Epigenome-wide meta-analysis of prenatal vitamin D insufficiency and cord blood DNA methylation
Bibliographic record
Abstract
Low maternal vitamin D concentrations during pregnancy have been associated with a range of offspring health outcomes. DNA methylation is one mechanism by which the maternal vitamin D status during pregnancy could impact offspring’s health in later life. We aimed to evaluate whether maternal vitamin D insufficiency during pregnancy was conditionally associated with DNA methylation in the offspring cord blood. Maternal vitamin D insufficiency (plasma 25-hydroxy vitamin D ≤ 75 nmol/L) during pregnancy and offspring cord blood DNA methylation, assessed using Illumina Infinium 450k or Illumina EPIC Beadchip, was collected for 3738 mother–child pairs in 7 cohorts as part of the Pregnancy and Childhood Epigenetics (PACE) consortium. Associations between maternal vitamin D and offspring DNA methylation, adjusted for fetal sex, maternal smoking, maternal age, maternal pre-pregnancy or early pregnancy BMI, maternal education, gestational age at measurement of 25(OH)D, parity, and cell type composition, were estimated using robust linear regression in each cohort, and a fixed-effects meta-analysis was conducted. The prevalence of vitamin D insufficiency ranged from 44.3% to 78.5% across cohorts. Across 364,678 CpG sites, none were associated with maternal vitamin D insufficiency at an epigenome-wide significant level after correcting for multiple testing using Bonferroni correction or a less conservative Benjamini–Hochberg False Discovery Rate approach (FDR, p > 0.05). In this epigenome-wide association study, we did not find convincing evidence of a conditional association of vitamin D insufficiency with offspring DNA methylation at any measured CpG site.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.035 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".