Actigraphy sleep patterns in presymptomatic Alzheimer’s Disease: the potential role of neuroinflammation
Bibliographic record
Abstract
Abstract Background Both short and long sleep duration have been associated with increased risk of developing Alzheimer’s Disease (AD). While short sleep has been hypothesized to be a risk factor promoting neurodegenerative processes. Long sleep has been viewed as a marker of ongoing neurodegeneration, potentially as a result of neuroinflammation. The objective of the study is to evaluate sleep patterns measured with actigraphy close to expected symptom onset in association with neuroinflammatory biomarkers. We leveraged the PREVENT‐AD cohort in which participants with a parental history of sporadic AD are studied before clinical onset. Method 203 participants (age: 68.5±5.4 years, 151 women) free of dementia were tested with a week of actigraphy. Nighttime sleep duration, time in bed, sleep latency, sleep efficiency, and number of sleep bouts were calculated using the Actiware scoring algorithm (medium threshold). The standard deviation of these characteristics was calculated across actigraphy days to assess day‐to‐day variability. Years to expected onset was calculated by the number of years remaining before the age of clinical onset of a parent, or the earliest age of clinical onset if both parents developed AD. A subset of 100 participants underwent a lumbar puncture, and IL‐6 and MCP‐1 levels were assessed using OLINK technology in cerebrospinal fluid (CSF). The association between sleep characteristics, years to expected onset and inflammatory markers was assessed using linear regressions adjusted for age, sex, and time interval between measurements. Result Being closer to expected onset was associated with longer nighttime sleep duration (b = ‐0.19, p = 0.015) and lower sleep bouts day‐to‐day variability (b = 0.17, p = 0.027). Higher CSF IL‐6 was associated with longer sleep duration and time in bed (b = 0.30, p = 0.006; b = 0.24, p = 0.028), whereas higher MCP‐1 was associated with longer nighttime sleep duration (b = 0.25, p = 0.019). Higher CSF MCP‐1 levels were associated with lower sleep bouts day‐to‐day variability and shorter sleep onset latency only in those within 10 years of expected onset (interaction p = 0.012 and 0.061; b = ‐0.31, p = 0.043; b = ‐0.31, p = 0.047). Conclusion Proximity to parental onset of sporadic AD onset was associated with a longer sleep profile that is more consolidated and stable, which may be due to proinflammatory processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".