Leptin Gene Epigenetic Adaptation to Impaired Glucose Metabolism During Pregnancy
Bibliographic record
Abstract
OBJECTIVE: To verify whether the leptin gene epigenetic (DNA methylation) profile is altered in the offspring of mothers with gestational impaired glucose tolerance (IGT). RESEARCH DESIGN AND METHODS: Placental tissues and maternal and cord blood samples were obtained from 48 women at term including 23 subjects with gestational IGT. Leptin DNA methylation, gene expression levels, and circulating concentration were measured using the Sequenom EpiTYPER system, quantitative real-time RT-PCR, and enzyme-linked immunosorbent assay, respectively. IGT was assessed after a 75-g oral glucose tolerance test (OGTT) at 24-28 weeks of gestation. RESULTS: We have shown that placental leptin gene DNA methylation levels were correlated with glucose levels (2-h post-OGTT) in women with IGT (fetal side: ρ=-0.44, P≤0.05; maternal side: ρ=0.53, P≤0.01) and with decreased leptin gene expression (n=48; ρ≥-0.30, P≤0.05) in the whole cohort. Placental leptin mRNA levels accounted for 16% of the variance in maternal circulating leptin concentration (P<0.05). CONCLUSIONS: IGT during pregnancy was associated with leptin gene DNA methylation adaptations with potential functional impacts. These epigenetic changes provide novel mechanisms that could contribute to explaining the detrimental health effects associated with fetal programming, such as long-term increased risk of developing obesity and type 2 diabetes.
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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".