Effects of long-term sleep disruption on cognitive function and brain amyloid-ß burden: a case-control study.

BACKGROUND: Recent evidence indicates that disrupted sleep could contribute to the development of Alzheimer's disease by influencing the production and/or clearance of the amyloid-ß protein. We set up a case-control study to investigate the association between long-term work-induced sleep disruption, cognitive function, and brain amyloid-ß burden. METHODS: Nineteen male maritime pilots (aged 48-60 years) with chronic work-related sleep disruption and a sex-, age-, and education-matched control sample (n = 16, aged 50-60 years) with normal sleep completed the study. Primary sleep disorders were ruled out with in-lab polysomnography. Additional sleep measurements were obtained at home using actigraphy, sleep-wake logs, and a single-lead EEG device. Cognitive function was assessed with a neuropsychological test battery, sensitive to early symptomatic Alzheimer's disease. Brain amyloid-ß burden was assessed in maritime pilots using 18F-flutemetamol amyloid PET-CT. RESULTS: Maritime pilots reported significantly worse sleep quality (Pittsburgh Sleep Quality Index (PSQI) = 8.8 ± 2.9) during work weeks, compared to controls (PSQI = 3.2 ± 1.4; 95% CI 0.01 to 2.57; p = 0.049). This was confirmed with actigraphy-based sleep efficiency (86% ± 3.8 vs. 89.3% ± 4.3; 95% CI 0.43 to 6.03; p = 0.03). Home-EEG recordings showed less total sleep time (TST) and deep sleep time (DST) during work weeks compared to rest weeks (TST 318.56 (250.21-352.93) vs. TST 406.17 (340-425.98); p = 0.001; DST 36.75 (32.30-58.58) vs. DST 51.34 (48.37-69.30); p = 0.005)). There were no differences in any of the cognitive domains between the groups. For brain amyloid-ß levels, mean global cortical standard uptake value ratios of 18F-flutemetamol were all in the normal range (1.009 ± 0.059; 95% CI 0.980 to 1.037), confirmed by visual reads. CONCLUSIONS: Capitalizing on the particular work-rest schedule of maritime pilots, this study with a small sample size observed that long-term intermittent sleep disruption had no effects on global brain amyloid-ß levels or cognitive function.

Slow wave sleep disruption increases cerebrospinal fluid amyloid-ß levels.

See Mander et al. (doi:10.1093/awx174) for a scientific commentary on this article.Sleep deprivation increases amyloid-ß, suggesting that chronically disrupted sleep may promote amyloid plaques and other downstream Alzheimer's disease pathologies including tauopathy or inflammation. To date, studies have not examined which aspect of sleep modulates amyloid-ß or other Alzheimer's disease biomarkers. Seventeen healthy adults (age 35-65 years) without sleep disorders underwent 5-14 days of actigraphy, followed by slow wave activity disruption during polysomnogram, and cerebrospinal fluid collection the following morning for measurement of amyloid-ß, tau, total protein, YKL-40, and hypocretin. Data were compared to an identical protocol, with a sham condition during polysomnogram. Specific disruption of slow wave activity correlated with an increase in amyloid-ß40 (r = 0.610, P = 0.009). This effect was specific for slow wave activity, and not for sleep duration or efficiency. This effect was also specific to amyloid-ß, and not total protein, tau, YKL-40, or hypocretin. Additionally, worse home sleep quality, as measured by sleep efficiency by actigraphy in the six nights preceding lumbar punctures, was associated with higher tau (r = 0.543, P = 0.045). Slow wave activity disruption increases amyloid-ß levels acutely, and poorer sleep quality over several days increases tau. These effects are specific to neuronally-derived proteins, which suggests they are likely driven by changes in neuronal activity during disrupted sleep.

Automated selective disruption of slow wave sleep.

BACKGROUND: Slow wave sleep (SWS) plays an important role in neurophysiologic restoration. Experimentally testing the effect of SWS disruption previously required highly time-intensive and subjective methods. Our goal was to develop an automated and objective protocol to reduce SWS without affecting sleep architecture. NEW METHOD: We developed a custom Matlab™ protocol to calculate electroencephalogram spectral power every 10s live during a polysomnogram, exclude artifact, and, if measurements met criteria for SWS, deliver increasingly louder tones through earphones. Middle-aged healthy volunteers (n=10) each underwent 2 polysomnograms, one with the SWS disruption protocol and one with sham condition. RESULTS: The SWS disruption protocol reduced SWS compared to sham condition, as measured by spectral power in the delta (0.5-4Hz) band, particularly in the 0.5-2Hz range (mean 20% decrease). A compensatory increase in the proportion of total spectral power in the theta (4-8Hz) and alpha (8-12Hz) bands was seen, but otherwise normal sleep features were preserved. N3 sleep decreased from 20±34 to 3±6min, otherwise there were no significant changes in total sleep time, sleep efficiency, or other macrostructural sleep characteristics. COMPARISON WITH EXISTING METHOD: This novel SWS disruption protocol produces specific reductions in delta band power similar to existing methods, but has the advantage of being automated, such that SWS disruption can be performed easily in a highly standardized and operator-independent manner. CONCLUSION: This automated SWS disruption protocol effectively reduces SWS without impacting overall sleep architecture.

User preferences and usability of iVitality: optimizing an innovative online research platform for home-based health monitoring.

BACKGROUND: The iVitality online research platform has been developed to gain insight into the relationship between early risk factors (ie, poorly controlled hypertension, physical or mental inactivity) and onset and possibly prevention of dementia. iVitality consists of a website, a smartphone application, and sensors that can monitor these indicators at home. Before iVitality can be implemented, it should fit the needs and preferences of users, ie, offspring of patients with dementia. This study aimed to explore users' motivation to participate in home-based health monitoring research, to formulate requirements based on users' preferences to optimize iVitality, and to test usability of the smartphone application of iVitality. METHODS: We recruited 13 participants (aged 42-64 years, 85% female), who were offspring of patients with dementia. A user-centered methodology consisting of four iterative phases was used. Three semistructured interviews provided insight into motivation and acceptance of using iVitality (phase 1). A focus group with six participants elaborated on expectations and preferences regarding iVitality (phase 2). Findings from phase 1 and 2 were triangulated by two semistructured interviews (phase 3). Four participants assessed the usability of the smartphone application (phase 4) using a think aloud procedure and a questionnaire measuring ease and efficiency of use (scale 1-7; higher scores indicated better usability). RESULTS: All participants were highly motivated to contribute to dementia research. However, the frequency of home-based health monitoring should not be too high. Participants preferred to receive feedback about their measurements and information regarding the relationship between these measurements and dementia. Despite minor technical errors, iVitality was considered easy and efficient to use (mean score 5.50, standard deviation 1.71). CONCLUSION: Offspring of patients with dementia are motivated to contribute to home-based monitoring research by using iVitality and are able to use the smartphone application. The formulated requirements will be embedded to optimize iVitality.