Associations between snoring and dental sleep conditions: A systematic review

Research routes on awake bruxism metrics: Implications of the updated bruxism definition and evaluation strategies

BACKGROUND

With time, due to the poor knowledge on it epidemiology, the need to focus on awake bruxism as a complement of sleep studies emerged.

OBJECTIVE

In line with a similar recent proposal for sleep bruxism (SB), defining clinically oriented research routes to implement knowledge on awake bruxism (AB) metrics is important for an enhanced comprehension of the full bruxism spectrum, that is better assessment and more efficient management.

METHODS

We summarised current strategies for AB assessment and proposed a research route for improving its metrics.

RESULTS

Most of the literature focuses on bruxism in general or SB in particular, whilst knowledge on AB is generally fragmental. Assessment can be based on non-instrumental or instrumental approaches. The former include self-report (questionnaires, oral history) and clinical examination, whilst the latter include electromyography (EMG) of jaw muscles during wakefulness as well as the technology-enhanced ecological momentary assesment (EMA). Phenotyping of different AB activities should be the target of a research task force. In the absence of available data on the frequency and intensity of wake-time bruxism-type masticatory muscle activity, any speculation about the identification of thresholds and criteria to identify bruxers is premature. Research routes in the field must focus on the improvement of data reliability and validity.

CONCLUSIONS

Probing deeper into the study of AB metrics is a fundamental step to assist clinicians in preventing and managing the putative consequences at the individual level. The present manuscript proposes some possible research routes to advance current knowledge. At different levels, instrumentally based and subject-based information must be gathered in a universally accepted standardised approach.

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.