Early Growth and Dysmetabolism at 11.5 years: A Cohort Analysis of the PROBIT Study
Bibliographic record
Abstract
Background Rapid infant weight gain predicts higher BMI in later life, but few studies have examined interactions with size at birth or associations with other cardiometabolic risk markers. Methods: In 13,576 Belarusian children enrolled at birth in the Promotion of Breastfeeding Intervention Trial, we modeled weight gain trajectories from birth to 8.5 years and at 11.5 years we measured BMI and calculated an internal metabolic risk z‐score from fasting insulin, glucose, SBP, Apo A1 and waist circumference. Using linear regression models we adjusted for parental sociodemographics and BMI, and child weight & length at prior ages. Results: Faster postnatal weight gain in all age periods was associated all markers of cardio‐metabolic risk except ApoA1, with increasingly stronger estimates at successively older ages. Associations were strongest for adiposity outcomes. Effect estimates (all expressed as kg/year) of weight gain on overall metabolic z‐score were 0.03 (95% CI: 0.02, 0.04) for 0‐3 months; 0.08 (0.06, 0.09) for 3‐12 months; 0.21 (0.18, 0.23) for 12‐34 months, and 0.31 (0.29, 0.33) for 34 months – 8.5 years.In SGA, AGA, and LGA infants growth during infancy had similar associations with outcomes, but faster weight gain after 12 months was much more strongly associated with adverse outcomes in SGA vs. LGA or AGA infants, whereas effects were similar in LGA vs. AGA children. In all 3 groups, associations with adverse outcomes were strongest for growth at older vs. younger ages. Conclusion Faster growth in later childhood predicted adverse metabolic outcomes more strongly compared with growth during 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".