Nature and Science of Sleep最新文献

Purpose: Approximately 30% of patients with sleep-disordered breathing (SDB) present with masked hypertension, primarily characterized by elevated nighttime blood pressure. This study aimed to develop a hypertension prediction model tailored for primary care physicians, utilizing simple, readily available predictors derived from type IV sleep monitoring devices.

Patients and methods: Participants were recruited from communities in Guangdong Province, China, between April and May 2021. Data collection included demographic information, clinical indicators, and results from type IV sleep monitors, which recorded oxygen desaturation index (ODI), average nocturnal oxygen saturation (MeanSpO2), and lowest recorded oxygen saturation (MinSpO2). Hypertension was diagnosed using blood pressure monitoring or self-reported antihypertensive medication use. A nomogram was constructed using multivariate logistic regression after Least Absolute Shrinkage and Selection Operator (LASSO) regression identified six predictors: waist circumference, age, ODI, diabetes status, family history of hypertension, and apnea. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA).

Results: The model, developed in a cohort of 680 participants and validated in 401 participants, achieved an AUC of 0.775 (95% CI: 0.730-0.820) in validation set. Calibration plots demonstrated excellent agreement between predictions and outcomes, while DCA confirmed significant clinical utility.

Conclusion: This hypertension prediction model leverages easily accessible indicators, including oximetry data from type IV sleep monitors, enabling effective screening during community-based SDB assessments. It provides a cost-effective and practical tool for prioritizing early intervention and management strategies in both primary care and clinical settings.

Objective: Patients with obstructive sleep apnea (OSA) frequently suffer from migraine, however the causal relationship between OSA and migraine is unknown. Investigating the causation will assist in understanding the etiology of OSA and migraine.

Methods: Bidirectional two-sample Mendelian randomization (MR) and multivariable MR (MVMR) approaches were carried out to investigate the causal link between OSA and migraine. The public genome-wide association study (GWAS) data for OSA, migraine, and subtypes were obtained from the IUE open GWAS project and the FinnGen consortium. To investigate the causal links between OSA and migraine, inverse variance weighted (IVW) analysis was used in conjunction with four additional statistical approaches. Furthermore, sensitivity studies were performed using heterogeneity and pleiotropy tests to assess the estimation's robustness.

Results: In general, our findings suggested that the OSA is causally associated with migraine with aura (MA, IVW: OR = 1.147; 95% CI = 1.016-1.295; P = 0.026), which was confirmed with the MVMR analysis further (OR = 1.184, 95% CI = 1.028-1.364, P = 0.020). In addition, increased risk of migraine and migraine without aura on OSA occurrence were identified in the reverse analysis, but these results were subsequently negated with MVMR analysis.

Conclusion: According to the current findings, there was a preliminary causal effect of OSA on MA among European descendants.

Clinical relevance: These findings suggest a potential causal effect of OSA on migraine and provide new insights to prevent and manage the two disorders.

Background: Simple and affordable methods for evaluating Insulin Resistance (IR) have been suggested, such as the Triglyceride-Glucose (TyG) index and its variants, including the TyG-Body Mass Index (TyG-BMI), TyG-Waist Circumference (TyG-WC), and TyG-Waist-to-Height Ratio (TyG-WHtR). The aim of this study is to investigate the relationship between these TyG-related indices, which measure IR, and Obstructive Sleep Apnea (OSA).

Methods: This study analyzed NHANES data from 2007-2008, 2015-2016, and 2017-2020. TyG and its derivatives were evaluated as continuous and categorical variables in relation to OSA using multivariable logistic regression models. Subgroup analyses, dose-response relationships, and threshold effects were explored, and the diagnostic performance of TyG-related indices was assessed using AUC curves.

Results: The study included 8,374 participants. The fully adjusted Model 3 analysis (Note: Body Mass Index was not adjusted for TyG-BMI) of continuous variables showed a positive correlation between OSA and all four indices. All four TyG-related indicators showed statistically significant relationships with OSA when grouped into quartiles (TyG: AOR = 1.448, 95% CI: 1.260-1.663; TyG-BMI: AOR = 3.785, 95% CI: 3.319-4.317; TyG-WC: AOR = 2.089, 95% CI: 1.629-2.677; TyG-WHtR: AOR = 1.913, 95% CI: 1.548-2.363). Subgroup analysis revealed a stronger association of TyG-WHtR with OSA in the 41-59 age group (AOR = 1.459, 95% CI: 1.254-1.698) and the low-income group (AOR = 1.451, 95% CI: 1.241-1.698). TyG showed a linear relationship with OSA, while TyG-BMI, TyG-WC, and TyG-WHtR exhibited nonlinear relationships. The diagnostic capability was highest for TyG-WC, with an AUC of 0.647.

Conclusion: The study confirms strong associations between OSA and the TyG indices, particularly TyG-WC, which demonstrates significant predictive power for OSA risk. Future longitudinal studies are recommended to further investigate these associations and enhance OSA management in resource-constrained environments.

Background: While bedtime procrastination is commonly associated with adverse outcomes such as poor sleep quality, the mechanisms mediating these effects remain underexplored. Grounded in the Self-Regulation Model of Behavior and the Transactional Model of Stress and Coping, this study examines the mediating role of cognitive reappraisal in the relationship between bedtime procrastination and sleep quality over time.

Methods: Employing a longitudinal design, the study examined the progression of bedtime procrastination, cognitive reappraisal, and sleep quality among university students at three distinct time points throughout an academic semester. Structural equation modeling and autoregressive time-lagged panel models were utilized to analyze the data, assessing both the direct effects and the mediating role of cognitive reappraisal over time.

Results: The results revealed that bedtime procrastination exhibited significant stability across time points (β = 0.619 to 0.658, p<0.001). Bedtime procrastination at earlier time points predicted poorer cognitive reappraisal (β= -0.169, p<0.05 to -0.215, p<0.01) and subsequent sleep quality (β=0.256, p<0.001). Additionally, cognitive reappraisal significantly mediated the relationship between bedtime procrastination and sleep quality (β= -0.359, Boot 95% CI: -0.51 to -0.234), emphasizing the role of emotional regulation strategies in sleep-related outcomes.

Conclusion: These findings underscored the impact of bedtime procrastination on sleep quality and highlight cognitive reappraisal as a key mediator. Interventions focusing on enhancing emotion regulation skills could mitigate the adverse effects of bedtime procrastination and improve sleep outcomes among university students.

Purpose: Two previously proposed modelling approaches to explain the bimodal pattern of activity and/or sleep in Drosophila melanogaster are based on 1) the concept of morning and evening oscillators underlying the peaks of activity in the morning and evening, respectively, and 2) the concept of two cycles of buildup and decay of sleep pressure, gated only by the circadian oscillator. Previously, we simulated 24-h alertness-sleepiness curves in humans using a model postulating the circadian modulation of the buildup and decay phases of two (wake and sleep) homeostatic processes. Here, we tested whether a similar model could be applied to simulate the bimodal 24-h rhythm of fly locomotor activity and sleep.

Methods: To obtain typical curves for the simulations, a sample of 4263 individual 24-h curves of locomotor activity and sleep were subjected to principal component analysis. It yielded three principal components, which explained more than 70% of the individual variations in these curves. We calculated the typical curves using scores on the 1^st, 2^nd, and 3^rd principal components and simulated these curves and the sample-averaged curves.

Results: We found that these curves are always characterized by two peaks with varying sizes and timings. They can be fitted by proposing the variation of some of the parameters of the two homeostatic processes reflecting the 24-h rhythmicity of the drive for wake and the 12-h rhythmicity of the drive for sleep.

Conclusion: Postulation of two separate circadian oscillators is not necessary to explain the bimodal curves in Drosophila melanogaster.

Background: COVID-19 has led to reports of fatigue and sleep problems. Brain function changes underlying sleep problems (SP) post-COVID-19 are unclear.

Purpose: This study investigated SP-related brain functional connectivity (FC) alterations.

Patients and methods: Fifty-five COVID-19 survivors with SP (COVID_SP) and 33 without SP (COVID_NSP), matched for demographics, completed PSQI and underwent rs-fMRI at baseline and 2-month follow-up. Correlations between FC and clinical data were analyzed by Pearson correlation analysis with Gaussian random field (GRF) correction. The repeated-measures analysis of variance (R-M ANOVA) was completed to explore the interaction with time.

Results: At baseline, COVID_SP exhibited elevated FC: right precentral gyrus (PrG) with left lateral occipital cortex (LOcC)/right PrG, left inferior parietal lobule (IPL) with right superior frontal gyrus (SFG), left hippocampus with right inferior frontal gyrus (IFG). Higher FC between left hippocampus and right SFG correlated with PSQI scores. At 2-month follow-up, decreased FC implicated in emotion regulation, executive function, and memory; increased FC in semantics, attention, and auditory-visual processing. The changes in these regions are correlated with the scores of PSQI, GAD, and PHQ. The Repeated-Measures Analysis of Variance (R-M ANOVA) revealed a significant time interaction effect between sleep and various emotion scales. Moreover, the analysis of the functional connectivity between the right PrG and the right PrG as well as that between the left IPL and the right SFG also discovered a significant time interaction effect.

Conclusion: This study provides insight into the changes in brain function associated with SP after COVID-19. These changes may partially explain the development of SP, and they also changed over time.

Background: The incidence of insomnia in cancer patients is significantly higher than in the general population. Chronic insomnia imposes pronounced physical and psychological burdens on cancer patients, affecting their quality of life and survival rate. This study aims to investigate insomnia in cancer patients and further analyze potentially related factors.

Methods: Oncology outpatients treated at Fudan University Shanghai Cancer Center were consecutively recruited. Demographic information and clinical features, such as type of cancer and treatment status, were collected. Insomnia was assessed using the Insomnia Severity Index (ISI).

Results: A total of 146 patients participated in the study, with the majority suffering from breast tumors (40.4%), gastrointestinal tract tumors (18.5%), and endocrine tumors (5.8%). Among these patients, 25 (17.1%) did not report insomnia, 69 (47.3%) had subclinical insomnia, and 52 (35.6%) reached the level of clinical insomnia. Older patients aged 41-50 years (Estimate = -3.49, 95% CI, -6.99 to 0.00, p = 0.05) and those with higher education levels (Estimate = -2.72, 95% CI, -4.88 to -0.55, p = 0.01) were less likely to have higher ISI total scores. In contrast, undergoing chemotherapy (Estimate = 3.86, 95% CI, 0.53 to 7.19, p = 0.02) was associated with higher ISI total scores. Gender, age, education, treatment modalities correlated with ISI subitem scores.

Conclusion: The prevalence of insomnia is higher in oncology patients and is associated with gender, age, education, tumor type, and treatment modality. Screening and interventions for insomnia should be emphasized in the whole-course management of oncology patients.