IGF2 DNA methylation is a modulator of newborn’s fetal growth and development
Bibliographic record
Abstract
The insulin-like growth factor 2 (IGF2) gene, located within a cluster of imprinted genes on chromosome 11p15, encodes a fetal and placental growth factor affecting birth weight. DNA methylation variability at the IGF2 gene locus has been previously reported but its consequences on fetal growth and development are still mostly unknown in normal pediatric population. We collected one hundred placenta biopsies from 50 women with corresponding maternal and cord blood samples and measured anthropometric indices, blood pressure and metabolic phenotypes using standardized procedures. IGF2/H19 DNA methylation and IGF2 circulating levels were assessed using sodium bisulfite pyrosequencing and ELISA, respectively. Placental IGF2 (DMR0 and DMR2) DNA methylation levels were correlated with newborn's fetal growth indices, such as weight, and with maternal IGF2 circulating concentration at the third trimester of pregnancy, whereas H19 (DMR) DNA methylation levels were correlated with IGF2 levels in cord blood. The maternal genotype of a known IGF2/H19 polymorphism (rs2107425) was associated with birth weight. Taken together, we showed that IGF2/H19 epigenotype and genotypes independently account for 31% of the newborn's weight variance. No association was observed with maternal diabetic status, glucose concentrations or prenatal maternal body mass index. This is the first study showing that DNA methylation at the IGF2/H19 genes locus may act as a modulator of IGF2 newborn's fetal growth and development within normal range. IGF2/H19 DNA methylation could represent a cornerstone in linking birth weight and fetal metabolic programming of late onset obesity.
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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".