Cargando…

Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers

OBJECTIVES: Coronavirus disease 2019 continues to spread rapidly with high mortality. We performed metabolomics profiling of critically ill coronavirus disease 2019 patients to understand better the underlying pathologic processes and pathways, and to identify potential diagnostic/prognostic biomark...

Descripción completa

Detalles Bibliográficos
Autores principales: Fraser, Douglas D., Slessarev, Marat, Martin, Claudio M., Daley, Mark, Patel, Maitray A., Miller, Michael R., Patterson, Eric K., O’Gorman, David B., Gill, Sean E., Wishart, David S., Mandal, Rupasri, Cepinskas, Gediminas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7587450/
https://www.ncbi.nlm.nih.gov/pubmed/33134953
http://dx.doi.org/10.1097/CCE.0000000000000272
_version_ 1783600177434591232
author Fraser, Douglas D.
Slessarev, Marat
Martin, Claudio M.
Daley, Mark
Patel, Maitray A.
Miller, Michael R.
Patterson, Eric K.
O’Gorman, David B.
Gill, Sean E.
Wishart, David S.
Mandal, Rupasri
Cepinskas, Gediminas
author_facet Fraser, Douglas D.
Slessarev, Marat
Martin, Claudio M.
Daley, Mark
Patel, Maitray A.
Miller, Michael R.
Patterson, Eric K.
O’Gorman, David B.
Gill, Sean E.
Wishart, David S.
Mandal, Rupasri
Cepinskas, Gediminas
author_sort Fraser, Douglas D.
collection PubMed
description OBJECTIVES: Coronavirus disease 2019 continues to spread rapidly with high mortality. We performed metabolomics profiling of critically ill coronavirus disease 2019 patients to understand better the underlying pathologic processes and pathways, and to identify potential diagnostic/prognostic biomarkers. DESIGN: Blood was collected at predetermined ICU days to measure the plasma concentrations of 162 metabolites using both direct injection-liquid chromatography-tandem mass spectrometry and proton nuclear magnetic resonance. SETTING: Tertiary-care ICU and academic laboratory. SUBJECTS: Patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until ICU day 10 if the patient tested positive (coronavirus disease 2019 positive). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Age- and sex-matched healthy controls and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients suffered bilateral pneumonia more frequently than coronavirus disease 2019 negative patients. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Feature selection identified the top-performing metabolites for identifying coronavirus disease 2019 positive patients from healthy control subjects and was dominated by increased kynurenine and decreased arginine, sarcosine, and lysophosphatidylcholines. Arginine/kynurenine ratio alone provided 100% classification accuracy between coronavirus disease 2019 positive patients and healthy control subjects (p = 0.0002). When comparing the metabolomes between coronavirus disease 2019 positive and coronavirus disease 2019 negative patients, kynurenine was the dominant metabolite and the arginine/kynurenine ratio provided 98% classification accuracy (p = 0.005). Feature selection identified creatinine as the top metabolite for predicting coronavirus disease 2019-associated mortality on both ICU days 1 and 3, and both creatinine and creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death with 100% accuracy (p = 0.01). CONCLUSIONS: Metabolomics profiling with feature classification easily distinguished both healthy control subjects and coronavirus disease 2019 negative patients from coronavirus disease 2019 positive patients. Arginine/kynurenine ratio accurately identified coronavirus disease 2019 status, whereas creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death. Administration of tryptophan (kynurenine precursor), arginine, sarcosine, and/or lysophosphatidylcholines may be considered as potential adjunctive therapies.
format Online
Article
Text
id pubmed-7587450
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-75874502020-10-29 Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers Fraser, Douglas D. Slessarev, Marat Martin, Claudio M. Daley, Mark Patel, Maitray A. Miller, Michael R. Patterson, Eric K. O’Gorman, David B. Gill, Sean E. Wishart, David S. Mandal, Rupasri Cepinskas, Gediminas Crit Care Explor Original Clinical Report OBJECTIVES: Coronavirus disease 2019 continues to spread rapidly with high mortality. We performed metabolomics profiling of critically ill coronavirus disease 2019 patients to understand better the underlying pathologic processes and pathways, and to identify potential diagnostic/prognostic biomarkers. DESIGN: Blood was collected at predetermined ICU days to measure the plasma concentrations of 162 metabolites using both direct injection-liquid chromatography-tandem mass spectrometry and proton nuclear magnetic resonance. SETTING: Tertiary-care ICU and academic laboratory. SUBJECTS: Patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until ICU day 10 if the patient tested positive (coronavirus disease 2019 positive). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Age- and sex-matched healthy controls and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients suffered bilateral pneumonia more frequently than coronavirus disease 2019 negative patients. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Feature selection identified the top-performing metabolites for identifying coronavirus disease 2019 positive patients from healthy control subjects and was dominated by increased kynurenine and decreased arginine, sarcosine, and lysophosphatidylcholines. Arginine/kynurenine ratio alone provided 100% classification accuracy between coronavirus disease 2019 positive patients and healthy control subjects (p = 0.0002). When comparing the metabolomes between coronavirus disease 2019 positive and coronavirus disease 2019 negative patients, kynurenine was the dominant metabolite and the arginine/kynurenine ratio provided 98% classification accuracy (p = 0.005). Feature selection identified creatinine as the top metabolite for predicting coronavirus disease 2019-associated mortality on both ICU days 1 and 3, and both creatinine and creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death with 100% accuracy (p = 0.01). CONCLUSIONS: Metabolomics profiling with feature classification easily distinguished both healthy control subjects and coronavirus disease 2019 negative patients from coronavirus disease 2019 positive patients. Arginine/kynurenine ratio accurately identified coronavirus disease 2019 status, whereas creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death. Administration of tryptophan (kynurenine precursor), arginine, sarcosine, and/or lysophosphatidylcholines may be considered as potential adjunctive therapies. Lippincott Williams & Wilkins 2020-10-21 /pmc/articles/PMC7587450/ /pubmed/33134953 http://dx.doi.org/10.1097/CCE.0000000000000272 Text en Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Clinical Report
Fraser, Douglas D.
Slessarev, Marat
Martin, Claudio M.
Daley, Mark
Patel, Maitray A.
Miller, Michael R.
Patterson, Eric K.
O’Gorman, David B.
Gill, Sean E.
Wishart, David S.
Mandal, Rupasri
Cepinskas, Gediminas
Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers
title Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers
title_full Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers
title_fullStr Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers
title_full_unstemmed Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers
title_short Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers
title_sort metabolomics profiling of critically ill coronavirus disease 2019 patients: identification of diagnostic and prognostic biomarkers
topic Original Clinical Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7587450/
https://www.ncbi.nlm.nih.gov/pubmed/33134953
http://dx.doi.org/10.1097/CCE.0000000000000272
work_keys_str_mv AT fraserdouglasd metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT slessarevmarat metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT martinclaudiom metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT daleymark metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT patelmaitraya metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT millermichaelr metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT pattersonerick metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT ogormandavidb metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT gillseane metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT wishartdavids metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT mandalrupasri metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers
AT cepinskasgediminas metabolomicsprofilingofcriticallyillcoronavirusdisease2019patientsidentificationofdiagnosticandprognosticbiomarkers