Adaptations of placental and cord blood<i>ABCA1 </i>DNA methylation profile to maternal metabolic status
Bibliographic record
Abstract
In utero environmental perturbations have been associated with epigenetic changes in the offspring and a lifelong susceptibility to cardiovascular diseases (CVD). DNA methylation at the ATP-binding cassette transporter A1 (ABCA1) gene was previously associated with CVD, but whether these epigenetic marks respond to changes in the maternal environment is unknown. This study was undertaken to assess the associations between the maternal metabolic profile and ABCA1 DNA methylation levels in placenta and cord blood. Placenta and cord blood samples were obtained at delivery from 100 women including 26 with impaired glucose tolerance (IGT) diagnosed following a 75 g-oral glucose tolerance test (OGTT) between week 24 and 28 of gestation. ABCA1 DNA methylation and mRNA levels were measured using bisulfite pyrosequencing and quantitative real-time PCR, respectively. We report that ABCA1 DNA methylation levels on the maternal side of the placenta are correlated with maternal high density lipoprotein cholesterol (HDL-C) levels (r<-0.21; P<0.04) and glucose levels 2 h post-OGTT (r = 0.25; P = 0.02). On the fetal side of the placenta, ABCA1 DNA methylation levels are associated with cord blood triglyceride levels (r = -0.28; P = 0.01). ABCA1 DNA methylation variability on both sides of the placenta are also associated with ABCA1 mRNA levels (r<-0.35; P = 0.05). As opposed to placenta, cord blood DNA methylation levels are negatively correlated with maternal glucose 2 h post-OGTT (r = -0.26; P = 0.02). In conclusion, the epivariations observed in placenta and cord blood likely contribute to an optimal materno-fetal cholesterol transfer. These in utero epigenetics adaptations may also potentially trigger the long-term susceptibility of the newborn to dyslipidemia and CVD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".