Mediation of genetic susceptibility to obesity through eating behaviours in children
Bibliographic record
Abstract
Summary Background/Objectives Few studies have examined the putative mediating role of eating behaviours linking genetic susceptibility and body weight. The goal of this study was to investigate the extent to which two polygenic scores (PGSs) for body mass index (BMI), based on child and adult data, predicted BMI through over‐eating and fussy eating across childhood. Subjects/Methods The study sample involved 692 participants from a birth cohort study. Height and weight were measured on six occasions between ages 6 and 13 years. Over‐eating and fussy eating behaviours were assessed five times between ages 2 and 6 years. Longitudinal growth curve mediation analysis was used to estimate the contributions of the PGSs to BMI z ‐scores mediated by over‐eating and fussy eating. Results Both PGSs predicted BMI z ‐scores (PGS child : β = 0.26, 95% CI: 0.19–0.33; PGS adult : β = 0.34, 95% CI: 0.27–0.41). Over‐eating significantly mediated these associations, but this mediation decreased over time from 6 years (PGS child : 18.0%, 95% CI: 3.1–32.9, p ‐value = 0.018; PGS adult : 14.2%, 95% CI: 2.8–25.5, p ‐value = 0.014) to 13 years (PGS child : 11.4%, 95% CI: −0.4‐23.1, p ‐value = 0.057; PGS adult : 6.2%, 95% CI: 0.4–12.0, p ‐value = 0.037). Fussy eating did not show any mediation. Conclusions Our results support the view that appetite is key to translating genetic susceptibility into changes in body weight.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".