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Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection

Authors :
Nicola Gray
Nathan G. Lawler
Annie Xu Zeng
Monique Ryan
Sze How Bong
Berin A. Boughton
Maider Bizkarguenaga
Chiara Bruzzone
Nieves Embade
Julien Wist
Elaine Holmes
Oscar Millet
Jeremy K. Nicholson
Luke Whiley
Source :
Metabolites, Vol 11, Iss 7, p 467 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

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.

Details

Language :
English
ISSN :
22181989
Volume :
11
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Metabolites
Publication Type :
Academic Journal
Accession number :
edsdoj.bc71669c66a44bb4bb780e384b8773ec
Document Type :
article
Full Text :
https://doi.org/10.3390/metabo11070467