Exposure to prenatal maternal stress is associated with epigenetic age acceleration and altered cell composition in the placenta: The QF2011 Queensland Flood Study
Bibliographic record
Abstract
BACKGROUND: Exposure to prenatal maternal stress (PNMS) in utero has been associated with several adverse perinatal outcomes, such as pre-term birth and perturbed cognitive development. As the interface between the fetal and maternal compartments during pregnancy, the placenta has been postulated to play a role in this process. We hypothesized that placental DNA methylation (DNAme) may be altered in association with natural disaster-mediated PNMS. METHODS: Pooled placental samples from the Queensland Flood Study, or QF2011, cohort (n = 105) were processed for assessment of DNAme using the Illumina Infinium MethylationEPIC BeadChip array. RESULTS: Overall, we did not find significant associations between placental DNAme and several stress measurements using linear modelling (FDR<0.05 and Δβ>0.03). While we found that XX placentas had slightly higher predicted cytotrophoblast to syncytiotrophoblast cell ratios than XY placentas (p = 0.01), this difference in cell ratio was not associated with PNMS exposure. However, we did observe associations between placental epigenetic age acceleration and all three types of PNMS investigated (objective hardship (QFOSS) (p = 0.03), subjective distress (COSMOSS) (p = 0.03), and maternal cognitive appraisal (CONSEQ) (p = 0.0005) scores). CONCLUSION: The lack of large global impacts of PNMS on placental DNAme possibly indicates that the placenta can buffer moderate levels of maternal stress during pregnancy. It remains unclear what the impact of increased placental epigenetic age acceleration is on fetal development or perinatal outcomes and will require further investigation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".