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

Cardiovascular Risk Factors and Diseases and Awareness of Related Burden in Women: Results of a Survey in Italian Pharmacies

INTRODUCTION

Cardiovascular diseases (CVD) represent the leading cause of morbidity and mortality for women worldwide, yet they are often unaware of this heavy burden.

AIM

To assess cardiovascular risk awareness among Italian women.

METHODS

Following World Heart Day 2023, a cardiovascular prevention campaign was conducted in Italian pharmacies to evaluate the effectiveness of screening activities offered by pharmacies and raise awareness of cardiovascular health status among Italian women. Cardiovascular risk profile and perception of CVD burden relative to other common female-specific diseases were assessed. Blood pressure (BP) measurement and ECG recording were performed.

RESULTS

A total of 1510 women (84.7% < 70 years), enrolled at 91 pharmacies, were included. The most prevalent cardiovascular risk factor was sedentary lifestyle (57.9%), followed by overweight/obesity (44.3%), hypercholesterolemia (37.9%), hypertension (31.3%), family history of early CVD (28.7%), smoking (20.6%), and diabetes (5%). CVD and/or kidney disease were uncommon (3.6%), but 1 in 4 women was classified as being at increased cardiovascular risk, and 47.5% had some type of ECG abnormalities, requiring further assessments in 18% cases. Less than 1 in 3 women was aware of the burden represented by CVD, being the majority mostly concerned with breast cancer and osteoporosis as potential health threats.

CONCLUSIONS

The burden of cardiovascular risk factors is high, and the perception of related health threat is low among the examined sample of Italian women, supporting the urgent need to raise awareness of CVD in women as a major health issue and to undertake effective, tailored preventive strategies to reduce such risk in a timely fashion.

Meta-analysis of oral microbiome reveals sex-based diversity in biofilms during periodontitis

Sex is an often overlooked, yet compulsory, biological variable when performing biomedical research. Periodontitis is a common yet progressively debilitating chronic inflammatory disorder affecting the tissues supporting teeth that ultimately leads to tooth loss if left untreated. The incidence of periodontitis is sex biased, with increased prevalence in males compared with females but with unknown etiology. We performed a sex-specific meta-analysis using publicly available oral microbiome data from different sampling sites of patients with periodontitis and periodontally healthy controls; sex balance was established for each periodontal health condition. Our results show sex-based diversity in oral biofilms of individuals with periodontitis but not in their saliva, with increased abundance of several periodontal pathogens in subgingival plaques from females compared with males. We devised a quantitative measure, uniquely defined as the Microsexome Index (MSI), which indicates that sexual dimorphism in subgingival bacterial composition is a distinct feature of reduced microbial diversity during periodontitis but not under healthy conditions. In addition, we found that smoking exacerbates microsexome diversity in supragingival biofilms, particularly during periodontitis. Taken together, we provide insights regarding sex-based diversity in periodontitis, a disease with multiorgan associations, and provide the rationale for further mechanistic, diagnostic, and therapeutic studies.

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.