Development of a screening and confirmatory method for the analysis of polar endogenous compounds in saliva based on a liquid chromatographic-tandem mass spectrometric system

Fernández-del-Campo-García, María Teresa; Casas-Ferreira, Ana María; Rodríguez‐Gonzalo, Encarnación; Cordero, Bernardo Moreno; Pavón, José Luis Pérez

doi:10.1016/j.chroma.2019.01.001

Cited by 12 publications

(1 citation statement)

References 47 publications

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“…It should be noted, however, that whilst this work was untargeted for discovery purposes, the features selected for the PLS-DA panel (valine, leucine, phenylalanine, tyrosine, proline and C 44 H 74 N 8 O 16 ) were on average present in 85% of participant samples and targeted methods are available that can reliably quantify amino acids for future investigation [ 33 ]. Therefore, whilst this is a preliminary and exploratory study, we see these results as encouraging first evidence for distinctive changes in the salivary metabolome of hospitalised individuals with severe COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity

et al. 2022

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Background The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes–and whether markers can be found in different biofluids–are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. 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. Methods 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, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. Results Positive percent agreement of 1.00 between a partial least squares–discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. Conclusions In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.

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Section: Discussionmentioning

confidence: 99%