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Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study
- Source :
- Metabolomics, Metabolomics, Springer Verlag, 2021, 17 (1), ⟨10.1007/s11306-020-01758-z⟩, Metabolomics, 2021, 17 (1), ⟨10.1007/s11306-020-01758-z⟩
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- International audience; 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.
- Subjects :
- Normalization (statistics)
[SDV]Life Sciences [q-bio]
Endocrinology, Diabetes and Metabolism
Clinical Biochemistry
Osmolality
Urine analysis
Urine
Urinalysis
01 natural sciences
Biochemistry
03 medical and health sciences
Metabolomics
Statistics
Humans
Trial registration
030304 developmental biology
Mathematics
0303 health sciences
Mass spectrometry
Untargeted metabolomics
Osmolar Concentration
010401 analytical chemistry
Confounding
Liquid Biopsy
Statistical model
Body Fluids
0104 chemical sciences
[SDV] Life Sciences [q-bio]
[SDV.TOX] Life Sciences [q-bio]/Toxicology
Normalization
Data Interpretation, Statistical
[SDV.TOX]Life Sciences [q-bio]/Toxicology
Metabolome
Statistical discrimination
Chromatography, Liquid
Subjects
Details
- ISSN :
- 15733890 and 15733882
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- Metabolomics
- Accession number :
- edsair.doi.dedup.....7d73270330e3e43e0f4fd818baf656cc