SLEEP

doi: 10.1093/sleep/zsac269pmid: 36356042

Study ObjectivesTo assess altitude-induced sleep and nocturnal breathing disturbances in healthy lowlanders 40 y of age or older and the effects of preventive acetazolamide treatment.MethodsClinical examinations and polysomnography were performed at 760 m and in the first night after ascent to 3100 m in a subsample of participants of a larger trial evaluating altitude illness. Participants were randomized 1:1 to treatment with acetazolamide (375 mg/day) or placebo, starting 24 h before and while staying at 3100 m. The main outcomes were indices of sleep structure, oxygenation, and apnea/hypopnea index (AHI).ResultsPer protocol analysis included 86 participants (mean ± SE 53 ± 7 y old, 66% female). In 43 individuals randomized to placebo, mean nocturnal pulse oximetry (SpO2) was 94.0 ± 0.4% at 760 m and 86.7 ± 0.4% at 3100 m, with mean change (95%CI) −7.3% (−8.0 to −6.5); oxygen desaturation index (ODI) was 5.0 ± 2.3 at 760 m and 29.2 ± 2.3 at 3100 m, change 24.2/h (18.8 to 24.5); AHI was 11.3 ± 2.4/h at 760 m and 23.5 ± 2.4/h at 3100 m, change 12.2/h (7.3 to 17.0). In 43 individuals randomized to acetazolamide, altitude-induced changes were mitigated. Mean differences (Δ, 95%CI) in altitude-induced changes were: ΔSpO2 2.3% (1.3 to 3.4), ΔODI -15.0/h (−22.6 to −7.4), ΔAHI -11.4/h (−18.3 to −4.6). Total sleep time, sleep efficiency, and N3-sleep fraction decreased with an ascent to 3100 m under placebo by 40 min (17 to 60), 5% (2 to 8), and 6% (2 to 11), respectively. Acetazolamide did not significantly change these outcomes.ConclusionsDuring a night at 3100 m, healthy lowlanders aged 40 y or older revealed hypoxemia, sleep apnea, and disturbed sleep. Preventive acetazolamide treatment improved oxygenation and nocturnal breathing but had no effect on sleep duration and structure.Trial registrationThe trial is registered at Clinical Trials, https://clinicaltrials.gov, NCT03561675

Study ObjectivesShift work is a risk factor for cardiometabolic disease, possibly through effects on sleep–wake rhythms. We hypothesized that evening (afternoon and night combined) and irregular (irregular/on-call or rotating combined) shift work during pregnancy is associated with increased odds of preeclampsia, preterm birth, and gestational diabetes mellitus (GDM), mediated by irregular sleep timing.MethodsThe Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) is a prospective cohort study (n = 10 038) designed to investigate risk factors for adverse pregnancy outcomes. Medical outcomes were determined with medical record abstraction and/or questionnaires; sleep midpoint was measured in a subset of participants with ≥5-day wrist actigraphy (ActiWatch). We estimated the association of evening and irregular shift work during pregnancy with preeclampsia, preterm birth, and GDM using logistic regression, adjusted for adversity (cumulative variable for poverty, education, health insurance, and partner status), smoking, self-reported race/ethnicity, and age. Finally, we explored whether the association between shiftwork and GDM was mediated by variability in sleep timing.ResultsEvening shift work is associated with approximately 75% increased odds of developing GDM (adjusted OR = 1.75, 95% CI: 1.12–2.66); we did not observe associations with irregular shifts, preterm birth, or preeclampsia after adjustment. Pregnant evening shift workers were found to have approximately 45 minutes greater variability in sleep timing compared to day workers (p < .005); sleep-timing variability explained 25% of the association between evening shift work and GDM in a mediation analysis.ConclusionsEvening shift work was associated with GDM, and this relationship may be mediated by variability in sleep timing.

doi: 10.1093/sleep/zsac121pmid: 35670608

Study ObjectivesPeriodic limb movement in sleep is a common sleep phenotype characterized by repetitive leg movements that occur during or before sleep. We conducted a genome-wide association study (GWAS) of periodic limb movements in sleep (PLMS) using a joint analysis (i.e., discovery, replication, and joint meta-analysis) of four cohorts (MrOS, the Wisconsin Sleep Cohort Study, HypnoLaus, and MESA), comprised of 6843 total subjects.MethodsThe MrOS study and Wisconsin Sleep Cohort Study (N = 1745 cases) were used for discovery. Replication in the HypnoLaus and MESA cohorts (1002 cases) preceded joint meta-analysis. We also performed LD score regression, estimated heritability, and computed genetic correlations between potentially associated traits such as restless leg syndrome (RLS) and insomnia. The causality and direction of the relationships between PLMS and RLS was evaluated using Mendelian randomization.ResultsWe found 2 independent loci were significantly associated with PLMS: rs113851554 (p = 3.51 × 10−12, β = 0.486), an SNP located in a putative regulatory element of intron eight of MEIS1 (2p14); and rs9369062 (p = 3.06 × 10−22, β = 0.2093), a SNP located in the intron region of BTBD9 (6p12); both of which were also lead signals in RLS GWAS. PLMS is genetically correlated with insomnia, risk of stroke, and RLS, but not with iron deficiency. Pleiotropy adjusted Mendelian randomization analysis identified a causal effect of RLS on PLMS.ConclusionsBecause PLMS is more common than RLS, PLMS may have multiple causes and additional studies are needed to further validate these findings.

doi: 10.1093/sleep/zsac254pmid: 36255119

Study ObjectivesEye movement quantification in polysomnograms (PSG) is difficult and resource intensive. Automated eye movement detection would enable further study of eye movement patterns in normal and abnormal sleep, which could be clinically diagnostic of neurologic disorders, or used to monitor potential treatments. We trained a long short-term memory (LSTM) algorithm that can identify eye movement occurrence with high sensitivity and specificity.MethodsWe conducted a retrospective, single-center study using one-hour PSG samples from 47 patients 18–90 years of age. Team members manually identified and trained an LSTM algorithm to detect eye movement presence, direction, and speed. We performed a 5-fold cross validation and implemented a “fuzzy” evaluation method to account for misclassification in the preceding and subsequent 1-second of gold standard manually labeled eye movements. We assessed G-means, discrimination, sensitivity, and specificity.ResultsOverall, eye movements occurred in 9.4% of the analyzed EOG recording time from 47 patients. Eye movements were present 3.2% of N2 (lighter stages of sleep) time, 2.9% of N3 (deep sleep), and 19.8% of REM sleep. Our LSTM model had average sensitivity of 0.88 and specificity of 0.89 in 5-fold cross validation, which improved to 0.93 and 0.92 respectively using the fuzzy evaluation scheme.ConclusionAn automated algorithm can detect eye movements from EOG with excellent sensitivity and specificity. Noninvasive, automated eye movement detection has several potential clinical implications in improving sleep study stage classification and establishing normal eye movement distributions in healthy and unhealthy sleep, and in patients with and without brain injury.

Study ObjectivesNo study has yet examined the prevalence of frequent nightmares in representative youth populations in Asia and how they may contribute to future mental health risks. We aimed to fill this gap using data from a large-scale household-based youth sample in Hong Kong.MethodsParticipants were consecutively recruited from a large-scale epidemiological youth mental health study in Hong Kong (n = 3132). A subset of participants were invited for a follow-up assessment after 1 year (n = 1154 in the final analyses). Frequent nightmares (≥1/week during the past month) were assessed using an item from the Pittsburgh Sleep Quality Index. Univariate analyses and multivariable logistic regression models were applied to examine the contribution of frequent nightmares at baseline to moderate-to-severe depressive and anxiety symptoms, post-traumatic stress disorder symptoms, and 30-day major depressive episode (MDE) or generalized anxiety disorder (GAD), both at baseline and follow-up. The long-term functional implications of frequent nightmares were also examined.ResultsThe prevalence of frequent nightmares was 16.3%. Females were more likely to experience frequent nightmares (20.4%) compared to males (12.1%), p < 0.001. Baseline frequent nightmares were significantly associated with all four mental health outcomes at 1 year. Notably, their prospective associations with depressive and anxiety symptoms and 30-day MDE/GAD remained significant even after adjusting for external stressors, resilience, and sociodemographic characteristics. Frequent nightmares were also significantly associated with both current and 1-year functional impairments.ConclusionsFrequent nightmares have significant long-term implications on mental health and functioning. Identifying young adults with frequent nightmares can improve early risk detection and intervention in the population.