Prenatal maternal distress and immune cell epigenetic profiles at 3‐months of age
Bibliographic record
Abstract
BACKGROUND: Prenatal maternal distress predicts altered offspring immune outcomes, potentially via altered epigenetics. The role of different kinds of prenatal maternal distress on DNA methylation profiles is not understood. METHODS: A sample of 117 women (APrON cohort) were followed from pregnancy to the postpartum period. Maternal distress (depressive symptoms, pregnancy-specific anxiety, stressful life events) were assessed mid-pregnancy, late-pregnancy, and 3-months postpartum. DNA methylation profiles were obtained from 3-month-old blood samples. Principal component analysis identified two epigenetic components, characterized as Immune Signaling and DNA Transcription through gene network analysis. Covariates were maternal demographics, pre-pregnancy body mass index, child sex, birth gestational age, and postpartum maternal distress. Penalized regression (LASSO) models were used. RESULTS: Late-pregnancy stressful life events, b = 0.006, early-pregnancy depressive symptoms, b = 0.027, late-pregnancy depressive symptoms, b = 0.014, and pregnancy-specific anxiety during late pregnancy, b = -0.631, were predictive of the Immune Signaling component, suggesting that these aspects of maternal distress could affect methylation in offspring immune signaling pathways. Only early-pregnancy depressive symptoms was predictive of the DNA Transcription component, b = -0.0004, suggesting that this aspect of maternal distress is implicated in methylation of offspring DNA transcription pathways. CONCLUSIONS: Exposure timing and kind of prenatal maternal distress could matter in the prediction of infant immune epigenetic profiles.
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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 | 0.000 | 0.000 |
| 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 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".