Literature DB >> 25285402

Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine.

William M B Edmands1, Pietro Ferrari, Augustin Scalbert.   

Abstract

Extraction of meaningful biological information from urinary metabolomic profiles obtained by liquid-chromatography coupled to mass spectrometry (MS) necessitates the control of unwanted sources of variability associated with large differences in urine sample concentrations. Different methods of normalization either before analysis (preacquisition normalization) through dilution of urine samples to the lowest specific gravity measured by refractometry, or after analysis (postacquisition normalization) to urine volume, specific gravity and median fold change are compared for their capacity to recover lead metabolites for a potential future use as dietary biomarkers. Twenty-four urine samples of 19 subjects from the European Prospective Investigation into Cancer and nutrition (EPIC) cohort were selected based on their high and low/nonconsumption of six polyphenol-rich foods as assessed with a 24 h dietary recall. MS features selected on the basis of minimum discriminant selection criteria were related to each dietary item by means of orthogonal partial least-squares discriminant analysis models. Normalization methods ranked in the following decreasing order when comparing the number of total discriminant MS features recovered to that obtained in the absence of normalization: preacquisition normalization to specific gravity (4.2-fold), postacquisition normalization to specific gravity (2.3-fold), postacquisition median fold change normalization (1.8-fold increase), postacquisition normalization to urinary volume (0.79-fold). A preventative preacquisition normalization based on urine specific gravity was found to be superior to all curative postacquisition normalization methods tested for discovery of MS features discriminant of dietary intake in these urinary metabolomic datasets.

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Year:  2014        PMID: 25285402     DOI: 10.1021/ac503190m

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  17 in total

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

Authors:  Loïc Mervant; Marie Tremblay-Franco; Emilien L Jamin; Emmanuelle Kesse-Guyot; Pilar Galan; Jean-François Martin; Françoise Guéraud; Laurent Debrauwer
Journal:  Metabolomics       Date:  2021-01-02       Impact factor: 4.290

2.  Comprehensive urinary metabolomic characterization of a genetically induced mouse model of prostatic inflammation.

Authors:  Ling Hao; Yatao Shi; Samuel Thomas; Chad M Vezina; Sagar Bajpai; Arya Ashok; Charles J Bieberich; William A Ricke; Lingjun Li
Journal:  Int J Mass Spectrom       Date:  2018-09-22       Impact factor: 1.986

3.  Metabolite quantification: A fluorescence-based method for urine sample normalization prior to 1H-NMR analysis.

Authors:  James Gerard Wolfsberger; Emily C Hunt; Sai Sumedha Bobba; Sharifa Love-Rutledge; Bernhard Vogler
Journal:  Metabolomics       Date:  2022-10-19       Impact factor: 4.747

4.  Metabolomic Profiling of Human Urine as a Screen for Multiple Inborn Errors of Metabolism.

Authors:  Adam D Kennedy; Marcus J Miller; Kirk Beebe; Jacob E Wulff; Anne M Evans; Luke A D Miller; V Reid Sutton; Qin Sun; Sarah H Elsea
Journal:  Genet Test Mol Biomarkers       Date:  2016-07-22

Review 5.  Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine.

Authors:  Abdul-Hamid Emwas; Edoardo Saccenti; Xin Gao; Ryan T McKay; Vitor A P Martins Dos Santos; Raja Roy; David S Wishart
Journal:  Metabolomics       Date:  2018-02-12       Impact factor: 4.290

6.  Baseline urine metabolic phenotype in patients with severe alcoholic hepatitis and its association with outcome.

Authors:  Jaswinder Singh Maras; Sukanta Das; Shvetank Sharma; Saggere M Shasthry; Benoit Colsch; Christophe Junot; Richard Moreau; Shiv Kumar Sarin
Journal:  Hepatol Commun       Date:  2018-04-16

7.  Diagnosis of bacterial urinary tract infection: Utility of urine myeloperoxidase concentration to predict urine culture results in dogs.

Authors:  Jillian Myers Smith; Courtney Thomason; Xiaocun Sun; Elizabeth M Lennon
Journal:  PLoS One       Date:  2020-05-22       Impact factor: 3.240

Review 8.  For what factors should we normalize urinary extracellular mRNA biomarkers?

Authors:  Pradeep Moon Gunasekaran; James Matthew Luther; James Brian Byrd
Journal:  Biomol Detect Quantif       Date:  2019-04-23

9.  Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy.

Authors:  Ting-Li Han; Yang Yang; Hua Zhang; Kai P Law
Journal:  F1000Res       Date:  2017-06-22

10.  Normalizing Untargeted Periconceptional Urinary Metabolomics Data: A Comparison of Approaches.

Authors:  Ana K Rosen Vollmar; Nicholas J W Rattray; Yuping Cai; Álvaro J Santos-Neto; Nicole C Deziel; Anne Marie Z Jukic; Caroline H Johnson
Journal:  Metabolites       Date:  2019-09-21
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