Self-reported periodontitis: A multiethnic community-based validation study

TY - JOUR

T1 - Self-reported periodontitis

T2 - A multiethnic community-based validation study

AU - Goh, Charlene E.

AU - CHEW, Jacob Ren Jie

AU - Lai, Clement

AU - Seah, Francine

AU - Lim, Maybritte

AU - Febriana, Eveline

AU - Lee, Michelle H.

AU - Feng, Jacqueline Kuo Ting

AU - Tan, Kai Soo

AU - Fu, Jiahui

AU - Yip, Julie K.

AU - Toh, Sue Anne

AU - Preshaw, Philip M.

N1 - Publisher Copyright: © 2025

PY - 2026/2

Y1 - 2026/2

N2 - Objective: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation efforts exist in Asia, where periodontitis is highly prevalent. Moreover, the diagnostic performance of individual self-reported measures and the relevant sociodemographic factors differs across populations. This study aims to clinically validate self-reported periodontal measures in a multiethnic community-based population and to develop several predictive models that combine sociodemographic characteristics and self-reported periodontal measures. Methods: We analysed cross-sectional data from 426 diabetes-free participants in Singapore who completed the Centers for Disease Control and Prevention/American Academy of Periodontology (CDC-AAP) self-reported periodontal questionnaire and underwent full-mouth periodontal examinations. Periodontitis status was defined using the 2012 CDC-AAP case definitions. Multivariable logistic regressions and area under the curve (AUC) analyses evaluated predictive models for periodontitis. Additionally, we performed an exploratory analysis, training and testing five machine learning models to predict periodontitis. Results: Participants had a mean age of 48.9±9.9 years, 55.6% were female, and 16.4%, 42.5%, and 18.1% had mild, moderate, and severe periodontitis, respectively. A multivariable model incorporating one self-reported question (loose teeth), age, and ethnicity demonstrated good discrimination for severe periodontitis (AUC=0.76; sensitivity/specificity=0.60/0.80). While the machine learning models achieved similar AUC (0.67-0.76), they tended to be highly specific (0.99-0.75) but had much lower sensitivity (0.12-0.63). Conclusion: Selected self-reported periodontitis questions were useful for detecting severe periodontitis in this population. While machine learning models integrated sociodemographic factors with all 8 self-reported questions, larger and more diverse datasets are needed to enhance model robustness and generalisability across international populations. Clinical significance: Self-reported periodontal measures validated in a multiethnic population demonstrated good predictive value for severe periodontitis when combining self-report of loose teeth with demographic variables age, gender, and ethnicity. The application of machine learning models further enhanced diagnostic performance, highlighting potential for scalable, data-driven periodontal screening and surveillance across diverse populations.

AB - Objective: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation efforts exist in Asia, where periodontitis is highly prevalent. Moreover, the diagnostic performance of individual self-reported measures and the relevant sociodemographic factors differs across populations. This study aims to clinically validate self-reported periodontal measures in a multiethnic community-based population and to develop several predictive models that combine sociodemographic characteristics and self-reported periodontal measures. Methods: We analysed cross-sectional data from 426 diabetes-free participants in Singapore who completed the Centers for Disease Control and Prevention/American Academy of Periodontology (CDC-AAP) self-reported periodontal questionnaire and underwent full-mouth periodontal examinations. Periodontitis status was defined using the 2012 CDC-AAP case definitions. Multivariable logistic regressions and area under the curve (AUC) analyses evaluated predictive models for periodontitis. Additionally, we performed an exploratory analysis, training and testing five machine learning models to predict periodontitis. Results: Participants had a mean age of 48.9±9.9 years, 55.6% were female, and 16.4%, 42.5%, and 18.1% had mild, moderate, and severe periodontitis, respectively. A multivariable model incorporating one self-reported question (loose teeth), age, and ethnicity demonstrated good discrimination for severe periodontitis (AUC=0.76; sensitivity/specificity=0.60/0.80). While the machine learning models achieved similar AUC (0.67-0.76), they tended to be highly specific (0.99-0.75) but had much lower sensitivity (0.12-0.63). Conclusion: Selected self-reported periodontitis questions were useful for detecting severe periodontitis in this population. While machine learning models integrated sociodemographic factors with all 8 self-reported questions, larger and more diverse datasets are needed to enhance model robustness and generalisability across international populations. Clinical significance: Self-reported periodontal measures validated in a multiethnic population demonstrated good predictive value for severe periodontitis when combining self-report of loose teeth with demographic variables age, gender, and ethnicity. The application of machine learning models further enhanced diagnostic performance, highlighting potential for scalable, data-driven periodontal screening and surveillance across diverse populations.

KW - Epidemiology

KW - Oral health

KW - Periodontitis

KW - Self-report

KW - Sensitivity

KW - Specificity

KW - Validation

UR - https://www.scopus.com/pages/publications/105024826489

U2 - 10.1016/j.jdent.2025.106294

DO - 10.1016/j.jdent.2025.106294

M3 - Article

C2 - 41371537

AN - SCOPUS:105024826489

SN - 0300-5712

VL - 165

JO - Journal of Dentistry

JF - Journal of Dentistry

M1 - 106294

ER -