Network-Based Approach for Analyzing Intra- and Interfluid Metabolite Associations in Human Blood, Urine, and Saliva
Network-Based Approach for Analyzing Intra- and Interfluid Metabolite Associations in Human Blood, Urine, and Saliva
复制标题
DOI：
10.1021/pr501130a
复制
发表时间：
2015-02-01
影响因子：
4.4
通讯作者：
Krumsiek, Jan
中科院分区：
文献类型：
--
作者：
Do, Kieu Trinh;Kastenmueller, Gabi;Krumsiek, Jan
大多数研究人类代谢组学测量的研究仅限于单一的生物流体，最常见的是血液或尿液。然而，生物体的生化库包含复杂的跨界关系，只有通过研究跨越人体多个部位的代谢相互作用和生理过程才能理解。因此，我们在这里提出了一个数据驱动的基于网络的方法来生成一个综合的图片代谢组学协会在多种液体。我们对来自卡塔尔糖尿病代谢组学研究（QMDiab）的374名参与者的血浆、尿液和唾液中测量的2251种代谢物进行了分析。使用高斯图形模型（GGM）估计数据集不同子集上的代谢物-代谢物相互作用。首先，我们比较了三种液体之间的代谢组和关联网络的异同。其次，我们通过分析流体之间发生的相关性来研究流体之间的串扰。第三，我们提出了一个框架，通过将2型糖尿病，性别，年龄和体重指数整合到我们的网络中来分析医学相关的表型。总之，我们提出了一种通用的，数据驱动的基于网络的方法，用于构建和可视化多种体液内和之间的代谢物相关性，从而能够公正地解释代谢组学多流体数据。
Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.