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Anal Bioanal Chem. 2016 Nov;408(29):8483-8493. Epub 2016 Nov 4.

Evaluation of dilution and normalization strategies to correct for urinary output in HPLC-HRTOFMS metabolomics.

Author information

1
Institute of Functional Genomics, University of Regensburg, Am BioPark 9, 93053, Regensburg, Germany.
2
Department of Epidemiology and Preventive Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93051, Regensburg, Germany.
3
Thrasos Therapeutics, Inc, 75 Federal Street, Boston, Massachusetts, 02110, USA.
4
Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Ulmenweg 18, 91054, Erlangen, Germany.
5
Institute of Functional Genomics, University of Regensburg, Am BioPark 9, 93053, Regensburg, Germany. Peter.Oefner@klinik.uni-regensburg.de.

Abstract

Reliable identification of features distinguishing biological groups of interest in urinary metabolite fingerprints requires the control of total metabolite abundance, which may vary significantly as the kidneys adjust the excretion of water and solutes to meet the homeostatic needs of the body. Failure to account for such variation may lead to misclassification and accumulation of missing data in case of less concentrated urine specimens. Here, different pre- and post-acquisition methods of normalization were compared systematically for their ability to recover features from liquid chromatography-mass spectrometry metabolite fingerprints of urine that allow distinction between patients with chronic kidney disease and healthy controls. Methods of normalization that were employed prior to analysis included dilution of urine specimens to either a fixed creatinine concentration or osmolality value. Post-acquisition normalization methods applied to chromatograms of 1:4 diluted urine specimens comprised normalization to creatinine, osmolality, and sum of all integrals. Dilution of urine specimens to a fixed creatinine concentration resulted not only in the least number of missing values, but it was also the only method allowing the unambiguous classification of urine specimens from healthy and diseased individuals. The robustness of classification could be confirmed for two independent patient cohorts of chronic kidney disease patients and yielded a shared set of 49 discriminant metabolite features. Graphical Abstract Dilution to a uniform creatinine concentration across urine specimens yields more comparable urinary metabolite fingerprints.

KEYWORDS:

Creatinine; LC-MS; Metabolic fingerprinting; Normalization; Osmolality; Urine

PMID:
27815612
DOI:
10.1007/s00216-016-9974-1
[Indexed for MEDLINE]

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