Air pollution, lung function and COPD: results from the population-based UK Biobank study
Bibliographic record
Abstract
Ambient air pollution increases the risk of respiratory mortality, but evidence for impacts on lung function and chronic obstructive pulmonary disease (COPD) is less well established. The aim was to evaluate whether ambient air pollution is associated with lung function and COPD, and explore potential vulnerability factors. We used UK Biobank data on 303 887 individuals aged 40–69 years, with complete covariate data and valid lung function measures. Cross-sectional analyses examined associations of land use regression-based estimates of particulate matter (particles with a 50% cut-off aerodynamic diameter of 2.5 and 10 µm: PM 2.5 and PM 10 , respectively; and coarse particles with diameter between 2.5 μm and 10 μm: PM coarse ) and nitrogen dioxide (NO 2 ) concentrations with forced expiratory volume in 1 s (FEV 1 ), forced vital capacity (FVC), the FEV 1 /FVC ratio and COPD (FEV 1 /FVC <lower limit of normal). Effect modification was investigated for sex, age, obesity, smoking status, household income, asthma status and occupations previously linked to COPD. Higher exposures to each pollutant were significantly associated with lower lung function. A 5 µg·m −3 increase in PM 2.5 concentration was associated with lower FEV 1 (−83.13 mL, 95% CI −92.50– −73.75 mL) and FVC (−62.62 mL, 95% CI −73.91– −51.32 mL). COPD prevalence was associated with higher concentrations of PM 2.5 (OR 1.52, 95% CI 1.42–1.62, per 5 µg·m −3 ), PM 10 (OR 1.08, 95% CI 1.00–1.16, per 5 µg·m −3 ) and NO 2 (OR 1.12, 95% CI 1.10–1.14, per 10 µg·m −3 ), but not with PM coarse . Stronger lung function associations were seen for males, individuals from lower income households, and “at-risk” occupations, and higher COPD associations were seen for obese, lower income, and non-asthmatic participants. Ambient air pollution was associated with lower lung function and increased COPD prevalence in this large study.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".