Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study

Secular trends of dental caries and association with nutritional status: a retrospective analysis of 16,199 Chinese students from three successive national surveys from 2010 to 2019

Introduction

The prevalence of dental caries (DC) among students in developing countries has increased at an alarming rate, and nutritional status has been shown to be associated with DC in children and adolescents with inconsistent conclusions. We aimed to understand the trends of DC prevalence in students aged 7, 9, 12, and 14 years and to explore the relationship between DC prevalence and nutritional status.

Methods

We recruited 16,199 students aged 7, 9, 12, and 14 years in China by multi-stage, stratified, random sampling methods from 2010 to 2019. Permanent caries were measured using the Decay, Loss, and Filling (DMF) index and prevalence rate. Deciduous caries were measured using the decay, loss, and filling (dmf) index and prevalence rate. Nutritional status was assessed using body mass index (BMI) and hemoglobin levels. Logistic regression analysis was used to assess the association between nutritional status and the DC prevalence in children and adolescents, incorporating information concerning family-related factors.

Results

The results indicated that DC prevalence increased from 39.75% in 2010 to 53.21% in 2019 in Henan province, with deciduous teeth and permanent teeth being 45.96 and 27.18%, respectively, in 2019. The total caries rate decreased with age (p < 0.05), and the caries rate of girls was higher than that of boys in 2019 (55.75% vs. 50.67%) (p < 0.001). The prevalence of dental caries among primary and secondary school students in areas with medium economic aggregate was the highest, followed by cities with the best economic development level, and cities with low economic levels have a lower prevalence of dental caries. The dental caries prevalence was negatively correlated with body mass index. In the fully adjusted model, underweight children had a higher caries prevalence (OR = 1.10, 95%CI: 0.86-1.41). Children with anemia had a higher prevalence of dental caries (OR = 1.18, 95%CI: 0.98-1.42).

Conclusion

The DC prevalence of students in Henan Province was high, with a tendency to increase. Females, young individuals, and those with a higher economic level showed a positive correlation with the prevalence of caries. In the process of economic development, particular attention should be paid to early childhood caries prevention. Nutritional status should be taken seriously among children and adolescents, and the oral health system should be improved to keep pace with economic development.

Obesity and Dental Caries in School Children

(1) Background: Childhood obesity and dental caries are common chronic conditions with multiple contributing factors, linked to negative health consequences and significant expenses in healthcare. The aim of this study was to assess the correlation between obesity and dental caries in school-aged children; (2) Methods: Data from 3323 6-12-year-old children from the National Health and Nutrition Examination Survey (NHANES) 2011-2016 were analyzed. The NHANES was conducted at the Centers for Disease Control and Prevention (CDC) in the United States. The CDC standard was used to define obesity. Dental caries was measured during clinical examinations and summarized using DMFT scores for caries experience and prevalence (dt > 0) for untreated caries. The study examined the correlation between obesity and dental caries using regression models that considered demographic variables, family socioeconomic status, and the child's intake of added sugars as controlling factors; (3) Results: The association between obesity and dental caries was not significant in either unadjusted or adjusted models; and (4) Conclusion: The data indicate that untreated caries and caries experiences are not directly correlated with childhood obesity. There are, however, common causes of poor dental health and childhood obesity: culture, poverty level, lifestyle, and family traditions and habits. Dentists must be aware of factors influencing the development of childhood caries so that they can intervene as early as possible.

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

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.

Using machine learning to study the association of sociodemographic indicators, biomarkers, and oral condition in older adults in Colombia

BACKGROUND

Chronic health conditions and socioeconomic problems that affect the well-being and life expectancy of older adults are common. The objective of this cross-sectional study was to analyze the association between sociodemographic variables, oral conditions, and general health and the biomarkers of older adults using machine learning (ML).

METHODS

A total of 15,068 surveys from the national study of Health, Well-Being and Aging (Salud, Bienestar y Envejecimiento) data set were used for this secondary analysis. Of these, 3,128 people provided blood samples for the analysis of blood biomarkers. Sociodemographic, oral health, and general health variables were analyzed using ML and logistic regression.

RESULTS

The results of clustering analysis showed that dyslipidemia was associated with poor oral condition, lower socioeconomic status, being female, and low education. The self-perception of oral health in older adults was not associated with the presence of teeth, blood biomarkers, or socioeconomic variables. However, the necessity of replacing a dental prosthesis was associated with the lowest self-perception of oral health. Edentulism was associated with being female, increased age, and smoking.

CONCLUSIONS

Socioeconomic and educational disparities, sex, and smoking are important factors for tooth loss and suboptimal blood biomarkers in older adults. ML is a powerful tool for identifying potential variables that may aid in the prevention of systemic and oral diseases in older adults, which would improve geriatric dentistry.

PRACTICAL IMPLICATIONS

These findings can help the academic community identify critical sociodemographic and clinical factors that influence the process of healthy aging and serve as a useful guide to enhance health care policies and geriatric oral health care services.

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis-Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

This study aimed to analyze the associations of obstructive sleep apnea (OSA) with dental parameters while controlling for socio-demographics, health-related habits, and each of the diseases comprising metabolic syndrome (MetS), its consequences, and related conditions. We analyzed data from the dental, oral, and medical epidemiological (DOME) cross-sectional records-based study that combines comprehensive socio-demographic, medical, and dental databases of a nationally representative sample of military personnel for one year. Analysis included statistical and machine learning models. The study included 132,529 subjects; of these, 318 (0.2%) were diagnosed with OSA. The following parameters maintained a statistically significant positive association with OSA in the multivariate binary logistic regression analysis (descending order from highest to lowest OR): obesity (OR = 3.104 (2.178-4.422)), male sex (OR = 2.41 (1.25-4.63)), periodontal disease (OR = 2.01 (1.38-2.91)), smoking (OR = 1.45 (1.05-1.99)), and age (OR = 1.143 (1.119-1.168)). Features importance generated by the XGBoost machine learning algorithm were age, obesity, and male sex (located on places 1-3), which are well-known risk factors of OSA, as well as periodontal disease (fourth place) and delivered dental fillings (fifth place). The Area Under Curve (AUC) of the model was 0.868 and the accuracy was 0.92. Altogether, the findings supported the main hypothesis of the study, which was that OSA is linked to dental morbidity, in particular to periodontitis. The findings highlight the need for dental evaluation as part of the workup of OSA patients and emphasizes the need for dental and general medical authorities to collaborate by exchanging knowledge about dental and systemic morbidities and their associations. The study also highlights the necessity for a comprehensive holistic risk management strategy that takes systemic and dental diseases into account.