Dataset Information

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

ABSTRACT:

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 40 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.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase

SUBMITTER: Loic Mervant

PROVIDER: MTBLS1765 | MetaboLights | 2023-09-04

REPOSITORIES: MetaboLights

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Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study.

Mervant Loïc L Tremblay-Franco Marie M Jamin Emilien L EL Kesse-Guyot Emmanuelle E Galan Pilar P Martin Jean-François JF Guéraud Françoise F Debrauwer Laurent L

Metabolomics : Official journal of the Metabolomic Society 20210102 1

<h4>Introduction</h4>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.<h4>Objectives</h4>To investigate the impact of several met ...[more]

PMID: 33389209

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			Action	DRS
	MTBLS1765	Other
	FILES	Other
	a_MTBLS1765_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	files-all.json	Other
	i_Investigation.txt	Txt