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.

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.

Irregular sleep and non-optimal sleep duration separately have been shown to be associated with increased disease and mortality risk. We used data from the prospective cohort Multi-Ethnic Study of Atherosclerosis sleep study (2010-2013) to investigate: do aging adults whose sleep is objectively high in regularity in timing and duration, and of sufficient duration tend to have increased survival compared with those whose sleep is lower in regularity and duration, in a diverse US sample? At baseline, sleep was measured by 7-day wrist actigraphy, concurrent with at-home polysomnography and questionnaires. Objective metrics of sleep regularity and duration from actigraphy were used for statistical clustering using sparse k-means clustering. Two sleep patterns were identified: "regular-optimal" (average duration: 7.0 ± 1.0 hr obtained regularly) and "irregular-insufficient" (duration: 5.8 ± 1.4 hr obtained with twice the irregularity). Using proportional hazard models with multivariate adjustment, we estimated all-cause mortality hazard ratios. Among 1759 participants followed for a median of 7.0 years (Q1-Q3, 6.4-7.4 years), 176 deaths were recorded. The "regular-optimal" group had a 39% lower mortality hazard than did the "irregular-insufficient" sleep group (hazard ratio [95% confidence interval]: 0.61 [0.45, 0.83]) after adjusting for socio-demographics, lifestyle, medical comorbidities and sleep disorders. In conclusion, a "regular-optimal" sleep pattern was significantly associated with a lower hazard of all-cause mortality. The regular-optimal phenotype maps behaviourally to regular bed and wake times, suggesting sleep benefits of adherence to recommended healthy sleep practices, with further potential benefits for longevity.

Exposure to light at night (LAN) may influence sleep timing and regularity. Here, we test whether greater light exposure during sleep (LEDS) is bidirectionally associated with greater irregularity in sleep onset timing in a large cohort of older adults in cross-sectional and short-term longitudinal (days) analyses. Light exposure and activity patterns, measured via wrist-worn actigraphy (ActiWatch Spectrum), were analyzed in 1933 participants with 6+ valid days of data in the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 5 Sleep Study. Summary measures of LEDS averaged across nights were evaluated in linear and logistic regression analyses to test the association with standard deviation (SD) in sleep onset timing (continuous variable) and irregular sleep onset timing (SD > 90 min, binary). Night-to-night associations between LEDS and absolute differences in nightly sleep onset timing were also evaluated with distributed lag non-linear models and mixed models. In between-individual linear and logistic models adjusted for demographic, health, and seasonal factors, every 5-lux unit increase in LEDS was associated with a 7.8-min increase in sleep onset SD (β = 0.13 h, 95%CI:0.09-0.17) and 32% greater odds (OR = 1.32, 95%CI:1.17-1.50) of irregular sleep onset. In within-individual night-to-night mixed model analyses, every 5-lux unit increase in LEDS the night prior was associated with a 2.2-min greater deviation of sleep onset the next night (β = 0.036 h, p < 0.05). Conversely, every 1-h increase in sleep deviation was associated with a 0.35-lux increase in future LEDS (β = 0.348 lux, p < 0.05). LEDS was associated with greater irregularity in sleep onset in between-individual analyses and subsequent deviation in sleep timing in within-individual analyses, supporting a role for LEDS in irregular sleep onset timing. Greater deviation in sleep onset was also associated with greater future LEDS, suggesting a bidirectional relationship. Maintaining a dark sleeping environment and preventing LEDS may promote sleep regularity and following a regular sleep schedule may limit LEDS.

RATIONALE: While patients with obstructive sleep apnea (OSA) have a higher risk for COVID-19 hospitalization, the causal relationship has remained unexplored.

OBJECTIVES: To understand the causal relationship between OSA and COVID-19 leveraging data from vaccination and electronic health records, genetic risk factors from genome-wide association studies (GWAS) and Mendelian randomization.

METHODS: We elucidated genetic risk factors for OSA using FinnGen (N total = 377,277 individuals) performing genome-wide association. We used the associated variants as instruments for univariate and multivariate Mendelian randomization (MR) analyses and computed absolute risk reduction (ARR) against COVID-19 hospitalization with or without vaccination.

MEASUREMENTS AND MAIN RESULTS: We identified 9 novel loci for OSA and replicated our findings in the Million Veterans Program. Furthermore, MR analysis showed that OSA was a causal risk factor for severe COVID-19 (P=9.41x10-4). Probabilistic modelling showed that the strongest genetic risk factor for OSA at the FTO locus reflected a signal of higher BMI, whereas BMI independent association was seen with the earlier reported SLC9A4 locus and a MECOM locus which is a transcriptional regulator with 210-fold enrichment in the Finnish population. Similarly, Multivariate MR (MVMR) analysis showed that the causality for severe COVID-19 was driven by body mass index (BMI), (P MVMR = 5.97x10-6, beta=0.47). Finally, vaccination reduced the risk for COVID-19 hospitalization more in the OSA patients than in the non-OSA controls: ARR = 13.3% vs. ARR = 6.3% in the OSA vs. non-OSA population.

CONCLUSIONS: Our analysis identified novel genetic risk factors for OSA and showed that OSA is a causal risk factor for severe COVID-19. The effect is predominantly explained by higher BMI and suggests BMI-dependent effects at the level of individual variants and at the level of comorbid causality.