BETTER4U has achieved major milestones in database integration, systematic reviews, AI model preparation, and evidence synthesis. Data harmonization across cohorts and trials led to the creation of a unified research database containing genetic, metabolic, anthropometric, and behavioral data. Common variable definitions and pre-analysis protocols were developed for streamlined analysis, including weight change metrics for longitudinal studies. Genome-wide association studies (GWAS) were initiated under WP3, and WP4 produced two systematic reviews: i) one on the impact of genetic risk communication on behavior change, highlighting the role of baseline motivation and health literacy; and ii) one on behavior change strategies, as well as emphasizng the effects in vulnerable populations, emphasizing cultural tailoring and community-based delivery. These reviews supported the design of RCT personalization arms and helped shape communication strategies. Statistical meta-analyses confirmed links between high BMI and low socioeconomic status and revealed interaction effects between genetics and lifestyle on weight trends. Metabolomic signatures of BMI also helped define biochemical subgroups for targeted interventions. WP3 and WP4 collaborated with WP5 on identifying causal variables for AI-based modeling. They highlighted key mediators and moderators and advised on fairness and transparency in model design to inform individualized strategies. This foundational work underpins the personalized prevention logic of BETTER4U. WP5 developed preliminary Directed Acyclic Graphs (DAGs) using harmonized data, clarifying exposure-mediator-outcome pathways. WP6 identified core behavioral indicators, using evidence reviews and stakeholder input, and led the pilot design focused on acceptability and feasibility. WP7 finalized the BETTER4ALL RCT protocol and recruitment plan, while WP8 launched the cost-effectiveness framework for simulating the economic value of personalized interventions. These joint efforts have built a foundation for testing scalable, tailored obesity interventions, deepening our understanding of the interplay among genetics, behavior, and environment in shaping obesity risk and treatment response.
