Sleep Quality as a Modifier of Plasma pTau217 and GFAP Associations with Cognitive Function
Bibliographic record
Abstract
Abstract Background Plasma biomarkers, such as neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated tau (pTau217), and total tau (tTau), are associated with cognitive decline. However, the role of sleep quality in modifying these associations remains unclear. This study examines whether subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index (PSQI), modifies the associations between plasma biomarkers and cognitive performance. Methods We analyzed cross-sectional data from 491 adults aged 36 years or older in the Aging Adult Brain Connectome study. Plasma levels of NfL, GFAP, pTau217, and total tTau were measured. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA) and the Preclinical Alzheimer’s Cognitive Composite (PACC). Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Generalized linear models were used to test main and interaction effects while adjusting for demographics. Sensitivity analyses included APOE ε4 status and body mass index. Results Higher plasma levels of NfL, GFAP, and pT217 were associated with lower cognitive performance on both MoCA and PACC (all P < 0.05). Poorer sleep quality was independently associated with worse PACC outcomes. Critically, significant interaction effects were observed: PSQI moderated the negative associations between GFAP and both PACC (β = 0.0003, P = 0.039) and MoCA (β = 0.0019, P = 0.021), and between pT217 and MoCA (β = 0.0299, P = 0.004), indicating a synergistic relationship between sleep quality and glial/ amyloid-related pathology in cognitive aging. Conclusion Sleep quality modifies biomarker-cognition associations, highlighting its potential as a behavioral target to support brain health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".