Maternal Gestational Diabetes Associated with Higher Child BMI Z-Score at Preschool and Lower Likelihood of Breastfeeding Initiation
Bibliographic record
Abstract
Objectives: To examine the association of maternal GDM with 1) child BMI z-score at preschool; 2) breastfeeding initiation and duration, while adjusting for child birthweight in addition to potential confounders. Method: Sample included 53 children (3 - 5 years old) recruited from two preschools in Jeddah, Saudi Arabia. Mothers completed a self-administered questionnaire. Child anthropometry was completed using standardized procedures. BMI z-scores were calculated using the WHO standards/reference data. Linear regression models were tested to examine the association between maternal GDM and child BMI z-score, as well as breastfeeding duration. Logistic regression models were tested to examine the association between maternal GDM and breastfeeding initiation. Models were adjusted for child birthweight, maternal BMI, and maternal age at pregnancy. Results: Mean child BMI z-score was 1.10 (SD= 1.22). About one quarter (24.5%) of mothers reported being diagnosed with GDM. Mean birthweight of children whose mothers were diagnosed with GDM was 3.10 kg (SD= 0.74). Adjusting for covariates, we found that maternal GDM was associated with increased child BMI z-score (B= 1.04, 95% CI= 0.14 - 1.94, P-value= 0.02), and lower odds of breastfeeding initiation (OR= 0.10, 95% CI= 0.02 – 0.49, P-value= 0.005). Maternal GDM was not associated with breastfeeding duration (B= -4.75, 95% CI: -11.79 – 2.29, P-value= 0.18). Conclusion: Findings suggest that maternal GDM is associated with higher child BMI z-score at preschool and lower likelihood of breastfeeding initiation. Studies are needed in order to identify the underlying mechanisms of associations. Obesity prevention programs may target children whose mothers were diagnosed with GDM; prenatal breastfeeding counseling may be offered.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".