Sleep, Alzheimer pathology and risk of clinical progression in cognitively unimpaired older adults
Bibliographic record
Abstract
Abstract Background Increasing evidence suggests a link between sleep and Alzheimer disease’s (AD) pathology and cognitive decline. We investigated whether sleep disturbances might be accompanied by faster AD pathology accumulation and/or cognitive decline before the onset of cognitive symptoms. Method We investigated cross‐sectional and longitudinal associations between sleep quality, AD pathology and cognition in 220 participants from the PREVENT‐AD cohort. A subsample of 99 participants had longitudinal amyloid and tau PET data (mean follow‐up:4.33±0.53y, range: 1.59 – 6.11y) and a subsample of 218 had longitudinal cognitive evaluations (mean follow‐up:0.80±0.50y, range 1‐9). We used the PSQI global score and the actigraphy day‐to‐day sleep efficiency and fragmentation index variability, amyloid and tau‐PET to measure amyloid and tau respectively and the Repeated Battery for the Assessment of Neuropsychological Status (RBANS) to assess cognition. All participants were cognitively unimpaired at their first sleep measurement and 32 individuals developed mild cognitive impairment (MCI) during the study. In supplementary analyses individuals were classified as having high or low to moderated levels of amyloid based on a centiloid of 40. We used robust linear models (RLM) and ANOVAs to assess the association between sleep, and AD pathology and cognition. Result We found that higher levels of amyloid pathology were associated with greater day‐to‐day sleep efficiency and fragmentation index variability (Fig. 1). Higher levels of tau pathology were associated with greater day‐to‐day sleep efficiency and fragmentation variability (Fig. 1). We further found longitudinal associations between annual amyloid change and greater day‐to‐day sleep efficiency variability (Fig. 1). These associations were only present in individuals who had centiloid values lower than or equal to 40 (Fig. 2). While no association was found between sleep quality and cognition, individuals who developed MCI (n = 32) had higher baseline PSQI scores (Fig. 3) and greater day‐to‐day sleep efficiency (Fig. 3) years before they were classified as MCI. Conclusion Higher variability in sleep quality and worst self‐reported sleep relates to AD pathology. Subjective and objective sleep impairments were also present years prior the development of MCI. Sleep variability and subjective sleep assessment changes might precede sleep disruptions observed later in the disease, which could promote further pathological processes in the brain.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".