Understanding the association of longitudinal body mass index patterns in children and their parents: A data-driven study from the TARGet Kids! cohort
Bibliographic record
Abstract
BACKGROUND: Obesity remains a persistent global health issue across generations. Targeting family-level factors may help improve child and adolescent body mass index (BMI) outcomes. While associations between parental and offspring BMI are well-documented, the temporal patterns and risk factors driving these relationships remain unclear. This study aimed to identify longitudinal family-level BMI patterns incorporating child, maternal, and paternal BMI and apply interpretable machine learning (ML) methods to uncover key predictors. METHODS: This longitudinal study included 6092 children and their parents from the TARGet Kids! cohort, with BMI measurements collected from birth to 150 months. Group-based multi-trajectory modeling identified joint trajectories of child BMI-for-age Z-scores (zBMI) and parental BMI. Five ML classifiers predicted group membership using 78 predictors spanning sociodemographic, dietary, parental health, and child lifestyle variables. To explore the modifying effect of parental overweight/obesity (OW/OB) on the relationship between child age and BMI, Bayesian generalized additive mixed models (GAMMs) with smoothed term interactions were applied. RESULTS: Five distinct joint trajectory groups were identified. Children in the highest BMI trajectory group typically had both parents following similar high BMI trajectories. Parental OW/OB status emerged as the strongest predictor of child OW/OB (37 % classification probability), followed by breastfeeding duration (17 %) and child physical activity (15 %). The influence of parental OW/OB was particularly pronounced in early childhood (0-60 months). Bayesian GAMMs confirmed the robust, longitudinal association between child and parental BMI trajectories. CONCLUSIONS: Parental BMI patterns strongly influence child BMI development, with age-dependent effects. These findings highlight the importance of early family-based interventions.
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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 | 0.017 | 0.029 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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".