Oral health and sleep disorders: A systematic review and meta-analysis
- Authors:
- Published online on: December 20, 2024 https://doi.org/10.3892/br.2024.1915
- Article Number: 37
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Copyright: © Lei et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
The relationship between oral health and sleep quality is a complex and bidirectional phenomenon that has garnered significant attention in recent years. This interplay affects not only the quality of life but also has profound implications for overall health and well-being (1). Sleep disturbances can significantly impact oral health (2). Individuals with poor sleep quality often neglect proper oral hygiene practices, increasing the risk of dental issues such as periodontitis and gingivitis (3). A study, using the National Health and Nutrition Examination Survey data, found that individuals with sleep disorders were more likely to report dental pain, periodontal issues and negative emotions regarding their oral health compared with those without sleep disorders (4). Poor sleep quality can also lead to xerostomia, reducing saliva flow and increasing the risk of oral health problems such as tooth decay and gingivitis (4).
Conversely, poor oral health can significantly impact sleep quality. Dental issues such as periodontitis, gingivitis and dental pain can disrupt sleep patterns and lead to restless nights. Thus, periodontal diseases have been identified as risk factors for developing sleep disorders, with inflammation caused by untreated gum disease potentially leading to systemic health issues that can affect sleep quality (5,6). Besides, the Oral Health Impact Profile (OHIP)-14 score, which reflects the subjective interpretation of oral health-related quality of life, was found to be higher in patients with poor sleep quality. This suggests that oral health can significantly affect sleep quality, highlighting the importance of addressing oral health issues to improve sleep (7).
Several mechanisms underlie the relationship between oral health and sleep quality. One key mechanism is the inflammatory pathway (8). Periodontal disease, for instance, leads to chronic inflammation, which can affect systemic health and sleep quality. The release of pro-inflammatory cytokines such as interleukin-6 and tumor necrosis factor-α can disrupt sleep patterns by altering the immune response and affecting the hypothalamic-pituitary-adrenal axis (2,4,9). High cortisol levels can disrupt sleep patterns, and conversely, poor sleep quality can increase cortisol levels (2). This bidirectional relationship is crucial in understanding how sleep disturbances affect the stress response of the body. Studies have shown that elevated cortisol levels are associated with insomnia, waking up during the night, and less sleep time overall (2,4).
However, the aforementioned findings are not universal and certain studies have not found an association between sleep quality and oral hygiene/health (10,11). Given the uncertainty regarding the bidirectional nature of this relationship, a comprehensive meta-analysis is essential to fully understand the magnitude and mechanisms of the interplay between oral health and sleep quality. Such an analysis would help to synthesize data from various studies, providing a clearer picture of how sleep disorders affect oral health and vice versa. Moreover, a meta-analysis could help in addressing the limitations of current studies (12). Therefore, the present systematic review and meta-analysis was performed to assess the relationship between sleep quality/disturbances and oral health/hygiene.
Materials and methods
The present systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (13).
Inclusion and exclusion criteria
Studies were included if they met the following criteria: i) Population: Individuals of any age group, irrespective of sex, in studies assessing oral health status and sleep quality; ii) intervention/exposure: Studies examining the relationship between oral health (such as periodontal disease, dental caries and oral hygiene) and sleep quality; iii) outcomes: Studies reporting on sleep quality using validated tools [such as Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI)] or on oral health using validated tools [such as Decayed, Missing, and Filled Teeth Index (DMFT) or OHIP]; and iv) study design: Cross-sectional, cohort and case-control studies were included. Review articles, case reports, commentaries and editorials were excluded. Only studies published in English were considered. No time limit was imposed on the publication date. The search was conducted until the end of September 2024.
Search terms and strategy
A comprehensive search of the literature was conducted in September 2024 using the following electronic databases: PubMed (https://pubmed.ncbi.nlm.nih.gov), Embase (https://www.embase.com), Scopus (https://www.scopus.com) and the Cochrane Library (https://www.cochranelibrary.com). Additionally, the references of included studies and relevant review articles were searched to identify any additional studies not captured in the initial database searches. The search strategy was developed using a combination of medical subject headings (MeSH) and free-text terms related to oral health and sleep quality. The search terms included: i) Oral health-related terms: ‘Oral health’, ‘periodontal disease’, ‘dental caries’, ‘oral hygiene’, ‘gingivitis’, ‘tooth loss’ and ‘edentulism’; and ii) sleep quality-related terms: ‘Sleep quality’, ‘sleep disturbance’, ‘insomnia’, ‘sleep disorders’, ‘sleep duration’, ‘Pittsburgh Sleep Quality Index’, ‘PSQI’, ‘Insomnia Severity Index’ and ‘ISI’.
The specific search strategy for PubMed was as follows: (‘oral health’[MeSH Terms] OR ‘periodontal disease’[MeSH Terms] OR ‘dental caries’[MeSH Terms] OR ‘oral hygiene’[MeSH Terms] OR ‘gingivitis’[MeSH Terms] OR ‘tooth loss’[MeSH Terms] OR ‘edentulism’[MeSH Terms] OR ‘oral health’[Title/Abstract] OR ‘periodontal disease’[Title/Abstract] OR ‘dental caries’[Title/Abstract] OR ‘oral hygiene’[Title/Abstract] OR ‘gingivitis’[Title/Abstract] OR ‘tooth loss’[Title/Abstract] OR ‘edentulism’[Title/Abstract]) AND (‘sleep quality’[MeSH Terms] OR ‘sleep disturbance’[MeSH Terms] OR ‘insomnia’[MeSH Terms] OR ‘sleep disorders’[MeSH Terms] OR ‘sleep duration’[MeSH Terms] OR ‘Pittsburgh Sleep Quality Index’[Title/Abstract] OR ‘PSQI’[Title/Abstract] OR ‘Insomnia Severity Index’[Title/Abstract] OR ‘ISI’[Title/Abstract] OR ‘sleep quality’[Title/Abstract] OR ‘sleep disturbance’[Title/Abstract] OR ‘insomnia’[Title/Abstract] OR ‘sleep disorders’[Title/Abstract] OR ‘sleep duration’[Title/Abstract]).
Study selection and data extraction
The studies identified through the database search were imported into EndNote X9 (Clarivate) and duplicates were removed. In total, two independent reviewers screened the titles and abstracts of the remaining studies. Full texts of potentially eligible studies were retrieved and assessed according to the inclusion criteria. Any disagreements between the reviewers were resolved through discussion or consultation with a third reviewer. Data were extracted independently by two reviewers using a standardized data extraction form. Extracted data included: i) Study characteristics: Author, year of publication, country and study design; ii) population characteristics: Sample size, age, sex and oral health status; iii) sleep quality assessment: Tools used (such as PSQI and ISI) and definitions; and iv) results: Mean ± standard deviation (SD).
Risk of bias assessment
In total, two independent reviewers assessed the risk of bias of each study using the Newcastle-Ottawa Scale (NOS) for observational studies. The NOS evaluates studies based on selection, comparability and exposure or outcome assessment (14). Each study was rated as low (score, 7-9), moderate (score, 4-6) or high (score, 0-3) risk of bias (14). Discrepancies in the assessments were resolved through discussion or by consulting a third reviewer.
Statistical analysis
Meta-analyses were performed using Comprehensive Meta-Analysis version 3 software (https://meta-analysis.com). The relationship between oral health and sleep quality was synthesized using Standardized Mean Differences (SMDs) to compare outcomes across studies with different measurement scales. The choice between a fixed-effect and a random-effects (in this case) meta-analysis should never be made on the basis of a statistical test for heterogeneity. In addition, since heterogeneity is expected for the intervention effects among multiple studies from different groups and geographical locations, a random effects model was used to calculate the SMDs. Heterogeneity among the studies was assessed using the precision interval approach, which examines the variability in effect sizes across studies. Unlike the I² statistic, the precision interval provides a range of plausible true effects, giving a more direct interpretation of heterogeneity. This method was chosen due to its robustness in handling the diverse study designs and populations included in the analysis (15). Publication bias was assessed through Egger's, and Begg and Mazumdar rank correlation tests and visually using funnel plots. Begg's test evaluates the asymmetry of the funnel plot, with P<0.05 indicating potential publication bias. In cases where publication bias was suggested, the trim-and-fill method was applied to estimate the impact of missing studies on the overall effect size.
Results
Using the predefined search strategy, a total of 311 articles were identified. After removing duplicates, irrelevant studies, reviews and editorials, 29 articles were deemed eligible for full-text assessment. Of these, 8 articles, encompassing 18 different comparisons, met the criteria for inclusion in the meta-analysis (Fig. 1) (5,6,16-21). Some studies included multiple comparisons with different populations and have therefore been split into multiple studies in the present analysis. The characteristics of the included studies are presented in Table I. The table includes 8 cross-sectional studies conducted between 2015 and 2023 in countries such as Greece, India, Spain, Italy, France, South Korea and the United States. Sample sizes ranged from 60 to 29,870 participants, with age groups spanning from young children (2-5 years) to adults. The total number of patients included across all the studies was 36,559. Oral health assessments used various indices, including the DMFT, Community Periodontal Index and gingival indices. Additionally, sleep quality was measured using tools such as the PSQI, Pediatric Sleep Questionnaire and Epworth Sleepiness Scale. The results showed that poorer oral health, such as higher DMFT scores and gingival inflammation, was often associated with poorer sleep quality, including increased sleep disturbances and reduced sleep duration.
 | Table ICharacteristics of the included studies, population characteristics, sleep quality assessment and the results. |
The results of the present study showed a significant association between oral health and sleep quality [SMD, 2.166; confidence interval (95% CI), 0.677-3.655; P=0.004; Fig. 2]. The Leave-One-Out sensitivity analysis showed that the exclusion of no single study significantly affected the total effect size of the studies and its significance (Fig. 3). Additionally, the funnel plot was reasonably symmetric and showed no evidence of publication bias (Fig. 4). The observed and adjusted point estimates were also the same (point estimate: 0.387). In the present analysis, the trim-and-fill method was applied to assess potential publication bias by evaluating the symmetry of the funnel plot. This method involves ‘trimming’ asymmetric studies and ‘filling’ in estimated missing studies to achieve symmetry. The results indicated that no studies needed to be trimmed or imputed on either side of the mean, suggesting an absence of publication bias. Consequently, both the observed and adjusted point estimates remained consistent, with a fixed-effect estimate of 0.387 and a random-effect estimate of 2.166. This consistency implies that the data distribution is symmetric, and the influence of publication bias on the present findings is minimal. Regarding the funnel plot visualization, it is important to note that the plot displays the original studies without any imputed data points, as no imputations were necessary. The appearance of points outside the funnel could be attributed to factors such as heterogeneity among studies or variations in study precision. While the trim-and-fill method did not identify missing studies, the presence of points outside the funnel suggests that other factors, such as study heterogeneity, may be influencing the plot's appearance. Therefore, while the trim-and-fill results suggest minimal publication bias, the funnel plot should be interpreted with caution, considering these potential influences (Fig. 4). Begg and Mazumdar rank correlation confirmed these findings by showing no evidence of publication bias (two-tailed P=0.225). Similarly, Egger's regression intercept revealed no evidence of publication bias (two-tailed P=0.065) (Table II).
The precision interval analysis yielded a mean effect size of 2.17 with a 95% confidence interval (CI) ranging from 0.68 to 3.66. This suggests that, based on the present sample data, it is with 95% confidence that the true effect size lies between 0.68 and 3.66. Additionally, the analysis provided a prediction interval from -4.83 to 9.16, indicating that in 95% of comparable populations, the true effect size is expected to fall within this range. The broader prediction interval reflects the variability observed across different populations, highlighting the potential for diverse outcomes in similar studies (Fig. 5).
Due to the high heterogeneity of the included studies, a moderator analysis was conducted to examine the effects of age (mean ± SD) and sex (percentage of male participants) on the overall effect size. The analysis revealed that the mean age of participants significantly influenced the study outcomes, with higher ages associated with lower SMDs (slope, P<0.00001; Fig. 6A). Conversely, an increase in the proportion of male participants was associated with a higher SMDs (slope, P<0.00001; Fig. 6B). To address this issue further, subgroup analyses based on the continent of origin (Europe, Asia and North America) and year of publication of the studies were performed. However, the heterogeneity of the subgroups remained high even at subgroup level (data not shown).
Based on the NOS study quality appraisal, 2 studies were deemed to have low quality while others were found to be of high quality (Table III). The decision to include the 2 studies of low quality was made to provide a comprehensive overview of the existing literature on the topic. Excluding these studies could have led to an incomplete synthesis of available evidence, potentially omitting valuable insights.
| Table IIIMethodological quality assessment of cross-sectional studies using the Newcastle-Ottawa Scalea. |
Discussion
The meta-analysis conducted in the present study on the association between oral health and sleep quality yielded significant findings, highlighting a robust link between these two critical aspects of overall health. The results indicated that individuals with poorer oral health, as measured by indices such as the DMFT and gingival indices, were more likely to experience sleep disturbances and reduced sleep duration. The Leave-One-Out sensitivity analysis showed that the exclusion of any single study did not significantly affect the total effect size, suggesting that the findings were robust and not heavily influenced by any one study. This consistency was further supported by the symmetry of the funnel plot and the absence of evidence for publication bias, as confirmed by the Begg and Mazumdar rank correlation and Egger's regression intercept. The absence of adjusted values indicated that the observed distribution of studies was balanced around the pooled effect size. Therefore, the results of the trim and fill analysis supported the robustness of the study findings, reinforcing that the significant association between oral health and sleep quality (SMD, 2.166; 95% CI, 0.677-3.655; P=0.004) was not notably impacted by potential publication bias.
A wide precision interval, as observed in the present study, suggests variability in the strength of the association across different populations, indicating that the true effect size may vary depending on the specific characteristics of each study. This reflects a high degree of heterogeneity, which the moderator analysis performed in the present study aimed to address (15). For instance, the analysis revealed that age played a significant role, with studies involving older populations tending to show lower SMDs. This finding suggests that age-related factors may influence the strength of the association between oral health and sleep quality. Additionally, the moderator analysis highlighted that the proportion of male participants also affected the results, with a higher percentage of males associated with larger SMDs. These findings indicate that demographic factors such as age and sex contribute to the observed variability, leading to the wide precision interval. This variability could arise due to different age groups and sex distributions responding differently to the factors influencing both sleep quality and oral health.
The association between sleep disorders and oral health is well-documented in the literature. For instance, a study using self-reported questionnaires found that individuals with sleep disorders reported worse self-perceived oral health, including a higher prevalence of tooth and temporomandibular joint pain (22). Another study highlighted that both short and long sleep durations are significantly associated with poor oral health status. Specifically, sleep durations of ≤5 h and ≥9 h per night were linked to higher odds of poor oral health compared with a normal sleep duration of 6-8 h (23). This supports the results of the present meta-analysis, suggesting that sleep quality and duration are critical factors influencing oral health. Additionally, a study examining the relationship between sleep duration and dental caries found a statistically significant negative relationship, indicating that individuals who sleep ≥7 h per night are less likely to experience dental caries compared with those who sleep <7 h (24). This finding is consistent with the broader theme that adequate sleep is essential for maintaining good oral health.
Poor oral health can directly affect sleep quality through various mechanisms. For instance, dental issues such as tooth decay, gum disease and oral infections can cause discomfort, leading to difficulty falling asleep or frequent awakenings (25). Additionally, conditions such as sleep apnea, which can be linked to oral health issues such as misalignment of the jaw or the tongue falling back into the throat, further disrupt sleep patterns (25). Poor sleep quality can exacerbate stress and inflammation, which in turn can worsen oral health. Chronic sleep deprivation is known to increase levels of inflammatory markers, which can contribute to conditions such as periodontitis and other oral health issues (22). The relationship between oral health and sleep quality may also be influenced by behavioral factors. For instance, individuals with poor sleep quality might have reduced motivation or ability to maintain good oral hygiene practices, leading to a vicious cycle of deteriorating oral health and sleep quality (25).
The present meta-analysis included a diverse range of studies conducted across different countries and populations, providing a comprehensive overview of the association between oral health and sleep quality. The use of SMD and sensitivity analyses ensured that the findings were robust and not unduly influenced by any single study. The absence of publication bias further strengthens the reliability of the results. The majority of the included studies were deemed to be of high quality according to the NOS study quality appraisal, which enhances the credibility of the findings. However, the present study had several shortcomings. The wide precision interval (-4.83 to 9.16) indicated significant variability in the strength of the association across different populations. This variability could be due to differences in study designs, population demographics and the measurement tools used. A key limitation is the reliance on self-reported sleep measures in a number of the included studies. Self-reported data can be prone to recall and social desirability biases, leading to possible misclassification of sleep quality or disorders, which may underestimate the true impact of sleep disturbances on oral health. Moreover, self-reports often lack the precision of objective measures (including polysomnography or actigraphy), which are more accurate in diagnosing conditions such as obstructive sleep apnea. This could result in the underreporting of certain sleep disorders, further influencing the observed association between sleep quality and oral health. The included studies were predominantly cross-sectional, which limits the ability to establish causality between oral health and sleep quality. Longitudinal studies are necessary to determine the temporal relationship between these variables. The use of various indices for oral health and sleep quality might also introduce some heterogeneity in the results. Therefore, standardization of measurement tools across studies could help in reducing this variability. While the present study adjusted for several covariates, there could be other unmeasured confounding variables that influence the relationship between oral health and sleep quality. Future studies should aim to control for a broader range of potential confounders.
Several gaps in knowledge remain in the field of oral health and sleep quality. One key area is the mechanistic pathways through which sleep quality impacts oral health, such as the role of inflammatory processes and immune responses. Additionally, more research is needed to understand the impact of specific sleep disorders, such as obstructive sleep apnea, on various oral health conditions beyond periodontitis
The findings of the present meta-analysis highlight important clinical implications. Clinicians should consider incorporating basic sleep assessments into routine dental check-ups, as poor sleep quality is linked to worsened oral health outcomes. Collaboration with sleep specialists for patients with severe oral health issues and suspected sleep disorders can help optimize patient care. Additionally, educating patients about the relationship between sleep and oral health can encourage improved sleeping habits, which may improve oral health maintenance and recovery. For older patients and those with chronic oral conditions, screening for sleep issues may be particularly beneficial in managing their overall health.
In conclusion, the present meta-analysis provided strong evidence for a significant association between poorer oral health and poorer sleep quality. This relationship is supported by various studies in the literature, which highlight the direct and indirect mechanisms through which oral health can impact sleep and vice versa. The robust statistical analysis and the high quality of the included studies are notable strengths of the present study. However, the variability in study populations and the cross-sectional nature of the included studies are limitations that need to be addressed in future research. Understanding this association can inform public health strategies aimed at improving both oral health and sleep quality, ultimately contributing to improved overall health outcomes. Given the cross-sectional nature of most included studies, future research should focus on longitudinal studies to establish causality between oral health and sleep quality. Long-term studies that follow individuals over time can clarify whether poor sleep quality leads to deteriorating oral health, or if existing oral health issues contribute to sleep disturbances.
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
The data generated in the present study may be requested from the corresponding author.
Authors' contributions
XH conceived, designed, revised, supervised and edited the manuscript, as well as analyzed and interpreted the data. FL acquired the data. FL and XH confirm the authenticity of the data. Both authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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