Journal of Sleep Research最新文献

C-reactive protein (CRP) appears to improve the ability to detect cardiometabolic risk in young and middle-aged adults with mild-to-moderate obstructive sleep apnea (mmOSA). The aim of this study is to assess utility of CRP in identifying the risk of hypertension and insulin resistance across a wide age range including older patients with mmOSA. Adults (n = 216) of a wide age range (28-90 years old, mean age 52.64 ± 12.74) with mmOSA (5 ≤ AHI < 30) completed in-lab polysomnography or home sleep apnea testing, physical examination including blood pressure (BP) measures, structured medical history questionnaire, and blood draw for CRP and fasting glucose and insulin levels. In adults < 60 years, lnCRP but not the apnea-hypopnea index (AHI) was associated with greater odds for hypertension (odds ratio [OR] = 2.40, 95% CI = 1.20-4.84, p = 0.01; OR = 1.00, 95% CI = 0.92-1.08, p = 0.92, respectively) and with higher average systolic and diastolic BP. Also, in adults < 60 years lnCRP but not AHI, was associated with higher lnHOMA values. In contrast, in adults > 60 years neither lnCRP nor AHI were associated with greater odds for hypertension, average systolic and diastolic BP, and lnHOMA. Receiver-operating characteristics curves revealed that adding CRP to standard clinical factors (age, sex, and BMI) yielded moderately good risk models for hypertension in patients < 60 years (AUC = 0.721). In conclusion, CRP improves the ability to detect cardiometabolic risk in young and middle-aged, but not older adults with mmOSA, suggesting that inflammation may be a primary pathogenetic mechanism in younger patients with OSA.

Central disorders of hypersomnolence (CDH) are chronic diseases that significantly impact the lives of affected individuals. We aimed to explore the perspectives of individuals with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH), and the challenges they encounter in their daily lives and within the healthcare systems in the Nordics. Interviews with patients (N = 41) and healthcare professionals (n = 14) and a patient survey (n = 70) were conducted in 2022 in Denmark, Sweden, Finland, and Norway to develop a patient journey map that visualises the patient with CDH journey and provides insights into the difficulties faced by these individuals. The patient journey mapping approach was chosen to focus on the processes and experiences of patients, highlighting the challenges they confront. Our findings revealed that the process of receiving a CDH diagnosis, as well as subsequent misdiagnoses and treatment, can be protracted and burdensome. CDH diagnoses remain poorly understood by neurologists, general practitioners, and the public, resulting in adverse consequences, with patients reporting a mean (standard deviation [SD]) time from symptom onset to diagnosis of 8.4 (5.11) years and a mean (SD) of 5.5 (4.17) productive hours lost/day. The available non-pharmaceutical support for patients with CDH, encompassing medical, psychological, educational, and professional assistance, was insufficient. The generalisability of the findings to one specific diagnosis is limited due to the collective analysis of the CDH. These findings are invaluable for identifying disruptions in the patient with CDH journeys and for designing improved pathways for those with NT1, NT2, and IH in the future.

Menstruation is an inflammatory process that involves changes in women's physiology leading to mental and physical complaints. Sleep is essential for optimal hormonal release, immune response, and wellbeing, becoming an important factor to be evaluated. We compared sleep, inflammatory mediators, fatigue, anxiety and depression symptoms, and quality of life in menstruating and non-menstruating women. We used the polysomnographic data of 232 women from EPISONO 2007, an epidemiological study from São Paulo city, Brazil. Women were distributed into menstruating (N = 61) and non-menstruating groups (N = 171). We applied questionnaires related to sleep quality, sleepiness, insomnia, fatigue, anxiety and depression symptoms, and quality of life. The serum levels of interleukin 6, tumour necrosis factor-alpha, and C-reactive protein were analysed. For statistical analysis the significance level adopted was p < 0.05. Sleep efficiency was statistically lower in menstruating women (81% ± 13) compared with the non-menstruating group (84.2% ± 13.3, p < 0.023). No statistical differences between the two groups were found in respect to the other parameters analysed. Both groups scored for fatigue symptoms, but no statistical significance was observed between the groups. Our findings indicate that menstruation was associated with lower objective sleep efficiency, suggesting that menstruation may be a physiological factor impairing sleep. Further studies evaluating menstrual variables, and each phase of the menstrual cycle, should be undertaken to detect the main factors associated with sleep complaints, fatigue, and objective parameters of sleep.

Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (κ = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (κ = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.

There is an ongoing debate in the United Kingdom and in other countries about whether twice-yearly changes into and out of Daylight Saving Time should be abolished. Opinions are divided about whether any abolition of Daylight Saving Time should result in permanent Standard Time, or year-long Daylight Saving Time. The British Sleep Society concludes from the available scientific evidence that circadian and sleep health are affected negatively by enforced changes of clock time (especially in a forward direction) and positively by the availability of natural daylight during the morning. Thus, our recommendation is that the United Kingdom should abolish the twice-yearly clock change and reinstate Standard Time throughout the year.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.e., in human-in-the-loop applications. To address these challenges, we propose an automatic sleep staging model (aSAGA) that effectively utilises both electroencephalography and electro-oculography channels while incorporating transparency of uncertainty in the decision-making process. We validated the model through extensive retrospective testing using a range of datasets, including open-access, clinical, and research-driven sources. Our channel-wise ensemble model, trained on both electroencephalography and electro-oculography signals, demonstrated robustness and the ability to generalise across various types of sleep recordings, including novel self-applied home polysomnography. Additionally, we compared model uncertainty with human uncertainty in sleep staging and studied various uncertainty mapping metrics to identify ambiguous regions, or "grey areas", that may require manual re-evaluation. The validation of this grey area concept revealed its potential to enhance sleep staging accuracy and to highlight regions in the recordings where sleep experts may struggle to reach a consensus. In conclusion, this study provides a technical basis and understanding of automatic sleep staging uncertainty. Our approach has the potential to improve the integration of automatic sleep staging into clinical practice; however, further studies are needed to test the model prospectively in real-world clinical settings and human-in-the-loop scoring applications.

Obstructive sleep apnea frequently coexists with epilepsy, potentially influencing its pathophysiology. However, the effect of obstructive sleep apnea severity on interictal epileptiform discharges is not well understood. To explore this, we studied 108 Asian patients with epilepsy who underwent single-night polysomnography. We utilized generalized linear models, adjusting for age, sex, epilepsy type (focal versus generalized), antiepileptic medication use and disease duration, to analyse the relationship between obstructive sleep apnea severity, as measured by the apnea-hypopnea index, and interictal epileptiform discharge frequency during non-rapid eye movement and rapid eye movement sleep. Our analysis revealed that severe obstructive sleep apnea (apnea-hypopnea index ≥ 30) was associated with a higher frequency of interictal epileptiform discharges during non-rapid eye movement sleep (p = 0.04), but no such association was observed during rapid eye movement sleep. Additionally, the frequency of interictal epileptiform discharges in non-rapid eye movement sleep was positively correlated with the wake time between sleep onset and offset (p = 0.03). Further studies are warranted to validate our findings across diverse ethnicities, and over multiple nights of sleep and interictal epileptiform discharge recordings.

Nightmares are a common symptom in narcolepsy that has not been targeted in prior clinical trials. This study investigated the efficacy of Cognitive Behavioural Therapy for Nightmares (CBT-N), adapted for narcolepsy, in a small group of adults. Given the high prevalence of lucid dreaming in narcolepsy, we added a promising adjuvant component, targeted lucidity reactivation (TLR), a procedure designed to enhance lucid dreaming and dream control. Using a multiple baseline single-case experimental design, adults with narcolepsy and frequent nightmares (≥3/week, N = 6) were randomised to a 2 or 4 week baseline and received seven treatment sessions (CBT-N or CBT-N + TLR). Across the groups, there was a large effect size (between-case standardised mean difference [BC-SMD] = -0.97, 95% CI -1.79 to -0.14, p < 0.05) for reduced nightmare frequency from baseline (M = 8.38/week, SD = 7.08) to posttreatment (M = 2.25/week, SD = 1.78). Nightmare severity improved significantly with large effect sizes on sleep diaries (BC-SMD = -1.14, 95% CI -2.03 to -0.25, p < 0.05) and the Disturbing Dream and Nightmare Severity Index (z = -2.20, p = 0.03, r = -0.64). Treatment was associated with a reduction for some participants in sleep paralysis, sleep-related hallucinations, and dream enactment. NREM parasomnia symptoms (z = -2.20, p = 0.03, r = -0.64) and self-efficacy for managing symptoms (z = -2.02, p = 0.04, r = -0.58) improved significantly with large effect sizes. Participants who underwent TLR (n = 3) all recalled dreams pertaining to their rescripted nightmare. In interviews, participants noted reduced shame and anxiety about sleep/nightmares. This study provides a proof of concept for the application of TLR as a therapeutic strategy with clinical populations, as well as preliminary evidence for the efficacy of CBT-N in treating narcolepsy-related nightmares.

The 'first night effect' (FNE) is a well-known phenomenon in polysomnographic (PSG) sleep studies, resulting in significant variations in the macrostructure of wakefulness and sleep states, particularly between the initial and subsequent sleep recording sessions. The FNE phenomenon during sleep has been studied in various species, revealing complex variations between several sessions of sleep recording. The present study used a non-invasive PSG method to examine differences between various vigilance states in four adult female dromedary camels during 4 consecutive nights and days of sleep recording. The results indicate the presence of a FNE in the architecture of the dromedary camel's vigilance states. On the first night, the proportions of wakefulness and light non-rapid eye movment (NREM) sleep (drowsiness) were higher, at a mean (standard error of the mean [SEM]) of 40.92% (0.88%) and 14.93% (0.37%), respectively; while the proportion of rumination (mean [SEM] 29.55% [0.92%]) was lower compared to consecutive nights. No FNE was found on deep NREM sleep, while night-time REM sleep had a shorter proportion during the first night compared to subsequent consecutive nights. A significantly lower REM/total sleep time (TST) ratio was observed on the first night. Daytime comparisons did not show any significant differences for the different vigilance states. The increase in wakefulness and light NREM sleep and the reduction in REM sleep and REM/TST sleep on the first night indicate a decline in sleep quality in the dromedary camel due to the FNE. Thus, we recommend excluding from a PSG sleep study at least the first session/night of the recordings to ensure accurate results.

We explored the effects of daylight saving time clock changes on sleep duration in a large accelerometer dataset. Our sample included UK Biobank participants (n = 11,780; aged 43-78 years) with accelerometer data for one or more days during the 2 weeks surrounding the Spring and Autumn daylight saving time transitions from October 2013 and November 2015. Between-individual t-tests compared sleep duration on the Sunday (midnight to midnight) of the clock changes with the Sunday before and the Sunday after. We also compared sleep duration on all other days (Monday-Saturday) before and after the clock changes. In Spring, mean sleep duration was 65 min lower on the Sunday of the clock changes than the Sunday before (95% confidence interval -72 to -58 min), and 61 min lower than the Sunday after (95% confidence interval -69 to -53). In Autumn, the mean sleep duration on the Sunday of the clock changes was 33 min higher than the Sunday before (95% confidence interval 27-39 min), and 38 min higher than the Sunday after (95% confidence interval 32-43 min). There was some evidence of catch-up sleep after both transitions, with sleep duration a little higher on the Monday-Friday than before, although this was less pronounced in Autumn. Future research should use large datasets with longer periods of accelerometer wear to capture sleep duration before and after the transition in the same individuals, and examine other aspects of sleep such as circadian misalignment, sleep fragmentation or daytime napping.