No abstract

In evaluating the pathogenesis of periodontal diseases, the diagnostic potential of gingival crevicular fluid has been extensively explored during the last twenty years, from initially just confirming health and disease states to more recently investigating it as a potential prognostic tool. As host susceptibility is a critical determinant in periodontal disease pathogenesis, the inflammatory mediator levels present in gingival crevicular fluid represent relevant risk indicators for disease activity. Considerable work has been carried out to identify the many different cytokine inflammatory pathways and microbial stimuli that are associated with periodontal disease pathogenesis. Now, ‘omics’ approaches aim to summarize how these pathways interact and probably converge to create critical inflammatory networks. More recently, gingival crevicular fluid metabolomics appears promising as an additional diagnostic method. Biofilm structure and the host inflammatory response to the microbial challenge may induce specific inflammatory signatures. Host genetics and epigenetics may also modulate microbial colonization, adding to the multiplicity of potential causal pathways. Omics analyses of gingival crevicular fluid, measuring microbial and host interactions in association with the onset and progression of periodontal diseases, still show the potential to expand the landscape for the discovery of diagnostic, prognostic and therapeutic markers.

Periodontal disease (PD) is a common dental disease associated with the interaction between dysbiotic oral microbiota and host immunity. It is a prevalent disease, resulting in loss of gingival tissue, periodontal ligament, cementum and alveolar bone. PD is a major form of tooth loss in the adult population. Experimental animal models have enabled the study of PD pathogenesis and are used to test new therapeutic approaches for treating the disease. The ligature-induced periodontitis model has several advantages as compared with other models, including rapid disease induction, predictable bone loss and the capacity to study periodontal tissue and alveolar bone regeneration because the model is established within the periodontal apparatus. Although mice are the most convenient and versatile animal models used in research, ligature-induced periodontitis has been more frequently used in large animals. This is mostly due to the technical challenges involved in consistently placing ligatures around murine teeth. To reduce the technical challenge associated with the traditional ligature model, we previously developed a simplified method to easily install a bacterially retentive ligature between two molars for inducing periodontitis. In this protocol, we provide detailed instructions for placement of the ligature and demonstrate how the model can be used to evaluate gingival tissue inflammation and alveolar bone loss over a period of 18 d after ligature placement. This model can also be used on germ-free mice to investigate the role of human oral bacteria in periodontitis in vivo. In conclusion, this protocol enables the mechanistic study of the pathogenesis of periodontitis in vivo.

The purpose of this study was to determine the role of saliva-derived biomarkers and periodontal pathogens during periodontal disease progression (PDP). One hundred human participants were recruited into a 12-month investigation. They were seen bi-monthly for saliva and clinical measures and bi-annually for subtraction radiography, serum and plaque biofilm assessments. Saliva and serum were analyzed with protein arrays for 14 pro-inflammatory and bone turnover markers, while qPCR was used for detection of biofilm. A hierarchical clustering algorithm was used to group study participants based on clinical, microbiological, salivary/serum biomarkers, and PDP. Eighty-three individuals completed the six-month monitoring phase, with 39 [corrected] exhibiting PDP, while 44 [corrected] demonstrated stability. Participants assembled into three clusters based on periodontal pathogens, serum and salivary biomarkers. Cluster 1 members displayed high salivary biomarkers and biofilm; 71% [corrected] of these individuals were undergoing PDP. Cluster 2 members displayed low biofilm and biomarker levels; 76% [corrected] of these individuals were stable. Cluster 3 members were not discriminated by PDP status; however, cluster stratification followed groups 1 and 2 based on thresholds of salivary biomarkers and biofilm pathogens. The association of cluster membership to PDP was highly significant (p < 0.0007). [corrected] The use of salivary and biofilm biomarkers offers potential for the identification of PDP or stability (ClinicalTrials.gov number, CT00277745).

Genome-wide association studies (GWAS) of chronic periodontitis (CP) defined by clinical criteria alone have had modest success to-date. Here, we refine the CP phenotype by supplementing clinical data with biological intermediates of microbial burden (levels of eight periodontal pathogens) and local inflammatory response (gingival crevicular fluid IL-1β) and derive periodontal complex traits (PCTs) via principal component analysis. PCTs were carried forward to GWAS (∼2.5 million markers) to identify PCT-associated loci among 975 European American adult participants of the Dental ARIC study. We sought to validate these findings for CP in the larger ARIC cohort (n = 821 participants with severe CP, 2031—moderate CP, 1914—healthy/mild disease) and an independent German sample including 717 aggressive periodontitis cases and 4210 controls. We identified six PCTs with distinct microbial community/IL-1β structures, although with overlapping clinical presentations. PCT1 was characterized by a uniformly high pathogen load, whereas PCT3 and PCT5 were dominated by Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis, respectively. We detected genome-wide significant signals for PCT1 (CLEC19A, TRA, GGTA2P, TM9SF2, IFI16, RBMS3), PCT4 (HPVC1) and PCT5 (SLC15A4, PKP2, SNRPN). Overall, the highlighted loci included genes associated with immune response and epithelial barrier function. With the exception of associations of BEGAIN with severe and UBE3D with moderate CP, no other loci were associated with CP in ARIC or aggressive periodontitis in the German sample. Although not associated with current clinically determined periodontal disease taxonomies, upon replication and mechanistic validation these candidate loci may highlight dysbiotic microbial community structures and altered inflammatory/immune responses underlying biological sub-types of CP.

Aim Assess the ability of a panel of gingival crevicular fluid (GCF) biomarkers as predictors of periodontal disease progression (PDP). Materials and Methods 100 individuals participated in a 12-month longitudinal investigation and categorized into 4 groups according to their periodontal status. GCF, clinical parameters, and saliva were collected bi-monthly. Sub-gingival plaque and serum were collected bi-annually. For 6 months, no periodontal treatment was provided. At 6-months, patients received periodontal therapy and continued participation from 6-12 months. GCF samples were analyzed by ELISA for MMP-8, MMP-9, OPG, CRP and IL-1β. Differences in median levels of GCF biomarkers were compared between stable and progressing participants using Wilcoxon Rank Sum test (p=0.05). Clustering algorithm was used to evaluate the ability of oral biomarkers to classify patients as either stable or progressing. Results Eighty-three individuals completed the 6-month monitoring phase. With the exception of GCF C-reactive protein, all biomarkers were significantly higher in the PDP group compared to stable patients. Clustering analysis showed highest sensitivity levels when biofilm pathogens and GCF biomarkers were combined with clinical measures, 74% (95% CI = 61,86). Conclusions Signature of GCF fluid-derived biomarkers combined with pathogens and clinical measures provides a sensitive measure for discrimination of PDP (ClinicalTrials.gov NCT00277745).

We confirm an independent association between periodontal disease and incident stroke risk, particularly cardioembolic and thrombotic stroke subtype. Further, we report that regular dental care utilization may lower this risk for stroke.

The decision-making chart provided may serve as a reference guide for dentists when making the decision to save or extract a compromised tooth.