Evidence map›Paper›PMID 32649962›Full record

ArticleBiochimie2020

High prevalence for obesity in severe COVID-19: Possible links and perspectives towards patient stratification.

Isabelle Dugail, Ez-Zoubir Amri, Nicolas Vitale

Open access · bronzeAbstract read
In one paragraph

Article in Biochimie, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
0.8field-weighted citation impact, top 15% 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

19 citing papers in PubMed, 36 citations in OpenAlex.

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  11. Obesity and Coronavirus Disease 2019.Journal of metabolic and bariatric surgery · 2021
    Review
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  19. Obesity altersbioRxiv : the preprint server for biology · 2020
    Article
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

3 authors at 3 institutions in 1 country.

Isabelle DugailUMRS 1269 INSERM/Sorbonne University, Nutriomics, Faculté de médecine Pitié-Salpêtrière, F-75013, Paris, France; Member of the scientific council of the Groupe d'Etude et de Recherche en Lipidomique (GERLI), France.
Ez-Zoubir AmriUniversité Côte d'Azur, CNRS, Inserm, iBV, Nice, France; Member of the scientific council of the Groupe d'Etude et de Recherche en Lipidomique (GERLI), France.
Nicolas VitaleCentre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives, F-67000, Strasbourg, France; Member of the scientific council of the Groupe d'Etude et de Recherche en Lipidomique (GERLI), France. Electronic address: vitalen@unistra.fr.
Centre National de la Recherche Scientifique · FRInserm · FRInstitut des Neurosciences Cellulaires et Intégratives · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is becoming obvious that in addition to aging and various hearth pathologies, excess of body weight, especially obesity is a major risk factor for severity of COVID-19 infection. Intriguingly the receptor for SARS-CoV-2 is ACE2, a member of the angiotensin receptor family that has a relatively large tissue distribution. This observation likely explains the multitude of symptoms that have been described from human patients. The adipose tissue also expresses ACE2, suggesting that adipocytes are potentially infected by SARS-CoV-2. Here we discuss some of the potential contribution of the adipose tissue to the severity of the infection and propose some aspects of obese patients metabolic phenotyping to help stratification of individuals with high risk of severe disease.

Indexed as

Adipose TissueCOVID-19CytokinesHumansObesityPrevalenceCytokines

Identifiers

PMID32649962
PMCPMC7340594
OpenAlexW3038595604

What OpenQuestion holds

Textmetadata
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.