Multi-trait genome-wide characterization of non-traditional glycemic biomarkers and type 2 diabetes

非传统血糖生物标志物和 2 型糖尿病的多特征全基因组表征

• 批准号: 10215925
• 负责人: Debashree Ray
• 金额: $ 24.56万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10215925
• 关键词: Atherosclerosis Risk in Communities; Biological; Biological Assay; Biological Markers; Biology; Caring; Clinical; Coronary Artery Risk Development in Young Adults Study; DNA Methylation; Data; Databases; Development; Diabetes Mellitus; Diagnosis; Environmental Risk Factor; Epidemic; Epidemiology; Ethnic group; Family Study; Fasting; Female; Fructosamine; Future; Gene Frequency; Genes; Genetic; Genetic Determinism; Genetic Predisposition to Disease; Genetic study; Genotype; Glucose; Glycosylated Hemoglobin; Glycosylated hemoglobin A; Guidelines; Heritability; Hour; Hyperglycemia; Impaired fasting glycaemia; Impairment; Insulin; Investigation; Joints; Knowledge; Lead; Maps; Measurement; Mendelian randomization; Methods; Modernization; Monitor; National Heart, Lung, and Blood Institute; Non-Insulin-Dependent Diabetes Mellitus; OGTT; Obesity; Participant; Pathway interactions; Patients; Pattern; Pharmacotherapy; Physiology; Plasma; Predisposition; Preparation; Prevention strategy; Proteomics; Research; Role; Sampling; Sex Differences; Signal Transduction; Smoking; Statistical Methods; Stress; Time; Trans-Omics for Precision Medicine; Twin Studies; Variant; base; causal variant; clinically relevant; cohort; diabetes mellitus genetics; diabetes risk; exome; fasting glucose; follow-up; genetic architecture; genetic association; genetic risk factor; genetic variant; genome wide association study; genome-wide; glycemic control; glycosylated serum albumin; improved; individual variation; innovation; insight; male; multi-ethnic; multiple omics; novel; novel strategies; novel therapeutics; pleiotropism; programs; rare variant; risk stratification; screening; sedentary lifestyle; sex; targeted treatment; trait; translational impact; whole genome

PROJECT SUMMARY Type 2 diabetes (T2D) and its long-term complications has reached epidemic proportions worldwide. Currently, there are major gaps in the knowledge of T2D genetics and its underlying mechanisms, which is critical for identifying novel therapies and strategies that can reduce the impact of T2D. Genetic studies of clinically relevant biomarkers in relation to T2D, beyond the classical glycemic biomarkers (e.g. glucose, HbA1c, and insulin- related traits), can help us get a wholesome view of T2D mechanisms. There is growing epidemiologic evidence that nontraditional biomarkers of hyperglycemia – fructosamine, glycated albumin, and 1,5-anhydroglucitol – improve risk stratification of diabetes and its long-term complications, provide information beyond the classical glycemic biomarkers, and hold promise in providing unique insights into hyperglycemia and diabetes physiology. A limited number of previous genome-wide association studies (GWAS) have examined genetic determinants of these newer biomarkers using data from the Atherosclerosis Risk in Communities (ARIC) Study. Thus far, ARIC (or any other study) has not been fully exploited to unravel genetic associations of these newer biomarkers or investigate underlying causal mechanisms using more advanced statistical methods that can leverage overlapping genetic architecture of these biomarkers, among themselves and with traditional glycemic biomarkers and T2D taking into account sex-specific differences. In this proposal, we will use modern statistical approaches to exploit the unique data available on these newer glycemic biomarkers, in two major ethnic groups (Black and White) in the ARIC Study to identify genetic associations across the entire allele-frequency spectrum (Aims 1, 3); to explore genetic overlap with traditional biomarkers (Aim 2) and with known T2D-relevant genes (Aims 2, 3); to investigate independent causal role of all biomarkers on T2D by leveraging genetic overlap (Aim 2); and characterize sex-specific and ancestry-specific differences if any (Aims 1-3). We will attempt to replicate our findings in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Our innovations include going beyond the current paradigm of T2D locus discovery using classical biomarkers; novel use of modern multi-trait approaches on existing data; novel follow-up analysis of pleiotropic loci to better characterize underlying causal mechanisms using Mendelian Randomization; use of trans-ethnic analysis to boost GWAS power; and exploring rare variant associations of fructosamine and glycated albumin for the first time. Successful completion of these aims will provide preliminary data for a subsequent multi-omics proposal to study these biomarkers using DNA methylation, proteomic, and whole genome sequence data in ARIC. Thus, our pipeline for systematic examination of unique and shared genetics of these newer glycemic biomarkers with classical biomarkers and T2D can lead to a better understanding of the mechanisms by which they act in concert in delineating T2D risk, and can impact clinical guidelines and diabetes care. Intriguing results from this proposal will encourage other cohorts to do assays of these newer biomarkers that will increase power of future GWAS.

项目摘要 2型糖尿病（T2 D）及其长期并发症在全球范围内已达到流行病的程度。目前， 在T2 D遗传学及其潜在机制的知识方面存在重大空白，这对于 确定可以减少T2 D影响的新疗法和策略。临床相关基因研究 除了经典的血糖生物标志物（例如葡萄糖、HbA 1c和胰岛素）之外， 相关性状），可以帮助我们获得T2 D机制的健康观点。越来越多的流行病学证据表明 高血糖症的非传统生物标志物-果糖胺、糖化白蛋白和1，5-脱水葡萄糖醇- 改善糖尿病及其长期并发症的风险分层，提供超越经典的 血糖生物标志物，并有望提供独特的见解高血糖症和糖尿病生理学。 有限数量的以前的全基因组关联研究（GWAS）已经检查了 这些新的生物标志物使用来自社区动脉粥样硬化风险研究（ARIC）的数据。到目前为止，ARIC (or任何其他研究）尚未被充分利用，以解开这些新的生物标志物的遗传关联，或 使用更先进的统计方法调查潜在的因果机制， 这些生物标志物的遗传结构相互重叠，并与传统的血糖 生物标志物和T2 D，同时考虑性别特异性差异。在本建议中，我们将使用现代统计 在两个主要种族群体中， （黑色和白色），以确定整个等位基因频谱的遗传关联 (Aims 1，3）;探索与传统生物标志物（目的2）和已知T2 D相关基因的遗传重叠 (Aims 2，3）;通过利用遗传重叠研究所有生物标志物对T2 D的独立因果作用（Aim 2）;并描述性别特异性和祖先特异性差异（如有）（目标1-3）。我们将尝试复制 我们在年轻人冠状动脉风险发展（CARDIA）研究中的发现。我们的创新包括 超越了使用经典生物标志物的T2 D基因座发现的当前范例;现代生物标志物的新用途 基于现有数据的多性状方法;对多效性基因座进行新的后续分析，以更好地表征 使用孟德尔随机化的潜在因果机制;使用跨种族分析来提高GWAS 功率;并首次探索果糖胺和糖化白蛋白的罕见变异关联。 这些目标的成功完成将为随后的多组学建议提供初步数据 在ARIC中使用DNA甲基化、蛋白质组学和全基因组序列数据来研究这些生物标志物。因此，在本发明中， 我们的管道系统检查这些新的血糖生物标志物的独特和共享的遗传学， 经典生物标志物和T2 D可以更好地理解它们协同作用的机制 在描述T2 D风险方面，可能会影响临床指南和糖尿病护理。这个提议的结果很有趣 将鼓励其他队列对这些新的生物标志物进行检测，这将增加未来GWAS的能力。