Pubblicazioni

Autori:

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

Titolo:

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

Anno:

2024

Tipologia prodotto:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Lingua:

Inglese

Formato:

Elettronico

Referee:

No

Nome rivista:

MSPHERE

ISSN Rivista:

2379-5042

N° Volume:

9

Numero o Fascicolo:

10

Intervallo pagine:

1-21

Parole chiave:

COVID-19 severity; gut microbiota; machine learning

Breve descrizione dei contenuti:

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.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.

Note:

Patrizia Brigidi and Adriana Tami contributed equally to this article. Epub 2024 Sep 19

Id prodotto:

141217

Handle IRIS:

11562/1137828

ultima modifica:

15 novembre 2024

Citazione bibliografica:

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, The gut microbiota as an early predictor of COVID-19 severity «MSPHERE» , vol. 9 , n. 10 , 2024 , pp. 1-21

Consulta la scheda completa presente nel repository istituzionale della Ricerca di Ateneo