The role of dry mouth in screening sleep apnea

The clinical characteristic of catathrenia: a new look at an old issue-a systematic review of existing literature

STUDY OBJECTIVES

The International Classification of Sleep Disorders categorized catathrenia as a respiratory disorder, but there are doubts whether episodes appear during rapid eye movement (REM) sleep or the non-rapid eye movement (NREM), their duration, and symptoms. The main objectives were to identify the most common features and relations of catathrenia.

METHODS

PubMed, Embase, and Web of Science were searched according to the PRISMA 2020 guidelines. The Joanna Briggs Institute and the ROBINS-I tools were chosen to assess the risk of bias.

RESULTS

A total of 288 records were identified, 31 articles were included. The majority of the studies had a moderate risk of bias. 49.57% of episodes occurred during the NREM sleep, while 46% took place during REM. In 60.34% females, catathrenia was more common in the NREM, while in 59.26% of males was in REM sleep (p < 0.05). Females and obese individuals were found to have shorter episodes (p < 0.05). Age was inversely correlated with minimal episodes duration (r =  - 0.34). The continuous positive airway pressure (CPAP) therapy was inversely correlated with the maximal episode duration (r =  - 0.48).

CONCLUSIONS

Catathrenia occurs with similar frequency in both genders. The most frequent symptoms embraced groaning, awareness of disturbing bedpartners, and daytime somnolence-not confirmed by the Epworth Sleepiness Scale. The episodes occur more frequently in NREM than in REM sleep. Catathrenia may be considered as a sex-specific condition. The effects of CPAP treatment leading to shortening episodes duration, which may indicate the respiratory origin of catathrenia.

Oral Health Implications of Obstructive Sleep Apnea: A Literature Review

BACKGROUND

Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by repeated episodes of partial or complete obstruction of the upper airway during sleep. While the systemic implications of OSA are well documented, the dental consequences are less frequently discussed yet equally significant. This review aims to elucidate the oral health impacts of OSA, emphasizing the importance of interdisciplinary care.

METHODS

A comprehensive literature search was conducted across several databases to identify studies examining the relationship between OSA and various oral health parameters. The review included observational studies, clinical trials, and systematic reviews published in English up to January 2024.

RESULTS

OSA was significantly associated with heightened risks of bruxism, dry mouth, periodontal disease, temporomandibular joint disorders, palatal and dental changes, and alterations in taste sensation. Mouth breathing associated with OSA was a critical factor in exacerbating xerostomia and dental caries. Furthermore, the systemic inflammation induced by OSA appeared to correlate with the severity of periodontal disease. Patients using oral appliance therapy for OSA also showed notable changes in dental occlusion and required ongoing dental monitoring.

CONCLUSIONS

The findings underscore the bidirectional relationship between OSA and oral health, highlighting the need for dental professionals to be integral participants in the management of OSA. Early dental evaluation and intervention can contribute to the overall health and quality of life of individuals with OSA. The review advocates for the development of clinical guidelines to facilitate the early identification and management of OSA-related oral health issues within dental practice and encourages a collaborative approach to patient care.

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Objective

To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.

Methods

We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.

Results

Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.

Conclusion

Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

Correlation of morning dry mouth with clinical features of OSA in a community population: a cross-sectional study

OBJECTIVES

Morning dry mouth, commonly seen in Obstructive Sleep Apnea (OSA) patients, is absent in current OSA screening tools. This study evaluated the link between morning dry mouth and OSA's clinical symptoms and complications, aiming to determine its viability as a screening indicator.

METHODS

This research analyses baseline data from a prospective cohort study (the PIFCOPD study). Demographic information, medical history, and the presence of morning dry mouth symptoms were collected. The STOP-Bang questionnaire was performed for OSA screening. Logistic regression analyses were employed to establish the correlations between morning dry mouth and the clinical symptoms and comorbidities of OSA.

RESULT

1291 participants (62.1±7.5 years; 501 males, 790 females) were included, of which 416 reported morning dry mouth (32.2%). 42.6% in the high-risk OSA group and 22.1% in the low-risk group reported morning dry mouth. Individuals with morning dry mouth also showed higher STOP-Bang scores (3.3±1.6 vs. 2.3±1.4, P<0.01). Significant associations were found between morning dry mouth and loud snoring, observed sleep apnea, daytime fatigue, and hyperlipidemia (P<0.01), but not with alcohol consumption, tea consumption, diabetes, or hypertension.

CONCLUSION

Morning dry mouth is associated with increased OSA risk and its clinical signs, suggesting its potential as an OSA screening symptom.

CLINICAL TRIAL REGISTRATION

This study has been registered at www.ClinicalTrials.gov (registration identifier: NCT03532893) on 21 May 2018.

Sleep quality in women with diabetes in pregnancy: a single-center retrospective study

PURPOSE

Sleep quality is an important indicator of individual quality of life, which not only affects people's mental health but is also closely related to the occurrence of many diseases. Sleep disorders associated with diabetes in pregnancy can greatly endanger the health of both mothers and babies, and their hazards are strongly associated with blood glucose levels. This study explored the quality of sleep and sleep disorders in pregnant women with diabetes.

METHODS

From June 2020 to July 2021, a total of 693 patients diagnosed with diabetes during pregnancy in Gansu Provincial Maternal and Child Health Hospital were used as the experiment group, including 626 patients with gestational diabetes mellitus (GDM) and 67 patients with pregestational diabetes mellitus (PGDM). At the same time, 709 women not having diabetes were randomly selected as the control group. To obtain the general situation of the participants, the participants were surveyed using the Pittsburgh Sleep Quality Index (PSQI) and the STOP-BANG (S, Snoring; T, Tiredness; O, Observed apnea; P, high blood Pressure; B, Body mass index > 35 kg/ m2; A, Age > 50 years; N, Neck circumference > 40 cm; G, male Gender) questionnaire. The differences in sleep quality and obstructive sleep apnea-hypopnea syndrome (OSAHS) were analyzed between the experiment group and the control group by using chi-square and t-test, and the clinical features and related factors of sleep disorder were analyzed.

RESULTS

Compared with the control group, the age, pre-pregnancy weight, body mass index (BMI), and neck circumference were larger in the experimental group (P < 0.05). The experimental group had higher PSQI scores for sleep quality, time to fall asleep score, sleep duration, sleep efficiency, sleep disorder, and daytime dysfunction than the control group (all P < 0.001). Specific analysis of the clinical features of sleep disorders indicated that the experimental group scored higher than the control group (P < 0.05). The analysis of the types of daytime dysfunction showed that the experiment group scored higher in terms of frequently feeling sleepy and lack of energy to do things than the control group (P < 0.05). Analysis of STOP-BANG scores indicated that the proportion of patients with GDM or PGDM having fatigue, hypertension, BMI > 35 kg/m2, and neck circumference > 40 cm was higher than that in the control group (P < 0.05). According to regression analysis, sleep quality of patients with GDM was significantly impacted by the increases in age (OR: 1.243, CI:1.197-1.290), neck circumference (OR: 1.350, CI: 1.234-1.476), PSQI score (OR: 2.124, CI:1.656-2.724), and sleep efficiency score (OR: 3.083, CI:1.534-6.195), whereas that of patients with PGDM was impacted by age (OR: 1.191, CI:1.086-1.305), neck circumference (OR: 1.981, CI: 1.469-2.673), and PSQI score (OR: 7.835, CI: 2.383-25.761).

CONCLUSIONS

Pregnant women with diabetes had poorer sleep quality and a higher risk of developing OSAHS than those without diabetes. There may be some link between sleep quality and the onset of diabetic.

Modified STOP-bang questionnaire incorporating morning dry mouth and BMI adjustment in China: a retrospective study of 590 patients

BACKGROUND

Morning dry mouth (MDM) is a common symptom of Obstructive Sleep Apnea (OSA) yet current OSA screening tools overlook it.

OBJECTIVE

To enhance the specificity of the Stop-Bang questionnaire (SBQ) by adding an MDM symptom.

METHOD

A retrospective analysis on 590 patients from Peking University First Hospital (2013-2018) suspected of OSA was conducted. They underwent polysomnography. The research incorporated the MDM symptom into SBQ and adjusted the body mass index (BMI) threshold to 28 kg/m2. Predictive parameters were then calculated.

RESULTS

83.1% patients were diagnosed with OSA, with 61.4% reporting MDM. Multivariate regression confirmed MDM significantly influenced Apnea-Hypopnea Index (AHI). Adjusted SBQ with MDM showed a slight decrease in sensitivity but improved specificity, especially when using a BMI threshold of > 28 kg/m2. For AHI ≥ 5 events/h and AHI ≥ 15 events/h, adjusted SBQ with MDM (BMI >28 kg/m2) obtained the highest Youden index.

CONCLUSION

Incorporating the MDM symptom into SBQ and adjusting the BMI threshold enhances the diagnostic specificity for OSA.

Association between preserved ratio impaired spirometry and sleep apnea in a Chinese community

OBJECTIVE

This study investigated the association between obstructive sleep apnea (OSA) and preserved ratio impaired spirometry (PRISm) in a community population.

METHODS

Baseline data from a prospective cohort study, the Predictive Value of Combining Inflammatory Biomarkers and Rapid Decline of FEV1 for COPD (PIFCOPD), were used for cross-sectional analysis. Participants aged 40-75 years were recruited from the community and their demographic information and medical history were collected. The STOP-Bang questionnaire (SBQ) was used to assess the risk of OSA. Pulmonary function tests were performed using a portable spirometer (COPD-6) and forced expiratory volume in 1 s (FEV1) and 6 s (FEV6) were measured. Routine blood, biochemical, high-sensitivity C-reactive protein (hs-CRP), and interleukin-6 tests were also performed. The pH of the exhaled breath condensate was determined.

RESULTS

A total of 1183 participants were enrolled, of which 221 with PRISm and 962 with normal lung function. The neck circumference, waist-to-hip ratio, hs-CRP concentration, proportion of males, cigarette exposure, number of current smoker, high risk of OSA, and prevalence of nasal and ocular allergy symptoms were significantly higher in the PRISm group than in the non-PRISm group (p < .05). Logistic regression showed that the risk of OSA (odds ratio, 1.883; 95% confidence interval, 1.245-2.848), waist-to-hip ratio, current smoking, and prevalence of nasal allergy symptoms were independently associated with PRISm after correcting for age and sex.

CONCLUSION

These findings showed that OSA prevalence is independently associated with PRISm prevalence. Further studies should confirm the relationship between systemic inflammation in OSA, localized inflammation of the airways, and impaired lung function.

Associations between snoring and dental sleep conditions: A systematic review

BACKGROUND

It is important for dentists to know if the presence of snoring is associated with the presence of other dental sleep conditions (e.g. obstructive sleep apnea [OSA], sleep bruxism [SB], gastroesophageal reflux disease [GERD], xerostomia and oro-facial pain). If so, dentists could play a significant role in the early recognition and management of these conditions.

OBJECTIVES

This systematic review aimed to: (i) investigate the associations between the presence of snoring and the presence of other dental sleep conditions; and (ii) determine if it is clinically relevant that dentists assess snoring in their population.

METHODS

The literature search was performed in PubMed and Embase.com in collaboration with a medical librarian. Studies were eligible if they employed regression models to assess whether snoring was associated with other dental sleep conditions, and/or investigated the incidence of snoring in patients with other dental sleep conditions and vice versa.

RESULTS

Of the 5299 retrieved references, 36 eligible studies were included. The available evidence indicates that the presence of snoring is associated with higher probabilities of OSA, GERD and headache. Due to limited evidence and conflicting findings, the currently available articles are not indicative of associations between the presence of snoring and the presence of SB and oral dryness.

CONCLUSION

Within the limitations of this study, it can be concluded that the presence of snoring is associated with higher probabilities of OSA, GERD and headache. Therefore, it is clinically relevant that dentists assess snoring in their patient population.

A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study

Purpose

Snoring patients, as a high-risk group for OSA, are prone to the combination of severe OSA and face serious health threats. The aim of our study was to develop and validate a nomogram to predict the occurrence of severe OSA in snorers, in order to improve the diagnosis rate and treatment rate in this population.

Patients and Methods

A training cohort of 464 snoring patients treated at our institution from May 2021 to October 2022 was divided into severe OSA and non-severe OSA groups. Univariate and multivariate logistic regression were used to identify potential predictors of severe OSA, and a nomogram model was constructed. An external hospital cohort of 210 patients was utilized as an external validation cohort to test the model. Area under the receiver operating characteristic curve, calibration curve, and decision curve analyses were used to assess the discriminatory power, calibration, and clinical utility of the nomogram, respectively.

Results

Multivariate logistic regression demonstrated that body mass index, Epworth Sleepiness Scale total score, smoking history, morning dry mouth, dream recall, and hypertension were independent predictors of severe OSA. The area under the curve (AUC) of the nomogram constructed from the above six factors is 0.820 (95% CI: 0.782-0.857). The Hosmer-Lemeshow test showed that the model had a good fit (P = 0.972). Both the calibration curve and decision curve of the nomogram demonstrated the corresponding dominance. Moreover, external validation further confirmed the reliability of the predicted nomograms (AUC=0.805, 95% CI: 0.748-0.862).

Conclusion

A nomogram predicting the occurrence of severe OSA in snoring patients was constructed and validated with external data for the first time, and the findings all confirmed the validity of the model. This may help to improve existing clinical decision making, especially at institutions that do not yet have devices for diagnosing OSA.

Association between prior cancer diagnosis and osteoporosis: a matched case-control study

Epidemiological studies show an inconsistent association between cancer and osteoporosis. In this nationally representative population-based study, we found that a prior cancer diagnosis was not associated with osteoporosis. This finding may primarily apply to cancer survivors seen many years after their cancer diagnosis.

BACKGROUND

Epidemiological studies show an inconsistent association between cancer and osteoporosis. We examined the association between a prior cancer diagnosis and osteoporosis in population-based data.

METHODS

We performed an age- and sex-matched case-control study (1:2 matching ratio) using the National Health and Nutrition Examination Survey, 2011-2018. Cases were determined by self-reported prior diagnosis of cancer; all controls were free of cancer at the time of bone density measurement with dual-energy x-ray absorptiometry. We defined osteoporosis as a T-score ≤  - 2.5 at femoral neck, total hip, or lumbar spine. Unconditional multivariable logistic regression was used to test the association between a prior cancer diagnosis and osteoporosis.

RESULTS

We identified 246 prior cancer cases and 492 controls (mean age: 65.8 years) in females, and 243 prior cancer cases and 486 controls (mean age: 68.0 years) in males. The most common types of cancer in females and males were breast cancer and prostate cancer, respectively. Osteoporosis prevalences were comparable between cases and controls among females (19.1% in cases vs. 18.7% in controls; P = 0.894) and males (5.8% in cases vs. 6.8% in controls; P = 0.594). After adjusting for covariates, a prior cancer diagnosis was not associated with osteoporosis in females (odds ratio [OR]: 0.83; 95% confidence interval [CI]: 0.54-1.29) or males (OR: 1.09; 95% CI: 0.51-2.30). Results were unaffected by cancer severity, cancer type, or time since cancer diagnosis.

CONCLUSIONS

A prior cancer diagnosis was not associated with osteoporosis in this nationally representative population.

Association between sleep-disordered breathing and periodontal diseases: A systematic review protocol

Background

Sleep-disordered breathing (SDB) is a chronic sleep-related breathing disorder, considered associated with increased risk of cardiovascular disorders, metabolic disorders, cognitive dysfunction and behavior changes. Periodontal diseases are chronic infectious diseases that are also believed to be associated with cardiovascular diseases, metabolic syndrome and cognitive dysfunction. Several studies have indicated that SDB may be associated with periodontal diseases through certain mechanisms such as inflammation response, oxidative stress and oral dryness. The aim of this systematic review is to explore the association between SDB and periodontal diseases in an integrated approach.

Materials and Methods

This systematic review will include cohort studies, cross-sectional studies and case-control studies that are identified by electronic and manual searches. Electronic searches will be conducted in the following databases: PubMed, Embase, Scopus and Web of Science. Our search will cover articles published from inception of databases to March 2022 without restrictions in language and settings. Pre-determined eligibility criteria include: participants (participants without a history of respiratory diseases, history of periodontal treatment within the past 6 months and history of medication that is known to influence SDB or periodontal diseases); exposure (participants who have been diagnosed with SDB or at high-risk for SDB); comparison (participants without SDB); and outcome (periodontal parameters, such as probing depth, clinical attachment level, bleeding on probing, radiographic bone loss). Two authors will perform study screening and data extraction independently and in duplicate. All discrepancies will be solved by discussion. The methodological quality of included studies will be assessed using the Newcastle-Ottawa Scale.

Discussion

This systematic review will summarize the existing evidence on the association between SDB and periodontal diseases, a topic of controversy and clinical significance. Its findings can provide evidence for the development of relevant prevention and treatment strategies. The results will be disseminated through peer-reviewed journals.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022313024. Registered on March 28th 2022.

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

Background

OSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.

Method

We collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.

Results

There are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ² = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.

Conclusion

The prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.

Leptin: A Potential Link Between Obstructive Sleep Apnea and Obesity

Chronic intermittent hypoxia (CIH), a pathophysiological manifestation of obstructive sleep apnea (OSA), is strongly correlated with obesity, as patients with the disease experience weight gain while exhibiting elevated plasma levels of leptin. This study was done to determine whether a relationship may exist between CIH and obesity, and body energy balance and leptin signaling during CIH. Sprague-Dawley rats were exposed to 96 days of CIH or normoxic control conditions, and were assessed for measures of body weight, food and water intake, and food conversion efficiency. At the completion of the study leptin sensitivity, locomotor activity, fat pad mass and plasma leptin levels were determined within each group. Additionally, the hypothalamic arcuate nucleus (ARC) was isolated and assessed for changes in the expression of proteins associated with leptin receptor signaling. CIH animals were found to have reduced locomotor activity and food conversion efficiency. Additionally, the CIH group had increased food and water intake over the study period and had a higher body weight compared to normoxic controls at the end of the study. Basal plasma concentrations of leptin were significantly elevated in CIH exposed animals. To test whether a resistance to leptin may have occurred in the CIH animals due to the elevated plasma levels of leptin, an acute exogenous (ip) leptin (0.04 mg/kg carrier-free recombinant rat leptin) injection was administered to the normoxic and CIH exposed animals. Leptin injections into the normoxic controls reduced their food intake, whereas CIH animals did not alter their food intake compared to vehicle injected CIH animals. Within ARC, CIH animals had reduced protein expression of the short form of the obese (leptin) receptor (isoform OBR₁₀₀) and showed a trend toward an elevated protein expression of the long form of obese (leptin) receptor (OBRb). In addition, pro-opiomelanocortin (POMC) protein expression was reduced, but increased expression of the phosphorylated extracellular-signal-regulated kinase 1/2 (pERK1/2) and of the suppressor of cytokine signaling 3 (SOCS3) proteins was observed in the CIH group, with little change in phosphorylated signal transducer and activator of transcription 3 (pSTAT3). Taken together, these data suggest that long-term exposure to CIH, as seen in obstructive sleep apnea, may contribute to a state of leptin resistance promoting an increase in body weight.