Influences of Gestational Obesity on Associations between Genotypes and Gene Expression Levels in Offspring following Maternal Gastrointestinal Bypass Surgery for Obesity
Bibliographic record
Abstract
METHODS: Whole-genome genotyping and gene expression analyses in blood of 22 BMS and 23 AMS offspring from 19 mothers were conducted using Illumina HumanOmni-5-Quad and HumanHT-12 v4 Expression BeadChips, respectively. Using PLINK we analyzed interactions between offspring gene variations and maternal surgical status on offspring gene expression levels. Altered biological functions and pathways were identified and visualized using DAVID and Ingenuity Pathway Analysis. RESULTS: Significant interactions (p ≤ 1.22 x 10(-12)) were found for 525 among the 16,060 expressed transcripts: 1.9% of tested SNPs were involved. Gene function and pathway analysis demonstrated enrichment of transcription and of cellular metabolism functions and overrepresentation of cellular stress and signaling, immune response, inflammation, growth, proliferation and development pathways. CONCLUSION: We suggest that impaired maternal gestational metabolic fitness interacts with offspring gene variations modulating gene expression levels, providing potential mechanisms explaining improved cardiometabolic risk profiles of AMS offspring related to ameliorated maternal lipid and carbohydrate metabolism.
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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".