Nature and Science of Sleep最新文献

Objective: Obstructive sleep apnea (OSA) is a common sleep disorder characterized by upper airway obstruction during sleep, leading to hypoxia and serious health consequences. Conventional diagnostic methods such as polysomnography and drug-induced sleep endoscopy (DISE) are often costly, invasive, or time-consuming. This study aimed to develop a safe, rapid, and fully automated AI-assisted platform for OSA detection using nasopharyngoscopic videos acquired during wakefulness, and to propose diagnostic criteria comparable to the apnea-hypopnea index (AHI).

Methods: Flexible nasopharyngoscopic videos of supine, awake patients were analyzed using an AI system comprising four Xception-based image classifiers to identify scan boundaries and classify anatomical regions (nasopharynx, velopharyngeal/oropharyngeal [VO] wall, tongue base, and epiglottis [TE]). Five U-Net-based semantic segmentation models were then applied to extract quantitative airway features. Key variables-including maximum and minimum airway cross-sectional areas, VO wall area ratio, and TE ratio-were entered into a support vector regression model to predict OSA. A total of 103 clinical samples (59 non-OSA, 44 OSA) were analyzed, with 35 cases reserved for testing.

Results: Classification accuracy for nostril, VO/TE, vocal fold, and nasopharynx regions was 100%, 95.8%, 95%, and 98.5%, respectively. The mean intersection over union (IoU) for segmentation models reached 82.72%. The prediction model achieved 97.14% accuracy on the test set. OSA-associated thresholds were identified-VO wall area ratio < 0.41 and TE ratio > 36.97-all comparable to AHI-based diagnosis. The complete diagnostic workflow, including video upload, classification, segmentation, and prediction, was completed in an average of 85 seconds.

Conclusion: This study is the first to implement a fully automated, AI-based dynamic endoscopic video analysis for OSA detection in awake patients. The system accurately predicts OSA and localizes potential obstruction sites in a non-invasive, real-time manner, offering a practical outpatient screening tool to help select candidates who require further evaluation with polysomnography or DISE.

Background: This study aimed to assess: (i) whether long-term continuous positive airway pressure (CPAP) reduces fractional exhaled nitric oxide (FeNO) in obstructive sleep apnea (OSA), and (ii) how FeNO change relates to baseline OSA severity. Unlike prior cross-sectional work, we performed a prospective longitudinal follow-up with FeNO at baseline, after short-term titration, and at three months. Primary and secondary endpoints were prespecified.

Methods: We enrolled 62 patients with moderate-to-severe OSA; 43 completed CPAP titration and three-month follow-up. FeNO was measured preCPAP, after two nights (postCPAP1), and at three months (postCPAP2). Secondary analyses examined correlations between FeNO change and baseline indices-apnea-hypopnea index (AHI), oxygen desaturation index (ODI), percent sleep time with SpO₂ < 90% (TS90%), and Epworth Sleepiness Scale (ESS)-and the effect of CPAP adherence. Statistics: normality (Shapiro-Wilk); t-test or Mann-Whitney U; χ²/Fisher's exact for categorical data; repeated-measures ANOVA with Huynh-Feldt for longitudinal change; Spearman correlations. Missing data were handled by complete-case analysis with multiple imputation (m = 5) as sensitivity.

Results: At baseline, FeNO was higher in OSA than in AHI < 15 reference subjects from our earlier cohort (21.49 ± 5.56 vs 13.28 ± 3.70 ppb, P < 0.001). Within the OSA cohort, FeNO rose slightly after titration (22.84 ± 4.58 ppb, P > 0.05) but decreased significantly after 3 months of treatment (17.95 ± 3.27 ppb, P < 0.001 vs baseline).FeNO reduction correlated with AHI (r = 0.674), ODI (r = 0.617), TS90% (r = 0.461), and ESS (r = 0.552; all P < 0.001). Greater reductions occurred with adherence ≥ 6 h/night.

Conclusion: Sustained CPAP significantly lowers FeNO, reflecting reduced airway inflammation in OSA. FeNO may serve as a promising non-invasive candidate biomarker for monitoring CPAP response, but larger multicenter studies are needed.

Background: Obstructive sleep apnea is a common condition worldwide, and prostate cancer is the most prevalent cancer among men. However, the link between OSA and prostate cancer remains unclear. This study aims to explore the relationship between OSA and prostate cancer.

Methods: Initially, a multivariable regression analysis was conducted to control for potential confounders and assess the impact of OSA on the risk of developing prostate cancer. Subsequently, age-stratified analyses were performed to further investigate the relationship between OSA and prostate cancer across different age groups. Finally, Mendelian randomization was employed to evaluate the causal relationship between OSA and prostate cancer risk.

Results: The multivariable regression analysis of the NHANES data showed no significant association between OSA and prostate cancer after controlling for age, smoking habits, hypertension, diabetes, cardiovascular diseases, stroke, alcohol consumption, body mass index (OR = 0.83; 95% CI: 0.46-1.48; p = 0.49). However, age-stratified analysis revealed a significant negative correlation between OSA and prostate cancer in the younger population (<60 years) (OR = 0.07; 95% CI: 0.01-0.74; p = 0.03), whereas no significant association was found in the elderly population (≥60 years) (OR = 1.01; 95% CI: 0.69-1.49; p = 0.96). Finally, our Mendelian randomization results did not find a causal relationship between OSA and prostate cancer (OR = 0.992; 95% CI: 0.876-1.124; p = 0.906).

Conclusion: The findings indicate that while there is no significant association between OSA and prostate cancer in the overall analysis, a significant negative correlation exists in the younger population. The lack of significant association in the Mendelian randomization analysis may be due to the inability to perform age stratification. Further prospective studies and mechanistic research are needed to better understand the biological mechanisms underlying this association.

Objective: To conduct a bibliometric analysis of scientific publications on mandibular advancement devices (MADs) for obstructive sleep apnea (OSA), evaluating publication trends, citation impact, productive countries, institutions, authorship patterns, and keyword co-occurrence to identify key contributors and research directions.

Materials and methods: A comprehensive search was performed in the Web of Science Core Collection on May 12, 2025, using topic-specific keywords related to MADs and oral appliances. The search covered literature published from 1984 to December 31, 2024. After excluding non-research items such as early access articles, letters, and corrections, 2,610 documents were included. VOSviewer and Microsoft Excel were used to extract and analyze data on publication metrics, collaboration networks, and keyword trends.

Results: Publication volume increased significantly over the past two decades, with 62.3% of the documents published between 2015 and 2024. The United States led in both number of publications and citations, while the Netherlands and Sweden demonstrated the highest citation impact. The University of Sydney, University of Antwerp, and University of British Columbia were among the top contributing institutions. Peter A. Cistulli was the most cited author. Collaboration among authors has increased, and multi-author studies tend to receive higher citations. Keyword analysis highlighted central research themes including OSA, MADs, CPAP, and diagnostic tools such as polysomnography.

Conclusion: Global research on MADs for OSA has expanded notably, especially in the last decade. High-impact work is linked to international collaboration and focused efforts from leading institutions.

Objective: We examined whether excessive screen time (ST) is associated with emotional and behavioral problems (EBPs) in young school children mediated by sleep disturbance.

Methods: This was a cross-sectional survey of 3883 children aged 7-9 years at 18 primary schools in Shenzhen. Data on children's ST on weekdays and weekends as well as household environment, parental ST, and parental accompaniment time were collected. Sleep disturbance and EBPs were assessed with the Sleep Disturbance Scale for Children and the Strengths and Difficulties Questionnaire (SDQ), respectively. Odds ratios (ORs) and 95% confidence intervals (CIs), the average causal mediation effects and the average direct effects were estimated.

Results: Overall, 15.7% of children had ST in excess of the recommended 2 hr/day, and 24.1% of parents reported that their child had a sleep disturbance. The total SDQ score was higher for children with than without ST ≥ 2 hr/day and sleep disturbance (P<0.001). After adjustment for potential confounders, children with ST ≥ 2 hours/day had increased odds of sleep disturbances (OR 1.99, 95% CI 1.65-2.33). The odds of EBPs were increased for children with ST ≥ 2 hr/day (OR 1.34, 95% CI 1.11-1.64) and sleep disturbance (OR 2.77, 95% CI 2.23-3.22). Mediation analysis indicated that sleep disturbance partially explained the association between ST and EBPs (all effects statistically significant, P < 0.001).

Conclusion: Excessive ST was significantly associated with EBPs and may be mediated by sleep disturbance. Reducing children's ST and encouraging good sleep practices may enhance mental health.

Background / objective: Down syndrome (DS) is the most common chromosomal disorder worldwide, and approximately ¾ of individuals with DS demonstrate multifactorial sleep disturbances, including sleep apnea. As the effects of chromosome 21 triplication are complex, mouse models may provide valuable insights into the causal mechanisms of disordered sleep in DS. Although the recently developed transchromosomic TcMAC21 mouse model offers the closest genetic similarity to human DS, its sleep-wake architecture is unexplored. We hypothesized that TcMAC21 mice would exhibit sleep disruption similar to human DS, specifically with increased wakefulness and sleep fragmentation compared to the euploid controls.

Methods: Using a non-invasive piezo-electric sleep recording system, we evaluated the sleep-wake architecture in male TcMAC21 (TS, n=9) and euploid (EU, n=9) male control mice under a 12-hour light/dark cycle. Analyzed metrics included: total sleep percentage, bout frequency, and bout length.

Results: Compared to EU controls, TS mice exhibited a significant reduction in sleep bout duration (-29.0%, p = 0.02) during the dark phase, with primary effect during the first 8 hours, culminating in an overall decrease in total sleep percentage (-24.2%, p = 0.04). The light phase did not demonstrate statistically significant changes in total sleep percentage or sleep architecture.

Conclusion: TcMAC21 mice demonstrated significant sleep fragmentation during the dark phase, potentially reproducing some aspects of sleep disruption in Down syndrome. Interestingly, these findings differed from descriptions of sleep in other DS animal models. Given the high degree of DS gene replication and non-mosaic nature of the TcMAC21 model, it may provide unique insight into the neurologic and anatomic mechanisms of sleep dysfunction in Down syndrome.

Background: The 24 Solar Terms of the traditional Chinese lunisolar calendar reflect seasonal and climatic changes that may influence sleep. Few large-scale studies have examined sleep quality and stability across these seasonal markers in chronic insomnia.

Methods: This retrospective observational study analyzed anonymized data from 25,428 chronic insomnia patients using the "Good Sleep 365" platform at Zhejiang University's Affiliated Mental Health Center (2018-2023). Sleep quality and stability were assessed via the Pittsburgh Sleep Quality Index (PSQI), incorporating the total score to reflect overall sleep quality, along with score reduction and reduction rate to capture changes and stability over time. Seasonal autoregressive integrated moving average (SARIMA) models were applied to time-series data; 2023 data validated the models, and 2024 predictions were generated.

Results: Sleep quality was poorer during Grain Rain (Guyu) and Cold Dew (Hanlu), with mean PSQI scores of 9.53 and 9.48, respectively, whereas it was better during Major Snow (Daxue) and Minor Snow (Xiaoxue), with mean PSQI scores of 8.91 and 8.96, respectively. Women were more sensitive to seasonal variations than men (P<0.05), while patients aged 45-59 showed greater fluctuations (P<0.05). No significant associations were found between sleep and key solar terms such as Beginning of Spring (Lichun), Beginning of Summer (Lixia), Beginning of Autumn (Liqiu), and Beginning of Winter (Lidong), etc. SARIMA(1,0,1)(0,1,1)[24] best modeled sleep quality and fluctuations, and SARIMA(1,0,1)(1,0,1)[24] for improvements; both models demonstrated good fit and predictions are mostly contained within the confidence intervals.

Conclusion: Sleep quality and stability vary across the 24 Solar Terms, with notable gender and age differences. SARIMA models are able to reflect these patterns to a certain extent, with the majority of predictions lying within the confidence intervals, which may contribute to personalized insomnia management. Cultural context of the 24 Solar Terms adds interpretive value. Limitations of this study include reliance on self-reported PSQI scores, lack of direct meteorological data, and other factors.

Background: Heightened inflammatory state is considered a key factor linking obstructive sleep apnea (OSA) with cardiovascular disease (CVD).

Objective: This study aimed to assess the level of novel hematologic inflammatory biomarkers including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), ratio of neutrophil count to HDL level (NHR), ratio of monocyte count to HDL level (MHR), monocyte count multiplied by neutrophil-to-lymphocyte ratio (SIRI) in OSA patients, and explored the relationships between these inflammatory biomarkers and cardiovascular risk.

Methods: This study enrolled 974 patients with OSA and all data were collected after admission. Spearman correlation was used to explore the correlations between sleep parameters and inflammatory indices. Logistic regression and receiver operating characteristic (ROC) analysis were employed to assess the association between the novel hematologic inflammatory indices and CVD in OSA patients.

Results: Correlation analysis showed that most inflammatory indices were closely related to nocturnal hypoxia in OSA patients. Multivariate logistic regression suggested that NLR (OR=1.085, 95% CI: 1.017-1.158), MLR (OR=3.708, 95% CI: 1.322-10.404), NHR (OR=1.074, 95% CI: 1.020-1.131), MHR (OR=2.116, 95% CI: 1.205-3.715), and SIRI (OR=1.148, 95% CI: 1.035-1.272) were positively correlated with CVD in OSA patients after adjusting all confounding factors. Moreover, the area under the curve (AUC) of NLR, MLR, NHR, MHR, and SIRI for discriminating OSA patients with CVD was 0.734, 0.735, 0.736, 0.734, and 0.735, respectively, after adjusting for all confounders.

Conclusion: Inflammatory indices including NLR, MLR, NHR, MHR, and SIRI were promising biomarkers for CVD in OSA patients, which might aid in the early identification of CVD risk in clinical. These easily obtainable markers may facilitate CVD risk stratification in OSA patients.