Daytime Dozing Research Articles

Associations of classical HLA alleles with sleep behaviours.

Immune dysregulation has been observed in individuals with sleep disturbance, with HLA molecules play a crucial role in the immune response. This study aimed to investigate the associations between HLA alleles and sleep behaviours, considering several environmental factors. Data were sourced from the UK Biobank. Logistic regression analyses were performed to explore the associations between 359 HLA alleles and 4 sleep behaviours, including chronotype (n = 204,636), insomnia (n = 227,553), snoring (n = 214,350) and daytime dozing (n = 227,197). Furthermore, gene-environmental interaction studies (GEIS) were conducted to evaluate the interactions of HLA alleles with environmental factors on sleep behaviours. This study analysed a total sample and subgroups stratified by sex to elucidate the impact of HLA alleles on sleep behaviours. Our findings revealed several associations between specific HLA alleles and sleep behaviours. Notably, HLA-A*23:01 was associated with evening chronotype in the total sample (OR = 0.918, 95%CI: 0.872-0.965), while HLA-A*32:01 was associated with evening chronotype in males (OR = 1.089, 95%CI: 1.037-1.144). Furthermore, GEIS identified multiple sets of interactions associated with sleep behaviours. For example, the interaction of HLA-DPA1*01:04 with alcohol consumption was associated with daytime dozing in the total sample (OR = 1.993, 95%CI: 1.351-2.941), while the interaction of HLA-DQB1*05:04 with ever suffered mental distress preventing usual activities was associated with insomnia in males (OR = 0.409, 95%CI: 0.254-0.658). Our findings highlight the involvement of HLA in sleep regulation and underscore the potential interactions between HLA alleles and environmental factors in modulating susceptibility to sleep behaviours.

Sleep traits and breast cancer risk: a two-sample Mendelian randomization study

Globally, breast cancer continues to be the leading cause of cancer-related incidence and mortality among females. Research has shown that sleep patterns significantly influence tumor onset and progression. In this research, the association was examined through the application of a two-sample Mendelian randomization (MR) approach. For the analysis of seven sleep patterns, genetic tools were sourced from both the UK Biobank and 23andMe, including morning/evening person (chronotype) n = 177,604, morning person (chronotype) n = 248,094, daytime dozing/sleepiness n = 193,472, getting up in the morning n = 193,717, and sleeplessness n = 193,987; sleep duration n = 192,810; and nap during the day n = 166,853. The Breast Cancer Association Consortium (BCAC) supplied genome-wide association studies (GWAS) data, including 133,384 breast cancer cases and 113,789 controls, alongside subtype-specific data with 106,278 cases and 91,477 controls. We discovered that chronotype encompasses both morning and evening types contributes to the risk of overall breast cancer. While daytime dozing and morning person (chronotype) are linked to a lower risk of breast cancer in general, In subtype-specific analyses, morning person (chronotype) was negatively associated with luminal B, HER2-negative-like, and daytime dozing was negatively correlated with luminal A-like, luminal B-like, and HER2-enriched-like. The study corroborates that chronotype is a danger element for breast cancer, aligning with previous observational findings. The association between being a morning person (chronotype) or having daytime dozing and a decreased risk of breast cancer underscores the significance of sleep patterns in formulating strategies for cancer prevention.

The causal relationship between sleep and risk of psychiatric disorders: A two-sample mendelian randomization study.

Sleep is associated with psychiatric disorders. However, their causality remains unknown. The study explored the causal relationship between seven sleep parameters (sleep duration, insomnia, sleep apnea, chronotype, daytime dozing, napping during the day, and snoring) and three psychiatric disorders including major depressive disorder (MDD), schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS) summary data for sleep parameters were obtained from the United Kingdom biobank, FinnGen biobank, and EBI databases. MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW(fixed effects) were used to perform the MR analysis. The heterogeneity was detected by Cochran's Q statistic. The horizontal pleiotropy was detected by MR Egger. The sensitivity was investigated by the leave-one-out analysis. Insomnia (OR = 2.02, 95%CI = 1.34-3.03, p = 0.001, False-discovery rate (FDR) corrected p-value = 0.011) and napping during the day (OR = 1.81, 95%CI = 1.34-2.44, FDR corrected p-value<0.001) were associated with an increased risk of MDD. Longer sleep duration (OR = 2.20, 95%CI = 1.24-3.90, FDR corrected p-value = 0.049) had an association with the increased risk of schizophrenia, while daytime dozing (OR = 4.44, 95%CI = 1.20-16.41, corrected p-value = 0.088)and napping during the day (OR = 2.11, 95%CI = 1.11-4.02, FDR corrected p-value = 0.088) had a suggestive association with an increased risk of schizophrenia. Longer sleep duration had a suggestive association with a decreased risk of ADHD (OR = 0.66, 95%CI = 0.42-0.93, FDR corrected p-value = 0.088). This study provides further evidence for a complex relationship between sleep and psychiatric disorders. Our findings highlight the potential benefits of addressing sleep problems in the prevention of psychiatric disorders.

A genome-wide association study identifies candidate genes for sleep disturbances in depressed individuals

ObjectiveThis study aimed to identify candidate loci and genes related to sleep disturbances in depressed individuals and clarify the co-occurrence of sleep disturbances and depression from the genetic perspective.MethodsThe study subjects (including 58,256 self-reported depressed individuals and 6,576 participants with PHQ-9 score ≥ 10, respectively) were collected from the UK Biobank, which were determined based on the Patient Health Questionnaire (PHQ-9) and self-reported depression status, respectively. Sleep related traits included chronotype, insomnia, snoring and daytime dozing. Genome-wide association studies (GWASs) of sleep related traits in depressed individuals were conducted by PLINK 2.0 adjusting age, sex, Townsend deprivation index and 10 principal components as covariates. The CAUSALdb database was used to explore the mental traits associated with the candidate genes identified by the GWAS.ResultsGWAS detected 15 loci significantly associated with chronotype in the subjects with self-reported depression, such as rs12736689 at RNASEL (P = 1.00 × 10− 09), rs509476 at RGS16 (P = 1.58 × 10− 09) and rs1006751 at RFX4 (P = 1.54 × 10− 08). 9 candidate loci were identified in the subjects with PHQ-9 ≥ 10, of which 2 loci were associated with insomnia such as rs115379847 at EVC2 (P = 3.50 × 10− 08), and 7 loci were associated with daytime dozing, such as rs140876133 at SMYD3 (P = 3.88 × 10− 08) and rs139156969 at ROBO2 (P = 3.58 × 10− 08). Multiple identified genes, such as RNASEL, RGS16, RFX4 and ROBO2 were reported to be associated with chronotype, depression or cognition in previous studies.ConclusionOur study identified several candidate genes related to sleep disturbances in depressed individuals, which provided new clues for understanding the biological mechanism underlying the co-occurrence of depression and sleep disorders.

Relationships between orofacial pain and sleep: Analysis of UK biobank and genome-wide association studies data

Background/purposeOrofacial pain is common in dental practices. This study aimed to explore relationships between orofacial pain and sleep using the UK Biobank dataset and, based on epidemiological associations, to investigate the causal association using genome-wide association studies data. Materials and methodsFirst, a cross-sectional study was conducted with 196,490 participants from UK Biobank. Information on pain conditions and sleep traits was collected. Multivariable models were used to explore the relationships with odds ratio (OR). Second, Mendelian randomization analyses were conducted using data for orofacial pain, including temporomandibular joint disorders-related pain (n = 377,277) and atypical facial pain (n = 331,749), and sleep traits, including sleep duration (n = 446,118), short sleep (n = 411,934), long sleep (n = 339,926), snoring (n = 359,916), ease of getting up (n = 385,949), insomnia (n = 453,379), daytime dozing (n = 452,071), daytime napping (n = 452,633), and chronotype (n = 403,195). ResultsThe cross-sectional study confirmed the bidirectionality between pain and sleep. Participants experiencing pain all over the body showed a significant association with an unhealthy sleep pattern (OR = 1.18, P < 0.001) and other sleep traits (P < 0.05). Risks of chronic orofacial pain were associated with sleep duration in a non-linear relationship (P = 0.032). The Mendelian randomization analyses indicated that long sleep was causally associated with temporomandibular joint disorders-related pain (OR = 6.77, P = 0.006). ConclusionThe relationship between pain and sleep is bidirectional. Long sleep is found to be causally associated with chronic orofacial pain.

Sleep Patterns and Traditional Cardiovascular Health Metrics: Joint Impact on Major Adverse Cardiovascular Events in a Prospective Cohort Study.

This study examines the association between traditional cardiovascular health (CVH) metrics and major adverse cardiovascular events (MACE) incidence in individuals with diverse sleep patterns. We analyzed data from 208 621 participants initially free of cardiovascular disease (CVD) in the UK Biobank study. Sleep patterns were assessed using scores for chronotype, duration, insomnia, snoring, and daytime dozing. Traditional CVH scores were derived from the Life's Simple 7 metrics. Cox proportional hazards multivariate regression assessed associations between distinct combinations of CVH and sleep scores and MACE, including nonfatal myocardial infarction, nonfatal stroke, and CVD mortality. Over a mean follow-up of 12.73 years, 9253 participants experienced incident MACE. Individuals with both a healthy sleep pattern and ideal CVH levels had the lowest MACE risk compared with those with a poor sleep pattern and poor CVH levels (hazard ratio, 0.306 [95% CI, 0.257-0.365]; P<0.001). Elevated CVH scores were associated with a reduced risk of MACE across different sleep patterns. Similar trends were observed for individual MACE components, heart failure, and all-cause mortality. These findings remained robust in sensitivity analyses and across various subgroups. In individuals without known CVD, maintaining a favorable sleep pattern and achieving optimal CVH levels, as measured by traditional metrics, were associated with the lowest MACE risk. Enhanced CVH significantly reduced CVD risk, even in individuals with a poor sleep pattern. These results emphasize the importance of considering multiple dimensions of sleep health alongside CVH to mitigate CVD risk. URL: https://www.ukbiobank.ac.uk; Unique identifier: 91090.

Association between sleep traits and sarcopenia-related traits: A two-sample bidirectional Mendelian randomization study.

Despite limited evidence regarding the impact of sleep quality on sarcopenia, it is widely recognized as being associated with various diseases. This study aimed to explore the causal relationship between sleep traits and sarcopenia-related traits. This study utilized a two-sample bidirectional Mendelian randomization analysis. Genetic genome-wide summary data of sleep quality indicators, including chronotype, morning wake-up time, sleep duration, daytime napping, insomnia and daytime dozing, were used. Data on sarcopenia-related traits, such as appendicular lean mass, grip strength of both hands, walking pace and waist circumference, were collected from a large cohort study. The primary method used was the inverse-variance weighted analysis. A causal association was found between chronotype and appendicular lean mass (odds ratio [OR] 1.019, 95% confidence interval [CI] 1.016-1.211, P = 0.021). Napping during the day was connected with walking pace (OR 0.879, 95% CI 0.834-0.928, P = 2.289 × 10-6) and waist circumference (OR 1.234, 95% CI 1.081-1.408, P = 0.002). Insomnia was related to lower grip strength of the right hand (OR 0.844, 95% CI 0.747-0.954, P = 0.007), left hand (OR 0.836, 95% CI 0.742-0.943, P = 0.003), as well as walking pace (OR 0.871, 95% CI 0.798-0.951, P = 0.002). Furthermore, the reverse Mendelian randomization analysis showed associations between certain sarcopenia-related traits and poor sleep quality. Some sleep traits were associated with the occurrence of sarcopenia. These findings emphasized the significance of prioritizing sleep quality as a preventive measure against sarcopenia. Geriatr Gerontol Int 2024; 24: 537-545.

No evidence linking sleep traits with white blood cell counts: Multivariable-adjusted and Mendelian randomization analyses.

Disturbances in habitual sleep have been associated with multiple age-associated diseases. However, the biological mechanisms underpinning these associations remain largely unclear. We assessed the possible involvement of the circulating immune system by determining the associations between sleep traits and white blood cell counts using multivariable-adjusted linear regression and Mendelian randomization. Cross-sectional multivariable-adjusted linear regression analyses were done using participants within the normal range of total white blood cell counts (>4.5 × 109 and <11.0 × 109/μL) from UK Biobank. For the sleep traits, we examined (short and long) sleep duration, chronotype, insomnia symptoms and daytime dozing. Two-sample Mendelian randomization analyses were done using instruments for sleep traits derived from European-ancestry participants from UK Biobank (over 410,000 participants) and using SNP-outcome data derived from European-ancestry participants from the Blood Cell Consortium (N = 563,946) to which no data from UK Biobank contributed. Using data from 357,656 participants (mean [standard deviation] age: 56.5 [8.1] years, and 44.4% men), we did not find evidence that disturbances in any of the studied sleep traits were associated with differences in blood cell counts (total, lymphocytes, neutrophiles, eosinophiles and basophiles). Also, we did not find associations between disturbances in any of the studied sleep traits and white blood cell counts using Mendelian Randomization. Based on the results from two different methodologies, disturbances in habitual sleep are unlikely to cause changes in blood cell counts and thereby differences in blood cell counts are unlikely to be underlying the observed sleep-disease associations.

Causal association of gastroesophageal reflux disease with obstructive sleep apnea and sleep-related phenotypes: a bidirectional two-sample Mendelian randomization study.

The interactions and associations between obstructive sleep apnea (OSA), sleep-related phenotypes (SRPs), and gastroesophageal reflux disease (GERD) are complex, thus it is hard to explore the effect and direction of causalities. A bidirectional Mendelian randomization (MR) study was performed to explore causal associations of GERD with OSA and SRPs (including insomnia, morningness, sleep duration, ease of getting up, daytime napping, daytime dozing, and snoring). First, we gathered summary statistics from publicly available databases. Subsequently, we identified single-nucleotide polymorphisms without strong linkage (r2 ≤ 0.001) by referencing relevant genome-wide association studies that met genome-wide significance criteria. Our primary analysis relied on inverse variance weighted to estimate the causal relationship. To ensure the validity of our findings, we also conducted several sensitivity analyses. These included MR Pleiotropy RESidual Sum and Outlier to detect and correct for potential pleiotropic effects, MR-Egger to assess directional pleiotropy, and weighted median analysis to further evaluate heterogeneity and pleiotropy. For the initial MR analysis, when causality was indicated by the results, instrumental variables that were significantly linked to the aforementioned confounding factors were removed. We will re-analyze the data after excluding outcome-related single nucleotide polymorphisms to confirm that the results are still consistent with the previous results. GERD was found to increase the risk of OSA (OR = 1.53, 95% CI = 1.37-1.70, p = 5.3 × 10-15), insomnia (OR = 1.14, 95% CI = 1.10-1.19, p = 1.3 × 10-10), snoring (OR = 1.09, 95% CI = 1.04-1.13, p = 6.3 × 10-5) and less sleep duration (OR = 0.94, 95% CI = 0.91-0.97, p = 3.7 × 10-4). According to the reverse-direction analysis, there is an elevated risk of GERD associated with OSA (OR = 1.07, 95% CI = 1.02-1.12, p = 0.005), insomnia (OR = 1.95, 95% CI = 1.60-2.37, p = 1.92 × 10-11) and snoring (OR = 1.74, 95% CI = 1.37-2.21, p = 4.4 × 10-6). Genetic susceptibility to GERD can elevate the likelihood of experiencing insomnia, snoring, and OSA, in addition to diminishing sleep duration. Conversely, a reverse MR analysis indicates that ameliorating any one of insomnia, snoring, or OSA can mitigate the risk of developing GERD.

Associations of Sleep Patterns With Dynamic Trajectory of Cardiovascular Multimorbidity and Mortality: A Multistate Analysis of a Large Cohort.

Background The purpose of this study was to explore the association of sleep patterns with the development of first cardiovascular diseases (FCVD), progression to cardiovascular multimorbidity (CVM), and subsequently to mortality. Methods and Results This prospective study included 381 179 participants without coronary heart disease, stroke, atrial fibrillation, or heart failure at baseline, and they were followed up until March 31, 2021. We generated sleep patterns by summing the scores for 5 sleep behaviors, whereby <7 or >8 hours/d of sleep, evening chronotype, frequent insomnia, snoring, and daytime dozing were defined as high-risk groups. We used a multistate model to estimate the impacts of sleep patterns on the dynamic progression of cardiovascular diseases. Over a median follow-up of 12.1 years, 41 910 participants developed FCVD, 7302 further developed CVM, and 20 707 died. We found that adverse sleep patterns were significantly associated with the transition from health to FCVD, from FCVD to CVM, and from health to death, with hazard ratio associated with 1-factor increase in sleep scores being 1.08 (95% CI, 1.07-1.09), 1.04 (95% CI, 1.02-1.06), and 1.04 (95% CI, 1.02-1.05), respectively. When further dividing FCVD into coronary heart disease, stroke, atrial fibrillation, and heart failure, adverse sleep patterns showed a significant and persistent effect on the transition from health to each cardiovascular disease, and from heart failure or atrial fibrillation to CVM. Conclusions Our study provides evidence that adverse sleep patterns might increase the risk for the progression from health to cardiovascular diseases and further to CVM. Our findings suggest that improving sleep behaviors might be helpful for the primary and secondary prevention of cardiovascular diseases.

Minimal effect of sleep on the risk of age-related macular degeneration: a Mendelian randomization study.

Observational studies have shown that sleep pattern is associated with age-related macular degeneration (AMD), but whether sleep pattern is a causal factor for AMD remains unclear. This study aims to use Mendelian randomization (MR) analysis to investigate the potential causal relationship between sleep traits and AMD. This is a two-sample MR study. The single-nucleotide polymorphisms associated with AMD and early AMD were selected as the outcome from two different genome-wide association studies (GWAS): the early AMD GWAS with 14,034 cases and 91,214 controls, and AMD GWAS with 3,553 cases and 147,089 controls. The datasets of sleep duration, daytime dozing, and sleeplessness were used as exposure, which comprised nearly 0.46 million participants. Inverse-variance weighted method was used as the main result, and comprehensive sensitivity analyses were conducted to estimate the robustness of identified associations and the impact of potential horizontal pleiotropy. Through MR analysis, we found that sleep duration was significantly associated with AMD (OR = 0.983, 95% CI = 0.970-0.996, P-value = 0.01). We also found suggestive evidence for the association of genetically predicted sleep duration with early AMD, which showed a consistent direction of effect with a marginal significance (OR = 0.724, 95% CI = 0.503-1.041, P-value = 0.08). Sensitivity analyses further supported the robustness of the causal relationship between sleep duration and AMD. However, we were unable to determine the relationship between daytime dozing or sleeplessness and AMD (including early AMD) (P-value > 0.05). Sleep duration affects the causal risk for AMD; that is, longer sleep duration reduces the risk of AMD, while shorter sleep duration increases the risk of AMD. Although the influence is minimal, keeping adequate sleep duration is recommended, especially for patients with intermediate or advanced AMD.

Sleep traits and risk of end-stage renal disease: a mendelian randomization study

BackgroundEpidemiological evidence relating sleep disorders to end-stage renal disease (ESRD) has been obscure. The present study is sought to examine the association between sleep traits and ESRD.MethodsFor this analysis, we selected genetic instruments for sleep traits from published genome-wide association studies (GWAS). As instrumental variables, independent genetic variations linked with seven sleep-related features (sleep duration, getting up in the morning, daytime napping, chronotype of morning/evening person, sleeplessness/insomnia, non-snoring, and daytime dozing) were chosen. A two-sample Mendelian randomization (TSMR) study was conducted to assess the causal relationship between sleep traits and ESRD (N = 33,061). The reverse MR analysis subsequently determined the causal relationship between ESRD and sleep traits. The causal effects were estimated using inverse variance weighted, MR-Egger, weighted median. To conduct sensitivity studies, Cochran’s Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis, and funnel plot were used. To study the potential mediators, multivariable mendelian randomization analyses were undertaken further.ResultsGenetically predicted sleeplessness/ insomnia (OR = 6.11, 95%CI 1.00-37.3, P = 0.049, FDR = 0.105), getting up in the morning easily(OR = 0.23, 95%CI 0.063–0.85; P = 0.0278, FDR = 0.105), non-snoring (OR = 4.76E-02, 95%CI 2.29E-03-0.985, P = 0.0488, FDR = 0.105) was suggestively associated with the risk of ESRD. However, we found no evidence favoring a causal association between other sleep traits and ESRD through the IVW method.ConclusionThe present TSMR found no strong evidence of a bidirectional causal association between genetically predicted sleep traits and ESRD.

Assessing the effect of interaction between lifestyle and longitudinal changes in brain structure on sleep phenotypes.

Longitudinal changes in brain structure and lifestyle can affect sleep phenotypes. However, the influence of the interaction between longitudinal changes in brain structure and lifestyle on sleep phenotypes remains unclear. Genome-wide association study dataset of longitudinal changes in brain structure was obtained from published study. Phenotypic data of lifestyles and sleep phenotypes were obtained from UK Biobank cohort. Using genotype data from UK Biobank, we calculated polygenetic risk scores of longitudinal changes in brain structure phenotypes. Linear/logistic regression analysis was conducted to evaluate interactions between longitudinal changes in brain structure and lifestyles on sleep duration, chronotype, insomnia, snoring and daytime dozing. Multiple lifestyle × longitudinal changes in brain structure interactions were detected for 5 sleep phenotypes, such as physical activity×caudate_age2 for daytime dozing (OR = 1.0389, P = 8.84 × 10-3) in total samples, coffee intake×cerebellar white matter volume_age2 for daytime dozing (OR = 0.9652, P = 1.13 × 10-4) in females. Besides, we found 4 overlapping interactions in different sleep phenotypes. We conducted sex stratification analysis and identified one overlapping interaction between female and male. Our results support the moderate effects of interaction between lifestyle and longitudinal changes in brain structure on sleep phenotypes, and deepen our understanding of the pathogenesis of sleep disorders.

Poorer sleep impairs brain health at midlife

Sleep is an emerging risk factor for dementia but its association with brain health remains unclear. This study included UK Biobank (n = 29,545; mean age = 54.65) participants at imaging visit with sleep measures and brain scans, and a subset (n = 14,206) with cognitive measures. Multiple linear regression analyses were conducted to study the associations between sleep and brain health. Every additional hour of sleep above 7 h/day was associated with 0.10–0.25% lower brain volumes. In contrast, a negative non-linear association was observed between sleep duration, grey matter, and hippocampal volume. Both longer (> 9 h/day) and shorter sleep (< 6 h/day) durations were associated with lower brain volumes and cognitive measures (memory, reaction time, fluid intelligence). Additionally, daytime dozing was associated with lower brain volumes (grey matter and left hippocampus volume) and lower cognitive measures (reaction time and fluid intelligence). Poor sleep (< 6 h/day, > 9 h/day, daytime dozing) at midlife was associated with lower brain health. Sleep may be an important target to improve brain health into old age and delay the onset of dementia.

Sleep disorders in Wilson's disease: a questionnaire study.

To examine the clinical characteristics and influencing factors related to sleep disorders in patients with Wilson's disease (WD), and investigate its potential mechanisms. A total of 150 patients with WD (76 hepatic, 42 neurological, 32 asymptomatic form) and 150 age- and sex-matched control subjects were investigated using 3 standardized sleep questionnaires. Differences among 3 subtypes were discussed. The mean Parkinson's disease sleep scale (PDSS) score of WD was lower than the controls (Z = - 4.426, P = 0.000), and their mean Epworth Sleepiness Scale (ESS) score as well as Pittsburgh sleep quality index (PSQI) score of WD was higher than that of the controls (t = 2.005, P = 0.048; t = 3.342, P = 0.001). The incidence of excessive daytime sleepiness (EDS) in WD group were significantly higher than the controls (X2 = 6.064, P = 0.014). Further analysis showed that total PDSS score of neurologic presentation group was lower than others (X2 = 6.131, P = 0.047), while the ESS score was higher (F = 3.817, P = 0.029). UWDRS showed a negative correlation with PDSS (r = - 0.440, P = 0.022) and has a higher negative correlation with PDSS in neurologic presentation group (r = - 0.732, P = 0.000). Patients with WD often suffer from sleep disturbances, mainly characterized by difficulty falling asleep, difficulty staying asleep, nocturnal motor symptoms (numbness, cramps, tremor), and daytime dozing. And the incidence of EDS is significantly higher than that of the controls. Sleep quality is worse in patients with WD of neurologic presentation than the other two groups. Furthermore, the worse of the symptoms, patients with WD suffer more serious of the sleep disorders especially in neurologic presentation group.

Association between telomere length and insomnia: A mendelian randomization and colocalization study

BackgroundPrevious studies have suggested a potential association between sleep and telomere length (TL), but its genetic basis remains unclear. In this study, we aimed to explore the genetic correlation and potential causal association between TL and insomnia. MethodsThe genome-wide association study (GWAS) datasets of TL and insomnia-related traits were used, including insomnia, snoring, daytime dozing and napping. Based on the polygenic risk scores (PRS) of TL, linear regression and linkage disequilibrium score (LDSC) regression were used to preliminarily explore the association between TL and insomnia parameters in the UK Biobank cohort. Then, we investigated the causal association between TL and insomnia by mendelian randomization (MR) analysis and colocalization analysis. ResultsIn the UK Biobank cohort, the association between TL and insomnia was observed in the female samples (t = 2.968, P = 3.00 × 10−3). LDSC detected a genetic correlation between short TL and insomnia (Rg = −9.27 × 10−2, P = 8.00 × 10−4). We found no evidence supporting significant causal association between insomnia and TL in IVW method (b = −5.95 × 10−3, P = 0.57), with horizontal pleiotropy and heterogeneity tests indicating the validity of our MR study. Finally, rs12638862 was classified as colocalized by COLOC (PP4 = 0.99), and TERC may be involved in regulating the association between insomnia and TL. ConclusionsOur study found no evidence for causal association between insomnia and TL in individuals of European ancestry. We detected a candidate gene associated with both insomnia and TL, providing novel clues for understanding the roles of this association.

The Lifestyle-Related Cardiovascular Risk Is Modified by Sleep Patterns

ObjectiveTo prospectively assess whether sleep patterns modified lifestyle-associated cardiovascular disease (CVD) risk. Patients and MethodsThis study included 393,690 participants without CVD at baseline measurements between March 13, 2006, and October 1, 2010, from UK Biobank. A lifestyle score was calculated on the basis of the 4 lifestyle factors (smoking, alcohol consumption, physical activity, and diet), and sleep patterns were constructed based on sleep duration, chronotype, insomnia, snoring, and daytime dozing. ResultsDuring a median follow-up of 8.93 years, we observed 10,218 incident CVD events, including 6595 myocardial infarctions (MIs) and 3906 strokes. We found that sleep patterns significantly modified the relations of the lifestyle score with incident CVD (P for interaction =.007) and MI (P for interaction =.004). Among participants with a poor sleep pattern, unfavorable lifestyle (per score increase) was associated with 25% (95% CI, 13% to 39%) and 29% (95% CI, 13% to 47%) increased risks for CVD and MI, while among participants with a healthy sleep pattern, unfavorable lifestyle was associated with 18% (95% CI, 15% to 21%) and 17% (95% CI, 13% to 21%) increased risks for CVD and MI. ConclusionOur results indicate that adherence to a healthy sleep pattern may attenuate the CVD risk associated with an unfavorable lifestyle.

Genome-Wide Association Study and Genetic Correlation Scan Provide Insights into Its Genetic Architecture of Sleep Health Score in the UK Biobank Cohort

PurposeMost previous genetic studies of sleep behaviors were conducted individually, without comprehensive consideration of the complexity of various sleep behaviors. Our aim is to identify the genetic architecture and potential biomarker of the sleep health score, which more powerfully represents overall sleep traits.Patients and MethodsWe conducted a genome-wide association study (GWAS) of sleep health score (overall assessment of sleep duration, snoring, insomnia, chronotype, and daytime dozing) using 336,463 participants from the UK Biobank. Proteome-wide association study (PWAS) and transcriptome-wide association study (TWAS) were then performed to identify candidate genes at the protein and mRNA level, respectively. We finally used linkage disequilibrium score regression (LDSC) to estimate the genetic correlations between sleep health score and other functionally relevance traits.ResultsGWAS identified multiple variants near known candidate genes associated with sleep health score, such as MEIS1, FBXL13, MED20 and SMAD5. HDHD2 (PPWAS = 0.0146) and GFAP (PPWAS = 0.0236) were identified associated with sleep health score by PWAS. TWAS identified ORC4 (PTWAS = 0.0212) and ZNF732 (PTWAS = 0.0349) considering mRNA expression level. LDSC found significant genetic correlations of sleep health score with 3 sleep behaviors (including insomnia, snoring, dozing), 4 psychiatry disorders (major depressive disorder, attention deficit/hyperactivity disorder, schizophrenia, autism spectrum disorder), and 9 plasma protein (such as Stabilin-1, Stromelysin-2, Cytochrome c) (all LDSC PLDSC < 0.05).ConclusionOur results advance the comprehensive understanding of the aetiology and genetic architecture of the sleep health score, refine the understanding of the relationship of sleep health score with other traits and diseases, and may serve as potential targets for future mechanistic studies of sleep phenotype.

Prevalence of sleep disruption and determinants of sleepiness in a cohort of Italian hospital physicians: The PRESOMO study.

Nightshift work can cause daytime somnolence and decreased alertness, and can increase risk of medical errors, occupational injuries and car accidents. We used a structured questionnaire, including the Epworth Sleepiness Scale (ESS), to assess the prevalence and the determinants of sleep disruption in 268 Italian University hospital physicians from Cagliari (N = 57), Milan (N = 180) and Pisa (N = 31), who participated in the multicentre study on the prevalence of sleep disturbance among hospital physicians (PRESOMO); 198 of them (74%) were engaged in nightshift work. We explored the association between history of nightshift work and poor sleep quality and daytime somnolence with multivariate logistic regression, adjusting by personal and lifestyle covariates. Age, female gender, taking medication interfering with sleep and an elevated ESS score were significant predictors of poor sleep quality and daytime somnolence. Nightshift work was associated with a higher prevalence of unrestful sleep (84% versus 70%; odds ratio [OR] = 2.4, 95% confidence interval [CI] 1.18–5.05) and daytime dozing (57% versus 35%; OR = 1.9, 95% CI 1.03–3.64), with an upward trend by years of engagement in nightshift work for both conditions (p = .043 and 0.017, respectively), and by number of nightshifts/year for unrestful sleep (p = .024). Such an association was not detected with the ESS scale. Our results suggest that nightshift work significantly affects sleep quality and daytime somnolence in hospital physicians, who might underestimate their daytime dozing problem, when asked to subjectively scale it.

547 Earlier-life sleep patterns and risk for delirium in elderly hospitalized patients from a 14-year longitudinal cohort

Abstract Introduction Delirium is an acute decline in attention and cognition that is associated with cognitive dysfunction in elderly patients. While accumulating evidence points to associations between sleep disturbances and neurocognitive disorders, the temporal relationship between sleep patterns and delirium remains unclear. We tested whether earlier-life sleep duration, daytime dozing, insomnia, and sleep apnea predict incident delirium during hospitalization. Methods We studied 315,989 participants (mean age 58.3±7.9; range 37.4–73.7) from the UK Biobank with up to 14 years follow-up, and at least one hospitalization episode. Delirium diagnosis was derived using ICD-10 coding from hospitalization records. Multivariate logistic regression models examined the associations of self-reported baseline sleep duration (less than 6h/6-9h/more than 9h), daytime dozing (often/rarely), insomnia (often/rarely), and presence of prior sleep apnea (ICD-10), with incident delirium. Models were adjusted for age, sex, education, Townsend deprivation index, and major confounders (including number of hospitalizations during follow-up, BMI, neurological/cardiovascular/respiratory diseases, depression/anxiety, chronotype, and sedatives). Results 4,025 developed delirium (12.7/1,000). There was a U-shaped association between sleep duration and delirium, where short [17.3/1,000; OR 1.18, 95% CI: 1.05–1.33, p=0.006] and long (28.8/1,000; OR 1.49, 95% CI: 1.30–1.70, p&amp;lt;0.001) sleepers had elevated risk compared to regular 6-9h sleepers. Often daytime dozing (25.3/1,000; OR 1.38, 95% CI: 1.20–1.58, p&amp;lt;0.001) and sleep apnea (21.7/1,000; OR 1.21, 95% CI: 1.03–1.42 p=0.02) also had increased the risk for delirium, but the latter was attenuated by the inclusion of BMI and hypertension. However, we did observe further risk when two or more of the above traits were present (OR 1.59, 95% CI: 1.29–1.95 p&amp;lt;0.001). No effects on incident delirium were observed from insomnia. Conclusion Earlier-life sleep patterns, in particular longer sleep and daytime dozing, are associated with an increased risk for delirium. Sleep patterns may reflect unmeasured health status; further work is warranted to confirm the associations using objective sleep/circadian measures, examine underlying mechanisms, and test whether optimizing sleep patterns can reduce the risk of developing delirium. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.