UPMC Clinical Center for the Study of Diabetes after Acute Pancreatitis

ABSTRACT. The UPMC Clinical Center for the Study of Diabetes after Acute Pancreatitis (UPMC CC) is a multidisciplinary research program at the University of Pittsburgh and UPMC and a member of the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC). The UPMC CC’s infrastructure allows comprehensive characterization of patients with acute pancreatitis from an epidemiological, clinical, and translational perspective. Its strengths include outstanding faculty with vast expertise in all areas relevant to the T1DAPC, high patient volumes, infrastructure to conduct high-quality clinical and translational studies, and a record of strong and successful collaborations and scientific output. The UPMC CC has an effective clinical and research infrastructure for participant accrual, sample processing, data management and transfer, and scientific analyses. The studies proposed in this application will directly address the objectives of the T1DAPC through 3 specific aims. Aim: 1) Continue to recruit and retain participants in DREAM and ancillary studies. As a participating site with a strong enrollment record, we will continue to recruit and retain participants in DREAM and ensure the successful completion of ongoing and ancillary studies approved by the Steering Committee. Aim 2) Identify the changes in glucose metabolism that occur following acute pancreatitis and ultimately contribute to the subsequent development of diabetes. We will test the hypothesis that patients with acute pancreatitis experience a progressive deterioration in glucose homeostasis after acute pancreatitis resolution. We will investigate how insulin secretion, beta cell function, and insulin sensitivity change with measurements obtained from detailed, serial, dynamic metabolic testing, including oral glucose tolerance tests, and frequently sample intravenous glucose tolerance tests. Aim 3) Identify T Cell ‘Immunotypes’ Based On T-cell receptor Repertoires: We will use TCR sequences to identify ‘immunotypes’ in acute pancreatitis patients and investigate their relationship with new-onset diabetes after acute pancreatitis. We will extract TCR sequences from RNAseq data that have been collected in the DREAM cohort using computational tools and perform supervised or unsupervised machine learning on the TCR repertoires to identify whether there are distinct subgroups of patients with distinct immunological profiles that correlate with biological outcomes. The proposed studies will provide crucial information to design personalized medicine approaches in pancreatitis-related diabetes and lay the foundation for ancillary studies related to both diabetes and pancreatitis.