Large-for-Gestational-Age, Leptin, and Adiponectin in Infancy
Bibliographic record
Abstract
CONTEXT: Fetal overgrowth "programs" an elevated risk of obesity and type 2 diabetes in adulthood. Plausibly, adipokines may be involved in programming metabolic health. OBJECTIVE: This work aimed to evaluate whether large-for-gestational-age (LGA), an indicator of fetal overgrowth, is associated with altered circulating leptin and adiponectin levels in infancy, and assess the determinants. METHODS: In the Canadian 3D birth cohort, we studied 70 LGA (birth weight > 90th percentile) and 140 optimal-for-gestational-age (OGA, 25th-75th percentiles) infants matched by maternal ethnicity, smoking, and gestational age at delivery. The primary outcomes were fasting leptin, and total and high-molecular-weight (HMW) adiponectin concentrations at age 2 years. RESULTS: LGA infants had higher body mass index (BMI) than OGA infants. However, there were no significant differences in leptin, and total and HMW adiponectin concentrations. Leptin concentrations were positively associated with female sex, weight (z score) gain 0 to 24 months, current BMI, and the sum of triceps and subscapular skinfold thickness, and negatively associated with maternal age and White ethnicity. Female sex was associated with lower total and HMW adiponectin concentrations. Weight (z score) gain 0 to 24 months and current BMI were positively correlated with total and HMW adiponectin concentrations in LGA infants only. CONCLUSION: This study is the first to demonstrate that LGA does not matter for circulating leptin and adiponectin concentrations in infancy, and there may be LGA-specific positive associations between weight gain or current BMI and adiponectin concentrations in infancy, suggesting dysfunction in establishing the adiposity-adiponectin negative feedback loop in LGA individuals.
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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 | 0.001 |
| 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".