Characterization of maternal plasma biomarkers associated with delivery of small and large for gestational age infants in the MIREC study cohort
Bibliographic record
Abstract
OBJECTIVE: Neonatal morbidity and mortality can be influenced by maternal health status. Information on maternal and fetal biomarkers of adverse health outcomes is limited. This work aims at identifying maternal biomarkers associated with low and high birth weight for gestational age groups. DESIGN AND SETTINGS: Population-based prospective cohort study of the potential adverse health effects of exposure to environmental contaminants on pregnancy and infant health. METHODS: Third trimester maternal plasma samples (n = 1588) from a pregnancy cohort (Maternal-Infant Research on Environmental Chemicals Study, MIREC) were analyzed for changes in a target spectrum of biomarkers of vascular health (e.g., matrix metalloproteinases MMPs, vascular endothelial cell growth factor VEGF), inflammation (e.g. cellular adhesion molecules CAMs, cytokines, chemokines) by affinity-based multiplex protein array analyses. Multivariate logistic regression analyses were done to examine associations between target plasma biomarkers, maternal-infant characteristics, and birth weight outcomes assessed as small for gestational age (SGA) ≤10th percentile and large for gestational age (LGA) ≥90th percentile groups. RESULTS AND OUTCOMES: Our results revealed that maternal plasma biomarkers monocyte chemoattractant protein-1 MCP-1 (p<0.05, +ve) and VEGF (p<0.05, -ve) along with parity = 1 (p<0.01, -ve) and gestational hypertension (p<0.05, +ve) were associated with SGA births. Meanwhile, LGA was associated with maternal plasma VEGF (p<0.05, +ve) and MMP-9 (p<0.05, -ve) and gestational hypertension (p<0.01, +ve), pre-pregnancy body mass index (p<0.01, +ve), parity (p<0.05, +ve) and education (p<0.05, -ve). CONCLUSIONS: Third trimester maternal plasma biomarkers in combination with maternal health and socioeconomic characteristics can be useful in predicting SGA and LGA outcomes. Maternal vascular health and inflammatory status may contribute to both SGA and LGA births through distinct molecular mechanisms.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".