Loïc Mervant1,2, Marie Tremblay-Franco3,4, Emilien L Jamin1,2, Emmanuelle Kesse-Guyot5, Pilar Galan5, Jean-François Martin1,2, Françoise Guéraud2, Laurent Debrauwer1,2. 1. Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France. 2. Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France. 3. Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France. marie.tremblay-franco@inrae.fr. 4. Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France. marie.tremblay-franco@inrae.fr. 5. Sorbonne Paris Nord University, Inserm, INRAE, Cnam, Nutritional Epidemiology, Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), 93017, Bobigny, France.
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 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.
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.
Keywords:
Mass spectrometry; Normalization; Osmolality; Untargeted metabolomics; Urine analysis
Authors: Warwick B Dunn; David Broadhurst; Paul Begley; Eva Zelena; Sue Francis-McIntyre; Nadine Anderson; Marie Brown; Joshau D Knowles; Antony Halsall; John N Haselden; Andrew W Nicholls; Ian D Wilson; Douglas B Kell; Royston Goodacre Journal: Nat Protoc Date: 2011-06-30 Impact factor: 13.491
Authors: Souhaila Bouatra; Farid Aziat; Rupasri Mandal; An Chi Guo; Michael R Wilson; Craig Knox; Trent C Bjorndahl; Ramanarayan Krishnamurthy; Fozia Saleem; Philip Liu; Zerihun T Dame; Jenna Poelzer; Jessica Huynh; Faizath S Yallou; Nick Psychogios; Edison Dong; Ralf Bogumil; Cornelia Roehring; David S Wishart Journal: PLoS One Date: 2013-09-04 Impact factor: 3.240