Sleep Disturbances, Cognitive Status, and Biomarkers of Dementia
Bibliographic record
Abstract
BACKGROUND: While sleep disturbances appear to be risk factors in Alzheimer's disease (AD) progression, information such as the prevalence across dementia severity and the influence on the trajectory of cognitive decline is unclear. OBJECTIVE: We evaluate the hypotheses that the prevalence of insomnia differs by cognitive impairment, that sleep disturbances track with AD biomarkers, and that longitudinal changes in sleep disorders affect cognition. METHODS: We used the National Alzheimer's Coordinating Center Database to determine the prevalence of clinician-identified insomnia and nighttime behaviors in normal, mild cognitive impairment (MCI), and demented individuals. We evaluated mean Montreal Cognitive Assessment (MoCA) scores, hippocampal volumes (HV), and CSF phosphorylated tau:amyloid-β ratios at first visit using analysis of variance with age as a covariate. In longitudinal evaluations, we assessed changes in MoCA scores and HV in insomnia and nighttime behaviors between the first and last visits. RESULTS: Prevalence of insomnia was 14%, 16%, and 11% for normal, MCI, and dementia groups. Prevalence of nighttime behaviors was 14%, 21%, and 29% respectively. Insomnia patients had higher MoCA scores, larger HV, and lower pTauBeta than individuals without insomnia, indicating less neurodegeneration. In contrast, nighttime behaviors were associated with worse cognition, smaller HV, and higher pTauBeta. Similar findings were seen between longitudinal associations of sleep disorders and cognition and HV. CONCLUSION: Our findings suggest that insomnia is unreliably recognized in patients with cognitive impairment. Nighttime behaviors may better indicate the presence of sleep disturbances and have diagnostic specificity in AD over insomnia.
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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".