Type 1 Diabetes Genetic Risk Score in TrialNet

TrialNet是一个由NIH/NIDK赞助的网络，它识别最初的非糖尿病胰岛自身抗体阳性 1型糖尿病(T1D)患者的亲属，并为他们提供旨在防止进展到临床的试验 疾病。T1D风险的准确预测对于评估预防性试验的风险-收益比至关重要。在……里面 此外，根据审判调整候选人的选择标准将有助于克服目前的成功障碍， 例如，T1D的异质性，从而提高了响应率。到目前为止，T1D遗传学的复杂性已经 限制了其在预测模型和试验资格算法中的使用。申请者已经开发并验证了 T1D遗传风险评分(GRS)，在成人糖尿病患者中，识别那些患有T1D的人。此外，我们的 对有限的TrialNet参与者子集的初步数据强烈表明，T1D GRS改善了 目前的预测模型(即胰岛自身抗体、年龄和代谢因素)可用于预测Pre-Pre T1D的临床分期。然而，必须验证和优化这些结果，然后才能使用T1D GRS 在研究实践中使用。长期目标是预测和预防T1D。总体目标是使用 遗传学与其他因素相结合，准确、及时地识别出将发生T1D的个体 并且会对预防性治疗有反应。这个应用的中心假设是T1D GRS可以 改进现有的T1D预测模型和干预试验候选者的选择。其基本原理是 因为这一建议是，及时预测T1D和准确选择干预候选人将导致 以安全有效地预防T1D。在强劲的初步数据的指导下，这一假设将得到以下检验 三个具体目标：(1)建立一个有效的包含T1D GRS的T1D预测模型， 自身抗体数据、临床和代谢参数。为了实现这一目标，我们将测试改进版本的 整个TrialNet观察队列(预防路径)上的T1D GRS，以确定最佳模型 预测T1D的整体和每个临床前阶段的进展。(2)确定T1D GRS的作用 在选择TrialNet干预试验的参与者方面。为了达到这一目标，我们将测试改进后的 T1DGRS与其他已知预测指标(例如年龄)相结合，可以区分应答者和非应答者 在TrialNet预防和新的发病试验中对疾病修改疗法的应答者，并开发模型 选择干预试验的候选人。(3)建立独特的遗传资源，可供 TrialNet和更广泛的研究社区，以加深我们对T1D的理解。在这个目标下，我们将使 可供其他研究人员使用本项目获得的关于表型极好的基因分型数据 TrialNet队列。这个项目意义重大，因为它最终有望改善试验结果，以 预防T1D。该项目具有创新性，因为它试图通过提议使用 遗传学作为一种新的、负担得起的、独立于时间的策略来识别T1D风险个体并选择 参加干预试验的候选人。

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.