Sleep Disorders and Cognitive Aging Among Cognitively Impaired Versus Unimpaired Older Adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sleep disorders often predict or co-occur with cognitive decline. Yet, little is known about how the relationship unfolds among older adults at risk for cognitive decline. To examine the associations of sleep disorders with cognitive decline in older adults with unimpaired cognition or impaired cognition (mild cognitive impairment and dementia). RESEARCH DESIGN AND METHODS: A total of 5,822 participants (Mage = 70) of the National Alzheimer's Coordinating Center database with unimpaired or impaired cognition were followed for 3 subsequent waves. Four types of clinician-diagnosed sleep disorders were reported: sleep apnea, hyposomnia/insomnia, REM sleep behavior disorder, or "other." Cognition over time was measured by the Montreal Cognitive Assessment (MoCA) or an estimate of general cognitive ability (GCA) derived from scores based on 12 neuropsychological tests. Growth curve models were estimated adjusting for covariates. RESULTS: In participants with impaired cognition, baseline sleep apnea was related to better baseline MoCA performance (b = 0.65, 95% confidence interval [95% CI] = [0.07, 1.23]) and less decline in GCA over time (b = 0.06, 95% CI = [0.001, 0.12]). Baseline insomnia was related to better baseline MoCA (b = 1.54, 95% CI = [0.88, 2.21]) and less decline in MoCA over time (b = 0.56, 95% CI = [0.20, 0.92]). Furthermore, having more sleep disorders (across the 4 types) at baseline predicted better baseline MoCA and GCA, and less decline in MoCA and GCA over time. These results were only found in those with impaired cognition and generally consistent when using self-reported symptoms of sleep apnea or insomnia. DISCUSSION AND IMPLICATIONS: Participants with sleep disorder diagnoses may have better access to healthcare, which may help maintain cognition through improved sleep.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".