Maternal adversities during pregnancy and cord blood oxytocin receptor (<i>OXTR</i>) DNA methylation
Bibliographic record
Abstract
The aim of this study was to investigate whether maternal adversities and cortisol levels during pregnancy predict cord blood DNA methylation of the oxytocin receptor (OXTR). We collected cord blood of 39 babies born to mothers participating in a cross-sectional study (N = 100) conducted in Basel, Switzerland (2007-10). Mothers completed the Inventory of Life Events (second trimester: T2), the Edinburgh Postnatal Depression Scale (EPDS, third trimester: T3), the Trier Inventory of Chronic Stress (TICS-K, 1-3 weeks postpartum) and provided saliva samples (T2, T3) for maternal cortisol profiles, as computed by the area under the curve with respect to ground (AUCg) or increase (AUCi) for the cortisol awakening response (CAR) and for diurnal cortisol profiles (DAY). OXTR DNA methylation was quantified using Sequenom EpiTYPER. The number of stressful life events (P = 0.032), EPDS score (P = 0.007) and cortisol AUCgs at T2 (CAR: P = 0.020; DAY: P = 0.024) were negatively associated with OXTR DNA methylation. Our findings suggest that distinct prenatal adversities predict decreased DNA methylation in a gene that is relevant for childbirth, maternal behavior and wellbeing of mother and offspring. If a reduced OXTR methylation increases OXTR expression, our findings could suggest an epigenetic adaptation to an adverse early environment.
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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 | 0.000 | 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".