TrialNet is a NIH/NIDK-sponsored network that identifies initially non-diabetic islet autoantibody-positive relatives of patients with type 1 diabetes (T1D) and offers them trials that aim to prevent progression to clinical disease. Accurate prediction of T1D risk is critical to assess the risk-benefit ratio of preventive trials. In addition, tailoring the selection criteria for candidates to trials will help overcome current barriers to success, e.g., heterogeneity of T1D, and thus, increase rates of response. Until now, the complexity of T1D genetics has limited its use in predictive models and trial eligibility algorithms. The applicants have developed and validated a T1D Genetic Risk Score (GRS) that, in adults with diabetes, identifies those with T1D. Furthermore, our preliminary data on a limited subset of TrialNet participants strongly suggests that the T1D GRS improves the current predictive model (i.e., islet autoantibodies, age and metabolic factors) for progression along the pre- clinical stages of T1D. However, these results must be validated and optimized before the T1D GRS can be used in research practice. The long-term goal is to predict and prevent T1D. The overall objective is to use genetics, in combination with other factors, to accurately and timely identify individuals who will develop T1D and will respond to preventive treatments. The central hypothesis of this application is that the T1D GRS can improve the current prediction model for T1D and selection of candidates for intervention trials. The rationale for this proposal is that timely prediction of T1D and accurate selection of candidates for intervention will lead to safe and effective prevention of T1D. Guided by strong preliminary data, this hypothesis will be tested by three specific aims: (1) Establish a validated T1D prediction model that incorporates T1D GRS, islet autoantibody data, clinical and metabolic parameters. To achieve this aim, we will test an improved version of the T1D GRS on the entire TrialNet observational cohort (Pathway to Prevention) to identify the best models to predict progression overall and at each of the preclinical stages of T1D. (2) Determine the role of the T1D GRS in selection of participants for TrialNet intervention trials. To achieve this aim, we will test whether the improved T1D GRS, in combination with other known predictors (e.g., age), can distinguish responders and non- responders to disease modifying therapies in TrialNet prevention and new onset trials, and develop models for selection of candidates for intervention trials. (3) Establish a unique genetic resource that can be used by TrialNet and wider research community for furthering our understanding of T1D. Under this aim, we will make available to other investigators genotyping data obtained by this project on the extremely well phenotyped TrialNet cohorts. This project is significant because it is ultimately expected to improve the outcomes of trials to prevent T1D. This project is innovative because it seeks to shift the current practice by proposing to utilize genetics as a novel, affordable, time-independent strategy to identify individuals at risk of T1D and select candidates for intervention trials.

Public Health Relevance

The proposed research is relevant to public health because it seeks to use a genetic risk score, in combination with other known factors, to predict type 1 diabetes in a timely fashion and select individuals who will respond to immunomodulatory therapies and thus, ultimately prevent type 1 diabetes, which is increasing in incidence and poses a large burden on individuals, families and society. This project will also create a unique genetic resource for further research in type 1 diabetes by other investigators. Thus, the proposed research is relevant to the part of the NIDDK's mission that pertains to conducting medical research on diabetes to improve people's health and quality of life.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project (R01)
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Kidney, Nutrition, Obesity and Diabetes Study Section (KNOD)
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Akolkar, Beena
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Baylor College of Medicine
Schools of Medicine
United States
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