Obstructive Sleep Apnea and Cognitive Decline: A Review of Potential Vulnerability and Protective Factors
Bibliographic record
Abstract
Around 40% of dementia risk is attributable to modifiable risk factors such as physical inactivity, hypertension, diabetes and obesity. Recently, sleep disorders, including obstructive sleep apnea (OSA), have also been considered among these factors. However, despite several epidemiological studies investigating the link between OSA and cognitive decline, there is still no consensus on whether OSA increases the risk of dementia or not. Part of the heterogeneity observed in previous studies might be related to some individual characteristics that modulate the association between OSA and cognitive decline. In this narrative review, we present these individual characteristics, namely, age, sex, menopause, obesity, diabetes mellitus, hypertension, cardiovascular diseases, smoking, excessive alcohol consumption, depression, air pollution, Apolipoprotein E ε4 allele, physical activity, and cognitive reserve. To date, large cohort studies of OSA and cognitive decline tended to statistically control for the effects of these variables, but whether they interact with OSA to predict cognitive decline remains to be elucidated. Being able to better predict who is at risk of cognitive decline when they have OSA would improve clinical management and treatment decisions, particularly when patients present relatively mild OSA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".