Tumor necrosis factor alpha (TNF-α) is one of the early proinflammatory cytokines that plays a key role in periodontal tissue destruction [1]. Clinical studies have demonstrated that elevated TNF-α is a risk factor for the progression of periodontal diseases. TNF-α mediates periodontal tissue destruction via at least two different pathways. Firstly, it stimulates production of osteoclasts that cause alveolar bone resorption [2, 3]. Secondly, TNF-α promotes the body’s early immune response to periodontal pathogens and regulates synthesis of matrix metalloproteinases (MMP) capable of damaging connective tissue. Besides, it is reported that TNF-α levels are increased systemically in patients with obesity or metabolic syndrome [4]. Adipose tissue cells secrete TNF-α; therefore, accumulation of excess fat leads to chronic systemic inflammation [5, 6]. It has been shown that TNF-α levels also correlate with insulin resistance [7]. TNF-α is a paracrine mediator: its local activity is aimed at reducing sensitivity of adipocytes to insulin [8]. There is a reciprocal connection between periodontal disease and metabolic syndrome. The severity of systemic inflammation in patients with metabolic syndrome can affect local inflammation in the periodontium, while the products of periodontal inflammation can stimulate secretion of systemic cytokines. In this study we propose a prognostic model for predicting the risk of severe generalized chronic periodontitis based on TNF-α concentrations in the exudate from a periodontal pocket (PP).

METHODS

We examined 537 patients (243 females and 294 males; 45.25% vs. 54.75%, respectively) aged 35 to 65 years with clinically diagnosed generalized chronic periodontitis (GCP) and metabolic syndrome. The patients were distributed into 3 age groups: group 1 included patients aged 35–44 years, with the mean age of 41.7 ± 2.1; group 2 consisted of patients aged 45–54 years, with the mean age of 52.2 ± 1.2; group 3 comprised individuals aged 55–65 years, with the mean age of 63.4 ± 1.1. Our study included patients of both sexes aged 35 to 65 years, with clinically diagnosed GCP, comorbid metabolic syndrome and a body mass index ≥ 25 kg/m², who gave written informed consent to participate. The following exclusion criteria were applied: age under 35 years; hematologic disorders; diseases of the central nervous system, both congenital and acquired; malignancies (cancers, sarcomas); decompensated chronic conditions (myocardial infarction, systemic thromboembolism); pregnancy.

Exudate samples were collected onto filter paper strips introduced into the PP for 30 s. Then, the strips were transferred into Eppendorf tubes containing 1 ml of sterile normal saline and left there for 40 min. After that, the strips were taken out with tweezers, and the content of the Eppendorf tubes was analyzed. TNF-α levels were measured using ELISA kits by BIOSOURCE (Europe S. A.; Belgium); spectrophotometry was done by a microplate reader at 450 nm wavelength. Cytokine concentrations were determined from a standard curve and expressed as pg/ml.

The next step was to create a prognostic model for predicting the risk of developing severe GCP based on TNF-α concentrations. A primary data matrix was generated in Statistica.10 (StatSoft; USA). Model coefficients were calculated in the output spreadsheet and included into the mathematical expression. Then, a ROC curve was constructed and a cut-off point was determined. The cut-off point allows using the model for practical tasks: new data can be assigned to one of the 2 classes depending on their position relative to the cut-off point. Besides, we applied the ROC-curve analysis to assess the diagnostic efficacy of our model by calculating the AUC value (Area Under Curve) using a trapezoidal rule.

RESULTS

Based on the obtained TNF-α concentrations (tab. 1), a prognostic model was built for predicting the risk of developing severe GCP.
The mathematical expression below can be used to calculate the risk of severe periodontal tissue destruction based on the TNF-α concentrations in the PP. The measured TNF-α concentrations should be plugged into the following formula:

W = –3.2 + 1.2 • log₁₀ (Y),

where W is a risk of developing severe GCP calculated from the cytokine profile of the oral cavity and Y is a TNF-α concentration in the PP expressed as pg/ml.
fig. 1 is the graphic representation of the relationship between the risk of developing severe GCP and the TNF-α concentrations in the PP. The risk for severe GCP increases as TNF-α levels grow in the PP.
The risk W for developing severe GCP was calculated for each study participant from TNF-α concentrations in the PP. Then, the ROC analysis was conducted to determine a critical (cut-off) value for W (0.3), above which a high risk for severe GCP could be predicted with maximum specificity and sensitivity.
W ≥ 0.3 means that the risk for severe GCP is high; W < 0.3 means it is low. The diagnostic sensitivity of the method is 91.2%, whereas its specificity is 70.8%.

fig. 2 features a ROC curve for different values of the prognostic coefficient W. tab. 2 shows sensitivity and specificity of the method at which W = 0.3 had the highest sensitivity and specificity.
The AUC value of 0.862 ± 0.05 (z = 7.3; p < 0.001) and the confidence interval of 0.765–0.959 suggest that W has a high diagnostic significance in predicting severe GCP based on the cytokine profile of the oral cavity.
W was computed in Microsoft Exсel 2010; individual TNF-α concentrations were entered into the highlighted cell (fig. 3).

DISCUSSION

TNF-α plays a key role in the pathogenesis of periodontal diseases. When bacterial lipopolysaccharides permeate periodontal tissue, macrophages assisted by CD14- lymphocytes activate a number of innate and adaptive immunity mechanisms through specific receptors. An inadequately strong immune response leads to chronic inflammation and periodontal tissue destruction [9, 10]. Prostaglandin Е2, IL1β and TNF-α are key proinflammatory mediators that activate tissue metalloproteinases and thereby stimulate bone resorption by osteoclasts and induce damage to the periodontium [11]. A number of nonimmune periodontal cells, such as epithelial cells and fibroblasts, can recognize and respond to proinflammatory IL1β and TNF-α. Tissue metalloproteinases produced by neutrophils, macrophages, fibroblasts, and osteoclasts promote proteolysis of collagen, gelatin and elastin, destroying the connective tissue components of tooth-supporting structures. Among the members of the TNF superfamily are osteotropic factors, such as the receptor activator of NF-kB ligand (RANKL) and RANK themselves that are synthesized by osteoclasts and promote bone resorption [12]. The binding of the RANK ligand to the RANK receptor is accompanied by a fusion of a few precursor cells into a mature multinucleated osteoclast that immediately starts to destroy bone tissue (fig. 4).
In light of this, the study of TNF-α levels in the PP of patients opens new possibilities for predicting the severity of GCP.

CONCLUSIONS

We have found that elevated TNF-α concentrations in the PP correlate with the severity of GCP in patients with metabolic syndrome: higher TNF-α levels are associated with a more severe course of the disease. The proposed prognostic model based on the TNF-α concentrations in the PP is a promising and informative noninvasive method that can be used to predict the progression of the disease. The advantages of our model include low costs, availability, fast results, and the simplicity of use, which is crucial for routine dental practice.