Network-Based Approach for Analyzing Intra- and Interfluid Metabolite Associations in Human Blood, Urine, and Saliva

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• 作者：Kieu Trinh Do ; Gabi Kastenm眉ller ; Dennis O. Mook-Kanamori ; Noha A. Yousri ; Fabian J. Theis ; Karsten Suhre ; Jan Krumsiek
• 刊名：Journal of Proteome Research
• 出版年：2015
• 出版时间：February 6, 2015
• 年：2015
• 卷：14
• 期：2
• 页码：1183-1194
• 全文大小：728K
• ISSN：1535-3907

文摘

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism鈥檚 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.