Maternal Profiles Account for Birth Weight Differences Across Ethnicities: Results from Three Canadian Birth Cohorts
Bibliographic record
Abstract
Background Marked differences in birth weight (BW) between South Asian and White European-origin populations are well-documented and pose public health concerns. Methods We analyzed fetal BW, the fat mass (FM), and fat-free mass (FFM) components in South Asian (n=938) and White European (n=3,044 and 804) newborns from three Canadian birth cohorts, examining the contribution of 16 maternal factors to observed BW differences using epidemiological and Mendelian randomization analyses. Findings South Asian newborns had on average, a significantly lower BW (3.3±0.4kg) than White Europeans (3.5±0.5kg), even after accounting for birth length (p<0.001). FFM was the primary driver of this difference, contributing to 0.22kg lower BW (p<2.2E-16), while FM had a significant but weaker counteracting effect of 0.01kg higher BW in South Asians (p=0.006). Five maternal factors demonstrated a direct maternal genetic influence: pre-pregnancy weight primarily increased BW via FFM, it also had a non-negligible increasing effect on FM. On the other hand, maternal glucose and gestational diabetes mellitus (GDM) causally increased BW through FM accumulation. Maternal height had a minimal effect only on FFM. After adjusting for these 5 maternal predictors, roughly 50% of the ethnic difference in BW (0.1kg; 95% CI: 0.067-0.13kg) was accounted for. Interpretation Different maternal factors influence specific components of BW. Targeting body fat reduction and maternal glucose regulation in South Asian mothers may help reduce the intergenerational transmission of increased FM and its associated adverse health outcomes. Funding This study was funded by the Canadian Institutes of Health Research DOHaD Team Grant: MWG-146332.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".