Prenatal Smoking Exposures and Epigenome-Wide Methylation in Newborn Blood
Bibliographic record
Abstract
BACKGROUND: Maternal sustained smoking during pregnancy is associated with thousands of differentially methylated CpGs in newborns, but impacts of other prenatal tobacco smoking exposures remain unclear. OBJECTIVE: To identify differential DNA methylation in newborns from maternal sustained smoking and less studied prenatal smoking exposures (i.e., maternal exposure to secondhand smoke [SHS] exposure during pregnancy, maternal quitting before pregnancy, paternal smoking around conception, and paternal quitting before pregnancy). METHODS: We conducted a large meta-analysis of prenatal tobacco smoking exposures and epigenome-wide newborn blood DNA methylation through the Pregnancy And Childhood Epigenetics Consortium (PACE). Across 19 cohorts, 11,175 parent-newborn pairs contributed information on at least one prenatal smoking exposure, mostly from questionnaires. Maternal blood or urine cotinine measurements, available in a few studies, provided objective data for maternal SHS and smoking during pregnancy. Primary analyses used Illumina450 K methylation data; secondary analyses in 5 cohorts examined CpGs unique to the EPIC array. RESULTS: Maternal sustained smoking associated with differential DNA methylation (false discovery rate [FDR] < 0.05) at 8,862 CpGs on the 450 K ( n = 8,148) and did not differ by infant sex. We identified over 300 novel genes not previously identified in EWAS of smoking. No differential methylation was associated with maternal SHS, maternal former smoking, or paternal smoking around conception. However, cg24805739 ( MED13L ) was associated with former paternal former smoking. Forty-one novel genes were identified using maternal cotinine measurements compared to questionnaire. In EPIC unique analyses ( n = 3,415), differential methylation was observed with maternal sustained smoking (211 CpGs), maternal SHS (5 CpGs), and paternal former smoking (4 CpGs). Smoking-associated CpGs in blood were strongly enriched for functional elements across multiple tissues. CONCLUSIONS: Maternal sustained smoking has the largest impact on newborn DNA methylation, suggesting a strong influence of the intrauterine environment. We observed minimal impacts for less studied exposures including SHS, maternal former smoking, and paternal smoking.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".