Sleep macro-architecture and dementia risk in adults: Meta-analysis of 5 cohorts from the Sleep and Dementia Consortium
Bibliographic record
Abstract
ABSTRACT Study objectives Poor sleep may play a role in the risk of dementia. However, few studies have investigated the association between polysomnography (PSG)-derived sleep architecture and dementia incidence. We examined the relationship between sleep macro-architecture and dementia incidence across five US-based cohort studies from the Sleep and Dementia Consortium (SDC). Methods Percent of time spent in stages of sleep (N1, N2, N3, REM sleep), wake after sleep onset and sleep maintenance efficiency were derived from a single night home-based PSG. Dementia was ascertained in each cohort using its cohort-specific criteria. Each cohort performed Cox proportional hazard regressions for each sleep exposure and incident dementia, adjusting for age, sex, body mass index, anti-depressant use, sedative use, and APOE e4 status. Results were then pooled in random effects meta-analyses. Results The pooled sample comprised 4,657 participants (30% women) aged ≥60 years (mean age was 74 years at sleep assessment). There were 998 (21.4%) dementia cases (median follow-up time of 5 to 19 years). Pooled effects of the five cohorts showed no association between sleep architecture and incident dementia. When meta-analyses were restricted to the three cohorts which had dementia case ascertainment based on DSM-IV/V criteria (n=2,374), higher N3% was marginally associated with an increased risk of dementia (HR: 1.06; 95%CI: 1.00-1.12, per percent increase N3, p=0.050). Conclusions There were no consistent associations between sleep macro-architecture measured and the risk of incident dementia. Implementing more nuanced sleep metrics remains an important next step for uncovering more about sleep-dementia associations. STATEMENT OF SIGNIFICANCE Poor sleep may represent a potential lifestyle risk factor for dementia. Sleep is thought to be important for the clearance of toxic Alzheimer’s disease proteins, but whether sleep is associated with dementia risk remains unclear. In the largest study of its kind, utilizing overnight polysomnographic assessment of sleep and data from 5 large U.S cohort studies, we examined the association between sleep macro-architecture and dementia risk. Meta-analysis revealed no clear associations between sleep measures and dementia risk, though there was a suggestion that a higher proportion of N3 sleep may be associated with greater dementia risk. Further exploration of sleep patterns across time, latent sleep traits across metrics, and sleep micro-architecture remain as important next steps for understanding sleep-dementia associations.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.050 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".