Identifying Temporal Sequences of Exposures in Type 1 Diabetes Etiology

项目概要/摘要 1 型糖尿病 (T1DM) 是一种慢性自身免疫性疾病，患者无法产生 胰岛素导致葡萄糖失调。 T1DM 患者承受着巨大的疾病负担，使得 疾病预防优先。环境暴露与免疫激活和进展有关 产生胰岛素的 β 细胞遭到破坏，导致明显的 T1DM。尽管有几项研究调查了这一作用 尽管环境暴露与 T1DM 病因学有关，但尚未确定任何致病暴露。原因之一是 对暴露的时间和组合（即时间序列）的理解有限，这有助于 T1DM 病因学。修改这些暴露可能提供安全且具有成本效益的方法来预防或延迟 T1DM 疾病发作。因此，更清楚地了解驱动的暴露时间序列 需要 T1DM 病因学来指导预防策略和减轻疾病负担。 目前的预防策略侧重于改变 T1DM 自然史中的早期疾病时间点， 包括免疫激活和渐进性 β 细胞破坏。使用胰岛监测免疫激活 自身抗体和 β 细胞破坏使用症状前 T1DM 分期进行近似。时间性的 这些早期疾病时间点背后的暴露顺序尚不清楚。因此，这 该提案旨在通过调节胰岛来识别增加 T1DM 风险的暴露时间序列 自身抗体的轨迹和症状前分期的进展。本提案的具体目标 1.) 确定共同改变胰岛自身抗体轨迹的暴露时间序列是否增加 T1DM 风险，以及 2.) 确定暴露的时间序列是否与 T1DM 阶段相关 可以预测 T1DM 症状前的进展。这项工作将使用最先进的时间 使用来自糖尿病环境决定因素的数据进行机器学习和数据挖掘方法 年轻（TEDDY）研究。该项目的成功完成将提高暴露的临床可转化性 通过进一步了解暴露时间和组合来修改 T1DM 预防 与早期 T1DM 疾病时间点有关。本提案中开发的分析流程将 也可作为阐明其他暴露驱动疾病中复杂的时间相互作用的框架。 该申请概述了犹他大学严格的科学和临床培训计划。 时间机器学习、联合轨迹建模和序列模式挖掘中的计算训练 生物医学信息学系将结合该科的纵向临床经验 儿科和内分泌学。这些活动在科学和临床的指导下得到加强 专家，将使申请人成为儿科内分泌学领域成功的医师科学家。

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.