Dietary exposure biomarker-lead discovery based on metabolomics analysis of urine samples
Dietary exposure biomarker-lead discovery based on metabolomics analysis of urine samples
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DOI：
10.1017/s0029665113001237
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发表时间：
2013-08-01
影响因子：
7
通讯作者：
Draper, John
中科院分区：
文献类型：
--
作者：
Beckmann, Manfred;Lloyd, Amanda J.;Draper, John
虽然从传统的观察流行病学中可以明显看出饮食摄入量与人群健康之间存在强有力的关联，但测试这些联系因果关系的大规模干预研究的结果往往证明是不确定的，或者未能证明因果关系。这种明显的冲突可能是由于公认的测量习惯性食物摄入量的困难，这可能导致观察流行病学的混淆。尿液生物标志物指示暴露于特定的食物提供信息补充依赖饮食摄入量自我评估工具，如FFQ，这是受个人偏见。使用非靶向代谢组学的生物标志物发现策略最近已被用于分析来自短期食物干预研究或来自参与者食用自由选择的饮食的队列研究的尿液。在后者中，对饮食日记或FFQ信息的分析允许根据特定饮食成分的消费频率对个体进行分类。我们回顾了这些方法的生物标志物的发现和说明，特别是参考两项研究的作者使用的方法相结合的代谢物指纹MS与监督的多变量数据分析。在这两种方法中，负责区分特定食物的尿液信号被识别出来，并且可能与原始食物的化学成分有关。当使用膳食数据时，食物的独特性和消费频率影响是否可以充分区分不同的膳食暴露。我们的结论是，代谢组学方法的指纹或分析过夜尿液，特别是，提供了一个强大的战略，饮食暴露生物标志物铅的发现。
Although robust associations between dietary intake and population health are evident from conventional observational epidemiology, the outcomes of large-scale intervention studies testing the causality of those links have often proved inconclusive or have failed to demonstrate causality. This apparent conflict may be due to the well-recognised difficulty in measuring habitual food intake which may lead to confounding in observational epidemiology. Urine biomarkers indicative of exposure to specific foods offer information supplementary to the reliance on dietary intake self-assessment tools, such as FFQ, which are subject to individual bias. Biomarker discovery strategies using non-targeted metabolomics have been used recently to analyse urine from either short-term food intervention studies or from cohort studies in which participants consumed a freely-chosen diet. In the latter, the analysis of diet diary or FFQ information allowed classification of individuals in terms of the frequency of consumption of specific diet constituents. We review these approaches for biomarker discovery and illustrate both with particular reference to two studies carried out by the authors using approaches combining metabolite fingerprinting by MS with supervised multivariate data analysis. In both approaches, urine signals responsible for distinguishing between specific foods were identified and could be related to the chemical composition of the original foods. When using dietary data, both food distinctiveness and consumption frequency influenced whether differential dietary exposure could be discriminated adequately. We conclude that metabolomics methods for fingerprinting or profiling of overnight void urine, in particular, provide a robust strategy for dietary exposure biomarker-lead discovery.