Sleep Quality and Day‐to‐Day Variability: Relation to Alzheimer’s pathology in Cognitively Unimpaired Older Adults At‐Risk of AD Dementia
Bibliographic record
Abstract
Abstract Background Increasing evidence suggests a link between sleep and late‐life Alzheimer disease’s (AD) pathology. We investigated whether sleep degradation might be accompanied by faster AD pathology accumulation and/or whether early AD pathologic changes might be accompanied by sleep changes before the onset of cognitive symptoms. Method We investigated 228 PREVENT‐AD participants with available sleep data who underwent Aβ, [18F] NAV‐4694, and tau,[18F] Flortaucipir, positron emission tomography (PET). Longitudinal subjective sleep quality data were available for 191 participants (follow‐up:1.03±0.19y), longitudinal objective sleep data measured with actigraphy was available for 152 participants (follow‐up:2.5±1.24y), and longitudinal PET scans were available for 104 participants (follow‐up:4.3±0.24y). We assessed sleep quality using the Pittsburgh sleep quality index (PSQI) global score along with 7‐day actigraphy to indicate sleep duration, efficiency, and fragmentation index. Daily variation in sleep was measured using cross‐sectional and longitudinal standard deviations across the 7 days of actigraphy. Linear mixed effect models tested for longitudinal association of baseline sleep measures with PET outcomes over time. We also examined whether an increase in PET AD tracer uptake predicted changes in sleep quality and day‐to‐day variability. Every model controlled for age and sex. Result We found no association between sleep measures and Aβ PET. Higher baseline PSQI scores were associated with higher levels of tau over time (p = 0.004, R2 = 0.034, 𝛽 = 0.007, Fig.1, A). Higher baseline variability of sleep duration and fragmentation index were also associated with higher levels of tau longitudinally (p = 0.001, R2 = 0.05, 𝛽 = 0.001, Fig.1, B; p = 0.029, R2 = 0.022, 𝛽 = 0.01; Fig.1, C). Furthermore, increased tau at baseline was associated with lower sleep quality (p = 0.006, R2 = 0.026, 𝛽 = 4.12, Fig.2, A), and with higher sleep duration variability over time (p = 0.004, R2 = 0.06, 𝛽 = 61.56, Fig.1, B.) Conclusion Poor sleep quality and higher day‐to‐day sleep variability in older age could contribute to faster rate of tau pathology accumulation, which could in turn disrupt sleep further. Targeting sleep disturbances may therefore serve in delaying tau burden and slowing down disease progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".