Evidence map›Paper›PMID 37511373›Full record

ArticleInternational journal of molecular sciences2023

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

Samantha Lodge, Nathan G Lawler, Nicola Gray, Reika Masuda, Philipp Nitschke, Luke Whiley, Sze-How Bong, Bu B Yeap, Girish Dwivedi, Manfred Spraul and 7 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

22 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Host metabolic responses to SARS-CoV-2 and influenza viruses: parallels and contrasts.Metabolomics : Official journal of the Metabolomic Society · 2026
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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

17 authors at 4 institutions in 6 countries.

Samantha LodgeAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.ORCID 0000-0001-9193-0462
Nathan G LawlerAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.ORCID 0000-0001-9649-425X
Nicola GrayAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.
Reika MasudaAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.ORCID 0000-0003-4891-7830
Philipp NitschkeAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.
Luke WhileyAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.ORCID 0000-0002-9088-4799
Sze-How BongAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.ORCID 0000-0002-3313-5097
Bu B YeapMedical School, University of Western Australia, Perth, WA 6150, Australia.
Girish DwivediMedical School, University of Western Australia, Perth, WA 6150, Australia.ORCID 0000-0003-0717-740X
Manfred SpraulBruker Biospin GmbH, 76287 Rheinstetten, Germany.
Hartmut SchaeferBruker Biospin GmbH, 76287 Rheinstetten, Germany.
Rubén Gil-RedondoPrecision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160 Derio, Spain.ORCID 0000-0002-3541-9085
Nieves EmbadePrecision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160 Derio, Spain.ORCID 0000-0001-9878-3290
Oscar MilletPrecision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160 Derio, Spain.ORCID 0000-0001-8748-4105
Elaine HolmesAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.
Julien WistAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.
Jeremy K NicholsonAustralian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.ORCID 0000-0002-8123-8349
Murdoch University · AUCIC bioGUNE · ESBruker (Germany) · DEThe University of Western Australia · AU

Funding

Medical Research Future Fund 2014349
6 · The paper itself

Abstract

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 (

Indexed as

COVID-19HumansLipidomicsPandemicsPlasmaSARS-CoV-2COVID-19diagnostic modellinglipidomicslipidslipoproteinsmass spectrometrymetabolic phenotypingmetabolomicsmortality predictionNMR spectroscopypatient stratificationplasma IVDrSARS-CoV-2

Identifiers

PMID37511373
PMCPMC10380980
OpenAlexW4384827502

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.