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: 10655281
• 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/10655281
• 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; Exclusion; 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; 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; Time; Translations; Veterans; Visit; 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; 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、死亡率和费用的主要原因。mvp 009有 对潜在遗传学的深入了解。由于糖尿病护理没有利用 遗传学，我们建议使用遗传学来支持临床翻译和机制发现。 在MVP 009中，我们利用高度特异性的表型进行全基因组关联研究（GWAS）， (i)射血分数保留与降低的心力衰竭（HF）;（ii）低血糖-重度（急诊） 和偶发疾病（门诊）;（iii）肾脏疾病;和（iv）眼部疾病。我们还发现，虽然典型， “青少年发病的1型糖尿病（T1 D）”不包括服兵役，至少10%的退伍军人患有假定的T2 D 在MVP可能有“成人发病T1 D”-很大程度上未被认识。我们现在建议扩展这些发现。 与《糖尿病精准医学共识报告》一致，我们的目标是在以下方面实现精准度： (i)诊断（遗传性T1 D vs. T2 D），（ii）治疗[结合遗传与传统风险因素（RF）]，和（iii） 预后（表观基因对并发症的贡献）-纳入遗传学，以便护理可以更好地 准确和个性化，并确定机制，可以导致发现新的治疗方法。 目的1：评估T1 D和T2 D遗传负荷对临床特征的贡献 和假定患有T2 D的人的疾病轨迹。我们将用T1 D模拟多种族遗传风险 和T2 D多基因风险评分（PRS，来自近期大型研究的多种族数据）;每名MVP退伍军人将 具有T1 D和T2 D PRS。结局将包括DM事件和疾病轨迹：年龄和BMI 发作时、至胰岛素Rx的时间、酮症酸中毒和低血糖。我们会评估生产者责任计划的效用， 确定糖尿病退伍军人谁将受益于明确的T1 D测试和/或早期使用胰岛素。 目的2：评估遗传/非遗传RF对发展的综合贡献。 并发症（子目标a）识别RF和并发症之间的效应修改。遗传 相互作用分析将包括生活方式、人口统计学和合并症（例如，血压，HbA 1c），如 由疾病位点和PRS赋予的并发症风险的修饰符。我们将使用假设检验 已知位点和PRS的方法，以及假设生成方法（使用全基因组G×E 模型），以检查与糖尿病性眼病（DED）、肾病（DKD）、HF和 低血糖和使用最新孟德尔随机化（MR）的因果关系，包括 多变量和中介MR（子目标B）开发和测试预测模型。我们将使用摘要 MVP GWAS和文献中的统计数据，使用最近的“最佳实践”开发单独的PRS 方法，用于DED、DKD、HF和低血糖，以及PheWAS与PRS，以阐明先前未知的 射频。利用PRS、PheWAS、来自SubAim（a）的信息、临床RF和治疗，我们将开发 将在eMERGE和最近的MVP参与者中评估的基因组信息预测模型。 目的3：确定糖尿病并发症的表观基因标记物和分子系统。 表观基因组变化调节基因表达，可以介导环境和生理效应， 与T2 D和相关血糖性状相关。我们假设，差异甲基化也将是 与DM并发症有关。使用EPIC芯片（> 850，000个位点）的甲基化信息将是 可用于> 30，000名退伍军人，并可用于其他退伍军人，允许表观基因组学和多组学 方法，如聚集分析和表观基因组关联研究，以（i）确定与 糖尿病的并发症以及低血糖症，和（ii）确定相关的基因和途径。 影响：诊断的遗传学，G×E，表观基因组学和预测模型都应该有所帮助 翻译-识别个体的风险，并帮助个性化治疗，以减少DM并发症， 低血糖-并支持发现新的治疗方法，以减轻潜在的过程。