Effect of Non-surgical Treatment of Periodontitis Patients Focusing on Inflammatory Mediators in Gingival Crevicular Fluid

Periodontitis is initiated by a dysbiotic microbiota that colonizes the tooth surface in the periodontal pocket, inducing chronic inflammation in the periodontal tissues and subsequently leading to the destruction and loss of tooth-supporting tissues. Severe periodontitis affects a substantial proportion of the global population and can lead to tooth loss. The host's immune responses in the periodontal tissues are responsible for the cascade of events that lead to bone loss. In periodontitis-affected gingival tissue, infiltrating immune cells and resident gingival cells produce cytokines and matrix-degrading enzymes to eliminate periodontopathogens. Gingival crevicular fluid (GCF) is a site-specific exudate that in inflamed gingiva serves as a reflection of the inflammatory process that takes place there, and inflammatory cytokines such as IL-6, IFN-γ, IL-β in GCF, have been shown to correlate with periodontitis severity. Additionally, GCF, is a non-invasive and easily collected fluid to analyze. Chemokines are a specialized subgroup of cytokines that primarily direct the movement of immune cells to sites of inflammation via chemotaxis. The homing of neutrophils is a highly selective process in which the expression of specific neutrophil chemoattractant, such as CXCL1, CXCL2 and CXCL8, plays a significant role in periodontitis pathogenesis. Moreover, macrophages are reported to have a spectrum of activation, ranging from pro-inflammatory to anti-inflammatory, in periodontal disease. Macrophage migration inhibitory factor (MIF) plays a crucial role in maintaining macrophage homeostasis. It is reported to have diverse functions, with both protective and detrimental effects of inflammatory process, bone metabolism, angiogenesis, and apoptosis. Vascular endothelial growth factor-C (VEGF-C) is an important family member of the vascular endothelial growth factor family, accelerating the growth of vascular and lymphatic vessels. Furthermore, the growth (lymphangiogenesis) and remodeling of lymphatic vessels in pathological conditions are influenced by chemokines and their receptors expressed by lymphatic endothelial cells (LECs) within and around the affected tissue. Lymphangiogenesis has been linked to periodontal inflammation in mice. In humans, CCL21, a ligand important for dendritic cell migration, is downregulated in lymphatics from patients with periodontitis. VEGF-C level is linked to poor prognosis in cancer, and studies have reported that the expression of MIF and VEGF-C are strongly correlated. To the best of our knowledge, VEGF-C expression in human GCF has never been investigated. In patients who receive non-surgical periodontal therapy (NSPT), the goal of the treatment is to control inflammation and promote healing of the periodontal tissue by re-establishing normal microbiota. In smokers, a hampered healing response after NSPT was reported previously . Smoking appears to modulate microbial composition and promote colonization of key periodontal pathogens. Emerging evidence suggests that certain cytokines and chemokines in GCF serve as promising biomarkers for determining the pathogenesis, prognosis of periodontitis, and response to NSPT. In this study, investigators analyzed the expression of chemokines/cytokines, and VEGF-C in GCF from smokers and non-smokers with severe periodontitis within deep pockets before and during NSPT. At baseline, investigators also compared the levels of these markers between shallow and deep periodontal pockets in smokers and non-smokers.

Material and methods Study population: Individuals referred to the specialist clinic at Oral Health Centre of Expertise, Western Norway for periodontal treatment, were invited to participate in the present study. Inclusion criteria were subjects diagnosed with stage III and IV periodontitis according to the EFP/AAP classification system. All eligible participants (n=30, aged >16-year) were asked about their smoking history and were identified as smokers if they were smokers for the last 5 years. Subjects were excluded if they reported use of antibiotics within the last 6 months, pregnancy, lactation, chronic high-dose steroid therapy, radiation, or immunosuppressive therapy. They were not excluded if they reported well-regulated diabetes. The present study was approved by the Regional Committee for Medical Research Ethics in West Norway (REK), 2017/1650/REK Nord. Informed consents were obtained from all patients before inclusion in the study.Clinical examination of sites included in the analysisProbing pocket depth (PPD), clinical attachment loss (CAL), and bleeding on probing (BOP) were measured from four sites per tooth except third molars, and diagnosis was carried out by one periodontist at the first screening visit. Information regarding smoking habits and diabetes was collected. Sampling of gingival crevicular fluid (GCF) was determined after the screening and comprised one shallow (˂4mm) and one deep pocket (≥5 mm) for each patient. Only teeth with deep pockets, estimated by the periodontist to persist after the three timepoints of treatment, were selected for sampling prior to NSPT at baseline (T0). GCF was collected from shallow and deep pockets at T0, and from deep pockets at T1 and T2 prior to NSPT.

The same periodontist examined and treated all patients with NSPT and collected the GCF samples. GCF was collected at each timepoint following supragingival plaque removal with curette, and teeth were isolated with cotton rolls to prevent contamination with saliva. GCF samples were collected using filter paper strips (PerioPaper, OraFlow Inc., Hewlett, New York, USA) were inserted subgingivally until there was gentle resistance, for 30s. Strips were immediately transferred to Eppendorf tubes and were stored at -80 °C until further analysis. The tubes with strips were weighed using Mettler Toledo AT261 (DeltaRange®, Switzerland readability: 0.01mg) before and after sampling to obtain GCF volume, by using the formula volume equal mass (one µl equal to one mg). The samples from deep and shallow pockets were kept in separate Eppendorf tubes. Tris-HCL buffer (200 µl) with a final concentration 12mM (pH 7.6) was added to each tube for protein elution. Total protein concentration for each sample was measured using the NanoDrop Lite Plus Micro-UV Spectrophotometer (Thermo Fisher Scientific, Wilgmington, DE, USA) following the supplier instruction. Bead-Based Immunoassay Using commercially available bead-based immunoassay Bio-Rad (catalogue number # 171AK99MR2, BioRad, Hercules, CA, USA), levels of 40 chemokines/cytokines were determined in pg/ml. In addition, VEGF-C was analyzed using Milliplex panel (catalogue number # HAGP1MAG-12K, Merck KGaA, Darmstadt, Germany). Manufacturer's protocols were followed for all multiplex immunoassays' panels. Before each measurement, calibration and validation of the system were done to ensure accuracy and reliability of the bead readings. The standard curve for each analyte was analyzed using five parametric logistic regression curves using Bio-Plex manager software (version 6.0, BioRad). Outliers were removed by the system.

Statistical analysisAll analyses were conducted in R v4.5.2 (28). Plots were created using the ggplot2 package in R (29). Descriptive statistics including mean, standard error (SE), median, and percentages, were used to summarize the demographic characteristics of the study participants. Baseline (T0) comparisons of deep vs shallow pockets:To evaluate the effects of pocket depth and smoking status, we fitted mixed-effects models using the lmer() function from the lme4 package in R (30) P-values for fixed effects were obtained with the lmerTest package (31). A mixed effects approach was chosen to account for dependency introduced by paired sampling within individuals (one shallow and one deep periodontal pocket per patient), with individual random intercepts included as the random effect. The model included pocket depth (shallow vs deep), smoking status, and their interaction as predictors; the interaction term was removed when clearly non-significant (P≥0.1). Due to sample size constraints, sex and age were not included as covariates in the primary models, although their potential influence was explored in separate analyses. Longitudinal analysis; effects of repeated treatment To evaluate changes in PPD, CAL, chemokine expression, and GCF volume following NSPT in deep pockets, we analyzed repeated measurements collected at three time points: baseline (T0), follow-up 1 (T1), and follow-up 2 (T2). Each measurement was obtained from the same site within each patient, yielding three observations per individual. Accordingly, we applied the same type of mixed-effects models described above, replacing pocket depth with repeated NSPT as an ordered categorical predictor (levels: T0, T1, T2). As an alternative of NSPT effects, we calculated the proportion of individuals who showed improvement from T0 to T2 for each inflammatory marker. Individuals were classified as successful if they exhibited a reduction after NSPT. For each marker, we tested the proportion of successes using a binomial test, assuming a null hypothesis of p=0.5 (i.e., a random distribution of successes and failures). Analyses were performed separately for smokers and non-smokers, as well as for the combined sample.Given the distributional characteristics of VEGF-C measurements - specifically, the high proportion of zero values - the data were dichotomized into two categories: no detection (0) and detection (1). This binary outcome was analyzed using a generalized linear mixed-effects model (GLMM) with a binomial error distribution, implemented via the glmr() function in lme4 package for R . The effect of smoking status, sex, or age was not assessed for VEGF-C.