Air pollution, lung function and mortality: survival and mediation analyses in UK Biobank
Bibliographic record
Abstract
Background Air pollution is associated with lower lung function, and both are associated with premature mortality and cardiovascular disease (CVD). Evidence remains scarce on the potential mediating effect of impaired lung function on the association between air pollution and mortality or CVD. Methods We used data from UK Biobank (n∼200 000 individuals) with 8-year follow-up to mortality and incident CVD. Exposures to particulate matter <10 µm (PM 10 ), particulate matter <2.5 µm (PM 2.5 ) and nitrogen dioxide (NO 2 ) were assessed by land-use regression modelling. Lung function (forced expiratory volume in 1 s (FEV 1 ), forced vital capacity (FVC) and the FEV 1 /FVC ratio) was measured between 2006 and 2010 and transformed to Global Lung Function Initiative (GLI) z-scores. Adjusted Cox proportional hazards and causal proportional hazards mediation analysis models were fitted, stratified by smoking status. Results Lower FEV 1 and FVC were associated with all-cause and CVD mortality, and incident CVD, with larger estimates in ever- than never-smokers (all-cause mortality hazard ratio per FEV 1 GLI z-score decrease 1.29 (95% CI 1.24–1.34) for ever-smokers and 1.16 (95% CI 1.12–1.21) for never-smokers). Long-term exposure to PM 2.5 or NO 2 was associated with incident CVD, with similar effect sizes for ever- and never-smokers. Mediated proportions of the air pollution–all-cause mortality estimates driven by FEV 1 were 18% (95% CI 2–33%) for PM 2.5 and 27% (95% CI 3–51%) for NO 2 . Corresponding mediated proportions for incident CVD were 9% (95% CI 4–13%) for PM 2.5 and 16% (95% CI 6–25%) for NO 2 . Conclusions Lung function may mediate a modest proportion of associations between air pollution and mortality and CVD outcomes. Results likely reflect the extent of either shared mechanisms or direct effects relating to lower lung function caused by air pollution.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".