Evidence map›Paper›PMID 34357361›Full record

ArticleMetabolites2021

Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection.

Nicola Gray, Nathan G Lawler, Annie Xu Zeng, Monique Ryan, Sze How Bong, Berin A Boughton, Maider Bizkarguenaga, Chiara Bruzzone, Nieves Embade, Julien Wist and 4 more

Abstract read
In one paragraph

Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

14 authors.

Nicola GrayAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0002-0094-5245
Nathan G LawlerAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0001-9649-425X
Annie Xu ZengAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0002-2367-2307
Monique RyanAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0001-9188-7097
Sze How BongAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0002-3313-5097
Berin A BoughtonAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0001-6342-9814
Maider BizkarguenagaCentro de Investigación Cooperativa en Biociencias-CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain.ORCID 0000-0003-0988-2006
Chiara BruzzoneCentro de Investigación Cooperativa en Biociencias-CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain.ORCID 0000-0003-4252-8180
Nieves EmbadeCentro de Investigación Cooperativa en Biociencias-CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain.ORCID 0000-0001-9878-3290
Julien WistAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0002-3416-2572
Elaine HolmesAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0002-0556-8389
Oscar MilletCentro de Investigación Cooperativa en Biociencias-CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain.ORCID 0000-0001-8748-4105
Jeremy K NicholsonAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0002-8123-8349
Luke WhileyAustralian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.ORCID 0000-0002-9088-4799

Funding

Department of Industry, Tourism and Trade of the Government of the Autonomous Community of the Basque Country Elkartek BG2019Medical Research Future Fund ARG76435 (MRFF- ARAPC)
6 · The paper itself

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.

Indexed as

COVID-19infectious diseaselipidomicslipidsliquid chromatography-mass spectrometry (LC-MS)metabolic phenotypingSARS-CoV-2

Identifiers

PMID34357361
PMCPMC8306636

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.