Type 1 Diabetes Genetic Risk Score in TrialNet

TrialNet 中的 1 型糖尿病遗传风险评分

• 批准号: 10398018
• 负责人: Maria Jose Redondo
• 金额: $ 36.07万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10398018
• 关键词: Address; Adult; Age; Algorithms; Autoantibodies; Beta Cell; Cell physiology; Characteristics; Childhood diabetes; Clinical; Communities; Complex; Data; Diabetes Mellitus; Diabetes prevention; Diabetic Ketoacidosis; Disease; Eligibility Determination; Ethnic Origin; Family; Future; Genetic; Genetic Risk; Genotype; Glucose; Goals; Haplotypes; Health; Heterogeneity; Immunologics; Incidence; Individual; Infrastructure; Insulin-Dependent Diabetes Mellitus; Intervention; Intervention Trial; Knowledge; Link; Medical Research; Metabolic; Mission; Modeling; Modification; National Institute of Diabetes and Digestive and Kidney Diseases; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Participant; Pathway interactions; Patients; Persons; Phenotype; Prevention; Prevention strategy; Prevention trial; Preventive; Preventive treatment; Process; Public Health; Quality of life; Research; Research Personnel; Risk; Role; SNP array; Selection Criteria; Societies; Testing; Therapeutic; Time; United States National Institutes of Health; Variant; arm; base; candidate selection; cohort; diabetes mellitus genetics; diabetes pathogenesis; diabetes risk; endocrine pancreas development; genetic information; genetic resource; high risk; immunomodulatory therapies; improved; improved outcome; individualized prevention; innovation; insulin dependent diabetes mellitus onset; islet cell antibody; next generation; non-diabetic; novel; pre-clinical; precision medicine; predicting response; prediction algorithm; predictive modeling; prevent; research in practice; responders and non-responders; response; risk benefit ratio; secondary analysis; success; tool

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.

TrialNet 是一个由 NIH/NIDK 赞助的网络，最初可识别非糖尿病胰岛自身抗体阳性 1 型糖尿病 (T1D) 患者的亲属，并为他们提供旨在预防进展到临床的试验 疾病。准确预测 T1D 风险对于评估预防性试验的风险效益比至关重要。在 此外，调整试验候选人的选择标准将有助于克服当前成功的障碍， 例如，T1D 的异质性，从而提高反应率。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。申请人已开发并验证 T1D 遗传风险评分 (GRS)，可在成人糖尿病患者中识别患有 T1D 的患者。此外，我们的 TrialNet 参与者有限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） T1D 的临床阶段。然而，在 T1D GRS 投入使用之前，必须对这些结果进行验证和优化。 用于研究实践。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，能够准确、及时地识别将患 T1D 的个体 并对预防性治疗有反应。该应用的中心假设是 T1D GRS 可以 改进当前 T1D 的预测模型和干预试验候选者的选择。理由 该建议的理由是，及时预测 T1D 并准确选择干预候选人将导致 安全有效地预防 T1D。在强有力的初步数据的指导下，这一假设将得到检验 三个具体目标： (1) 建立一个经过验证的 T1D 预测模型，其中包含 T1D GRS、胰岛 自身抗体数据、临床和代谢参数。为了实现这个目标，我们将测试一个改进版本 T1D GRS 对整个 TrialNet 观察队列（预防途径）进行评估，以确定最佳模型 预测 T1D 的总体进展和每个临床前阶段。 (2)确定T1D GRS的作用 选择 TrialNet 干预试验的参与者。为了实现这个目标，我们将测试改进后的 T1D GRS 与其他已知的预测因子（例如年龄）相结合，可以区分应答者和非应答者 TrialNet 预防和新发试验中对疾病修饰疗法的反应者，并开发模型 选择干预试验的候选人。 （三）建立独特的遗传资源 TrialNet 和更广泛的研究社区加深了我们对 T1D 的了解。在这个目标下，我们将 可供其他研究人员使用该项目获得的关于极其良好表型的基因分型数据 TrialNet 队列。该项目意义重大，因为它最终有望改善试验结果 预防 T1D。该项目具有创新性，因为它试图通过提议利用 遗传学作为一种新颖、负担得起、与时间无关的策略来识别有 T1D 风险的个体并选择 干预试验的候选人。