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

Abstract

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.

Keywords: Gingival disease; Logistic regression; Periodontitis; Prediction model; Random forest model; Type 2 diabetes mellitus.

Figure 1

Flow diagram.

Figure 2

Importance of variables in the random forest model. HbA1c: Glycosylated hemoglobin; TC: Total cholesterol; TG: Triglyceride.

Figure 3

Receiver operating characteristic curves of the random forest (black) and logistic regression (blue) models. A: For the training set; B: For the validation set. AUC: Area under curve.