Nature and Science of Sleep最新文献

Background: Survival in patients with sleep apnoea (SA) can be reduced by variables such as age, sex, and comorbidities. However, survival data in patients with SA in Colombia remains scarce.

Methods: This is a retrospective cohort study of patients diagnosed with SA between 2005 and 2022. Five-year survival was assessed using the Kaplan-Meier method, and survival curves were stratified by age, sex, and cardiovascular disease. Risk factors associated with survival were evaluated using Hazard Ratio (HR) by adjusting for confounding variables with a Cox regression model. A minimum sample size of 1537 patients were estimated to be necessary to estimate a survival incidence rate with a 5% precision.

Results: The five-year survival rate in the general population was 94.6%, with lower survival in patients over 65 years (88.5% vs 97.9%; p < 0.001) and in patients with cardiovascular disease (89% vs 95.2%; p < 0.001) compared to the control group. In the Cox regression, age showed an HR of 1.05 (95% CI: 1.02-1.07; p < 0.001). Male sex had an HR of 2.31 (95% CI: 1.25-4.25; p = 0.007), congestive heart failure an HR of 4.00 (95% CI: 2.31-6.94; p < 0.001), chronic obstructive pulmonary disease (COPD) an HR of 1.75 (95% CI: 1.04-2.96; p = 0.035), chronic kidney disease (CKD) an HR of 2.23 (95% CI: 1.31-3.78; p = 0.003), and metastatic cancer an HR of 4.96 (95% CI: 1.95-12.60; p = 0.001).

Conclusion: The study showed a high five-year survival rate in patients with SA. The risk factors associated with decreased overall five-year survival were age, male sex, cardiovascular disease, COPD, CKD, and metastatic cancer.

Purpose: The COVID-19 pandemic resulted in a shift to working and learning from home and a concomitant rise in the use of virtual communication technology, such as videoconferencing. The current study prospectively examined the association between videoconferencing and sleep in a sample of young adults attending a university during the pandemic. The effects of videoconferencing on health and wellness outcomes and academic performance were also evaluated.

Patients and methods: Participants completed the core Consensus Sleep Diary and reported engagement in videoconferencing, the use of electronic devices, and physical activity daily for 8 consecutive days. They also completed baseline measures of sleep, communication technology use, physical activity, and mental distress, as well as released their end-of-term GPA. Results were evaluated via multilevel modeling and path analysis.

Results: Participants with a heavier videocall volume lost 17 m of sleep and suffered nearly a 1% reduction in sleep efficiency for each additional hour of videoconferencing compared to those with a lower call volume. They also tended to spend more time awake during the night, have earlier sleep midpoints, and report worse sleep, although those trends did not reach statistical significance. For everyone, including individuals with lower videocall volume, earlier sleep midpoints, lower sleep quality, somewhat shorter sleep, and higher fatigue were reported on days with a relatively high videocall load compared to days with a low videocall load. Increased academic engagement with videoconferencing predicted lower academic performance and higher psychological distress: Both relationships were mediated by sleep. Use of videoconferencing for personal reasons, however, was directly associated with a reduction in distress.

Conclusion: Videoconferencing is an important determinant of sleep and may impact health and wellness as well as academic outcomes in young adults. The effects of virtual communication on sleep and human behavior warrant further study in this and other populations.

Purpose: The prevalence of Obstructive Sleep Apnea (OSA) is high, and there are many complications. Few studies have reported the relationship between OSA and kidney stones. The purpose of this study is to explore whether people at risk of OSA will increase the risk of kidney stones.

Methods: This was a cross-sectional study, and information was collected through the National Health and Nutrition Examination Survey conducted from 2015 to 2018. Multiple logistic regression analyses were employed to calculate the odds ratios (ORs) and their 95% confidence intervals (CIs) for the link between obstructive sleep apnea and the presence of kidney stones. Additionally, to assess causality and reduce observational biases, five distinct two-sample Mendelian randomization techniques were applied.

Results: Following the adjustment for relevant confounders, findings indicated a statistically significant correlation between obstructive sleep apnea (OSA) and higher prevalence of kidney stones (OR = 1.29; 95% CI: 1.00-1.66). Additionally, using the inverse-variance weighted approach in Mendelian randomization, results suggested a genetic predisposition to OSA might be causally linked to an elevated risk of developing kidney stones (OR: 1.00221, 95% CI 1.00056-1.00387).

Conclusion: OSA promotes the formation of kidney stones, and the treatment and management of OSA can improve or mitigate the occurrence of kidney stones.

Purpose: This study investigates the prevalence, risk factors, and clinical characteristics of positional obstructive sleep apnea (POSA) among pediatric patients diagnosed with obstructive sleep apnea (OSA).

Patients and methods: A total of 1,236 children aged 0 to 17 years who underwent nocturnal polysomnography (PSG) and completed the Sleep Questionnaire were included. After excluding those with an AHI <1, neurological or muscular disorders, or insufficient sleep time in specific positions, 908 patients remained: 158 with POSA and 750 with non-positional OSA (NPOSA). Propensity score matching (PSM) was applied at a 1:2 ratio, resulting in a final sample of 153 POSA and 306 NPOSA patients. Data analyses were performed using R software (version 4.2.3).

Results: The prevalence of POSA was 12.8%. After PSM, patients with POSA had a lower overall AHI (8.66 vs 10.30), REM-AHI (14.30 vs 17.40), and NREM-AHI (7.43 vs 8.77) compared to those with NPOSA. POSA patients also had a shorter total sleep time (411 vs 427 minutes), spent less time in the supine position (168 vs 225 minutes), and more time in non-supine positions (241 vs 202 minutes) than NPOSA patients. Additionally, while the supine AHI was higher in POSA patients (15.60 vs 10.30), the non-supine AHI was lower (5.00 vs 11.00) compared to NPOSA patients. The minimum oxygen saturation was slightly higher in POSA patients (0.88 vs 0.87). All differences were statistically significant (P < 0.05). Risk factors for POSA included mild OSA, allergic rhinitis, non-allergic rhinitis, and obesity.

Conclusion: The prevalence of POSA in children is lower than in adults, and its severity is less than that of NPOSA. Compared to NPOSA patients, POSA patients had significantly higher AHI during supine sleep and lower AHI during non-supine sleep. POSA patients also spent more time in non-supine positions, suggesting that avoiding supine sleep may help reduce apnea events. These findings highlight the importance of monitoring and managing sleep posture in POSA patients.

Background: Central obesity, as measured by examination instruments, has been shown to be associated with both OSA and short sleep duration. However, objective measurement tools like CT, MRI, and DXA are expensive, cause radiation exposure, and have limited availability, especially in resource-limited settings. Thus, this study aimed to demonstrate the relevance of Body Mass Index (BMI) and Waist-to-Height Ratio (WHtR) as surrogate indicators of visceral obesity in the assessment of OSA and short sleep duration. We also intend to evaluate whether WHtR, in combination with BMI, can be a suitable surrogate marker for visceral adiposity.

Methods: We recruited 333 adults with complete polysomnographic (PSG) records retrospectively. Logistic regression helped to assess the association of BMI and WHtR as surrogates for central adiposity with OSA and short sleep duration. Moreover, ROC curve analysis was conducted to evaluate the predictive ability of BMI and WHtR.

Results: Following the relevant adjustments, logistic regression analysis results showed that the combination of WHtR and BMI acting as central obesity surrogates was significantly associated with OSA and short sleep duration (p<0.05). According to univariate regression analysis, sleep latency and wake after sleep onset were independent predictors of the risk of central obesity in patients with short sleep duration and OSA. Additionally, ROC curve analysis demonstrated that the combination of BMI and WHtR provided a better assessment of central adiposity in patients with OSA and short sleep duration, compared to each measure alone.

Conclusion: BMI and WHtR are significantly associated with OSA and short sleep duration, and might serve as a potential surrogate marker for central obesity. Sleep latency and wake after sleep onset can independently predict the risk of central obesity in patients with short sleep time and OSA. Thus, larger prospective studies are needed to verify our findings.

Objective: The aim of this study was to analyze the relationship between the characteristics of the intestinal microbiota and cytokine levels in individuals with different degrees of obstructive sleep apnea-hypopnea syndrome (OSAHS) as well as to investigate intestinal microbiota imbalances in patients with OSAHS and the associated mechanisms.

Methods: Based on their sleep apnea hypopnea index (AHI), a total of 37 adults were assigned to a control group, a mild OSAHS group, or a moderate-to-severe OSAHS group. Fecal samples were collected to characterize the intestinal microbiota using metagenomic next-generation sequencing (mNGS), while blood samples were collected to detect levels of interleukin-17a (IL-17a), interleukin-10 (IL-10), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) in each group.

Results: 1. There was no significant difference in the Shannon index among the three groups (P > 0.05). The three groups showed significant difference in the relative abundance of Faecalibacterium prausnitzii and Bifidobacterium adolescentis (with F values of 3.955 and 7.24, respectively, P < 0.05), while showed no significant difference in the relative abundance of B. pseudocatenulatum, Bifidobacterium longum, Klebsiella pneumoniae, and Haemophilus parainfluenzae (P > 0.05). 2. The three groups showed significant difference in the expression of serum IL-17A and TNF-α levels (with F values of 18.119 and 10.691, respectively, P < 0.05), while showed no significant difference in the expression of IL-10, IL-6, and CRP levels (P > 0.05). 3. Multiple linear regression analysis revealed that the relative abundance of F. prausnitzii was correlated with changes in BMI and AHI (with β values of 2.585 and -0.157, respectively, P < 0.05), while the relative abundance of B. adolescentis was correlated with changes in IL-17a (with β value of -0.161, P < 0.05).

Conclusion: The study revealed a significant correlation between intestinal microbiota abundance and cytokine levels, suggesting that gut microbiota disruption in OSAHS patients may be linked to systemic chronic inflammation.

Purpose: The diagnosis of severe OSA still relies on polysomnography, which causes a strong sense of restraint in patients with obesity. However, better prediction tools for severe OSA applicable to patients with obesity have not been developed.

Patients and methods: Relevant clinical data of 1008 patients with OSA who underwent bariatric surgery in our hospital were collected retrospectively. Patients were divided into training and test cohorts by machine learning. Univariate and multivariate logistic regression analysis was used to screen associations, including liver stiff measurement (LSM) and abdominal visceral tissue (aVAT), and to construct a severe OSA risk prediction nomogram. Then, we evaluated the effectiveness of our model and compared our model with the traditional Epworth Sleepiness Scale (ESS) model. Finally, our associations were used to explore the correlation with other indicators of OSA severity.

Results: Our study revealed that age, biological sex, BMI, LSM, aVAT, and LDL were independent risk factors for severe OSA in patients with obesity. A severe OSA risk prediction nomogram constructed by six indicators possessed high AUC (0.845), accuracy (77.6%), and relatively balanced specificity and sensitivity (72.4%, 82.8%). The Hosmer-Lemeshow test (P=0.296, 0.785), calibration curves, and DCA of the training and test cohorts suggested better calibration and more net clinical benefit. Compared with the traditional ESS model, our model had higher AUC (0.829 vs 0.545), sensitivity (78.9% vs 12.2%), PPV (77.9% vs 53.3%), and accuracy (75.4% vs 55.2%). In addition, the associations in our model were independently correlated with other indicators reflecting OSA severity.

Conclusion: We provided a simple, cheap, and non-invasive nomogram of severe OSA risk prediction for patients with obesity, which would be helpful for preventing further complications associated with severe OSA.

Purpose: Clinical studies focusing on the association between the gut microbiota and obstructive sleep apnea (OSA) are limited. This study aimed to explore the relationship between intermittent hypoxia and the composition of gut microbiota in adults by analyzing the differences in the characteristics and functional distribution of gut microbiota between patients with different severities of OSA and healthy individuals.

Patients and methods: A cohort of 113 individuals from the First Affiliated Hospital of Sun Yat-sen University underwent overnight polysomnography from July 2019 to August 2021. The individuals included 16 healthy controls and 97 patients with OSA, categorized by the apnea-hypopnea index into mild, moderate, and severe groups. Fecal samples were analyzed using high-throughput sequencing of the 16S rRNA V3-V4 region to assess gut microbiota composition and function. Correlation analysis was used to evaluate the association between clinical indicators and microbiota markers.

Results: In patients with OSA, the gut microbiota diversity and the abundance of specific microbes that produce short-chain fatty acids decreased (P<0.05). The phyla Verrucomicrobia and Candidatus Saccharibacteria, genera Gemmiger and Faecalibacterium, and the species Gemmiger formicilis exhibited decreasing abundance with increasing OSA severity. Correlation analysis revealed a robust association between the proportion of total sleep time, characterized by nighttime blood oxygen saturation below 90%, and the alterations in the gut microbiota, demonstrating that elevated levels of desaturation are correlated with pronounced microbiota dysbiosis (P<0.05).

Conclusion: Compared to the control group, the intermittent hypoxia exhibited by patients with OSA may be related to alterations in the composition and structure of the gut microbiota. Our results demonstrate the importance of monitoring hypoxia indicators in future clinical practice.