Dataset related to article "A 'multiomic' approach to separate COVID-19 inpatients from outpatients."

*These authors contributed equally to the work^#Corresponding author This record contains raw data related to article “A ‘multiomic’ approach to separate COVID-19 inpatients from outpatients." 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.