1. The gut microbiota as an early predictor of COVID-19 severity.
- Author
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Fabbrini M, D'Amico F, van der Gun BTF, Barone M, Conti G, Roggiani S, Wold KI, Vincenti-Gonzalez MF, de Boer GC, Veloo ACM, van der Meer M, Righi E, Gentilotti E, Górska A, Mazzaferri F, Lambertenghi L, Mirandola M, Mongardi M, Tacconelli E, Turroni S, Brigidi P, and Tami A
- Subjects
- Humans, Female, Male, Machine Learning, Middle Aged, Adult, Feces microbiology, Feces virology, Biomarkers, Aged, Bacteria classification, Bacteria isolation & purification, COVID-19 microbiology, Gastrointestinal Microbiome, Severity of Illness Index, SARS-CoV-2
- Abstract
Several studies reported alterations of the human gut microbiota (GM) during COVID-19. To evaluate the potential role of the GM as an early predictor of COVID-19 at disease onset, we analyzed gut microbial samples of 315 COVID-19 patients that differed in disease severity. We observed significant variations in microbial diversity and composition associated with increasing disease severity, as the reduction of short-chain fatty acid producers such as Faecalibacterium and Ruminococcus , and the growth of pathobionts as Anaerococcus and Campylobacter . Notably, we developed a multi-class machine-learning classifier, specifically a convolutional neural network, which achieved an 81.5% accuracy rate in predicting COVID-19 severity based on GM composition at disease onset. This achievement highlights its potential as a valuable early biomarker during the first week of infection. These findings offer promising insights into the intricate relationship between GM and COVID-19, providing a potential tool for optimizing patient triage and streamlining healthcare during the pandemic.IMPORTANCEEfficient 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., Competing Interests: The authors declare no conflict of interest.
- Published
- 2024
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