Periodontitis and Metabolic Syndrome: Statistical and Machine Learning Analytics of a Nationwide Study

Abstract

This study aimed to analyze the associations between periodontitis and metabolic syndrome (MetS) components and related conditions while controlling for sociodemographics, health behaviors, and caries levels among young and middle-aged adults. We analyzed data from the Dental, Oral, and Medical Epidemiological (DOME) record-based cross-sectional study that combines comprehensive sociodemographic, medical, and dental databases of a nationally representative sample of military personnel. The research consisted of 57,496 records of patients, and the prevalence of periodontitis was 9.79% (5630/57,496). The following parameters retained a significant positive association with subsequent periodontitis multivariate analysis (from the highest to the lowest OR (odds ratio)): brushing teeth (OR = 2.985 (2.739–3.257)), obstructive sleep apnea (OSA) (OR = 2.188 (1.545–3.105)), cariogenic diet consumption (OR = 1.652 (1.536–1.776)), non-alcoholic fatty liver disease (NAFLD) (OR = 1.483 (1.171–1.879)), smoking (OR = 1.176 (1.047–1.322)), and age (OR = 1.040 (1.035–1.046)). The following parameters retained a significant negative association (protective effect) with periodontitis in the multivariate analysis (from the highest to the lowest OR): the mean number of decayed teeth (OR = 0.980 (0.970–0.991)); North America as the birth country compared to native Israelis (OR = 0.775 (0.608–0.988)); urban non-Jewish (OR = 0.442 (0.280–0.698)); and urban Jewish (OR = 0.395 (0.251–0.620)) compared to the rural locality of residence. Feature importance analysis using the eXtreme Gradient Boosting (XGBoost) machine learning algorithm with periodontitis as the target variable ranked obesity, OSA, and NAFLD as the most important systemic conditions in the model. We identified a profile of the “patient vulnerable to periodontitis” characterized by older age, rural residency, smoking, brushing teeth, cariogenic diet, comorbidities of obesity, OSA and NAFLD, and fewer untreated decayed teeth. North American-born individuals had a lower prevalence of periodontitis than native Israelis. This study emphasizes the holistic view of the MetS cluster and explores less-investigated MetS-related conditions in the context of periodontitis. A comprehensive assessment of disease risk factors is crucial to target high-risk populations for periodontitis and MetS.

Keywords

• big data
• machine learning
• periodontal medicine
• systemic health/disease
• dental informatics
• electronic medical record