Mendelian randomization supports causality between maternal hyperglycemia and epigenetic regulation of leptin gene in newborns
Bibliographic record
Abstract
Leptin is an adipokine that acts in the central nervous system and regulates energy balance. Animal models and human observational studies have suggested that leptin surge in the perinatal period has a critical role in programming long-term risk of obesity. In utero exposure to maternal hyperglycemia has been associated with increased risk of obesity later in life. Epigenetic mechanisms are suspected to be involved in fetal programming of long term metabolic diseases. We investigated whether DNA methylation levels near LEP locus mediate the relation between maternal glycemia and neonatal leptin levels using the 2-step epigenetic Mendelian randomization approach. We used data and samples from up to 485 mother-child dyads from Gen3G, a large prospective population-based cohort. First, we built a genetic risk score to capture maternal glycemia based on 10 known glycemic genetic variants (GRS10) and showed it was an adequate instrumental variable (β = 0.046 mmol/L of maternal fasting glucose per additional risk allele; SE = 0.007; P = 7.8 × 10(-11); N = 467). A higher GRS10 was associated with lower methylation levels at cg12083122 located near LEP (β = -0.072 unit per additional risk allele; SE = 0.04; P = 0.05; N = 166). Direction and effect size of association between the instrumental variable GRS10 and methylation at cg12083122 were consistent with the negative association we observed using measured maternal glycemia. Lower DNA methylation levels at cg12083122 were associated with higher cord blood leptin levels (β = -0.17 log of cord blood leptin per unit; SE = 0.07; P = 0.01; N = 170). Our study supports that maternal glycemia is part of causal pathways influencing offspring leptin epigenetic regulation.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".