Outdoor air pollution, road traffic noise, and allostatic load in children aged 6–11 years: evidence from six European cohorts
Bibliographic record
Abstract
Abstract Emerging evidence suggests that exposure to air pollutants and road traffic noise triggers stress responses, which mediate physiological responses in multiple organs and tissues. However, epidemiological study in children is sparse. We aimed to evaluate whether outdoor air pollution and road traffic noise are associated with physiological response measured by allostatic load in children. We studied 919 children aged 6–11 years from the HELIX (Human Early Life Exposome) cohort in 6 European countries with 19 biomarkers assessed in four physiological systems—cardiovascular, metabolic, immune/inflammation, and neuroendocrine systems. We then calculated both count-based and continuous scores for each physiological system and generated allostatic load scores (range 0–19). Exposure to air pollution (NO2, PM2.5, PM10, PM2.5absorbance) and road traffic noise (LDEN) based on participants’ home, school, and commuting route addresses were estimated for the year prior to outcome assessment. Higher exposure to all air pollutants was associated with a higher allostatic load, although only the association of PM10 survived correction for multiple testing (for count-based allostatic load score: RR = 1.27, 95%CI: 1.08, 1.48; for continuous allostatic load score: β = 0.56, 95% CI: 0.27–0.84, per each 10 µg/m3 increase in PM10). Examining physiological systems separately, higher exposure to air pollution was mainly associated with higher allostatic load in the immune/inflammatory and metabolic systems. No associations between road traffic noise and allostatic load were observed. Our findings suggest that air pollution act as a chronic stressor in manifesting multi-systemic physiological dysregulation in childhood, which may be a precursor of air pollution-related diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".