Large birth size, infancy growth pattern, insulin resistance and β-cell function
Bibliographic record
Abstract
OBJECTIVE: Large birth size programs an elevated risk of type 2 diabetes in adulthood, but data are absent concerning glucose metabolic health impact in infancy. We sought to determine whether the large birth size is associated with insulin resistance and β-cell function in infancy and evaluate the determinants. DESIGN AND PARTICIPANTS: In the Canadian 3D birth cohort, we conducted a nested matched (1:2) study of 70 large-for-gestational-age (LGA, birth weight >90th percentile) and 140 optimal-for-gestational-age (OGA, 25th-75th percentiles) control infants. The primary outcomes were homeostasis model assessment of insulin resistance (HOMA-IR) and beta-cell function (HOMA-β) at age 2-years. RESULTS: HOMA-IR and HOMA-β were similar in LGA and OGA infants. Adjusting for maternal and infant characteristics, decelerated growth in length during early infancy (0-3 months) was associated with a 25.8% decrease (95% confidence intervals 6.7-41.0%) in HOMA-β. During mid-infancy (3-12 months), accelerated growth in weight was associated with a 25.5% (0.35-56.9%) increase in HOMA-IR, in length with a 69.3% increase (31.4-118.0%) in HOMA-IR and a 24.5% (0.52-54.3%) increase in HOMA-β. Decelerated growth in length during late infancy (1-2 years) was associated with a 28.4% (9.5-43.4%) decrease in HOMA-IR and a 21.2% (3.9-35.4%) decrease in HOMA-β. Female sex was associated with higher HOMA-β, Caucasian ethnicity with lower HOMA-IR, and maternal smoking with lower HOMA-β. CONCLUSIONS: This study is the first to demonstrate that large birth size is not associated with insulin resistance and β-cell function in infancy but infancy growth pattern matters. Decelerated infancy growth may be detrimental to beta-cell function.
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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 | 0.001 |
| 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".