Modeling Homeostasis of Human Blood Metabolites

人体血液代谢物稳态建模

• 批准号: 10408272
• 负责人: DANIEL RAFTERY
• 金额: $ 9.72万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10408272
• 关键词: Accounting; Adult; Affect; Age; Biological; Biological Markers; Blood; Blood specimen; Body mass index; Clinical; Collection; Communities; Data; Demographic Factors; Disease; Disease Marker; Failure; Gender; Geography; Glucose; Homeostasis; Human; Individual; Investigation; Machine Learning; Measures; Modeling; Monitor; Organ; Outcome; Performance; Sampling; Site; Smoking; Statistical Methods; Statistical Models; Stress; System; Systems Biology; Whole Organism; Work; base; disease diagnosis; disorder risk; improved; metabolomics; multidimensional data; predictive modeling; risk prediction; small molecule; user friendly software

PROJECT SUMMARY Metabolite levels in human blood are regulated by a relatively strict system of homeostatic control. Previous investigations of homeostasis have taken a number of approaches, and models of glucose and a few other metabolites have been developed, typically focused on a single organ. However, while potentially extremely useful, an accurate and quantitative model of blood metabolite levels under homeostasis does not currently exist. It is well known that numerous demographic and clinical factors such as gender, age, BMI, smoking, etc., as well as pre-analytical factors and many diseases, significantly affect the levels of blood metabolites. Numerous studies in the field of metabolomics have attempted to account for the effects of many such factors. However, efforts to quantify these effects and validate them across different studies have so far been challenging, and resulted in consistent failures to validate discovered putative biomarkers. The challenges to integrate metabolite profiles with clinical and demographic factors are complicated by the high dimensionality of the data and the numerous correlations among the metabolites. Traditional statistical methods are incapable of accounting for these factors, and hence, investigations suffer from a high false discovery rate (FDR). To overcome these challenges, we propose to develop quantitative statistical models of blood metabolite levels in healthy adults, and thereby produce a predictive model of homeostasis. Our preliminary work indicates that we can predict metabolite levels with much reduced variance using the reproducibly measured levels of a large pool of blood metabolites and clinical and demographic variables. We propose to develop sophisticated models of homeostasis based on advanced statistical methods and evaluate their predictive performance across different sample sets and metabolite classes. The proposed project has four main Aims: (1) Obtain broad-based metabolomics data on blood samples collected from geographically distinct sites to explore the effects of a range of confounding effects on metabolite levels. (2) Model individual or biologically related groups of metabolite levels using multivariate statistical approaches to determine the contribution of clinical/demographic and pre-analytical variables and their predictability across collection site. (3) Investigate the interactions between metabolites and clinical/demographic variables using machine learning approaches to identify stable metabolites and key interactions. (4) Provide the community with user-friendly software packages for the prediction of blood metabolite levels under homeostasis. An overall model of the metabolite concentrations in blood will be highly useful for a number of applications that include a better understanding of systems biology at the whole organism level, and ultimately improved risk prediction, disease diagnosis, treatment monitoring and outcomes analysis.

项目总结 人体血液中的代谢物水平受到相对严格的体内平衡控制系统的调节。上一首 对动态平衡的研究采取了许多方法，并建立了葡萄糖和其他一些模型 代谢物已经被开发出来，通常集中在单个器官上。然而，尽管潜在的极端 有用的是，目前还不存在稳态下血液代谢物水平的准确和定量模型。 众所周知，许多人口统计和临床因素，如性别、年龄、体重指数、吸烟等，如 以及分析前因素和许多疾病，显著影响血液代谢物的水平。数不胜数 代谢组学领域的研究试图解释许多这样的因素的影响。然而， 迄今为止，量化这些影响并在不同研究中验证它们的努力是具有挑战性的。 导致一直未能验证已发现的推定生物标志物。整合代谢物的挑战 具有临床和人口统计因素的档案因数据的高维度和 代谢物之间存在着大量的相关性。传统的统计方法无法解释 这些因素，因此，调查受到高错误发现率(FDR)的影响。 为了克服这些挑战，我们建议开发血液代谢物的定量统计模型 在健康成年人中的水平，从而产生一个可预测的动态平衡模型。我们的初步工作表明 我们可以使用可重复测量的代谢物水平预测代谢物水平 大量血液代谢物以及临床和人口统计学变量。我们建议开发复杂的 基于先进统计方法的动态平衡模型及其预测性能评估 不同的样本组和代谢物类别。 拟议的项目有四个主要目标：(1)获得血液样本的广泛代谢组学数据 从地理上不同的地点收集，以探索一系列混杂影响对代谢物的影响 级别。(2)使用多变量统计方法对代谢物水平的个体或生物相关群体进行建模 确定临床/人口学和分析前变量的贡献及其影响的方法 整个收集站点的可预测性。(3)研究代谢产物与临床/人口学之间的相互作用 变量使用机器学习方法来确定稳定的代谢物和关键的相互作用。(4)提供 社区具有用户友好的软件包，用于预测体内平衡状态下的血液代谢物水平。 血液中代谢物浓度的总体模型将对许多应用非常有用 这包括在整个有机体水平上更好地理解系统生物学，并最终改善风险。 预测、疾病诊断、治疗监测和结果分析。