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

基本信息

批准号：
10670168
负责人：
Mark Goodarzi
金额：
$ 28.51万
依托单位：
CEDARS-SINAI MEDICAL CENTER
依托单位国家：
美国
项目类别：
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/10670168
关键词：
Address Artificial Intelligence Autoantibodies Autoimmune Biological Assay Blood California Clinical Collection County Data Data Set Defect Development Diabetes Mellitus Early identification Endocrinology Enrollment Epidemiology Etiology Fasting Frequencies Future Gastroenterology Genetic Genetic Anticipation Genetic Risk Genotype Glucose Goals Health Homeostasis Hormonal Hospitalization Hospitals Image Incidence Individual Insulin Insulin Resistance Insulin-Dependent Diabetes Mellitus Intake Liquid substance Los Angeles MRI Scans Magnetic Resonance Imaging Measures Mediator 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 artificial intelligence method chronic pancreatitis clinical center cohort design diabetes risk ethnic diversity experience 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

项目摘要

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.

该应用程序描述了一个位于南加州的强大临床中心，用于参与 1 型急性胰腺炎联盟 (T1DAPC) 中的糖尿病。拟议的方案解决代谢机制以及急性胰腺炎 (AP) 和复发性急性胰腺炎患者的遗传、蛋白质和影像学特征胰腺炎（RAP）患者未来患糖尿病的风险较高。 AP 是最常见的原因胰源性糖尿病。荟萃分析显示，糖尿病的发病率为 23% 美联社，他们还没有阐明所发生的糖尿病类型，其中可能包括自身免疫性或特发性糖尿病 1 型糖尿病 (T1DM)、2 型糖尿病 (T2DM) 或独特的糖尿病病理学。详细了解通过采用新的方法来预测、预防和治疗 AP 后发生的糖尿病，将产生巨大的益处。治疗这种形式的糖尿病。为实现这些目标，提出以下目标：具体目标 1. 招募一组最近发生过 AP 或 RAP 的非糖尿病患者，并进行前瞻性研究使用频繁采样的静脉注射来表征其胰岛自身免疫和葡萄糖/胰岛素稳态出院后1个月以及3、6、6时进行糖耐量试验和混合餐耐量试验 12、18 和 24 个月，以及此后每年。该目标的目标是 (a) 确定糖尿病的发病率 AP 后，(b) 确定 AP 后发生的糖尿病类型，(c) 确定早期代谢轨迹与 AP 后糖尿病相关，(d) 组建将成为目标 2-4 平台的队列。具体目标 2. 评估 AP 或 RAP 患者糖尿病的遗传和蛋白质危险因素。这个目标将评估 T1DM 和 T2DM 遗传风险评分与 AP 后糖尿病的关联。十三名候选人初步研究中与 AP 后糖尿病相关的蛋白质将被评估与事件的关联 AP 后的糖尿病，产生一组关键的蛋白质，不仅可用于糖尿病预测，还可用于未来的预防或治疗措施。具体目标 3. 表征预测 AP 或 RAP 后糖尿病发展的影像表型。 AP 住院期间获得的回顾性 CT 扫描以及 CT 和新型多参数 MRI 1 个月和 12 个月后获得的扫描将接受人工智能分析以识别成像表明糖尿病风险的生物标志物。具体目标 4. 开发多因素模型来预测 AP 或 RAP 后糖尿病的发展。一笔财富将从目标 1-3 收集数据，并将其与临床因素相结合来构建和验证（在独立数据集）AP 后糖尿病的综合预测模型。目标是创建一个模型可用于临床环境中识别高危人群，促进有针对性的糖尿病预防措施。这项创新研究将由内分泌学领域经验丰富的研究人员团队进行，胃肠病学、影像学、生理学和流行病学，解决具有重大公共卫生意义的问题。