Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment. Using digital pathology, machine learning algorithms were applied to provide high-resolution, whole-slide images of human pancreata to determine if the tissue composition in individuals with or at risk for type 1 diabetes differs from those without diabetes. Transplant-grade pancreata from organ donors were evaluated from 16 nondiabetic autoantibody negative controls, 8 nondiabetic autoantibody positive subjects who have increased type 1 diabetes risk, and 19 persons with type 1 diabetes (0 to 12 years' duration). HALO image analysis algorithms were implemented to compare architecture of the main pancreatic duct as well as cell size, density, and area of acinar, endocrine, ductal, and other nonendocrine, nonexocrine tissues. Type 1 diabetes was found to affect exocrine area, acinar cell density, and size, whereas the type of difference correlated with the presence or absence of insulin-positive cells remaining in the pancreas. These changes were not observed before disease onset, as indicated by modeling cross-sectional data from pancreata of autoantibody positive subjects and those diagnosed with type 1 diabetes. These data provide novel insights into anatomic differences in type 1 diabetes pancreata and demonstrate that machine learning can be adapted for the evaluation of disease processes from cross-sectional data sets.
Institut(e)Institute for Pancreatic Beta Cell Research (IPI)
FörderungenJuvenile Diabetes Research Foundation Leona M. & Harry B. Helmsley Charitable Trust IH National Institute of Diabetes and Digestive and Kidney Diseasesesupported Human Islet Research Network NIH National Institute of Allergy and Infectious Diseases program project NIH Common Fund supported Stimulating Peripheral Activity to Relieve Conditions program NIH National Institute of Diabetes and Digestive and Kidney Diseases Network for Pancreatic Organ Donors with Diabetes (nPOD)