White matter integrity changes in mild cognitive impairment associated with Aspirin use
Bibliographic record
Abstract
Objectives: Alzheimer's disease (AD) represents a significant public health challenge, particularly as its prevalence is projected to rise sharply. Aspirin, known for its anti-inflammatory and antiplatelet properties, has been hypothesized to affect AD progression, although findings from observational studies and clinical trials remain inconsistent.Methods: This study utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate the potential association between aspirin use and white matter (WM) microstructural changes in a cohort of 148 mild cognitive impairment (MCI) subjects. Diffusion tensor imaging (DTI) was employed to assess WM integrity, with fractional anisotropy (FA) and diffusivity metrics serving as primary outcomes. Statistical analyses were conducted using ANCOVA, adjusting for age, sex, APOE ε4 genotype, and MMSE score.Results: Aspirin users exhibited significantly higher FA values in the anterior corona radiata and left external capsule, alongside lower axial and radial diffusivity values in the right cingulum, indicating better-preserved WM microstructure compared to non-users.Conclusion: These findings suggest that aspirin may confer neuroprotective effects on WM in early AD, potentially delaying cognitive decline. Further research is warranted to confirm these results and explore the underlying mechanisms. Aspirin is widely prescribed to millions of adults, yet its impact on WM regions in the brain remains largely unclear. Further research is necessary to replicate these findings and to assess whether the effects of aspirin on WM structure could contribute to delaying or preventing cognitive decline.
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 | 0.002 |
| 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 | 0.000 | 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".