Effect of Maternal Glycemia on Neonatal Adiposity in a Multiethnic Asian Birth Cohort
Bibliographic record
Abstract
CONTEXT: Gestational hyperglycemia increases the risk of obesity and diabetes in offspring later in life. OBJECTIVE: We examined the relationship between gestational glycemia and neonatal adiposity in a multiethnic cohort of Singaporean neonates. DESIGN: A prospective mother-offspring cohort study recruited 1247 pregnant mothers (57.2% Chinese, 25.5% Malay, 17.3% Indian) and performed 75-g, 2-hour oral glucose tolerance tests at 26-28 weeks' gestation; glucose levels were available for 1081 participants. Neonatal anthropometry (birth weight, length, triceps, and subscapular skinfolds) was measured, and percentage body fat (%BF) was derived using our published equation. Associations of maternal glucose with excessive neonatal adiposity [large for gestational age; %BF; and sum of skinfolds (∑SFT)>90th centile] were assessed using multiple logistic regression analyses. RESULTS: Adjusting for potential confounders we observed strong positive continuous associations across the range of maternal fasting and 2-hour glucose in relation to excessive neonatal adiposity; each 1 SD increase in fasting glucose was associated with 1.31 [95% confidence interval (CI) 1.10-1.55], 1.72 (95% CI 1.31-2.27) and 1.64 (95% CI 1.32-2.03) increases in odds ratios for large for gestational age and %BF and ∑SFT greater than the 90th centile, respectively. Corresponding odds ratios for 2-hour glucose were 1.11 (95% CI 0.92-1.33), 1.55 (95% CI 1.10-2.20), and 1.40 (95% CI 1.10-1.79), respectively. The influence of high maternal fasting glucose on neonatal ∑SFT was less pronounced in Indians compared with Chinese (interaction P=.005). CONCLUSIONS: A continuous relationship between maternal glycemia and excessive neonatal adiposity extends across the range of maternal glycemia. Compared with Chinese infants, Indian infants may be less susceptible to excessive adiposity from high maternal glucose levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".