A cohort study of the multi-pollutant effects of PM2.5, NO2, and O3 on C-reactive protein levels during pregnancy
Bibliographic record
Abstract
Background and Aim Individual air pollutants and green space are associated with inflammation in pregnancy, but their combined effects are not well understood. Our objective was to study the multipollutant effects of PM2.5, NO2, and O3 on C-reactive protein (CRP) levels in maternal blood (a marker of inflammation implicated in adverse pregnancy outcomes). Methods We analyzed data collected from 1,170 pregnant Canadian women enrolled in the MIREC Study. Maternal blood concentrations of CRP were measured in the third trimester. Residentially-based ambient concentrations of PM2.5, NO2, and O3 during the 14 days prior to blood draw were estimated using satellite-derived concentrations and land use regression models. Green space was measured using the Normalized Difference Vegetative Index (NDVI). We fit multipollutant linear regression models using 14-day average estimates of PM2.5, NO2, and O3. We also evaluated the effects of pollutant mixtures using Weighted Quantile Sum Regression (WQSR), and by calculating the Air Quality Health Index (AQHI), a Canadian risk communication tool that derives a weighted average of the pollutants. Results In multipollutant models that included NO2 and O3, each interquartile range (IQR) increase in 14-day average PM2.5 (IQR: 6.9 µg/m3) was associated with 27.1% (95% CI: 6.2, 50.7) higher CRP concentrations. In air pollution mixture models, each IQR increase in AQHI was associated with 37.7% (95% CI: 13.9, 66.5) higher CRP levels; and an IQR increase in the WQSR was associated with 78.6% (95% CI: 29.7, 146.0) higher CRP levels. Associations between air pollution and CRP were not confounded or modified by NDVI. Conclusions We provide evidence for stronger effects of the combined mixture of PM2.5, NO2, and O3 on inflammation levels during pregnancy compared with individual pollutants. This research emphasizes the importance of examining multipollutant analyses in future investigations. Keywords PM2.5, NO2, O3, green space, C-reactive protein, pregnancy
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".