04 Oct Untargeted metabolic profiling of saliva by liquid chromatography-mass spectrometry for the identification of potential diagnostic biomarkers of asthma
Researchers in England have developed a non-invasive technique for diagnosing asthma that detects metabolic biomarkers in a saliva sample. The technique diagnosed asthma with greater than 80% accuracy, according to results of a study published in Analytical Methods.
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.
The benefits of a simple, non-invasive sampling technique for asthma diagnosis are obvious and a great deal of work has focussed on the development of breath sampling, including exhaled nitric oxide, measurement of volatile organic compounds and the e-nose. The discriminatory ability of these tests varies. Importantly the passive drool technique can be used in conjunction with or as a preliminary alternative to the existing diagnostic tests such as FEV1%, has good discriminatory ability, and provides metabolic information on the status of the airway. The identification, validation and the biological significance of the discriminant molecular features identified in this study also need to be validated and quantified using a larger cohort of moderate and/or severe asthmatics versus healthy control populations using a targeted metabolite analysis approach. However, the passive drool technique combined with LC-MS offers a novel non-invasive approach to differentiating patients with asthma from healthy controls.
Anal. Methods, 2016,8, 5407-5413