Sleep Patterns and Prospective Diffusion Weighted Imaging Biomarkers: the Sleep and Dementia Consortium (SDC)
Bibliographic record
Abstract
Abstract Background Adequate sleep is necessary to maintain brain health, with sleep disturbances associated with higher Alzheimer’s disease (AD) risk. Diffusion‐weighted imaging (DWI) metrics are increasingly recognized as useful neuroimaging biomarkers to detect AD‐related white matter degeneration. We assessed the relationship between sleep patterns and prospective DWI metrics in the Sleep and Dementia Consortium (SDC). The SDC studies associations between polysomnography (PSG)‐derived sleep with dementia risk and cognitive and MRI endophenotypes. Method The SDC includes five community‐based cohorts, two of which have DWI acquisitions: The Framingham Heart Study (FHS) and the Atherosclerosis Risk in Communities study (ARIC). Dementia‐free participants who underwent both PSG and DWI‐MRI were selected, including 354 FHS participants (56.6±7.1y, 57%W), and 184 ARIC participants (61.6±5.0y, 52%W). The MRI session was approximately 15 years after the PSG on average (FHS:17.0±1.3y; ARIC:15.8±0.8y). Fractional anisotropy (FA) and mean diffusivity (MD) were considered for both cohorts, in addition to free‐water (FW) in the FHS. Sleep metrics were harmonized centrally, distributed for cohort‐specific linear regressions, and study‐level estimates were pooled in random effects meta‐analyses. Analyses were adjusted for demographics, obesity, time between PSG and MRI, antidepressants and sedative medication usage. An interaction term by APOE4 allele carrier status in regression models was used to test its moderating effect. Result In the FHS, sleep fragmentation was associated with DWI measures in the expected direction: Higher Wake After Sleep Onset and lower Sleep Maintenance Efficiency were associated with lower FA (β±SE = ‐0.17±0.09,p = 0.04; β±SE = 0.23±0.10,p = 0.03), higher MD (β±SE = 0.15±0.07,p = 0.04; β±SE = ‐0.20±0.09,p = 0.03), and higher FW (β±SE = 0.17±0.07,p = 0.02; β±SE = ‐0.22±0.08,p = 0.008). Meta‐analysis of FHS and ARIC revealed significant pooled effects between lower Sleep Maintenance Efficiency and lower FA (β±SE = 0.17±0.08,p = 0.04). In the FHS, APOE4 allele significantly moderated the association between REM sleep proportion with FA and MD, where lower REM sleep percentage was associated with higher MD (β±SE = ‐4.89±1.95,p = 0.02) and lower FA (β±SE = 4.93±2.26,p = 0.03) in APOE4 carriers only. Conclusion In the SDC, sleep fragmentation was associated with MRI markers of poorer white matter integrity 15 years later. Less REM sleep was associated with poorer white matter integrity in APOE4 allele carriers only, suggesting that disrupted sleep architecture may contribute and interact with neurodegenerative processes to affect brain integrity.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".