Research Questions:1.Which genetic, environmental, and lifestyle factors-independently or interactively-are associated with the onset and progression of common digestive disorders (e.g., IBD, cirrhosis, colorectal cancer) in the UK Biobank cohort?2.How do these factors contribute to disease risk disparities across demographic subgroups (age, sex, ethnicity, socioeconomic status)?3.Are there associations between IBD progression and systemic conditions (e.g., circulatory/immune disorders, infections)? Could shared mechanisms (e.g., immune dysregulation) remain underexplored?4.Can integrated multi-omics models (genomics, proteomics, radiomics, metabolomics, psychological data) improve early identification of high-risk individuals and precision therapeutic strategies
Objectives:1.Leverage UK Biobank’s multi-omics data (phenotypic, genetic, environmental, imaging, metabolomic, proteomic) to identify modifiable/non-modifiable risk factors for digestive diseases.2.Develop machine learning-based multi-modal tools to predict disease onset, progression, therapeutic response, complications, and prognosis.3.Investigate IBD’s systemic associations (e.g., circulatory/immune/metabolic disorders) and shared mechanisms (e.g., microbiota-host crosstalk).4.Propose evidence-based interventions to reduce disease burden through personalized prevention strategies.
Scientific Rationale !Digestive diseases (e.g., IBD) arise from complex genetic-environmental-lifestyle interactions, but their population-specific effects and cross-disease mechanisms (e.g., gut-brain axis) are underexplored. The UK Biobank’s multi-omics data allows comprehensive risk factor analysis beyond traditional models. Machine learning integration of these data may enable precise risk prediction and personalized interventions.
