Abstract Background The red blood cell distribution width‐to‐albumin ratio (RAR) is a novel composite biomarker of inflammation and nutritional status, yet its association with insomnia and underlying mechanisms remain poorly understood. Objective To investigate the association between the RAR and insomnia in two independent cohorts, comprising a nationally representative U.S. population and a Chinese clinical population, and to explore the mediating roles of adiposity and metabolic indicators. Methods We analyzed 20,571 adults from National Health and Nutrition Examination Survey (NHANES) 2005–2014 and 270 participants from the Hangzhou Hospital of Traditional Chinese Medicine (HZ‐TCM) cohort in China. Insomnia was defined using validated questionnaires or clinical diagnoses. Logistic regression, restricted cubic spline analyses, two‐piecewise threshold analyses, and mediation analyses were performed. Results Threshold analysis identified RAR inflection points of 3.52 (NHANES) and 3.44 (HZ‐TCM). Below these thresholds, each unit increase in RAR was associated with a 2.86‐fold increase in insomnia odds in NHANES (OR = 2.86, 95% CI: 2.23–3.68) and a 27.74‐fold increase in the HZ‐TCM cohort (OR = 27.74, 95% CI: 9.11–84.45) (both p < 0.001), whereas no significant association was observed above the thresholds. Conclusions RAR is an independent and robust biomarker for insomnia risk, demonstrating a consistent nonlinear threshold association across both population‐based and clinical cohorts. Central obesity and insulin resistance appear to be important mediating pathways. As an easily accessible composite indicator, RAR holds potential clinical utility for insomnia risk assessment.
Abstract Polysomnography (PSG), the reference standard for sleep assessment, has limited scalability for large‐scale screening, driving the development of wearable and unobtrusive monitors. However, the physiological signals these devices capture are physiologically correlated with PSG channels but are not completely equivalent. Their reliability and validity for clinical use therefore require careful evaluation. The advent of artificial intelligence (AI) provides a technical bridge to close the representation gap between PSG signals and wearable/unobtrusive signals. This review outlines the core role of PSG signals in sleep assessment and sleep disorder diagnosis, including sleep structure analysis, respiratory event detection, and abnormal behavior identification. It then explores the current research progress of wearable and unobtrusive sleep monitoring devices, such as smartwatches, sleep monitoring pads, and radars, for sleep staging and the diagnosis of common sleep disorders such as sleep apnea, and discusses the use of AI in such devices, with a particular focus on building interpretable models to enhance the clinical trustworthiness of out‐of‐hospital sleep monitoring. This article aims to provide valuable insights into the development of low‐burden, easy‐to‐operate, trustworthy assessment tools for out‐of‐hospital sleep monitoring, with the goal of facilitating early detection, personalized management, and long‐term follow‐up of patients with sleep disorders.
Abstract Objective This pilot study aimed to explore the effects of supplemental sleep on cognitive recovery after acute sleep deprivation. Methods Thirty healthy adults were randomized to a morning nap group (n = 15; median age [IQR]: 20.0 [19.0–26.0] years; 33% male) or afternoon nap group (n = 15; median age: 19.0 [19.0–25.0] years; 53% male). Cognitive assessments included the Karolinska Sleepiness Scale (KSS), reaction time test, Schulte grid test, and N‐BACK test. These tests were administered at a fixed time each day (Day 1). Next, sleep deprivation was performed, and then the cognitive tests were performed again on the second morning (Day 2). Subjects were required to complete morning nap or afternoon nap according to randomized groups. Final cognitive tests were completed on the third day (Day 3). Results (1) Acute sleep deprivation significantly impaired reaction time (Δ = +32.1 ms, p < 0.001) and subjective alertness (KSS score: 3→6, p < 0.001); (2) Supplemental sleep restored performance to baseline levels (all p > 0.05 vs. Day 1); (3) No differences emerged between morning and afternoon naps (all p > 0.05). Conclusions In conclusion, combined nocturnal and daytime sleep effectively restored alertness and attention. Morning and afternoon naps offered comparable benefits, supporting flexible scheduling. These pilot data provide effect size estimates for future trials.
Abstract Sleep plays a critical role in overall health, and modern lifestyles, such as shift work and late‐night activities, can significantly impact sleep condition. Insufficient sleep has been associated with various adverse health outcomes, including an increased risk of tumor development. Previous research has primarily focused on the effects of circadian rhythm disruption and related genetic mechanisms on tumorigenesis. However, an overlooked perspective is that the human body regulates and restores sleep through multiple mechanisms; especially, inflammatory pathways may also impair homeostasis and promote tumor development. Compensatory sleep recovery is associated with elevated levels of inflammatory cytokines such as IL‐1, IL‐6, and activation of the NF‐κB pathway, which may suppress the anti‐tumor inflammatory rebound of immune cells. These processes create a tumor‐promoting microenvironment that supports tumor survival and progression. Therefore, we propose that the inflammatory rebound during sleep recovery is a potential key contributor to tumor promotion. This review explores the molecular pathways through which insufficient sleep and the recovery process contribute to tumor progression.
Abstract Background Climate anxiety is prevalent among youth and is closely associated with sleep disturbance. This study aimed to examine the cross‐sectional and longitudinal associations between sleep disturbance and climate anxiety in a large cohort of college students. Methods A two‐wave longitudinal survey was conducted in Hunan and Shanxi provinces in China. A total of 11,010 college students completed the 6‐month follow‐up survey. The Youth Self‐Rating Insomnia Scale and the Climate Change Anxiety Scale were used to assess sleep disturbance and climate anxiety, respectively. A series of logistic regression analyses were performed to examine the associations between each sleep‐related variable and climate anxiety. Results At T 1 and T 2, 8.4% and 11.3% of college students reported climate anxiety, respectively. Sleep disturbance and sleep loss at T 1 were significantly associated with an increased risk of climate anxiety at T 2 after adjusting for covariates. Furthermore, sleep disturbance and sleep loss also predicted the new onset and persistence of climate anxiety. Conclusions Study findings provide evidence that sleep disturbance was not only cross‐sectionally and longitudinally associated with an increased risk of climate anxiety but also linked to its trajectories. Addressing sleep problems may serve as an effective strategy for alleviating climate anxiety among youth.
Abstract Purpose This study aimed to analyze the effectiveness of sleep apnea‐specific hypoxic burden (SASHB) and sleep disordered breathing index (SBII) in assessing cognitive dysfunction in adults with obstructive sleep apnea (OSA) and to compare them with the apnea‐hypopnea index (AHI). Methods The clinical data of 100 adult OSA patients aged 18–59 years admitted to the Department of Otorhinolaryngology–Head and Neck Surgery, the Second Affiliated Hospital of Xi 'an Jiaotong University, from December 2023 to December 2024 were retrospectively collected and divided into mild, moderate, and severe OSA groups according to AHI. Results In terms of cognitive assessment, there were significant differences in MoCA scores between the two groups ( p < 0.001). Correlation analysis showed that AHI, SASHB and SBII were negatively correlated with MoCA score, and the correlation between SASHB and SBII was stronger after excluding confounding factors. SBII (AUC = 0.846) and SASHB (AUC = 0.835) were better than AHI (AUC = 0.662) in evaluating cognitive function. Conclusion SASHB and SBII are more important in assessing cognitive dysfunction in adults with OSA, and may be important indicators for assessing cognitive impairment in OSA patients. In the future, larger prospective studies are needed to clarify further the relationship between SASHB, SBII and cognitive function.
Abstract Study Objectives Obesity is a major determinant of obstructive sleep apnea (OSA) severity, but its influence may vary across adulthood. We examined age‐related patterns of body mass index (BMI), apnea–hypopnea index (AHI), and hypoxemic burden, and assessed whether the association between BMI and OSA severity attenuates with age. Methods We analyzed 32,031 men and 10,572 women aged 20–90 years who underwent overnight polysomnography for suspected OSA. Segmented linear regression with a prespecified knot at 50 years evaluated age‐associated differences in BMI, AHI, and hypoxemia adjusted for clinic site and testing year. Pearson correlations between BMI and AHI and between BMI and hypoxemia were calculated across 10‐year age bins with differences tested using Fisher's r‐to‐z transformation. Results BMI increased up to midlife and declined at older ages in both sexes, whereas AHI and hypoxemia increased with age. In men, age‐related increases in AHI and hypoxemia were attenuated after midlife, whereas in women AHI slope did not change significantly. BMI–AHI and BMI–hypoxemia correlations were moderate in midlife but weakened at older ages, particularly in men. Fisher's comparisons confirmed significant attenuation in older men, with less consistent findings in women. Conclusions These findings demonstrate an age‐related decoupling between adiposity and OSA severity. BMI becomes a less informative marker of OSA severity in older individuals, particularly in men, suggesting increasing contributions of BMI‐independent mechanisms with aging.
Abstract Background Insomnia is highly prevalent, and non‐pharmacological strategies to facilitate sleep initiation remain an important clinical priority. Aims This study aimed to evaluate the tolerability, safety, and preliminary sleep‐related effects of a herbal aromatherapy liquid administered using a Traditional Chinese Medicine Aromatherapy Sleep Aid Device (TCMASAD) in adults with insomnia predominantly characterized by difficulty initiating sleep. Materials and Methods This single‐center, single‐arm pre–post pilot study enrolled 30 adults with insomnia. Participants received a 4‐week home‐based aromatherapy intervention. Outcomes were assessed using the Pittsburgh Sleep Quality Index (PSQI), daily sleep diaries, and overnight polysomnography (PSG). Results Within‐subject comparisons showed reductions in total PSQI scores and six of seven PSQI component scores after the intervention (all p < 0.01), suggesting improved subjective sleep quality. PSG further showed reductions in sleep‐onset latency and the number of nocturnal awakenings ( p < 0.05). The intervention was generally well tolerated, and no serious adverse events were reported. Discussion These preliminary findings suggest that a 4‐week intervention with herbal aromatherapy liquid delivered via a TCMASAD is feasible, well tolerated, and associated with improvements in sleep initiation and overall sleep quality in adults with insomnia characterized by difficulty falling asleep. Conclusion Herbal aromatherapy delivered through the TCMASAD may represent a feasible and acceptable non‐pharmacological approach for insomnia management. Controlled trials are warranted to confirm efficacy and establish comparative effectiveness.
Abstract Background Hypertension is a primary cardiovascular complication in type 2 diabetes associated with increased morbidity and mortality. Ambulatory blood pressure monitoring (ABPM) is essential for capturing circadian BP variation, which is closely influenced by sleep. Methods Twenty patients (63.75 ± 4.44 years old, 40% female, duration of T2D: 12.3 ± 6.24 years) underwent ABPM and actigraphy in a free‐living condition. Multi‐day sleep parameters including total sleep time, sleep efficiency, wake after sleep onset, sleep onset latency and number of awakenings were assessed by wrist actigraphy supplemented with sleep diary, ABPM was measured for 24 h during the sleep assessment period. Associations between parameters and ABPM across total sleep measurement period and the exact date of ABPM measurement were investigated, respectively. Linear regression was conducted for the relationship between objective sleep parameters and ABPM‐derived variables, adjusting for Age, sex, body mass index, duration of diabetes, antidiabetic medication uses and total physical activity MET (min/week). Results Sleep onset latency was associated with 24‐h coefficient of variance of systolic BP (β‐coefficient: 0.323, 95% CI: [0.014, 0.632], p = 0.04). Inter‐day sleep efficiency was inversely associated with SD and CV (SD: β‐coefficient:−0.373, 95% CI: [−0.676, −0.071], p = 0.02; CV: β‐coefficient:−0.588, 95% CI: [−1.141, −0.036], p = 0.04) of diastolic BP.
Abstract The exploration of salivary biomarkers has emerged as a promising avenue in the diagnosis and management of sleep‐related disorders, such as obstructive sleep apnea (OSA), insomnia, and sleep deprivation. Saliva is a noninvasive biofluid that contains a wealth of biological markers, reflecting both local and systemic physiological changes, making it an ideal medium for clinical applications. This review summarizes the development of salivary biomarkers, explores their role in various sleep‐related diseases, evaluates existing challenges, and discusses future research directions aimed at enhancing their clinical utility.
Abstract A 41‐year‐old male patient presented with a 13‐year history of recurrent nocturnal penile pain associated with erections during sleep. Over the course of his illness, the patient attended outpatient clinics on 298 occasions and was hospitalized three times. The primary symptom reported was erectile pain occurring during sleep. Physical examination and urinary ultrasounds revealed no significant abnormalities. Polysomnography (PSG) combined with nocturnal penile tumescence and rigidity monitoring demonstrated painful awakenings occurred during erections in rapid eye movement sleep, with no evidence of organic erectile dysfunction. Consequently, a diagnosis of sleep‐related painful erection was established. Treatment with sertraline and baclofen was initiated; however, the medications were discontinued by the patient due to adverse effects. The patient remains under clinical follow‐up.
Abstract Background Obstructive sleep apnea (OSA) frequently co‐occurs with major depressive disorder (MDD). However, the underlying biological mechanisms remain unclear. This study aimed to identify OSA‐related transcriptional signatures associated with depressive status using integrated bioinformatics and machine learning. Methods Transcriptomic datasets GSE98793 and GSE135917 were obtained from the Gene Expression Omnibus. Shared differentially expressed genes (DEGs) were defined as OSA‐related DEGs (OSA‐DEGs). Functional enrichment analyses were conducted to explore the underlying biological pathways. Three machine learning algorithms LASSO, support vector machine‐recursive feature elimination, and random forest, were applied to identify key genes. A support vector machine (SVM) model was constructed to evaluate diagnostic performance. Drug–gene interaction analysis was performed to identify potential therapeutic targets. Results OSA‐DEGs were enriched in pathways related to ribosomal biogenesis, cytoplasmic translation, and immune responses. Six core genes were identified (KLRB1, SPINK2, PPP1R17, ILF3‐AS1, BLOC1S6, and TROVE2). The six‐gene SVM model achieved a sensitivity of 0.842 and an area under the curve (AUC) of 0.723 in internal validation. External validation using an independent MDD dataset yielded AUCs of 0.646 using equal weighting. Drug–gene interaction analysis identified potential agents targeting BLOC1S6. Conclusions This study identified an OSA‐related transcriptional signature associated with depressive status, providing preliminary transcriptomic evidence for OSA‐MDD comorbidity. All findings are hypothesis‐generating and require independent validation.
Abstract Objective This study evaluates and enhances wearable sleep monitoring by comparing two feature extraction methods: traditional activity counts and deep learning‐derived features. By identifying optimal machine learning architectures, we aim to improve sleep stage classification accuracy, providing a robust, noncontact tool for clinical chronic disease management. Method We employed dual feature extraction strategies: (1) traditional activity counts (triaxial acceleration signal summation and windowed averaging) and (2) a two‐tier 1D‐CNN architecture with batch normalization/ReLU/max‐pooling for hierarchical feature learning. The extracted features were subsequently modeled using four machine learning approaches (convolutional neural network [CNN], XGBoost, SVM, RF) to assess their relative performance in sleep stage classification. Results The CNN‐derived features consistently outperformed traditional features across all classifiers, achieving superior sleep quality assessment accuracy (Macro AUC: CNN 0.97 vs. XGBoost 0.98 with CNN features). Notably, the CNN model showed particular advantages in capturing transient movement patterns and cross‐channel physiological relationships critical for sleep staging. Conclusion This comparative analysis validates that although conventional activity counts maintain interpretability, CNN‐based feature extraction provides significant performance gains for wearable sleep monitoring, especially when integrated with ensemble methods. The findings establish an optimized framework for clinical sleep assessment in chronic disease management, balancing computational efficiency with diagnostic accuracy.
Abstract Purpose This prospective study aimed to develop a predictive score for obstructive sleep apnea (OSA) risk using participants' somesthetic perception of airflow in their oropharynx. The primary goal was to establish a two‐step clinical screening protocol: first, using participants' somesthetic information, then integrating additional clinical observations. Methods The assessment involved two phases. First, mechanical modifications of the oropharynx were induced by experimenters, in its volume and wall tension to create either “facilitating” or “aggravating” airflow conditions. Participants' somesthetic response accuracy was scored by item, yielding an intermediate score (IS) of 0–13. Second, anatomical and clinical observations were added to the IS to produce a total score (TS) ranging from 0 to 23. The study included 101 participants: 41 undergoing polysomnography (PSG), categorized by OSA severity, and 60 controls. Results A two‐part score was obtained. Obstructive sleep apnea suspicion thresholds were IS ≥ 7.5 and TS ≥ 10. Sensitivity was 73% for IS and 80% for TS; specificity was 77% for IS and 70% for TS ( p < 0.001). Conclusion This study established a clinical score for OSA suspicion using participants' oropharyngeal airflow perception, offering a complementary, low‐cost method for identifying at‐risk OSA patients in outpatient settings. Clinical Trial Registration Registered at clinicaltrials.gov, ID: NCT06092710, URL: https://clinicaltrials.gov/study/NCT06092710 .
Abstract Study objectives To investigate the use of automated analysis to detect heart rate accelerations (HRAs) to screen for sleep‐disordered breathing (SDB) among patients who use chronotropic medications. Methods Nocturnal polysomnography (NPSG) recordings from 933 participants in the Sleep Heart Health Study were analyzed using an automated method to detect HR changes with pulse signals. Participants were included in the study if aged 40 years or older, had complete NPSG and used chronotropic medications prior to enrolling in the study. Nocturnal respiratory‐related HRAs were analyzed for any correlation to apnea–hypopnea index (AHI). The heart rate acceleration index (HRAI) is determined by the HRAs based on pulse signal per hour for an entire NPSG. Results The total HRAI has a mean ± SD value of 30 ± 19/h. Total AHI and respiratory HRAI has a Spearman correlation coefficient of 0.79 (p < 0.001). In the receiver operating characteristics curve, the area under the curve is greatest when AHI = 5 events/h, yielding a value of 0.96. This indicates that respiratory‐related HRAI had the greatest screening ability when AHI = 5 events/h, as compared to other cut‐off points of AHI. Conclusion The novel automated analysis for nocturnal HR changes correlates with AHI in patients with SDB who use chronotropic medications and provides an accurate prediction for the screening of SDB among these patients. This method may be used for patients irrespective of chronotropic medication use.
Abstract Objectives This study aimed to explore the association between excessive daytime sleepiness (EDS) and frequent nightmares and the risk for suicidal ideation (SI) among psychiatric inpatients. Methods In this cross‐sectional study, 650 inpatients were consecutively recruited from the inpatient department of Shantou University Mental Health Center. EDS was defined as Epworth Sleepiness Scale (ESS) > 10. Frequent nightmares were defined as nightmares ≥1 time per week during past 1 month. Pittsburgh Sleep Quality Index (PSQI) was used to evaluate nightmares and sleep‐related features. Beck Scale for Suicide Ideation‐Chinese Version (BSI‐CV) was used to assess SI. Results Among the 650 inpatients, 39 (6.00%) presented SI. Patients with SI had higher proportion of EDS (p < 0.001) and total scores of ESS (p < 0.001) compared to those without SI. Patients with EDS and frequent nightmares had higher odds for SI (OR = 5.203, p = 0.005; OR = 3.077, p = 0.012) after adjusting for the confounders. Similarly, dose–response associations between the higher ESS scores (p for trend < 0.001), frequent nightmare (p for trend = 0.014) and higher risk of SI were also observed. Conclusions EDS and frequent nightmares are risk factors for SI among psychiatric inpatients. These findings underscore the importance of assessment and treatment of EDS and frequent nightmares for identifying and mitigating SI among psychiatric inpatients.
Abstract Aims Insomnia increases the risk of cardiovascular diseases (CVDs), but whether insulin resistance (IR) or its related traits mediate the underlying associations is unclear. We conducted a two‐step two‐sample Mendelian randomization (MR) study to address these questions. Methods and Results We selected genetic variants of insomnia, IR, and its traits as instrumental variables, whereas summary‐level data of five CVDs served as the main outcomes, which were derived from previous genome‐wide association studies. In the MR analysis, genetically predicted insomnia symptoms were significantly associated with five CVD risks and six IR‐related traits after correcting for multiple tests, whereas genetically predicted IR and its related traits, such as T2DM, TG, and high‐density lipoprotein cholesterol (HDL‐C), were associated with four CVD risks. In the mediation analysis, we found strong evidence for the mediating effects of IR, TG, HDL‐C, and T2DM in the causal pathway from insomnia to four CVDs, except for atrial fibrillation. The multivariable MR analysis provided further evidence supporting the potential mediation effects of IR and its related traits in the causal pathway between insomnia and CVDs. Conclusions These results suggest that genetically predicted insomnia symptoms are associated with a higher risk of CVDs, with considerable mediation by IR and T2DM.
Abstract The bidirectional interplay between sleep and metabolic homeostasis is fundamental to physiological health. While the roles of glucose and lipid metabolism in sleep regulation have been extensively characterized, bile acids (BAs), which are traditionally viewed as digestive surfactants, are emerging as critical metabolic messengers with distinct circadian rhythmicity and pleiotropic signaling functions. This review systematically elucidates the signaling network of the gut–liver–brain axis mediated by BAs through the nuclear Farnesoid X receptor and the membrane Takeda G protein‐coupled receptor 5. Accumulating evidence suggests that BAs are not only precisely regulated by the hepatic circadian clock but also modulate the central nervous system function by crossing the blood–brain barrier or via vagal afferent pathways. Specifically, recent findings highlight that aberrantly elevated BAs can infiltrate the central nervous system to disrupt the master circadian clock and modulate neurocircuitry governing arousal, thereby contributing to sleep fragmentation and circadian misalignment. Furthermore, this review discusses the potential of BA profiles as systemic biomarkers in obstructive sleep apnea, chronic insomnia, and related metabolic comorbidities. Finally, we propose that targeting BA metabolic receptors and the gut microbiota represents a promising translational strategy for the management of sleep disorders and their metabolic consequences.
Abstract Background This study presents SIMSleepSM, a novel single‐channel electroencephalography (EEG) sleep staging model. It addresses two primary challenges: insufficient modeling of long‐range temporal dependencies combined with limited multi‐scale feature extraction, and poor accuracy in identifying the N1 stage. Methods SIMSleepSM extends the SleePyCo architecture through three principal innovations. First, the spatial‐channel synergistic attention (SCSA) module is adapted into a 1D variant, SCSA_1D, tailored for EEG signals and inserted into every feature layer of the backbone network. The spatial attention extracts local temporal dependencies across various time scales, whereas the channel attention captures relationships among feature channels. Together these attentions strengthen temporal dependency modeling and emphasize N1‐specific features. Second, an interactive convolution block (ICB) is integrated into the feature pyramid. The ICB employs a two‐branch interactive convolution to refine multi‐scale feature extraction. Finally, a bidirectional Mamba‐based classifier is designed. Its bidirectional state space mechanism captures long‐range temporal dependencies in the EEG and thereby strengthens representation of sleep‐stage dynamics. Results On the Sleep‐EDF‐20, Sleep‐EDF‐78, and Sleep Heart Health Study (SHHS) datasets, SIMSleepSM achieves accuracy values of 88.1%, 86.2%, and 84.1%; records macro F1 scores of 82.7%, 81.0%, and 77.9%; obtains Cohen's Kappa coefficients of 0.839, 0.810, and 0.791; and attains F1‐scores on the N1 stage of 53.7%, 54.4%, and 50.9% for Sleep‐EDF‐20, Sleep‐EDF‐78, and SHHS, surpassing the second‐best models by 1.3%, 4.0%, and 4.8%, respectively. Conclusion Experimental results demonstrate that SIMSleepSM outperforms thirteen state‐of‐the‐art baseline models, with particularly notable improvements in N1‐stage identification. These results indicate that SIMSleepSM provides an effective and reliable solution for automatic sleep staging using single‐channel EEG, highlighting it as a robust and high‐performing model.
