Type 2 diabetes (T2D) is a chronic disease currently affecting around 500 million people worldwide with often severe health consequences. Yet, histopathological analyses are still inadequate to infer the glycaemic state of a person based on morphological alterations linked to impaired insulin secretion and β-cell failure in T2D. Giga-pixel microscopy can capture subtle morphological changes, but data complexity exceeds human analysis capabilities. In response, we generated a dataset of pancreas whole-slide images with multiple chromogenic and multiplex fluorescent stainings and trained deep learning models to predict the T2D status. Using explainable AI, we made the learned relationships interpretable, quantified them as biomarkers, and assessed their association with T2D. Remarkably, the highest prediction performance was achieved by simultaneously focusing on islet α-and δ-cells and neuronal axons. Subtle alterations in the pancreatic tissue of T2D donors such as smaller islets, larger adipocyte clusters, altered islet-adipocyte proximity, and fibrotic patterns were also observed. Our innovative data-driven approach underpins key findings about pancreatic tissue alterations in T2D and provides novel targets for research.

The authors have declared no competing interest.

The DZD is funded by the German Federal Ministry for Education and Research and the states where its partner institutions are located (01GI0925). The authors acknowledge the project specific financial support of the Helmholtz Association (project DIADEM, ZT-1-PF-5 139). This project has received funding from the European Union's Horizon Europe research and innovation program under grant agreement No 1010954433 (Intercept-T2D). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This work was funded by Helmholtz Imaging (HI), a platform of the Helmholtz Incubator on Information and Data Science. Birkenfeld A. was supported by the German Federal Ministry for Education and Research (01GI0925) via the German Center for Diabetes Research (DZD eV); Ministry of Science, Research and the Arts Baden-Wuerttemberg; and Helmholtz Munich.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

We analyzed clinical, laboratory, and histologic data obtained within the Studying Islets from Living Donors (SILDS) programs at two academic sites of the German Center for Diabetes Research network, the University Hospital Tuebingen and the University Hospital Dresden. Patients undergoing pancreatic surgery for different indications provided written informed consent to donate blood samples and pancreas tissue, and share health records and laboratory data for research purposes at both study sites (Tuebingen and Dresden). The study was approved by the Ethical Committees of the Technische Universitaet Dresden (Reference EK 151062008) and Eberhard Karls Universitaet Tuebingen (Reference 697/2011BO1). We obtained macroscopically healthy tissue resected during surgery but not required for further pathology workup. All patients were of European ethnicity. Additionally, fasting blood was drawn pre-surgery for detailed metabolic phenotyping. Fasting glucose and C-peptide levels were measured as previously described (Babbar et al., 2018), and homeostatic model assessment (HOMA) of insulin secretion was calculated using the computer model-based HOMA-2B using glucose and C-peptide (Levy et al., 1998). None of the participants had depleted endogenous insulin production as measured by C-peptide-based HOMA2B (lowest HOMA2B: 6 with a diabetes duration of 24 years), excluding type 1 diabetes among the participants. Information on medical history was collected by a physician. Documented by their health records, T2D patients were diagnosed as having T2D at least one year before admission to pancreatic surgery. This excludes diabetes in the context of exocrine pancreatic disease. In contrast, patients without diabetes neither had diabetes nor did they fulfill diagnostic criteria of T2D based on glycated hemoglobin (HbA1c) and fasting glucose, as defined by the American Diabetes Association (American Diabetes Association, 2020).

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