The research will leverage data from the UK Biobank to explore the role of metabolic communication in healthy aging. We aim to investigate associations between metabolic regulatory genes, plasma metabolites, and lifestyle factors with aging-related diseases such as diabetes and cardiovascular conditions. Using statistical models (Cox, logistic, Poisson) and machine learning techniques, we will develop predictive models for disease prognosis and treatment efficacy. The study will also incorporate GWAS and metabolomics analyses to uncover genetic and metabolic pathways linked to aging. Key lifestyle factors like diet and exercise will be examined for their influence on metabolic communication and disease development. This comprehensive approach will highlight the interactions among genetics, metabolism, and lifestyle factors in aging and related diseases. We aim to identify metabolic biomarkers and genetic pathways that can be targeted for personalized interventions to delay aging and prevent age-related diseases. The innovative integration of metabolic communication with healthy aging in a large-scale population study offers a novel framework for understanding and promoting longevity.
