Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France.
Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Metabolomics. 2021 Jan 2;17(1):2. doi: 10.1007/s11306-020-01758-z.
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.
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.
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.
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.
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.
NCT03335644.
由于尿液采集方便，因此它是代谢组学研究中最常用的生物基质之一。然而，与其他生物体液不同，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淆不一致性。尽管有许多现有的方法可以对尿液样本进行归一化，但对于哪种方法最合适或如何评估这些方法，仍然没有共识。
研究在简单代谢组学研究中，尿液代谢组学中常规使用的几种方法和方法组合对两组统计区分的影响。
我们将 14 种不同的归一化策略应用于通过液相色谱-高分辨率质谱联用（LC-HRMS）分析的四十个尿液样本。为了评估这些不同策略的影响，我们依赖于每种方法减少混杂变异性的能力，同时保留感兴趣的变异性，以及统计模型的可预测性。
在所有测试的归一化方法中，渗透压归一化法的效果最佳。此外，我们证明了在分析之前使用特定稀释液进行归一化优于在采集后进行归一化。我们还证明了各种归一化方法的组合不一定能提高统计区分度。
本研究再次强调了对代谢组学研究中尿液样本进行归一化的重要性。此外，方法的选择似乎对结果质量有重大影响。因此，我们建议渗透压归一化为尿液样本的最佳归一化方法。
NCT03335644。
