Xu HM, Shen XJ, Liu J. Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus. World J Diabetes 2023; 14(12): 1793-1802 [PMID: 38222787 DOI: 10.4239/wjd.v14.i12.1793]
Corresponding Author of This Article
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
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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
ARTICLE HIGHLIGHTS
Research background
Periodontitis is a complication of type 2 diabetes mellitus (T2DM). With lifestyle changes and the acceleration of the aging process, the prevalence of periodontitis and diabetes is increasing annually.
Research motivation
Periodontitis can lead to tooth loosening and loss, decline in oral function, and reduced living standards.
Research objectives
This study aimed to explore and analyze the factors influencing periodontal disease in patients with T2DM, and construct prediction models for the risk of periodontal disease in patients with T2DM.
Research methods
We conducted a retrospective study in patients with T2DM hospitalized in our hospital to analyze the factors influencing periodontitis in patients with T2DM. We used random forest and logistic regression prediction models to assess the risk of specific factors in periodontitis.
Research results
This study found that the factors influencing periodontal disease in patients with T2DM were age, brushing frequency, education level, and glycosylated hemoglobin, total cholesterol, and triglyceride levels. The prediction models both had good predictive value.
Research conclusions
In this study, a random forest model was established and compared to a logistic regression model. The results showed that the random forest and logistic regression models had good predictive value and can accurately predict the risk of periodontitis in patients with T2DM.
Research perspectives
In the future, we will expand the sample size, combine samples from multiple regions, and include additional influencing factors to build a more complete prediction model.
