Prenatal antidepressant exposure associated with<i>CYP2E1</i>DNA methylation change in neonates
Bibliographic record
Abstract
Some but not all neonates are affected by prenatal exposure to serotonin reuptake inhibitor antidepressants (SRI) and maternal mood disturbances. Distinguishing the impact of these 2 exposures is challenging and raises critical questions about whether pharmacological, genetic, or epigenetic factors can explain the spectrum of reported outcomes. Using unbiased DNA methylation array measurements followed by a detailed candidate gene approach, we examined whether prenatal SRI exposure was associated with neonatal DNA methylation changes and whether such changes were associated with differences in birth outcomes. Prenatal SRI exposure was first associated with increased DNA methylation status primarily at CYP2E1(β(Non-exposed) = 0.06, β(SRI-exposed) = 0.30, FDR = 0); however, this finding could not be distinguished from the potential impact of prenatal maternal depressed mood. Then, using pyrosequencing of CYP2E1 regulatory regions in an expanded cohort, higher DNA methylation status--both the mean across 16 CpG sites (P < 0.01) and at each specific CpG site (P < 0.05)--was associated with exposure to lower 3rd trimester maternal depressed mood symptoms only in the SRI-exposed neonates, indicating a maternal mood x SRI exposure interaction. In addition, higher DNA methylation levels at CpG2 (P = 0.04), CpG9 (P = 0.04) and CpG10 (P = 0.02), in the interrogated CYP2E1 region, were associated with increased birth weight independently of prenatal maternal mood, SRI drug exposure, or gestational age at birth. Prenatal SRI antidepressant exposure and maternal depressed mood were associated with altered neonatal CYP2E1 DNA methylation status, which, in turn, appeared to be associated with birth weight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".