Application value of combination therapy of periodontal curettage and root planing on moderate-to-severe chronic periodontitis in patients with type 2 diabetes

Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is associated with periodontitis. Currently, there are few studies proposing predictive models for periodontitis in patients with T2DM.

AIM

To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models.

METHODS

In this a retrospective study, 300 patients with T2DM who were hospitalized at the First People's Hospital of Wenling from January 2022 to June 2022 were selected for inclusion, and their data were collected from hospital records. We used logistic regression to analyze factors associated with periodontitis in patients with T2DM, and random forest and logistic regression prediction models were established. The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve (AUC).

RESULTS

Of 300 patients with T2DM, 224 had periodontitis, with an incidence of 74.67%. Logistic regression analysis showed that age [odds ratio (OR) = 1.047, 95% confidence interval (CI): 1.017-1.078], teeth brushing frequency (OR = 4.303, 95%CI: 2.154-8.599), education level (OR = 0.528, 95%CI: 0.348-0.800), glycosylated hemoglobin (HbA1c) (OR = 2.545, 95%CI: 1.770-3.661), total cholesterol (TC) (OR = 2.872, 95%CI: 1.725-4.781), and triglyceride (TG) (OR = 3.306, 95%CI: 1.019-10.723) influenced the occurrence of periodontitis (P < 0.05). The random forest model showed that the most influential variable was HbA1c followed by age, TC, TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showed that in the training dataset, the AUC of the random forest model was higher than that of the logistic regression model (AUC = 1.000 vs AUC = 0.851; P < 0.05). In the validation dataset, there was no significant difference in AUC between the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915; P > 0.05).

CONCLUSION

Both random forest and logistic regression models have good predictive value and can accurately predict the risk of periodontitis in patients with T2DM.

The role of IL-31 and IL-34 in the diagnosis and treatment of chronic periodontitis

This study was devoted to demonstrating the role of IL-31 and IL-34 in the diagnosis and treatment of chronic periodontitis (CP). From the results, we found that the IL-31 and IL-34 levels were significantly increased in GCF and serum of CP patients compared with healthy controls or obese patients. Meanwhile, the area under the curve results further verified the diagnostic potential of IL-31 and IL-34 in terms of discriminating CP from obese patients at the GCF and serum levels. Finally, after 1 year of continuous treatment, we found that IL-31 and IL-34 levels were decreased in CP, suggesting their potential as biomarkers in CP treatment response. Monitoring GCF and serum levels of IL-31 and IL-34 contributed to CP detection and treatment response.

Changes of Oral Flora, Inflammatory Factors, and Immune Function Indicators in Patients with Chronic Periodontitis and Their Clinical Significance

This research focuses on investigating the changes of oral flora, inflammatory factors, and immune function indicators in patients with chronic periodontitis (CP) and their clinical significances. Clinical indices such as gingival index (GI) and sulcus bleeding index (SBI) of the study subjects were recorded. The levels of oral flora, inflammatory factors and T lymphocyte subsets in gingival crevicular fluid (GCF) of the study subjects were measured. To analyze the correlation between GI and gingival SBI and oral flora, inflammatory factors, and immune function indicators, Pearson correlation analysis was performed. Porphyromonas gingivalis, Streptococcus digestiveis, Prevotella intermedia, Veronococcus, tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), IL-8, CD3+, CD4+, and CD4+/CD8+ had a positive correlation with GI and SBI, while IL-10 and CD8+ were negatively correlated with GI and SBI. Oral flora, inflammatory factors and immune function indicators levels are largely elevated in patients with CP and they are correlated with CP clinical indicators.

Non-Surgical Periodontal Therapy Could Improve the Periodontal Inflammatory Status in Patients with Periodontitis and Chronic Hepatitis C

Non-surgical periodontal therapy (NSPT) is the first essential step for the management of any periodontitis patient. This study aims to evaluate the impact of NSPT on pro-inflammatory mediators’ regulation and on clinical parameters in periodontitis patients who suffer from chronic hepatitis C. At baseline, selected patients were clinically evaluated for their periodontal status. A subsequent quantitative assessment of C-reactive protein and pentraxin-3 in samples of gingival fluid was performed by Enzyme-Linked Immunosorbent Assay (ELISA). Afterwards, NSPT was performed. Three months after NSPT, the clinical and ELISA assessments were repeated. The results show an improvement of the clinical parameters in periodontitis patients at the three-month recall. In chronic hepatitis C patients with periodontitis, the gingival fluid levels of pro-inflammatory markers reduced significantly. The targeted markers also expressed significant correlations with the clinical parameters used for the assessment of periodontitis’ severity. The results suggest that, while chronic hepatitis C patients exhibited a more negative periodontal status at baseline as compared to non-hepatitis ones, NSPT is effective in decreasing the local periodontal inflammatory reaction and in proving the periodontal status of this type of patients. Given the limitation of the study, periodontal screening and NSPT should be included in the integrated therapeutical approach of chronic hepatitis C patients, for its impact on the local inflammatory response.