Trial reportEnvironmental health perspectives2024
Development and Validation of a Novel Placental DNA Methylation Biomarker of Maternal Smoking during Pregnancy in the ECHO Program.
Trial report in Environmental health perspectives, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Paediatric DNA methylation profile scores: a systematic review and open-source atlas.EBioMedicine · 2026Pooled it
- The Consequences of Prenatal Tobacco Exposure: A Systematic Review of Birth Outcomes and Developmental Trajectories.Cureus · 2026Review
- Exploring the epigenetic mechanisms of recurrent pregnancy loss: from genetic predisposition to environmental influences.Journal of assisted reproduction and genetics · 2025Review
- Immediate and durable effects of maternal tobacco consumption on placental DNA methylation: a replication and discovery study.Environmental epigenetics · 2025Article
- Epigenetic Biomarker: Improving Estimates of Fetal Exposure to Cigarette Smoke.Environmental health perspectives · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundMaternal cigarette smoking during pregnancy (MSDP) is associated with numerous adverse health outcomes in infants and children with potential lifelong consequences. Negative effects of MSDP on placental DNA methylation (DNAm), placental structure, and function are well established.
objectiveOur aim was to develop biomarkers of MSDP using DNAm measured in placentas (
methodsWe compared the ability of four machine learning methods [logistic least absolute shrinkage and selection operator (LASSO) regression, logistic elastic net regression, random forest, and gradient boosting machine] to classify MSDP based on placental DNAm signatures. We developed separate models using the complete EPIC array dataset and on the subset of probes also found on the 450K array so that models exist for both platforms. For comparison, we developed a model using CpGs previously associated with MSDP in placenta. For each final model, we used model coefficients and normalized beta values to calculate placental smoking index (PSI) scores for each sample. Final models were validated in two external datasets: the Extremely Low Gestational Age Newborn observational study,
resultsLogistic LASSO regression demonstrated the highest performance in cross-validation testing with the lowest number of input CpGs. Accuracy was greatest in external datasets when using models developed for the same platform. PSI scores in smokers only ( DISCUSSION: To our knowledge, we have developed the first placental DNAm-based biomarkers of MSDP with broad utility to studies of prenatal disease origins. https://doi.org/10.1289/EHP13838.
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Registered trials
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