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Objective: Regulators of peri-implant bone loss in patients with diabetes appear to involve multiple risk factors that have not been clearly elucidated. This study was conducted to explore putative local etiologic factors on implant bone loss in relation to type 2 diabetes mellitus, including clinical, microbial, salivary biomarker, and psychosocial factors., Materials and Methods: Thirty-two subjects (divided into type 2 diabetes mellitus and non-diabetic controls), having at least one functional implant and six teeth, were enrolled in a 1-year longitudinal investigation. Analyses of clinical measurements and standardized intra-oral radiographs, saliva and serum biomarkers (via protein arrays for 20 selected markers), and plaque biofilm (via qPCR for eight periodontal pathogens) were performed at baseline and 1 year. In addition, the subjects were asked to respond to questionnaires to assess behavioral and psychosocial variables., Results: There was a significant increase from baseline to 1 year in the probing depth of implants in the diabetes group (1.95 mm to 2.35 mm, P = 0.015). The average radiographic bone loss during the study period marginally increased at dental implants compared to natural teeth over the study period (0.08 mm vs. 0.05 mm; P = 0.043). The control group harbored higher levels of Treponema denticola at their teeth at baseline (P = 0.046), and the levels of the pathogen increased significantly over time around the implants of the same group (P = 0.003). Salivary osteoprotegerin (OPG) levels were higher in the diabetes group than the control group at baseline only; in addition, the salivary levels of IL-4, IL-10, and OPG associated with host defense were significantly reduced in the diabetes group (P = 0.010, P = 0.019, and P = 0.024), while controls showed an increase in the salivary OPG levels (P = 0.005). For psychosocial factors, there were not many significant changes over the observation period, except for some findings related to coping behaviors at baseline., Conclusions: The study suggests that the clinical, microbiological, salivary biomarker, and psychosocial profiles of dental implant patients with type 2 diabetes who are under good metabolic control and regular maintenance care are very similar to those of non-diabetic individuals. Future studies are warranted to validate the findings in longer-term and larger clinical trials (ClinicalTrials.gov # NCT00933491)., (© 2013 John Wiley & Sons A/S. Published by Blackwell Publishing Ltd.)

Background: The aim of this human investigation is to explore the relationship of gingivitis with salivary biomarkers, periodontal pathogens, and interleukin (IL)-1 polymorphism after a transient inflammatory burden., Methods: Thirty healthy human participants were randomized by IL-1 genotype status to control for potential influences of this particular single nucleotide polymorphism on the inflammatory profile. Oral hygiene practices ceased for 21 days to induce gingivitis (induction), after which home care was reinstated until 35 days (resolution). Clinical parameters included plaque (PI) and gingival (GI) indices and papillary bleeding score (PBS). Levels and proportions of 40 subgingival bacteria were determined using checkerboard DNA-DNA hybridization. Saliva was analyzed using a multiplex protein array for 30 biomarkers associated with host defense, inflammation, tissue destruction, and angiogenesis., Results: Mean PI, GI, and PBS values were significantly increased during induction and decreased during resolution as measured at 35 days (P <0.01), although no differences were observed between IL-1 groups. Participants were stratified as either "high" or "low" responders based on inflammatory response (high: GI >1.5; low: GI ≤1.5). Baseline levels of salivary IL-6 and IL-8 demonstrated the highest ability to discriminate between high and low responders (area under the curve [AUC] of 0.81 and 0.72, respectively). Salivary biomarkers, matrix metalloproteinases (MMPs), and bacterial biofilm were combined to generate receiver operating characteristic curves. High levels of IL-6 and MMP-1 at baseline demonstrated the strongest ability to predict high responders (AUC of 0.89; odds ratio of 17.0; 95% confidence interval, 1.7 to 171.7)., Conclusion: In this proof-of-concept investigation, we identified specific biomarker and microbial signatures that are associated with gingival inflammation (ClinicalTrials.gov number NCT00980525).

Periodontal disease is a bacteria-induced chronic inflammatory disease affecting the soft and hard supporting structures encompassing the teeth. When left untreated, the ultimate outcome is alveolar bone loss and exfoliation of the involved teeth. Traditional periodontal diagnostic methods include assessment of clinical parameters and radiographs. Though efficient, these conventional techniques are inherently limited in that only a historical perspective, not current appraisal, of disease status can be determined. Advances in the use of oral fluids as possible biological samples for objective measures of current disease state, treatment monitoring, and prognostic indicators have boosted saliva and other oral-based fluids to the forefront of technology. Oral fluids contain locally and systemically derived mediators of periodontal disease, including microbial, host-response, and bone-specific resorptive markers. Although most biomarkers in oral fluids represent inflammatory mediators, several specific collagen degradation and bone turnover-related molecules have emerged as possible measures of periodontal disease activity. Pyridinoline cross-linked carboxyterminal telopeptide (ICTP), for example, has been highly correlated with clinical features of the disease and decreases in response to intervention therapies, and has been shown to possess predictive properties for possible future disease activity. One foreseeable benefit of an oral fluid-based periodontal diagnostic would be identification of highly susceptible individuals prior to overt disease. Timely detection and diagnosis of disease may significantly affect the clinical management of periodontal patients by offering earlier, less invasive, and more cost-effective treatment therapies.

Aim Assess the ability of a panel of gingival crevicular fluid ( GCF) biomarkers as predictors of periodontal disease progression ( PDP). Materials and methods In this study, 100 individuals participated in a 12-month longitudinal investigation and were categorized into four groups according to their periodontal status. GCF, clinical parameters and saliva were collected bi-monthly. Subgingival 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 to 12 months. GCF samples were analysed by ELISA for MMP-8, MMP-9, Osteoprotegerin, C-reactive Protein and IL-1 β. 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). [ABSTRACT FROM AUTHOR]

Background: Periodontitis is the major cause of tooth loss in adults and is linked to systemic illnesses, such as cardiovascular disease and stroke. The development of rapid point-of-care (POC) chairside diagnostics has the potential for the early detection of periodontal infection and progression to identify incipient disease and reduce health care costs. However, validation of effective diagnostics requires the identification and verification of biomarkers correlated with disease progression. This clinical study sought to determine the ability of putative host- and microbially derived biomarkers to identify periodontal disease status from whole saliva and plaque biofilm. Methods: One hundred human subjects were equally recruited into a healthy/gingivitis group or a periodontitis population. Whole saliva was collected from all subjects and analyzed using antibody arrays to measure the levels of multiple proinflammatory cytokines and bone resorptive/turnover markers. Results: Salivary biomarker data were correlated to comprehensive clinical, radiographic, and microbial plaque biofilm levels measured by quantitative polymerase chain reaction (qPCR) for the generation of models for periodontal disease identification. Significantly elevated levels of matrix metalloproteinase (MMP)-8 and -9 were found in subjects with advanced periodontitis with Random Forest importance scores of 7.1 and 5.1, respectively. The generation of receiver operating characteristic curves demonstrated that permutations of salivary biomarkers and pathogen biofilm values augmented the prediction of disease category. Multiple combinations of salivary biomarkers (especially MMP-8 and -9 and osteoprotegerin) combined with red-complex anaerobic periodontal pathogens (such as Porphyromonas gingivalis or Treponema denticola) provided highly accurate predictions of periodontal disease category. Elevated salivary MMP-8 and T denticola biofilm levels displayed robust combinatorial characteristics in predicting periodontal disease severity (area under the curve = 0.88; odds ratio = 24.6; 95% confidence interval: 5.2 to 116.5). Conclusions; Using qPCR and sensitive immunoassays, we identified host- and bacterially derived biomarkers correlated with periodontal disease. This approach offers significant potential for the discovery of biomarker signatures useful in the development of rapid POC chairside diagnostics for oral and systemic diseases. Studies are ongoing to apply this approach to the longitudinal predictions of disease activity. [ABSTRACT FROM AUTHOR]