Maternal Circulating Placental Growth Factor and Neonatal Metabolic Health Biomarkers in Small for Gestational Age Infants
Bibliographic record
Abstract
Small-for-gestational-age (SGA) infants are at increased risk of type 2 diabetes in adulthood. It is unknown whether any prenatal biomarkers are helpful for identifying SGA infants with altered metabolic health profile at birth or later life. In a nested study of 162 SGA (birth weight <10th percentile) and 161 optimal birth weight (25th-75th percentiles) control infants in the 3D (design, develop and discover) birth cohort in Canada, we assessed whether maternal circulating placental growth factor (PlGF), a biomarker of placental function, is associated with metabolic health biomarkers in SGA infants. Main outcomes were cord plasma insulin, proinsulin, insulin-like growth factor-I (IGF-I), leptin and high-molecular-weight (HMW) adiponectin concentrations. Maternal PlGF concentrations at 32-35 weeks of gestation were substantially lower in SGA versus control infants (P<0.001), so as were cord plasma proinsulin (P=0.005), IGF-I (P<0.001), leptin (P<0.001) and HMW adiponectin (P=0.002) concentrations. In SGA infants with both low (<25th percentile) and normal maternal PlGF concentrations, cord plasma IGF-I and leptin concentrations were lower than control infants, but the decreases were to a greater extent in SGA infants with low maternal PlGF. Cord blood leptin levels were lower comparing SGA infants with low vs. normal maternal PlGF levels (P=0.01). SGA infants with low maternal circulating PlGF levels at late gestation were characterized by greater decreases in cord blood IGF-I and leptin concentrations. Maternal circulating PlGF appears to be associated with neonatal metabolic health profile in SGA infants.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".