Introduction

Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods.

Objectives

To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study.

Methods

We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models.

Results

Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination.

Conclusions

This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples.

Trial registration number: NCT03335644

中文翻译：

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基于渗透压的归一化增强了非靶向代谢组学尿液分析的统计区分：比较研究的结果

介绍

由于其易于收集，尿液是代谢组学研究中最常用的基质之一。然而，与其他生物流体不同，尿液表现出巨大的可变性，这可能会在结果解释过程中引入混杂的不一致。尽管有许多现有的尿液样本标准化技术，但对于哪种方法最合适或如何评估这些方法仍然没有达成共识。

目标

调查尿液代谢组学中常用的几种方法和方法组合对简单代谢组学研究中两组统计区分的影响。

方法

我们对通过液相色谱与高分辨率质谱 (LC-HRMS) 分析的 40 份尿液样本应用了 14 种不同的归一化策略。为了评估这些不同策略的影响，我们依赖于每种方法在保留感兴趣的可变性的同时减少混杂可变性的能力，以及统计模型的可预测性。

结果

在所有测试的归一化方法中，基于渗透压的归一化给出了最好的结果。此外，我们证明了在分析之前使用特定稀释的归一化优于收购后的归一化。我们还证明了各种归一化方法的组合不一定能提高统计辨别力。

结论

该研究再次强调了尿液样本标准化对代谢组学研究的重要性。此外，方法的选择似乎对结果质量有显着影响。因此，我们建议将基于渗透压的归一化作为尿液样本归一化的最佳方法。

试用注册号：NCT03335644