Identifying Temporal Sequences of Exposures in Type 1 Diabetes Etiology

项目摘要/摘要 1型糖尿病是一种慢性自身免疫性疾病，在这种疾病中， 胰岛素会导致血糖失调。T1糖尿病患者经历了沉重的疾病负担，使 预防疾病是当务之急。环境暴露与免疫激活和渐进性有关 导致显性T1 DM的产生胰岛素的β细胞的破坏。尽管有几项研究调查了这一角色 在T1 DM病因学中的环境暴露中，尚未发现致因性暴露。其中一个原因是 对接触的时间和组合--即时间序列--的了解有限 T1 DM病因学。修改这些暴露可能提供安全且经济高效的方法来预防或延迟 T1糖尿病起病。因此，更清楚地了解导致辐射的时间序列 需要T1糖尿病病因学来为预防策略提供信息，减少疾病负担。 目前的预防策略侧重于改变T1 DM自然病程中的早期疾病时间点， 包括免疫激活和渐进性的β细胞破坏。使用胰岛监测免疫激活 自身抗体和β细胞破坏可用症状前T1 DM分期来近似。世俗的 人们对这些早期疾病时间点背后的暴露序列知之甚少。因此，这 一项提案寻求通过调节胰岛来确定增加T1 DM风险的暴露的时间序列 自身抗体在症状前分期中的轨迹和进展。这项建议的具体目的 为1。)确定联合改变胰岛自身抗体轨迹的暴露时间序列是否增加 对T1 DM的风险，以及2。)确定与T1 DM分期相关的暴露时间序列 可以预测T1 DM的症状前进展。这项工作将使用最先进的时间 基于糖尿病环境决定因素数据的机器学习和数据挖掘方法 年轻(泰迪)研究。该项目的成功完成将提高暴露的临床可译性 通过提供对暴露的时间和组合的进一步洞察，在预防T1 DM方面进行修改 与早期T1 DM疾病的时间点有关。本提案中开发的分析管道将 也可以作为一个框架，用于阐明其他暴露驱动的疾病中复杂的时间相互作用。 这份申请概述了犹他大学严格的科学和临床培训计划。 在时间机器学习、联合轨迹建模和序列模式挖掘方面的计算培训 生物医学信息学系将与该司的纵向临床经验相结合 儿科和内分泌学。这些活动得到了来自科学和临床的指导 专家将使申请者成为一名成功的儿科内分泌学内科科学家。

PROJECT SUMMARY/ABSTRACT Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease in which an inability to produce insulin results in glucose dysregulation. Patients with T1DM experience significant disease burden, making disease prevention a priority. Environmental exposures are implicated in immune activation and progressive destruction of insulin-producing β-cells that lead to overt T1DM. Despite several studies investigating the role of environmental exposures in T1DM etiology, no causative exposures have been identified. One reason is limited understanding of the timing and combination, i.e., temporal sequences, of exposures that contribute to T1DM etiology. Modifying these exposures may offer safe and cost-effective approaches to prevent or delay T1DM disease onset. Therefore, a clearer understanding of the temporal sequences of exposures that drive T1DM etiology is needed to inform prevention strategies and reduce disease burden. Current prevention strategies focus on altering early disease timepoints in the natural history of T1DM, including immune activation and progressive β-cell destruction. Immune activation is monitored using islet autoantibodies, and β-cell destruction is approximated using presymptomatic T1DM staging. The temporal sequences of exposures underlying these early disease timepoints are poorly understood. Therefore, this proposal seeks to identify temporal sequences of exposures that increase risk for T1DM by modulating islet autoantibody trajectories and progression through presymptomatic staging. The specific aims of this proposal are to 1.) determine if temporal sequences of exposures that jointly alter islet autoantibody trajectories increase risk for T1DM, and 2.) determine if temporal sequences of exposures that are associated with stages of T1DM can predict presymptomatic progression of T1DM. This work will be investigated using state-of-the-art temporal machine learning and data mining methods with data from the Environmental Determinants of Diabetes in the Young (TEDDY) study. Successful completion of this project will improve the clinical translatability of exposure modification in T1DM prevention by providing further insight into the timings and combinations of exposures that are implicated in early T1DM disease timepoints. The analytical pipelines developed in this proposal will also serve as a framework for elucidating complex temporal interactions in other exposure-driven diseases. This application outlines a rigorous scientific and clinical training plan at the University of Utah. Computational training in temporal machine learning, joint trajectory modeling, and sequential pattern mining in the Department of Biomedical Informatics will be combined with longitudinal clinical experiences in the Division of Pediatrics and Endocrinology. These activities, augmented with mentorship from scientific and clinical experts, will enable the applicant to become a successful physician-scientist in pediatric endocrinology.