Sleep advances : a journal of the Sleep Research Society最新文献

Study objectives: There is a higher risk of cardiovascular disease (CVD) in patients with obstructive sleep apnea (OSA) and innate immunity is a potential pathophysiologic pathway. The objective of this study was to determine whether nocturnal hypoxemia is associated with circulating markers of innate immunity in OSA.

Methods: This was a cross-sectional study of an observational cohort from the multicentre, clinic-based, Canadian Sleep and Circadian Network. Oxygen desaturation index 4% (ODI4%) was used to diagnose and determine the severity of OSA. The percentage of time spent below SpO₂ <90% (T90) and minimum SpO₂ we considered as other measures of nocturnal hypoxemia. Multiple linear regressions were used to assess associations between total white blood cell (WBC) and subsets and ODI and hypoxemia indices.

Results: A total of 1296 patients were included in the analysis. There was a positive association between ODI4% and lymphocyte count, adjusting for confounders. For every 1-SD increase in ODI4%, lymphocyte counts increased by 0.08 × 10⁹ L (95% CI 0.01 to 0.15) SD. Patients with severe OSA (ODI4% ≥ 30 events/hour) had significantly higher total WBC and lymphocyte count than non-OSA cohort, in the adjusted model, p-value <.02, for both. There was a positive association between T90 and total WBC, neutrophil, lymphocyte, and monocyte count, adjusting for confounders. Minimum SpO₂ was independently associated with total WBC, neutrophil, and monocyte counts.

Conclusions: In this pan-Canadian clinic-based cohort of individuals with suspected OSA, nocturnal hypoxemia indices were associated with an increase in total WBC and subset counts.

Study objectives: Trazodone is an antidepressant with robust hypnotic effects, frequently prescribed off-label to treat insomnia. Trazodone has gained recent attention in the context of neurodegenerative diseases because sleep has been proposed as a novel target for disease-modifying therapeutics. Preclinical research in rodents examining the effects of trazodone on sleep is limited, so here we aimed to develop a translationally focused protocol to study the sleep-promoting effects of trazodone in mice.

Methods: We investigated the effects of voluntary oral trazodone administration at doses of 10, 40, and 60 mg/kg on sleep in C57BL/6J mice (n = 15; females = 6; age 10-13 months). Mice were dosed with trazodone for 6 consecutive nights, while being recorded with intracranially implanted 2-channel electroencephalogram (EEG) and electromyography (EMG). EEG/EMG recordings were analyzed for time spent in each vigilance state and power spectra.

Results: A single dose of trazodone, administered prior to the onset of the 12-h rest phase, dose-dependently increased non-rapid eye movement (NREM) sleep and delta power during NREM sleep, at the expense of rapid eye movement (REM) sleep. These effects on sleep persisted after six consecutive days of dosing, albeit to a lesser extent.

Conclusions: We have validated a novel voluntary oral administration protocol for trazodone use in mice and have shown that trazodone effectively promotes NREM in mice. Our novel protocol will be useful for future research investigating the effects of trazodone on sleep in mouse models of disease.

The function of rapid eye movement (REM) sleep in consolidating emotional memories and reducing emotional charge has been studied, but evidence remains conflicting. Our study employed the targeted memory reactivation (TMR) technique, which posits that specific sleep memories can be reactivated through sensory stimuli during sleep. Additionally, the late positive potential (LPP), a component of event-related brain potentials, was measured while participants (N = 16, 22.5 ± 1.2 years) viewed negative, neutral, or positive images (old images) paired with an odor stimulus. During subsequent REM sleep, the same odor was presented in the TMR condition, while an odorless stimulus was presented in the control condition. Upon awakening, participants performed the same task as before sleep, with new images added to test memory. The results demonstrated that TMR increased the LPP amplitude between 500 and 800 ms after image onset following sleep for negative old images; however, no changes were observed in the LPP in the same range for negative new images and neutral or positive images. TMR during REM sleep did not influence performance on the memory task, nor did it affect levels of arousal or emotional valence immediately after viewing the emotional images. These preliminary findings from our pilot study suggest that either the presentation of phenylethyl alcohol itself or the reprocessing induced by TMR during REM sleep selectively enhances the LPP in emotional processing of previously encountered negative stimuli. Due to the small sample size of this study, further investigation is warranted to evaluate the robustness of the results.

In a dynamic process that ultimately affords memories their persistence, memory reconsolidation can serve to strengthen associations following reactivation, particularly in sleep, where active processes may effect overnight enhancement. Reactivation can also occur in wake, where improvement would be unexpected. In an earlier study using performance on the Weather Prediction Task (WPT) as a measure of probabilistic category learning, we looked at the effect of sleep and found significant improvement after a daytime nap, where improvement correlated with the amount of REM sleep obtained. When we introduced interference training following sleep, this REM sleep benefit vanished: post-learning task memory was otherwise preserved. Here, we follow up on these results and test whether reactivation itself-wake reactivation-might be sufficient to induce the improvement found after REM sleep. Our results show that it is not: we saw no improvement on the WPT following reactivation in wake, suggesting sleep may be unique in supporting memory improvement. When we looked at interference effects, we saw unexplained differences between wake and sleep that suggest that while interference is uniformly destabilizing of WPT memories during wake, interference after REM show effects on the memory trace formed during initial learning that are distinctly different from its effects on the subsequently sleep-enhanced memory.

This paper is a review of my life as a sleep researcher and clinician. I chanced into sleep by being at the right place at the right time. Over the last 45 years, I became an expert on sleep and circadian rhythms in aging, in Alzheimer's disease and Parkinson's disease and in cancer. We were one of the first to show how common sleep apnea and periodic limb movements in sleep are in the elderly. We "moved" into the nursing home and showed how disrupted sleep is in these patients. We used light therapy to improve sleep in the nursing home. We studied the effect of CPAP on sleep and cognition in both Alzheimer's disease and Parkinson's disease. And we were some of the first to study sleep and circadian rhythms in women with breast cancer, starting the evaluations before they started their chemotherapy. These studies were both observational and treatment studies. I am very proud of the work we did. For me, everything revolves around sleep. And that is the beginning of my story.

Analyzing scored sleep is a fundamental prerequisite to understanding how sleep changes between health and disease. Classically, this is accomplished by manually calculating various measures (e.g. percent of non-rapid eye movement sleep) from a collection of scored sleep files. This process can be tedious and error-prone, especially when studies include large animal numbers or involve long recording sessions. To address this issue, we present SleepInvestigatoR, a versatile tool that can quickly organize and analyze multiple scored sleep files into a single output. The function is written in the open-source statistical language R and has a total of 25 parameters that can be set to match a wide variety of experimental needs. SleepInvestigatoR delivers a total of 23 unique measures of sleep, including all measures commonly reported in the rodent literature. A simple plotting function is also provided to quickly graph and visualize the scored data. All code is designed to be implemented with little formal coding knowledge, and step-by-step instructions are provided on the corresponding GitHub page. Overall, SleepInvestigatoR provides the sleep researcher a critical tool to increase efficiency, interpretation, and reproducibility in analyzing scored rodent sleep.

Study objectives: Chronotype, a manifestation of circadian rhythms, affects morning or evening preferences and ease of getting up. This study explores the genetic basis of morning chronotype and ease of getting up, focusing on the G-protein-coupled receptor locus, GPR61.

Methods: We analyzed the genetic correlation between chronotype and ease of getting up using linkage disequilibrium score regression with summary statistics from the UK Biobank (n = 453,379). We prioritized shared signals between chronotype and ease of getting up using the Human Genetic Evidence (HuGE) score. We assessed the significance of GPR61 and the lead variant rs12044778 through co-localization and in silico analyses from ENCODE, Genotype-Tissue Expression, Hi-C, and Knockout Mouse Project databases to explore potential regulatory roles of causal genes.

Results: We identified a strong genetic correlation (Rg = 0.80, p = 4.9 × 10³²⁴) between chronotype and ease of getting up. Twenty-three genes, including three circadian core clock components, had high HuGE scores for both traits. Lead variant rs12044778 in GPR61 was prioritized for its high HuGE score (45) and causal pleiotropy (posterior probability = 0.98). This morningness variant influenced gene expression in key tissues: decreasing GPR61 in tibial nerve, increasing AMIGO1 in subcutaneous adipose, and increasing ATXN7L2 in the cerebellum. Functional knockout models showed GPR61 knockout increased fat mass and activity, AMIGO1 knockout increased activity, and ATXN7L2 knockout reduced body weight without affecting activity.

Conclusions: Our findings reveal pleiotropic genetic factors influencing chronotype and ease of getting up, emphasizing GPR61's rs12044778 and nearby genes like AMIGO1 and ATXN7L2. These insights advance our understanding of circadian preferences and suggest potential therapeutic interventions.

The "sleep to forget and sleep to remember" hypothesis states that sleep attenuates the emotional tone of a memory while strengthening its factual content. However, previous experimental research has yielded inconsistent results, associating sleep with the reduction, enhancement, or maintenance of the emotional tone of memories. Although the hypothesized process may necessitate multiple nights of sleep, most studies have relied on single-night protocols. To address this, we further investigated whether immediate sleep diminishes emotional reactivity triggered by memory reactivation after one week. In a karaoke paradigm, we recorded participants' singing of two songs and played back one of their recordings (rec1) to induce an embarrassing episode either in the early afternoon (delayed sleep group; N = 25) or the evening (immediate sleep group; N = 25). One week later, we assessed participants' emotional reactions to the re-exposed recording (rec1) and a newly introduced recording (rec2). Emotional reactivity was assessed using facial blushing as a primary physiological measure and subjective ratings of embarrassment, valence, and blushing. Sleep was monitored using diaries. While the embarrassing episode was successfully induced, Bayesian mixed-effects models revealed reduced facial blushing and more negative valence ratings from initial exposure to re-exposure (rec1) after both a shorter and longer interval to sleep. These changes were nonspecific to the reactivated recording (rec1) and were also observed for the new recording (rec2). Other subjective measures remained unchanged. This study demonstrates that neither the time interval to sleep following encoding nor memory reactivation influenced long-term emotional reactivity, leaving sleep's role in emotional memory processing elusive.

The manual evaluation of mouse sleep studies is labor-intensive and time-consuming. Although several approaches for automatic sleep stage classification have been proposed, no automatic pipeline for detecting a specific mouse phenotype has yet been developed. Here, we present a fully automated pipeline for estimating the probability of Narcolepsy Type 1 (NT1) in the hypocretin-tTA;TetO-Diphteria toxin A (DTA) mouse model using unlabeled electroencephalographic (EEG) and electromyographic (EMG) data. The pipeline is divided into three modules: (1) automatic sleep stage classification, (2) feature extraction, and (3) phenotype classification. We trained two automatic sleep stage classifiers, Usleep_EEG and Usleep_EMG, using data from 83 wild-type (WT) mice. We next computed features such as EEG spectral power bands, EMG root mean square, and bout metrics from 11 WT and 19 DTA mice. The features were used to train an L1-penalized logistic regression classifier in a Leave-One-Subject-Out approach, achieving an accuracy of 97%. Finally, we validated the pipeline in a held-out dataset of EEG/EMG recordings at four different timepoints during disease development in seven DTA mice, finding that the pipeline captured disease progression in all mice. While our pipeline generalizes well to data from other laboratories, it is sensitive to artifacts, which should be considered in its application. With this study, we present a pipeline that facilitates a fast assessment of NT1 probability in the DTA model and thus can accelerate large-scale evaluations of NT1 treatments.

Study objectives: Unfamiliar environments are often poorly conducive to quality sleep, especially for patients within health and aged care settings. This scoping review aims to map available evidence regarding the sleep environment in rehabilitation, subacute, and aged care settings. It examines how these factors are measured and seeks to identify any reported standard metrics, guidelines, or methodologies.

Methods: Searches were conducted within PubMed, EMBASE, Cochrane Library, Cumulated Index to Nursing and Allied Health Literature, PsycINFO, and Web of Science from database inception to May 2023. Eligibility criteria included original studies of any design reporting on the measurement properties of care environment factors affecting the sleep of adult patients admitted to rehabilitation, subacute wards, and aged care facilities.

Results: Seventy-four studies were reviewed that included 5055 participants, mostly (78.4%, 58/74) from aged care facilities. From 102 identified care environment factors, the spectral measurements of light were most reported (65.7%, 67/102), with methodologies varying from actigraphy and illuminance meters to pendant-style light monitors. Other environmental factors (sound, temperature, and air quality/humidity), room characteristics (mattress/bedding, room cohabitation), and hospital functioning (imposed schedules) were measured considerably less often and displayed similar variations in reported units and devices. Eighteen studies reported international, national, and methodological standards or guidelines.

Conclusions: This review provides a comprehensive overview of the care environment factors affecting sleep studied within rehabilitation, subacute, and aged care settings. Various units and devices were used in measuring these factors, and standard metrics and methodology were not consistently used. Future care environment studies incorporating interventions that employ standardized devices, units, and methodologies, will thereby enhance the reliability and comparability of findings within this field.