Maternal glucose in pregnancy is associated with child's adiposity and leptin at 5 years of age
Bibliographic record
Abstract
BACKGROUND: Exposure to maternal hyperglycaemia in pregnancy has been associated with childhood obesity. Leptin regulation might be involved in this 'adiposity programming' and may depend on timing of exposure. OBJECTIVES: To investigate associations of maternal glycaemia at different periods in pregnancy with childhood adiposity and leptin levels at 5 years of age. METHODS: In a prospective pre-birth cohort, we measured maternal glucose levels after a 50 g oral glucose challenge test at first trimester (9.8 ± 2.3 weeks) and during a 75 g oral glucose tolerance test at second trimester (26.5 ± 0.9 weeks). We followed up children at 5 years; we measured anthropometry and body composition using dual-energy X-ray absorptiometry (DXA). We measured fasting leptin levels using immunoassays (Luminex) in 328 children. We conducted linear regression analyses, adjusting for potential confounders. RESULTS: Maternal glycaemia at first trimester was associated with childhood leptin levels at 5 years, independently of maternal pre-pregnancy BMI and other confounders (β = .09 ± .04; P = .03). Higher post-load glucose levels at second trimester were associated with greater total body fat percentage measured by DXA (1 hour-glucose: β = .010 ± .004; P = .03 and 2 hours-glucose: β = .016 ± .005; P = .002), but not with leptin levels. CONCLUSIONS: Our results suggest that programming of leptin regulation may be sensitive to maternal hyperglycaemia specifically in early pregnancy.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".