Identifying Temporal Sequences of Exposures in Type 1 Diabetes Etiology

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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.