Diabetic Complications and Genetic Variants in the Million Veterans Program

百万退伍军人计划中的糖尿病并发症和遗传变异

• 批准号: 10368695
• 负责人: LAWRENCE S PHILLIPS
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10368695
• 关键词: Adolescent; Adult; Age; Blood Pressure; Body mass index; Caring; Characteristics; Clinical; Complications of Diabetes Mellitus; Consensus; DNA; Data; Development; Diabetes Mellitus; Diabetic Ketoacidosis; Diagnosis; Disease; EFRAC; Emergency Situation; Epigenetic Process; Ethnic Origin; Eye diseases; Gene Expression; Genes; Genetic; Genetic Load; Genetic Risk; Genetic study; Genome; Glycosylated hemoglobin A; Goals; Health; Heart failure; Hypoglycemia; Individual; Insulin; Insulin-Dependent Diabetes Mellitus; Kidney Diseases; Lead; Libraries; Life Style; Literature; Mediating; Mediation; Mendelian randomization; Methods; Methylation; Modeling; Modification; Molecular; Morbidity - disease rate; Mutation; Natural History; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Outpatients; Participant; Pathway interactions; Pattern; Persons; Phenotype; Physiological; Process; Prognosis; Proteinuria; Race; Reporting; Retinal Diseases; Risk; Risk Factors; Site; System; Testing; Thromboplastin; Time; Translations; Veterans; Visit; base; clinical risk; clinical translation; comorbidity; cost; demographics; diabetic; epigenome-wide association studies; epigenomics; gene environment interaction; genetic disorder diagnosis; genetic variant; genome wide association study; genome-wide; genomic locus; improved; insulin dependent diabetes mellitus onset; macrovascular disease; military service; mortality; multi-ethnic; multiple omics; non-genetic; novel therapeutics; personalized care; personalized medicine; phenomics; polygenic risk score; precision medicine; predictive modeling; predictive test; preservation; programs; statistics; trait

Diabetes (DM) complications are the major cause of its morbidity, mortality, and costs. MVP009 has advanced understanding of the underlying genetics. Since DM care does not take advantage of progress in genetics, we propose to use genetics to support both clinical translation and mechanistic discovery. In MVP009, we utilized highly specific phenotypes in genome-wide association studies (GWAS) of (i) heart failure (HF) with preserved vs. reduced ejection fraction; (ii) hypoglycemia – severe (emergency visits) and incidental (outpatient visits); (iii) kidney disease; and (iv) eye disease. We also found that although typical, “juvenile-onset type 1 diabetes (T1D)” excludes military service, at least 10% of Veterans with presumed T2D in MVP may have “adult onset T1D” – largely unrecognized. We now propose to extend these findings. Consistent with the Precision Medicine in Diabetes Consensus Report, our Aims target precision in (i) diagnosis (genetic T1D vs. T2D), (ii) treatment [combining genetic with traditional risk factors (RF)], and (iii) prognosis (epigenomic contributions to complications) – to incorporate genetics so that care can be more accurate and individualized, and identify mechanisms that can lead to discovery of new treatments. Aim 1: Assess the contributions of T1D and T2D genetic loads to the clinical characteristics and disease trajectories of people presumed to have T2D. We will model multiethnic genetic risk with T1D and T2D polygenic risk scores (PRS, with multiethnic data from large recent studies); each MVP Veteran will have both a T1D and a T2D PRS. Outcomes will include incident DM, and the disease trajectory: age and BMI at onset, time to insulin Rx, and ketoacidosis and hypoglycemia. We will evaluate the utility of the PRS to identify Veterans with DM who would benefit from definitive T1D testing and/or early use of insulin. Aim 2: Assess the combined contributions of genetic/nongenetic RF to development of complications. (SubAim a) Identify effect modifications between RF and complications. Genetic interaction analyses will include lifestyle, demographics, and comorbidities (e.g., blood pressure, HbA1c), as modifiers of the risk of complications conferred by disease loci and PRS. We will use both hypothesis-testing approaches for known loci and PRS, and hypothesis-generating approaches (using genome-wide G×E modeling) to examine interactions associated with diabetic eye disease (DED), kidney disease (DKD), HF, and hypoglycemia, and causal associations using state-of-the-art Mendelian Randomization (MR), including multivariable and mediation MR. (SubAim b) Develop and test predictive models. We will use summary statistics from the MVP GWAS and the literature, to develop separate PRS using the “best practice” recent method, for DED, DKD, HF, and hypoglycemia, and PheWAS with the PRSs to elucidate previously unknown RFs. Utilizing the PRS, PheWAS, information from SubAim (a), clinical RF, and treatments, we will develop genome-informed predictive models that will be evaluated in eMERGE and more recent MVP participants. Aim 3: Identify epigenomic markers and molecular systems underlying DM complications. Epigenomic changes regulate gene expression, can mediate environmental and physiologic effects, and have been associated with T2D and related glycemic traits. We hypothesize that differential methylation will also be associated with DM complications. Methylation information using the EPIC chip (>850,000 sites) will be available on >30,000 Veterans, and can be imputed in other Veterans, allowing epigenomic and multi-omic methods such as aggregation analysis and epigenome-wide association studies to (i) identify associations with the complications of DM as well as hypoglycemia, and (ii) identify the genes and pathways involved. Impact: The genetics of diagnosis, G×E, epigenomics, and predictive models should both aid translation – to identify risk in individuals, and help personalize treatment to reduce DM complications and hypoglycemia – and support discovery of new therapies to mitigate the underlying processes.

糖尿病（DM）并发症是其发病率，死亡率和成本的主要原因。 MVP009具有 对基本遗传学的高级理解。由于DM护理没有利用进度 遗传学，我们建议使用遗传学来支持临床翻译和机理发现。 在MVP009中，我们在全基因组关联研究（GWAS）中利用了高度特异性的表型（GWAS） （i）保留的心力衰竭（HF）与减少的射血分数； （ii）低血糖 - 严重（紧急就诊） 和偶然（门诊就诊）； （iii）肾脏疾病； （iv）眼病。我们还发现，尽管典型， “少年发作1型糖尿病（T1D）”不包括军事服务，至少有10％的退伍军人使用T2D 在MVP中，可能具有“成人发作T1D”，这在很大程度上无法识别。我们现在建议扩展这些发现。 与糖尿病共识报告中的精确医学一致，我们的目标精度 （i）诊断（遗传T1D与T2D），（ii）治疗[将遗传与传统危险因素（RF）结合]和（iii） 预后（对并发症的表观基因组贡献） - 纳入遗传学，以使护理可以更多 准确和个性化，并确定可能导致新疗法的机制。 目标1：评估T1D和T2D遗传负荷对临床特征的贡献 和具有T2D的人的疾病轨迹。我们将用T1D对多种族遗传风险进行建模 和T2D多基因风险评分（PRS，具有来自大型研究的多种族数据）；每位MVP老兵将 具有T1D和T2D PR。结果将包括事件DM和疾病轨迹：年龄和BMI 在发病时，胰岛素RX以及酮症酸中毒和低血糖的时间。我们将评估PR的实用性 确定将从确定的T1D测试和/或早期使用胰岛素中受益的DM的退伍军人。 AIM 2：评估遗传/非核RF对发展的综合贡献 并发症。 （subaim a）确定射频和并发症之间的效果修改。遗传 互动分析将包括生活方式，人口统计和合并症（例如，血压，HBA1C），如 疾病基因座和PR带来的并发症风险的修饰符。我们将使用两个假设测试 已知基因座和PR的方法以及假设生成的方法（使用全基因组G×E 建模）检查与糖尿病眼病（DED），肾脏疾病（DKD），HF和HF相关的相互作用 使用最先进的孟德尔随机化（MR）的低血糖和因果关系，包括 多变量和调解MR。 （subaim b）开发和测试预测模型。我们将使用摘要 MVP GWAS和文献的统计数据使用最近的“最佳实践”开发单独的PR 用于DED，DKD，HF和低糖和PHEWA的方法，以及PRSS以阐明以前未知的方法 RFS。利用PRS，Phewas，Subaim（a），临床RF和治疗的信息，我们将开发 基因组信息的预测模型将在出现和最新的MVP参与者中进行评估。 AIM 3：确定DM并发症基础的表观基因组标记和分子系统。 表观基因组变化调节基因表达，可以中位环境和生理作用，并且具有 我们假设差异甲基化也将是 与DM并发症相关。使用史诗芯片（> 850,000个地点）的甲基化信息将是 在> 30,000名退伍军人上可用，可以在其他退伍军人中估算，允许表观基因组和多运动 诸如聚集分析和表观基因组范围的关联研究等方法（i）确定与 DM以及低血糖的并发症以及（ii）确定所涉及的基因和途径。 影响：诊断，G×E，表观基因组学和预测模型的遗传学应有助于 翻译 - 确定个人的风险，并帮助个性化治疗以减少DM并发症和 低血糖 - 并支持发现新疗法以减轻基本过程。