Salivary microbiota composition may discriminate between patients with eosinophilic oesophagitis ( EoE) and non‐EoE subjects
Summary Background Data on the role of the microbiome in adult patients with eosinophilic oesophagitis (EoE) are limited. Aims To prospectively collect and characterise the salivary, oesophageal and gastric microbiome in patients with EoE, further correlating the findings with disease activity. Methods Adult patients with symptoms of oesophageal dysfunction undergoing upper endoscopy were consecutively enrolled. Patients were classified as EoE patients, in case of more than 15 eosinophils per high‐power field, or non‐EoE controls, in case of lack of eosinophilic infiltration. Before and during endoscopy, saliva, oesophageal and gastric fundus biopsies were collected. Microbiota assessment was performed by 16 s rRNA analysis. A Sparse Partial Least Squares Discriminant Analysis (sPLS‐DA) was implemented to identify biomarkers. Results Saliva samples were collected from 29 EoE patients and 20 non‐EoE controls;, biopsies from 25 EoE and 5 non‐EoE controls. In saliva samples, 23 Amplicon Sequence Variants (ASVs) were positively associated with EoE and 27 ASVs with controls, making it possible to discriminate between EoE and non‐EoE patients with a classification error (CE) of 24%. In a validation cohort, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of this model were 78.6%, 80%, 75%, 80% and 60%, respectively. Moreover, the analysis of oesophageal microbiota samples observed a clear microbial pattern able to discriminate between active and inactive EoE (CE = 8%). Conclusion Our preliminary data suggest that salivary metabarcoding analysis in combination with machine learning approaches could become a valid, cheap, non‐invasive test to segregate between EoE and non‐EoE patients..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:56 |
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| Enthalten in: |
Alimentary Pharmacology & Therapeutics - 56(2022), 3, Seite 450-462 |
| Beteiligte Personen: |
Facchin, Sonia [VerfasserIn] |
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| BKL: |
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| Anmerkungen: |
Copyright © 2022 John Wiley & Sons Ltd |
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| Umfang: |
13 |
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| doi: |
10.1111/apt.17091 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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WLY002345439 |
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| 520 | |a Summary Background Data on the role of the microbiome in adult patients with eosinophilic oesophagitis (EoE) are limited. Aims To prospectively collect and characterise the salivary, oesophageal and gastric microbiome in patients with EoE, further correlating the findings with disease activity. Methods Adult patients with symptoms of oesophageal dysfunction undergoing upper endoscopy were consecutively enrolled. Patients were classified as EoE patients, in case of more than 15 eosinophils per high‐power field, or non‐EoE controls, in case of lack of eosinophilic infiltration. Before and during endoscopy, saliva, oesophageal and gastric fundus biopsies were collected. Microbiota assessment was performed by 16 s rRNA analysis. A Sparse Partial Least Squares Discriminant Analysis (sPLS‐DA) was implemented to identify biomarkers. Results Saliva samples were collected from 29 EoE patients and 20 non‐EoE controls;, biopsies from 25 EoE and 5 non‐EoE controls. In saliva samples, 23 Amplicon Sequence Variants (ASVs) were positively associated with EoE and 27 ASVs with controls, making it possible to discriminate between EoE and non‐EoE patients with a classification error (CE) of 24%. In a validation cohort, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of this model were 78.6%, 80%, 75%, 80% and 60%, respectively. Moreover, the analysis of oesophageal microbiota samples observed a clear microbial pattern able to discriminate between active and inactive EoE (CE = 8%). Conclusion Our preliminary data suggest that salivary metabarcoding analysis in combination with machine learning approaches could become a valid, cheap, non‐invasive test to segregate between EoE and non‐EoE patients. | ||
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