Early and Late Postnatal Accelerated Growth Have Distinct Effects on Metabolic Health in Normal Birth Weight Infants
Bibliographic record
Abstract
Accelerated growth in postnatal life in low birth weight infants has been associated with insulin resistance and metabolic syndrome related disorders in later life. Postnatal accelerated growth in also common in normal birth weight infants, but little is known about the impact on metabolic health. In a prospective cohort study of 203 term normal birth weight infants, we evaluated the impacts of accelerated (ΔWeight Z score >0.5) or decelerated (ΔWeight ΔZ <-0.5) growth during early (0-3 months) and late (3-12 months) postnatal life on metabolic health indicators at age 1-y. The primary outcomes were homeostasis model assessment of insulin resistance (HOMA-IR), β-cell function (HOMA-β) and fasting plasma lipids. Adjusting for maternal, paternal and infant characteristics, accelerated growth during the first 3 months of life was associated with a 41.6% (95% confidence interval 8.9-84.2%) increase in HOMA-β, and a 8.3% (0.7-15.4%) decrease in fasting plasma total cholesterols, and was not associated with HOMA-IR in infants at age 1-y. Accelerated growth during 3-12 months was associated with a 30.9% (3.3-66.0%) increase in HOMA-IR, and was not associated with HOMA-β. Neither accelerated nor decelerated growth was associated with fasting plasma triglycerides, HDL or LDL cholesterol concentrations in infants at age 1-y. Accelerated growth during early postnatal life may be beneficial for β-cell function, but during late postnatal life harmful for insulin sensitivity in normal birth weight 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".