Modeling Homeostasis of Human Blood Metabolites

项目摘要 人体血液中的代谢物水平由相对严格的稳态控制系统调节。先前 对体内平衡的研究已经采取了许多方法，并且葡萄糖和一些其他的模型已经被用于体内平衡的研究。 已经开发了代谢物，通常集中在单个器官上。然而，尽管可能极其 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，许多人口统计学和临床因素，如性别、年龄、BMI、吸烟等，作为 以及分析前因素和许多疾病，显着影响血液代谢物的水平。许多 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，在这方面， 迄今为止，量化这些影响并在不同研究中验证它们的努力具有挑战性， 导致一致的失败，以验证发现的推定的生物标志物。整合代谢物的挑战 具有临床和人口统计学因素的概况由于数据的高维性而复杂化， 代谢物之间的许多相关性。传统的统计方法无法解释 这些因素以及因此调查遭受高错误发现率（FDR）。 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 水平，从而产生稳态的预测模型。我们的初步工作表明 我们可以预测代谢物水平，使用可重复测量的水平， 大量的血液代谢物以及临床和人口统计学变量。我们建议开发先进的 动态平衡模型的基础上，先进的统计方法，并评估其预测性能， 不同的样品组和代谢物类别。 该项目有四个主要目标：（1）获得广泛的血液样本代谢组学数据 从地理位置不同的研究中心收集，以探索一系列混杂效应对代谢物的影响 程度. (2)使用多变量统计模型对代谢物水平的个体或生物学相关组进行建模 确定临床/人口统计学和分析前变量及其 整个收集站点的可预测性。(3)研究代谢物与临床/人口统计学之间的相互作用 变量使用机器学习方法来识别稳定的代谢物和关键相互作用。(4)提供 该社区提供用户友好的软件包，用于预测稳态下的血液代谢物水平。 血液中代谢物浓度的总体模型对于许多应用将是非常有用的 这包括在整个生物体水平上更好地理解系统生物学， 预测、疾病诊断、治疗监测和结果分析。

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.