Sleep Disturbance Forecasts β-Amyloid Accumulation across Subsequent Years

Abstract

Experimental sleep-wake disruption in rodents and humans causally modulates β-amyloid (Aβ) dynamics (e.g., [1-3]). This leads to the hypothesis that, beyond cross-sectional associations, impaired sleep structure and physiology could represent prospective biomarkers of the speed with which Aβ accumulates over time. Here, we test the hypothesis that initial baseline measures of non-rapid eye movement (NREM) sleep slow-wave activity (SWA) and sleep quality (efficiency) provide future forecasting sensitivity to the rate of Aβ accumulation over subsequent years. A cohort of clinically normal older adults was assessed using objective sleep polysomnography in combination with longitudinal tracking of Aβ accumulation with [¹¹C]PiB positron emission tomography (PET) imaging. Both the proportion of NREM SWA below 1 Hz and the measure of sleep efficiency predicted the speed (slope) of subsequent Aβ deposition over time, and these associations remained robust when taking into account additional cofactors of interest (e.g., age, sex, sleep apnea). Moreover, these measures were specific, such that no other macro- and microphysiological architecture metrics of sleep demonstrated such sensitivity. Our data support the proposal that objective sleep markers could be part of a set of biomarkers that statistically forecast the longitudinal trajectory of cortical Aβ deposition in the human brain. Sleep may therefore represent a potentially affordable, scalable, repeatable, and non-invasive tool for quantifying of Aβ pathological progression, prior to cognitive symptoms of Alzheimer's disease (AD).

Keywords: Alzheimer's disease; PET; aging; sleep; slow-wave sleep; β-amyloid.

Figure 1.. Group-Level Patterns of Longitudinal [¹¹C]PiB β-Amyloid Increase

(A) Voxelwise mean group annual [¹¹C]PiB DVR increase, highlighting regions of Aβ plaque accumulation during the study period. (B) Predicted group longitudinal trajectory of cortical Aβ plaque deposition (global [¹¹C]PiB DVR increase) over time, extracted from a linear mixed-effects model. Light blue shading represents 95% confidence intervals.

Figure 2.. Baseline Sleep Metrics Predict Longitudinal β-Amyloid Plaque Deposition

(A) Scatterplot of significant association between proportion (prop.) <1 Hz slow-wave activity (SWA) at baseline and subsequent rate of increase in cortical [¹¹C]PiB DVR. Individuals with lower baseline prop. <1 Hz SWA went on to experience higher rates of increase in cortical Aβ relative to those with higher initial prop. <1 Hz SWA. (B) Correlation between NREM slow-wave sleep (SWS) spectral power in 1 Hz bins (0.6–40 Hz) and subsequent rate of [¹¹C]PiB DVR increase. The shaded area represents the a priori SWA frequency range (0.6–4 Hz). The dashed line denotes a correlation of 0. (C) Scatterplot of bivariate association between baseline sleep efficiency and future rate of [¹¹C]PiB DVR increase. Lower sleep efficiency was predictive of a higher rate of increase in cortical Aβ.

Figure 3.. Regional Differences in Patterns of Annual Longitudinal β-Amyloid Accumulation on the Basis of Baseline Sleep Measures

(A) Median split of individuals with high versus low prop. <1 Hz NREM SWA at baseline illustrates distinct patterns of annual [¹¹C]PiB DVR increase, with a greater mean increase in the low prop. <1 Hz SWA group. (B) Median split by baseline sleep efficiency. Mean annual [¹¹C]PiB DVR increase is higher in the low-sleep-efficiency group. All images show the medial surface of the right hemisphere.