Sleep quality profiles related to cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Females have a higher risk of developing Alzheimer's disease (AD) and report poorer sleep quality with age compared to males. Because poor sleep quality is also a risk factor for AD, it may contribute to sex-specific differences in AD risk. Females differ in the prevalence, presentation, and severity of sleep disorders, and tend to show greater mismatch between objective and subjective sleep metrics. To better understand these factors, this study investigated sex differences in self-reported sleep quality metrics across varying levels of cognitive impairment. METHOD: Cross-sectional self-report data from COMPASS-ND/CIMA-Q were analyzed across three groups [cognitively unimpaired (CU, n=104), subjective cognitive impairment (SCI, n=108), amnestic mild cognitive impairment (MCI, n=290)], including components of the Pittsburgh Sleep Quality Index, metrics of daytime fatigue/dysfunction, sleep medication use, sleep apnea, rapid eye movement (REM) sleep behaviour disorder, and restless leg syndrome. Principal component analyses including these sleep variables were run separately for males (n =246) and females (n =256). Linear models assessed whether principal component scores differed by sex and group (CU, SCI, MCI). RESULT: In both sexes, the first principal component (PC1) reflected overall sleep quality and daytime fatigue/dysfunction, with higher scores indicating better sleep and lower fatigue/dysfunction. The second component (PC2) captured discordance between sleep quality and fatigue, with higher scores indicating poor sleep but low fatigue. Linear models showed that both PC1 and PC2 scores were significantly lower in MCI compared to CU. Thus, MCI was associated with both poor sleep and high fatigue (low PC1), as well as good sleep but high fatigue (low PC2), suggesting a mismatch between perceived sleep quality and daytime functioning for some participants. While no significant sex-by-group interactions were found, subtle sex differences were noted: Compared to males, females with low PC2 scores reported more symptoms of REM sleep behaviour disorder and restless leg syndrome. CONCLUSION: Findings highlight distinct sleep and cognitive impairment patterns, with potential sex-specific sleep symptom profiles. Understanding these profiles could improve early identification of cognitive decline and support targeted sleep interventions. Tailoring approaches to these differences may enhance both sleep quality and cognitive outcomes in aging populations.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".