Sleep最新文献

Study objectives: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by the American Academy of Sleep Medicine rely on relatively broad categorizations. These traditional sleep stages are not optimized to reflect health status. Here, we propose health-oriented sleep states to better associate with pre-existing health conditions.

Methods: This observational retrospective cohort study involved 8673 participants from the Massachusetts General Hospital sleep laboratory. We examined seven pre-existing conditions: mild cognitive impairment, ischemic stroke, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, and depression. We clustered a sleep staging model's hidden layer within each stage, where clusters represent sleep states. The number of sleep states was selected to maximize the average association with the health conditions, using the average area under the receiver operating characteristic curve across outcomes based on time spent in these states. We also assessed the area under the precision-recall curve.

Results: We identified three states within N3, 14 in N2, 6 in N1, 3 in R, and 9 in W. Average area under the receiver operating characteristic curve ranged from 0.608 to 0.723 across the seven outcomes, and area under the precision-recall curve from 0.064 to 0.524. Among these outcomes, mild cognitive impairment/dementia, atrial fibrillation, myocardial infarction, and hypertension demonstrated significantly stronger associations with the health conditions compared to conventional American Academy of Sleep Medicine sleep stages.

Conclusions: Novel sleep states are linked to health conditions. A better understanding of the physiology behind these sleep states may further enhance the concept of using sleep as a window into overall health. Statement of Significance The conventional sleep staging describes sleep physiology rather than indicating health conditions. In contrast to the macrostructure (i.e. sleep stages), the microstructure of sleep, as reflected in multi-organ physiological signals during sleep, contains profound information about health. It would be a conceptual innovation to summarize the multi-organ microstructure of sleep into novel sleep states that better reflect health conditions than the current sleep stages. These sleep states should still align with the conventional sleep stages. We propose health-oriented sleep states, which are data-driven states optimized to associate with health conditions. This approach directly links health to sleep states and interprets them similarly to sleep stages, marking a significant step toward a more comprehensive understanding of the clinical relevance of sleep.

Study objectives: Depression frequently occurs after diagnosis of breast cancer diagnosis. This study examines the incidence of depression and whether insomnia exaggerates depression risk in long-term breast cancer survivors (BCSs).

Methods: A sample of 636 nondepressed females, aged 55 to 85 years, was recruited between 2015 and 2020 from a diverse community-based health plan; 315 were BCSs at least 2 years postdiagnosis, and 321 were an aged-matched comparison cohort. Both groups were stratified by the presence or absence of insomnia at baseline. The primary outcome, incident and recurrent major depressive disorder, was diagnosed over 32 months. Cox proportional hazards models estimated risk of depression (hazard ratio [HR], 95% confidence interval [CI]).

Results: A total of 310 (98.4%) BCSs, and 309 (96.3%) comparisons completed 32 months follow-up. Relative to the comparisons, risk of depression was elevated in BCSs (HR = 5.94; 95% CI = 3.34% to 10.54%, p < .001). Insomnia, as defined by the Insomnia Severity Index (>8), further increased depression risk BCS (HR = 9.91; 95% CI = 4.92% to 19.96%; p < .001), but not in the comparisons.

Conclusions: Long-term BCSs have a heightened risk of major depressive disorder, and even subthreshold insomnia exaggerates that risk. Given that insomnia treatment can effectively prevent depression, insomnia screening and treatment have implications for depression prevention in BCSs. Statement of Significance Breast cancer survivors with insomnia show a markedly elevated risk of depression, which supports the urgent need to screen for insomnia in cancer survivors and to develop and implement insomnia treatment to prevent depression in this high-risk population.

Study objectives: To examine the combined impact of four circadian-alignment behaviors-morning sunlight exposure, time-restricted eating, Zone 2 cardiovascular training, and breathwork-on sleep consistency, cardiorespiratory fitness, and parasympathetic activity in adults.

Methods: A pre-post quasi-experimental design involving 38 838 healthy adults who participated in the "Core Four Challenge" over 31 days. Participants were monitored using a wrist-worn device (WHOOP Strap 3.0 and 4.0; Boston, MA) tracking sleep and cardiorespiratory metrics. Data were collected across 3 months: baseline, intervention, and postintervention.

Results: Participants significantly increased engagement in all four behaviors compared to matched controls (F ≥ 5610.45, p < .001), leading to improved sleep consistency (F > 11.6, p = .001), resting heart rate (F > 9.2, p ≤ .002), and heart rate variability (F > 6.1, p ≤ .013). Mediation analyses showed that engagement in these four behaviors enhanced cardiorespiratory fitness and parasympathetic activity partially through improved sleep consistency, independent of sleep duration and sleep duration variability.

Conclusions: Increased engagement in the Core Four behaviors was associated with improvements in sleep consistency, which in turn was linked to enhanced cardiorespiratory fitness and parasympathetic activity. This increased engagement persisted for at least 1 month postintervention, suggesting that integrating simple circadian-alignment practices into daily routines may support sustainable improvements in sleep regularity and health outcomes.

Study objectives: Nonrestorative sleep (NRS) refers to the subjective experience of feeling unrefreshed upon awakening that is not attributed to a lack of sleep. NRS may lead to the development of various lifestyle-related diseases, including cardiovascular disease. We investigated the relationships among NRS status, major adverse cardiac and cerebrovascular events (MACCEs), and newly diagnosed sleep apnea syndrome (SAS) status among health check-up participants via the health insurance claims database.

Methods: We followed 86 009 participants who underwent health check-ups in 2014 and answered a sleep-related question for up to 6 years via health insurance claims and a health check-up database. MACCEs were defined as the initial recording of a diagnostic code for MACCEs that required hospitalization. Cox proportional hazards models were constructed to determine whether NRS status was significantly associated with MACCE risk.

Results: The mean age of the participants was 50.7 ± 15.8 years; 58.8% were male, and 32.9% had NRS status. Even after adjusting for other factors, NRS status was a significant risk factor for MACCE development (hazard ratio: 1.14, 95% CI = 1.07% to 1.23%). A total of 75.9% of the participants who had both MACCEs and newly diagnosed SAS during the follow-up period had heart disease.

Conclusions: NRS status is an important indicator of sleep hygiene, and improving NRS might reduce the risk of MACCE development. Further screening tests (e.g. home sleep apnea testing) and consequent appropriate treatment may reduce MACCE risk and maintain their health status in people with NRS identified during check-ups.

Study objective: Sleep and circadian rhythms impact nearly all aspects of physiology and are critical for optimal organismal function. Disruption of the clock can lead to significant metabolic disorders, neuropsychiatric illness, and cognitive dysfunction. Our lab has shown that environmental circadian desynchronization (ECD) in adults alters the anatomical structure and neurophysiological function of prefrontal cortex (PFC) neurons, PFC-mediated behaviors, and sleep quality. As the PFC undergoes significant development in utero and early life, and maternal disturbances during this period can have significant long-term ramifications, we hypothesized that disrupting the circadian environment during the perinatal period would alter sleep and PFC function in adult offspring.

Methods: Using a mouse model of ECD, we investigated how perinatal ECD (pECD) modulates sleep quality in adult offspring. We also determined how pECD impacts PFC neural function in adult offspring using ex vivo patch-clamp electrophysiology, exploring how pECD alters synaptic function and action potential dynamics.

Results: We found that male pECD mice trended toward increased total sleep during the inactive (light) period with shorter sleep bouts during the active (dark) period, with no changes in female mice. Independent of time of day, pECD altered postsynaptic dynamics of excitatory release onto PFC pyramidal neurons. There was also a loss of time-of-day effects on cell endogenous properties in male pECD mice.

Conclusion: Thus, pECD clearly alters sleep behavior and PFC function in male mice. However, female mice appear protected against the effects of pECD. Together, these experiments form the foundation for future studies to understand the lifelong neurobehavioral impact of pECD. Statement of Significance Disruptions to the body's natural circadian rhythms during early development may have lasting consequences for brain function and behavior. This study demonstrates that desynchronizing rhythms during perinatal period has consequences for both sleep quality and cortical function later in adulthood, particularly in males. Male mice exposed to perinatal circadian desynchronization exhibited fragmented sleep and changes in synaptic properties of prefrontal cortex neurons, while female mice appeared resilient. These findings suggest that early life circadian disruptions could contribute to cognitive and behavioral disorders linked to cortical dysfunction, such as mental illness and learning deficits. Understanding these mechanisms is crucial in an era where artificial lighting and shift work disrupt natural sleep cycles, potentially affecting neurodevelopment and lifelong brain health.

Recent developments in mathematical modeling of electroencephalography (EEG) enable the tracking of otherwise-inaccessible neurophysiological parameters throughout sleep. Likewise, advancements in wearable electronics have enabled easy and affordable collection of sleep EEG at home. The convergence of these two advances, namely neurophysiological modeling for mobile sleep EEG, can boost preclinical and clinical assessments of sleep. However, this subject area has received limited attention in existing literature. To address this, we used an established model of the corticothalamic system to analyze EEG power spectra from five datasets, spanning from research-grade systems to at-home mobile EEG. In the present work, we compare the convergent and divergent features of the data and the estimated physiological model parameters. While data quality and characteristics differ considerably, key patterns consistent with previous theoretical and empirical work are observed. During the transition from lighter to deeper NREM, (1) exponent of the aperiodic (1/f) spectral component is increased, (2) bottom-up thalamocortical drive is reduced, (3) corticocortical connection strengths are increased. This effect is observed in healthy subjects but is interestingly absent when taking SSRI antidepressants, suggesting possible effects of ascending neuromodulation on corticothalamic oscillations. We further show a month-long increase in REM% in one mobile EEG subject, associated with boosted high-frequency activity in spectra and higher thalamothalamic gains in the model, pointing to possible changes of thalamic inhibition in REM parasomnias. Our results provide a proof-of-principle for the utility and feasibility of this physiological modeling-based approach to analyzing mobile EEG data, providing a mechanistic measure of brain physiology during sleep.

Study objectives: This study aims to investigate the association between daytime napping frequency and cancer incidence and elucidate the underlying biological mechanisms.

Methods: Using data from the UK Biobank, Cox regression was employed to assess the association between self-reported daytime napping frequency and risks for overall and site-specific cancers. A polygenic risk score (PRS) was conducted to assess whether genetic predisposition to daytime napping influenced cancer incidence. Finally, mediation analysis was performed on a panel of 325 nuclear magnetic resonance (NMR) metabolites to identify potential biological pathways, which linked napping to cancer risk.

Results: In this cohort of 460 923 participants, both sometimes [Hazard ratios (HR) = 1.04, p<.001] and usually (HR = 1.03, p=.046) napping were significantly associated with a higher risk of overall cancer compared to never napping. Site-specific analysis showed an elevated breast cancer risk for sometimes napping (HR = 1.06, p=.005) and esophageal cancer risk for usually napping (HR = 1.21, p=.038). Furthermore, a high PRS for daytime napping also predicted increased cancer incidence (HR = 1.02, p=.017), suggesting a role for genetic predisposition. Mediation analysis revealed 29 NMR biomarkers that each explained over 10 per cent of the napping-cancer association. The most significant mediator is the percentage of cholesterol in large LDL particles (LDL_C_pct), which accounted for 13.3 per cent of napping-cancer relationship.

Conclusions: Increased daytime napping frequency is potentially associated with elevated cancer risk. Further research is warranted to validate this association and elucidate the underlying biological mechanisms. Statement of Significance This study provides the comprehensive investigation of how daytime napping influence cancer risk through biological mechanisms. By analyzing data from 461 000 individuals in the UK Biobank, we demonstrated that increased daytime napping frequency is associated with elevated cancer risk, particularly for breast and esophageal cancers. Our research employed both observational and genetic approaches, using PRS to assess the contribution of genetic liability and to provide evidence which was less susceptible to traditional confounding. Through mediation analysis of 325 NMR metabolites, we identified lipid metabolic dysfunction as the key biological pathway linking napping to cancer development. Specifically, cholesterol composition in small low-density lipoproteins explained 13.3 per cent of this association. These findings provide the biological explanation for how daytime napping influences cancer development and highlight lipid metabolism as a potential intervention target for cancer prevention. Future studies are essential to validate this association and elucidate the underlying biological mechanisms.

Study objectives: Insomnia is a common sleeping disorder in military personnel and is linked to the development and maintenance of other mental health symptoms. How insomnia symptoms develop long-term, up to 10 years following deployment and what pre-deployment risk factors underpin this development is not yet clear.

Methods: A cohort of Dutch military personnel (n = 846, PRISMO cohort) deployed to Afghanistan was studied from pre-deployment to 10-years post-deployment. Longitudinal trajectories of insomnia symptoms were explored with a latent class growth analysis. Both linear and nonlinear predictive modeling were performed to assess which pre-deployment demographic, psychological, and biological variables predicted insomnia symptoms.

Results: We identified five trajectories of insomnia symptoms in military personnel from pre- to 10 years post-deployment: resilient sleepers (44%), recovery from pre-deployment insomnia (15%), insomnia symptoms, minor decrease following deployment (22%), minor increase (8%), and incident insomnia since deployment (11%). These groups did not differ in demographic variables. Both linear and nonlinear models could distinguish trajectories with post-deployment insomnia symptoms from resilient sleepers based on pre-deployment variables with hyperarousal as top predictor.

Conclusions: Our findings demonstrate that insomnia symptoms among military personnel are mainly affected by deployment and stable over a 10-year period post-deployment. Predictive modeling can help identify vulnerable subpopulations, though additional measurements might improve accuracy. Early interventions may prevent chronicity of the symptoms and the development of other mental health symptoms. Statement of Significance The long-term development of insomnia symptoms remains understudied, especially in military personnel, despite its links to other mental health issues. Here, we report distinct trajectories of insomnia symptoms of military personnel up to 10 years post-deployment. Predictive models, using pre-deployment psychological and biological factors and deployment experiences, enabled us to distinguish post-deployment trajectories, with pre-deployment hyperarousal emerging as top predictor of vulnerability. Future research should focus on external validation of the findings, enhancing predictive power with additional variables and exploring early interventions for prevention of chronicity of insomnia symptoms and associated mental health conditions following military deployment.

Study objectives: Large muscle group movements during sleep (LMMS) have recently been recognized as a prevalent feature in patients with restless legs syndrome (RLS), yet their autonomic profile remains insufficiently characterized. This study aimed to compare heart rate (HR) changes associated with LMMS to those accompanying short-interval (SILMS), periodic (PLMS), and isolated leg movements (ISOLMS) during non-REM sleep in RLS.

Methods: Thirty drug-free RLS patients (20 women, mean age 57.6 ± 12.73 years) underwent full-night polysomnography. For each subject, five arousal-associated events per movement type were selected, provided they were isolated by at least 30 seconds of motor/arousal-free sleep. HR changes were analyzed by computing R-R intervals and expressing them as a percentage of baseline, synchronized to movement onset. The area under the curve (AUC,-10 to +20 s), HR change peak, and movement durations were statistically compared using non-parametric tests.

Results: LMMS were significantly longer than other movement types (mean duration: 9.3 s vs. <3.0 s for others) and induced the highest HR response (peak: 129.6%, AUC: 369.3%), followed by SILMS (peak: 125.4%, 266.3%), ISOLMS (peak: 118.2%, 173.4%), and PLMS (peak: 118.5%, 166.9%). SILMS and LMMS were associated with rapid and sustained HR increases, without post-peak bradycardia, while PLMS and ISOLMS showed a modest transient bradycardia following the peak.

Conclusions: LMMS are associated with strong autonomic activation indicating parasympathetic withdrawal and/or sympathetic activation, distinguishing them from other sleep-related leg movements in RLS. The absence of post-peak bradycardia suggests reduced parasympathetic buffering, potentially reflecting more sustained arousal mechanisms. Statement of Significance This study provides the first detailed characterization of the heart rate dynamics associated with large muscle group movements during sleep (LMMS) in patients with restless legs syndrome (RLS). By comparing LMMS with established motor patterns such as periodic, isolated, and short-interval leg movements during sleep, we show that LMMS induce the strongest and most sustained autonomic responses. These responses are likely driven by sympathetic activation and/or parasympathetic withdrawal due to sustained arousal-related central autonomic commands. These findings support the hypothesis that LMMS represent a physiologically distinct class of sleep-related motor events with unique implications for cardiovascular and sleep disruption risk in RLS.