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The prevalence of chronic non-communicable diseases such as obesity hasnoticeably increased in the last decade. The study of these diseases in earlylife is of paramount importance in determining their course in adult life andin supporting clinical interventions. Recently, attention has been drawn onapproaches that study the alteration of metabolic pathways in obese children.In this work, we propose a novel joint modelling approach for the analysis ofgrowth biomarkers and metabolite concentrations, to unveil metabolic pathwaysrelated to child obesity. Within a Bayesian framework, we flexibly model thetemporal evolution of growth trajectories and metabolic associations throughthe specification of a joint non-parametric random effect distribution whichalso allows for clustering of the subjects, thus identifying risk sub-groups.Growth profiles as well as patterns of metabolic associations determine theclustering structure. Inclusion of risk factors is straightforward through thespecification of a regression term. We demonstrate the proposed approach ondata from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohortstudy, based in Singapore. Posterior inference is obtained via a tailored MCMCalgorithm, accommodating a nonparametric prior with mixed support. Our analysishas identified potential key pathways in obese children that allows forexploration of possible molecular mechanisms associated with child obesity.

Joint modelling of association networks and longitudinal biomarkers: an application to child obesity