Impact of maternal cardiometabolic status after bariatric surgery on the association between telomere length and adiposity in offspring
Bibliographic record
Abstract
The impact of bariatric surgery on metabolic and inflammatory status are reflected in the epigenetic profile and telomere length mediated by the changes in the metabolic status of the patients. This study compared the telomere length of children born before versus after maternal bariatric surgery as a surrogate to test the influence of the mother's metabolic status on children's telomere length. DNA methylation telomere length (DNAmTL) was estimated from Methylation-EPIC BeadChip array data from a total of 24 children born before and after maternal bariatric surgery in the greater Quebec City area. DNAmTL was inversely associated with chronological age in children (r = - 0.80, p < 0.001) and significant differences were observed on age-adjusted DNAmTL between children born before versus after the maternal bariatric surgery. The associations found between body mass index and body fat percentage with DNAmTL in children born after the surgery were influenced by maternal triglycerides, TG/HDL-C ratio and TyG index. This study reports the impact of maternal bariatric surgery on offspring telomere length. The influence of maternal metabolic status on the association between telomere length and markers of adiposity in children suggests a putative modulating effect of bariatric surgery on the cardiometabolic risk in offspring.
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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".