A Narrative Review on the Effect of Valproic Acid on the Placenta
Bibliographic record
Abstract
BACKGROUND: Valproic acid (VPA) is an antiepileptic and mood-stabilizing drug with well-established teratogenic risks when taken during pregnancy. While its harmful effects on fetal development are well known, less attention has been given to its impact on placental development and function, despite the placenta's critical role in pregnancy. AIM: This narrative review examines how VPA exposure affects placental growth, morphology, nutrient transport, and epigenetic modifications. It also considers whether placental dysfunction may contribute VPA's teratogenic effects. RESULTS: Evidence suggests that VPA disrupts placental structure and growth, alters the expression of nutrient transporters, such as those for folate, glucose, and amino acids, and modifies the placental epigenome, including globally decreased DNA methylation and increased histone acetylation. DISCUSSION: It is hypothesized that these epigenetic changes may influence chromatin remodelling and trophoblast gene expression, though this connection has not been fully established. Such epigenetic dysregulation may result in aberrant gene expression that underlies the structural and functional impairments observed in the placenta, potentially compromising its ability to support fetal development and contributing to VPA's teratogenic effects. Findings across studies, however, are inconsistent, varying with dose, timing of exposure, and model system. Furthermore, there is a lack of research examining sex-specific differences in placental responses to VPA, despite evidence that male and female placentas exhibit distinct growth patterns, gene expression profiles, and susceptibilities to environmental insults. CONCLUSION: Addressing these knowledge gaps through targeted research will improve our understanding of how VPA affects the placenta and its role in teratogenesis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".