Southern California Clinical Center of the Type 1 Diabetes in Acute Pancreatitis Consortium

南加州 1 型糖尿病急性胰腺炎联盟临床中心

• 批准号: 10461111
• 负责人: Mark Goodarzi
• 金额: $ 28.67万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10461111
• 关键词: Address; Artificial Intelligence; Autoantibodies; Autoimmune; Biological Assay; Blood; California; Clinical; Collection; County; Data; Data Set; Defect; Development; Diabetes Mellitus; Endocrinology; Enrollment; Epidemiology; Etiology; Fasting; Frequencies; Future; Gastroenterology; Genetic; Genetic Risk; Genotype; Glucose; Goals; Health; Homeostasis; Hormonal; Hospitalization; Hospitals; Image; Incidence; Individual; Insulin; Insulin Resistance; Insulin-Dependent Diabetes Mellitus; Intake; Lead; Light; Liquid substance; Los Angeles; MRI Scans; Magnetic Resonance Imaging; Measures; Mediator of activation protein; Meta-Analysis; Metabolic; Methods; Modeling; Necrosis; Non-Insulin-Dependent Diabetes Mellitus; Pancreas; Pancreatitis; Participant; Patients; Phenotype; Physiological; Physiology; Preventive; Proteins; Proteomics; Protocols documentation; Public Health; Recurrence; Research; Research Personnel; Risk; Risk Factors; Sampling; Scanning; Set protein; Severities; Signal Transduction; Swelling; Testing; Therapeutic; United States; X-Ray Computed Tomography; acute pancreatitis; base; chronic pancreatitis; clinical center; cohort; design; diabetes risk; ethnic diversity; experience; genetic association; genome wide association study; genome-wide; high risk; imaging biomarker; improved; innovation; insulin secretion; intravenous glucose tolerance test; islet; islet autoimmunity; islet cell antibody; non-diabetic; novel; novel strategies; pancreas imaging; predictive marker; predictive modeling; prevent; prospective; recruit; response; type I and type II diabetes

This application describes a robust Southern California-based Clinical Center for participation in the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC). Proposed protocols address the metabolic mechanisms and the genetic, protein, and imaging signature of patients with acute pancreatitis (AP) and recurrent acute pancreatitis (RAP) who are at high risk for future development of diabetes. AP is the most common cause of pancreatogenic diabetes. While meta-analyses have revealed an incidence rate of 23% for diabetes arising after AP, they have not shed light on the type of diabetes that develops, which may comprise autoimmune or idiopathic type 1 diabetes (T1DM), type 2 diabetes (T2DM), or a unique diabetes pathobiology. A detailed understanding of diabetes developing after AP will yield great benefit by facilitating novel approaches to predict, prevent, and treat this form of diabetes. The following aims are proposed to address these goals: Specific Aim 1. Recruit a cohort of non-diabetic patients with a recent episode of AP or RAP and prospectively characterize their islet autoimmunity and glucose/insulin homeostasis using the frequently sampled intravenous glucose tolerance test and mixed meal tolerance tests performed 1 month after hospital discharge, and at 3, 6, 12, 18, and 24 months, and yearly thereafter. The goals of this aim are to (a) determine the incidence of diabetes after AP, (b) identify the types of diabetes that develop after AP, (c) identify early metabolic trajectories associated with post-AP diabetes, (d) assemble the cohort that will be the platform for Aims 2-4. Specific Aim 2. Evaluate genetic and protein risk factors for diabetes in patients with AP or RAP. This Aim will evaluate association of genetic risk scores for T1DM and T2DM with post AP diabetes. Thirteen candidate proteins, associated with post AP diabetes in preliminary studies, will be assessed for association with incident diabetes after AP, yielding a key set of proteins with utility not only in diabetes prediction but also targets for future preventive or therapeutic measures. Specific Aim 3. Characterize the imaging phenotype that predicts development of diabetes after AP or RAP. Retrospective CT scans obtained during hospitalization for AP as well as CT and novel multiparametric MRI scans obtained 1 and 12 months afterward will undergo artificial intelligence analysis to identify the imaging biomarkers that signal diabetes risk. Specific Aim 4. Develop a multi-factorial model to predict development of diabetes after AP or RAP. A wealth of data will be collected from Aims 1-3, which will be combined with clinical factors to build and validate (in independent datasets) an integrative predictive model of post AP diabetes. The goal is to create a model that can be used in clinical settings to identify those at highest risk, facilitating targeted measures to prevent diabetes. This innovative research will be conducted by an experienced team of investigators in endocrinology, gastroenterology, imaging, physiology, and epidemiology to solve a problem of great public health significance.

这个应用程序描述了一个健壮的南加州临床中心，用于参与Type 1