The role of early life growth development, the FTO gene and exclusive breastfeeding on child BMI trajectories
Bibliographic record
Abstract
Background: Recent studies have implicated the FTO gene in child and adult obesity. A longer duration of exclusive breastfeeding (EXBF) has been shown to reduce body mass index (BMI) and the risk of being overweight in the general population and among FTO gene carriers. However, it remains unclear whether the preventive effect of EXBF could be explained by its impact on early life growth development, e.g. ages at adiposity peak (AP) and adiposity rebound (AR) and BMI velocities in the first years of life, which are major determinants of overweight and obesity later in life. Methods: We studied 5590 children from the British Avon Longitudinal Study of Parents and Children (ALSPAC) cohort and modelled their longitudinal BMI profiles with mixed effects models from birth to 16 years of age, as well as their ages at AP, AR and BMI velocities in relation to the FTO gene variant and EXBF. Results: A longer duration of EXBF (i.e. at least 5 months) has substantial impact on BMI growth trajectories among children carrying the FTO adverse variant by modulating the age at AP, age at AR and BMI velocities. EXBF acts antagonistically to the FTO rs9939609 risk allele and by the age of 15, the predicted reduction in BMI after 5 months of EXBF is 0.56 kg/m2 [95% confidence interval (CI) 0.11-1.01; P = 0.003] and 1.14 kg/m2 (95% CI 0.67-1.62; P < 0.0001) in boys and girls, respectively. Conclusions: EXBF influences early life growth development and thus plays a critical role in preventing the risks of overweight and obesity even when those are exacerbated by genetic factors.
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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 | 0.000 | 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) | 0.002 | 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".