ABSTRACT
BackgroundThe COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at pace, there is an unmet clinical need to develop tests that are prognostic, to triage the high volumes of patients arriving in hospital settings. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. 1 In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. We demonstrate here for the first time that saliva metabolomics can reveal COVID-19 severity. Methods88 saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing. COVID severity was classified using clinical descriptors first proposed by SR Knight et al. Metabolites were extracted from saliva samples and analysed using liquid chromatography mass spectrometry. ResultsIn this work, positive percent agreement of 1.00 between a PLS-DA metabolomics model and the clinical diagnosis of COVID severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, for overall percent agreement of 1.00. ConclusionsThis research demonstrates that liquid chromatography-mass spectrometry can identify salivary biomarkers capable of separating high severity COVID-19 patients from low severity COVID-19 patients in a small cohort study.
ABSTRACT
The COVID-19 pandemic has led to an urgent and unprecedented demand for testing - both for diagnosis and prognosis. Here we explore the potential for using sebum, collected via swabbing of a patients skin, as a novel sampling matrix to fulfil these requirements. In this pilot study, sebum samples were collected from 67 hospitalised patients (30 PCR positive and 37 PCR negative). Lipidomics analysis was carried out using liquid chromatography mass spectrometry. Lipid levels were found to be depressed in COVID-19 positive participants, indicative of dyslipidemia. Partial Least Squares-Discriminant Analysis (PLS-DA) modelling showed promising separation of COVID-19 positive and negative participants when comorbidities and medication were controlled for, with sensitivity of 75% and specificity of 81% in stratified subsets. Given that sebum sampling is rapid and non-invasive, this work highlights the potential of this alternative matrix for testing for COVID-19.
