1. A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019
- Author
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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, Alberto Mantovani, Giuseppe Penna, Riccardo Barbieri, Letterio S. Politi, Maria Rescigno, Aghemo Alessio, Anfray Clement, Badalamenti Salvatore, Belgiovine Cristina, Bertocchi Alice, Bombace Sara, Brescia Paola, Calcaterra Francesca, Calvi Michela, Cancellara Assunta, Capucetti Arianna, Carenza Claudia, Carloni Sara, Carnevale Silvia, Cazzetta Valentina, Cecconi Maurizio, Ciccarelli Michele, Coianiz Nicolò, Darwich Abbass, Lleo de Nalda Ana, De Paoli Federica, Di Donato Rachele, Digifico Elisabeth, Durante Barbara, FARINA Floriana Maria, Ferrari Valentina, Fornasa Giulia, Franzese Sara, Gil Gomez Antonio, Giugliano Silvia, Gomes Ana Rita, Lizier Michela, Lo Cascio Antonino, Melacarne Alessia, Mozzarelli Alessandro, My Ilaria, Oresta Bianca, Pasqualini Fabio, Pastò Anna, Pelamatti Erica, Perucchini Chiara, Pozzi Chiara, Rimoldi Valeria, Rimoldi Monica, Scarpa Alice, Selmi Carlo, Silvestri Alessandra, Sironi Marina, Spadoni Ilaria, Spano' Salvatore, Spata Gianmarco, Supino Domenico, Tentorio Paolo, Ummarino Aldo, Valentino Sonia, Voza Antonio, Zaghi Elisa, and Zanon Veronica
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PCA, principal component analysis ,SVM, support vector machine ,ESI, electrospray ionization ,FDR, false discovery rate ,Microbiota ,IgG, immunoglobulin G ,COVID-19 ,LR, logistic regression ,Original Research—Basic ,CHI3L1, chitinase 3-like-1 ,ELISA, enzyme-linked immunosorbent assay ,PTX3, pentraxin 3 ,CI, confidence interval ,AUC, area under the curve ,RFE, recursive feature elimination ,Metabolome ,CHI3L1 ,COVID-19, coronavirus disease 19 ,DT, decision tree - Abstract
Background and Aims The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often 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 coronavirus disease 19 (COVID-19) inpatients from outpatients. Methods Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. Results We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. Conclusion This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.
- Published
- 2021