Insulin-Glucose-Glucagon Network: Defining a type 1 diabetes progression model

胰岛素-葡萄糖-胰高血糖素网络：定义 1 型糖尿病进展模型

• 批准号: 8974151
• 负责人: SUE A BROWN
• 金额: $ 148.19万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8974151
• 关键词: Achievement; Address; Ancillary Study; Artificial Pancreas; Autoantibodies; Autoimmune Process; Blood Glucose; C-Peptide; Cell physiology; Clinical Trials; Clinical Trials Network; Collection; Control Groups; Data; Development; Diabetes autoantibodies; Disease; Enrollment; Epinephrine; Evaluation; Fasting; First Degree Relative; Future; Glucagon; Glucose; Goals; Homebound Persons; Hospitals; Hour; Hypoglycemia; Individual; Inpatients; Insulin; Insulin-Dependent Diabetes Mellitus; Intervention Trial; Life; Link; Measures; Metabolic; Methodology; Modeling; OGTT; Participant; Pathogenesis; Pathway interactions; Patients; Persons; Phenotype; Population; Prevention; Preventive Intervention; Recruitment Activity; Regulation; Reporting; Research Design; Risk; Risk Factors; Sampling; Second Degree Relative; Signal Transduction; Staging; System; Testing; Urine; base; biobank; blood glucose regulation; counterregulation; design; diaries; drinking; experience; field study; first phase insulin response; glucose monitor; high risk; improved; insulin secretion; insulin sensitivity; islet; minimally invasive; network models; prevent; public health relevance; research clinical testing; response; simulation; tool

 DESCRIPTION (provided by applicant): The goal of the proposed ancillary studies is to establish whether characterization of the insulin-glucagon-glucose (IGG) interactions in first degree relatives of patients with type 1 diabetes (T1D) can provide new information about the pathogenesis, prediction, and progression of the early stages of the disease. The project will enroll individuals from the "Living Biobank" of the TrialNet Pathway to Prevention (PTP) study who are phenotyped with respect to a variety of risk factors, including immunological abnormalities. To the best of our knowledge, the IGG relationships in general and the glucagon phenotype in particular have not been studied in this population. It is known, however, that in T1D the release of glucagon is altered, which is manifested by abnormal postprandial suppression and defective response to hypoglycemia. Several reports indicate that glucagon becomes dysregulated prior to the development of T1D, but comprehensive studies aiming to understand in detail the insulin-glucagon co-dynamics in people at risk for T1D have never been performed. Thus, our goals now are to transfer our expertise in clinical testing and analysis of the IGG interactions and to expand our methodology to characterize the IGG interactions in individuals at risk for developing T1D.To achieve our goals, we will first quantify the primary intra-islet interactions using our Minimal Control Network models of glucagon secretion and counterregulation. Further, our preliminary data show that in patients with T1D, continuous glucose monitoring (CGM) field data provide information about the patient's insulin sensitivity, and glucagon and epinephrine counterregulation. Accordingly, our second goal is to test whether a simple minimally invasive test performed in the field can estimate the state of the IGG interactions. We hypothesize that in first degree relatives of patients with T1D, immunological abnormalities are associated with alterations in the interactions between blood glucose, insulin and glucagon which can be: (i) detected with a combined 10-hour mixed-meal and induced hypoglycemia inpatient test and (ii) correlated with self-collected minimally-invasive CGM field data. The following Specific Aims are proposed for studies in first degree relatives of patients with T1D enrolled in (or screened for participation in) the T1D TrialNet clinical trials network. Aim 1 is to test the hypothesis that immunological abnormalities are associated with abnormally high glucagon responses to a meal and reduced glucagon counterregulatory response to insulin induced hypoglycemia. Aim 2 is to correlate metrics derived from a minimally-invasive CGM-based field test with glucagon responses to a meal and induced hypoglycemia measured in the hospital. When completed, this study will improve the understanding of the pathogenesis of the early stages of T1D and will provide new quantitative tools for prediction and evaluation of insulin-glucagon-glucose interactions relevant to individuals at risk for developing T1D, thereby enabling future preventive intervention trials.

 描述（由申请方提供）：拟定辅助研究的目的是确定1型糖尿病（T1 D）患者一级亲属中胰岛素-胰高血糖素-葡萄糖（IGG）相互作用的表征是否可以提供有关疾病早期发病机制、预测和进展的新信息。该项目将从TrialNet Pathway to Prevention（PTP）研究的“Living Biobank”中招募个体，这些个体的表型与各种风险因素有关，包括免疫异常。据我们所知，尚未在该人群中研究一般IGG关系，特别是胰高血糖素表型。然而，已知在T1 D中，胰高血糖素的释放改变，这表现为异常的餐后抑制和对低血糖症的反应缺陷。一些报告表明，胰高血糖素在T1 D发展之前变得失调，但从未进行过旨在详细了解T1 D风险人群中胰岛素-胰高血糖素协同动力学的全面研究。因此，我们现在的目标是转移我们在临床测试和分析IGG相互作用方面的专业知识，并扩展我们的方法来表征处于发展T1 D风险的个体中的IGG相互作用。为了实现我们的目标，我们将首先使用胰高血糖素分泌和反调节的最小控制网络模型来量化主要的胰岛内相互作用。此外，我们的初步数据显示，在T1 D患者中，连续葡萄糖监测（CGM）现场数据提供了有关患者胰岛素敏感性以及胰高血糖素和肾上腺素反调节的信息。因此，我们的第二个目标是测试在现场进行的简单微创测试是否可以估计IGG相互作用的状态。我们假设，在T1 D患者的一级亲属中，免疫异常与血糖、胰岛素和胰高血糖素之间相互作用的改变相关，这可以：（i）通过10小时混合餐和诱导低血糖住院试验检测到，（ii）与自我收集的微创CGM现场数据相关。针对入组（或筛选参与）T1 D TrialNet临床试验网络的T1 D患者一级亲属的研究，拟定了以下特定目的。目的1是检验免疫异常与对进餐的异常高胰高血糖素反应和对胰岛素诱导的低血糖的降低胰高血糖素反调节反应相关的假设。目的2是将来自基于微创CGM的现场测试的度量与对膳食的胰高血糖素反应和在医院中测量的诱导低血糖相关联。完成后，这项研究将提高对T1 D早期发病机制的理解，并将提供新的定量工具，用于预测和评估与T1 D风险个体相关的胰岛素-胰高血糖素-葡萄糖相互作用，从而使未来的预防性干预试验成为可能。

项目成果

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning.

使用 1 周 CGM 家庭测试、夜间血糖增量和标准化液体混合早餐，并通过机器学习增强分类来预测 1 期和 2 期糖尿病的免疫风险。

• DOI: 10.1089/dia.2023.0064
• 发表时间: 2023
• 期刊: Diabetes technology & therapeutics
• 影响因子: 5.4
• 作者: Montaser,Eslam;Breton,MarcD;Brown,SueA;DeBoer,MarkD;Kovatchev,Boris;Farhy,LeonS
• 通讯作者: Farhy,LeonS

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study.

使用为期一周的连续血糖监测家庭测试并通过机器学习增强分类来预测患 1 型糖尿病的风险：一项探索性研究。

• DOI: 10.1177/19322968231209302
• 发表时间: 2024
• 期刊: Journal of diabetes science and technology
• 影响因子: 5
• 作者: Montaser,Eslam;Brown,SueA;DeBoer,MarkD;Farhy,LeonS
• 通讯作者: Farhy,LeonS