# Type 1 Diabetes Genetic Risk Score in TrialNet

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2021 · $544,609

## Abstract

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. Thi...

## Key facts

- **NIH application ID:** 10145671
- **Project number:** 5R01DK121843-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Maria Jose Redondo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $544,609
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145671

## Citation

> US National Institutes of Health, RePORTER application 10145671, Type 1 Diabetes Genetic Risk Score in TrialNet (5R01DK121843-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10145671. Licensed CC0.

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