Simple Saliva Test Developed to Diagnose Asthma

Current clinical tests employed to diagnose asthma are inaccurate and limited by their invasive nature, but a new test that can diagnose asthma from a patient's saliva has now been developed.

To diagnose an asthmatic condition doctors usually measure a person's airflow lung capacity, however lung function tests can be inaccurate and do not reflect underlying changes associated with asthma. Other tests, such as blood, urine or sputum analysis can be distressing, particularly for younger patients.

Scientists at Loughborough University (UK) and their colleagues developed 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.

The team performed an assessment 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.

Colin Creaser, PhD, a professor of Chemistry and senior author of the study, said, “Unlike other sampling methods, such as expired breath analysis, saliva can be collected by passive drool from the very young to the very old without causing distress. We were therefore interested to know if techniques for metabolic profiling of saliva to identify physiological stress from exercise, which was developed by Loughborough University, could be applied to asthma diagnosis. We were very excited to discover that they could.” The study was published in the August 2016 issue of the journal Analytical Methods.

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