Epigenome-wide association study of placental co-methylated regions in newborns for prenatal opioid exposure
Bibliographic record
Abstract
Abstract The increasing incidence of opioid use during pregnancy has led to a rise in the number of infants exposed to opioids in utero. Prenatal opioid exposure may have consequences for health and (neuro)development, including neonatal opioid withdrawal syndrome (NOWS). It is unknown which infants are at greatest risk for NOWS. DNA methylation (DNAm) is an epigenetic mark reflecting both allelic variation and environmental exposures, which may provide biomarkers for prenatal opioid exposure and infant NOWS. The placenta is an accessible, biologically relevant tissue in which to directly investigate the epigenetic effects of prenatal opioid exposure. Therefore, the aims of this study were to examine whether prenatal opioid exposure is associated with differential DNAm, including epigenetic age acceleration (EAA) in the placenta. We performed an epigenome-wide association study based on co-methylated regions and single CpG sites in placental samples from in utero opioid-exposed (n = 19) and nonexposed infants (n = 143), correcting for potential confounders. We did not identify statistically significant differential DNAm profiles, but the strongest associations were found for cg06621211; cg18688392 (ZMIZ1, adjusted P = .068) and cg04460738 (KCNMA1, adjusted P = .068), although effect sizes were very small. One of these DNAm patterns (cg06621211) was in part under control of genetic variants through methylation quantitative trait loci. The involved single nucleotide polymorphism did not show significant associations in recent genome-wide association studies for phenotypes related to substance use, and the finding was not driven by potential co-occurring substance use based on sensitivity analyses. There was also no association between placental EAA and in utero opioid exposure. In conclusion, placental DNAm showed limited associations with in utero opioid exposure and NOWS diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".