Association between metabolic syndrome components and gingival bleeding is women-specific: a nested cross-sectional study

Role of oral health in heart and vascular health: A population-based study

BACKGROUND AND AIM

Conditions such as hypertension, cardiovascular diseases, and hypercholesterolemia, are a major public health challenge. This study investigates the influence of oral health indicators, including gum bleeding, active dental caries, tooth mobility, and tooth loss, on their prevalence in Hungary, considering socioeconomic, demographic, and lifestyle factors.

MATERIALS AND METHODS

Data from the 2019 Hungarian European Health Interview Survey with 5,603 participants informed this analysis. Data were accessed from the records maintained by the Department of Health Informatics at the University of Debrecen between September and November 2023. Variable selection employed elastic net regularization and k-fold cross-validation, leading to high-performing predictors for weighted multiple logistic regression models. Sensitivity analysis confirmed the findings' validity.

RESULTS

Significant links were found between poor oral health and chronic cardiac conditions. Multiple teeth extractions increased hypertension risk (OR = 1.67, 95% CI: [1.01-2.77]); dental prosthetics had an OR of 1.45 [1.20-1.75]. Gum bleeding was associated with higher cardiovascular disease (OR = 1.69 [1.30-2.21]) and hypercholesterolemia risks (OR = 1.40 [1.09-1.81]).

CONCLUSIONS

Oral health improvement may reduce the risk of cardiac conditions. This underscores oral health's role in multidisciplinary disease management.

Oral health's role in diabetes risk: a cross-sectional study with sociodemographic and lifestyle insights

Introduction

Diabetes, a key chronic non-communicable disease, poses a substantial public health burden. The role of oral health as a determinant in the epidemiology of diabetes mellitus, particularly in the Central Eastern European region, remains underexplored. This research aims to examine the impact of specific oral health parameters, including gum bleeding, active dental caries, tooth mobility, and tooth loss, on diabetes prevalence. Additionally, it seeks to clarify the moderating effects of socio-demographic and lifestyle variables on this relationship.

Materials and methods

Data were extracted from the 2014 and 2019 datasets of the Hungarian European Health Interview Survey, comprising a combined nationally representative sample of 11,429 participants. Descriptive statistics were presented as weighted proportions and unweighted counts, and weighted Pearson's chi-squared tests were employed for assessing associations and goodness-of-fit. Significant predictors were integrated into weighted multiple logistic regression models for analysis. Sensitivity analysis was then conducted to confirm the robustness of the findings.

Results

The study identified 'Bad' self-perceived oral health as a diabetes risk (OR=1.35; 95% CI: [1.04-1.75]), with filled teeth being protective (0.65 [0.51-0.84]). Subgroup analysis revealed higher diabetes odds among individuals with primary education (1.41 [1.02-1.96]) and rural residents with tooth loss from decay (3.54 [1.36-9.19]). The bootstrap analysis with 1,000 iterations reaffirmed the model's stability and predictive accuracy for diabetes.

Discussion

Enhanced oral health is associated with lower risk factors for diabetes. This research highlights the importance of including oral health measures in comprehensive diabetes management approaches.

Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease

Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45-79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome.