Dysbiosis of the Subgingival Microbiome and Relation to Periodontal Disease in Association with Obesity and Overweight

Abstract

Obesity causes gut dysbiosis; nevertheless, little is known about the oral microbiome. We aimed to identify differences in the subgingival microbiota influenced by body weight and periodontal status. Patients (n = 75) recruited at the University Dental Hospital Sharjah, United Arab Emirates, were distributed into three equal groups (healthy weight, overweight, and obese) sub-divided into having either no-mild (NM) or moderate-severe (MS) periodontitis. Subgingival plaques were collected. Microbiota were identified by 16S rRNA sequencing using nanopore technology. Linear discriminant analysis demonstrated significant bacterial biomarkers for body weight and periodontal health. Unique microbiota signatures were identified, with enrichment of periopathogens in patients with MS periodontitis (Aggregatibacter actinomycetemcomitans in obese, Tannerella forsythia and Treponema denticola in overweight, Porphyromonas gingivalis and Fusobacterium nucleatum in healthy weight), thus reflecting differences in the microbiota affected by body weight. Other pathogenic bacteria, such as Salmonella enterica and Klebsiella pneumoniae, were enriched in overweight subjects with NM periodontitis, suggesting an increase in the relative abundance of pathogens even in patients with good periodontal health if they were overweight. Alpha and beta diversities were significantly different among the groups. Dysbiosis of the subgingival microbiota in obese and overweight individuals was associated with increased prevalence and severity of periodontal disease, which was correlated with the body mass index. This study highlights the immense importance of the oral microbiome and the need for lifestyle and dental interventions to resolve oral dysbiosis and restore normal homeostasis.

Higher Prevalence of the Periodontal Pathogen Selenomonas noxia among Pediatric and Adult Patients May Be Associated with Overweight and Obesity

“…These results suggest that Selenomonas may play an important and significant role in a two-way system that influences the development of overweight and obesity, although this may not be the only periodontal pathogen with specific properties that influence this relationship. For example, other studies have demonstrated that additional periodontal pathogens, including Aggregatibacter actinomycetemcomitans , Treponema denticola, and Tannerella forsythia, may also be observed to have different prevalence between normal weight and overweight or obese patients with periodontal disease [ 52 , 53 ]. However, the bidirectional relationships observed between these pathogens and systemic diseases, such as diabetes, metabolic syndrome, and obesity, appear to be more functionally related to inflammatory cytokines and the inhibition of other metabolic pathways rather than on directly increasing caloric recovery from dietary intake, as has been demonstrated with Selenomonas [ 54 , 55 ].…”

Oral administration of Manuka honey induces IFNγ-dependent resistance to tumor growth that correlates with beneficial modulation of gut microbiota composition

“…Linear discriminant analysis (LDA) effect size (LEfSe) was used to detect biomarkers from microbial profiles ( 31 ) using the Microbiome Analyst 2.0 platform (McGill, Canada), which was also used to generate the graph of relative abundance and the heatmap for groups comparison ( 32 ). Venn diagrams were generated to compare the taxa exhibiting significant differences based on the LDA analysis for the identification of shared and unique OTUs ( 33 ).…”

Deciphering salivary microbiome signature in Crohn’s disease patients with different factors contributing to dysbiosis

“…The full-length bacterial 16S rRNA gene (1500 bp) was sequenced with Mk1C Oxford Nanopore sequencer using 16S Barcoding kit 1-24 (SQK-16S024; Oxford Nanopore Technologies, UK) 18 . DNA was amplified by PCR using LongAmp™ Taq 2 × Master Mix (New England Biolabs, UK), and purified by AMPure XP beads (Beckman Coulter, USA), then quantified by Qubit 2 (Thermo Scientific, USA).…”

“…Operational Taxonomic Units (OTUs) were analyzed using Microbiome Analyst 2.0 platform (McGill, Canada) 21 , which was also used for other downstream analyses. Linear discriminant analysis (LDA) effect size (LEfSe) was used to detect biomarkers from microbial profiles, at an LDA threshold of 2 18 , 22 . For α-diversity, different indices were used including Shannon and Simpson diversity (richness, and evenness measures), Chao1 (estimator for diversity from abundance data), ACE (Abundance-based Coverage Estimator), and Observed Species (counts of unique OTUs per sample).…”