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Diagnostic potential of the plasma lipidome in infectious disease: Application to acute SARS-CoV-2 infection

Gray, N., Lawler, N., Zeng, A., Ryan, M., Bong, S-H, Boughton, B.ORCID: 0000-0001-6342-9814, Bizkarguenaga, M., Bruzzone, C., Embade, N., Wist, J., Holmes, E., Millet, O., Nicholson, J. and Whiley, L.ORCID: 0000-0002-9088-4799 (2021) Diagnostic potential of the plasma lipidome in infectious disease: Application to acute SARS-CoV-2 infection. Metabolites, 11 (7). Article 467.

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Abstract

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography–mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal–Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.

Item Type: Journal Article
Murdoch Affiliation(s): Australian National Phenome Center
Centre for Computational and Systems Medicine
Health Futures Institute
Publisher: MDPI
Copyright: © 2021 by the authors
United Nations SDGs: Goal 3: Good Health and Well-Being
URI: http://researchrepository.murdoch.edu.au/id/eprint/61692
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