Sleep Disturbance is Associated with Incident Dementia and Mortality
Bibliographic record
Abstract
People with Alzheimer' s disease (AD) commonly complain of sleep disturbances, which are seen in a wide variety of conditions that become more common in late life. It is not known whether sleep-related symptoms are associated with AD independently of their association with other illnesses. Secondary analyses of sleep-related measures collected through the Survey of Health, Ageing and Retirement in Europe (SHARE; i.e., sleeping problems, fatigue, taking sleeping medication, and trouble sleeping or a change in pattern) were conducted on those who reported the absence of AD or dementia at baseline. A 'sleep disturbance index' (SDI) using sleep-related measures was created and compared to a frailty index reflecting overall health status. Each sleep measure independently predicted self-reported AD or dementia and mortality within ~4 years. Combined, the SDI was associated with an increased risk of developing AD or dementia (OR= 1.23, 95%CI = 1.11-1.36) and mortality (OR = 1.18, 95% CI = 1.12-1.24), and remained a strong factor for dementia when overall health status was added to the risk model (p = 0.054). These findings indicate that sleep disturbance may exist prior to the manifestation of other typical symptoms observed in AD (e.g., memory loss). Sleep-related questions may be useful for screening individuals at risk for dementia and may allow for the earlier detection of AD at the preclinical stage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".