The impact of breastfeeding on FTO-related BMI growth trajectories: an application to the Raine pregnancy cohort study
Bibliographic record
Abstract
INTRODUCTION: For years, body mass index (BMI) has been used by scientists to track weight problems and obesity in children and adults. Recent studies have implicated the fat mass and obesity gene (FTO) in the increase of BMI in young adults. A longer duration of breastfeeding is known to reduce the risk of being overweight later in life, but its ability to modify the effect because of FTO is not known. METHODS: We studied 1096 children from the Western Australian Pregnancy (Raine) cohort who were followed up from birth to 14 years of age. Linear mixed-effects models were used to investigate BMI growth trajectories in boys and girls separately. RESULTS: An association was found between BMI growth and the duration of exclusive breastfeeding (EXBF) among carriers of the risk allele of the FTO SNP rs9939609. In girls, EXBF interacts with the SNP at baseline and can reverse the increase in BMI because of SNP risk allele by age 14 years after 3 months of EXBF. In boys, EXBF reduces BMI both in carriers and non-carriers of the risk allele with an association found after 10 years of age. Six months of EXBF will put the boys' BMI growth curves back to the normal range. CONCLUSIONS: Our study could have major health implications by providing new perspectives for the prevention of growth problems in children carrying risk alleles in the FTO gene.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".