Nature and Science of Sleep最新文献

Background: Premature birth poses a major challenge in global obstetric clinical practice. The relationship between preterm infants and behavioral problems in school-aged children remains debatable, and the mediating role of sleep-disordered breathing (SDB) in this connection has not been investigated. This study aimed to address these gaps through a large-scale cross-sectional survey.

Methods: We recruited 18,138 children aged 6-10 from schools. Data on demographics, prematurity, SDB, and childhood behavioral problems were collected. The Paediatric Sleep Questionnaire (PSQ), a validated screening tool, assessed SDB symptoms, and the Conners' Parent Rating Scale (CPRS) evaluated behavioral problems. Path analysis with bootstrap methods was used for statistical analysis.

Results: Among 18,138 participants, 8% (n = 1,450) were premature. After adjusting for age, gender, BMI z-score, maternal age, and maternal education level, prematurity showed a positive association with total PSQ score (B = 0.411, p < 0.01). Higher total PSQ scores were significantly associated with all six CPRS dimensions (all p < 0.05). While prematurity was not directly associated with Conduct, Psychosomatic, Impulsive-hyperactive, or Hyperactivity scores in CPRS (all p > 0.05), it demonstrated significant associations with Learning problems (β = 0.063, p = 0.005) and Anxiety scores (β = 0.076, p = 0.003). Mediation analysis showed PSQ accounted for a large proportion of associations between prematurity and Conduct, Psychosomatic, Impulsive - hyperactive, and Hyperactivity problems (95% Bootstrap CI excluded 0).

Conclusion: Premature infants may exhibit behavioral problems significantly associated with SDB, though our cross-sectional design precludes causal inference and parent-reported SDB severity may bias true associations. Future studies should utilize longitudinal cohorts to explore whether SDB is involved in the relationship between prematurity and behavioral problems (eg, anxiety). Additionally, they should conduct pilot randomized controlled trials of SDB interventions in preterm infants to assess neurodevelopmental benefits. Final conclusions require subsequent causal validation.

Purpose: Insomnia places significant physical and psychological burdens on female cancer patients undergoing chemotherapy, affecting their quality of life. This study aimed to investigate the prevalence of insomnia and its associated factors in female outpatients receiving chemotherapy.

Patients and methods: A cross-sectional study was conducted with female cancer patients receiving chemotherapy. The questionnaire included items assessing sociodemographic and clinical characteristics. Insomnia was measured using the Insomnia Severity Index.

Results: A total of 206 female patients undergoing chemotherapy were included, with a mean age of 56.1 years (SD ± 11.7). The most common cancer types were breast (57.3%), gastrointestinal (22.8%), and gynecological malignancies (19.9%). Based on the Insomnia Severity Index (ISI), 34.0% of participants had subclinical insomnia and 17.0% had clinical insomnia. Increasing age was significantly associated with lower insomnia severity (aOR: 0.971; 95% CI: 0.945-0.998; p = 0.038). Among gynecological cancer patients, insomnia was more prevalent in those receiving treatment for metastatic disease (76.2% vs 35.0%). Psychiatric conditions (depression and/or anxiety) requiring medication and the presence of pain were both significantly associated with higher rates of insomnia (p < 0.001 for both).

Conclusion: Insomnia was highly prevalent among female cancer patients undergoing chemotherapy. Younger age, presence of pain, psychiatric comorbidities (particularly depression and/or anxiety), and metastatic disease status emerged as significant correlates. Considering the relationship between insomnia and physical and psychological distress, it is anticipated that regular screening and treatment approaches for insomnia will contribute to the holistic cancer care process by improving patient quality of life.

Background: Obstructive Sleep Apnea (OSA) patients experience significant illness uncertainty, impacting coping. Social support mitigates uncertainty, while coping styles influence management. Research predominantly examines individual patients, neglecting dyadic interactions between patients and co-residents.

Objective: To examine the dyadic interrelationships of illness uncertainty, social support, and coping styles in OSA patient-co-resident pairs using the Actor-Partner Interdependence Model (APIM).

Methods: A cross‑sectional study of 242 patient-co‑resident dyads from a tertiary hospital examined self‑reported illness uncertainty, social support, and coping styles. APIM analyzed actor and partner effects.

Results: Patients reported higher illness uncertainty (P<0.001), whereas co‑residents reported greater social support (P<0.001). Social support was positively associated with active coping and negatively associated with passive coping within dyads. Actor effects indicated that illness uncertainty in both patients and co-residents was associated with lower levels of their own social support, which in turn correlated with decreased active coping and increased passive coping (β=0.203 and 0.038, P<0.05). Partner effects analyses indicated that one member's uncertainty or social support was associated with the other member's coping via specific indirect paths.

Conclusion: The findings reveal bidirectional, dyadic interdependence among illness uncertainty, social support, and coping styles in OSA patient-co-resident pairs, with social support appearing as a prominent within‑individual associative pathway. These results support considering family‑oriented strategies that aim to strengthen mutual social support to be explored further as a means to promote adaptive coping in this population.

Obstructive sleep apnea (OSA) is a global health problem. Patients with OSA may experience the upper airway collapsing during sleep, resulting in decreased oxygen saturation and sleep disruption, which is characterized by hypoxemia and sleep fragmentation, thereby reducing sleep quality and harming quality of life. In addition, OSA is associated with the occurrence of a variety of systemic diseases, which brings a huge burden to public health. Therefore, timely diagnosis of OSA is crucial. Polysomnography (PSG) is the most accurate method for diagnosing OSA at present, which can be used to determine the severity of sleep apnea and to monitor therapeutic efficacy. However, the PSG is difficult to be popularized because of its cumbersome operation, patients' non-compliance, and expensive medical expenses. Therefore, it is imperative to find a convenient and fast OSA diagnosis method. In recent years, the development of machine learning prediction models and their application in the medical field have provided a new method for OSA severity diagnosis, making it possible to identify OSA severities efficiently and accurately. The purpose of this paper is to review relevant research on machine learning prediction models for OSA severity diagnosis and to provide sleep specialists with recommendations for more effective early identification and diagnosis of OSA. In addition, the challenges faced by machine learning at the level of diagnostic applications are summarized and future trends are envisioned.

Background: Insomnia significantly impairs both mental and physical health, and its bidirectional relationship with chronic diseases exacerbates outcomes for both conditions. While insomnia risk factors are well-studied in general populations, little is known about its prevalence and determinants among chronic disease patients in Bangladesh. Using machine learning (ML) alongside traditional analyses may improve prediction and early identification of insomnia risk in this high-vulnerability group.

Methods: This cross-sectional study recruited 1,222 adult chronic disease patients from healthcare facilities in Dhaka and Chattogram between May and November 2024. Insomnia was assessed using the Insomnia Severity Index (ISI-7). Multivariable logistic regression identified significant risk and protective factors. Six ML classifiers, K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost), were trained and tested (with Synthetic Minority Over-sampling Technique for class imbalance), and model performance was evaluated using accuracy, precision, F1 score, log loss, and the area under the receiver operating characteristic curve (AUC-ROC). Feature importance was determined via SHapley Additive exPlanations (SHAP) and gain values.

Results: Insomnia affected 41.3% of patients. Risk factors included female gender, joint family, urban residence, smokeless tobacco and substance use, prolonged daytime napping, late disease onset, presence of other chronic diseases, and unmet mental healthcare needs. Protective factors were physical activity, 7-9 hours of nighttime sleep, met mental healthcare needs, and notably, presence of urinary disease. Among ML models, CatBoost outperformed others (accuracy 71.67%, AUC 77.27%, F1 score 71.23%), followed closely by RF and SVM. Feature importance analysis consistently identified mental healthcare need fulfillment and nighttime sleep duration as the strongest predictors of insomnia.

Conclusion: Insomnia was common among Bangladeshi chronic disease patients and linked to sociodemographic, behavioral, clinical, and mental health factors. CatBoost and other ML models showed strong predictive ability, supporting their use in early screening. Prospective studies are needed to validate these findings and guide targeted interventions.

Objective: This study aimed to appraise the association between urinary enterolactone and sleep quality in American obese adults.

Methods: Our study analyzed data from 913 obese adults (2005-2008) in the National Health and Nutrition Examination Survey (NHANES) database. Enterolactone was tested in urine specimens. The Pittsburgh Sleep Quality Index (PSQI)-like measure reconstructed for NHANES based on prior literature was used to assess sleep quality. Multivariable logistic regression models were used to calculate the associations between urinary enterolactone and sleep quality in American obese adults. We also carried out linear tests utilizing restricted cubic splines to investigate the dose-response relationship between urinary enterolactone and sleep quality. Furthermore, we conducted stratified and interaction analyses to determine whether this relationship remained consistent across various subgroups.

Results: A total of 913 obese participants were included in the analyses. After adjusting for potential confounding factors, each one-unit change in log-transformed urinary enterolactone was associated with 8% lower odds of poor sleep quality (OR=0.92, 95% CI: 0.85-0.99, p=0.027). When urinary enterolactone was presented in tertiles, this inversely correlation became more significant with increasing levels of urinary enterolactone. Moreover, in stratified analyses, the relationship between urinary enterolactone and sleep quality persisted.

Conclusion: Urinary enterolactone, an indicator of gut microbiome health, is inversely associated with poor sleep quality in American obese adults.

Purpose: Sleep apnea is a prevalent sleep disorder with serious health implications. This study introduces ApneaWhisper, a Transformer-based audio segmentation model designed for noninvasive detection of sleep apnea subtypes using PSG-Audio data.

Patients and methods: We utilized a PSG-Audio dataset from 284 patients. ApneaWhisper leverages a pretrained Whisper encoder to extract 10 ms-resolution frame-level features from 20-second audio clips. A lightweight Transformer decoder with token-based segmentation and a classification head aggregates these features for both frame-level and clip-level predictions. The model was fine-tuned using class-balanced cross-entropy loss to address data imbalance across apnea subtypes.

Results: ApneaWhisper achieved strong performance for sleep apnea detection, with a clip-level F1-score of 0.82 and a frame-level F1-score of 0.70, outperforming conventional baselines including MFCC+DNN, VGGish+bi-LSTM, and VAD-based models. It also showed promising ability in distinguishing between OSA, MSA, CSA, and hypopnea, though with varying success.

Conclusion: The model's fine-grained temporal resolution enables precise apnea event localization, duration estimation, and subtype classification. While ApneaWhisper performs robustly for OSA, challenges remain in distinguishing central (CSA) and mixed (MSA) sleep apnea, due to subtle or ambiguous acoustic patterns. The frame-level segmentation also facilitates accurate apnea-hypopnea index (AHI) estimation, which could reduce dependence on full PSG studies in certain clinical and home-monitoring scenarios. Future improvements may involve multimodal integration (eg, oxygen saturation) and noise-robust training techniques.

Background: Elevated systemic oxidative stress contributes to endometrial damage. Individuals with obstructive sleep apnea (OSA) exhibit significantly elevated oxidative stress; however, the potential role of oxidative stress in OSA-induced endometrial injury remains unclear.

Objective:  To investigate the effects of OSA on systemic oxidative stress and endometrial morphological alterations in a female rat model.

Methods: We randomly assigned 15 female Sprague-Dawley (SD) rats to three groups: (1) Control group: Normal feeding for 8 weeks; (2) Short-term OSA group: 4 weeks of normal feeding followed by 4 weeks of Sleep Apnea (SA) modeling; (3) Long-term OSA group: 8 weeks of SA modeling.Assessments included: Body weight; uterine index; Oxidative stress markers: superoxide dismutase (SOD), reactive oxygen species (ROS) and malondialdehyde (MDA);Endometrial histomorphology: thickness, microvessel density and gland count via Hematoxylin and Eosin (H&E) staining; immunohistochemical (IHC) analysis of Kiel 67 (Ki-67) antigen and vascular endothelial growth factor (VEGF); Apoptosis detection by terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL) assay.

Results: Long-term OSA exposure significantly increased body weight vs control (P<0.05). Both OSA groups showed reduced uterine indices and elevated oxidative stress (P<0.05). Progressive structural impairment was observed with OSA duration: endometrial thickness and microvessel density decreased sequentially (control > short-term > long-term; P<0.05), and gland number was reduced in the long-term group vs control (P<0.05). IHC showed duration-dependent suppression of Ki-67 (proliferation) and VEGF (angiogenesis) expression (P<0.05), while apoptosis increased with OSA exposure (P<0.05).

Conclusion: In a preclinical model, OSA-like exposure promoted weight gain, uterine atrophy, and progressive endometrial damage. Mechanistic analyses revealed that this impairment resulted from oxidative stress-mediated inhibition of cellular proliferation (reflected by reduced Ki-67 expression) and suppression of angiogenesis (indicated by decreased VEGF levels), concurrent with enhanced apoptotic activity. Given the observed duration-dependent pathological progression, our findings establish that sleep apnea contributes to female reproductive dysfunction, warranting early clinical intervention in women with sleep-disordered breathing.