Differential Methylation Of Inflammatory And Insulinotropic Genes After Metabolic Surgery In Women
Bibliographic record
Abstract
Context Biliopancreatic diversion with duodenal switch (BPD-DS), a metabolic bariatric operation, induces durable loss of excess weight and reduced cardiometabolic risk. Altered epigenetic marks are mechanistically associated with environment-driven phenotypic variations. Objective The current study aimed to compare gene methylation levels before and after BPD-DS to identify epigenetic marks potentially linked to metabolic improvements induced by BPD-DS. Design and patients Metabolic risk factors and gene methylation levels of 20 women studied mean 12 years (range 4-22) after BPD-DS were compared to those of 20 severely obese surgical candidates as controls, matched for pre-surgical age, body mass index and dyslipidemia and hypertension prevalences. Whole-genome blood DNA methylation analysis enabled between-group differential methylation analyses. We calculated correlations between methylation levels of the most differentially methylated CpG sites and plasma glucose and insulin levels and HOMA-IR. Results Differential methylation analysis identified 15,343 genes demonstrating at least one differentially methylated CpG site (p<1.43x10-7). Diabetic and inflammation/immune functions were among the most overrepresented from the 200 genes exhibiting the largest group differences in methylation levels. CpG sites methylation levels of genes related to insulin action correlated significantly with fasting insulin levels and homeostatic model of insulin resistance (p≤0.002 for all). Conclusion These findings suggest that differential methylation levels in obese controls versus treated women may partially explain the durable metabolic improvements after BPD-DS.
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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 | 0.000 |
| 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".