Ambient air pollution and inflammatory effects in a Canadian pregnancy cohort
Bibliographic record
Abstract
Background: Epidemiologic studies have consistently reported associations between air pollution and pregnancy outcomes including preeclampsia and gestational diabetes. However, the biologic mechanisms underlying these relationships remain unclear as few studies have collected relevant biomarker data. We examined relationships between ambient PM2.5 and NO2 with markers of inflammation during pregnancy in a prospective cohort of Canadian women. Methods: We analyzed data from 1170 women enrolled in the Maternal-Infant Research on Environmental Chemicals study. Daily residential PM2.5 and NO2 exposures during pregnancy were estimated using satellite-based and land-use regression models and used to create 14-day and 30-day exposure windows before blood-draw. Inflammatory markers C-reactive protein, interleukin-6, interleukin-8, and tumor necrosis factor-α were measured in third trimester plasma samples. Multivariable linear regression was used to estimate associations for an interquartile range (IQR) increase in PM2.5 and NO2 and markers of inflammation, while adjusting for individual-level confounders. Results: Fourteen-day (IQR: 6.85 µg/m3) and 30-day (IQR: 6.15 µg/m3) average PM2.5 exposures before blood-draw were positively associated with C-reactive protein after adjustment for covariates (24.6% [95% CI = 9.4, 41.9] and 17.4% [95% CI = 1.0, 35.0] increases, respectively). This association was found to be robust in several sensitivity analyses. Neither PM2.5 nor NO2 exposures were associated with interleukin-6, interleukin-8, or tumor necrosis factor-α. Conclusion: Exposure to ambient PM2.5 is positively associated with maternal inflammatory pathways in late pregnancy. This may contribute to positive associations between ambient PM2.5 and risk of adverse pregnancy outcomes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".