Oral‐gut bacterial profiles discriminate between periodontal health and diseases

This investigation explored oral-gut microbial signatures with potential to distinguish among periodontal conditions.The interplay between the oral and gut microbiomes may be a critical pathway linking periodontal diseases and systemic inflammatory disorders. The mechanisms by which oral microorganisms translocate to the gut and cause microbial dysbiosis, favoring an inflammatory state, are still unknown. As a first approach, characterization of oral-gut microbial profiles associated with periodontal health and diseases can provide insights on such mechanisms of etiology and pathogenesis.Fecal and saliva samples from individuals with periodontal health (PH, 8), gingivitis (GG, 17), and periodontitis (PD, 24) were analyzed for their microbial composition by 16S rRNA gene sequencing. Microbial taxa were compared and correlated to periodontal parameters. Multivariate discriminant analysis (MDA) was carried out to identify profiles related to health and disease.Few significant differences in oral-gut taxa were detected among clinical groups, although increase in fecal Fusobacterium nucleatum ss vincentii and salivary Aggregatibacter actinomycetemcomitans, Parvimonas micra, and Fretibacterium sp. HMT358 were strongly correlated with deep pockets and inflammation (p < .01). Over 50% of the fecal microbiota comprised microorganisms shared between oral and gut sites, whereas oral taxa were detected in approximately 9%, particularly enriched in GG fecal samples (p = .04). Trends for lower fecal richness and higher salivary diversity in PD compared to PH were observed. MDA was able to classify correctly 82% of the patients into the clinical groups. Main classifiers of periodontitis were high BMI, older age, and enrichment of oral-fecal Leptotrichia sp. HMT4, Peptostreptococcus stomatis, Dialister invisus, and a novel Lautropia sp. HMTC89-like organism.Within the limitations of an exploratory investigation, specific profiles of oral-gut taxa, including known and potential novel organisms, combined with social-demographic features were able to discriminate individuals with periodontal diseases in this study population.