Does ambient air pollution influence biochemical markers of liver injury? Findings of a cross-sectional population-based survey
Bibliographic record
Abstract
There is limited evidence supporting an adverse effect of ambient air pollution on the liver. To test the association between exposure to residential air pollution and serum biochemical indicators of liver injury. We used a nationally representative sample of 32,989 participants aged 3–79 years old who participated in the Canadian Health Measures Survey between 2007 and 2019. Cross-sectional associations were assessed by generalized linear mixed models incorporating survey-specific sampling weights. The joint effect of an interquartile range (IQR) increase in nitrogen dioxide (NO2), ozone (O3) and fine particulate matter (PM2.5) was positively and significantly associated with all measures of liver injury adjusting for age, sex, education, income, smoking, alcohol consumption, body mass index (BMI), total cholesterol, diabetes, hypertension, and physical activity. The ranking of effect sizes from largest to smallest percent increases were 8.72% (95% confidence interval [CI] 7.56, 9.88) for alanine aminotransferase (ALT), 5.54% (95%CI 3.31, 7.77) for gamma-glutamyl transferase (GGT), 4.81% (95%CI 3.87, 5.74) for aspartate aminotransferase (AST), 2.46% (95%CI 0.26, 4.65) for total bilirubin (TBIL) and 1.18% (95%CI 0.62, 1.75) for alkaline phosphatase (ALP). Findings were not significantly different when stratified by age (≤16, >16 yr), sex, smoking (current, other), cholesterol (≤6.18, >6.18 mmol/l) and BMI (<30, ≥30 kg/m2). These findings suggest that ambient air pollution may have a relatively small impact on the liver, but these changes may have significant impact from a population health perspective, considering the ubiquitous nature of air pollution, or for individuals exposed to very high levels of 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".