The gut microbiota as an early predictor of COVID-19 severity

Efficient patient triage for COVID-19 is vital to manage healthcare resources effectively. This study underscores the potential of gut microbiota (GM) composition as an early biomarker for COVID-19 severity. By analyzing GM samples from 315 patients, significant correlations between microbial divers...

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Veröffentlicht in:mSphere Jg. 9; H. 10; S. e0018124
Hauptverfasser: Fabbrini, Marco, D’Amico, Federica, van der Gun, Bernardina T. F., Barone, Monica, Conti, Gabriele, Roggiani, Sara, Wold, Karin I., Vincenti-Gonzalez, María F., de Boer, Gerolf C., Veloo, Alida C. M., van der Meer, Margriet, Righi, Elda, Gentilotti, Elisa, Górska, Anna, Mazzaferri, Fulvia, Lambertenghi, Lorenza, Mirandola, Massimo, Mongardi, Maria, Tacconelli, Evelina, Turroni, Silvia, Brigidi, Patrizia, Tami, Adriana
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States American Society for Microbiology 29.10.2024
Schlagworte:
Adult
Aged
Bacteria - classification
Bacteria - isolation & purification
Biomarkers
COVID-19
COVID-19 - microbiology
COVID-19 severity
Feces - microbiology
Feces - virology
Female
Gastrointestinal Microbiome
Gut microbiota
Health care
Hospitalization
Human Microbiome
Humans
Infections
Infectious diseases
Intestinal microflora
Machine Learning
Male
Microbiota
Middle Aged
Neural networks
Pandemics
Patients
Public health
Research Article
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
Severity of Illness Index
Italy
Netherlands
machine learning
COVID-19 severity
gut microbiota
ISSN:2379-5042, 2379-5042
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Efficient patient triage for COVID-19 is vital to manage healthcare resources effectively. This study underscores the potential of gut microbiota (GM) composition as an early biomarker for COVID-19 severity. By analyzing GM samples from 315 patients, significant correlations between microbial diversity and disease severity were observed. Notably, a convolutional neural network classifier was developed, achieving an 81.5% accuracy in predicting disease severity based on GM composition at disease onset. These findings suggest that GM profiling could enhance early triage processes, offering a novel approach to optimizing patient management during the pandemic.
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The authors declare no conflict of interest.
Patrizia Brigidi and Adriana Tami contributed equally to this article.
ISSN:2379-5042
2379-5042
DOI:10.1128/msphere.00181-24