingham, Wash.)2025
Data-driven abdominal phenotypes of type 2 diabetes in lean, overweight, and obese cohorts from computed tomography.
Article in Journal of medical imaging (Bellingham, Wash.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Although elevated body mass index (BMI) is a well-known risk factor for type 2 diabetes, the disease's presence in some lean adults and absence in others with obesity suggests that more detailed measurements of body composition may uncover abdominal phenotypes of type 2 diabetes. With artificial intelligence (AI) and computed tomography (CT), we can now leverage robust image segmentation to extract detailed measurements of size, shape, and tissue composition from abdominal organs, abdominal muscle, and abdominal fat depots in 3D clinical imaging at scale. This creates an opportunity to empirically define body composition signatures linked to type 2 diabetes risk and protection using large-scale clinical data. Approach: We studied imaging records of 1728 de-identified patients from Vanderbilt University Medical Center with BMI collected from the electronic health record. To uncover BMI-specific diabetic abdominal patterns from clinical CT, we applied our design four times: once on the full cohort ( Results: Across the full, lean, overweight, and obese cohorts, the random forest classifier achieved a mean area under the receiver operating characteristic curve (AUC) of 0.72 to 0.74. SHAP highlighted shared type 2 diabetes signatures in each group-fatty skeletal muscle, older age, greater visceral and subcutaneous fat, and a smaller or fat-laden pancreas. Univariate logistic regression confirmed the direction of 14 to 18 of the top 20 predictors within each subgroup ( Conclusions: We found similar abdominal signatures of type 2 diabetes across the separate lean, overweight, and obese groups, which suggests that the abdominal drivers of type 2 diabetes may be consistent across weight classes. Although our model had a modest AUC, the explainable components allowed for a clear interpretation of feature importance. In addition, in both lean and obese subgroups, the most important feature for identifying type 2 diabetes was fatty skeletal muscle.
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