Association of a History of Sleep Disorder With Risk of Mild Cognitive Impairment and Alzheimer’s Disease Dementia
Bibliographic record
Abstract
OBJECTIVE: We explored whether a history of sleep disorder affected a current diagnosis of cognitive impairment and clinical conversion in a non-demented elderly population. METHODS: Comprehensive clinical data collected as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) was analyzed. A history of sleep disorder was recorded in the recent ADNI medical database. Standard clinical and neuropsychological tests were performed both at baseline and follow-up visit. Multiple logistic regression analysis was performed after adjusting for age, sex, education, apolipoprotein E ε4 status, vascular risk score, body mass index, Geriatric Depression Scale score, and use of sleeping pills. RESULTS: A total of 391 cognitively normal individuals, 303 with early mild cognitive impairment (MCI) and 364 with late MCI were included. Sleep disorder history was significantly associated with an increased risk of MCI but not with clinical conversion. A history of insomnia or obstructive sleep apnea (OSA) significantly increased the risk of MCI, but only an OSA history predicted progression to Alzheimer's disease (AD) dementia. CONCLUSION: Our findings suggest that a sleep disorder history usefully aids early detection of cognitive impairment and emphasize that such sleep disorder, particularly OSA, is important as potential target for AD prevention.
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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 | 0.000 |
| 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".