Air pollution, lung function and mortality: survival and mediation analyses in UK Biobank
Bibliographic record
Abstract
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 the UK Biobank cohort (∼200,000 individuals) with 8-year follow-up to mortality and incident CVD. Exposures to PM 10 , PM 2.5 , and NO 2 at baseline were assessed by land-use regression modelling. Lung function [forced expiratory volume in 1 second (FEV 1 ), Forced Vital Capacity (FVC)] was measured by spirometry and transformed to Global Lung Function Initiative 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. Point estimates for the mortality outcomes were larger in ever-smokers than never-smokers (all-cause mortality Hazard Ratio (HR) per decrease FEV 1 GLI z-score, ever smokers: 1.29 [95%CI:1.24-1.34], versus never-smokers: HR 1.16 [95%CI:1.12-1.21]). 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 . The estimated mediated proportions for air pollution—incident CVD were 9% [95%CI: 4%-13%] for PM 2.5 and 16% [95%CI: 6%-25%] for NO 2 . Conclusions Lower FEV 1 may mediate some associations between air pollution on mortality and CVD outcomes, with more pronounced effect sizes for NO 2 than for PM 2.5 . Take-home message Adverse effects of air pollution on lower lung function (FEV 1 ) potentially mediate 10-30% of the effects of PM 2.5 or NO 2 on mortality and incident cardiovascular disease.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".