Association Between Single Nucleotide Polymorphisms in the Aquaporin‐4 Gene and Longitudinal Changes in White Matter Free Water and Cognitive Function in Non‐Demented Older Adults
Bibliographic record
Abstract
We investigated whether aquaporin-4 (AQP4) single-nucleotide polymorphisms (SNPs) influence Alzheimer's disease (AD) progression through changes in the glymphatic system. We included 242 non-dementia participants and chose six SNPs previously shown to be related to AD. We analyzed the associations between AQP4 SNPs and glymphatic markers, including enlarged perivascular spaces (PVS), white matter free water (FW), and diffusion tensor image analysis along the perivascular space (DTI-ALPS), in both cross-sectional and longitudinal data. We investigated whether AQP4-related glymphatic markers are associated with AD pathology progression and cognitive impairment, and whether they mediate the relationship between AQP4 SNPs and AD progression. There was no association between AQP4 SNPs and glymphatic markers at baseline. Carriers of the AQP4 SNP rs72878794 minor allele status exhibited slower FW increase in the amyloid-positive group (SNP*time: β = -0.0040, t(46.25) = -2.062, p = 0.045, 95% CI = -0.0078 ~ -0.0001), whereas the rs9951307 minor allele carrier showed a faster FW increase in the amyloid-negative group (SNP*time: β =0.0033, t(81.19) = 2.245, p = 0.027, 95% CI = 0.0004 ~ 0.0062). The higher FW was associated with faster cognitive decline at follow-ups. AQP4 SNPs influence interstitial fluid accumulation, contributing to cognitive decline but not amyloid deposition in AD. Further studies are needed to clarify the pathways linking AQP4 SNPs and AD progression.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".