Sleep最新文献

Study objective: To examine the association between light at night (LAN) and multiple sleep health dimensions.

Methods: Among 47 765 Sister Study participants, indoor LAN (TV on in the room, light(s) on in room, light from outside the room, nightlight, no light) and sleep dimensions were self-reported at baseline (2003-2009). We used Poisson regression with robust variance to estimate adjusted prevalence ratios (PR) and 95% confidence intervals (CI) for the cross-sectional associations between LAN and short sleep duration (<7 hours/night), insomnia symptoms (difficulty falling or staying asleep), frequent napping (≥3 naps/week), inconsistent sleep/wake time (differed day-to-day and week-to-week), sleep debt (≥2 hours between longest and shortest duration), recent sleep medication use, and a cumulative poor sleep score (≥3 poor sleep dimensions). Population-attributable risks (PARs) were determined for any light exposure vs. none by race/ethnicity.

Results: Compared to sleeping with no light in the bedroom, sleeping with a TV on was associated with a higher prevalence of most dimensions of poor sleep (e.g. short sleep duration: PR = 1.38, 95% CI: 1.32 to 1.45; inconsistent sleep/wake time: PR = 1.55, 95% CI: 1.44 to 1.66; sleep debt: PR = 1.36, 95% CI: 1.29 to 1.44; poor sleep score: PR = 1.58, 95% CI: 1.48-1.68). PARs tended to be higher for non-Hispanic black women compared to non-Hispanic white women.

Conclusions: Sleeping with a TV on was associated with poor sleep health among US women, and non-Hispanic black women may be disproportionately burdened.

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ± 13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ± 1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (β = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.

Study objectives: People with diabetes and prediabetes are more likely to have sleep-disordered breathing (SDB), but few studies examined sleep architecture in people with diabetes or prediabetes in the absence of moderate-severe SDB, which was the aim of our cross-sectional study.

Methods: This cross-sectional sample is from the Baependi Heart Study, a family-based cohort of adults in Brazil. About 1074 participants underwent at-home polysomnography (PSG). Diabetes was defined as fasting glucose >125 mg/dL or HbA1c > 6.4 mmol/mol or taking diabetic medication, and prediabetes was defined as HbA1c ≥ 5.7 & <6.5 mmol/mol or fasting glucose ≥ 100 & ≤125 mg/dl. We excluded participants with an apnea-hypopnea index (AHI) ≥ 30 in primary analyses and ≥ 15 in secondary analysis. We compared sleep stages among the 3 diabetes groups (prediabetes, diabetes, neither).

Results: Compared to those without diabetes, we found shorter REM duration for participants with diabetes (-6.7 min, 95%CI -13.2, -0.1) and prediabetes (-5.9 min, 95%CI -10.5, -1.3), even after adjusting for age, gender, BMI, and AHI. Diabetes was also associated with lower total sleep time (-13.7 min, 95%CI -26.8, -0.6), longer slow-wave sleep (N3) duration (+7.6 min, 95%CI 0.6, 14.6) and higher N3 percentage (+2.4%, 95%CI 0.6, 4.2), compared to those without diabetes. Results were similar when restricting to AHI < 15.

Conclusions: People with diabetes and prediabetes had less REM sleep than people without either condition. People with diabetes also had more N3 sleep. These results suggest that diabetes and prediabetes are associated with differences in sleep architecture, even in the absence of moderate-severe sleep apnea.

Sleep spindles are isolated transient surges of oscillatory neural activity present during sleep stages 2 and 3 in the nonrapid eye movement (NREM). They can indicate the mechanisms of memory consolidation and plasticity in the brain. Spindles can be identified across cortical areas and classified as either slow or fast. There are spindle transients across different frequencies and power, yet most of their functions remain a mystery. Using several electroencephalogram (EEG) databases, this study presents a new method, called the "spindles across multiple channels" (SAMC) method, for identifying and categorizing sleep spindles in EEGs during the NREM sleep. The SAMC method uses a multitapers and convolution (MT&C) approach to extract the spectral estimation of different frequencies present in sleep EEGs and graphically identify spindles across multiple channels. The characteristics of spindles, such as duration, power, and event areas, are also extracted by the SAMC method. Comparison with other state-of-the-art spindle identification methods demonstrated the superiority of the proposed method with an agreement rate, average positive predictive value, and sensitivity of over 90% for spindle classification across the three databases used in this paper. The computing cost was found to be, on average, 0.004 seconds per epoch. The proposed method can potentially improve the understanding of the behavior of spindles across the scalp and accurately identify and categories sleep spindles.

Study objectives: Patients with isolated rapid-eye-movement sleep behavior disorder (iRBD) have an increased risk of developing neurodegenerative diseases. This study assessed cerebrospinal-fluid (CSF) biomarkers of neurodegeneration and blood-brain barrier (BBB) alteration in patients with iRBD compared to controls and ascertain whether these biomarkers may predict phenoconversion to alpha-synucleinopathies (Parkinson's Disease (PD), Dementia with Lewy bodies (DLB), Multiple System Atrophy (MSA)).

Methods: Patients and controls underwent between 2012 and 2016 a neurological assessment, a lumbar puncture for CSF biomarker analysis (β-amyloid42 - Aβ42; total-tau, and phosphorylated tau), and BBB alteration (CSF/serum albumin ratio). All patients with iRBD were followed until 2021 and then classified into patients who converted to alpha-synucleinopathies (iRBD converters, cRBD) or not (iRBD non-converters, ncRBD).

Results: Thirty-four patients with iRBD (mean age 67.12 ± 8.14) and 33 controls (mean age 64.97 ± 8.91) were included. At follow-up (7.63 ± 3.40 years), eight patients were ncRBD and 33 patients were cRBD: eleven converted to PD, 10 to DLB, and two to MSA. Patients with iRBD showed lower CSF Aβ42 levels and higher CSF/serum albumin ratio than controls. Cox regression analysis showed that the phenoconversion rate increases with higher motor impairment (hazard ratio [HR] = 1.23, p = 0.032). CSF Aβ42 levels predicted phenoconversion to DLB (HR = 0.67, p = 0.038) and BBB alteration predicted phenoconversion to PD (HR = 1.20, p = 0.038).

Discussion: This study showed that low CSF Aβ42 levels and high BBB alteration may predict the phenoconversion to DLB and PD in patients with iRBD, respectively. These findings highlight the possibility to discriminate phenoconversion in iRBD patients through CSF biomarkers; however, further studies are needed.

This is the first English translation of the work Periodic phenomena in the sleep in children, published in 1926 in the Journal Novoe v refleksologii i fiziologii nervnoi sistemy (Vol. 2, pp. 338-345) by Maria Denisova and Nicholai Figurin; it is the first study to report data on what is currently termed rapid eye movement (REM) sleep. The authors acquired continuous quantitative respiration data, as well as, eye and body movements during sleep in children for up to 6 hours, and discovered several novel features of sleep cycles in healthy infants from birth to about 1 year of age. First, the study reports cyclical periods of increased respiration and eye and body movements, with rapid ocular movements visible under relaxed eyelids (separation: 0.5-1 mm). These observations suggest atonia of REM sleep. Second, the length of the complete cycle (alternating active and quiet sleep phases or states) is about 50 minutes, an estimate that is consistent with later work. Third, the study identifies infant-specific ordering of sleep states, with the active phase beginning after sleep onset, followed by the quiescence phase. Importantly, these published data on sleep cycles precede all published studies related to the state now termed REM sleep by about 30 years (i.e. publishing in Science and in the Journal of Applied Physiology in the 1950s by Eugene Aserinski and Nathaniel Kleitman). In the historical commentary accompanying this translation, the findings of those later works are carefully compared to the original data on respiration and ocular and body motility cycles during sleep in infants, first reported and published by Denisova and Figurin (1926).

Study objectives: The sleep apnea multi-level surgery (SAMS) randomized clinical trial showed surgery improved outcomes at 6 months compared to ongoing medical management in patients with moderate or severe obstructive sleep apnea (OSA) who failed continuous positive airway pressure therapy. This study reports the long-term outcomes of the multi-level surgery as a case series.

Methods: Surgical participants were reassessed >2 years postoperatively with the same outcomes reported in the main SAMS trial. Primary outcomes were apnea-hypopnea index (AHI) and Epworth sleepiness scale (ESS), with secondary outcomes including other polysomnography measures, symptoms, quality of life, and adverse events. Long-term effectiveness (baseline to long-term follow-up [LTFU]) and interval changes (6 month to LTFU) were assessed using mixed effects regression models. Control participants were also reassessed for rate of subsequent surgery and outcomes.

Results: 36/48 (75%) of surgical participants were reevaluated (mean (standard deviation)) 3.5 (1.0) years following surgery, with 29 undergoing polysomnography. AHI was 41/h (23) at preoperative baseline and 21/h (18) at follow-up, representing persistent improvement of -24/h (95% CI -32, -17; p < 0.001). ESS was 12.3 (3.5) at baseline and 5.5 (3.9) at follow-up, representing persistent improvement of -6.8 (95% CI -8.3, -5.4; p < 0.001). Secondary outcomes were improved long term, and adverse events were minor. Interval change analysis suggests stability of outcomes. 36/43 (84%) of the control participants were reevaluated, with 25 (69%) reporting subsequent surgery, with symptom and quality of life improvements.

Conclusion: Multi-level upper airway surgery improves OSA burden with long-term maintenance of treatment effect in adults with moderate or severe OSA in whom conventional therapy failed.

Clinical trial: Multi-level airway surgery in patients with moderate-severe obstructive sleep apnea (OSA) who have failed medical management to assess change in OSA events and daytime sleepiness; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366019&isReview=true; ACTRN12614000338662.