ArticlemSystems2023
The intestinal microbiota predicts COVID-19 severity and fatality regardless of hospital feeding method.
Article in mSystems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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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.
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.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- Longitudinal multi-omic dynamics in hospitalized COVID-19 patients based on disease severity.Gut microbes reports · 2026Article
- The Role of Probiotics and Their Postbiotic Metabolites in Post-COVID-19 Syndrome.Molecules (Basel, Switzerland) · 2025Review
- Bacteriome Signature in SARS-CoV-2-Infected Patients Correlates with Increased Gut Permeability and Systemic Inflammatory Cytokines.Microorganisms · 2025Article
- The Role of Beneficial Microbiota in COVID-19: Insights from Key Bacterial Genera.Microorganisms · 2025Review
- In Silico Analysis of Probiotic Bacteria Changes Across COVID-19 Severity Stages.Microorganisms · 2024Article
- Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data.Scientific reports · 2024Article
- Inhibition of SARS-CoV-2 infection byMicrobiology spectrum · 2024Article
- Gut microbiome and clinical and lifestyle host factors associated with recurrent positive RT-PCR for SARS-CoV-2.Frontiers in cellular and infection microbiology · 2024Article
Corrections and comments
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Authors and funding
18 authors at 1 institution in 1 country.
Funding
Abstract
SARS-CoV-2-positive patients exhibit gut and oral microbiome dysbiosis, which is associated with various aspects of COVID-19 disease (1-4). Here, we aim to identify gut and oral microbiome markers that predict COVID-19 severity in hospitalized patients, specifically severely ill patients compared to moderately ill ones. Moreover, we investigate whether hospital feeding (solid versus enteral), an important cofounder, influences the microbial composition of hospitalized COVID-19 patients. We used random forest classification machine learning models with interpretable secondary analyses. The gut, but not the oral microbiota, was a robust predictor of both COVID-19-related fatality and severity of hospitalized patients, with a higher predictive value than most clinical variables. In addition, perturbations of the gut microbiota due to enteral feeding did not associate with species that were predictive of COVID-19 severity. IMPORTANCE SARS-CoV-2 infection leads to wide-ranging, systemic symptoms with sometimes unpredictable morbidity and mortality. It is increasingly clear that the human microbiome plays an important role in how individuals respond to viral infections. Our study adds to important literature about the associations of gut microbiota and severe COVID-19 illness during the early phase of the pandemic before the availability of vaccines. Increased understanding of the interplay between microbiota and SARS-CoV-2 may lead to innovations in diagnostics, therapies, and clinical predictions.
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Registered trials
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.