Maternal Insulin Sensitivity During Pregnancy Predicts Infant Weight Gain and Adiposity at 1 Year of Age
Bibliographic record
Abstract
Emerging evidence suggests that fetal environmental exposures impact on future development of obesity. The objectives of this study were to assess the relationships between (i) maternal insulin sensitivity and glucose tolerance status in pregnancy and (ii) early infant weight gain and adiposity in the first year of life. In this prospective cohort study, 301 women underwent oral glucose tolerance testing for assessment of glucose tolerance status and insulin sensitivity (IS(OGTT)) in pregnancy. Their infants underwent anthropometric assessment at 12 months of age, including determination of weight gain in the first year of life and sum of skinfold thickness (SFT), a measure of infant adiposity. Infant weight gain and sum of SFT at 12 months did not differ according to maternal glucose tolerance status. On univariate analyses, weight gain from 0 to 12 months and sum of SFT were negatively associated with maternal IS(OGTT) during pregnancy. On multiple linear regression analysis, negative independent predictors of weight gain from 0 to 12 months were maternal IS(OGTT) during pregnancy (t = -2.73; P = 0.007), infant female gender (t = -3.16; P = 0.002), and parental education (t = -1.98; P = 0.05), whereas white ethnicity was a positive independent predictor (t = 2.68; P = 0.008). Maternal IS(OGTT) (t = -2.7; P = 0.008) and parental education (t = -2.58; P = 0.01) were independent negative predictors of sum of SFT at 12 months. Independent of maternal glucose tolerance status, maternal insulin resistance during pregnancy is associated with increased infant weight gain and adiposity over the first year of life. Further longitudinal study to evaluate obesity in this group of children will increase our understanding of the contribution of the intrauterine environment to their long-term health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".