Evidence map›Paper›PMID 36081770›Full record

ArticleFrontiers in cellular and infection microbiology2022

COVID-19 severity is associated with population-level gut microbiome variations.

Eva Lymberopoulos, Giorgia Isabella Gentili, Sanjay Budhdeo, Nikhil Sharma

Abstract read
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In one paragraph

Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

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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

4 authors.

Eva LymberopoulosThe Sharma Lab, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, England.
Giorgia Isabella GentiliThe Sharma Lab, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, England.
Sanjay BudhdeoThe Sharma Lab, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, England.
Nikhil SharmaThe Sharma Lab, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, England.

Funding

Wellcome TrustWellcome Trust 566701
6 · The paper itself

Abstract

The human gut microbiome interacts with many diseases, with recent small studies suggesting a link with COVID-19 severity. Exploring this association at the population-level may provide novel insights and help to explain differences in COVID-19 severity between countries. We explore whether there is an association between the gut microbiome of people within different countries and the severity of COVID-19, measured as hospitalisation rate. We use data from the large (n = 3,055) open-access gut microbiome repository curatedMetagenomicData, as well as demographic and country-level metadata. Twelve countries were placed into two groups (high/low) according to COVID-19 hospitalisation rate before December 2020 (ourworldindata.com). We use an unsupervised machine learning method, Topological Data Analysis (TDA). This method analyses both the local geometry and global topology of a high-dimensional dataset, making it particularly suitable for population-level microbiome data. We report an association of distinct baseline population-level gut microbiome signatures with COVID-19 severity. This was found both with a PERMANOVA, as well as with TDA. Specifically, it suggests an association of anti-inflammatory bacteria, including

Indexed as

COVID-19Gastrointestinal MicrobiomeBacteriaBifidobacteriumHumansCOVID-19gut microbiomepopulation healthprobioticstopological data analysis

Identifiers

PMID36081770
PMCPMC9445151

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.