Immunological and Microbiological Profiling of Cumulative Risk Score for Periodontitis.

The cumulative risk score (CRS) is a mathematical salivary diagnostic model to define an individual's risk of having periodontitis. In order to further validate this salivary biomarker, we investigated how periodontal bacteria, lipopolysaccharide (LPS), and systemic and local host immune responses relate to CRS. Subgingival plaque, saliva, and serum samples collected from 445 individuals were used in the analyses. Plaque levels of 28 microbial species, especially those of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Porphyromonas endodontalis, Prevotella intermedia, and Tannerella forsythia, and serum and salivary levels of IgA and IgG against these five species were determined. Additionally, LPS activity was measured. High CRS associated strongly with all IgA/IgG antibody and LPS levels in saliva, whereas in serum the associations were not that obvious. In the final logistic regression model, the best predictors of high CRS were saliva IgA burden against the five species (OR 7.04, 95% CI 2.25-22.0), IgG burden (3.79, 1.78-8.08), LPS (2.19, 1.38-3.47), and the sum of 17 subgingival Gram-negative species (6.19, 2.10-18.3). CRS is strongly associated with microbial biomarker species of periodontitis and salivary humoral immune responses against them.

Salivary biomarkers in association with periodontal parameters and the periodontitis risk haplotype.

Genetic factors play a role in periodontitis. Here we examined whether the risk haplotype of MHC class III region BAT1-NFKBIL1-LTA and lymphotoxin-α polymorphisms associate with salivary biomarkers of periodontal disease. A total of 455 individuals with detailed clinical and radiographic periodontal health data were included in the study. A 610 K genotyping chip and a Sequenom platform were used in genotyping analyses. Phospholipid transfer protein activity, concentrations of lymphotoxin-α, IL-8 and myeloperoxidase, and a cumulative risk score (combining Porphyromonas gingivalis, IL-1ß and matrix metalloproteinase-8) were examined in saliva samples. Elevated IL-8 and myeloperoxidase concentrations and cumulative risk scores associated with advanced tooth loss, deepened periodontal pockets and signs of periodontal inflammation. In multiple logistic regression models adjusted for periodontal parameters and risk factors, myeloperoxidase concentration (odds ratio (OR); 1.37, P = 0.007) associated with increased odds for having the risk haplotype and lymphotoxin-α concentration with its genetic variants rs2857708, rs2009658 and rs2844482. In conclusion, salivary levels of IL-8, myeloperoxidase and cumulative risk scores associate with periodontal inflammation and tissue destruction, while those of myeloperoxidase and lymphotoxin-α associate with genetic factors as well.

Salivary Concentrations of Interleukin (IL)-1ß, IL-17A, and IL-23 Vary in Relation to Periodontal Status.

BACKGROUND: Interleukin (IL)-23-induced T helper (Th) 17 pathway is involved in the pathogenesis of periodontal disease. This study's aim is to determine levels of IL-1ß, IL-17A, IL-23, and lipopolysaccharide (LPS) in saliva, and to examine whether their salivary concentrations are associated with periodontal health status. METHODS: Saliva samples originated from 220 participants; 76 had generalized periodontitis (GP) and 65 had localized periodontitis (LP), whereas 79 without periodontitis were used as controls. Cytokine analyses were performed by a flow cytometry-based technique and LPS analyses from pellet by commercially optimized assay coupled with chromogenic substrate. RESULTS: Salivary concentrations of IL-17A and IL-23 were elevated significantly in patients with LP compared with controls and patients with GP. Salivary IL-1ß concentrations were significantly higher in patients with GP than in patients with LP, whereas no difference was found between LP and control groups. Significant correlation was found between concentrations of IL-17A and IL-23 or IL-1ß. LPS concentrations did not have significant associations with any of the tested ILs. CONCLUSION: Elevated salivary IL-1ß concentrations are related to GP, whereas distinct elevation and reduction profiles of IL-17A and IL-23 are detected in saliva of patients with LP and GP.

Associations Between Salivary Bone Metabolism Markers and Periodontal Breakdown.

BACKGROUND: A dual relationship between glycemic status and bone remodeling was suggested recently. The present study aimed to 1) analyze salivary levels of receptor activator for nuclear factor κ-B ligand (RANKL), osteoprotegerin, osteocalcin, and osteopontin as potential biomarkers of alveolar bone loss and 2) determine whether the glycemic status affects the relationship between bone remodeling markers and periodontal status. METHODS: Salivary levels of RANKL, osteoprotegerin, osteocalcin, osteopontin, and serum glycosylated hemoglobin A1c, insulin, and glucose were analyzed in 220 participants divided into four groups according to their periodontal health status: 1) 79 participants had at least 14 teeth with probing depth (PD) ≥4 mm (generalized periodontitis [GP]); 2) 65 participants had either two or seven teeth with PD ≥4 mm (two groups of localized periodontitis [LP1 and LP2, respectively]); and 3) 76 participants had no teeth with PD ≥4 mm (non-periodontitis control group). RESULTS: Salivary concentrations of RANKL, osteocalcin, and osteopontin were higher, and osteoprotegerin was lower in females than in males. Salivary osteoprotegerin concentrations were higher in the GP and LP2 groups than in the control group, whereas RANKL, osteocalcin, and osteopontin were not related with periodontal status. Salivary osteopontin correlated positively with serum and salivary insulin. The association observed between increased osteoprotegerin concentrations and periodontitis was lost after salivary insulin was included into the analyses as a confounding factor. CONCLUSIONS: Salivary concentrations of bone markers are either affected by glycemic status or detected at very low levels. These factors hinder their use as salivary biomarkers of periodontitis.

Pregnancy-induced gingivitis and OMICS in dentistry: in silico modeling and in vivo prospective validation of estradiol-modulated inflammatory biomarkers.

Pregnancy-associated gingivitis is a bacterial-induced inflammatory disease with a remarkably high prevalence ranging from 35% to 100% across studies. Yet little is known about the attendant mechanisms or diagnostic biomarkers that can help predict individual susceptibility for rational personalized medicine. We aimed to define inflammatory proteins in saliva, induced or inhibited by estradiol, as early diagnostic biomarkers or target proteins in relation to pregnancy-associated gingivitis. An in silico gene/protein interaction network model was developed by using the STITCH 3.1 with "experiments" and "databases" as input options and a confidence score of 0.700 (high confidence). Salivary estradiol, interleukin (IL)-1ß and -8, myeloperoxidase (MPO), matrix metalloproteinase (MMP)-2, -8, and -9, and tissue inhibitor of matrix metalloproteinase (TIMP)-1 levels from 30 women were measured prospectively three times during pregnancy and twice during postpartum. In silico analysis revealed that estradiol interacts with IL-1ß and -8 by an activation link when the "actions view" was consulted. In saliva, estradiol concentrations associated positively with TIMP-1 and negatively with MPO and MMP-8 concentrations. When the gingival bleeding on probing percentage (BOP%) was included in the model as an effect modifier, the only association, a negative one, was found between estradiol and MMP-8. Throughout gestation, estradiol modulates the inflammatory response by inhibiting neutrophilic enzymes, such as MMP-8. The interactions between salivary degradative enzymes and proinflammatory cytokines during pregnancy suggest promising ways to identify candidate biomarkers for pregnancy-associated gingivitis, and for personalized medicine in the field of dentistry. Finally, we call for greater investments in, and action for biomarker research in periodontology and dentistry that have surprisingly lagged behind in personalized medicine compared to other fields, such as cancer research.