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Title: Exploring salivary microbiota as a COPD biomarker
Author: Melo-Dias, Sara
Tavares, Ana
Valente, Carla
Andrade, Lília
Marques, Alda
Sousa, Ana
Keywords: COPD
Salivary microbiota
Issue Date: 2020
Publisher: Universidade do Algarve
Abstract: Background: Evidence of the clinical implications of microbiota dysbiosis in chronic obstructive pulmonary disease (COPD) is still lacking, needs validation and is fundamental as microbiota might be a promising biomarker of this disease. We aimed at exploring saliva’s microbiota of patients with COPD to query its relationship with disease-specific clinical parameters and evaluate its potential to be used as a COPD biomarker. Methods: Thirty-eight outpatients with COPD (33 male, 66±8y, BMI 25.0±4.9, FEV1pp 33±7, GOLD III-26, IV-12) and 38 matched healthy controls (33 male, 66±9y, BMI 27.5±3.7, FEV1pp 103±18) were characterised based on sociodemographic, anthropometric, clinical parameters and 16S rRNA profiling of their salivary microbiota. An unsupervised clustering analysis based exclusively on microbiota beta diversity was performed. Moreover, a classification model was developed to assess the microbiota predictive ability of COPD. Results: Proteobacteria (~30% of patients’ salivary microbiota), particularly Neisseria, Haemophilus and Helicobacter, was significantly more abundant in patients with COPD, whereas Firmicutes was significantly enriched in healthy individuals. Furthermore, patients’ microbiota was less diverse than in healthy. The unsupervised clustering analysis distinguished 25% of the most severe and symptomatic patients and 20% of the healthy population. These groups were even more disparate in Proteobacteria abundance (~40% and 10%, respectively). 80% of accuracy was achieved in classifying individuals as sick or healthy based on a small group of salivary bacteria. In sum, saliva’s microbiota shows a strong association with COPD, particularly in more severe cases of the disease, and a remarkable predictive power for disease classification.
Peer review: yes
Appears in Collections:CIDMA - Comunicações
ESSUA - Comunicações
DCM - Comunicações
IBIMED - Comunicações
Lab3R - Comunicações

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