Investigating the relationship between sleep disturbances and white matter hyperintensities in healthy older adults, MCI, and AD
Bibliographic record
Abstract
Abstract Background Recent studies have shown sleep disturbances occur in people with Alzheimer’s disease (AD), with sleep being impacted before the onset of clinical symptoms. White matter hyperintensities (WMHs) are cerebrovascular disease‐related pathological changes that develop with increased age and are also associated with AD. While studies have shown that sleep disturbance can have profound negative effects on brain vasculature, its impact on WMH remains relatively unexplored. Our objective was to examine the relationship between sleep disturbances and WMHs in older adults. This study examined sleep disturbance and regional WMH differences between normal controls (NCs), people with mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Method We included participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). History of insomnia was extracted from the ADNI database along with the Neuropsychiatric Inventory Sleep (NPI sleep) Questionnaire used to assess sleep disturbance. Participants were included if they had at least two WMH measurements and completed the NPI. A total of 1782 participants with 8771 follow‐ups met the inclusion criteria. Linear mixed‐effects models examined group differences in sleep disturbance and insomnia and the association between WMH burden and sleep in NC, MCI, and AD. Result People with more sleep disturbance were found to have a greater WMH burden across all regions except temporal (p < .05). People with AD and MCI had more sleep disturbance than NC across all regions (p < .0001). Additionally, people with insomnia were found to have greater WMH burden across all regions (p < .001). Conclusion These results suggest WMH burden and sleep disturbance increases from aging to AD. These findings indicate that people with MCI and AD with worse sleep have a higher WMH burden. In older adults, insomnia and sleep disturbances are strongly associated with increased WMH accumulation over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".