Research into biomarkers of periodontitis is driven by mainly three targets: to identify 'at risk' patients before periodontal tissue destruction occurs; to determine disease activity and progression; and to build up our understanding of this complex disease with the purpose of finding new therapeutic targets. Whilst blood and gingival crevicular fluid were previously the biological samples of choice, saliva has recently gained more attention as a readily accessible oral fluid which has a mediator profile similar to that of serum and gingival crevicular fluid. The aim of this paper was to give a comprehensive overview of salivary cytokines in periodontitis, highlighting extensively studied cytokines such as interleukin-1beta and interleukin-6, but also cytokines that have been the subject of only a few studies and which warrant further investigation. Cross-sectional and longitudinal studies of salivary cytokines, and the potential of cytokines as periodontitis biomarkers, are evaluated. Finally, a discussion of potential confounding factors, such as concurrent systemic diseases and smoking, is presented.

SummaryPorphyromonas gingivalis lipopolysaccharide (LPS) (strain W50) interacts with Toll-like receptor 2 (TLR-2) leading to cytokine expression and inflammation, and thereby plays a key role in the pathogenesis of periodontal disease. The aims of this study were to investigate gene expression of key regulatory mediators of innate immune responses in a human monocytic cell line (THP-1) to P. gingivalis LPS and to compare these results with those obtained using the TLR-4 ligand, Escherichia coli LPS. Custom-made Taqman low-density arrays were used for expression profiling of 45 different cytokinerelated genes. Both types of LPS highly up-regulated interleukin (IL)-1a and IL-1b, IL-18 receptor (IL-18R), IL-18R accessory protein and IL-1 family (IL-1F)9. Expression levels of IL-1F6, IL-1F7 and caspase-1 were unaltered by either LPS. Genes for tumour necrosis factor-a, IL-6, leukaemia inhibitory factor and IL-32 were also highly induced by both LPS. For a subset of genes, including CXC chemokine ligand 5 (CXCL5), expression was induced only by E. coli LPS or was up-regulated more highly by E. coli compared with P. gingivalis LPS in THP-1 monocytes. A similar expression pattern was also observed in dendritic cells. Analysis of signalling pathways which lead to CXCL5 expression indicated that the mechanisms underpinning the differential responses did not involve the recruitment of different adaptor proteins by TLR-2 and TLR-4, and therefore occur downstream of the receptoradaptor complex. We conclude that differences in signalling pathways activated by TLR-2 and TLR-4 ligands lead to differential innate immune responses which may be important in polymicrobial diseases such as periodontal disease.

Theoretically, the measurement of cytokines in saliva may have utility for studies of brain, behavior, and immunity in youth. Cytokines in saliva and serum were analyzed across three annual assessments in healthy adolescent girls (N = 114, 11-17 years at enrollment). Samples were assayed for GM-CSF, IFNγ, IL-1β, IL-2, IL-6, IL-8, IL-10, IL-12p70, TNFα, adiponectin, and cotinine. Results revealed: (1) cytokine levels, except IFNγ and IL-10, were detectable in saliva, and salivary levels, except IL-8 and IL-1β, were lower than serum levels; (2) salivary cytokine levels were lower in older girls and positively associated with adiponectin; (3) compared to serum levels, the correlations between salivary cytokines were higher, but salivary cytokines were less stable across years; and (4) except for IL-1β, there were no significant serum-saliva associations. Variation in basal salivary cytokine levels in healthy adolescent girls reflect compartmentalized activity of the oral mucosal immune system, rather than systemic cytokine activity.

Aims:To assess the impact of periodontal treatment on systemic inflammation in type 2 diabetes. Materials and Methods:Adults with type 2 diabetes (n = 83) and without diabetes (controls, n = 75) were recruited, and participants with periodontitis received periodontal treatment and 12 months' follow-up. Biomarkers for periodontal inflammation (gingival crevicular fluid interleukin-6, tumour necrosis factor-α, interleukin-1β, interferon-γ, matrix metalloproteinase-8, matrix metalloproteinase-9, adiponectin) and serum markers of inflammation and diabetes control (glycated haemoglobin, high sensitivity C-reactive protein, interleukin-6, tumour necrosis factor-α, interleukin-1β, interferon-γ, leptin, adiponectin) were measured. Structural equation modelling was used to evaluate periodontal treatment effects on oral and systemic inflammation.Results: Periodontal treatment resulted in significant improvements in clinical status and reductions in gingival crevicular fluid biomarkers from baseline to month 12.Structural equation modelling identified that, at baseline, individuals with diabetes and periodontitis had significantly higher systemic inflammation than non-diabetic controls with periodontitis (Δ = 0.20, p = .002), with no significant differences between groups for oral inflammation. There was a greater reduction in systemic inflammation following periodontal treatment in individuals with diabetes and periodontitis compared to those with periodontitis but not diabetes (Δ = −0.25, p = .01). Conclusions:Diabetes and periodontitis together appear to increase systemic inflammation, with evidence of reductions following periodontal treatment. K E Y W O R D Sinflammation, periodontitis, diabetes mellitus, type 2

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Aim To discover and validate differential protein biomarker expression in saliva and gingival crevicular fluid (GCF) to discriminate objectively between periodontal health and plaque‐induced periodontal disease states. Materials and Methods One‐hundred and ninety participants were recruited from two centres (Birmingham and Newcastle upon Tyne, UK) comprising healthy, gingivitis, periodontitis, and edentulous donors. Samples from the Birmingham cohort were analysed by quantitative mass spectrometry proteomics for biomarker discovery. Shortlisted candidate proteins were then verified by enzyme‐linked immunosorbent assay in both cohorts. Leave‐one‐out cross validation logistic regression analysis was used to identify the best performing biomarker panels. Results Ninety‐five proteins were identified in both GCF and saliva samples, and 15 candidate proteins were selected based upon differences discovered between the donor groups. The best performing panels to distinguish between: health or gingivitis and periodontitis contained matrix metalloproteinase‐9 (MMP9), S100A8, alpha‐1‐acid glycoprotein (A1AGP), pyruvate kinase, and age (area under the curve [AUC] 0.970); health and gingivitis contained MMP9, S100A8, A1AGP, and pyruvate kinase, but not age (AUC 0.768); and mild to moderate and advanced periodontitis contained MMP9, S100A8, A1AGP, pyruvate kinase, and age (AUC 0.789). Conclusions Biomarker panels containing four proteins with and without age as a further parameter can distinguish between periodontal health and disease states.

Periodontitis is an economically important disease which is highly prevalent worldwide. Current diagnostic approaches are time-consuming and require interpretation of multiple aspects of clinical and radiographic assessment. Chair-side monitoring of inflammatory mediators of periodontitis could provide immediate information about disease activity, which can inform patient management. We aimed to develop a novel prototype biosensor to measure salivary matrix metalloproteinase-8 (MMP-8) using specific antibodies and surface acoustic wave (SAW) technology. The analytical performance of the prototype biosensor was compared to standard enzyme-linked immunosorbent assay (ELISA) using unstimulated saliva samples obtained from patients with periodontitis before and after non-surgical treatment (N = 58), patients with gingivitis (N = 54) and periodontally healthy volunteers (N = 65). Receiver operator characteristic (ROC) analysis for distinguishing periodontitis from health revealed an almost identical performance between the sensor and ELISA assays (area under curve values (AUC): ELISA 0.93; SAW 0.89). Furthermore, both analytical approaches yielded readouts which distinguished between heath, gingivitis and periodontitis, correlated identically with clinical measures of periodontal disease and recorded similar post-treatment decreases in salivary MMP-8 in periodontitis. The assay time for our prototype device is 20 minutes. The prototype SAW biosensor is a novel and rapid method of monitoring periodontitis which delivers similar analytical performance to conventional laboratory assays.

This study addresses gaps in our understanding about the validity and utility of using salivary adiponectin to index serum adiponectin levels. Matched blood and saliva samples were collected on a single occasion from healthy adults (n= 99; age 18-36 years, 53% male). Serum and saliva was assayed for adiponectin and inflammatory cytokines (IL-1β, IL-6, IL-8, TNFα), and saliva was also assayed for markers of blood contamination (transferrin), total protein (salivary flow rate) and matrix metalloproteinase-8 (MMP-8). We examined the extent to which salivary adiponectin was associated with serum adiponectin, and the influence of potential confounders on the serum-saliva correlation, including age, sex, body mass index, and markers of inflammation, oral health, salivary blood contamination, and flow rate. Findings revealed a modest serum-saliva association for adiponectin, and strong positive associations between salivary adiponectin and salivary levels of inflammatory cytokines, MMP-8, transferrin, and total protein. By contrast, salivary adiponectin was not related to serum levels of inflammatory activity. The magnitude of the serum-saliva association was strengthened when controlling for total protein in saliva, blood leakage into oral fluid, salivary inflammatory cytokines, and MMP-8. The pattern of findings extends our understanding of salivary adiponectin and its potential use as an index of circulating adiponectin levels.