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Issue 27, 2016
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Untargeted metabolic profiling of saliva by liquid chromatography-mass spectrometry for the identification of potential diagnostic biomarkers of asthma

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Abstract

Current clinical tests employed to diagnose asthma are inaccurate and limited by their invasive nature. New metabolite profiling technologies offer an opportunity to improve asthma diagnosis using non-invasive sampling. A rapid analytical method for metabolite profiling of saliva is reported using ultra-high performance liquid chromatography combined with high resolution time-of-flight mass spectrometry (UHPLC-MS). The only sample pre-treatment required was protein precipitation with acetonitrile. The method has been applied to a pilot study of saliva samples obtained by passive drool from well phenotyped patients with asthma and healthy controls. Stepwise data reduction and multivariate statistical analysis was performed on the complex dataset obtained from the UHPLC-MS analysis to identify potential metabolomic biomarkers of asthma in saliva. Ten discriminant features were identified that distinguished between moderate asthma and healthy control samples with an overall recognition ability of 80% during training of the model and 97% for model cross-validation. The reported method demonstrates the potential for a non-invasive approach to the clinical diagnosis of asthma using mass spectrometry-based metabolic profiling of saliva.

Graphical abstract: Untargeted metabolic profiling of saliva by liquid chromatography-mass spectrometry for the identification of potential diagnostic biomarkers of asthma

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Publication details

The article was received on 31 Mar 2016, accepted on 17 Jun 2016 and first published on 20 Jun 2016


Article type: Paper
DOI: 10.1039/C6AY00938G
Citation: Anal. Methods, 2016,8, 5407-5413
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    Untargeted metabolic profiling of saliva by liquid chromatography-mass spectrometry for the identification of potential diagnostic biomarkers of asthma

    A. Malkar, E. Wilson, T. Harrrison, D. Shaw and C. Creaser, Anal. Methods, 2016, 8, 5407
    DOI: 10.1039/C6AY00938G

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