Evidence map›Paper›PMID 34226622›Full record

ArticleScientific reports2021

Understanding protection from SARS-CoV-2 using metabolomics.

Elettra Barberis, Elia Amede, Matteo Tavecchia, Emilio Marengo, Micol G Cittone, Eleonora Rizzi, Anita R Pedrinelli, Stelvio Tonello, Rosalba Minisini, Mario Pirisi and 2 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.1field-weighted citation impact, top 10% 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

23 citing papers in PubMed, 39 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Special Issue: Molecular Research and Insights into COVID-19: 2nd Edition.International journal of molecular sciences · 2025
    Article
  6. Observational
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Observational
  14. Protective Barriers Provided by the Epidermis.International journal of molecular sciences · 2023
    Review
  15. Review
  16. Article
  17. Article
  18. Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022
    Review
  19. Article
  20. Observational
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 1 institution in 1 country.

Elettra BarberisDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Elia AmedeDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Matteo TavecchiaDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Emilio MarengoCenter for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale, Novara, Italy.
Micol G CittoneInternal and Emergency Medicine Departments, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Eleonora RizziInternal and Emergency Medicine Departments, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Anita R PedrinelliInternal and Emergency Medicine Departments, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Stelvio TonelloDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Rosalba MinisiniDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Mario PirisiDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Marcello ManfrediDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy. marcello.manfredi@uniupo.it.ORCID http://orcid.org/0000-0003-0632-5618
Pier Paolo SainaghiDepartment of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Università degli Studi del Piemonte Orientale “Amedeo Avogadro” · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic is still raging in most countries. Although the recent mass vaccination campaign has opened a new chapter in the battle against SARS-CoV-2, the world is still far from herd immunity. There is an urgent need to identify healthy people at high risk of contracting COVID-19, as well as supplements and nutraceuticals that can reduce the risk of infection or mitigate symptoms. In the present study, a metabolic phenotype that could protect individuals from SARS-CoV-2 infection or predispose them to developing COVID-19 was investigated. Untargeted metabolomics was performed on serum samples collected from 51 healthcare workers who were in good health at the beginning of the COVID-19 outbreak in Italy, and who were later exposed to the same risk of developing COVID-19. Half of them developed COVID-19 within three weeks of the blood collection. Our results demonstrate the presence of a specific signature associated with protection from SARS-CoV-2. Circulating monolaurin, which has well-known antiviral and antibacterial properties, was higher in protected subjects, suggesting a potential defensive role against SARS-CoV-2 infection; thus, dietary supplements could boost the immune system against this infection. In addition, our data demonstrate that people with higher levels of cholesterol are at higher risk of developing COVID-19. The present study demonstrates that metabolomics can be of great help for developing personalized medicine and for supporting public healthcare strategies. Studies with larger cohorts of subjects are necessary to confirm our findings.

Indexed as

MetabolomicsAntiviral AgentsCOVID-19Disease OutbreaksHealth PersonnelHumansImmunity, HerdItalySARS-CoV-2Antiviral Agents

Identifiers

PMID34226622
PMCPMC8257707
OpenAlexW3178281500

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.