Metabolic surgery influences gene expression profiles in offspring through gene‐environment interactions (636.1)
Bibliographic record
Abstract
Gestational weight gain and obesity predispose offspring to lifelong obesity and its comorbidities. We demonstrated that offspring born after maternal gastrointestinal bypass surgery (AMS) were less obese and exhibited improved cardiometabolic risk profiles compared to siblings born before maternal surgery (BMS). Objective: To examine relationships between gestational obesity and offspring gene variations and expression levels. Methods: Whole‐genome genotyping and gene expression of 22 BMS and 23 AMS siblings from 19 mothers were conducted using Illumina HumanOmni‐5‐Quad and HumanHT‐12 v4 Expression BeadChips, respectively. We tested gene‐by‐maternal surgical status interactions on offspring gene expression levels in whole blood using PLINK. Altered biological pathways were identified and visualized by Ingenuity Pathway Analysis (IPA). Results: A total of 16,060 genes were expressed in siblings. Significant gene‐by‐maternal surgical status interactions (p 蠄 1.22x10 ‐12 ) were found for 525 transcripts. Eighteen pathways were enriched for these transcripts, including 12 related to cellular stress and signaling, immune response and inflammation, and growth, proliferation and development. Conclusion: This study implies that gestational metabolic obesity interacts with offspring’s gene variations to modulate gene expression levels. Grant Funding Source : Supported by the CIHR
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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) | 0.003 | 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".