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

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.

Aging is associated with disruptions in circadian rhythms, lower brain white matter integrity, and cognitive changes. However, whether white matter integrity serves as a potential mechanism linking circadian dysfunction to age-related cognitive abilities in older adults is unclear. We investigated cross-sectional associations of actigraphic circadian rest/activity rhythms (RARs) with whole-brain white matter tract fractional anisotropy (FA) and executive function performance in 156 older adults without dementia from the BIOCARD study (mean age = 71.3 years, including 19 with mild cognitive impairment and 137 cognitively unimpaired). We studied non-parametric metrics of RAR strength (relative amplitude [RA]), day-to-day stability (interdaily stability [IS]), and fragmentation (intradaily variability [IV]). After adjusting for age, sex, education, APOE-e4 genotype, vascular risk, and diagnostic group, we found that greater rhythm strength (higher RA) was associated with better executive function. Additionally, higher rhythm strength (RA) and stability (IS) were associated with greater whole-brain FA, reflecting better white matter integrity, whereas greater fragmentation (IV) was associated with lower FA. Greater white matter integrity was also associated with better executive function and statistically mediated the association of higher RA with better executive function performance. Findings underscore the relationships between RAR strength and cognitive health in older adults and suggest that white matter integrity may be a key mechanism underlying these associations.

Story retelling is an important form of communication, cultural practice, and message transmission. Insufficient sleep is known to affect relevant cognitive skill areas necessary for story retelling or transmission fidelity. We conducted a preregistered randomized cross-over study on n = 155 young adults with exogenously assigned nightly sleep levels experienced in their at-home environments. A serial story reproduction task was administered online, and chains of up to three retells of a given story involved varied numbers of sleep restricted (SR) versus well-rested (WR) retellers. While story content decayed with each retell, group-level analysis showed that additional SR retellers in a chain was associated with greater decay, which mostly resulted from the introduction of an initial SR reteller at the first retell. Supporting the group-level effect, individual-level analysis confirmed that the number of details and the story's key event were significantly less preserved during a participant's SR treatment week. Exploratory analysis showed an attenuation of this effect in those reporting a higher level of affective response (interest or surprise) in the story. This suggests that emotional engagement can combat the deleterious effects of SR on successful story retelling, and perhaps on other types of content recollection.

Study objectives: The microbiome is proposed as a contributor to the adverse health impacts from altered sleep. The oral microbiome is a multifaceted microbial community that influences many health functions. However, data on the relationship between sleep and the oral microbiome are limited, and no studies have incorporated lifestyle and environmental exposures.

Methods: Within a subset (N=1,139) of the NIH-AARP cohort, we examined the association between self-reported sleep duration and the oral microbiome via 16S rRNA gene amplicon sequencing. Statistical models were adjusted for demographic characteristics. Additional models examined the role of various lifestyle and neighborhood exposures on the sleep-oral microbiome association.

Results: Compared to participants reporting the recommended 7-8 hours average sleep duration (n=702), those reporting short sleep (6 or fewer hours, n=284) had consistently decreased within-sample oral microbial diversity [e.g. number of observed amplicon sequence variants difference -8.681, p-value=0.009]. Several bacterial genera were more likely to be absent in the short sleep group. We found a higher relative abundance of Streptococcus and Rothia, and lower abundance of Fusobacterium, Atopobium, and Campylobacter in the short compared to the recommended sleep duration group. Results were consistent when controlling for lifestyle and neighborhood factors.

Conclusions: Our findings provide evidence for an association of short sleep duration with oral microbial diversity and composition. This suggests that oral bacteria may play a possible mechanistic role related to sleep health. Improved understanding of physiological pathways can aid in the design of interventions that may beneficially improve overall sleep health.