Genetic Susceptibility and Physical Activity Modified the Long‐Term Effect of Propylene Oxide Exposure on Lung Function: A Repeated‐Measurement Cohort Study
Bibliographic record
Abstract
Background The gene–environment interaction between propylene oxide (PO) exposure and genetic susceptibility on lung function, as well as the potential influence of physical activity (PA) on lung function alteration related to PO exposure, remains unclear. Methods Among 2228 Chinese community residents, urinary N ‐acetyl‐S‐(2‐hydroxypropyl)‐L‐cysteine (2HPMA) (PO exposure biomarker) and lung function were repeatedly measured at baseline and 3‐ and 6‐year follow‐ups. Polygenetic risk score (PRS) was calculated based on lung function–associated single‐nucleotide polymorphisms. The interactions of 2HPMA with PRS and PA on lung function were assessed in cross‐sectional and longitudinal analyses. Results Urinary 2HPMA was cross‐sectionally ( β = −1.02 % ; 95% confidence interval [CI]: −1.83%, −0.22%) and longitudinally (−0.39%; −0.76%, −0.03%) associated with the ratio of forced expiration volume in 1 s to forced vital capacity (FEV 1 /FVC). These associations were significantly modified by PRS and PA ( p for interaction < 0.05), with more pronounced associations observed among participants with inactive PA or high PRS. After 6 years, participants with persistent high 2HPMA and high PRS had a 2.17% (95% CI: −4.05%, −0.30%) decline in FEV 1 /FVC, compared with those with low 2HPMA and low PRS ( p for interaction = 0.007); participants with persistent high 2HPMA and inactive PA had a 3.36% (95% CI: −5.96%, −0.76%) decline in FEV 1 /FVC when compared to those with active PA and low 2HPMA ( p for interaction = 0.003). Conclusions Exposure to PO in Chinese community residents was associated with lung function decline, which was exacerbated by genetic susceptibility while mitigated by PA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".