Buying time: a rationale for examining the use of circadian rhythm and sleep interventions to delay progression of mild cognitive impairment to Alzheimer’s disease
Bibliographic record
Abstract
As of 2010, the worldwide economic impact of dementia was estimated at $604 billion USD; and without discovery of a cure or effective interventions to delay disease progression, dementia's annual global economic impact is expected to surpass $1 trillion USD as early as 2030. Alzheimer's disease (AD) is the leading cause of dementia accounting for over 75% of all cases. Toxic accumulation of amyloid beta (Aβ), either by overproduction or some clearance failure, is thought to be an underlying mechanism of the neuronal cell death characteristic of AD-though this amyloid hypothesis has been increasingly challenged in recent years. A compelling alternative hypothesis points to chronic neuroinflammation as a common root in late-life degenerative diseases including AD. Apolipoprotein-E (APOE) genotype is the strongest genetic risk factor for AD: APOE-ε4 is proinflammatory and individuals with this genotype accumulate more Aβ, are at high risk of developing AD, and almost half of all AD patients have at least one ε4 allele. Recent studies suggest a bidirectional relationship exists between sleep and AD pathology. Sleep may play an important role in Aβ clearance, and getting good quality sleep vs. poor quality sleep might reduce the AD risk associated with neuroinflammation and the ε4 allele. Taken together, these findings are particularly important given the sleep disruptions commonly associated with AD and the increased burden disrupted sleep poses for AD caregivers. The current review aims to: (1) identify individuals at high risk for dementia who may benefit most from sleep interventions; (2) explore the role poor sleep quality plays in exacerbating AD type dementia; (3) examine the science of sleep interventions to date; and (4) provide a road map in pursuit of comprehensive sleep interventions, specifically targeted to promote cognitive function and delay progression of dementia.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".