BackgroundPeriodontal disease (PD) is characterized by inflammatory tissue destruction in tooth supporting apparatus. Many studies indicate that the underlying pathogenesis is in concordance with rheumatoid arthritis (RA) sharing immune-inflammatory events affect both diseases. The aim of this study was to investigate serum cytokines, chemokines, growth factors, enzymes and costimulatory proteins in association with periodontal conditions in PD and RA subjects.Materials & methodsPeriodontal examination was performed in RA (n = 38), PD (n = 38) and healthy subjects (n = 14). Bleeding on probing (BOP) and probing pocket depth (PPD) were measured. Marginal bone loss (MBL) for premolars and molars was measured on digital panoramic radiographs. PD was defined as present if the PPD was ≥5mm in ≥ 3 different sites. Serum samples were collected from all subjects. A multiplex proximity extension assay (PEA) was used to analyze the samples for simultaneous measurement of 92 cytokines. Cytokines with ≥ 60% quantitative results were included.ResultsA significant positive correlation was seen for ST1A1, FGF-19 and NT-3 whereas EN-RAGE, DNER, CX3CL1 and TWEAK associated inversely with BOP, PPD≥ 5mm and MBL but positively with number of teeth. Several CD markers (CD244, CD40, CDCP1, LIF-R, IL-10RA, CD5 and CD6) were found to be associated with BOP, shallow and deep pockets, MBL and number of teeth, either directly or inversely. Most chemokines (CCL8, CX3CL1, CXCL10, CXCL11, CCL11, CCL4, CCL20, CXCL5, CXCL6, and CCL23) were positively associated with number of teeth and some inversely related to MBL (CCL8, CXCL10). Proteins with enzymatic activity (ST1A1, HGF and CASP-8) were directly related to the severity of periodontal conditions and inversely related to number of teeth. Aside from FGF-19, other growth factors were also directly associated with MBL (HGF), number of teeth (VEGF-A, LAP TGF-beta-1) and, inversely to, shallow pockets (LAP TGF-beta-1, TGFA and Beta-NGF). Out of 33 cytokines, 32 associated inversely with shallow pockets, whereas only CD40 associated positively. Associations between cytokines and periodontal parameters in the RA group were comparatively less. Statistical analyses were adjusted for multivariate effects using the Benjamini–Hochberg false discovery rate method.ConclusionSystemic inflammatory burden, via known and novel markers, is associated with periodontal conditions in PD and RA subjects. Shallow pockets are not associated with a higher inflammatory state.

No abstract

Exercise can increase plasma inflammatory cytokine concentrations in humans, but tissue responses are not well studied. We examined plasma concentrations and tissue expression of TNFalpha, IL-1beta, and IL-6 following treadmill running in mice. C57B1/6 mice were randomly assigned to: non-exercise control (CON), sacrifice at 0 or 1.5 h after 60 min running (MOD0, MOD 1.5), sacrifice at 0, 1.5, or 3 h after fatiguing running (approximately 3 h) (EX0, EX1.5, EX3), or lipopolysaccharide (25 microg) with no exercise (LPS). Lung, liver, muscle, and brain mRNA expression was analyzed (n = 4-6/group) using reverse transcriptase-rapid polymerase chain reaction (RT-RPCR). Plasma cytokine concentrations were determined (n =4-10/group) by ELISA. Plasma IL-6 was higher in EX1.5, and lung TNFalpha mRNA was higher in EX1.5 and EX3 compared to CON (P < 0.05). No significant increases in plasma cytokine concentrations or tissue cytokine expression were found in other EX groups. LPS significantly increased these cytokine measures in tissues and plasma, with the exception of plasma IL-1beta which was undetectable. The source of the plasma IL-6 following exercise does not appear to be lung, liver, muscle, or brain tissue, and remains to be determined. These data also suggest that tissue level cytokine expression may not necessarily lead to increased plasma cytokine concentrations.

BackgroundPeriodontal disease (PD) and rheumatoid arthritis (RA) are bone pathologies mediated through immuno-inflammatory mechanisms. The aim of this study was to investigate the serum markers osteopontin (OPN), tumor necrosis factor receptors 1 (TNFR1) and 2 (TNFR2) receptor activator of nuclear factor‐kappa B ligand (RANKL) and RANKL/ osteoprotegerin (OPG) ratio and compare them in PD and RA groups.Materials & methodsRA (with PD = 19 and without PD = 19), PD (n = 38) and 14 healthy subjects underwent bleeding on probing (BOP) and probing pocket depth (PPD) measurement. PD was defined as PPD measuring ≥5mm registered in ≥3 sites. Marginal bone loss (MBL) for premolars and molars was measured on digital panoramic radiographs. Serum samples were collected from all subjects. OPN, TNFR1, TNFR2 and RANKL were measured by enzyme-linked immunosorbent assays (ELISAs). OPG was measured as part of a multiplex proximity extension assay (PEA).ResultsOPN, TNFR1, TNFR2 and RANKL serum levels were the highest in the RA group with PD, while the RA group without PD were comparable to PD subjects only. The RANKL/OPG ratios were comparable between PD group and both RA groups with (p = 0.051) and without PD (p = 0.37). Serum RANKL levels were associated with MBL (p = 0.008) and PPD ≥ 5mm (p = 0.01).ConclusionPeripheral osteoclastogenesis is a feature of periodontal disease with systemic levels of osteoclastogenic markers comparable to the effects observed in rheumatoid arthritis.

Objectives: Periodontal disease (PD) and rheumatoid arthritis (RA) are kno­wn chronic conditions with sustained inflammation leading to osteolysis. Cardiovascular diseases (CVD) are frequent comorbidities that may arise from sustained inflammation associated with both PD and RA. In order to determine CVD risk, alterations at the molecular level need to be identified. The objective of this study, therefore, was to assess the relationship of CVD associated biomarkers in RA patients and how it is influenced by PD. Methods: The study consisted of patient (26 RA with PD, 21 RA without PD, 51 patients with PD only) and systemically and periodontally healthy control (n = 20) groups. Periodontal parameters bleeding on probing, probing pocket depth, and marginal bone loss were determined to characterize the patient groups. Proteomic analysis of 92 CVD-related protein biomarkers was performed using a multiplex proximity extension assay. Biomarkers were clustered using the search tool for retrieval of interacting genes (STRING) to determine protein–protein interaction (PPI) networks. Results: RA patients with PD had higher detection levels for 47% of the measured markers (ANGPT1, BOC, CCL17, CCL3, CD4, CD84, CTRC, FGF-21, FGF-23, GLO1, HAOX1, HB-EGF, hOSCAR, HSP 27, IL16, IL-17D, IL18, IL-27, IL6, LEP, LPL, MERTK, MMP12, MMP7, NEMO, PAPPA, PAR-1, PARP-1, PD-L2, PGF, PIgR, PRELP, RAGE, SCF, SLAMF7, SRC, THBS2, THPO, TNFRSF13B, TRAIL-R2, VEGFD, VSIG2, and XCL1) as compared to RA without PD. Furthermore, a strong biological network was identified amongst these proteins (clustering coefficient = 0.52, PPI enrichment p-value < 0.0001). Coefficients for protein clusters involved in CVD (0.59), metabolic (0.53), and skeletal (0.51) diseases were strongest in the PD group. Conclusion: Periodontal disease augments CVD-related biomarkers in RA through shared pathological clusters, concurrently enhancing metabolic and skeletal disease protein interactions, independent of autoimmune status.