Fine particular matter and its constituents in air pollution and gestational diabetes mellitus
Bibliographic record
Abstract
Ambient air pollution has been linked to the development of gestational diabetes mellitus (GDM). However, previous studies provided inconsistent findings and no study has examined the effects of complex chemical constituents of the particular matter on GDM, especially in developing countries. Therefore, we aim to investigate the associations of exposure to PM2.5 (particular matter ≤ 2.5 μm) and its constituents with GDM, and to identify susceptible exposure window in a large survey in China. The China Labor and Delivery Survey was a cross-sectional investigation conducted in 24 provinces in China between 2015 and 2016. A random sample of all deliveries in each participating hospital was selected and detailed obstetric and newborn information was extracted from medical records. Average concentrations of PM2.5 and six constituents (organic matter, black carbon, sulfate, nitrate, ammonium and soil dust) were estimated (1 km × 1 km) using a combined geoscience-statistical model. GDM was diagnosed based on an oral glucose tolerance test (OGTT) between 24 to 28 weeks of gestation and according to IADPSG criteria. Generalized linear mixed models were used to adjust for potential confounders. A total of 54,517 subjects from 55 hospitals were included. The incidence of GDM was 10.8%. An interquartile range (IQR) increase in PM2.5 exposure in the 2nd trimester of pregnancy was associated with an increased GDM risk in the single pollutant model, [adjusted odds ratio (aOR) = 1.11 and 95% confidence interval (CI): 1.01–1.22]. Exposure to organic matter (aOR = 1.14; 95%CI: 1.05–1.23), black carbon (aOR = 1.15; 95%CI: 1.07–1.25) and nitrate (aOR = 1.13; 95%CI: 1.02–1.24) during 2nd trimester were associated with increased risks of GDM. Associations between constituents and GDM were robust after controlling for total PM2.5 mass and accounting for multi-collinearity. Exposure to PM2.5 in 2nd trimester of pregnancy was associated with an increased risk of GDM. Organic matter, black carbon and nitrate may be the main culprits for the association.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".