INTEGRATION OF METABOLOMICS AND GENOMICS DATA TO INVESTIGATE CARDIOVASCULAR RISK

整合代谢组学和基因组学数据来调查心血管风险

• 批准号: 9222852
• 负责人: Sven Bergmann
• 金额: $ 13.45万
• 依托单位: UNIVERSITY OF LAUSANNE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9222852
• 关键词: Address; Biological; Biological Markers; Blood Pressure; Body mass index; Cardiovascular Diseases; Chemicals; Clinical; Collaborations; Collection; Complex; Data; Data Set; Development; Diagnosis; Disease; Familiarity; Genotype; Goals; Individual; Intervention Trial; Linear Regressions; Link; Lipids; Logistic Regressions; Measurement; Measures; Methods; Modeling; Molecular; Monitor; NMR Spectroscopy; Nuclear Magnetic Resonance; Obesity; Overweight; Pathway interactions; Performance; Phenotype; Population; Prevention; Regression Analysis; Reproducibility; Research Personnel; Risk; Risk Factors; Science; Sensitivity and Specificity; Software Tools; Specificity; Sum; Techniques; Technology; Testing; Time; Translations; Weight; Work; base; biomarker discovery; cardiovascular risk factor; clinical biomarkers; clinical practice; clinically relevant; disease diagnosis; genetic association; genomic data; improved; innovation; interest; metabolomics; new technology; novel; novel marker; outcome forecast; personalized medicine; potential biomarker; rosuvastatin; tool; trait; treatment choice; user friendly software; user-friendly

Project summary Diagnosis, choice of treatment, and prognosis of common and complex diseases, such as cardiovascular disease are still mostly based on a few biomarkers. While these biomarkers improve clinical practice at the population level, their sensitivity and specificity in particular subpopulation groups may be more limited. With the advent of affordable and highly reproducible omics measurements, there is a great opportunity to advance personalized medicine through the integrative analysis of much larger sets of markers. A particularly promising avenue is metabolomics, as it allows for the identification and quantification of hundreds of metabolites enabled by high performance technologies such as nuclear magnetic resonance (NMR) spectroscopy. Here, we propose to test the hypothesis that the aggregation of the NMR features that correlate with genotypic data and which can be matched to one or several metabolites could function as a novel type of quantitative biomarker. Such markers could be more powerful than individual spectral features or feature combinations that cannot be linked to known metabolites. The primary goals of the proposal are 1) to test whether these pseudo-compounds achieve stronger genetic association than any of their individual features and 2) to investigate whether such pseudo-compounds associate with established cardiovascular risk factors. For this purpose, we will leverage existing genotype, metabolomics, and other phenotype data, which has been measured for a subset of 983 individuals from the CoLaus (Cohorte Lausannoise) study. Then, we will validate our findings using a subset (n=500) of the JUPITER (Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin) study. Regression analysis will be used to model the relationship between genotype and metabolomics data, as well as to model the relationship between pseudo-compounds and established risk factors. Our proposal is innovative because it is not limited to a predefined set of metabolites as in targeted metabolomics. At the same time, a great advantage of our proposed concept of pseudo-compounds is that often it may be feasible to match them to one or several metabolites with a known spectrum, thus providing a tangible entity that can be related to the chemical and biological pathways of these metabolites. We will make our method publicly available for non-experts as user-friendly software, allowing clinical researchers with a specific interest in a disease or a related trait to directly extract and test pseudo-compounds as potential biomarkers. This will facilitate the translation of untargeted metabolomic data into potentially clinically relevant biomarkers.

项目总结 常见和复杂疾病的诊断、治疗选择和预后，例如 心血管疾病仍然主要基于少数几个生物标志物。在这些生物标志物改善的同时 人群水平的临床实践及其在特定亚群中的敏感性和特异性 可能会更加有限。随着负担得起和高度可重复性的组学测量的出现， 是通过更大规模的综合分析来推进个性化医疗的绝佳机会 一组记号笔。一个特别有希望的途径是代谢组学，因为它允许识别和 通过核等高性能技术实现数百种代谢物的量化 核磁共振光谱学。 在这里，我们建议测试这一假设，即与以下各项相关的核磁共振特征的聚合 可以与一种或几种代谢物匹配的基因类型数据可以作为一种新类型发挥作用 数量生物标记物。这样的标记可能比单独的光谱特征或 无法与已知代谢物相关联的特征组合。该提案的主要目标是 1)测试这些伪化合物是否实现了比它们的任何一个更强的遗传关联 个体特征和2)调查这些伪化合物是否与已建立的 心血管危险因素。 为此，我们将利用现有的基因、代谢组学和其他表型数据，这些数据 是对CoLaus(Cohorte LausanNoise)研究中983人的子集进行了测量。然后, 我们将使用木星的一个子集(n=500)来验证我们的发现(在 预防：一项评估罗舒伐他汀研究的干预试验。回归分析将被用于 对基因和代谢组学数据之间的关系进行建模，并对这种关系进行建模 假化合物和已确定的风险因素之间的关系。 我们的建议是创新的，因为它不限于像靶向的那样预先定义的代谢物集合 代谢组学。同时，我们提出的伪化合物概念的一个很大的优点是 通常，将它们与一个或几个具有已知光谱的代谢物进行匹配可能是可行的，因此 提供一个有形的实体，可以与这些物质的化学和生物途径相关 代谢物。我们将把我们的方法作为用户友好的软件向非专家公开提供， 允许对一种疾病或相关特征有特殊兴趣的临床研究人员直接提取和 测试假性化合物作为潜在的生物标志物。这将有助于翻译无针对性的 将代谢数据转化为潜在的临床相关生物标记物。

项目成果

Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine.

• DOI: 10.1021/acs.jproteome.1c00585
• 发表时间: 2021-11-05
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Flitman, Reyhan Sonmez;Khalili, Bita;Kutalik, Zoltan;Rueedi, Rico;Bruemmer, Anneke;Bergmann, Sven
• 通讯作者: Bergmann, Sven