Nature and Science of Sleep最新文献

Objective: This study aims to explore the mediating role of social support among the elderly in the relationship between psychological resilience and sleep quality.

Methods: From December 2024 to March 2025, researchers conducted a questionnaire survey among elderly individuals aged ≥65 years in Shaanxi Province and Shanghai Municipality. The Connor-Davidson Resilience Scale, Perceived Social Support Scale, and Pittsburgh Sleep Quality Index (PSQI) were used to assess the psychological resilience, social support, and sleep quality of the elderly. Structural equation modeling was employed to explore the relationships among the variables.

Results: This study included a total of 359 elderly participants, with an average sleep quality score of 12.69 (standard deviation = 4.15), indicating poor sleep quality. Psychological resilience was significantly associated with sleep quality in the elderly (r = -0.781, p < 0.001). In the model constructed in this study, social support was considered a partial mediating factor in the relationship between psychological resilience and sleep quality among the elderly, with the mediating effect accounting for 33.7% of the total effect (indirect effect β = -0.070, 95% CI = -0.108 to -0.025, p = 0.006).

Conclusion: In the elderly population, psychological resilience and sleep quality are significantly associated, with social support acting as a mediator in this relationship. The above findings provide scientific basis for the formulation of intervention strategies.

Purpose: This prospective study developed and validated a nomogram to assess the likelihood of early postoperative sleep disturbance (PSD occurring within 7 days after surgery) following non-cardiovascular surgical procedures.

Participants and study protocol: The study enrolled 3851 patients receiving non-cardiac procedures recruited in the First Affiliated Hospital of USTC from April 2024 to December 2024. These 3851 patients were randomly allocated into training cohort (n=2682, 70%) and validation cohorts (n=1169, 30%). Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by logistic regression analyses (univariate and multivariate) to identify independent risk factors. Based on these identified factors, a prognostic nomogram was developed and underwent comprehensive validation, including receiver operating characteristic (ROC) curve assessment, calibration plotting, and decision curve analysis (DCA) to evaluate its discriminative performance and clinical applicability.

Results: About 37.7% of patients developed PSD within the first 7 postoperative days, with 12.1% persisting at 1 month after surgery. The analysis revealed ten independent PSD risk factors (all p < 0.05): female, higher ASA class (III), moderate-to-severe anemia, dissatisfaction with ward environment, anxiety, non-use of dexmedetomidine, older age, extended anesthesia time, lower sufentanil doses and higher postoperative NRS score. The nomogram incorporating these ten predictors demonstrated excellent discriminative performance, with AUC values of 0.826 (95% CI: 0.810-0.843) in the training cohort and 0.822 (95% CI: 0.797-0.847) in the validation cohort, complemented by optimal calibration.

Conclusion: The prediction model incorporating ten routinely available clinical variables demonstrated excellent predictive accuracy and good calibration, highlighting its clinical utility in identifying short-term high-risk PSD patients (within 7 days postoperatively). This tool facilitates timely interventions to reduce PSD incidence and improve recovery outcomes.

Introduction: Sleep deprivation often leads to marked neurobehavioral and cognitive deficits, yet few well-defined interventions exist to address these effects. Dexmedetomidine (DEX), a highly selective α₂-adrenoceptor agonist, possesses sedative, hypnotic, analgesic, and sympathetic-blocking properties, closely mimic natural sleep state. In this study, we aim to investigate whether DEX protects hippocampal tissue against rapid eye movement sleep deprivation (RSD)-induced injury in rats and to explore the underlying molecular mechanisms.

Methods: In this study, a rapid eye movement sleep deprivation (RSD) rat model was created using a modified multi-platform method. The influence of dexmedetomidine (DEX) on hippocampal tissue morphology, the BDNF/TrkB signaling pathway, and cognitive function was then evaluated. Group comparisons were analyzed using one-way ANOVA followed by appropriate post hoc tests.

Results: In comparison with the control group, DEX significantly alleviated the impaired spatial learning and memory as reflected escape latency and increased the time spent in the garget quadrant. ANA-12 reversed these improvements, indicating DEX's cognitive benefits. HE staining showed that DEX protected neurons from RSD-induced injury by preserving structural integrity and TUNEL assay demonstrated reduced neuron apoptosis in the DEX group. Co-treatment with ANA-12 abolished these protective effects, resulting in neuronal damage and apoptosis levels similar to those observed in RSD rats. Moreover, compared with the level of TNA alpha in RSD rats, IL 6, IL 1beta and MDA levels were lower in the hippocampus of DEX group, while SOD activity was enhanced. Western blot analysis revealed that DEX increased hippocampal BDNF (0.586 ± 0.036 vs 0.315 ± 0.034, ~1.86‑fold, P < 0.01), TrkB (0.774 ± 0.039 vs 0.518 ± 0.033, ~1.49‑fold, P < 0.01) and pro-TrkB expression. However, co-administration of ANA‑12 abolished these effects, returning expression levels close to those in the RSD group, implying that DEX's neuroprotection is mediated via the BDNF/TrkB pathway.

Conclusion: These findings indicate that DEX exerts neuroprotective effects in RSD by activating the BDNF/TrkB pathway, offering valuable evidence for DEX-based therapeutic approaches to sleep deprivation-related brain injury.

Uric acid, the end product of purine metabolism, is closely associated with metabolic disorders, including gout and metabolic syndrome. Emerging evidence highlights a bidirectional relationship between uric acid metabolism and sleep regulation. Elevated uric acid levels can adversely affect sleep quality via oxidative stress and neuroinflammatory pathways, while sleep deprivation may promote uric acid synthesis and impair its excretion. This reciprocal interaction may form a vicious cycle, potentially accelerating the onset and progression of various metabolic diseases. This review summarizes recent clinical and experimental findings, focusing on the molecular mechanisms underlying the bidirectional regulation between uric acid metabolism and sleep. The implications of this relationship in the pathophysiology of metabolic disorders are discussed. Understanding this interplay underscores the importance of targeting uric acid levels and sleep quality as integrated strategies for managing metabolic diseases. These insights provide a foundation for the development of novel therapeutic interventions designed to enhance clinical outcomes in metabolic syndrome.

Background: Obstructive sleep apnea (OSA) affects nearly one billion adults globally and significantly increases cardiovascular and metabolic risks, largely due to insulin resistance (IR). The triglyceride-glucose (TyG) index is a validated, cost-effective surrogate for IR. However, studies report conflicting associations between the TyG index and the presence or severity of OSA, possibly due to confounding factors such as age, gender, and obesity (BMI). This study aimed to clarify the independent TyG-OSA relationship by adjusting for confounders using propensity score matching (PSM).

Methods: This cross-sectional study included 394 patients with OSA (apnea-hypopnea index [AHI] ≥5 events/hour) and 285 controls (AHI <5 events/hour). PSM (1:1) balanced groups for age, gender, height, weight, and BMI. Differences in the TyG index between groups and across OSA severity (mild, moderate, severe) were analyzed pre- and post-PSM. Predictive performance was assessed using receiver operating characteristic (ROC) curves.

Results: Pre-PSM, the TyG index was significantly higher in patients with OSA than in controls (p < 0.001) and increased with severity (p < 0.001). Post-PSM (185 matched pairs), the TyG index remained significantly higher in moderate or severe OSA versus matched controls (p < 0.05) but not in mild OSA. ROC analysis demonstrated that PSM reduced the area under the curve (AUC) for predicting any OSA (from 0.709 to 0.628; p < 0.001) but substantially increased the AUC for predicting severe OSA (from 0.752 to 0.843; p < 0.001), improving sensitivity (0.754 to 0.796) and specificity (0.796 to 0.843).

Conclusion: This PSM analysis provides robust evidence of an independent association between the TyG index and OSA, particularly in moderate-to-severe cases. The TyG index demonstrates strong predictive value for severe OSA, supporting its utility for risk stratification and monitoring in clinical practice.

This case report describes a patient with neck pain who was recommended by his gym coach to perform chin tuck exercises to manage his pain. After a period of 3 weeks of daily chin tuck exercise, each session consisting of 10 repetitions of a 20-second hold, performed three times per day, the patient started to experience symptoms of snoring, witnessed gasping, morning headache, and excessive daytime sleepiness. Polysomnography performed by a sleep physician confirmed the diagnosis of mild obstructive sleep apnea with an apnea-hypopnea index of 10 times/h and a significant desaturation of oxygen of 86% (BMI: 24 kg/m²). The patient was recommended to discontinue the exercise. His obstructive sleep apnea symptoms disappeared in 2 weeks, and follow-up polysomnography was conducted, which confirmed the complete resolution of obstructive sleep apnea with improved sleep parameters. To our knowledge, this is the first case report linking chin tuck exercise to the onset of obstructive sleep apnea, emphasizing the need for clinicians, physiotherapists, and fitness coaches to be aware of this potential risk.

Purpose: Could a simple sleep questionnaire reveal more than just obstructive sleep apnea (OSA) risk? Our study suggests it might. OSA is the most prevalent yet frequently underdiagnosed sleep disorder. Vitamin D deficiency shares multiple risk factors with OSA, such as obesity, older age, and hypertension. The STOP-Bang questionnaire, a simple, self-report screening tool originally designed to identify individuals at risk for OSA, may also highlight those susceptible to vitamin D deficiency. Therefore, this study aimed to investigate the associations between STOP-Bang parameters and vitamin D deficiency risk among elderly individuals.

Materials and methods: This prospective observational study included 451 elderly individuals from the Healthy Aging Project at Chang Gung Memorial Hospital. Participants completed the STOP-Bang questionnaire for OSA screening and underwent clinical evaluation and blood testing for serum 25(OH)D levels.

Results: There was a statistically significant inverse correlation between vitamin D levels and body mass index (BMI). Additionally, participants with larger neck circumferences and female sex were significantly more likely to have vitamin D deficiency. In addition, people with more daytime tiredness had significantly lower serum 25(OH)D levels than did those in the other group.

Conclusion: We suggest screening for vitamin D deficiency among OSA patients with several risk factors identified with the STOP-Bang parameters, such as a larger neck circumference, higher BMI levels, and increased daytime sleepiness, to enable timely interventions addressing vitamin D deficiency and its associated consequences. Further research should investigate the causal relationship between vitamin D deficiency and OSA risk in elderly populations.

Background: Carotid atherosclerosis (CAS) is a critical cardiovascular complication in elderly patients with obstructive sleep apnea (OSA). Current risk assessment tools inadequately capture OSA-specific pathophysiological mechanisms for CAS prediction in this high-risk population.

Methods: This multicenter retrospective study included 1196 elderly patients (≥60 years) with polysomnography-confirmed OSA from six tertiary hospitals in China. CAS was diagnosed by carotid ultrasound. LASSO (Least Absolute Shrinkage and Selection Operator) regression identified optimal predictive features from 18 candidate variables. Four machine learning algorithms were developed and validated using 5-fold cross-validation. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis.

Results: Among participants, 273 (22.8%) had CAS. LASSO regression selected eight optimal features. XGBoost achieved the best performance with test AUC of 0.854, accuracy of 79.8%, sensitivity of 81.2%, and specificity of 78.5%. SHAP analysis revealed systolic blood pressure (importance: 0.3148) and percentage of sleep time with oxygen saturation <90% (T90, importance: 0.2660) as the most influential predictors, surpassing traditional apnea-hypopnea index. Other significant predictors included alcohol consumption, mean oxygen saturation, body mass index, age, platelet count, and smoking status.

Conclusion: This study developed the first machine learning-based CAS prediction model for elderly OSA patients, achieving clinically relevant performance (AUC=0.854). The prominence of T90 over conventional apnea-hypopnea index suggests nocturnal hypoxemic burden is more important than respiratory event frequency for cardiovascular risk stratification in this population.

Purpose: Given rising numbers in sleep disorders like insomnia and insufficient availability of treatment options, the need for well-validated digital interventions rises. This pilot study aims at assessing the feasibility of an app-program which combines sleep-training based on core elements of Cognitive Behavioural Therapy for Insomnia (CBT-I) with reliable sleep-monitoring based on heart rate variability via an ECG-sensor.

Patients and methods: About 48 participants (26 females) aged 30-73 (M = 50.33 ± 11.88) were included in the study. At the beginning of the baseline (T0), at start (T1) and end (T2) of the 6-week training phase as well as 4 weeks after the end of the program (T3; follow-up) several questionnaires assessing sleep quality, insomnia severity, general psychological symptom severity, depression, anxiety as well as quality of life were completed. Furthermore, ambulatory polysomnography (PSG) was conducted three times at T0, T1 and T2. General feasibility was assessed by conducting interviews.

Results: Overall, the app-program as well as the study protocol was deemed as feasible according to the participants, besides some difficulties regarding app-instructions and certain technical issues, as well as some expected complaints about worse sleep quality during PSG-recordings. For statistical results, insomnia severity (p < 0.001, r = 0.67), sleep quality (p < 0.001, r = 0.56), general psychological symptom severity (p < 0.001, r = 0.68), depression (p = 0.002, r = 0.50) and anxiety (p < 0.001, r = 0.60) improved significantly during the training phase, while quality of life [physical (p = 0.014, r = 0.41) and psychological health (p = 0.049, r = 0.35)] improved significantly during the follow-up-period. PSG data revealed a significant decrease in Wake After Sleep Onset over the course of the study (p = 0.025, r = 0.36), yet no significant changes were found for other sleep parameters.

Conclusion: The app-program was largely feasible and potentially effective in improving sleep and well-being. PSG-derived WASO changes highlight the value of objective sleep measures. Future studies should refine protocols and include control conditions for greater generalizability.