Periodontitis is a chronic disease composed of a group of inflammatory conditions affecting the supporting structures of the dentition (Armitage, 1999
). Traditional periodontal diagnostic methods are limited to the evaluation of parameters that assess only periodontal destruction. Development of innovative diagnostic tests enabling active phases of periodontal disease to be detected and identifying individuals at risk for future disease occurrence is the focus of numerous clinical investigations.
As previously described by our group, analysis of our data identified putative biomarkers from saliva and anaerobic pathogens that were strongly related to disease status (Ramseier et al., 2009
). Among the salivary biomarkers, IL-1β, MMP-8, MMP-9, and OPG demonstrated the highest correlation with disease status. Further, the use of multiple time-points of two-month intervals of saliva biomarkers allows for an improved understanding of biomarker fluctuations over time.
During periodontal disease, host inflammatory cells are recruited, and inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, are released from fibroblasts, macrophages, connective tissue, and junctional epithelial cells. As a result, host-derived enzymes, such as MMP-8, MMP-9, and calprotectin, are released by PMNs and osteoclasts, leading to connective tissue and alveolar bone degradation.
Currently, studies have demonstrated the association of host-response salivary biomarkers and periodontal pathogens with periodontal disease (Herr et al., 2007
; Gursoy et al., 2009
; Ramseier et al., 2009
; Teles et al., 2010
. However, there is a gap in the literature regarding longitudinal studies in this area. To the best of our knowledge, this study is unique in that it provides a longitudinal analysis ofhost-response biomarkers and periodontal pathogens during the course of periodontal disease progression and recovery.
Analysis of data from a cross-sectional study demonstrated elevated concentrations of IL-1β and MMP-8 from whole saliva of participants with periodontal disease compared with healthy control individuals (Christodoulides et al., 2007
). Recently, Fine et al
. longitudinally evaluated PDP on children at risk for aggressive periodontitis and reported that IL-1β demonstrated a high specificity and sensitivity to predict alveolar bone loss (Fine et al., 2009
Regarding biofilm pathogens, analysis of our data revealed that periodontal pathogens, specifically the “red complex” pathogens (Socransky et al., 1998
), were able to predict PDP. Our findings are supported by a recent report demonstrating an association of periodontal pathogens, inflammatory biomarkers, and periodontal disease (Teles et al., 2010
). Results demonstrated a positive correlation among mean levels of IL-1β, IL-8, and MMP-8, and the proportions of periodontal pathogenic bacteria in individuals with periodontitis (Teles et al., 2010
Although serum biomarkers have been studied by several authors (Tonetti et al., 2007
; Renvert et al., 2009
), our study demonstrated that they did not appear to be good predictors of PDP. Interestingly, no significant changes on serum biomarkers after non-surgical periodontal treatment in pregnant women with periodontitis were shown (Michalowicz et al., 2009
). Furthermore, it has also been reported that analysis of serum biomarkers was inconsistent across individuals and largely not sustainable (Behle et al., 2009
Our results support the concept of combining clusters of salivary biomarkers and periodontal pathogens for prediction of future disease progression. The use of panels of host biomarkers and pathogens for disease diagnosis may hold promise (Ramseier et al., 2009
). Among the indicators for PDP, the elevated presence of “red complex” pathogens, F. nucleatum
, C. rectus
, and P. intermedia
, demonstrated the ability to predict PDP for 82% of individuals. Salivary biomarkers, specifically MMP-8, MMP-9, OPG, and IL-1β, present in low concentrations were able to predict stability for 78% of individuals who were clinically stable during disease monitoring. Interestingly, a selected group of individuals was classified as indeterminate regarding their clinical disease progression. A second cluster analysis within this specific group showed that those undergoing clinical disease progression also had high concentrations of periodontal pathogens. In addition, those who demonstrated stability tended to have low levels of salivary biomarkers, as did those initially considered stable. Offenbacher et al
. proposed a diagnostic periodontal disease classification scheme called the “biologic systems model” (Offenbacher et al., 2007
). This model is based on medical and dental findings and contributory biologic phenotypes. Underlying “biologic phenotypes” consider the biofilm and the host inflammatory and immune response to be at the biofilm-gingival interface. As a whole, the biologic system model is built on a framework of components, starting with the recognition of subject-level exposures interacting with genetic and epigenetic factors, and including cellular and molecular processes and inflammatory biomarkers to define different clinical phenotypes of periodontal disease detection and prediction. Limitations of this investigation with the sample evaluated include measurement error of PDP indices, the lack of body mass index assessments, serum cotinine levels, and analysis of the smoking contributions to biomarker assessments. Future investigations in larger populations may provide greater insights into these risk factors that may have confounded some of the results in the study sample evaluated.
In summary, this investigation supports the use of microbial and host-response biomarkers as indicators for periodontal disease progression. The use of saliva and biofilm biomarkers offers potential for the prediction of periodontal disease progression or stability to potentially determine periodontal signatures of disease in larger patient populations.