Sleep Trait Research Articles

The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be ‘objective’ measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

Association of sleep traits with risk of hypertensive disorders of pregnancy: a mendelian randomization study.

Unhealthy sleep patterns are common during pregnancy and have been associated with an increased risk of developing hypertensive disorders of pregnancy (HDPs) in observational studies. However, the causality underlying these associations remains uncertain. This study aimed to evaluate the potential causal association between seven sleep traits and the risk of HDPs using a two-sample Mendelian randomization study. Genome-wide association study (GWAS) summary statistics were obtained from the FinnGen consortium, UK Biobank, and other prominent consortia, with a focus on individuals of European ancestry. The primary analysis utilized an inverse-variance-weighted MR approach supplemented by sensitivity analyses to mitigate potential biases introduced by pleiotropy. Furthermore, a two-step MR framework was employed for mediation analyses. The data analyzed included 200 000-500 000 individuals for each sleep trait, along with approximately 15 000 cases of HDPs. Genetically predicted excessive daytime sleepiness (EDS) exhibited a significant association with an increased risk of HDPs [odds ratio (OR) 2.96, 95% confidence interval (95% CI) 1.40-6.26], and the specific subtype of preeclampsia/eclampsia (OR 2.97, 95% CI 1.06-8.3). Similarly, genetically predicted obstructive sleep apnea (OSA) was associated with a higher risk of HDPs (OR 1.27, 95% CI 1.09-1.47). Sensitivity analysis validated the robustness of these associations. Mediation analysis showed that BMI mediated approximately 25% of the association between EDS and HDPs, while mediating up to approximately 60% of the association between OSA and the outcomes. No statistically significant associations were observed between other genetically predicted sleep traits, such as chronotype, daytime napping, sleep duration, insomnia, snoring, and the risk of HDPs. Our findings suggest a causal association between two sleep disorders, EDS and OSA, and the risk of HDPs, with BMI acting as a crucial mediator. EDS and OSA demonstrate promise as potentially preventable risk factors for HDPs, and targeting BMI may represent an alternative treatment strategy to mitigate the adverse impact of sleep disorders.

Poor subjective sleep quality and trait impulsivity in patients with bipolar disorder.

Sleep disturbance and impulsivity are key components of mood vulnerability in bipolar disorder (BD), but few studies have assessed the association between these two symptoms among patients with BD. Forty-seven euthymic patients with bipolar I disorder (BDI) or bipolar II disorder (BDII) and 58 age- and sex-matched healthy controls were enrolled in this cross-sectional study. Trait impulsivity was measured using the Barratt Impulsiveness Scale Version 11 (BIS-11), which yielded 3 second-order factors: attention, motor, and non-planning. Subjective sleep quality was assessed using the self-reported Pittsburgh Sleep Quality Index (PSQI). General linear models (GLMs) were used to assess the associations between subjective poor sleep and trait impulsivity with multiple testing corrections. Patients with BD scored higher in BIS-11 and PSQI than healthy controls. PSQI total scores positively correlated with BIS-11 total scores, while sleep disturbance and daytime dysfunction were associated with attentional impulsiveness after controlling for covariates. Participants with higher PSQI total scores (>10) had higher scores in BIS-11 total, attention, and non-planning than those with low PSQI scores (≤5). These findings support the hypothesis that poor sleep quality might lead to impulsivity and add to the growing evidence that improving sleep quality may be a therapeutic target for patients with BD.

Causal effects of sleep traits on metabolic syndrome and its components: a Mendelian randomization study.

Previous observational studies have suggested an association between sleep disturbance and metabolic syndrome (MetS). However, it remains unclear whether this association is causal. This study aims to investigate the causal effects of sleep-related traits on MetS using Mendelian randomization (MR). Single-nucleotide polymorphisms strongly associated with daytime napping, insomnia, chronotype, short sleep, and long sleep were selected as genetic instruments from the corresponding genome-wide association studies (GWAS). Summary-level data for MetS were obtained from two independent GWAS datasets. Univariable and multivariable MR analyses were conducted to investigate and verify the causal effects of sleep traits on MetS. The univariable MR analysis demonstrated that genetically predicted daytime napping and insomnia were associated with increased risk of MetS in both discovery dataset (OR daytime napping = 1.630, 95% CI 1.273, 2.086; OR insomnia = 1.155, 95% CI 1.108, 1.204) and replication dataset (OR daytime napping = 1.325, 95% CI 1.131, 1.551; OR insomnia = 1.072, 95% CI 1.046, 1.099). For components, daytime napping was positively associated with triglycerides (beta = 0.383, 95% CI 0.160, 0.607) and waist circumference (beta = 0.383, 95% CI 0.184, 0.583). Insomnia was positively associated with hypertension (OR = 1.101, 95% CI 1.042, 1.162) and waist circumference (beta = 0.067, 95% CI 0.031, 0.104). The multivariable MR analysis indicated that the adverse effect of daytime napping and insomnia on MetS persisted after adjusting for BMI, smoking, drinking, and another sleep trait. Our study supported daytime napping and insomnia were potential causal factors for MetS characterized by central obesity, hypertension, or elevated triglycerides.

Sleep traits and associated factors among people living with HIV/AIDS in Iran: a two-step clustering analysis

Sleep plays an essential role in improving the quality of life of people living with HIV (PLWH); however, sleep traits in this population are not well studied. This study aims to evaluate the sleep traits and related associated factors among PLWH in Iran. A nationwide cross-sectional study was conducted with 1185 PLWH who attended Voluntary Counseling and Testing centers in 15 provinces in Iran between April 2021 and March 2022. The Berlin Obstructive Sleep Apnea questionnaire, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and Insomnia Severity Index were used. A two-step clustering method was employed to identify the number of sleep clusters in PLWH. Prevalence of poor sleep quality, sleepiness and insomnia were 49.6%, 21.15% and 42.7% respectively. Three sleep trait clusters were identified: I. minor sleep problems (45.6%); II. Snoring & sleep apnea (27.8%), and III. poor sleep quality and insomnia (26.7%). Age (Odds Ratio (OR) 1.033, 95% Confidence Interval (CI) 1.017–1.050), academic education (OR 0.542, 95% CI 0.294–0.998) and HIV duration were associated with being in Snoring & sleep apnea cluster, while age (OR = 1.027, 95% CI 1.009–1.040) was associated with being in Poor sleep quality and insomnia cluster. PLWH with depression had higher odds of being in Poor sleep quality and insomnia cluster, and those with anxiety had higher odds of being in Snoring & sleep apnea cluster and Poor sleep quality and insomnia cluster. A significant proportion of PLWH have poor sleep quality, sleepiness, and insomnia. The identification of three distinct sleep trait clusters underscores the need for increased attention and tailored interventions to address the specific sleep issues experienced by PLWH.

Causal relationship between sleep traits and cognitive impairment: A Mendelian randomization study.

Observational studies had demonstrated a link between sleep disturbances and cognitive decline. Here, we aimed to investigate the causal association between genetically predicted sleep traits and cognitive impairment using Mendelian randomization (MR). Using strict criteria, we selected genetic variants from European ancestry Genome-wide association studies (GWAS) from the Sleep Disorders Knowledge Portal and UK Biobank as instrumental variables for several sleep traits, including insomnia, sleep duration, daytime sleepiness, daytime napping, and chronotype. Summary statistics related to cognitive impairment were derived from five different GWAS, including the Social Science Genetic Association Consortium. The role of self-reported sleep trait phenotypes in the etiology of cognitive impairment was explored using inverse-variance weighted (IVW) tests, MR-Egger tests, and weighted medians, and sensitivity analyses were conducted to ensure robustness. In the main IVW analysis, sleep duration (reaction time: β=-0.05, 95% CI -0.07 to -0.04, p=1.93×10-12 ), daytime sleepiness (average cortical thickness: β=-0.12, 95% CI -0.22 to -0.02, p=0.023), and daytime napping (fluid intelligence: β=-0.47, 95% CI -0.87 to -0.07, p=0.021; hippocampal volume in Alzheimer's disease: β=-0.99, 95% CI -1.64 to -0.35, p=0.002) were significantly negatively correlated with cognitive performance. However, any effects of insomnia and chronotype on cognitive impairment were not determined. Our findings highlighted that focusing on sleep behaviors or distinct sleep patterns-particularly sleep duration, daytime sleepiness, and daytime napping, was a promising approach for preventing cognitive impairment. This study also shed light on risk factors for and potential early markers of cognitive impairment risk factors.

The Relation Between Sleep Quality and Anger Expression That Occur in Nurses: A Cross-Sectional Study

AimThe study examined the relationship between sleep quality and anger expression in nurses. MethodsThis cross-sectional study was carried out in a city hospital in the Mediterranean Region of Turkey from February 2020 to November 2020. The sample included 340 nurses. The data collection form, Pittsburgh Sleep Quality Index, Trait Anger, and Anger Expression Style Scales were used. Data were evaluated with Mann–Whitney U-test, Kruskal–Wallis H, Bonferroni correction test, and Spearman correlation coefficient. ResultsNurses' Pittsburgh Sleep Quality Index was found as 8.26 ± 2.88, the trait anger score was 20.42 ± 5.11, the anger control score was 21.49 ± 4.49, and the anger-out score was 14.28 ± 3.73. There was a statistically significant relationship between nurses' Pittsburgh Sleep Quality Index and trait anger; a negative relationship in anger control, and a positive relationship in anger-in (p ˂ .05). ConclusionIt was concluded that there is a relationship between sleep quality and anger expression style occurring in nurses. In-service training on anger expression and anger control for nurses after graduation is believed to be essential and also to arrange nurses' working hours to increase sleep quality and to provide anger management.

Investigating the causal interplay between sleep traits and risk of acute myocardial infarction: a Mendelian randomization study

BackgroundFew studies have investigated the joint effects of sleep traits on the risk of acute myocardial infarction (AMI). No previous study has used factorial Mendelian randomization (MR) which may reduce confounding, reverse causation, and measurement error. Thus, it is prudent to study joint effects using robust methods to propose sleep-targeted interventions which lower the risk of AMI.MethodsThe causal interplay between combinations of two sleep traits (including insomnia symptoms, sleep duration, or chronotype) on the risk of AMI was investigated using factorial MR. Genetic risk scores for each sleep trait were dichotomized at their median in UK Biobank (UKBB) and the second survey of the Trøndelag Health Study (HUNT2). A combination of two sleep traits constituting 4 groups were analyzed to estimate the risk of AMI in each group using a 2×2 factorial MR design.ResultsIn UKBB, participants with high genetic risk for both insomnia symptoms and short sleep had the highest risk of AMI (hazard ratio (HR) 1.10; 95% confidence interval (CI) 1.03, 1.18), although there was no evidence of interaction (relative excess risk due to interaction (RERI) 0.03; 95% CI −0.07, 0.12). These estimates were less precise in HUNT2 (HR 1.02; 95% CI 0.93, 1.13), possibly due to weak instruments and/or small sample size. Participants with high genetic risk for both a morning chronotype and insomnia symptoms (HR 1.09; 95% CI 1.03, 1.17) and a morning chronotype and short sleep (HR 1.11; 95% CI 1.04, 1.19) had the highest risk of AMI in UKBB, although there was no evidence of interaction (RERI 0.03; 95% CI −0.06, 0.12; and RERI 0.05; 95% CI –0.05, 0.14, respectively). Chronotype was not available in HUNT2.ConclusionsThis study reveals no interaction effects between sleep traits on the risk of AMI, but all combinations of sleep traits increased the risk of AMI except those with long sleep. This indicates that the main effects of sleep traits on AMI are likely to be independent of each other.

The Influence of Emotional Regulation and Cognitive Flexibility on Sleep Habits in Spanish Children and Adolescents through the Lens of Parents.

Previous research studies have suggested the importance of studying the relationship between emotional regulation and sleep habits. Some investigations have especially focused on how emotional regulation could impact sleep habits in children and adolescents. Therefore, these researchers have stated there exists a two-way direction in this relationship. This study aimed to analyze the influence of emotional regulation on sleep habits in Spanish children and adolescents and the mediating role of anxiety in this relationship. Participants were 953 Spanish parents who completed the assessment protocol according to their children and adolescents' information. The results revealed moderate-strong correlations between emotional regulation problems and sleep habit disturbances (r = 0.375, p < 0.001), trait (r = 0.488, p < 0.001) anxiety, and state (r = 0.589, p < 0.001) anxiety. Additionally, emotional regulation showed a direct impact on sleep habits (β = 0.011, p = 0.005). Trait and state anxiety demonstrated a significant mediating role in the relationship between emotional regulation and sleep habits. Emotional regulation may have an impact on sleep habits during childhood and adolescence, suggesting the importance of early intervention focused on the emotions management and the prevention of sleep habit disturbances.

Effect of sleep ambient music on sleep quality and mental health in college students: a self-controlled study.

To verify the effect of sleep ambient music intervention (SAMI) on sleep quality and mental status of college students, and to further explore the minimum effective duration of SAMI, this study was designed as a pre-and post-intervention self-controlled exploratory study. Participants were subjected to a one-week no-intervention test, then 4 weeks of music intervention followed. Subjective sleep quality data were collected using the Pittsburgh Sleep Quality Index (PSQI); objective sleep quality data were collected with Actigraphy; and mental status data were collected using the State-Trait Anxiety Inventory (STAI) and the Beck Depression Inventory-II (BDI-II). Data were analyzed and processed using mixed-effects models and repeated measures. The results showed that compared with the no-intervention week, college students' subjective sleep quality, objective sleep onset latency (SOL), trait anxiety, and depression symptom were reduced at week 1; week 2; week 3; week 4 under SAMI; state anxiety of college students at week 3 and week 4 under SAMI were also reduced. And there were differences in sleep quality among college students of different genders too. Compared with females, males had worse sleep efficiency (SE), shorter total sleep time (TST), and more awaking times (AT). In addition, 3 days was the minimum effective length for SAMI to shorten objective SOL, and 2 days was the minimum effective length to shorten the subjective SOL of college students. The findings of this study suggest that SAMI can improve subjective sleep quality, shorten objective SOL, and reduce anxiety and depression in college students. Interventions for more than 3 days had a significant effect on shortening SOL and long-term effects seemed to emerge after 3 weeks.

0622 Clustering bipolar disorder risk variants by their effect on sleep and circadian traits

Abstract Introduction Bipolar disorder (BD) is a mood disorder that causes noticeable, sometimes extreme, changes in mood and behaviour. BD affects nearly 50 million individuals worldwide including 2-4% of the US population. Previous studies have reported sleep and circadian disturbances in patients with BD. Genetic studies have identified 63 risk variants for bipolar disorder (BD). Methods To enable new insights into BD disease causing pathways, we clustered BD risk variants by their effect on sleep and circadian rhythm traits. A Bayesian soft-clustering approach was used to determine if BD variants cluster according to effect on eight subjectively and two objectively measured sleep and circadian traits (using sleep and circadian trait GWAS from the UK Biobank in European ancestry, n = 453,000). Results We found evidence of genetic correlation between BD and sleep/circadian traits (p&amp;lt; 0.05). Bipolar risk variants cluster into two clusters based on their association with sleep and circadian traits. The two clusters are: Cluster 1: late chronotype, shorter time in bed (by actigraphy), difficulty waking up, longer sleep duration (self-report), longer periods of daytime sleepiness, longer and frequent naps and increasing insomnia and Cluster 2: early chronotype, longer time in bed (by actigraphy), longer sleep duration (self-report) and less insomnia. In general, cluster 1 appears to be contributing to sleep insufficiency, while cluster 2 appears to be contributing to hypersomnia or more “typical” sleep. Conclusion In conclusion, sleep and circadian trait clusters indicate bipolar genetic risk may function through two distinct mechanisms. Some genetic risk variants appear to be related to sleep insufficiency, while others are related to hypersomnia, suggesting that it may be possible to genetically subtype BD. This finding may allow classification of BD-patients by distinct genetic pathways linking to their sleep-circadian disturbances, potentially offering a way forward toward genetically informed diagnosis, surveillance and management of BD-patients. Support (if any) N/A

Smartphone Keyboard Interaction Monitoring as an Unobtrusive Method to Approximate Rest-Activity Patterns: Experience Sampling Study Investigating Interindividual and Metric-Specific Variations

Sleep is an important determinant of individuals' health and behavior during the wake phase. Novel research methods for field assessments are required to enable the monitoring of sleep over a prolonged period and across a large number of people. The ubiquity of smartphones offers new avenues for detecting rest-activity patterns in everyday life in a noninvasive an inexpensive manner and on a large scale. Recent studies provided evidence for the potential of smartphone interaction monitoring as a novel tracking method to approximate rest-activity patterns based on the timing of smartphone activity and inactivity throughout the 24-hour day. These findings require further replication and more detailed insights into interindividual variations in the associations and deviations with commonly used metrics for monitoring rest-activity patterns in everyday life. This study aimed to replicate and expand on earlier findings regarding the associations and deviations between smartphone keyboard-derived and self-reported estimates of the timing of the onset of the rest and active periods and the duration of the rest period. Moreover, we aimed to quantify interindividual variations in the associations and time differences between the 2 assessment modalities and to investigate to what extent general sleep quality, chronotype, and trait self-control moderate these associations and deviations. Students were recruited to participate in a 7-day experience sampling study with parallel smartphone keyboard interaction monitoring. Multilevel modeling was used to analyze the data. In total, 157 students participated in the study, with an overall response rate of 88.9% for the diaries. The results revealed moderate to strong relationships between the keyboard-derived and self-reported estimates, with stronger associations for the timing-related estimates (β ranging from .61 to .78) than for the duration-related estimates (β=.51 and β=.52). The relational strength between the time-related estimates was lower, but did not substantially differ for the duration-related estimates, among students experiencing more disturbances in their general sleep quality. Time differences between the keyboard-derived and self-reported estimates were, on average, small (<0.5 hours); however, large discrepancies were also registered for quite some nights. The time differences between the 2 assessment modalities were larger for both timing-related and rest duration-related estimates among students who reported more disturbances in their general sleep quality. Chronotype and trait self-control did not significantly moderate the associations and deviations between the 2 assessment modalities. We replicated the positive potential of smartphone keyboard interaction monitoring for estimating rest-activity patterns among populations of regular smartphone users. Chronotype and trait self-control did not significantly influence the metrics' accuracy, whereas general sleep quality did: the behavioral proxies obtained from smartphone interactions appeared to be less powerful among students who experienced lower general sleep quality. The generalization and underlying process of these findings require further investigation.

Exploring Fatigue Patterns and Associated Factors Among Patients on Hemodialysis

Fatigue in patients on hemodialysis varies based on disease and treatment situations. Most studies have assessed the effectiveness of interventions based on average fatigue during the past week. However, two different types of fatigue are identified: post-dialysis fatigue and continuous fatigue. Identifying different types of fatigue and related factors may facilitate the development of more-effective, type-specific measures to reduce patient fatigue. The purpose of this study was to explore fatigue patterns and predictors in patients on hemodialysis during their eight-day dialysis cycle. A perspective, observational research design was adopted. One hundred and two patients were recruited from three hospitals and one hemodialysis clinic in New Taipei City. During the dialysis, patients answered a structured questionnaire that included a demographic datasheet, Taiwanese depression questionnaire, hemodialysis social support scale, trait anxiety inventory, Pittsburgh sleep quality index, and fatigue visual analogy scale. After dialysis on each day, patients completed the fatigue visual analogy scale for a period of eight consecutive days every morning, noon, and night. Biochemical data and inter-dialysis weight gain were collected from medical charts. This study found three distinct fatigue status groups: fatigue adaptation, rapid change and continuous fatigue. After the day of dialysis, fatigue level was the lowest in the fatigue adaptation group, followed by the continuous fatigue group. The rapid change group reported the highest level of fatigue. With regard to the degree of increase in fatigue after dialysis (fatigue score after dialysis minus fatigue score before dialysis), the rapid change group had the highest average increase in score after dialysis, while the continuous fatigue group had the lowest. Sleep quality, trait anxiety, and health caregiver support were also found to be predictors of group affiliation. Nurses can provide individual social support interventions and methods to improve sleep quality and alleviate anxiety to reduce fatigue in patients on hemodialysis.

Genetic associations between sleep traits and cognitive ageing outcomes in the Hispanic Community Health Study/Study of Latinos.

Sleep phenotypes have been reported to be associated with cognitive ageing outcomes. However, there is limited research using genetic variants as proxies for sleep traits to study their associations. We estimated associations between Polygenic Risk Scores (PRSs) for sleep duration, insomnia, daytime sleepiness, and obstructive sleep apnoea (OSA) and measures of cogntive ageing in Hispanic/Latino adults. We used summary statistics from published genome-wide association studies to construct PRSs representing the genetic basis of each sleep trait, then we studied the association of the PRSs of the sleep phenotypes with cognitive outcomes in the Hispanic Community Healthy Study/Study of Latinos. The primary model adjusted for age, sex, study centre, and measures of genetic ancestry. Associations are highlighted if their p-value <0.05. Higher PRS for insomnia was associated with lower global cognitive function and higher risk of mild cognitive impairment (MCI) (OR=1.20, 95% CI [1.06, 1.36]). Higher PRS for daytime sleepiness was also associated with increased MCI risk (OR=1.14, 95% CI [1.02, 1.28]). Sleep duration PRS was associated with reduced MCI risk among short and normal sleepers, while among long sleepers it was associated with reduced global cognitive function and with increased MCI risk (OR=1.40, 95% CI [1.10, 1.78]). Furthermore, adjustment of analyses for the measured sleep phenotypes and APOE-ε4 allele had minor effects on the PRS associations with the cognitive outcomes. Genetic measures underlying insomnia, daytime sleepiness, and sleep duration are associated with MCI risk. Genetic and self-reported sleep duration interact in their effect on MCI. Described in Acknowledgments.

Just sleep it off. The effects of sleep quality and trait anxiety on working memory.

Just sleep it off. The effects of sleep quality and trait anxiety on working memory.

The molecular mechanism of natural short sleep: A path towards understanding why we need to sleep

Sleep constitutes a third of human life and it is increasingly recognized as important for health. Over the past several decades, numerous genes have been identified to be involved in sleep regulation in animal models, but most of these genes when disturbed impair not only sleep but also health and physiological functions. Human natural short sleepers are individuals with lifelong short sleep and no obvious adverse outcomes associated with the lack of sleep. These traits appear to be heritable, and thus characterization of the genetic basis of natural short sleep provides an opportunity to study not only the genetic mechanism of human sleep but also the relationship between sleep and physiological function. This review focuses on the current understanding of mutations associated with the natural short sleep trait and the mechanisms by which they contribute to this trait.

An excitatory peri-tegmental reticular nucleus circuit for wake maintenance

Sleep is a necessity for our survival, but its regulation remains incompletely understood. Here, we used a human sleep duration gene to identify a population of cells in the peri-tegmental reticular nucleus (pTRNADRB1) that regulate sleep-wake, uncovering a role for a poorly understood brain area. Although initial ablation in mice led to increased wakefulness, further validation revealed that pTRNADRB1 neuron stimulation strongly promotes wakefulness, even after stimulation offset. Using combinatorial genetics, we found that excitatory pTRNADRB1 neurons promote wakefulness. pTRN neurons can be characterized as anterior- or posterior-projecting neurons based on multiplexed analysis of projections by sequencing (MAPseq) analysis. Finally, we found that pTRNADRB1 neurons promote wakefulness, in part, through projections to the lateral hypothalamus. Thus, human genetic information from a human sleep trait allowed us to identify a role for the pTRN in sleep-wake regulation.

Worsening sleep predicts lower life space mobility during the onset of the COVID-19 pandemic.

BackgroundPoor sleep health is an understudied yet potentially modifiable risk factor for reduced life space mobility (LSM), defined as one's habitual movement throughout a community. The objective of this study was to determine whether recalled changes in sleep traits (e.g., sleep quality, refreshing sleep, sleep problems, and difficulty falling asleep) because of the COVID‐19 pandemic were associated with LSM in older adults.MethodsData were obtained from a University of Florida‐administered study conducted in May and June of 2020 (n = 923). Linear regression models were used to assess the impact of COVID‐related change in sleep traits with summary scores from the Life Space Assessment. Analyses were adjusted for demographic, mental, and physical health characteristics, COVID‐related avoidant behaviors, and pre‐COVID sleep ratings.ResultsIn unadjusted models, reporting that any sleep trait got “a lot worse” or “a little worse” was associated with a decrease in LSM (all p < 0.05). Results were attenuated when accounting for demographic, mental, and physical health characteristics. In fully adjusted models, reporting that problems with sleep got “a lot worse” or that refreshing sleep got “a little worse” was associated with a lower standardized LSM score (β = −0.38, 95% CI: −0.74, −0.01, and β = −0.19, 95% CI: −0.37, −0.00, respectively).ConclusionsWhile additional research is needed in diverse people and environments, the results demonstrate an association between sleep traits that worsen in response to a health threat and reduced LSM. This finding suggests that interventions that focus on maintaining sleep health in times of heightened stress could preserve LSM.

PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals.

Many biological studies of transcriptional control mechanisms produce lists of genes and non-coding genomic intervals from corresponding gene expression and epigenomic assays. In higher organisms, such as eukaryotes, genes may be regulated by distal elements, with these elements lying 10s-100s of kilobases away from a gene transcription start site. To gain insight into these distal regulatory mechanisms, it is important to determine comparative enrichment of genes of interest in relation to genomic regions of interest, and to be able to do so at a range of distances. Existing bioinformatics tools can annotate genomic regions to nearest known genes, or look for transcription factor binding sites in relation to gene transcription start sites. Here, we present PEGS ( Peak set Enrichment in Gene Sets). This tool efficiently provides an exploratory analysis by calculating enrichment of multiple gene sets, associated with multiple non-coding elements (peak sets), at multiple genomic distances, and within topologically associated domains. We apply PEGS to gene sets derived from gene expression studies, and genomic intervals from corresponding ChIP-seq and ATAC-seq experiments to derive biologically meaningful results. We also demonstrate an extended application to tissue-specific gene sets and publicly available GWAS data, to find enrichment of sleep trait associated SNPs in relation to tissue-specific gene expression profiles.

PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals

Many biological studies of transcriptional control mechanisms produce lists of genes and non-coding genomic intervals from corresponding gene expression and epigenomic assays. In higher organisms, such as eukaryotes, genes may be regulated by distal elements, with these elements lying 10s–100s of kilobases away from a gene transcription start site. To gain insight into these distal regulatory mechanisms, it is important to determine comparative enrichment of genes of interest in relation to genomic regions of interest, and to be able to do so at a range of distances. Existing bioinformatics tools can annotate genomic regions to nearest known genes, or look for transcription factor binding sites in relation to gene transcription start sites. Here, we present PEGS ( Peak set Enrichment in Gene Sets). This tool efficiently provides an exploratory analysis by calculating enrichment of multiple gene sets, associated with multiple non-coding elements (peak sets), at multiple genomic distances, and within topologically associated domains. We apply PEGS to gene sets derived from gene expression studies, and genomic intervals from corresponding ChIP-seq and ATAC-seq experiments to derive biologically meaningful results. We also demonstrate an extended application to tissue-specific gene sets and publicly available GWAS data, to find enrichment of sleep trait associated SNPs in relation to tissue-specific gene expression profiles.