The fetal exposome and preterm birth: a systematic synthesis of environmental exposures and multi-omics evidence
Bibliographic record
Abstract
OBJECTIVES: Preterm birth (PTB), defined as delivery before 37 weeks of gestation, is a leading cause of neonatal mortality and long-term developmental impairment. Its complex etiology, spanning environmental, genetic, psychosocial, and socio-economic domains, limits effective prediction and prevention. We systematically synthesized evidence on how environmental exposures influence PTB risk through multi-omic disruptions within a fetal exposome framework. METHODS: A comprehensive literature search was conducted in major biomedical databases, following PRISMA guidelines. Ninety-five human studies published through May 2025 were included, encompassing exposures such as ambient air pollution, endocrine-disrupting chemicals, maternal stress, nutrition, occupational hazards, climate variability, and microbiome alterations. Two reviewers independently extracted data (exposure type, omics platform, biospecimen, PTB subtype) with inter-rater reliability assessment, and study quality was evaluated using the Newcastle-Ottawa Scale. Findings were narratively stratified by exposure category, study design, and spontaneous vs. indicated PTB. RESULTS: Environmental exposures were consistently associated with disruptions in oxidative stress, inflammation, immune regulation, hormonal signaling, placental aging, and microbial ecology, mediated by multi-omic signatures in maternal, placental, and fetal tissues. Candidate biomarkers show promise for early risk stratification but lack validation and population-level predictive performance due to heterogeneous exposure assessment and study design. CONCLUSIONS: Integrating fetal exposome concepts with multi-omics enhances mechanistic insight into PTB risk and may support biomarker discovery and precision-guided prenatal interventions. Clinical translation requires standardized exposure measurement, biomarker validation, and equity-focused implementation.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".