Background: The duodenum is integral as a metabolic signaling center, making it a promising therapeutic target for Type 2 Diabetes (T2D). However, the specific pathophysiological alterations in the duodenum under metabolic stress, especially in human, are not well-understood. This study aims to elucidate these changes, taking advantage of the duodenum's accessibility for sampling via esophagogastroduodenoscopy (EGD) and the sophisticated analytical capabilities of artificial intelligence (AI).
Methods: We analyzed duodenal biopsies from T2D patients and non-diabetic controls (non-DM) (2010-2017), with grossly normal esophagogastroduodenoscopy (EGD) exam. Biopsies were selected based on normal histology, confirmed by a blinded GI pathologist. These samples were digitized into whole slide images (WSIs). Using a deep Gaussian process (DGP)-based model in a weakly supervised learning setup, we segmented each WSI into 256x256 pixel tiles. The DGP model evaluated each tile for T2D markers, with a geometric pooling model aggregating these probabilities for slide-level T2D likelihood after performing 5X cross-validation. Additionally, we applied the Getis-Ord-Gi* statistic to identify 'lymphocyte hotspots' in the slides, employing deep learning for insights into stroma and lymphocyte distribution.
Results: Our study encompassed 231 patients, comprising 111 with Type 2 Diabetes (T2D) and 120 non-diabetic (non-DM) individuals. Detailed baseline demographics for both groups are presented in Table 1. A total of 320 duodenal biopsy slides were analyzed, including 172 from T2D patients and 148 from non-DM patients. The Deep Gaussian Process (DGP) model's predictive efficacy for T2D was evaluated, with its overall performance indicated by an Area Under the Curve (AUC) of 0.7591 (p=0.0507). Furthermore, the model achieved an F1 score of 0.7471 (p=0.0378), reflecting its accuracy in binary classification. Utilizing the Getis-Ord-Gi* statistic, we identified a significantly higher prevalence of lymphocyte clustering, termed 'lymphocyte hotspots,' accompanied by stromal expansion in the lamina propria of T2D patient biopsies compared to controls (p < 0.001), as illustrated in Figure 1. This finding underscores a distinct immunological pattern in the duodenal mucosa of T2D patients.
Conclusion: Our investigation identified significant lamina propria and submucosal inflammation in the duodenum of Type 2 Diabetes (T2D) patients, suggesting a new mechanistic pathway for T2D pathology and potential therapeutic interventions. These findings highlight the duodenum as a novel target for T2D treatment, warranting further research into strategies for modulating duodenal inflammation.

Table 1. Baseline characteristics of the diabetic and non-diabetic cohorts.

<b>Figure 1. </b>A) Heatmap displaying lymphocyte hotspots and lamina propria stroma expansion (shown in red) associated with T2D, B) Brightfield image showing 3D reconstruction and cellular segmentation of the lamina propria, C) Box plot illustrating increased lymphocyte hotspots and lamina propria expansion in T2D.

Figure 1. A) Heatmap displaying lymphocyte hotspots and lamina propria stroma expansion (shown in red) associated with T2D, B) Brightfield image showing 3D reconstruction and cellular segmentation of the lamina propria, C) Box plot illustrating increased lymphocyte hotspots and lamina propria expansion in T2D.

Yoon Ho Choi

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