Effect of excess weight and insulin resistance on DNA methylation in prepubertal children
Bibliographic record
Abstract
Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play a role in the development of insulin resistance. This study evaluates how the BMI z-score (BMIz) and the homeostatic model assessment of insulin resistance (HOMA-IR), alone or in combination, relate to clinical outcomes and DNA methylation patterns in prepubertal children. DNA methylation in peripheral blood mononuclear cells (PBMCs) and clinical outcomes were measured in a cohort of 41 prepubertal children. Children with higher HOMA-IR had higher blood pressure and plasma lactate levels while children with higher BMIz had higher triglycerides levels. Moreover, the DNA methylation analysis demonstrated that a 1 unit increase in the BMIz was associated with a 0.41 (95% CI: 0.29, 0.53) increase in methylation of a CpG near the PPP6R2 gene. This gene is important in the regulation of NF-kB expression. However, there was no strong evidence that the BMIz and the HOMA-IR were synergistically related to any clinical or DNA methylation outcomes. In summary, the results suggest that obesity and insulin resistance may impact metabolic health both independently in prepubertal children. In addition, obesity also has an impact on the DNA methylation of the PPP6R2 gene. This may be a novel underlying starting point for the systemic inflammation associated with obesity and insulin resistance, in this population.
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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 | 0.001 | 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".