The Effects of Outdoor Air Pollution on Inflammation and Preeclampsia
Bibliographic record
Abstract
Background: Outdoor air pollution is a ubiquitous and deleterious environmental exposure, and its relationship with several chronic diseases and acute health outcomes is well established. Outdoor air pollution is also associated with pregnancy complications such as preeclampsia. PM2.5, NO2, and O3 are some of the most commonly studied air pollutants, and can influence important biological processes in pregnancy, such as inflammation. Methods: This thesis explored different aspects of the relationship between air pollution and inflammation, and air pollution and preeclampsia. In Component 1, effects between PM2.5 and NO2 with four inflammation biomarkers (C-reactive protein, interleukin-6, interleukin-8, tumour necrosis factor-α) during pregnancy were assessed. In Component 2, the multipollutant effects of PM2.5, NO2, and O3 as a mixture on C-reactive protein were assessed, with consideration of surrounding green space as a confounder or effect modifier. In Components 3 and 4, a dose-response meta-analysis and burden of disease analysis were conducted, respectively. The Maternal Infant Research on Environmental Chemicals birth cohort was used for nested analyses for Components 1 and 2. Component 3 used publicly available data from previously published studies, and Component 4 used ecologic measures of PM2.5. Statistical methods included linear regression, restricted cubic splines, and mixture methods in Components 1 and 2, and dose-response meta-analyses, and burden of disease analyses in Components 3 and 4. Results: In Components 1 and 2 a consistent relationship between PM2.5 and C-reactive protein, an important inflammation biomarker was observed. In Component 2 specifically, the relationship between a mixture of PM2.5, NO2, and O3 was stronger then when compared with individual pollutant effects. In the meta-analysis for a relationship between PM2.5 and preeclampsia, the summary RR estimate was 1.07 (95% CI: 0.99-1.15) per 10μg/m3 average increase in PM2.5, which was lower than previously published estimates. In burden of disease calculations, it was estimated that PM2.5 is responsible for 4.8% of preeclampsia cases in Canada. Discussion: This thesis adds to the literature on the relationship between air pollution and preeclampsia by elucidating on a biological mechanism through which PM2.5 might increase risk of preeclampsia, and providing summary burden of disease estimates for the relationship between PM2.5 and preeclampsia. The work highlights the importance of studying environmental exposures in pregnancy, and contributes to the understanding of inflammation as a mechanism through which PM2.5 might impact preeclampsia incidence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".