Placental lipoprotein lipase DNA methylation levels are associated with gestational diabetes mellitus and maternal and cord blood lipid profiles
Bibliographic record
Abstract
Placental lipoprotein lipase (LPL) is crucial for placental lipid transfer. Impaired LPL gene expression and activity were reported in pregnancies complicated by gestational diabetes mellitus (GDM) and intra-uterine growth restriction. We hypothesized that placental LPL DNA methylation is altered by maternal metabolic status and could contribute to fetal programming. The objective of this study was thus to assess whether placental LPL DNA methylation is associated with GDM and both maternal and newborn lipid profiles. Placenta biopsies were sampled at delivery from 126 women including 27 women with GDM diagnosed following a post 75 g-oral glucose tolerance test (OGTT) between weeks 24 and 28 of gestation. Placental LPL DNA methylation and expression levels were determined using bisulfite pyrosequencing and quantitative real-time PCR, respectively. DNA methylation levels within LPL proximal promoter region (CpG1) and intron 1 CpG island (CpGs 2 and 3) were lower in placenta of women with GDM. DNA methylation levels at LPL-CpG1 and CpG3 were also negatively correlated with maternal glucose (2-h post OGTT; r=-0.22; P=0.02) and HDL-cholesterol levels (third trimester of pregnancy; r=-0.20; p=0.03), respectively. Moreover, we report correlation between LPL-CpG2 DNA methylation and cord blood lipid profile. DNA methylation levels within intron 1 CpG island explained up to 26% (r⩽-0.51; P<0.001) of placental LPL mRNA expression variance. Overall, we showed that maternal metabolic profile is associated with placental LPL DNA methylation dysregulation. Our results suggest that site-specific LPL epipolymorphisms in the placenta are possibly functional and could potentially be involved in determining the future metabolic health of the newborn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".