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: 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
• 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

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.

此应用程序描述了一个强大的南加州临床中心，用于参与类型1 糖尿病合并急性胰腺炎联盟(T1DAPC)。拟议的方案解决了代谢机制 急性胰腺炎(AP)和复发性急性胰腺炎患者的遗传、蛋白质和影像特征 胰腺炎(RAP)是糖尿病未来发展的高风险人群。AP是最常见的原因 胰源性糖尿病。而荟萃分析显示，糖尿病的发病率为23%，发生在 美联社，他们还没有阐明糖尿病的类型，这可能包括自身免疫性或特发性 1型糖尿病(T1 DM)、2型糖尿病(T2 DM)或一种独特的糖尿病病理生物学。详细了解 AP后发生的糖尿病将通过促进新的方法来预测、预防和 治疗这种形式的糖尿病。为实现这些目标，提出了以下目标： 具体目标1.招募一组最近发生AP或RAP的非糖尿病患者，并前瞻性地 用频繁采集的静脉血检测胰岛自身免疫功能和血糖/胰岛素稳态 分别于出院后1个月、3、6、6个月进行糖耐量试验和混合餐耐量试验。 12个月、18个月和24个月，此后每年。这一目标的目的是(A)确定糖尿病的发病率 急性胰腺炎后，(B)确定急性胰腺炎后发展的糖尿病类型，(C)确定早期代谢轨迹 与急性胰腺炎后糖尿病相关的，(D)集合将成为AIMS 2-4平台的队列。 具体目的2.评估AP或RAP患者糖尿病的遗传和蛋白质危险因素。这一目标 将评估T1 DM和T2 DM的遗传风险评分与AP后糖尿病的相关性。13名候选人 在初步研究中，与AP后糖尿病相关的蛋白质将被评估为与事件有关 AP后糖尿病，产生一组关键的蛋白质，不仅在糖尿病预测中有用，而且在 未来的预防或治疗措施。 具体目的3.描述AP或RAP后预测糖尿病发展的影像表型。 AP住院期间的回顾性CT扫描以及CT和新的多参数磁共振成像 在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 预示糖尿病风险的生物标记物。 具体目的4.建立预测AP或RAP后糖尿病发展的多因素模型。一笔财富 的数据将从AIMS 1-3收集，这些数据将与临床因素相结合以建立和验证(在 独立数据集)AP后糖尿病的综合预测模型。我们的目标是创建一个 可在临床环境中用于识别高危人群，促进采取有针对性的措施预防糖尿病。 这项创新研究将由一支经验丰富的内分泌学研究团队进行， 解决胃肠病学、影像学、生理学和流行病学问题具有重大的公共卫生意义。