1244-P: Impact of Maternal Fasting Glucose in Pregnancy on Excess Weight at Preschool Age in the Offspring
Bibliographic record
Abstract
Aim: To examine the association between elevated maternal fasting plasma glucose (FPG) during pregnancy and obesity in the offspring. Methods: For women without pre-existing diabetes in 2 zones in Alberta, Canada with births between 2005 - 2013, maternal data were linked to offspring’s birth registry and preschool immunization records (w/height, weight) between 2009-2017. A 50-g glucose challenge test (GCT) followed by a 75-g OGTT was used to diagnose GDM. Pregnancies were grouped as follows: 1) GCT negative; 2) OGTT negative; 3) elevated FPG on the OGTT; and 4) elevated 1-hour and/or 2-hour OGTT only. WHO criteria were used to identify children who were overweight, obese, or extremely obese. Results: Of 79,156 pregnancies, 80.3% were GCT negative, 14.7% were OGTT negative, 1.3% had elevated FPG, and 3.7% had elevated post-load glucose only. Both LGA and pre-school obesity rates were highest in pregnancies with abnormal FPG (Figure). Relative to children of GCT negative pregnancies, children of pregnancies with elevated maternal FPG had adjusted odds ratio (aOR, 95% CI) 3.0, (2.6-3.6) for LGA; and aOR 1.3 (1.1 - 1.6) for overweight, aOR 2.4 (1.9 - 3.0) for obesity, aOR 3.5 (2.7 - 4.7) for extreme obesity at preschool age. Conclusion: The effect of elevated maternal FPG in pregnancy on offspring weight extends to early childhood. These children may be candidates for early pediatric weight and health interventions. Disclosure P. Kaul: None. A. Savu: None. L.E. Moore: None. R.O. Yeung: Research Support; Self; AstraZeneca, Novo Nordisk Inc. Speaker’s Bureau; Self; Merck & Co., Inc. E.A. Ryan: None. Funding Canadian Institutes of Health Research; University Hospital Foundation
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".