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Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction

An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020...

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Autores principales: Lodge, Samantha, Lawler, Nathan G., Gray, Nicola, Masuda, Reika, Nitschke, Philipp, Whiley, Luke, Bong, Sze-How, Yeap, Bu B., Dwivedi, Girish, Spraul, Manfred, Schaefer, Hartmut, Gil-Redondo, Rubén, Embade, Nieves, Millet, Oscar, Holmes, Elaine, Wist, Julien, Nicholson, Jeremy K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380980/
https://www.ncbi.nlm.nih.gov/pubmed/37511373
http://dx.doi.org/10.3390/ijms241411614
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author Lodge, Samantha
Lawler, Nathan G.
Gray, Nicola
Masuda, Reika
Nitschke, Philipp
Whiley, Luke
Bong, Sze-How
Yeap, Bu B.
Dwivedi, Girish
Spraul, Manfred
Schaefer, Hartmut
Gil-Redondo, Rubén
Embade, Nieves
Millet, Oscar
Holmes, Elaine
Wist, Julien
Nicholson, Jeremy K.
author_facet Lodge, Samantha
Lawler, Nathan G.
Gray, Nicola
Masuda, Reika
Nitschke, Philipp
Whiley, Luke
Bong, Sze-How
Yeap, Bu B.
Dwivedi, Girish
Spraul, Manfred
Schaefer, Hartmut
Gil-Redondo, Rubén
Embade, Nieves
Millet, Oscar
Holmes, Elaine
Wist, Julien
Nicholson, Jeremy K.
author_sort Lodge, Samantha
collection PubMed
description An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff’s delta 0.91–0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff’s delta = −0.98 and PE.P 16:0/18:1, Cliff’s delta = −0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these “high risk” patients in the early disease stages.
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spelling pubmed-103809802023-07-29 Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction Lodge, Samantha Lawler, Nathan G. Gray, Nicola Masuda, Reika Nitschke, Philipp Whiley, Luke Bong, Sze-How Yeap, Bu B. Dwivedi, Girish Spraul, Manfred Schaefer, Hartmut Gil-Redondo, Rubén Embade, Nieves Millet, Oscar Holmes, Elaine Wist, Julien Nicholson, Jeremy K. Int J Mol Sci Article An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff’s delta 0.91–0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff’s delta = −0.98 and PE.P 16:0/18:1, Cliff’s delta = −0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these “high risk” patients in the early disease stages. MDPI 2023-07-18 /pmc/articles/PMC10380980/ /pubmed/37511373 http://dx.doi.org/10.3390/ijms241411614 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Lodge, Samantha
Lawler, Nathan G.
Gray, Nicola
Masuda, Reika
Nitschke, Philipp
Whiley, Luke
Bong, Sze-How
Yeap, Bu B.
Dwivedi, Girish
Spraul, Manfred
Schaefer, Hartmut
Gil-Redondo, Rubén
Embade, Nieves
Millet, Oscar
Holmes, Elaine
Wist, Julien
Nicholson, Jeremy K.
Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction
title Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction
title_full Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction
title_fullStr Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction
title_full_unstemmed Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction
title_short Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction
title_sort integrative plasma metabolic and lipidomic modelling of sars-cov-2 infection in relation to clinical severity and early mortality prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380980/
https://www.ncbi.nlm.nih.gov/pubmed/37511373
http://dx.doi.org/10.3390/ijms241411614
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