Association between periodontal disease and non-apnea sleep disorder: a systematic review

Association of Sleep Duration with Tooth Loss and Periodontitis: Insights from the National Health and Nutrition Examination Surveys (2005-2020)

OBJECTIVE

Sleep disorders such as insomnia, apnea, and restless leg syndrome can negatively affect a person's overall health and may cause hypertension, heart failure, and coronary heart disease. Likewise, periodontitis, a gum disease, can lead to both physical and psychological health issues, exerting a considerable effect on one's overall well-being-periodontitis stands as a primary cause of tooth loss. Nevertheless, there has been insufficient research on the correlation between the amount of sleep individuals get and the occurrence of periodontitis/tooth loss among Americans. Therefore, this study aimed to assess the influence of sleep length on periodontitis in the American population.

METHODS

Periodontitis severity was classified (none, mild, moderate, and severe) using American Periodontal Association criteria. Sleep duration was assessed by self-reported data and categorized into three groups (deficient, adequate, and excessive). Tooth loss was assessed by the oral examination. To establish a connection between the duration of sleep and periodontitis/tooth loss, a weighted multivariable logistic regression analysis was employed. A GAM analysis and smooth curve fitting assessment were conducted to identify non-linear relationships. Subgroup, interaction, and mediation analyses were also performed.

RESULTS

The prevalence of tooth loss was significantly high, affecting 96.4% of the individuals, whereas 46.6% of the study sample experienced moderate to severe periodontitis. The average age of participants was 52.7 years. After adjusting for potential confounding factors, the analysis of weighted multivariable logistic regression revealed a significant association between sleep insufficiency and moderate/severe periodontitis (OR 1.15, 95% CI 1.01-1.30, P = 0.0298), as well as tooth loss (OR 1.16, 95% CI 1.01-1.33, P = 0.0371). Additionally, the research showed a correlation between the length of sleep and periodontitis that followed a U-shaped pattern. In addition, the analysis of mediation revealed that high blood pressure explained 7.0% (95% CI 4.0% to 12.9%; P < 0.0001) of the link between the amount of sleep and the likelihood of losing teeth.

CONCLUSION

Sleep duration was independently correlated with moderate/severe periodontitis/tooth loss and had a non-linear relationship.

Enamel Matrix Derivatives as an Adjunct to Alveolar Ridge Preservation-A Systematic Review

PURPOSE

To systematically assess the current evidence regarding the adjunctive application of enamel matrix derivatives (EMDs) during alveolar ridge preservation (ARP) following tooth extraction.

METHODS

A comprehensive literature search was conducted in MEDLINE, Cochrane Library, PsycINFO, Web of Science, Google Scholar, and Scopus to identify relevant randomized controlled clinical trials (RCTs). The primary outcome parameters of this systematic review were histomorphometric and radiographic data; secondary outcomes were the feasibility of implant placement after ARP as well as patient-related outcomes such as postoperative discomfort.

RESULTS

The search identified 436 eligible articles published from 2011 to 2022, but only five were ultimately included for data extraction (146 patients). Given the substantial heterogeneity among the included studies, no meta-analysis could be performed. The authors' qualitative analysis showed marginally improved outcomes regarding an increased percentage of new bone formation after tooth extraction and a reduction in postoperative discomfort.

CONCLUSIONS

Given the potential value of EMDs in other fields of regenerative dentistry, more consideration should be given to EMDs as an adjunctive treatment option in ARP. However, more well-controlled randomized clinical trials are necessary to evaluate the exact potential and impacts of EMDs.

Multimorbidity and Sleep Patterns among Adults in a Peruvian Semi-Urban Area

Objective  To assess if the duration and quality of sleep vary due to the presence of multimorbidity. Materials and Methods  We performed a secondary analysis using data from a population-based study involving adult subjects aged between 30 and 69 years residing in a semi-urban area of Tumbes, Peru. The duration (normal, short or prolonged) and quality (good or poor) of sleep were our outcome variables, whereas the exposure was multimorbidity (two or more chronic conditions). Crude and adjusted Poisson regression models were built to assess the association of interest, and prevalence ratios (PRs) and 95% confidence intervals (95%CIs) were reported. Results  We analyzed data from 1,607 subjects with a mean age of 48.2 (standard deviation [SD]: ± 10.6) years, 809 (50.3%) of whom were women. Multimorbidity was present in 634 (39.5%; 95%CI: 37.1-41.9%) subjects, and 193 (12.1%; 95%CI: 10.5-13.7%) were short sleepers, 131 (8.2%; 95%CI: 6.9-9.6%) were long sleepers, and 312 (19.5%; 95%CI: 17.5-21.5%) had poor sleep quality. In the multivariable model, multimorbidity was associated with prolonged sleep duration (PR = 1.45; 95%CI: 1.03-2.04) and poor sleep quality (PR = 2.04; 95%CI: 1.65-2.52). Conclusions  Multimorbidity was associated with prolonged, but not short, sleep duration, as well as with poor sleep quality. Our results suggest the need of assessing sleep patterns among adults with multimorbidity.

A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions

Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.

mTOR Signaling in the Regulation of CD4+ T Cell Subsets in Periodontal Diseases

Periodontal disease results from the inflammatory infiltration by the microbial community which is marked through tooth mobility and alveolar bone resorption. The inflammation in periodontal disease is mediated by CD4⁺ T cells through cytokine secretion and osteoclastogenetic activity. Historically, the inflammatory model in periodontal disease is described through disruption of the balance between two subsets of T helper cells which are T-helper type 1 (Th1) and T-helper type 2 (Th2). However, more and more studies have found that apart from subsets of helper T cells, regulatory T-cells and Th17 cells are also involved in the pathogenesis of periodontal diseases. Growing evidence proves that helper T cells differentiation, activation, and subset determination are under the strong impact of mTOR signaling. mTOR signaling could promote Th1 and Th17 cell differentiation and inhibit Treg commitment through different mTOR complexes, therefore we anticipate a regulation effect of mTOR signaling on periodontal diseases by regulating CD4⁺ T cell subsets. This review aims to integrate the topical researches about the role of different types of Th cells in the pathogenesis of periodontal diseases, as well as the regulation of mTOR signaling in the specification and selection of Th cell commitment.

Are periodontal diseases associated with sleep duration or sleep quality? A systematic review

OBJECTIVES

This study aimed to systematically review the associations between periodontal diseases/tooth loss and sleep duration/quality.

MATERIAL AND METHODS

PubMed, Scopus, and Embase databases were searched (up to May 2021) to identify studies that assessed the association between periodontal diseases or number of teeth with sleep quality and sleep duration. Two researchers independently selected the studies and extracted the data. Considering the high heterogeneity among the included studies, meta-analysis was deemed unviable. Results are presented descriptively for sleep quality (studies that have used PSQI), self-reported sleep hours, and other tools that assessed sleep patterns.

RESULTS

Twenty studies (16 cross-sectional, two case-control, and two cohort) were included. Eight studies used the Pittsburgh Sleep Quality Index (PSQI) to assess quality of sleep. Six of these studies demonstrated that individuals with worse periodontal conditions demonstrated poorer sleep quality. However, most of the included studies that performed adjusted analysis showed no statistically significant association between self-reported hours of sleep and periodontitis. The mean number of present teeth was assessed in four studies; three of them demonstrated lower numbers of present teeth in individuals with inadequate sleep.

CONCLUSION

The literature shows conflicting results for the association between sleep hours and periodontitis. However, inadequate sleep may be associated with lower number of present teeth and periodontal diseases. Further studies are necessary in order to confirm these results.

Relationship between an Oral Health Risk Assessment Using a Salivary Multi-Test System and Woman's Subjective Oral Health Symptoms and Sleep Disorder

Saliva is used as a diagnosis and monitoring tool for various diseases because it can maintain the balance of the oral ecosystem and reflect the physiological and pathological state of the body. Because women suffer more fatigue than men because of physiological, psychological, and social factors, individual management strategies are needed to evaluate mental health and oral diseases. Therefore, this study examined the oral health risk level from seven saliva factors using a saliva multi-test system for adult women to confirm the possibility of screening for sleep disorders. The saliva of 83 adult female participants was surveyed along with a self-reported questionnaire consisting of seven subjective oral health symptoms and three questions about sleep disorders. Seven saliva factors were evaluated using the saliva multi-test system (SiLL-Ha ST-4910) to assess the oral health risk levels. In the tooth health risk groups, the acidity was high, while the buffering capacity was low (p < 0.001). The periodontal health risk groups showed significant differences in acidity, occult blood, leukocytes, proteins, and ammonia (p < 0.05). The oral malodor risk group had higher levels of cariogenic bacteria, occult blood, leukocytes, and ammonia (p < 0.05). In groups with 'irregular sleep times' and 'insomnia', the acidity was high, and the buffering capacity was low (p < 0.001). This study confirmed the relevance of saliva factors and sleep disorder. Therefore, an evaluation using saliva was confirmed for oral health risk assessments and as an early screening tool for sleep disorders.