Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Dec 15, 2023; 14(12): 1793-1802
Published online Dec 15, 2023. doi: 10.4239/wjd.v14.i12.1793
Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus
Hong-Miao Xu, Xuan-Jiang Shen, Jia Liu
Hong-Miao Xu, Department of Stomatology, The First People’s Hospital of Wenling, Taizhou 317500, Zhejiang Province, China
Xuan-Jiang Shen, Jia Liu, Department of Stomatology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou 318000, Zhejiang Province, China
Author contributions: Xu HM designed the study and collected the data; Shen XJ analyzed the data; Liu J provided administrative support; and all authors have approved the manuscript.
Institutional review board statement: The study was reviewed and approved by the First People’s Hospital of Wenling (approval No. KY-2023-2035-01).
Informed consent statement: Informed consent was waived due to the retrospective nature of this study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets used in this study can be obtained from the corresponding author upon reasonable request.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jia Liu, MM, Attending Doctor, Department of Stomatology, Taizhou Central Hospital (Taizhou University Hospital), No. 999 Donghai Avenue, Jiaojiang District, Taizhou 318000, Zhejiang Province, China. liujia_861217@163.com
Received: August 8, 2023
Peer-review started: August 8, 2023
First decision: September 29, 2023
Revised: October 20, 2023
Accepted: November 27, 2023
Article in press: November 27, 2023
Published online: December 15, 2023
Processing time: 128 Days and 7.1 Hours
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: Type 2 diabetes mellitus; Periodontitis; Logistic regression; Prediction model; Random forest model; Gingival disease

Core Tip: With the rapid increase in the number of patients with type 2 diabetes mellitus (T2DM), the number of cases complicated by periodontitis has also increased. Without timely intervention, periodontitis can lead to tooth loosening and loss, and a decline in oral function, reducing patient quality of life. We retrospectively analyzed the data of 300 patients with T2DM to determine the factors influencing periodontitis. Random forest and logistic regression models were constructed to provide a theoretical basis for predicting periodontitis in patients with T2DM.