Research Questions:
How do genetic variants interact with lifestyle factors (diet, smoking) to influence CRC risk?
Can gut microbiome profiles, blood metabolites, or inflammatory markers serve as early biomarkers for adenoma progression?
Can a multimodal model integrating genetic, imaging, and clinical data improve CRC risk prediction?
What are the cost-effective strategies for CRC screening in diverse populations?
Objectives:
Identify gene-environment interactions driving CRC susceptibility.
Discover novel biomarkers for adenoma-to-CRC transition.
Develop a dynamic risk prediction tool using UK Biobank’s multi-omics and imaging data.
Simulate population-level outcomes of screening interventions to guide policy.
Scientific Rationale:
CRC development involves complex interplay between genetic predisposition, environmental exposures, and gut dysbiosis. While GWAS have identified CRC risk loci, interactions with modifiable factors (e.g., diet) remain poorly understood. Similarly, most biomarkers (e.g., fecal blood) lack sensitivity for early-stage lesions like adenomas. Emerging evidence suggests the gut microbiome and host metabolites (e.g., bile acids) may fill this gap, but large-scale validation is lacking. UK Biobank’s unique combination of genomic, lifestyle, imaging, and longitudinal health data enables integrative analyses to address these gaps. By leveraging its >500,000 participants, this project will:
(1) Uncover gene-lifestyle synergies to refine prevention strategies.
(2) Validate multi-omics biomarkers for early detection of precancerous lesions.
(3) Build AI-driven risk models that outperform traditional tools.
(4) Inform equitable screening guidelines through cost-effectiveness analyses.
We confirm that we have read and will fully comply with the UK Biobank’s policy on the “Use of Artificial Intelligence (AI) applications and models”.
