Alzheimer’s disease-like cortical atrophy mediates the effect of air pollution on global cognitive function
Bibliographic record
Abstract
Little is known about the effect of air pollution on Alzheimer’s disease (AD)-specific brain structural pathologies. There is also a lack of evidence on whether this effect leads to poorer cognitive function. We investigated whether, and the extent to which, AD-like cortical atrophy mediated the association between air pollution exposures and cognitive function in dementia-free adults. We used cross-sectional data from 640 participants who underwent brain magnetic resonance imaging and the Montreal Cognitive Assessment (MoCA). Mean cortical thickness (as the measure of global cortical atrophy) and machine learning-based AD-like cortical atrophy score were estimated from brain images. Concentrations of particulate matter with diameters ≤ 10 μm (PM10) and ≤ 2.5 μm (PM2.5) and nitrogen dioxide (NO2) were estimated based on each participant’s residential address. Following the product method, a mediation effect was tested by conducting a series of three regression analyses (exposure to outcome; exposure to mediator; and exposure and mediator to outcome). A 10 μg/m3 increase in PM10 (β = -1.13; 95 % CI, −1.73 to −0.53) and a 10 ppb increase in NO2 (β = -1.09; 95 % CI, −1.40 to −0.78) were significantly associated with a lower MoCA score. PM10 (β = 0.27; 95 % CI, 0.06 to 0.48) and NO2 (β = 0.35; 95 % CI, 0.25 to 0.45) were significantly associated with an increased AD-like cortical atrophy score. Effects of PM10 and NO2 on MoCA scores were significantly mediated by mean cortical thickness (proportions mediated: 25 %–28 %) and AD-like cortical atrophy scores (13 %–16 %). The findings suggest that air pollution exposures may induce AD-like cortical atrophy, and that this effect may lead to poorer cognitive function in dementia-free adults.
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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 | 0.000 | 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) | 0.009 | 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".