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Klein, L.* ; Ziegler, S.* ; Gerst, F. ; Morgenroth. Y. ; Gotkowski, K.* ; Schöniger, E. ; Heni, M.* ; Kipke, N. ; Friedland, D. ; Seiler, A. ; Geibelt, E.* ; Yamazaki, H.* ; Häring, H.-U. ; Wagner, S.* ; Nadalin, S.* ; Königsrainer, A.* ; Mihaljevic, A.L.* ; Hartmann, D.* ; Fend, F.* ; Aust, D.* ; Weitz, J. ; Jumpertz von Schwartzenberg, R. ; Distler, M. ; Maier-Hein, K.* ; Birkenfeld, A.L. ; Ullrich, S. ; Jäger, P.F.* ; Isensee, F.* ; Solimena, M. ; Wagner, R.

Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetes.

Nat. Commun. 17:1558 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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 generate a dataset of pancreas whole-slide images from living donors with multiple chromogenic and multiplex immunofluorescence stainings and train deep learning models to predict the T2D status. Using explainable AI, we make the learned relationships interpretable, quantify them as biomarkers, and assess their association with T2D. Remarkably, the highest prediction performance is achieved by simultaneously focusing on islet α- and δ-cells and neuronal axons, alongside subtle pancreatic alterations in T2D donors such as larger adipocyte clusters, altered islet-adipocyte proximity and smaller islets. This data-driven approach provides a foundation for future research into relevant diagnostic and therapeutic targets, refining several hypotheses regarding tissue alterations associated with T2D.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Islet-associated Macrophages; Platelet Activation; Insulin; Fibrosis; Mellitus; Health; Cells
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 17, Heft: 1, Seiten: , Artikelnummer: 1558 Supplement: ,
Verlag Springer
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes Research and Metabolic Diseases (IDM)
Institute of Pancreatic Islet Research (IPI)
Förderungen Helmholtz Association