OBJECTIVES: Exposure to allergens may trigger inflammatory pathways contributing to poor sleep. We investigated the associations between indoor allergen concentrations with multiple sleep dimensions in children.

METHODS: Bedroom dust aeroallergens were collected in participants' homes. The outcomes were caregiver-reported sleep-related daytime impairment and sleep disturbance (pediatric PROMIS instruments), sleep-disordered breathing (SDB; apnea-hypopnea index or oxygen desaturation index >5), and actigraphy-based short sleep (<8 hours) duration and poor sleep continuity (sleep fragmentation index > 75th percentile). Logistic regression was used to examine associations between aeroallergens (>50% detectable levels) and sleep outcomes, adjusting for potential confounders (i.e., sociodemographic, environmental, and health-related).

RESULTS: The sample included an urban cohort of 256 children (41% Hispanic, 29% Black; 43% female) aged 6-12 years. Mouse (Mus m 1), cat (Fel d 1), and dog (Can f 1) allergens were detected in 81%, 72%, and 53% of households, respectively. Elevated mouse allergen exposure (>0.55 μg/g-75th percentile) was associated with a 2.6-fold (95% CI: 1.34, 5.03) increased odds for sleep-related daytime impairment (PROMIS T-score > 55) after adjusting for demographic factors. This association persisted after further adjusting for inflammatory-related health factors (asthma, allergic rhinitis, obesity, and environmental tobacco smoke), neighborhood disadvantage, and SDB. There was attenuation of this association with poor sleep consolidation. Associations were not observed for other allergens or other sleep outcomes.

CONCLUSIONS: Exposure to elevated mouse dust was associated with increased sleep-related daytime impairment symptoms. The role of household pest exposure as a potentially modifiable target for improving sleep health should be further studied.

BACKGROUND: Sleep traits, including sleep apnoea (SA), insomnia, daytime sleepiness, and snoring, frequently co-occur with cardiometabolic diseases (CMDs), with shared genetic factors suspected to underlie these associations. However, the contribution of shared genetic determinants to these associations is not fully understood.

METHODS: We conducted a genome-wide pleiotropic association study applying sequential genetic methods to identify shared genetic variants, genes, pathways and causal associations between the four sleep traits and seven CMDs, including LDSC, high-definition likelihood analysis, colocalisation, gene-based tests, enrichment analysis and Mendelian randomisation. Next, validation of those pleiotropic variants was performed in individuals from the All of Us and MVP studies.

FINDINGS: Among 28 pairs of sleep traits and CMDs, 25 showed significant genetic correlations. Pleiotropic analysis identified 754 independent SNPs (691 unique) and 102 colocalized loci (85 unique). Among these, 47 SNPs (44 unique) were validated as significantly associated with both traits in the pairs, and notably, rs429358 (19q13.32, APOE) demonstrated pleiotropic effects across SA, insomnia and type 2 diabetes (T2D). Forty-eight annotated genes were validated by gene-based tests. Shared genes were enriched in phenotypes related to mortality and growth. Pathway analysis highlighted Cushing syndrome, hormone secretion, and cGMP-PKG, Ras and calcium signalling pathways. After adjusting for glycaemic traits and blood pressure, genetically predicted T2D increased risk of SA, sleepiness, and snoring. Conversely, SA was positively associated with heart failure and T2D independently.

INTERPRETATION: This study of sleep traits and CMDs reveals shared genetic determinants that may partially explain their epidemiologic association and suggests potential treatment targets.

FUNDING: Described in Acknowledgements.

BACKGROUND AND AIMS: Deviations from the population mean in sleep duration have been associated with increased risk for developing dyslipidemia and atherosclerotic cardiovascular disease, but the mechanism of effect is poorly characterized. We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets.

METHODS: We collected data from 55 cohorts with a combined sample size of 732,564 participants (87 % European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20 % of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for one-degree of freedom tests of interaction and two-degree of freedom joint tests of the SNP-main and -interaction effect on lipid levels.

RESULTS: The one-degree of freedom variant-sleep interaction test identified 10 novel loci (Pint<5.0e-9), and we additionally identify 7 loci within the two-degree of freedom analyses (Pjoint<5.0e-9 in combination with Pint<6.6e-6). Multiple loci, including those mapped to APSH (target for aspartic and succinic acid) and SLC8A1 showed biological plausibility and druggability potential based on literature.

CONCLUSIONS: Collectively, the 17 (9 with short and 8 with long sleep) loci provided evidence into the biomolecular mechanisms underlying sleep-associated lipid changes, including potential involvement of the vitamin D receptor pathway. Collectively, these findings may contribute developing novel interventions for treating dyslipidemia in people with sleep disturbances.

INTRODUCTION: Sex hormones shape biological sex differences and alter the onset and severity of sleep and metabolic diseases in a sex-specific manner. To better understand relationships and underlying mechanisms, we develop summary proteomics and metabolomics scores for sex hormones and investigate their associations with sleep and metabolic disorders.

METHODS: We used proteome- (n= 3680) and metabolome- wide (n= 1649) data from the baseline exam of the Multi-Ethnic Study of Atherosclerosis (MESA) cohort to develop female- and male-specific omics scores for sex hormones including total (Total T), bioavailable (Bio T), and free (Free T) testosterone, estradiol (E2) and sex hormone binding protein (SHBG). Each omics dataset was randomly split assigning 80% of participants to a training dataset and the remaining 20% to a test dataset. We applied linear regression with bootstrap standard errors, adjusting for age, BMI, self-reported race/ ethnicity and study site, to identify sex hormone-associated proteins and metabolites (i.e FDR< .05). Lasso penalized regression was then used to select independent features, from which weighted protein (ProtS) and metabolite scores (MetS) were constructed as weighted sums, and examined in the validation dataset. Subsequently, we conducted sex-stratified association analysis of the validated omics scores using data from MESA baseline, exams 4 (proteomics) and 5 (proteomics, metabolomics) with sleep and metabolic phenotypes, timepoints where sex hormones were not measured.

RESULTS: All constructed omics scores were significantly associated with their corresponding hormones in the test dataset. Higher omics scores of SHBG and lower omics scores of Free T were associated with lower diabetes risk in both sexes; and higher E2 scores with higher incident hypertension risk only in men. In males, Total T had protective diabetes associations, whereas in females they were linked to greater risk. Similarly, higher ProtS-Free T and lower ProtS-SHBG were associated with increased risk for OSA in both sexes. Finally, higher E2 scores were associated with higher risk of insomnia only in males.

CONCLUSIONS: Summary omics-based scores reveal sex-specific cross-sectional associations with sleep and incident metabolic disorders. These findings highlight the potential of these omics proxies to improve risk stratification and generate insights into mechanisms linking sex hormones with disease.

STUDY OBJECTIVES: Since genome-wide association studies (GWAS) of sleep phenotypes have been conducted in differing populations and definitions of sleep phenotypes vary across studies, we investigated associations between several polygenic risk scores (PRSs) and potential sleep definitions among multiethnic cohorts.

METHODS: Using data from four cohorts (HCHS/SOL, ARIC, MESA, BHS, N = 16 895), we considered multiple definitions of short and long sleep, insomnia, and excessive daytime sleepiness (EDS). PRSs were developed based on summary statistics from GWAS in European ancestry individuals from the UK Biobank (UKB) and from GWAS conducted in a multiethnic population from the Million Veteran Program (MVP). Study-specific analyses estimated associations between sleep PRSs and corresponding sleep measures per 1 standard deviation increase in the PRS. Models were adjusted for age, sex, ancestral principal components, and center and race as appropriate. Results were meta-analyzed across studies.

RESULTS: PRSs based on European ancestry UKB GWAS had statistically significant associations with multiple definitions of the corresponding sleep phenotypes. Associations that were most consistent across studies included: short sleep PRS with ≤6 hours (OR = 1.23,p = 1.80x10-7,phet = 0.97); long sleep PRS with ≥9 hours (OR = 1.09,p = 6.76x10-4,phet = 0.77); insomnia PRS with the Women's Health Initiative Insomnia Rating Scale (WHIIRS) ≥10 or a subset of three questions ≥6 in ARIC (OR = 1.17,p = 5.51x10-10,phet = 0.69); and EDS PRS with Epworth Sleepiness Scale (ESS) ≥11 (OR = 1.23,p = 1.83x10-13,phet = 0.83). PRSs based on multi-ancestry MVP GWAS had weaker associations compared to those based on European ancestry only.

CONCLUSIONS: By evaluating several types of sleep PRSs and sleep phenotypes, we were able to highlight which sleep PRS performed well across diverse populations and which sleep definitions better captured genetic underpinnings.

BACKGROUND: Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep-related phenotypes and blood metabolites.

METHODS: Utilising data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep-related phenotypes, grouped in several domains (sleep disordered breathing (SDB), sleep duration, sleep timing, self-reported insomnia symptoms, excessive daytime sleepiness (EDS), and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualise and interpret the associations between sleep phenotypes and metabolites.

FINDINGS: The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, primary bile acid metabolism showed the highest cumulative percentage of statistically significant associations across all sleep phenotype domains except for SDB and EDS phenotypes. Several metabolites were associated with multiple sleep phenotypes, from a few domains. Glycochenodeoxycholate, vanillyl mandelate (VMA) and 1-stearoyl-2-oleoyl-GPE (18:0/18:1) were associated with the highest number of sleep phenotypes, while pregnenolone sulfate was associated with all sleep phenotype domains except for sleep duration. N-lactoyl amino acids such as N-lactoyl phenylalanine (lac-Phe), were associated with sleep duration, SDB, sleep timing and heart rate during sleep.

INTERPRETATION: This atlas of sleep-metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.

FUNDING: R01HL161012, R35HL135818, R01AG80598.

BACKGROUND: People share health-related experiences and treatments, such as for insomnia, in digital communities. Natural language processing tools can be leveraged to understand the terms used in digital spaces to discuss insomnia and insomnia treatments.

OBJECTIVE: The aim of this study is to summarize and chart trends of insomnia treatment terms on a digital insomnia message board.

METHODS: We performed a natural language processing analysis of the r/insomnia subreddit. Using Pushshift, we obtained all r/insomnia subreddit comments from 2008 to 2022. A bag of words model was used to identify the top 1000 most frequently used terms, which were manually reduced to 35 terms related to treatment and medication use. Regular expression analysis was used to identify and count comments containing specific words, followed by sentiment analysis to estimate the tonality (positive or negative) of comments. Data from 2013 to 2022 were visually examined for trends.

RESULTS: There were 340,130 comments on r/insomnia from 2008, the beginning of the subreddit, to 2022. Of the 35 top treatment and medication terms that were identified, melatonin, cognitive behavioral therapy for insomnia (CBT-I), and Ambien were the most frequently used (n=15,005, n=13,461, and n=11,256 comments, respectively). When the frequency of individual terms was compared over time, terms related to CBT-I increased over time (doubling from approximately 2% in 2013-2014 to a peak of over 5% of comments in 2018); in contrast, terms related to nonprescription over-the-counter (OTC) sleep aids (such as Benadryl or melatonin) decreased over time. CBT-I-related terms also had the highest positive sentiment and showed a spike in frequency in 2017. Terms with the most positive sentiment included "hygiene" (median sentiment 0.47, IQR 0.31-0.88), "valerian" (median sentiment 0.47, IQR 0-0.85), and "CBT" (median sentiment 0.42, IQR 0.14-0.82).

CONCLUSIONS: The Reddit r/insomnia discussion board provides an alternative way to capture trends in both prescription and nonprescription sleep aids among people experiencing sleeplessness and using social media. This analysis suggests that language related to CBT-I (with a spike in 2017, perhaps following the 2016 recommendations by the American College of Physicians for CBT-I as a treatment for insomnia), benzodiazepines, trazodone, and antidepressant medication use has increased from 2013 to 2022. The findings also suggest that the use of OTC or other alternative therapies, such as melatonin and cannabis, among r/insomnia Reddit contributors is common and has also exhibited fluctuations over time. Future studies could consider incorporating alternative data sources in addition to prescription medication to track trends in prescription and nonprescription sleep aid use. Additionally, future prospective studies of insomnia should consider collecting data on the use of OTC or other alternative therapies, such as cannabis. More broadly, digital communities such as r/insomnia may be useful in understanding how social and societal factors influence sleep health.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, so together may provide a more complete picture of sleep health, while illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p < 8.3e-9), along with 341 previously reported loci (p < 5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2 = 0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post-GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections (including potential causal links) to behavioral, psychological, and cardiometabolic traits.