Maternal and Fetal IGF-I and IGF-II Levels, Fetal Growth, and Gestational Diabetes
Bibliographic record
Abstract
CONTEXT: It remains uncertain whether maternal IGF-I is associated with fetal growth. Little is known about the role of maternal IGF-II in fetal growth and whether IGF-I or IGF-II is implicated in fetal hypertrophy in gestational diabetes. OBJECTIVE: The objective of the study was to assess maternal and fetal IGF-I and IGF-II levels in association with fetal growth and gestational diabetes. STUDY DESIGN, POPULATION, AND OUTCOMES: A singleton pregnancy cohort study (n = 307). The primary outcome was birth weight. RESULTS: Maternal plasma concentrations increased by an average of 55.4% for IGF-I and 11.8% for IGF-II between 24-28 and 32-35 weeks of gestation. The maternal IGF-I but not IGF-II level was correlated with birth weight and placental weight. Adjusting for maternal and infant characteristics, each SD increase in maternal IGF-I level at 24-28 weeks was associated with a 75-g (95% confidence intervals 29-120) increase in birth weight, a 20-g (7-33) increase in placental weight, and a 1.91-fold (1.28-2.86) higher odds of macrosomia (birth weight > 90th percentile). Similar associations were observed for the maternal IGF-I level at 32-35 weeks. Maternal and fetal IGF-I (but not IGF-II) levels were significantly higher in gestational diabetic than in nondiabetic pregnancies. The significantly higher birth weight z scores in diabetic pregnancies disappeared after adjusting for maternal and fetal IGF-I levels alone. CONCLUSIONS: Higher maternal IGF-I (but not IGF-II) levels at mid- and late gestation may indicate greater placental and fetal growth. IGF-I (but not IGF-II) may be implicated in fetal hypertrophy in gestational diabetes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".