Sleep最新文献

Study objectives: To investigate the associations of periodic limb movements during sleep (PLMS) and restless legs syndrome (RLS) symptoms with longitudinal changes in cognitive performance among older adults.

Methods: We analyzed data from 382 community-dwelling older adults without dementia participating in the HypnoLaus study (mean age: 71.0 ± 4.1 years; 42.9% male). Participants underwent polysomnography and baseline cognitive testing, followed by a second cognitive assessment after 4.7 ± 0.6 years. Individuals were categorized by PLMS index (PLMI) as <15/h (reference), 15-29.9/h, and ≥30/h. Associations between PLMI categories and annualized changes in executive function (Stroop Test), verbal fluency, and verbal memory were examined using multiple linear regression models adjusted for potential confounders. Additional analyses evaluated the role of RLS symptoms and PLMI × RLS symptoms interactions.

Results: A PLMI ≥30/h was associated with a steeper deterioration in executive function compared with the reference group (B = -0.06 standard deviations [SD]/year, p = .010), equivalent to -0.3 SD and -1.5 SD declines over 5 years relative to the baseline performance and to the distribution of change scores, respectively. This association was independent of RLS symptoms and other sleep-related conditions. No significant associations were observed between PLMI and changes in verbal fluency or verbal memory. RLS symptoms were not associated with changes in any cognitive outcome, and no PLMI × RLS symptoms interaction was detected.

Conclusions: High PLMS frequency was independently associated with accelerated deterioration in executive function, suggesting a link between PLMS and cognitive aging. In contrast, RLS symptoms showed no association with changes in cognitive function. Statement of Significance This study suggests that periodic limb movements during sleep (PLMS) may be related to early cognitive vulnerability and could represent an early marker of accelerated cognitive aging. A higher frequency of PLMS was associated with a steeper deterioration in executive function over about 5 years in community-dwelling older adults, independent of restless legs syndrome symptoms, other sleep conditions, and relevant clinical factors. These findings highlight the need for future research to elucidate the neurobiological mechanisms linking PLMS to accelerated cognitive aging, and to determine whether reducing PLMS could help preserve cognitive health, or whether PLMS primarily represent a marker rather than a direct contributor to cognitive deterioration.

Study objectives: We examined sex differences in within-person bidirectional and between-person associations between dimensions of Fitbit-measured sleep and multiple self-reported emotional health measures among a large sample of U.S. adults across ~1 year.

Methods: Data were from American Life in Realtime (ALiR), a probability-based cohort representative of U.S. adults. Participants wore Fitbits to measure sleep and answered monthly questionnaires on depression, anxiety, and stress symptoms. Linear mixed models examined bidirectional associations of nighttime Fitbit sleep measures (mean and SD of total sleep time [TST], clock time of sleep onset [CTSO], and sleep maintenance efficiency [SMEff]) across 14-day intervals that immediately preceded or succeeded each monthly report of emotional health and between-person associations. Analyses adjusted for sociodemographic variables and examined sex differences.

Results: The sample (n = 733-754) had a mean age of 47.1 ± 14.6, 48.7%F. Generally, within-person findings indicated that shorter and longer TST, poorer SMEff, and greater variability in CTSO and SMEff than one's average predicted higher depression, anxiety, and stress scores in the subsequent monthly report. In reverse, worse-than-average emotional health for a given month predicted poorer sleep health in the succeeding 14 days, with fewer significant associations than models with sleep predicting emotional health. Between-person results consistently showed that, on average, adults with later CTSO and more sleep variability had poorer emotional health. Some findings were stronger in females.

Conclusions: Poor sleep predicts worse emotional health and vice versa in a large representative sample, particularly among females, highlighting the need for tailored intervention strategies that consider sex-specific patterns in sleep-emotional health dynamics. Statement of Significance The present study examined bidirectional associations between sleep and emotional health in a population-based sample of several hundred U.S. adults using objective sleep measures (Fitbit-derived) across 1 year. Within-person findings indicated that poor sleep health (shorter and longer total sleep time, lower sleep maintenance efficiency, and more variability in these measures) predicted worse emotional health. Poor sleep more frequently predicted poor emotional health than vice versa. On average, in between-person findings, people with poorer sleep had worse emotional health. Some findings were stronger in females. Using novel longitudinal, high-frequency wearable data from a nationally representative cohort over a year, these findings highlight the need for tailored intervention strategies that consider sex-specific patterns in sleep-emotional health dynamics.

Sleep disturbances are prominent across neurodevelopmental disorders (NDDs) and may reflect specific abnormalities in brain development and function. Overnight polysomnography (PSG) allows for detailed investigation of sleep architecture, offering a unique window into neurocircuit function. Analysis of existing pediatric PSGs from clinical studies could enhance the availability of sleep studies in pediatric patients with NDDs towards a better understanding of mechanisms underlying abnormal development in NDDs. Here, we introduce and characterize a retrospective collection of 1527 clinical pediatric overnight PSGs across five different sites. We first developed an automated stager trained on independent pediatric sleep data, which yielded better performance compared to a generic stager trained primarily on adults. Using consistent staging across cohorts, we derived a panel of electroencephalography (EEG) micro-architectural features. This unbiased approach replicated broad trajectories previously described in typically developing sleep architecture. Further, we found sleep architecture disruptions in children with Down's syndrome (DS) that were consistent across independent cohorts. Finally, we built and evaluated a model to predict age from sleep EEG metrics, which recapitulated our previous findings of younger predicted brain age in children with DS. Altogether, by creating a resource pooled from existing clinical data we expanded the available datasets and computational resources to study sleep in pediatric populations, specifically towards a better understanding of sleep in NDDs. This Retrospective Analysis of Sleep in Pediatric cohorts dataset, including staging annotation derived from our automated stager is deposited at https://sleepdata.org/datasets/rasp. Statement of Significance We introduce the Retrospective Analysis of Sleep (RASP) cohorts, a collection of 1527 clinical pediatric overnight polysomnographies that includes typically developing and neurodevelopmental disorder cases. As a first step towards addressing the analytic bottleneck inherent in manual sleep staging, we developed and validated a pediatric-specific sleep stager. Leveraging the retrospective RASP cohort's dataset, we redemonstrated known developmental trajectories in sleep architecture. To summarize changes in brain function reflected in sleep, we developed a model to predict brain age from sleep measures. We recapitulate younger predicted age in RASP Down's syndrome cases. This valuable resource underscores the utility of existing clinical polysomnography studies for studying sleep disturbances in pediatric neurodevelopmental disorders populations.