Dataset related to article "A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19"
| Main Authors: | Chiara Pozzi*, Riccardo Levi*, Daniele Braga*, Francesco Carli, Abbass Darwich, Ilaria Spadoni, Bianca Oresta, Carola Conca Dioguardi, Clelia Peano, Leonardo Ubaldi, Giovanni Angelotti, Barbara Bottazzi, Cecilia Garlanda, Antonio Desai, Antonio Voza, Elena Azzolini, Maurizio Cecconi, ICH COVID-19 Task-force, Alberto Mantovani, Giuseppe Penna, Riccardo Barbieri, Letterio S. Politi, Maria Rescigno# |
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| Format: | info dataset Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5873701 |
Daftar Isi:
- *These authors contributed equally to the work #Corresponding author This record contains raw data related to article “A ‘Multiomic’ Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19" Abstract: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the healthcare systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized or discharged. Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration. Often discharged patients returned to the hospital for aggravation of their condition. Here we have developed a new combined approach of omics to identify factors that could distinguish COVID-19 inpatients from outpatients. We tested the metabolome in the saliva and identified nine metabolites that separated the inpatient from the outpatient population, but not completely. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrollidine acetic acid) and one serum protein, Chitinase 3-like-1(CHI3L1) were sufficient to separate the two groups completely. These metabolites positively or negatively correlated with four modulated microbiota taxa. This is a proof-of-concept that a combined omic analysis can be used to stratify patients.