Virtual metabolomics as a discovery tool for novel cardiometabolic disease biology

项目概要/摘要 代谢失调是美国许多死亡率和发病率的主要原因， 心脏代谢疾病代谢组学研究可以识别新的疾病生物标志物， 靶点和与病理相关的生物学途径。代谢组学的新兴技术 对来自不同途径的大量代谢物的询问。然而，这些方法仍然存在。 昂贵且耗时。将代谢组学应用于非常大的人群， 流行病学研究是不可行的，由于实施这些测定的实际挑战和成本 在规模上。这些挑战限制了新的生物标志物-疾病关联的发现。我们建议 通过基于遗传学的“虚拟”代谢物研究设计来解决这些限制， 代谢物浓度的遗传预测因子，在测量代谢物的小群体中， 然后使用这些遗传预测因子来估算代谢物浓度在一个大的人群中， 未测量代谢物。这种方法极大地扩大了发现的样本量， 鉴定用于下游验证新生物标志物。本提案的主要目的是：1）建立 基于单核苷酸多态性（SNP）循环代谢物预测因子，并确定相关性 心脏代谢表型，包括2型糖尿病和冠状动脉疾病; 2）验证 与直接代谢物测量的关联; 3）确定代谢物之间的多效性关联 遗传预测因子和临床表现。这些分析是通过遗传学方法实现的， 整合来自大规模心脏代谢疾病全基因组关联研究（GWAS）的数据， 电子健康记录链接DNA生物库的集合，包括超过70万名受试者。创新 这种方法的特点包括分析的效率和规模，包括代表性不足和 脆弱人群的需求，并实施可重复使用和可扩展的分析框架， 加速生物标志物的发现和实施。在这项工程完成后，我们会兴建一个 可公开访问的代谢疾病协会的在线资源，将作为一个 假设检验和生成的来源。最终，这些研究将推动这一领域的发展。 代谢组学通过快速推进将代谢物与临床相关疾病联系起来的过程。

PROJECT SUMMARY / ABSTRACT Dysregulated metabolism underlies many of the leading causes of mortality and morbidity in the US including cardiometabolic diseases. Metabolomics studies can identify novel disease biomarkers, novel therapeutic targets, and biological pathways with pathological relevance. Emerging technologies in metabolomics allow the interrogation of large numbers of metabolites from diverse pathways. However, these approaches remain expensive and time-consuming. Applying metabolomics to very large cohorts of individuals to conduct epidemiological studies is not feasible, due to the practical challenges and costs of implementing these assays at scale. These challenges have limited discovery of novel biomarker-disease associations. We propose to address these limitations with a genetics-based “virtual” metabolite study design that will allow us to define genetic predictors of metabolite concentrations in a small population in whom the metabolite was measured, and then use these genetic predictors to impute metabolite concentrations in a large population in whom the metabolite was not measured. This approach vastly amplifies the sample size for discovery, and can rapidly identify novel biomarkers for downstream validation. The primary aims of this proposal are to: 1) construct single nucleotide polymorphism (SNP)-based predictors of circulating metabolites, and identify associations with cardiometabolic phenotypes, including type 2 diabetes and coronary artery disease; 2) validate the associations with direct metabolite measurements; 3) identify pleiotropic associations between metabolite genetic predictors and the clinical phenome. These analyses are enabled by genetic approaches that allow us to integrate data from large scale genome-wide association studies (GWAS) of cardiometabolic diseases and a collection of electronic health record linked-DNA biobanks comprising over 700,000 subjects. Innovative features of this approach include the efficiency and scale of the analysis, inclusion of under-represented and vulnerable populations and implementation of a re-usable and scalable analytical framework that will accelerate biomarker discovery and implementation. Upon completion of this project, we will construct a publicly accessible online resource of metabolite-disease associations that will be available to researchers as a source for both hypothesis testing and generation. Ultimately, these studies will advance the field of metabolomics by rapidly advancing the process of linking metabolites to clinically-relevant diseases.