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

Sleep and periodontal health

Sleep is fundamental for health and well-being. An adequate amount and quality of sleep is a cardinal component of a healthy lifestyle at the basis of the prevention of many non-communicable chronic diseases. Recent evidence suggests that sleep disorders, particularly obstructive sleep apnea, represent an emerging risk factor for periodontal health. This review article provides a critical appraisal of the existing literature concerning the association between sleep duration, sleep quality, sleep disorders in general, and obstructive sleep apnea with periodontal diseases, including gingivitis and periodontitis. The putative mechanisms underlying these associations are described as well as the potential clinical implications for diagnosis and treatment.

Periodontitis associated with risk of obstructive sleep apnea in Peruvian adult patients: A cross-sectional study

BACKGROUND

Patients at high risk for obstructive sleep apnea (OSA) are characterized by intermittent transient hypoxias and upper respiratory tract collapse, with reactive oxygen production and oxidative imbalance which causes an inflammatory cascade. This can generate negative effects on the periodontium, causing severe tooth attachment loss. This study aimed to determine the association between periodontitis and the risk of OSA in adults who attend outpatient otolaryngology consultations.

MATERIAL AND METHODS

A cross-sectional observational study was carried out with 118 patients seen in the otolaryngology service of the Hospital Victor Lazarte Echegaray-ESSALUD in Trujillo (Peru), between September and October 2023. The presence and severity of periodontitis were determined using the Page and Eke criteria, and the presence and severity of OSA risk were determined using the STOP-BANG questionnaire. The results were analyzed using Chi-square, Spearman correlation and logistic regression tests, considering a significance level of p<0.05.

RESULTS

An association was found between periodontitis and risk of OSA (p=0.000), obtaining a positive relationship with rs=0.527. In addition, there was an association between periodontitis and DM2 (p=0.028) and bronchial asthma (0.017). No association was found between periodontitis and sex (0.503) or age (0.741).

CONCLUSIONS

There is an association between periodontitis and the risk of OSA in Peruvian adult patients who attended outpatient otolaryngology consultations. This association was also found according to age, sex, smoking, DM2 and asthma. In addition, an association was found between periodontitis and DM2 and asthma, but not with sex and age.

Metabolic syndrome facilitates histopathological changes and the risk of postoperative recurrence in chronic rhinosinusitis with nasal polyps

BACKGROUND

The relationship between metabolic syndrome (MS) and chronic rhinosinusitis with nasal polyps (CRSwNP) remains unclear. This study aimed to examine the effects of MS on histopathological features and postoperative recurrence in patients with CRSwNP.

METHODS

We recruited 529 patients with CRSwNP who underwent functional endoscopic sinus surgery. They were divided into MS and non-MS groups and followed up for 2 years to evaluate postoperative recurrence. Clinical characteristics, histopathological features, the immunoactivity of signature cytokines, and the risk of postoperative recurrence were compared between the two groups.

RESULTS

In total, 490 patients with CRSwNP were included in the study, 145 of whom experienced postoperative recurrence. The recurrence rate, tissue eosinophil count and percentage, and expression levels of IL-5 and IL-17A were significantly higher in the MS group compared to the non-MS group. Furthermore, within the MS group, patients who experienced recurrence exhibited higher tissue eosinophil counts and IL-5 and IL-17A levels than those in the non-MS group. Notably, the eosinophil count and IL-5 and IL-17A levels were higher in tissues collected during revision surgery than in those collected during primary surgery, particularly in patients with MS. Binary logistic regression analysis and Kaplan-Meier survival curves consistently indicated that MS independently increased the risk of postoperative recurrence in patients with CRSwNP. Furthermore, the risk increased with the number of MS components presented.

CONCLUSION

MS promoted tissue eosinophil infiltration, and IL-5 and IL-17A expression, and increased the risk of postoperative recurrence in patients with CRSwNP. MS was identified as an independent risk factor for postoperative recurrence, and the risk increased with an increase in the number of MS components.

Statistical Methods and Machine Learning Algorithms for Investigating Metabolic Syndrome in Temporomandibular Disorders: A Nationwide Study

The objective of this study was to analyze the associations between temporomandibular disorders (TMDs) and metabolic syndrome (MetS) components, consequences, and related conditions. This research analyzed data from the Dental, Oral, Medical Epidemiological (DOME) records-based study which integrated comprehensive socio-demographic, medical, and dental databases from a nationwide sample of dental attendees aged 18-50 years at military dental clinics for 1 year. Statistical and machine learning models were performed with TMDs as the dependent variable. The independent variables included age, sex, smoking, each of the MetS components, and consequences and related conditions, including hypertension, hyperlipidemia, diabetes, impaired glucose tolerance (IGT), obesity, cardiac disease, obstructive sleep apnea (OSA), nonalcoholic fatty liver disease (NAFLD), transient ischemic attack (TIA), stroke, deep venous thrombosis (DVT), and anemia. The study included 132,529 subjects, of which 1899 (1.43%) had been diagnosed with TMDs. The following parameters retained a statistically significant positive association with TMDs in the multivariable binary logistic regression analysis: female sex [OR = 2.65 (2.41-2.93)], anemia [OR = 1.69 (1.48-1.93)], and age [OR = 1.07 (1.06-1.08)]. Features importance generated by the XGBoost machine learning algorithm ranked the significance of the features with TMDs (the target variable) as follows: sex was ranked first followed by age (second), anemia (third), hypertension (fourth), and smoking (fifth). Metabolic morbidity and anemia should be included in the systemic evaluation of TMD patients.

Analysis of General Knowledge on Obstructive Sleep Apnea Syndrome (OSAS) among Italian Pediatricians

INTRODUCTION

Obstructive sleep apnea syndrome (OSAS) is a disorder characterized by partial or total airway obstruction during sleep. Studies have shown variability in the level of knowledge and awareness about OSAS among pediatricians. The management of childhood obstructive sleep apnea syndrome (OSAS) depends on the severity of the disease, the presence of comorbidities, and the child's age. The American Pediatric Academy recommends a multidisciplinary approach involving a pediatrician, a sleep specialist, and an otolaryngologist to provide comprehensive care for children with OSAS. The aim of this cross-sectional study is to evaluate the level of knowledge among pediatricians in Italy regarding the diagnosis of pediatric OSAS.

MATERIAL AND METHODS

An anonymized survey was conducted among Italian pediatricians. The survey was administered electronically using Google Forms, and a total of 350 pediatricians were invited to participate. Out of the 350 invitations, 299 pediatricians responded to the survey. The statistical analysis performed consisted of descriptive analysis. The study included 297 pediatricians.

RESULTS

Pediatricians demonstrated proficiency in identifying common nocturnal and day symptoms of OSAS. A majority (68.9%) considered the oral and otorhinolaryngologist areas during checkups. Approximately 70.6% took patient weight into account, and 62.8% were aware of the regional diagnostic-therapeutic-assistance pathway.

CONCLUSIONS

According to the results of this manuscript, there is evidence of a good level of knowledge about OSAS, but disseminating more information about OSAS and all the health issues associated with this syndrome is suggested. This study also has limitations caused by the complexity of the pathology.

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.

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.