Could salivary microbiota be used as a COPD biomarker?

Abstract

Background: Chronic Obstructive Pulmonary Disease (COPD), an inflammatory disease of the airways with high morbidity and mortality (3rd leading cause of death worldwide), is highly heterogeneous in terms of clinical phenotype being very difficult to treat and manage [1–3]. Precision Medicine holds great promise for this type of diseases but relies on the existence of validated biomarkers for disease prognosis or treatment prescription [4]. So, it is important to unravel and validate new biomarkers that allow the definition of endotypes to help managing COPD. The airway microbiota is a likely candidate for this purpose as it has been implicated in COPD stratification. Nevertheless, evidence of the clinical implications of microbiota dysbiosis in COPD is still lacking, needs validation and is fundamental before we can consider it in a multiple-biomarker approach [5–7]. Objective: Here we aimed at exploring saliva’s microbiota of patients with COPD to evaluate its potential as disease 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. A Random-Forest classification model was developed to assess the microbiota predictive ability of COPD. Results: Using the total number of available operational taxonomic units (OTUs) (n=97) a mean accuracy of 82.5% (IC 95% 82.2-82.9) was achieved. Performing a refinement of the model to include as few OTUs as possible, has shown that 9 OTUs, S24_7, Helicobacter, Peptococcus, Clostridiales, Peptostreptococcus, Lactococcus, Lachnoanaerobaculum, Atopobium and Mogibacteriaceae, were sufficient to achieve almost maximum mean accuracy (86.2%, IC 95% 86.5), with a sensitivity of 89.5% (IC 95% 82.6-96.4) (classification error: 10.5% for “COPD”) and a specificity of 84.2%( IC 95% 76-92.4) (classification error: 15.8% for “healthy”). Conclusion: Our work explores the predictive power of the salivary microbiota for disease classification supporting its use as a valuable biomarker.

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