Polysomnography derived sleep metrics and cognition in the Sleep and Dementia Consortium (SDC): a study of 5 population‐based cohorts
Bibliographic record
Abstract
Abstract Background Good sleep is essential for health, yet the role of sleep in dementia risk is incompletely understood. The Sleep and Dementia Consortium (SDC) was established to study associations between polysomnography (PSG)‐derived sleep metrics and the risk of dementia and related cognitive and brain MRI endophenotypes. This study presents the associations of sleep architecture and obstructive sleep apnea (OSA) with cognitive function across participating cohorts. Method The SDC curates data from five population‐based cohorts with methodologically consistent, overnight, home‐based PSG and neuropsychological assessments over 5 years of follow‐up. Cohorts include the Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Health Study (CHS), Framingham Heart Study (FHS), Osteoporotic Fractures in Men Study (MrOS), and Study of Osteoporotic Fractures (SOF). Global cognitive composite scores were derived from principal component analysis as the primary outcome. All sleep metrics were harmonized centrally and then distributed to the participating cohorts for cohort‐specific analysis using linear regression; study‐level estimates were pooled in random effects meta‐analyses. Results are adjusted for demographic variables, the time interval between the PSG and neuropsychological assessment (0‐5 years), body mass index, antidepressant use, and sedative medication use. Result The mean age of the cohorts ranged from 58 to 89 years (Figure 1) with a pooled sample of 5,946 participants. As shown in Figure 2, across cohorts, higher REM sleep percentage (pooled β±SE = 0.49±0.18 per % increase; p = 0.008) and Sleep Maintenance Efficiency (pooled β±SE = 0.08±0.03 per % increase; p = 0.01) were associated with better global cognition whereas OSA (Apnea‐hypopnea index [AHI] ≥ 5) was associated with worse global cognition (pooled β±SE = ‐0.06±0.02 vs. AHI<5; p = 0.03). Differences in N3 sleep were not associated with cognition. Conclusion Better sleep consolidation and higher REM sleep percentage were associated with better cognition whereas OSA was associated with worse cognition over 5‐years follow‐up. The role of interventions to improve sleep for maintaining cognitive function and potentially reduce dementia risk requires investigation.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".