Body mass index rebound and pubertal timing in girls with and without a family history of breast cancer: the LEGACY girls study
Bibliographic record
Abstract
BACKGROUND: Heavier body mass index (BMI) is the most established predictor of earlier age at puberty. However, it is unknown whether the timing of the childhood switch to heavier BMI (age at BMI rebound) also matters for puberty. METHODS: In the LEGACY Girls Study (n = 1040), a longitudinal cohort enriched with girls with a family history of breast cancer, we collected paediatric growth chart data from 852 girls and assessed pubertal development every 6 months. Using constrained splines, we interpolated individual growth curves and then predicted BMI at ages 2, 4, 6, 8 and 9 years for 591 girls. We defined age at BMI rebound as the age at the lowest BMI between ages 2 and 8 years and assessed its association with onset of thelarche, pubarche and menarche using Weibull survival models. RESULTS: The median age at BMI rebound was 5.3 years (interquartile range: 3.6-6.7 years). A 1-year increase in age at BMI rebound was associated with delayed thelarche (HR = 0.90; 95% CI = 0.83-0.97) and menarche (HR = 0.86; 95% CI = 0.79-0.94). The magnitude of these associations remained after adjusting for weight between birth and 2 years, was stronger after adjusting for BMI at age 9, and was stronger in a subset of girls with clinically assessed breast development. CONCLUSIONS: Earlier BMI rebound is associated with earlier pubertal timing. Our observation that BMI rebound may be a driver of pubertal timing in girls with and without a family history of breast cancer provides insight into how growth and pubertal timing are associated with breast cancer risk.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".