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Type 1 Diabetes Genetic Risk Score in TrialNet

TrialNet 中的 1 型糖尿病遗传风险评分

基本信息

批准号：
10650137
负责人：
Maria Jose Redondo
金额：
$ 35.32万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
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/10650137
关键词：
Address Adult Age Algorithms Asthma 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 Goals Haplotypes Health Heterogeneity Immunologics Incidence Individual Infrastructure Insulin-Dependent Diabetes Mellitus Intervention Intervention Trial Knowledge Link Malignant Neoplasms 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 Risk Reduction Risk-Benefit Assessment Role SNP array Selection Criteria Societies Testing Therapeutic Time United States National Institutes of Health Variant arm blood glucose regulation 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

项目摘要

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NIH/NIDK赞助的网络，用于识别最初非糖尿病胰岛自身抗体阳性的 1型糖尿病（T1 D）患者的亲属，并为他们提供旨在防止进展为临床的试验，疾病准确预测T1 D风险对于评估预防性试验的风险-获益比至关重要。在此外，根据审判情况调整候选人的甄选标准将有助于克服目前阻碍成功的障碍，例如，在一个实施例中，T1 D异质性，并因此增加响应率。到目前为止，T1 D遗传学的复杂性限制了其在预测模型和试验合格性算法中的使用。申请人已经开发并验证了一个T1 D遗传风险评分（GRS），在成人糖尿病，确定那些与T1 D。而且我们的有限的TrialNet参与者子集的初步数据强烈表明，T1 D GRS改善了当前预测模型（即，胰岛自身抗体、年龄和代谢因素），以便沿着 T1 D的临床分期然而，在T1 D GRS可以被使用之前，这些结果必须被验证和优化。用于研究实践。长期目标是预测和预防T1 D。总体目标是利用遗传学，结合其他因素，以准确和及时地确定谁将发展T1 D的个人并对预防性治疗有反应。本申请的中心假设是T1 D GRS可以改进目前的T1 D预测模型和干预试验候选人的选择。的理由及时预测T1 D和准确选择干预候选人将导致安全有效地预防T1 D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）建立一个有效的T1 D预测模型，该模型结合了T1 D GRS，胰岛自身抗体数据、临床和代谢参数。为了实现这一目标，我们将测试一个改进的在整个TrialNet观察性队列（预防途径）中使用T1 D GRS，以确定最佳模型，预测T1 D的总体进展和每个临床前阶段。(2)确定T1 D GRS的作用选择TrialNet干预试验的参与者。为了达到这个目的，我们将测试改进后的 T1 D GRS与其他已知的预测因子（例如，年龄），可以区分应答者和非应答者，在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。(3)建立一个独特的遗传资源， TrialNet和更广泛的研究社区，以促进我们对T1 D的理解。在这一目标下，我们将其他研究者可获得本项目获得的关于非常好的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T1 D。这个项目是创新的，因为它试图改变目前的做法，建议利用遗传学作为一种新的，负担得起的，不依赖于时间的策略，以识别T1 D风险个体，并选择干预试验的候选人。