Association of prenatal exposure to gestational diabetes with offspring body composition and regional body fat distribution
Bibliographic record
Abstract
Summary The aim of this cohort study was to compare body composition and regional body fat distribution between children exposed (GDM+) or unexposed (GDM−) in utero to gestational diabetes mellitus (GDM) and to investigate the association with the glycaemic and the insulin profile. Data from 56 GDM+ and 30 GDM− were analysed. Height, weight and waist circumference were measured. Total and regional body composition was measured by dual‐energy X‐ray absorptiometry. Insulin, glucose and HbA1c were obtained from a fasting plasma sample, and the HOMA‐IR index was calculated. anova was performed to compare adiposity measures between GDM+ and GDM−. Associations between the glycaemic and insulin profile and adiposity measures were studied using partial Pearson correlations. Mean age was 6.6 ± 2.3 years. Waist circumference, fat mass percentage, android fat mass, android fat mass percentage and android‐to‐gynoid fat mass ratio were higher among GDM+, and lean mass percentage was lower (P < 0.05). Among GDM+ children, body mass index (BMI) z score, waist circumference, fat mass percentage, android fat mass percentage and android‐to‐gynoid fat mass ratio were all positively correlated with HbA1C (r = 0.32–0.43, P < 0.05). Prenatal exposure to GDM is associated with increased total and abdominal adiposity. This increased adiposity observed among GDM+ children is associated with an altered glycaemic profile. This study is registered in the Clinical Trials.gov registry (NCT01340924).
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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) | 0.002 | 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".