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

Title: Diagnostic potential of the plasma lipidome in infectious disease: application to acute SARS-CoV-2 infection
Authors: Gray, N
Lawler, NG
Zeng, AX
Ryan, M
Bong, SH
Boughton, BA
Bizkarguenaga, M
Bruzzone, C
Embade, N
Wist, J
Holmes, E
Millet, O
Nicholson, JK
Whiley, L
Item Type: Journal Article
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.
Issue Date: 20-Jul-2021
Date of Acceptance: 12-Jul-2021
URI: http://hdl.handle.net/10044/1/91211
DOI: 10.3390/metabo11070467
ISSN: 2218-1989
Publisher: MDPI AG
Start Page: 1
End Page: 17
Journal / Book Title: Metabolites
Volume: 11
Issue: 7
Copyright Statement: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
metabolic phenotyping
infectious disease
lipids
lipidomics
liquid chromatography-mass spectrometry (LC-MS)
SARS-CoV-2
COVID-19
FALSE DISCOVERY RATE
OMICS ANALYSIS
INBORN-ERRORS
METABOLISM
CERAMIDE
DYSREGULATION
ABNORMALITIES
INFLAMMATION
COVID-19
SARS-CoV-2
infectious disease
lipidomics
lipids
liquid chromatography-mass spectrometry (LC-MS)
metabolic phenotyping
Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
metabolic phenotyping
infectious disease
lipids
lipidomics
liquid chromatography-mass spectrometry (LC-MS)
SARS-CoV-2
COVID-19
FALSE DISCOVERY RATE
OMICS ANALYSIS
INBORN-ERRORS
METABOLISM
CERAMIDE
DYSREGULATION
ABNORMALITIES
INFLAMMATION
0301 Analytical Chemistry
0601 Biochemistry and Cell Biology
1103 Clinical Sciences
Publication Status: Published
Article Number: ARTN 467
Online Publication Date: 2021-07-20
Appears in Collections:Department of Metabolism, Digestion and Reproduction
Department of Surgery and Cancer
Imperial College London COVID-19



This item is licensed under a Creative Commons License Creative Commons