Periodic Reporting for period 1 - BETTER4U (Preventing obesity through Biologically and bEhaviorally Tailored inTERventions for you - BETTER4U)
Okres sprawozdawczy: 2023-11-01 do 2025-04-30
BETTER4U is an EU-funded project addressing the global obesity crisis through personalized interventions. It aims to identify weight gain determinants and develop scalable, AI-driven, and evidence-based strategies. Drawing on previous biobanks and datasets, BETTER4U investigates how genetic predispositions influence the success of lifestyle changes in people with overweight or obesity. The project brings together public and private sector experts, including universities, NGOs, and regional governments, to promote personalized, effective, and equitable obesity prevention. The project pursues six core objectives:
i) Understand genetic, metabolomic, microbiota, socioeconomic, geographic, and lifestyle factors influencing weight gain using meta-analyses of data from ~1 million individuals.
ii) Develop a novel prevention methodology informed by causal AI modeling and pilot studies in 7 European countries.
iii) Use real-time monitoring tools to track behavioral data in context.
iv) Test this approach through an RCT offering personalized lifestyle guidance.
v) Evaluate implementation feasibility and barriers across settings.
vi) Disseminate sustainable, people-centered prevention guidelines.
i) Understand genetic, metabolomic, microbiota, socioeconomic, geographic, and lifestyle factors influencing weight gain using meta-analyses of data from ~1 million individuals.
ii) Develop a novel prevention methodology informed by causal AI modeling and pilot studies in 7 European countries.
iii) Use real-time monitoring tools to track behavioral data in context.
iv) Test this approach through an RCT offering personalized lifestyle guidance.
v) Evaluate implementation feasibility and barriers across settings.
vi) Disseminate sustainable, people-centered prevention guidelines.
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.
BETTER4U has delivered several innovations: i) a harmonized, multi-dimensional European obesity database; ii) evidence from systematic reviews and meta-analyses clarifying how genetic and behavioral tailoring can enhance obesity prevention in vulnerable groups; iii) a core behavioral indicator set, and; iv) preliminary causal models via DAGs. These outputs advance AI-driven personalization in public health. They support scalable digital tools and policy integration for more efficient prevention strategies. The framework addresses obesity in at-risk and low-SES populations and has the potential to be embedded in health platforms and screening protocols. Cost-effectiveness analyses will guide public investment and reimbursement decisions. Future efforts must focus on real-world deployment and further development of interoperable health data systems linking genomic, clinical, and behavioral data in real time. Commercialization of AI-based coaching tools using the lifestyle risk score may be pursued, with attention to ethical use, IP protection, and international regulation. Long-term studies are needed to understand sustained behavior change and evaluate equity, scalability, and acceptability across diverse health systems.