Cord Blood IGF-I, Proinsulin, Leptin, HMW Adiponectin, and Ghrelin in Short or Skinny Small-for-Gestational-Age Infants
Bibliographic record
Abstract
CONTEXT: Small-for-gestational-age (SGA) is an indicator of poor fetal growth "programming" an elevated risk of type 2 diabetes in adulthood. Little is known about early-life endocrine characteristics in SGA subtypes. Stunting (short) and wasting (skinny) are considered distinct SGA phenotypes in neonatal prognosis. OBJECTIVES: This work aimed to assess whether SGA infants with stunting or wasting have similar alterations in neonatal endocrine metabolic health biomarkers. METHODS: This was a nested case-control study based on the 3D (Design, Develop, and Discover) birth cohort in Canada. The study subjects were 146 SGA (birth weight < 10th percentile) and 155 optimal-for-gestational age (OGA, 25th-75th percentiles) infants. Stunting was defined as birth length less than the 10th percentile, and wasting as body mass index less than the 10th percentile for sex and gestational age, respectively. Main outcome measures included cord plasma concentrations of insulin-like growth factor I (IGF-I), proinsulin, leptin, high-molecular-weight (HMW) adiponectin, and ghrelin. RESULTS: Comparing to OGA infants adjusted for maternal and neonatal characteristics, SGA infants with either stunting only or wasting only had lower cord plasma IGF-I and leptin concentrations. HMW adiponectin concentrations were lower in SGA infants with wasting only (P = .004), but similar in SGA infants with stunting only (P = .816). Only SGA infants with both stunting and wasting had substantially lower proinsulin (P < .001) and higher ghrelin concentrations (P < .001) than OGA infants. CONCLUSION: This study is the first to demonstrate that SGA infants with wasting only are characterized by low HMW adiponectin concentrations, whereas those with stunting only are not. SGA with both stunting and wasting are characterized by low proinsulin and high ghrelin concentrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".