BMC medical research methodology · 2026
Bayesian semi-parametric inference for joint modelling of childhood growth and appetite phenotypes.
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Cremaschi Andrea, Franzolini Beatrice, De Iorio Maria, Chong Mary, Toh Jia Ying, Michael Navin, Gupta Varsha, Yap Fabian, Lee Yung Seng, Erikkson Johan, Fogel Anna
Аннотация
PURPOSE: Early eating behaviours have been associated with later weight outcomes, yet their longitudinal development and relationship with growth trajectories remain insufficiently understood. This study aims to investigate how appetite-related behaviours in early childhood co-evolve with growth patterns and contribute to obesity risk.
METHODS: We develop a Bayesian semi-parametric joint modelling framework to analyse repeated measures of growth indicators, such as body mass index, alongside questionnaire-based eating behaviour scores collected at multiple time points in children from the Singaporean GUSTO cohort ("Growing Up in Singapore Towards Healthy Outcomes"). The approach extends established models for ordinal questionnaire data to accommodate longitudinal observations and covariate effects, while growth trajectories are flexibly represented using spline-based regressions. Subject-specific demographic and clinical covariates are incorporated into both components of the model, allowing their effects on growth and eating behaviour to be assessed simultaneously. The two components are linked through a Bayesian nonparametric prior, specifically a Normalised Generalised Gamma Process, that enables data-driven identification of subgroups of children with similar developmental profiles.
RESULTS: The proposed framework captures the dynamic interplay between appetite phenotypes and growth trajectories over time, allowing the identification of clinically meaningful clusters characterised by distinct behavioural and growth patterns.
CONCLUSION: The proposed integrated modelling strategy provides a nuanced understanding of how eating behaviours and growth co-develop in early life, offering new insights into mechanisms underlying childhood obesity risk and supporting the design of targeted early interventions.
METHODS: We develop a Bayesian semi-parametric joint modelling framework to analyse repeated measures of growth indicators, such as body mass index, alongside questionnaire-based eating behaviour scores collected at multiple time points in children from the Singaporean GUSTO cohort ("Growing Up in Singapore Towards Healthy Outcomes"). The approach extends established models for ordinal questionnaire data to accommodate longitudinal observations and covariate effects, while growth trajectories are flexibly represented using spline-based regressions. Subject-specific demographic and clinical covariates are incorporated into both components of the model, allowing their effects on growth and eating behaviour to be assessed simultaneously. The two components are linked through a Bayesian nonparametric prior, specifically a Normalised Generalised Gamma Process, that enables data-driven identification of subgroups of children with similar developmental profiles.
RESULTS: The proposed framework captures the dynamic interplay between appetite phenotypes and growth trajectories over time, allowing the identification of clinically meaningful clusters characterised by distinct behavioural and growth patterns.
CONCLUSION: The proposed integrated modelling strategy provides a nuanced understanding of how eating behaviours and growth co-develop in early life, offering new insights into mechanisms underlying childhood obesity risk and supporting the design of targeted early interventions.