Periods of susceptibility for associations between phthalate exposure and preterm birth: Results from a pooled analysis of 16 US cohorts
Bibliographic record
Abstract
BACKGROUND: Phthalate exposure during pregnancy has been associated with preterm birth, but mechanisms of action may depend on the timing of exposure. OBJECTIVE: Investigate critical periods of susceptibility during pregnancy for associations between urinary phthalate metabolite concentrations and preterm birth. METHODS: Individual-level data were pooled from 16 US cohorts (N = 6045, n = 539 preterm births). We examined trimester-averaged urinary phthalate metabolite concentrations. Most phthalate metabolites had 2248, 3703, and 3172 observations in the first, second, and third trimesters, respectively. Our primary analysis used logistic regression models with generalized estimating equations (GEE) under a multiple informant approach to estimate trimester-specific odds ratios (ORs) of preterm birth and significant (p < 0.20) heterogeneity in effect estimates by trimester. Adjusted models included interactions between each covariate and trimester. RESULTS: Differences in trimester-specific associations between phthalate metabolites and preterm birth were most evident for di-2-ethylhexyl phthalate (DEHP) metabolites. For example, an interquartile range increase in mono (2-ethylhexyl) phthalate (MEHP) during the first and second trimesters was associated with ORs of 1.15 (95 % confidence interval [CI]: 0.99, 1.33) and 1.11 (95 % CI: 0.97, 1.28) for preterm birth, respectively, but this association was null in the third trimester (OR = 0.91 [95 % CI: 0.76, 1.09]) (p-heterogeneity = 0.03). CONCLUSION: The association of preterm birth with gestational biomarkers of DEHP exposure, but not other phthalate metabolites, differed by the timing of exposure. First and second trimester exposures demonstrated the greatest associations. Our study also highlights methodological considerations for critical periods of susceptibility analyses in pooled studies.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.017 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".