Association between maternal depression during pregnancy and newborn DNA methylation
Bibliographic record
Abstract
Around 15-65% of women globally experience depression during pregnancy, prevalence being particularly high in low- and middle-income countries. Prenatal depression has been associated with adverse birth and child development outcomes. DNA methylation (DNAm) may aid in understanding this association. In this project, we analyzed associations between prenatal depression and DNAm from cord blood from participants of the South African Drakenstein Child Health Study. We examined DNAm in an epigenome-wide association study (EWAS) of 248 mother-child pairs. DNAm was measured using the Infinium MethylationEPIC (N = 145) and the Infinium HumanMethylation450 (N = 103) arrays. Prenatal depression scores, obtained with the Edinburgh Postnatal Depression Scale (EPDS) and the Beck Depression Inventory-II (BDI-II), were analyzed as continuous and dichotomized variables. We used linear robust models to estimate associations between depression and newborn DNAm, adjusted for measured (smoking status, household income, sex, preterm birth, cell type proportions, and genetic principal components) and unmeasured confounding using Cate and Bacon algorithms. Bonferroni correction was used to adjust for multiple testing. DMRcate and dmrff were used to test for differentially methylated regions (DMRs). Differential DNAm was significantly associated with BDI-II variables, in cg16473797 (Δ beta = -1.10E-02, p = 6.87E-08), cg23262030 (Δ beta per BDI-II total IQR = 1.47E-03, p = 1.18E-07), and cg04859497 (Δ beta = -6.42E-02, p = 1.06E-09). Five DMRs were associated with at least two depression variables. Further studies are needed to replicate these findings and investigate their biological impact.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".