Evidence map›Paper›PMID 33134953›Full record

ArticleCritical care explorations2020

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

Douglas D Fraser, Marat Slessarev, Claudio M Martin, Mark Daley, Maitray A Patel, Michael R Miller, Eric K Patterson, David B O'Gorman, Sean E Gill, David S Wishart and 2 more

Open access · goldAbstract read
In one paragraph

Article in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 4 pooled it
6.1field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

84 citing papers in PubMed, 4 syntheses or guidelines pooled it, 121 citations in OpenAlex.

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  14. Utility of Protein Markers in COVID-19 Patients.International journal of molecular sciences · 2025
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24 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Douglas D FraserLawson Health Research Institute, London, ON, Canada.
Marat SlessarevLawson Health Research Institute, London, ON, Canada.
Claudio M MartinLawson Health Research Institute, London, ON, Canada.
Mark DaleyLawson Health Research Institute, London, ON, Canada.
Maitray A PatelDepartment of Computer Science, Western University, London, ON, Canada.
Michael R MillerLawson Health Research Institute, London, ON, Canada.
Eric K PattersonLawson Health Research Institute, London, ON, Canada.
David B O'GormanLawson Health Research Institute, London, ON, Canada.
Sean E GillLawson Health Research Institute, London, ON, Canada.
David S WishartDepartment of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Rupasri MandalDepartment of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Gediminas CepinskasLawson Health Research Institute, London, ON, Canada.
Lawson Health Research Institute · CAWestern University · CAThe Metabolomics Innovation Centre · CAUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesCoronavirus 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.

designBlood 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.

settingTertiary-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).

interventionsNone. MEASUREMENTS AND MAIN

resultsAge- 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 (

conclusionsMetabolomics 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.

Indexed as

biomarkercoronavirus disease 2019diagnosesintensive care unitmetabolomicsprognoses

Identifiers

PMID33134953
PMCPMC7587450
OpenAlexW3094237064

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.