Sleep Characteristics and Cognitive Function in Older Adults Without Dementia: The CABLE Study
Bibliographic record
Abstract
BACKGROUND: The associations between sleep characteristics and cognition are complicated. Alzheimer's disease (AD) pathologies have been proven to be associated with sleep characteristics. OBJECTIVE: We aimed to investigate the associations between sleep characteristics and cognitive function and examine the roles of AD pathologies in modulating the association of sleep duration with cognition. METHODS: A total of 974 participants who had measurements of cerebrospinal fluid (CSF) amyloid-β (Aβ), phosphorylated tau (P-tau), total tau proteins (T-tau), cognitive function, and sleep characteristics were included from the Chinese Alzheimer's Biomarker and Lifestyle (CABLE) study. Linear regression analyses were utilized to explore the associations of sleep characteristics with cognition. Non-linear regression analyses were utilized to explore the associations of sleep habits with cognition. Causal mediation analyses were conducted to explore the mediation effects of AD pathologies on cognition. RESULTS: The Pittsburgh Sleep Quality Index (PSQI) total score was significantly negatively correlated with Montreal Cognitive Assessment (MoCA) score (p = 0.0176). Long latency (p = 0.0054) and low efficiency (p = 0.0273) were associated with cognitive impairment. Habitual nap behavior was associated with lower MoCA scores (p = 0.0045). U-shaped associations were observed between sleep habits (bedtime and nocturnal sleep duration) and cognition. A causal mediation analysis indicated that P-tau/Aβ42 mediated the association of sleep duration with cognition. CONCLUSION: These findings showed sleep characteristics were associated with cognitive functions. Sleep habits (duration, bedtime) had U-shaped associations with cognition. AD core pathologies might partially mediate the influence of sleep duration on cognitive impairments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".