Nature and Science of Sleep最新文献

Objective: Both tobacco use and obstructive apnea-hypopnea syndrome (OSA) can affect sleep, and it is speculated that tobacco use may further affect the sleep of those with OSA. Our primary objective is to clarify the associations between tobacco use and the macrostructure and microstructure of sleep in patients with OSA.

Methods: This retrospective study encompasses a cohort of 1017 patients who were hospitalized between January 2020 and January 2023 for the investigation of sleep disorders. Rigorous inclusion criteria were applied, and all patients underwent a comprehensive polysomnography (PSG) assessment and completed a Pittsburgh Sleep Quality Index (PSQI) questionnaire.

Results: Patients with OSA who concurrently used tobacco exhibited markedly inferior sleep quality than those who did not. Notably, there was no association between the degree of tobacco dependence and sleep quality. Those with OSA who used tobacco demonstrated a significant prolongation of stage 1 light sleep and a reduction of deep sleep duration (N3). In this group, those who demonstrated poor sleep quality had more pronounced alterations in light sleep stages with prolonged N1 and shortened N2 stages.

Conclusion: Our findings reveal a substantial reduction in sleep quality amongst OSA patients who also use tobacco, compared to those with OSA who do not use tobacco. The rate of poor sleep quality was not linearly associated with the level of tobacco dependence. Tobacco use was associated with alterations in both light and slow wave sleep in those with OSA. Importantly, the effects of tobacco dependence on sleep structure were more pronounced in those with more severe OSA.

Purpose: To develop a sleep-staging algorithm based on support vector machine (SVM) and extreme gradient boosting model (XB Boost) and evaluate its performance.

Methods: In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. One hundred and twenty training sets and 80 test sets were randomly composed of the first 200 groups of data from the SHH1 database. The polysomnography (PSG) data of 20 real individuals in the clinic were selected as the experimental data. The C3 electroencephalogram (EEG), left and right electrooculogram (EOG), electromyogram (EMG), and other signals were analyzed. Finally, the stages were adjusted based on human sleep laws. The standard staging of the database and the doctor's diagnosis staging was used as the standard.

Results: The SHHS1 database test results were as follows: the average accuracy was 83.24%, the precision and recall of Stage Wake and Stage 2 NREM sleep (N2) were over 80%, and the precision, F1-Score and recall of Stage 3 NREM sleep (N3) and Rapid Eye Movement (REM) were more than 70%. The clinical data test results were as follows: the average accuracy rate was 76.37%; for Wake and N3, the precision reached 85%; for Wake, N2, and REM, the recall rate reached over 70%; for Wake, the F-1 Score reached over 90%.

Conclusion: This study shows that the sleep staging results of the algorithm for the database and clinical data were similar. The staging results meet the requirements at the medical level.

Mendelian randomization (MR) has become an influential method for elucidating causal links between sleep traits and disorders, and health outcomes. This article provides sleep medicine specialists with an overview of MR, emphasizing its applications and limitations in health research, particularly in the context of sleep research. The article addresses key challenges in conducting and interpreting MR studies on sleep, focusing on the core assumptions of relevance, exchangeability, and exclusion restriction. The importance of proper genetic instrument selection, bias mitigation, and cautious result interpretation is emphasized. Strategies are recommended to enhance the quality of MR studies in sleep medicine, including collaborations between MR experts and sleep specialists. The paper also explores sleep medicine-specific issues like analyzing binary traits and addressing heterogeneity in pooled analyses. Guidance is provided on transparent reporting of MR findings, stressing the need for comprehensive effect estimates, confidence intervals, and p-values. We conclude by advocating for rigorous MR implementation in sleep research to deepen our understanding of sleep-health relationships. By following best practices in study design, analysis, and reporting, researchers can reinforce the credibility and impact of MR findings in sleep medicine, ultimately improving patient care and public health strategies.

Background: Obesity is a significant risk factor for obstructive sleep apnea (OSA). The weight-adjusted-waist index (WWI) reflects weight-independent centripetal obesity. Our study aims to evaluate the relationship between WWI and OSA.

Methods: The data used in the current cross-sectional investigation are from the National Health and Nutrition Examination Survey (NHANES), which was carried out between 2017 and 2020. We utilized weighted multivariable-adjusted logistic regression to evaluate the relationship between WWI and the risk of OSA. In addition, we applied various analytical methods, including subgroup analysis, smoothing curve fitting, threshold effect analysis and the receiver operating characteristic (ROC) curve. To further explore the relationship, we conducted a MR study using genome-wide association study (GWAS) summary statistics. We performed the main inverse variance weighting (IVW) method along with other supplementary MR methods. In addition, a meta-analysis was conducted to provide an overall evaluation.

Results: WWI was positively related to OSA with the full adjustment [odds ratio (OR)=1.14, 95% confidence interval (95% CI): 1.06-1.23, P<0.001]. After converting WWI to a categorical variable by quartiles (Q1-Q4), compared to Q1 the highest WWI quartile was linked to an obviously increased likelihood of OSA (OR=1.26, 95% CI: 1.06-1.50. P=0.01). Subgroup analysis revealed the stability of the independent positive relationship between WWI and OSA. Smoothing curve fitting identified a saturation effect of WWI and OSA, with an inflection point of 11.62. In addition, WWI had the strongest prediction for OSA (AUC=0.745). Sensitivity analysis was performed to verify the significantly positive connection between WWI and stricter OSA (OR=1.18, 95% CI: 1.05-1.32, P=0.005). MR meta-analysis further supported our results (OR=2.11, 95% CI: 1.94-2.30, P<0.001). Sensitivity analysis confirmed the robustness and reliability of these findings.

Conclusion: WWI was significantly associated with the risk of OSA, suggesting that WWI could potentially serve as a predictor for OSA.

Objective: This study aims to analyze the tongue body shape and upper airway anatomical parameters in patients with Obstructive Sleep Apnea (OSA) and to explore the anatomical causes of OSA.

Methods: A total of 345 subjects participated in this study. Lateral pharyngeal images of the upper respiratory tract were captured in both normal and mandibular advancement states using X-ray plain film. Measurements were taken for the following parameters: Tongue Length, Tongue Thickness, Distance from the Mandibular Plane to the Hyoid, Soft Palate Length, Posterior Oropharyngeal Depth, Palatal Airway Space, Tongue Depth Space, and Mental Posterior Space. The correlation between the Apnea-Hypopnea Index (AHI) and these upper airway anatomical factors was analyzed using both univariate and multivariate analyses to develop a predictive model for OSA.

Results: The anatomical structure of the upper airway in patients with OSA is narrower compared to non-OSA individuals, and these patients exhibit a longer and thicker tongue. During mandibular advancement, the pharyngeal airway widens; however, the tongue length decreases while its thickness increases. Univariate correlation analysis revealed that the severity of OSA was significantly associated with tongue length, the ratio of tongue length to tongue thickness, the distance from the mandibular plane to the hyoid, soft palate length, and body mass index (BMI) in both the normal position and during mandibular advancement (p < 0.001). Multivariate linear analysis indicated that the severity of OSA is linked with the mandibular plane to hyoid distance in the normal position (MPH(N)) and BMI. A nomogram was utilized to develop a predictive model for OSA, achieving an area under the receiver operating characteristic curve of 0.838.

Conclusion: The pathogenesis of OSA is related to pharyngeal anatomy and tongue length in the state of mandibular advancement, which can be predicted by the measurement indexes of normal and anterior mandibular displacement lateral pharyngeal radiograph. This may potentially aid in early screening and diagnosis of OSA.

Purpose: Sleep disruption (SD) impairs sustained attention, and impairment is quantified with the psychomotor vigilance test (PVT) in humans. In rats, food restriction attenuates SD's effects on sustained attention, limiting translation of rodent vigilance tests. The goal of the current study was to determine if a rodent PVT (rPVT) requiring high baseline performance using food restriction and reinforcement is sensitive to the effects of SD.

Methods: Male Long-Evans rats (n=4) were trained on the rPVT using food reinforcement. Once baseline acquisition criteria were achieved, rats experienced acute SD using an automated sweep bar that moved across the home cage. Rats were tested in the rPVT the day following SD to assess performance-impairing effects.

Results: SD significantly increased lapses, and this effect was specific to shorter response-stimulus intervals. Decreased percent correct responses and increased slow reaction times were also found. These data suggest that many of the performance-impairing effects of SD are not attenuated by food restriction in this procedure.

Conclusion: The rPVT is sensitive to the performance impairing effects of SD in food restricted rats, a common methodology used to train and maintain performance on operant behavioral tests. Thus, food restriction does not appear to attenuate the effects of SD in all attention-related behavioral procedures.

Objective: To explore the characteristics of elderly patients with central sleep apnea (CSA).

Methods: This retrospective study divided 123 patients with CSA into elderly and non-elderly groups, and compared them in terms of demographic characteristics (age, BMI, etc), underlying diseases (hypertension, coronary heart disease, and cardiac arrhythmias, etc). and polysomnography parameters. Multiple linear regression analysis was conducted to investigate the potential risk factors of central apnea index (CAI).

Results: Compared with the non-elderly group, patients in the elderly group had lower body mass index, a higher proportion of comorbidities of coronary heart disease, arrhythmias, and diabetes, lower apnea-hypopnea index (AHI), obstructive apnea index (OAI) and oxygen desaturation index (ODI). CAI of the elderly group showed a trend higher than that of the non-elderly group with no statistical difference. However, the ratio of CAI to AHI in the elderly group was significantly higher (0.264 vs 0.154, P=0.003). True CSA was less prevalent than companion CSA in both groups. The results of multiple regression analysis indicated CAI was independently associated with age (β=0.256, P=0.005), OAI (β=-0.543, P<0.001), MAI (β=-0.267, P=0.005), ODI (β=0.538, P<0.001), heart failure (β=0.300, P<0.001).

Conclusion: CSA typically coexists with other types of sleep apnea. Elderly CSA patients have characteristics such as a lower BMI, and a milder decrease in blood oxygen saturation, along with higher prevalence of arrhythmia and coronary heart disease. Age may be a potential risk factor for CSA.

Background: White blood cell (WBC) and its subset counts are standard, inexpensive, direct markers of inflammation. Obstructive sleep apnea (OSA) is implicated in changes in inflammation markers, and sex differences are evident in both OSA and inflammation. It is unknown whether sex modulates the relationship between OSA severity and leukocyte measures.

Methods: 1222 patients (914 males, 308 females) underwent overnight laboratorial polysomnography and measurement of WBC and its subset (lymphocyte, neutrophil, monocyte) counts. Patients were divided into primary snoring and mild, moderate, and severe OSA groups, and differences in leukocyte parameters were analyzed separately by sex in multivariable analyses.

Results: In multiple regression models, higher apnea-hypopnea index (AHI) was independently associated with neutrophil counts only in men, and with higher total WBC, lymphocyte and monocyte counts both in women and men. Further ordinal logistic regression analysis revealed a significant association between AHI and total WBC (OR 1.87, 95% CI 1.09-3.23) and neutrophil (OR 1.77, 95% CI 1.02-3.07) counts in men only. Correlation analysis also revealed more robust relationships between leukocyte measures and cardiometabolic risk markers in men than in women.

Conclusion: This study provides novel data suggesting a significant association between neutrophil count and OSA severity only in men but not women. Similarly, the relationship between leukocyte parameters and cardiometabolic risk markers were more pronounced in men than women. Our findings suggest a sex-specific impact of OSA on leukocyte measures and on their relationship with indices of cardiometabolic risk.

Background: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but its long-term efficacy is limited by poor patient adherence. This study aimed to develop and validate a predictive nomogram for CPAP non-adherence in patients with OSA.

Methods: This is a secondary analysis of a retrospective study. A cohort of 695 Danish patients with OSA were followed for 3 years after initiating CPAP therapy. Independently associated factors were evaluated using multivariate Cox regression, and then nomogram predicting adherence to CPAP use were constructed. The discrimination of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA).

Results: Pulmonary disease, oxygen desaturation index (ODI), Epworth Sleepiness Score (ESS) and severity of OSA were identified as predictors and incorporated into the nomogram. The nomogram demonstrated good discrimination with concordance index in training dataset (0.73, 95% CI: 0.69-0.78) and validation dataset (0.72, 95% CI: 0.66-0.79). ROC curve, calibration curve, and DCA indicated the nomogram had good clinical utility.

Conclusion: This study provided an effective nomogram for predicting CPAP non-adherence in OSA patients.