DNA methylation changes in cord blood and the developmental origins of health and disease – a systematic review and replication study
Bibliographic record
Abstract
BACKGROUND: Environmental exposures in utero which modify DNA methylation may have a long-lasting impact on health and disease in offspring. We aimed to identify and replicate previously published genomic loci where DNA methylation changes are attributable to in utero exposures in the NutriGen birth cohort studies Alliance. METHODS: We reviewed the literature to identify differentially methylated sites of newborn DNA which are associated with the following five traits of interest maternal diabetes, pre-pregnancy body mass index (BMI), diet during pregnancy, smoking, and gestational age. We then attempted to replicate these published associations in the Canadian Healthy Infant Longitudinal Development (CHILD) and the South Asian birth cohort (START) cord blood epigenome-wide data. RESULTS: We screened 68 full-text articles and identified a total of 17 cord blood epigenome-wide association studies (EWAS) of the traits of interest. Out of the 290 CpG sites reported, 19 were identified in more than one study; all of them associated with maternal smoking. In CHILD and START EWAS, thousands of sites associated with gestational age were identified and maintained significance after correction for multiple testing. In CHILD, there was differential methylation observed for 8 of the published maternal smoking sites. No other traits tested (i.e., folate levels, gestational diabetes, birthweight) replicated in the CHILD or START cohorts. CONCLUSIONS: Maternal smoking during pregnancy and gestational age are strongly associated with differential methylation in offspring cord blood, as assessed in the EWAS literature and our birth cohorts. There are a limited number of reported methylation sites associated in more than two independent studies related to pregnancy. Additional large studies of diverse populations with fine phenotyping are needed to produce robust epigenome-wide data in order to further elucidate the effect of intrauterine exposures on the infants' methylome.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".