Sleep最新文献

Study objectives: Weight gain after continuous positive airway pressure (CPAP) initiation in obstructive sleep apnea (OSA) is common, but its mechanism and relevance remain unclear. This open-label randomized trial evaluated CPAP effects on energy expenditure, intake, body composition, physical activity, and appetite-regulatory hormones.

Methods: Patients with OSA were randomized (1:1) to 12-week CPAP or inactive control. The primary outcome was resting energy expenditure (REE). Secondary outcomes included dietary intake, eating behavior, fat mass (FM), fat-free mass (FFM), and activity count. Tertiary outcomes included appetite-regulatory hormones. CPAP effects were assessed as baseline-adjusted between-group differences using intention-to-treat (ITT) analysis; Per-protocol analysis (completers) served as sensitivity analysis.

Results: Of 52 randomized participants, 45 completed the study. In ITT analysis, CPAP had no effect on REE (8.6 kcal/day [95% CI = -51.5 to 68.7]; p = .774) or caloric intake (144.4 kcal/day [95% CI = -123.1 to 411.9]; p = .283). Although insignificant in morning, CPAP significantly increased evening body weight (p = .017) and body mass index in morning and evening (p = .040 and .030). CPAP also increased FFM, raised acylated ghrelin and insulin-like growth factor 1, and reduced cortisol and cognitive restraint. No changes were observed in macronutrient intake, FM, activity, insulin resistance, leptin, or neuropeptide Y. Per-protocol findings were similar.

Conclusions: CPAP-induced weight gain, probably primarily from FFM, occurred without measurable changes in REE, activity, or significant increases in caloric intake. Accompanying hormonal and behavioral changes suggest a subtle positive energy balance. This gain may not reflect adverse metabolic effects and supports evaluating CPAP's metabolic impact through body composition, not weight alone.

Clinical trial registration: Registry: ClinicalTrials.gov; Name: Validation of Sleep Healthcare System; URL: https://clinicaltrials.gov/study/NCT04252482; Identifier: NCT04252482. Statement of Significance The physiological basis and clinical relevance of weight gain following CPAP therapy remain insufficiently defined. This study found that CPAP-induced weight gain was probably primarily due to increases in FFM, without measurable changes in REE, physical activity, or significant increases in reported caloric intake. Given the potential underestimation in dietary reporting, the observed gain-together with hormonal and behavioral changes-suggests a subtle positive energy balance. These findings indicate that post-CPAP weight gain may not reflect adverse metabolic effects and highlight the value of assessing body composition, rather than weight alone, in clinical follow-up.

Study objectives: Teams that work together across location, time, and organization are known as "distributed teams." These teams often work in demanding environments that are stressful and fatiguing due to extended periods of wakefulness, intense work, and working during night hours. The aim of this study was to examine the impact of prolonged wakefulness on team performance and cohesion.

Methods: N = 22 healthy young individuals (M = 22.60, SD = 4.41 years, 11f) participated in a 5-day laboratory study with 62 h of wakefulness. Throughout the sleep deprivation period, four-person distributed teams completed the Capturing Objective Human Econometric Social Interactions in Organizations and Networks (COHESION) team task while physically isolated from one another. This task assessed cooperation, productivity, individual performance, team performance, and team dynamics. Fatigue and self-reported measures of team cohesion were also administered.

Results: There were statistically significant changes in team member cooperation and team dynamics across the sleep deprivation period (p < .05, ƞp2 > 0.14), with steep declines in cooperation and team dynamics after 21 h of prior wake. There were statistically significant productivity, team performance, and team cohesion over the sleep deprivation period (p < .05, ƞp2 > 0.14), with deficits after 36 h of wake.

Conclusions: Team members acted more selfishly than cooperatively after 21 h of total sleep deprivation, resulting in poorer team dynamics. Distributed team members were no longer able to engage effectively with their teams after 36 h of total sleep deprivation due to fatigue, which was associated with poorer distributed team performance and cohesion. These findings show impairments for distributed teams who operate with severe fatigue in safety-critical working environments.

Sleep is critical for physical, cognitive, and mental health, but how sleep supports these domains likely fluctuates across the lifespan. While traditional observational and experimental study designs-often cross-sectional or limited (two-wave) longitudinal designs-have provided valuable insights, they fall short of capturing the dynamic nature of sleep and its effects over time. To fully understand these complex and evolving relationships, multimethod, multi-time point longitudinal designs are required. These approaches can illuminate the temporal dynamics of sleep and its outcomes, offering stronger and potentially causal conclusions. In this article, we aim to empower sleep scientists, clinicians, and trainees with research methods focused on studying change-methods that can be applied across both observational and experimental designs. To truly advance the field, it is critical to examine sleep throughout the lifespan, from infancy through older adulthood, with repeated and nuanced assessments of sleep and its related outcomes. We outline a variety of statistical analysis approaches and corresponding design considerations that support the rigorous study of change in sleep. Finally, we offer forward-looking recommendations for scientific training, research program evaluation and funding, and the development of research infrastructure and collaborations. Together, these strategies have the potential to propel the field of sleep research forward, generating richer insights and change-based conclusions. Statement of Significance As a field, we are driven by a fundamental question: does sleep temporally precede and cause changes to our physical, mental, and cognitive health? While many existing studies use cross-sectional or limited (two-wave) longitudinal designs, these approaches often fall short of capturing the full picture needed to understand the timing and impact of sleep. Encouragingly, we have the tools and methods needed to pursue this important work. In this article, we highlight statistical approaches and research designs that can help move the field forward. With thoughtful application of these methods, we can strengthen our conclusions, generate more impactful findings, and bring us closer to understanding the role of sleep across the lifespan.

Study objectives: Excessive daytime sleepiness (EDS), influenced by environmental and social-behavioral factors, is reported by a subset of patients with sleep apnea-a group that may be at elevated cardiovascular risk. However, it is unclear whether sleep apnea with and without EDS have distinct genetic underpinnings. In this study, we perform gene-by-EDS interaction analyses for apnea hypopnea index, a diagnostic marker of sleep apnea severity, to understand EDS's influence on its underlying genetic risk.

Methods: Discovery interaction analyses for common variants and gene-based rare variants were conducted respectively using multi-ethnic Trans-Omics for Precision Medicine (N = 11 619) data, followed by replication and subsequent meta-analysis in additional Trans-Omics for Precision Medicine-imputed data (N = 8904). The 1 degree-of-freedom (1df) G × E test and the 2df joint G,G × E tests were utilized. Sex-stratified analyses were additionally performed.

Results: Discovery analysis revealed two common intronic variants-rs13118183 (CCDC3) and rs281851 (MARCHF1)-and three rare variant gene sets mapped to SCUBE2, TMEM26, and CPS4FL-to exhibit interaction with EDS. Meta-analysis revealed EDS interaction with 11 rare variant gene sets mapped to UBLCP1, MED31, RAP1GAP, CPNE5, MYMX, YY1, ZNF773, YBEY, IQCB1, PI4K2B, and CORO1A.

Conclusion: Genetic loci reveal connections to cardiovascular risk, insulin resistance, thiamine deficiency, and resveratrol mechanism. Discovered genetic signals may offer insight into pertinent biological pathways for sleep apnea patients with an excessively sleepy subtype. Statement of Significance Sleep apnea is a complex sleep disorder. Exemplifying this is the disparately varying estimates of presence of excessive daytime sleepiness (EDS) in patients, and persistent EDS that lingers despite treatment. Some data indicate that the excessively sleepy subtype of sleep apnea carries heightened cardiovascular risk. Whether EDS influences genetic risk factors underlying sleep apnea has not yet been investigated. This study addresses this gap, as the first genome-wide gene × EDS interaction study for apnea hypopnea index, the standard sleep apnea severity metric. Genetic loci that have been previously unconsidered for sleep apnea are revealed. Discovered interaction signals highlight pathways in metabolism, genes associated with cardiometabolic traits, and therapeutic agents influencing obesity, blood pressure, oxidative stress, and apnea hypopnea index.

Study objectives: Prior research has suggested that disrupted and weakened rest-activity rhythms measured by accelerometry may be associated with risks of many diseases, including cardiometabolic diseases, cancer, and dementia, but the mechanisms underlying this are not fully understood. This study is the second of two studies aimed at using an untargeted approach to identify metabolomic markers associated with rest-activity rhythm characteristics and focuses on older women.

Methods: The analysis included 688 women in the Women's Health Initiative. Rest-activity rhythms were characterized by parametric and non-parametric algorithms applied to accelerometry data. Metabolomics data were measured from fasting serum samples with ultra high-performance liquid-phase chromatography and gas chromatography coupled with mass spectrometry and tandem mass spectrometry. Associations between rest-activity rhythms and metabolomics were determined by multiple linear regression models and Ingenuity Pathway Analysis.

Results: Of the 934 metabolites included, 280 showed an association (false discovery rate < 0.1) with one of the three primary rest-activity variables (pseudo F-statistic, intradaily variability, and interdaily stability). These metabolites represent a wide range of biochemical classes and metabolic pathways, including sulfur amino acids, fibrinopeptides, plasmalogens, amino sugar metabolites, and nucleotides. The PEX5 gene network was identified by the Ingenuity Pathway Analysis as the most significantly enriched genetic pathway in relation to rest-activity rhythms.

Conclusions: We found numerous metabolites and pathways that were associated with rest-activity rhythm variables in older women, suggesting a potentially wide-reaching role of diurnal behaviors in human metabolism and health. Statement of Significance In this metabolomics study in older women, we found a large number of metabolites that were associated with rest-activity rhythms. These metabolites represented a wide range of biochemical classes and metabolic pathways. This analysis also confirmed numerous metabolite associations we have recently found in a sample of older men in the Osteoporotic Fractures in Men study, lending further support to a wide-reaching role of circadian rhythms and diurnal behaviors in human health. To the best of our knowledge, our two studies were the first metabolomics investigations focusing on rest-activity rhythm characteristics. With further validation studies, we anticipate that findings from these studies will contribute to the broader endeavor to understand, diagnose, and treat circadian rhythm-related disorders, with potential benefits for human health.