Nature and Science of Sleep最新文献

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.

Purpose: Sleep deprivation (SD), a common sleep disease in clinic, has certain risks, and its pathogenesis is still unclear. This study aimed to identify ferroptosis-cuproptosis-related genes (FCRGs) associated with SD through bioinformatics and machine learning, thus elucidating their biological significance and clinical value.

Methods: SD-DEGs were obtained from GEO. We intersected key WGCNA module genes of DE-FCRGs with SD-DEGs to obtain SD-DE-FCRGs. GO and KEGG analyses were performed. Machine learning was used to screen SD-DE-FCRGs, and filtered genes were intersected to obtain SD characteristic genes. ROC curves were used to evaluate the accuracy of SD characteristic genes. CIBERSORT was used to analyze the correlation between SD-DE-FCRGs and immune cells. We constructed a ceRNA network of SD-DE-FCRGs and used DGIbd to predict gene drug targets.

Results: The 156 DEGs were identified from GSE98566. Five SD-DE-FCRGs from DE- FCRGs and SD-DEGs were analyzed via WGCNA, and enrichment analysis involved mainly ribosome regulation, mitochondrial pathways, and neurodegenerative diseases. Machine learning was used to obtain Four SD-DE-FCRGs (IKZF1, JCHAIN, MGST3, and UQCR11), and these gene analyses accurately evaluated the distribution model (AUC=0.793). Immune infiltration revealed that SD hub genes were correlated with most immune cells. Unsupervised cluster analysis revealed significant differential expression of immune-related genes between two subtypes. GSVA and GSEA revealed that enriched biological functions included oxidative phosphorylation, ribonucleic acid, metabolic diseases, activation of oxidative phosphorylation, and other pathways. Four SD-DE-FCRGs associated with 29 miRNAs were identified via the construction of a ceRNA network. The important target lenalidomide of IKZF1 was predicted.

Conclusion: We first used bioinformatics and machine learning to screen four SD-DE-FCRGs. These genes may affect the involvement of infiltrating immune cells in pathogenesis of SD by regulating FCRGs. We predicted that lenalidomide may target IKZF1 from SD-DE-FCRGs.

Background: Phase-amplitude coupling (PAC) between the phase of low-frequency signals and the amplitude of high-frequency activities plays many physiological roles and is involved in the pathological processed of various neurological disorders. However, how low-frequency and high-frequency neural oscillations or information synchronization activities change under chronic central hypoxia in OSA patients and whether these changes are closely associated with OSA remains largely unexplored. This study arm to elucidate the long-term consequences of OSA-related oxygen deprivation on central nervous system function.

Methods: : We screened 521 patients who were clinically suspected of having OSA at our neurology and sleep centers. Through polysomnography (PSG) and other clinical examinations, 103 patients were ultimately included in the study and classified into mild, moderate, and severe OSA groups based on the severity of hypoxia determined by PSG. We utilized the phase-amplitude coupling (PAC) method to analyze the modulation index (MI) trends between different frequency bands during NREM (N1/N2/N3), REM, and wakefulness stages in OSA patients with varying severity levels. We also examined the correlation between the MI index and OSA hypoxia indices.

Results: Apart from reduced N2 sleep duration and increased microarousal index, the sleep architecture remained largely unchanged among OSA patients with varying severity levels. Compared to the mild OSA group, patients with moderate and severe OSA exhibited higher MI values of PAC in the low-frequency theta phase and high-frequency beta amplitude in the frontal and occipital regions during N1 sleep and wakefulness. No significant differences in the MI of phase-amplitude coupling were observed during N2/3 and REM sleep. Moreover, the MI of phase-amplitude coupling in theta and beta bands positively correlated with hypoxia-related indices, including the apnea-hypopnea index (AHI) and oxygenation desaturation index (ODI), and the percentage of oxygen saturation below 90% (SaO2<90%).

Conclusion: OSA patients demonstrated increased MI values of theta phase and beta amplitude in the frontal and occipital regions during N1 sleep and wakefulness. This suggests that cortical coupling is prevalent and exhibits sleep-stage-specific patterns in OSA. Theta-beta PAC during N1 and wakefulness was positively correlated with hypoxia-related indices, suggesting a potential relationship between these neural oscillations and OSA severity. The present study provides new insights into the relationship between neural oscillations and respiratory hypoxia in OSA patients.