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The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples

机译:质谱在流行病学中分析代谢物生物标志物的用途:应用于大量生物样品的方法和统计学考虑

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摘要

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.
机译:数据质量对流行病学至关重要,并且随着科学认识的扩展,流行病学研究可用的数据范围和用于测量的工具类型也已扩展。流行病学家必须掌握与不同测量工具有关的问题。由于基于MS的方法具有很高的特异性和敏感性,并且可以测量的生物标志物范围不断扩大,因此在流行病学队列中越来越多地用于测量生物标志物的工具是质谱(MS)。此外,与单个生物标志物方法相比,有利地将MS同时量化许多生物标志物的能力。但是,与用于测量生物标记物的所有方法一样,在流行病学中使用时,要考虑许多陷阱,这些陷阱可能会对结果产生影响。在这篇综述中,我们讨论了将MS用于生物标志物分析的方法,重点是代谢物及其应用以及与大规模流行病学研究有关的潜在问题,使用“组学”方法进行生物标志物发现以及如何使用基于MS的结果用于增加从流行病学研究中获得的生物学知识。更好地了解与基于MS的测量有关的可能性和问题,将有助于流行病学家与分析化学家进行讨论,并为这些数据使用最合适的统计工具。

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