Possible type 1 diabetes risk prediction: Using ultrasound imaging to assess pancreas inflammation in the inducible autoimmune diabetes BBDR model

Background/Aims Studies of human cadaveric pancreas specimens indicate that pancreas inflammation plays an important role in type 1 diabetes pathogenesis. Due to the inaccessibility of pancreas in living patients, imaging technology to visualize pancreas inflammation is much in need. In this study, we investigated the feasibility of utilizing ultrasound imaging to assess pancreas inflammation longitudinally in living rats during the progression leading to type 1 diabetes onset. Methods The virus-inducible BBDR type 1 diabetes rat model was used to systematically investigate pancreas changes that occur prior to and during development of autoimmunity. The nearly 100% diabetes incidence upon virus induction and the highly consistent time course of this rat model make longitudinal imaging examination possible. A combination of histology, immunoblotting, flow cytometry, and ultrasound imaging technology was used to identify stage-specific pancreas changes. Results Our histology data indicated that exocrine pancreas tissue of the diabetes-induced rats underwent dramatic changes, including blood vessel dilation and increased CD8+ cell infiltration, at a very early stage of disease initiation. Ultrasound imaging data revealed significant acute and persistent pancreas inflammation in the diabetes-induced rats. The pancreas micro-vasculature was significantly dilated one day after diabetes induction, and large blood vessel (superior mesenteric artery in this study) dilation and inflammation occurred several days later, but still prior to any observable autoimmune cell infiltration of the pancreatic islets. Conclusions Our data demonstrate that ultrasound imaging technology can detect pancreas inflammation in living rats during the development of type 1 diabetes. Due to ultrasound's established use as a non-invasive diagnostic tool, it may prove useful in a clinical setting for type 1 diabetes risk prediction prior to autoimmunity and to assess the effectiveness of potential therapeutics.