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 MVP009 发病率、死亡率和费用的主要原因。 由于 DM 治疗并没有利用进展的优势。 遗传学，我们建议使用遗传学来支持临床转化和机制发现。 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 (i) 射血分数保留与降低的心力衰竭 (HF)；(ii) 低血糖 – 严重（紧急就诊） 和偶然（门诊就诊）；（iii）肾脏疾病；和（iv）眼部疾病。 “青少年发病的 1 型糖尿病 (T1D)”不包括服兵役，至少 10% 的退伍军人被推定患有 T2D MVP 可能患有“成人发病的 T1D”——基本上未被认识到。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，我们的目标是精准治疗 (i) 诊断（遗传性 T1D 与 T2D），(ii) 治疗 [结合遗传与传统危险因素 (RF)]，以及 (iii) 预后（表观基因组对并发症的影响）——与遗传相结合，以便护理可以更加有效 准确和个性化，并确定可以导致新疗法发现的机制。 目标 1：评估 T1D 和 T2D 遗传负荷对临床特征的贡献 我们将用 T1D 来模拟多种族遗传风险。 T2D 多基因风险评分（PRS，来自近期大型研究的多种族数据）； 同时患有 T1D 和 T2D PRS 的结果将包括发病 DM 和疾病轨迹：年龄和 BMI。 我们将评估 PRS 的效用。 确定患有 DM 的退伍军人，他们将受益于明确的 T1D 检测和/或早期使用胰岛素。 目标 2：评估遗传/非遗传 RF 对发育的综合贡献 并发症（子目标 a）确定 RF 和并发症之间的影响变化。 交互分析将包括生活方式、人口统计数据和合并症（例如血压、HbA1c），如 疾病位点和 PRS 赋予并发症风险的修正因子 我们将同时使用假设检验。 已知基因座和 PRS 的方法，以及假设生成方法（使用全基因组 G×E 模型）来检查与糖尿病眼病（DED）、肾病（DKD）、心衰和糖尿病相关的相互作用 使用最先进的孟德尔随机化 (MR) 来确定低血糖和因果关系，包括 多变量和中介 MR（SubAim b）开发和测试预测模型。 来自 MVP GWAS 和文献的统计数据，使用最近的“最佳实践”开发单独的 PRS 方法，针对 DED、DKD、HF 和低血糖，以及 PheWAS 和 PRS，以阐明以前未知的情况 利用 PRS、PheWAS、SubAim (a) 的信息、临床 RF 和治疗，我们将开发 基于基因组的预测模型将在 eMERGE 和最近的 MVP 参与者中进行评估。 目标 3：识别 DM 并发症背后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，可以介导环境和生理效应，并具有 我们认为差异甲基化也与 T2D 和相关血糖特征有关。 使用 EPIC 芯片（>850,000 个位点）将获得与 DM 并发症相关的甲基化信息。 可用于超过 30,000 名退伍军人，并且可以推算到其他退伍军人中，从而实现表观基因组和多组学分析 聚合分析和全表观基因组关联研究等方法，以（i）确定与 糖尿病并发症和低血糖，以及 (ii) 确定相关基因和途径。 影响：诊断遗传学、G×E、表观基因组学和预测模型都应该有所帮助 翻译——识别个人风险，帮助个性化治疗，以减少糖尿病并发症和 低血糖——并支持发现新疗法以减轻潜在的过程。