Relationships between maternal body mass index and child cognitive outcomes at 3 years of age are buffered by specific early environments in a prospective Canadian birth cohort
Bibliographic record
Abstract
Abstract Fetal and child development are shaped by early life exposures, including maternal health states, nutrition and educational and home environments. We aimed to determine if suboptimal pre-pregnancy maternal body mass index (BMI; underweight, overweight, obese) would associate with poorer cognitive outcomes in children, and whether early life nutritional, educational and home environments modify these relationships. Self-reported data were obtained from mother-infant dyads from the pan-Canadian prospective Maternal-Infant Research on Environmental Chemicals cohort. Relationships between potential risk factors (pre-pregnancy maternal BMI, breastfeeding practices and Home Observation Measurement of the Environment [HOME] score) and child cognitive development at age three (Weschler’s Preschool and Primary Scale of Intelligence, Third Edition scale and its subcategories) were each evaluated using analysis of variance, multivariable regression models and moderating analyses. Amongst the 528 mother−child dyads, increasing maternal pre-pregnancy BMI was negatively associated with scores for child full-scale IQ (β [95% CI]; −2.01 [−3.43, −0.59],p= 0.006), verbal composite (−1.93 [−3.33, −0.53],p= 0.007), and information scale (−0.41 [−0.70, −0.14],p= 0.003) scores. Higher maternal education level or HOME score attenuated the negative association between maternal pre-pregnancy BMI and child cognitive outcome by 30%–41% and 7%–22%, respectively, and accounted for approximately 5%–10% greater variation in male children’s cognitive scores compared to females. Maternal education and higher quality home environment buffer the negative effect of elevated maternal pre-pregnancy BMI on child cognitive outcomes. Findings suggest that relationships between maternal, social and environmental factors must be considered to reveal pathways that shape risk for, and resiliency against, suboptimal cognitive outcomes in early life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".