Cerebral amyloid deposition correlates with objectively measured sleep dysfunction
Bibliographic record
Abstract
Abstract Background Sleep and circadian problems are known to be common along the Alzheimer’s disease (AD) spectrum. Previous research correlated an increase in CSF Aβ with poor sleep quality through self‐reported questionnaires in cognitively unimpaired (CU) individuals. Research on AD patients however showed an increase in tau‐PET. Today, there is increasing evidence that sleep disturbances are an early marker for AD pathology and marker of future risk of cognitive impairment. Our main objective was to study the relationship between sleep efficiency as assessed with polysomnography and amyloid, tau and neuroinflammation. Method 28 individuals (21 CU and 7 mild cognitive impairment (MCI)) underwent a [18F]AZD4694 amyloid‐PET scan, [18F]MK6240 tau‐PET scan, [11C]PBR28 neuroinflammation‐PET scan, an MRI a full neuropsychological evaluation as well as a polysomnography. [18F]AZD4694, [18F]MK6240 and [11C]PBR28 standardized uptake value ratios (SUVRs) used the cerebellum grey matter as the reference region and were calculated between 40‐70 min, 90‐110 min and 0‐90 min post‐injection respectively. A voxel based regression model evaluated the relationship between the different pathophysiologies of AD, amyloid, tau and neuroinflammation, and sleep efficiency. The model’s covariates were age and diagnosis. Result We found a negative correlation between sleep efficiency as measured with a polysomnography and amyloid. The most impacted regions were the temporal poles, the inferior parietal cortex and subcortical regions such as the thalamus. However, we did not find a correlation with [18F]MK6240 and [11C]PBR28. When we corrected for apnea‐hypopnea index, sex and years of education, results were similar. Conclusion These preliminary results show that poor sleep quality correlates with amyloid deposition in the brain of CU and MCI individuals. The regions impacted have been related to sleep regulation, such as the thalamus. They are also known to be vulnerable to AD pathophysiology, for example, temporal and inferior parietal cortices. Our study adds to previous research by identifying anatomical correlates, and also using objective assessment of sleep quality. It also corroborates with the idea that sleep deprivation promote Aβ deposition.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".