The rising incidence of pediatric obesity and associated comorbidities has created an urgent need to identify effective therapeutic approaches. In response to this concern, an innovative approach centered on source-driven metabolite predictions, integrating dietary, microbiome, lifestyle, and psychological parameters is proposed. By elucidating links between metabolite sources and their distinctive biomarker profiles, I aim to establish a foundation for tailoring treatments according to the 4P (predictive, preventive, personalized and participatory) principle. This involves integrating data from three pediatric cohorts— MetaBEAse, FAME, and ENVIRONAGE (n>1400). Utilizing advanced machine learning techniques, I will construct a predictive model aimed at uncovering relationships between sources and metabolites, ultimately enabling the personalization of therapeutic interventions. To fortify the robustness of and infer causality to the prediction model, I will conduct specific in vitro digestions, with emphasis on interrogating predictions most pertinent to the clinically obese phenotype. Subsequently, I will conduct in vivo experiments utilizing humanized murine models to further elucidate causality. By integrating multidisciplinary methodologies encompassing computational analysis, in vitro, and in vivo experiments, this research initiative aims to provide valuable insights into the mechanisms of pediatric obesity as a basis to develop personalized therapeutic interventions.