Evidence map›Paper›PMID 42063908›Full record

ArticleAfrican health sciences2026

Gut microbial profiles of COVID-19 patients in Uganda.

David Patrick Kateete, Christopher Lubega, Emmanuel Nasinghe, Monica Mbabazi, Ronald Galiwango, Daudi Jjingo

Abstract read
In one paragraph

Article in African health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

David Patrick KateeteDepartment of Immunology and Microbiology, School of biomedical Sciences, Makerere University, Kampala Uganda.
Christopher LubegaDepartment of Immunology and Microbiology, School of biomedical Sciences, Makerere University, Kampala Uganda.
Emmanuel NasingheDepartment of Immunology and Microbiology, School of biomedical Sciences, Makerere University, Kampala Uganda.
Monica MbabaziDepartment of Immunology and Microbiology, School of biomedical Sciences, Makerere University, Kampala Uganda.
Ronald GaliwangoAfrican Center of Excellence in Bioinformatics and Data Intensive Sciences, College of Health Sciences, Makerere University, Kampala, Uganda.
Daudi JjingoAfrican Center of Excellence in Bioinformatics and Data Intensive Sciences, College of Health Sciences, Makerere University, Kampala, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The role of the microbiome in COVID-19 outcomes remains an area of exploration. We comprehensively explored the gut microbiome of Ugandan COVID-19 patients and inferred potential implications. Methods: Stool and demographic data were collected from 100 COVID-19 confirmed cases at the covid isolation and treatment centers in Kampala during the first and second waves of the pandemic in Uganda (2020 and 2021, respectively). 16S rRNA sequencing was performed on the DNA extracted from stool, followed by bioinformatics analysis. Machine-learning techniques were used to determine microbes that were associated with disease severity. Results: We observed differences in microbial composition between COVID-19 patients and healthy controls. Pathogenic bacteria such as Klebsiella oxytoca, Salmonella enterica and Serratia marcescens had an increased presence in COVID-19 disease states, especially severe cases. Additionally, there was an increase in opportunistic pathogens like Enterococcus species, along with a decrease in beneficial microbes, such as Alphaproteobacteria, when comparing mild and severe cases. Machine-learning identified age and microbes like Ruminococcaceae, Bacilli, Enterobacteriales, porphyromonadaceae and Prevotella copri as predictive of severity. Conclusion: The microbiome likely plays a role in the dynamics of SARS-CoV-2 infection in Ugandan patients. The shift in abundance of specific microbes can moderately predict severity of COVID-19 in this population. Clinical trial number: Not applicable.

Indexed as

BacteriaCOVID-19Gastrointestinal MicrobiomeAdolescentAdultFecesFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16SSARS-CoV-2Severity of Illness IndexUgandaYoung AdultRNA, Ribosomal, 16SCOVID-19Gut microbiomeKampalaMachine LearningMetagenomicsUganda

Identifiers

PMID42063908
PMCPMC13126132

What OpenQuestion holds

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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.