Cord blood insulin, <scp>IGF</scp>‐I, <scp>IGF</scp>‐<scp>II</scp>, leptin, adiponectin and ghrelin, and their associations with insulin sensitivity, β‐cell function and adiposity in infancy
Bibliographic record
Abstract
AIM: Vulnerability to insulin resistance and Type 2 diabetes may originate in early life, but little is known about whether any perinatal biomarkers are predictive of later metabolic health. We sought to assess whether cord blood insulin, insulin-like growth factor (IGF)-I, IGF-II, leptin, adiponectin and ghrelin are associated with metabolic health indicators in infancy. METHODS: In a prospective singleton birth cohort, we assessed cord blood insulin, IGF-I, IGF-II, leptin, adiponectin and ghrelin concentrations in relation to the homeostasis model assessment of insulin resistance (HOMA-IR), β-cell function (HOMA-β), fasting proinsulin-to-insulin ratio, BMIz-score, and the sum of triceps and subscapular skinfold thickness (an indicator of adiposity) in infants at age 1 year (n = 185). RESULTS: Adjusting for maternal and infant characteristics, one standard deviation (sd) increase in cord blood adiponectin was associated with an 11.1% (95% confidence interval 1.8-19.5%) decrease in HOMA-β (P = 0.02) and a 13.6% (1.8-26.8%) increase in proinsulin-to-insulin ratio (P = 0.02), indicating worse β-cell function in infants at age 1 year. One sd increase in cord blood insulin was associated with a 0.5 (0.1-1.0) mm increase in skinfold thickness (P = 0.01). One sd increase in cord blood ghrelin was associated with a 0.2 (0.02-0.3) decrease in BMIz-score (P = 0.02) and a 0.5 (0.1-0.9) mm decrease (P = 0.02) in skinfold thickness. Cord blood IGF-I and IGF-II were not associated with the observed metabolic health indicators at age 1 year. CONCLUSION: The study is the first to show that cord blood adiponectin may be negatively predictive of β-cell function, whereas cord blood ghrelin may be negatively predictive of adiposity in infancy.
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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 | 0.000 | 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) | 0.001 | 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".