Associations between sleep quality, plasma neurofilament light, and cognition in older adults without dementia
Bibliographic record
Abstract
The relationship between sleep quality, neurofilament light chain (NFL), and cognitive impairment, including the potential effect of plasma NFL in this association, remains unclear. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, we excluded individuals with dementia or a history of sleep-related medication use at baseline, including 640 participants with complete sleep assessments and covariates. Sleep quality was assessed using the Neuropsychiatric Inventory sleep subscale, which includes ratings of frequency, severity, and their product, with higher scores indicating poorer sleep quality. Baseline and follow-up demographics, sleep indices, plasma NFL levels, and cognition scores (including Mini-Mental State Examination [MMSE], Montreal Cognitive Assessment [MoCA], Alzheimer's Disease Assessment Scale-Cognitive Subscale [ADAS13], Clinical Dementia Rating Scale-Sum of Boxes [CDRSB], Executive Function [EF], Language [LAN], and Memory [MEM]) were also collected. Multivariable linear regression examined the associations between baseline sleep quality, plasma NFL, and cognition, as well as the relationship between sleep quality and longitudinal cognitive decline, calculated using linear mixed-effects models. Mediation analysis evaluated the role of plasma NFL in the sleep-cognition association. Multiple testing significance was corrected using false discovery rate, with results presented as Q-values. Poor sleep quality scores were associated with elevated plasma NFL levels (β: 0.055 to 2.645, P < 0.05), poorer cognition (ADAS13, CDRSB, EF, LAN, MEM; β: -0.188 to 1.279, Q < 0.05), and accelerated longitudinal cognitive decline (MoCA; β: -0.005, Q < 0.05) in both models, with sensitivity analyses supporting these findings. Furthermore, plasma NFL levels partially mediated the relationship between sleep quality and both baseline cognition (ADAS13, CDRSB, LAN, MEM; P < 0.05) and longitudinal cognitive decline (MoCA; P < 0.05), with mediation proportions ranging from 9.2% to 26.7%. Poorer sleep quality was associated with cognitive impairment and accelerated cognitive decline, suggesting its potential role in Alzheimer's disease. These associations may be partially mediated by neuroaxonal injury.
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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 | 0.002 |
| 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) | 0.001 | 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".