A systematic review with a Burden of Proof meta-analysis of health effects of long-term ambient fine particulate matter (PM2.5) exposure on dementia
Bibliographic record
Abstract
Abstract Previous studies have indicated increased dementia risk associated with fine particulate matter (PM2.5) exposure; however, the findings are inconsistent. In this systematic review, we assessed the association between long-term PM2.5 exposure and dementia outcomes using the Burden of Proof meta-analytic framework, which relaxes log-linear assumptions to better characterize relative risk functions and quantify unexplained between-study heterogeneity (PROSPERO, ID CRD42023421869). Here we report a meta-analysis of 28 longitudinal cohort studies published up to June 2023 that investigated long-term PM2.5 exposure and dementia outcomes. We derived risk–outcome scores (ROSs), highly conservative measures of effect size and evidence strength, mapped onto a 1–5-star rating from ‘weak and/or inconsistent evidence’ to ‘very strong and/or consistent evidence’. We identified a significant nonlinear relationship between PM2.5 exposure and dementia, with a minimum 14% increased risk averaged across PM2.5 levels between 4.5 and 26.9 µg m−3 (the 15th to 85th percentile exposure range across included studies), relative to a reference of 2.0 µg m−3 (n = 49, ROS = 0.13, two stars). We found a significant association of PM2.5 with Alzheimer’s disease (n = 12, ROS = 0.32, three stars) but not with vascular dementia. Our findings highlight the potential impact of air pollution on brain aging.
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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 | 0.037 | 0.134 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".