Small-for-gestational-age and determinants of HOMA indices, leptin and adiponectin in infancy
Bibliographic record
Abstract
Abstract Background/Objective: Poor fetal growth “programs” an elevated risk of obesity and related metabolic dysfunctional disorders in adulthood. How this vulnerability was developed in early life remains unclear. We sought to assess whether small-for-gestational-age (SGA) - an indicator of poor fetal growth, is associated with altered metabolic health biomarkers in infancy and explore the determinants.Methods: This was a nested matched (1:2) study of 65 SGA (birth weight <10th percentile) and 130 optimal-for-gestational-age (OGA, 25th-75th) infants in the 3D birth cohort. The outcomes included homeostasis model assessment of insulin resistance (HOMA-IR) and beta-cell function (HOMA-β), circulating leptin and adiponectin concentrations at age 2 years.Results: HOMA-IR, HOMA-β, leptin and adiponectin concentrations were similar in SGA vs. OGA infants at age 2 years. Female sex and accelerated growth in length during mid-infancy (3-12 months) were associated with higher HOMA-IR. Caucasian ethnicity and decelerated growth in weight during late infancy (12-24 months) were associated with lower HOMA-IR. Decelerated growth in weight during mid-infancy was associated with lower HOMA-β. Circulating leptin was positively correlated with female sex and current BMI. Current BMI was positively correlated with circulating adiponectin in SGA infants only; each SD increase in BMI was associated with a 13.4% (4.0%-23.7%) increase in circulating adiponectin in SGA subjects.Conclusions: Insulin resistance and secretion, circulating leptin and adiponectin levels are normal in SGA subjects in infancy at age 2 years. The study is the first to report an SGA-specific positive correlation between current BMI and circulating adiponectin, suggesting dysfunctional adiposity-adiponectin negative feedback loop development during infancy in SGA subjects. This could be a mechanism in adverse metabolic programming in poor fetal growth.
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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".