The effect of outdoor air pollution on Alzheimer’s disease: a systematic review
Bibliographic record
Abstract
Abstract Air pollution is one of the largest global environmental risks to public health. Over recent years, neuroimaging techniques have started to uncover the detrimental impact air pollution has had on brain health, including the development of Alzheimer’s Diseases (AD). We systematically reviewed the literature to evaluate the effects of long-term exposure (months to years) to outdoor air pollutants on the development of AD-like neurology. This review was registered on PROSPERO (CRD42023482979) and followed PRISMA guidelines. Four large databases (MEDLINE, Embase, Scopus, and CINAHL) were searched in November 2023 using terms associated with air pollution, neuroimaging, and AD. Only peer-reviewed primary research articles using neuroimaging data to examine AD-like pathology after long-term exposure to air pollutants (Particulate Matter (PM2.5, PM10), SO2, NO2, O3, and/or CO) were included. Titles, abstracts and full-texts were screened, and included articles were quality assessed using the Newcastle Ottawa Scale. A narrative synthesis was conducted to analyse the studies. Our search yielded 397 results, of which eight articles met our inclusion criteria. Articles focused on changes to white matter (n = 5), cortical thickness (n = 6), and grey matter (n = 2). Exposure to PM2.5 was commonly associated with white matter reductions, and PM10 and NO2 exposure was associated with reduced cortical thickness. The effect of exposure to different outdoor pollution on grey matter was inconclusive, with both increases and decreases in grey matter volume observed. This review highlighted how PM2.5, PM10 and NO2 exposure was associated with neurological changes commonly seen in AD. These findings can be used by policymakers and researchers to identify specific pollutants that need greater restrictions and regulations to improve population health. Key messages • PM2.5, PM10 and NO2 exposure are associated with neurological changes commonly seen in AD. • Greater restrictions and regulations are needed on specific pollutants to improve population health.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".