Data-driven abdominal phenotypes of type 2 diabetes in lean, overweight, and obese cohorts from computed tomography

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.

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 ( $n=1728$ ) and once on lean ( $n=497$ ), overweight ( $n=611$ ), and obese ( $n=620$ ) subgroups separately. Briefly, our experimental design transforms abdominal scans into collections of explainable measurements, identifies which measurements most strongly predict type 2 diabetes and how they contribute to risk or protection, groups scans by shared model decision patterns, and links those decision patterns back to interpretable abdominal phenotypes in the original explainable measurement space of the abdomen using the following steps. (1) To capture abdominal composition: we represented each scan as a collection of 88 automatically extracted measurements of the size, shape, and fat content of abdominal structures using TotalSegmentator. (2) To learn key predictors: we trained a 10-fold cross-validated random forest classifier with SHapley Additive exPlanations (SHAP) analysis to rank features and estimate their risk-versus-protective effects for type 2 diabetes. (3) To validate individual effects: for the 20 highest-ranked features, we ran univariate logistic regressions to quantify their independent associations with type 2 diabetes. (4) To identify decision-making patterns: we embedded the top-20 SHAP profiles with uniform manifold approximation and projection and applied silhouette-guided K-means to cluster the random forest's decision space. (5) To link decisions to abdominal phenotypes: we fit one-versus-rest classifiers on the original anatomical measurements from each decision cluster and applied a second SHAP analysis to explore whether the random forest's logic had identified abdominal phenotypes.

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 ( $p<0.05$ ). Clustering the model's decision space further revealed type 2 diabetes-enriched abdominal phenotypes within the lean, overweight, and obese subgroups.

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.