Sleep Phenotypes Research Articles

Rescuable sleep and synaptogenesis phenotypes in a Drosophila model of O-GlcNAc transferase intellectual disability.

O-GlcNAcylation is an essential intracellular protein modification mediated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA). Recently, missense mutations in OGT have been linked to intellectual disability, indicating that this modification is important for the development and functioning of the nervous system. However, the processes that are most sensitive to perturbations in O-GlcNAcylation remain to be identified. Here, we uncover quantifiable phenotypes in the fruit fly Drosophila melanogaster carrying a patient-derived OGT mutation in the catalytic domain. Hypo-O-GlcNAcylation leads to defects in synaptogenesis and reduced sleep stability. Both these phenotypes can be partially rescued by genetically or chemically targeting OGA, suggesting that a balance of OGT/OGA activity is required for normal neuronal development and function.

Accelerometer-Derived Sleep Metrics in Adolescents Reveal Shared Genetic Influences with Obesity and Stress in a Brazilian Birth Cohort Study.

We aimed to test the association between sleep-related polygenic scores (PGSs) and accelerometer-based sleep metrics among Brazilian adolescents, and to evaluate potential mechanisms underlying the association through the enrichment of obesity, and cortisol pathway-specific polygenic scores (PRSet). Utilizing data from The 2004 Pelotas (Brazil) Birth Cohort, sleep time window and sleep efficiency were measured at the 11-year-old follow-up using ActiGraph accelerometers. Three sleep-PGSs were developed based on the most recent genome-wide association study (GWAS) of accelerometer-based sleep measures. PRSet, calculated using variants linked to body mass index (BMI) and plasmatic cortisol concentration, aimed to assess pleiotropic effects. Linear regression models, adjusted for sex and the first 10 principal components of ancestry, were employed to explore the impact of sleep-PGS and specific-PRSet on sleep phenotypes. The number of nocturnal sleep episodes-PGS was positively associated with sleep time window (β =2.306, SE: 0.92, p=0.011). Nocturnal sleep episodes were also associated with sleep time window when restricted to BMI-PRSet (β=2.682, SE: 0.912, competitive-p=0.003). Both number of sleep episodes and sleep time window cortisol-PRSets were associated (β=0.002, SE: 0.001, p=0.013; β=0.003, SE: 0.001, p=0.003, respectively) and exhibited enrichment in molecular pathways (competitive-p=0.011; competitive-p=0.003, respectively) with sleep efficiency. Sleep polygenetic components observed in European adults may partially explain accelerometer-based sleep time window in Brazilian adolescents. Specific BMI molecular pathway strengthened the association between sleep-PGS and sleep time window, while cortisol concentration pathway had a significant impact on the genetic liability for sleep efficiency. Our results suggest genetic overlap as a potential etiological pathway for sleep-related comorbidities, emphasizing common genetic mechanisms.

Racial and ethnic and sex differences in at-home estimates of sleep-disordered breathing parameters among Mexican American, Black, and Non-Hispanic White adults.

Background Racial and ethnic and sex differences in sleep may exist, but there are limited data directly comparing objective estimates of sleep-disordered breathing (SDB), particularly in rapid eye movement (REM) versus non-rapid eye movement (NREM) sleep, among Black, Mexican American (MA) and non-Hispanic White (NHW) men and women. Our goal is to investigate health disparities in SDB in a new, diverse cohort of older adults. Research Question Do SDB parameters during REM and NREM sleep differ by race and ethnicity or sex in community-dwelling older adults?. Methods The Dormir Study conducted a comprehensive sleep examination among eligible participants enrolled in the ongoing community-based Health and Aging Brain Study-Health Disparities (HABS-HD) cohort (2020-4), among Black, MA, and NHW adults aged 50 years and older. Here we characterize racial and ethnic and sex differences in SDB indices assessed by an FDA-approved Peripheral Arterial Tonometry (PAT)-based home sleep testing system. Results We examined 821 participants, including 543 (66.1%) women, and 284 (34.6%) MA and 174 (21.2%) Black individuals, with a mean age of 66.6±8.5 years. Around half (50.5%) of the participants had moderate to severe SDB as defined by the respiratory event index (REI based on 3% desaturations) of ≥15/ hour, 72.7% with moderate to severe REM SDB (REM-REI ≥ 15/hour) and 39.5% with moderate to severe NREM SDB (NREM-REI ≥15/hour). The prevalence of SDB did not differ by race or sex. However, significant racial and ethnic and sex differences were observed for REM-specific SDB metrics. Overall, Black women had the highest REM REI, and NHW men had the lowest REM REI and REM ODI. After controlling for age, sex, education, income, employment status, cognitive status, BMI, history of hypertension, diabetes, stroke, and coronary heart disease, and sleep medication use, Black participants had a REM-REI that was 3 events per hour higher than that of NHW adults, while NREM-REI were similar. MA individuals had similar REM or NREM SDB parameters compared to NHW adults but exhibited higher average blood oxygen levels. Conclusions In this community-based cohort of middle- to older-aged NHW, MA, and Black adults, PAT-based measures of in-home sleep indicate a higher prevalence of REM SDB in Black adults, particularly Black women, compared to their NHW counterparts; this contrasts with the similar NREM parameters observed across racial and ethnic groups. Given the close link between REM SDB and adverse health outcomes, clinicians should pay more attention to this sleep apnea phenotype, especially in minoritized populations.

Phenotypes of sleep health among adults with chronic heart failure in a randomized controlled trial of cognitive behavioral therapy for insomnia.

Poor sleep contributes to adverse health in heart failure. However, studies are limited to isolated sleep characteristics. To evaluate changes in sleep health phenotypes after cognitive behavioral therapy for insomnia or attention control and associations between sleep health phenotypes, symptoms, stress, functional performance, and emergency department visits and hospitalizations. Secondary analysis of a randomized controlled trial of cognitive behavioral therapy for insomnia among adults with heart failure. We measured sleep (rest-activity rhythms, sleep duration, quality, and efficiency, insomnia severity, daytime sleepiness), symptoms, cognitive ability, vigilance, and 6-minute walk distance at baseline and 3-, 6-, and 12-month postintervention and collected hospitalizations and emergency department visits. We used K-means cluster analysis and generalized linear mixed models, generalized estimating equations, and Cox proportional hazard models. Among 166 participants (M age=63.2 (SD=12.6) years; 57% male; 23% NewYork Heart Association Class III/IV), there were four sleep health phenotypes ("Unstable Sleep" (15%); "Short Sleep" (39%); "Low Sleep Efficiency" (25%); and "Good Sleep" (21%)) at baseline. The healthiest phenotype was associated with the lowest fatigue. The proportions of participants in the healthiest sleep group increased from pre- to post-treatment. Low sleepiness (p=.0188) and a robust circadian quotient (p=.007) predicted transition to the healthiest phenotype. The poorest sleep phenotype at baseline predicted time to hospitalizations and emergency department visits (hazard ratios 0.35-0.60) after adjusting for covariates. Sleep phenotypes predict heart failure outcomes. Tailored interventions targeting phenotypes may be more effective than approaches that focus on single sleep characteristics.

Associations of Everyday Discrimination With Insomnia and Short Sleep Duration Among Older Women.

Discrimination may contribute to sleep health disparities among women, yet limited research has investigated the association between discrimination and insomnia with short sleep. Among a racially and ethnically diverse sample of women (N=25 920; mean age, 72.2±6.1 years), we investigated the relationship of discrimination with insomnia symptoms and sleep duration. Poisson models with robust variance were fit to examine discrimination with insomnia, sleep duration (short <7 hours or long >9 hours versus recommended 7-9 hours), and insomnia short sleep phenotype adjusted for covariates. Insomnia symptoms, short and long sleep, and high discrimination were reported by 53%, 11%, 15%, and 40% of women, respectively. Women reporting high versus low discrimination were more likely to report insomnia, short sleep, and insomnia short sleep phenotype (insomnia: adjusted prevalence ratio, 1.15 [95% CI, 1.13-1.18]; short sleep: adjusted prevalence ratio, 1.24 [95% CI, 1.16-1.34]; insomnia short sleep phenotype: adjusted prevalence ratio, 1.45 [95% CI, 1.31-1.61]). In exploratory analyses, the association between discrimination and insomnia symptoms was present among Asian and White women, whereas the association between discrimination and sleep duration was among Hispanic (long sleep) and White (short sleep) women. Further, the association between discrimination and insomnia symptoms was more pronounced among those with less than a bachelor's degree, whereas women with a bachelor's degree or higher were less vulnerable to the association between discrimination and long sleep. Discrimination was associated with insomnia and short sleep, a more severe phenotype for adverse cardiovascular health. Discrimination may be a target for reducing sleep problems among older women.

Nocturnal sleep phenotypes in idiopathic hypersomnia – A data-driven cluster analysis

Nocturnal sleep phenotypes in idiopathic hypersomnia – A data-driven cluster analysis

Genetically predicted effects of 10 sleep phenotypes on revision of knee arthroplasty: a mendelian randomization study

BackgroundAccumulating evidence has suggested that sleep disturbances and disorders are common in patients who undergo knee arthroplasty. Revision surgery represents one of the most catastrophic outcomes of knee arthroplasty. However, it remains unclear whether sleep traits are the causes or consequences of knee arthroplasty revision. This study aimed to genetically examine the relationships between sleep traits and knee arthroplasty revision.MethodsTo determine the causal relationship between sleep traits and knee arthroplasty revision, we employed two-sample Mendelian randomization (MR) using summary statistics from the largest publicly available genome-wide association studies (GWASs). The MR design uses genetic variants as instrumental variables to help separate causal relationships from non-causal associations. The main analyses included an inverse variance weighted (IVW) meta-analysis to obtain primary effect estimates. Sensitivity analyses involving the weighted median approach and MR-Egger regression were also conducted to check for potential pleiotropic biases. Numerous complementary sensitivity analyses were also performed to identify statistically significant causal correlations when there were horizontal pleiotropy and heterogeneity across variants. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.ResultsIn the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that genetically-predicted short sleep duration short sleep duration (average sleep duration of 24 h is 6 h or less) was positively correlated with the risk of knee arthroplasty revision (odds ratio = 1.03, 95% confidence interval = 1.01–1.05, and P = 0.003), while the association between genetically-predicted long sleep duration and knee arthroplasty was negative. The reverse MR analysis did not yield evidence supporting reverse causality relation between knee arthroplasty revision and sleep phenotypes.ConclusionThis research indicated that, of the 10 sleep phenotypes we analyzed, only sleep duration was causally associated with knee arthroplasty revision. These discoveries added to the understanding of the role of sleep traits in the etiology of knee arthroplasty revision, which might further expand our insights into the prevention of knee arthroplasty revision.

Analysis of C-reactive protein omics-measures associates methylation risk score with sleep health and related health outcomes.

DNA methylation (DNAm) predictors of high sensitivity C-reactive protein (CRP) offer a stable and accurate means of assessing chronic inflammation, bypassing the CRP protein fluctuations secondary to acute illness. Poor sleep health is associated with elevated inflammation (including elevated blood CRP levels) which may explain associations of sleep insufficiency with metabolic, cardiovascular and neurological diseases. Our study aims to characterize the relationships among sleep health phenotypes and CRP markers -blood, genetic, and epigenetic indicators-within the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). In HCHS/SOL, methylation risk scores (MRS)-CRP and polygenetic risk score (PRS)-CRP were constructed separately as weighted sums of methylation beta values or allele counts, respectively, for each individual. Sleep health phenotypes were measured using self-reported questionnaires and objective measurements. Survey-weighted linear regression established the association between the multiple sleep phenotypes (obstructive sleep apnea (OSA), sleep duration, insomnia and excessive sleepiness symptom), cognitive assessments, diabetes and hypertension with CRP markers while adjusting for age, sex, BMI, study center, and the first five principal components of genetic ancestry in HCHS/SOL. We included 2221 HCHS/SOL participants (age range 37-76 yrs, 65.7% female) in the analysis. Both the MRS-CRP (95% confidence interval (CI): 0.32-0.42, p = 3.3 × 10-38) and the PRS-CRP (95% CI: 0.15-0.25, p = 1 × 10-14) were associated with blood CRP level. Moreover, MRS-CRP was associated with sleep health phenotypes (OSA, long sleep duration) and related conditions (diabetes and hypertension), while PRS-CRP markers were not associated with these traits. Circulating CRP level was associated with sleep duration and diabetes. Associations between OSA traits and metabolic comorbidities weakened after adjusting for MRS-CRP, most strongly for diabetes, and least for hypertension. MRS-CRP is a promising estimate for systemic and chronic inflammation as reflected by circulating CRP levels, which either mediates or serves as a common cause of the association between sleep phenotypes and related comorbidities, especially in the presence of diabetes.

Novel murine closed-loop auditory stimulation paradigm elicits macrostructural sleep benefits in neurodegeneration.

Boosting slow-wave activity (SWA) by modulating slow waves through closed-loop auditory stimulation (CLAS) might provide a powerful non-pharmacological tool to investigate the link between sleep and neurodegeneration. Here, we established mouse CLAS (mCLAS)-mediated SWA enhancement and explored its effects on sleep deficits in neurodegeneration, by targeting the up-phase of slow waves in mouse models of Alzheimer's disease (AD, Tg2576) and Parkinson's disease (PD, M83). We found that tracking a 2 Hz component of slow waves leads to highest precision of non-rapid eye movement (NREM) sleep detection in mice, and that its combination with a 30° up-phase target produces a significant 15-30% SWA increase from baseline in wild-type (WTAD)and transgenic (TGAD) mice versus a mock stimulation group. Conversely, combining 2 Hz with a 40° phase target yields a significant increase ranging 30-35% in WTPD and TGPD mice. Interestingly, these phase-target-triggered SWA increases are not genotype dependent but strain specific. Sleep alterations that may contribute to disease progression and burden were described in AD and PD lines. Notably, pathological sleep traits were rescued by mCLAS, which elicited a 14% decrease of pathologically heightened NREM sleep fragmentation in TGAD mice, accompanied by a steep decrease in microarousal events during both light and dark periods. Overall, our results indicate that model-tailored phase targeting is key to modulate SWA through mCLAS, prompting the acute alleviation of key neurodegeneration-associated sleep phenotypes and potentiating sleep regulation and consolidation. Further experiments assessing the long-term effect of mCLAS in neurodegeneration may majorly impact the establishment of sleep-based therapies.

Causal relationship between sleep phenotype and idiopathic normal pressure hydrocephalus: a two-sample bidirectional Mendelian randomization study

To explore the causal relationship between sleep phenotype and idiopathic normal pressure hydrocephalus (iNPH) using two-sample bidirectional Mendelian randomization. The exposure data including 8 sleep phenotypes used in this study were obtained from GWAS catalog, FinnGenR10 and MRCIEU GWAS. The outcome data for idiopathic normal-pressure hydrocephalus were obtained from FinnGen R10. We used the inverse-variance weighted (IVW) method to perform the principal analyses. Cochrane Q-statistics test was used to assess the heterogeneity and MR Egger‑intercept test performed to evaluate the pleiotropy for sensitivity analyses. IVW result showed that frequent daytime nap was associated with higher odds of iNPH (OR=3.3393, 95 CI% : 1.0646-10.4742, P=0.0270). Cochrane Q-statistics test and MR Egger‑intercept test showed that the MR analysis had no pleiotropy or heterogeneity (P > 0.05). The external validation reproduced this result (OR=2.5660, 95 CI% : 1.1680-5.6373, P=0.0189; OR=4.0424, 95 CI% : 1.5709-10.4024, P=0.0038). Reverse Mendelian randomization suggested that iNPH did not have significant impact on sleep phenotype. The frequency of daytime naps is causally associated with iNPH, and reducing the frequency of weekly daytime naps can reduce the risk of iNPH in the elderly population.

GBA-AAV mitigates sleep disruptions and motor deficits in mice with REM sleep behavior disorder

Sleep disturbances, including rapid eye movement sleep behavior disorder (RBD), excessive daytime sleepiness, and insomnia, are common non-motor manifestations of Parkinson’s disease (PD). Little is known about the underlying mechanisms, partly due to the inability of current rodent models to adequately mimic the human PD sleep phenotype. Clinically, increasing studies have reported that variants of the glucocerebrosidase gene (GBA) increase the risk of PD. Here, we developed a mouse model characterized by sleep–wakefulness by injecting α-synuclein preformed fibronectin (PFF) into the sublaterodorsal tegmental nucleus (SLD) of GBA L444P mutant mice and investigated the role of the GBA L444P variant in the transition from rapid eye movement sleep behavior disorder to PD. Initially, we analyzed spectral correlates of REM and NREM sleep in GBA L444P mutant mice. Importantly, EEG power spectral analysis revealed that GBA L444P mutation mice exhibited reduced delta power during non-rapid eye movement (NREM) sleep and increased theta power (8.2–10 Hz) in active rapid eye movement (REM) sleep phases. Our study revealed that GBA L444P-mutant mice, after receiving PFF injections, exhibited increased sleep fragmentation, significant motor and cognitive dysfunctions, and loss of dopaminergic neurons in the substantia nigra. Furthermore, the over-expression of GBA-AAV partially improved these sleep disturbances and motor and cognitive impairments. In conclusion, we present the initial evidence that the GBA L444P mutant mouse serves as an essential tool in understanding the complex sleep disturbances associated with PD. This model further provides insights into potential therapeutic approaches, particularly concerning α-synuclein accumulation and its subsequent pathological consequences.

Ageing-related modification of sleep and breathing in orexin-knockout narcoleptic mice.

Narcolepsy type-1 (NT1) is a lifelong sleep disease, characterised by impairment of the orexinergic system, with a typical onset during adolescence and young adulthood. Since the wake-sleep cycle physiologically changes with ageing, this study aims to compare sleep patterns between orexin-knockout (KO) and wild type (WT) control mice at different ages. Four groups of age-matched female KO and WT mice (16 weeks of age: 8 KO-YO and 9 WT-YO mice; 87 weeks of age: 13 KO-OLD and 12 WT-OLD mice) were implanted with electrodes for discriminating wakefulness, rapid-eye-movement sleep (REMS), and non-REMS (NREMS). Mice were recorded for 48 h in their home cages and for 7 more hours into a plethysmographic chamber to characterise their sleep-breathing pattern. Regardless of orexin deficiency, OLD mice spent less time awake and had fragmentation of this behavioural state showing more bouts of shorter length than YO mice. OLD mice also had more NREMS bouts and less frequent NREMS apneas than YO mice. Regardless of age, KO mice showed cataplexy-like episodes and shorter REMS latency than WT controls and had a faster breathing rate and an increased minute ventilation during REMS. KO mice also had more wakefulness, NREMS and REMS bouts, and a shorter mean length of wakefulness bouts than WT controls. Our experiment indicated that the lack of orexins as well as ageing importantly modulate the sleep and breathing phenotype in mice. The narcoleptic phenotype caused by orexin deficiency in female mice was substantially preserved with ageing.

Five million nights: temporal dynamics in human sleep phenotypes

Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night’s sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual’s sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e−100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e−100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2–10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses.

Distinct glutamatergic projections of the posteroventral medial amygdala play different roles in arousal and anxiety.

Sleep disturbance usually accompanies anxiety disorders and exacerbates their incidence rates. The precise circuit mechanisms remain poorly understood. Here, we found that glutamatergic neurons in the posteroventral medial amygdala (MePVGlu neurons) are involved in arousal and anxiety-like behaviors. Excitation of MePVGlu neurons not only promoted wakefulness but also increased anxiety-like behaviors. Different projections of MePVGlu neurons played various roles in regulating anxiety-like behaviors and sleep-wakefulness. MePVGlu neurons promoted wakefulness through the MePVGlu/posteromedial cortical amygdaloid area (PMCo) pathway and the MePVGlu/bed nucleus of the stria terminals (BNST) pathway. In contrast, MePVGlu neurons increased anxiety-like behaviors through the MePVGlu/ventromedial hypothalamus (VMH) pathway. Chronic sleep disturbance increased anxiety levels and reduced reparative sleep, accompanied by the enhanced excitability of MePVGlu/PMCo and MePVGlu/VMH circuits but suppressed responses of glutamatergic neurons in the BNST. Inhibition of the MePVGlu neurons could rescue chronic sleep deprivation-induced phenotypes. Our findings provide important circuit mechanisms for chronic sleep disturbance-induced hyperarousal response and obsessive anxiety-like behavior and are expected to provide a promising strategy for treating sleep-related psychiatric disorders and insomnia.

The role of glial and neuronal Eph/ephrin signaling in Drosophila mushroom body development and sleep and circadian behavior

The Eph receptor, a prototypically large receptor protein tyrosine kinase, interacts with ephrin ligands, forming a bidirectional signaling system that impacts diverse brain functions. Eph receptors and ephrins mediate forward and reverse signaling, affecting neurogenesis, axon guidance, and synaptic signaling. While mammalian studies have emphasized their roles in neurogenesis and synaptic plasticity, the Drosophila counterparts are less studied, especially in glial cells, despite structural similarities. Using RNAi to modulate Eph/ephrin expression in Drosophila neurons and glia, we studied their roles in brain development and sleep and circadian behavior. Knockdown of neuronal ephrin disrupted mushroom body development, while glial knockdown had minimal impact. Surprisingly, disrupting ephrin in neurons or glial cells altered sleep and circadian rhythms, indicating a direct involvement in these behaviors independent from developmental effects. Further analysis revealed distinct sleep phenotypes between neuronal and glial knockdowns, underscoring the intricate interplay within the neural circuits that govern behavior. Glia-specific knockdowns showed altered sleep patterns and reduced circadian rhythmicity, suggesting an intricate role of glia in sleep regulation. Our findings challenge simplistic models of Eph/ephrin signaling limited to neuron-glia communication and emphasize the complexity of the regulatory networks modulating behavior. Future investigations targeting specific glial subtypes will enhance our understanding of Eph/ephrin signaling's role in sleep regulation across species.

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis

BackgroundDisturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. Research questionTo identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. Study design and methodsA longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. ResultsAmong 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. InterpretationWe identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.

Workflow for the unsupervised clustering of sleep stages identifies light and deep sleep in electrophysiological recordings in mice

BackgroundSleep physiology plays a critical role in brain development and aging. Accurate sleep staging, which categorizes different sleep states, is fundamental for sleep physiology studies. Traditional methods for sleep staging rely on manual, rule-based scoring techniques, which limit their accuracy and adaptability. New methodWe describe, test and challenge a workflow for unsupervised clustering of sleep states (WUCSS) in rodents, which uses accelerometer and electrophysiological data to classify different sleep states. WUCSS utilizes unsupervised clustering to identify sleep states using six features, extracted from 4-second epochs. ResultsWe gathered high-quality EEG recordings combined with accelerometer data in diverse transgenic mouse lines (male ApoE3 versus ApoE4 knockin; male CNTNAP2 KO versus wildtype littermates). WUCSS showed high recall, precision, and F1-score against manual scoring on awake, NREM, and REM sleep states. Within NREM, WUCSS consistently identified two additional clusters that qualify as deep and light sleep states. Comparison with existing methodsThe ability of WUCSS to discriminate between deep and light sleep enhanced the precision and comprehensiveness of the current mouse sleep physiology studies. This differentiation led to the discovery of an additional sleep phenotype, notably in CNTNAP2 KO mice, showcasing the method's superiority over traditional scoring methods. ConclusionsWUCSS, with its unsupervised approach and classification of deep and light sleep states, provides an unbiased opportunity for researchers to enhance their understanding of sleep physiology. Its high accuracy, adaptability, and ability to save time and resources make it a valuable tool for improving sleep staging in both clinical and preclinical research.

Association of Sleep Pattern and Genetic Susceptibility with Obstructive Sleep Apnea: A Prospective Analysis of the UK Biobank.

The prevalence of obstructive sleep apnea (OSA) is high worldwide. This study aimed to quantify the relationship between the incidence of OSA and sleep patterns and genetic susceptibility. A total of 355,133 white British participants enrolled in the UK Biobank between 2006 and 2010 with follow-up data until September 2021 were recruited. We evaluated sleep patterns using a customized sleep scoring method based on the low-risk sleep phenotype, defined as follows: morning chronotype, 7-8 hours of sleep per day, never/rarely experience insomnia, no snoring, no frequent daytime sleepiness, never/rarely nap, and easily getting up early. The polygenic risk score was calculated to assess genetic susceptibility to OSA. Cox proportional hazard models were used to evaluate the associations between OSA and sleep patterns and genetic susceptibility. During a mean follow-up of 12.57 years, 4618 participants were diagnosed with OSA (age: 56.83 ± 7.69 years, women: 31.3%). Compared with those with a poor sleep pattern, participants with a normal (HR: 0.42, 95% CI: 0.38-0.46), ideal (HR: 0.21, 95% CI: 0.19-0.24), or optimal (HR: 0.15, 95% CI: 0.12-0.18) sleep pattern were significantly more likely to have OSA. The genetic susceptibility of 173,239 participants was calculated, and the results showed that poor (HR: 3.67, 95% CI: 2.95-4.57) and normal (HR: 1.89, 95% CI: 1.66-2.16) sleep patterns with high genetic susceptibility can increase the risk for OSA. This large-scale prospective study provides evidence suggesting that sleep patterns across seven low-risk sleep phenotypes may protect against OSA in individuals with varying degrees of genetic susceptibility.

371. Objectively-Characterized Peritraumatic Sleep Phenotypes Are Associated With Both Pre-Trauma Characteristics and Peritraumatic Symptom Outcomes

371. Objectively-Characterized Peritraumatic Sleep Phenotypes Are Associated With Both Pre-Trauma Characteristics and Peritraumatic Symptom Outcomes

No bidirectional relationship between sleep phenotypes and risk of proliferative diabetic retinopathy: a two-sample Mendelian randomization study

This study aimed to investigate the probable existence of a causal relationship between sleep phenotypes and proliferative diabetic retinopathy (PDR). Single nucleotide polymorphisms associated with sleep phenotypes were selected as instrumental variables at the genome-wide significance threshold (P < 5 × 10−8). Inverse‐variance weighted was applied as the primary Mendelian randomization (MR) analysis method, and MR Egger regression, weighted median, simple mode, and weighted mode methods were used as complementary analysis methods to estimate the causal association between sleep phenotypes and PDR. Results indicated that genetically predicted sleep phenotypes had no causal effects on PDR risk after Bonferroni correction (P = 0.05/10) [Chronotype: P = 0.143; Daytime napping: P = 0.691; Daytime sleepiness: P = 0.473; Insomnia: P = 0.181; Long sleep duration: P = 0.671; Morning person:P = 0.113; Short sleep duration: P = 0.517; Obstructive sleep apnea: P = 0.091; Sleep duration: P = 0.216; and snoring: P = 0.014]. Meanwhile, there are no reverse causality for genetically predicted PDR on sleep phenotypes [Chronotype: P = 0.100; Daytime napping: P = 0.146; Daytime sleepiness: P = 0.469; Insomnia: P = 0.571; Long sleep duration: P = 0.779; Morning person: P = 0.040; Short sleep duration: P = 0.875; Obstructive sleep apnea: P = 0.628; Sleep duration: P = 0.896; and snoring: P = 0.047]. This study’s findings did not support the causal effect of between sleep phenotypes and PDR. Whereas, longitudinal studies can further verify results validation.