tetano
Editor, Senior Moderator
Gastro Hep Adv
. 2022;1(2):194-209.
doi: 10.1016/j.gastha.2021.12.006. Epub 2022 Feb 7.
A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019
Chiara Pozzi[SUP] 1 [/SUP], Riccardo Levi[SUP] 2 [/SUP], Daniele Braga[SUP] 1 [/SUP], Francesco Carli[SUP] 3 [/SUP], Abbass Darwich[SUP] 2 [/SUP], Ilaria Spadoni[SUP] 2 [/SUP], Bianca Oresta[SUP] 1 [/SUP], Carola Conca Dioguardi[SUP] 4 [/SUP], Clelia Peano[SUP] 4 [/SUP], Leonardo Ubaldi[SUP] 2 [/SUP], Giovanni Angelotti[SUP] 1 [/SUP], Barbara Bottazzi[SUP] 1 [/SUP], Cecilia Garlanda[SUP] 1 2 [/SUP], Antonio Desai[SUP] 1 2 [/SUP], Antonio Voza[SUP] 1 2 [/SUP], Elena Azzolini[SUP] 1 2 [/SUP], Maurizio Cecconi[SUP] 2 1 [/SUP], ICH COVID-19 Task-force; Alberto Mantovani[SUP] 1 2 5 [/SUP], Giuseppe Penna[SUP] 1 [/SUP], Riccardo Barbieri[SUP] 6 [/SUP], Letterio S Politi[SUP] 1 2 [/SUP], Maria Rescigno[SUP] 1 2 [/SUP]
Collaborators, Affiliations
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.
Keywords: AUC, area under the curve; CHI3L1; CHI3L1, chitinase 3-like-1; CI, confidence interval; COVID-19; COVID-19, coronavirus disease 19; DT, decision tree; ELISA, enzyme-linked immunosorbent assay; ESI, electrospray ionization; FDR, false discovery rate; IgG, immunoglobulin G; LR, logistic regression; Metabolome; Microbiota; PCA, principal component analysis; PTX3, pentraxin 3; RFE, recursive feature elimination; SVM, support vector machine.
. 2022;1(2):194-209.
doi: 10.1016/j.gastha.2021.12.006. Epub 2022 Feb 7.
A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019
Chiara Pozzi[SUP] 1 [/SUP], Riccardo Levi[SUP] 2 [/SUP], Daniele Braga[SUP] 1 [/SUP], Francesco Carli[SUP] 3 [/SUP], Abbass Darwich[SUP] 2 [/SUP], Ilaria Spadoni[SUP] 2 [/SUP], Bianca Oresta[SUP] 1 [/SUP], Carola Conca Dioguardi[SUP] 4 [/SUP], Clelia Peano[SUP] 4 [/SUP], Leonardo Ubaldi[SUP] 2 [/SUP], Giovanni Angelotti[SUP] 1 [/SUP], Barbara Bottazzi[SUP] 1 [/SUP], Cecilia Garlanda[SUP] 1 2 [/SUP], Antonio Desai[SUP] 1 2 [/SUP], Antonio Voza[SUP] 1 2 [/SUP], Elena Azzolini[SUP] 1 2 [/SUP], Maurizio Cecconi[SUP] 2 1 [/SUP], ICH COVID-19 Task-force; Alberto Mantovani[SUP] 1 2 5 [/SUP], Giuseppe Penna[SUP] 1 [/SUP], Riccardo Barbieri[SUP] 6 [/SUP], Letterio S Politi[SUP] 1 2 [/SUP], Maria Rescigno[SUP] 1 2 [/SUP]
Collaborators, Affiliations
- PMID: 35174369
- PMCID: PMC8818445
- DOI: 10.1016/j.gastha.2021.12.006
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.
Keywords: AUC, area under the curve; CHI3L1; CHI3L1, chitinase 3-like-1; CI, confidence interval; COVID-19; COVID-19, coronavirus disease 19; DT, decision tree; ELISA, enzyme-linked immunosorbent assay; ESI, electrospray ionization; FDR, false discovery rate; IgG, immunoglobulin G; LR, logistic regression; Metabolome; Microbiota; PCA, principal component analysis; PTX3, pentraxin 3; RFE, recursive feature elimination; SVM, support vector machine.