Research Questions and Objectives
Chronic non-communicable diseases, including cancers, cardiovascular diseases, and diabetes, and other conditions, pose a major global health burden due to their complex etiology and heterogeneous molecular mechanisms. Current research often focuses on isolated mechanisms or disease subtypes, failing to capture the continuum from risk exposure to molecular changes and clinical outcomes. This study aims to address these gaps using the UK Biobank’s large-scale multi-omics and prospective health data. Specific objectives include:
a) Identifying and validating novel genetic, psychosocial, environmental, and biomarker-based risk factors associated with chronic disease incidence and progression;
b) Applying multiple statistical analysis methods such as multivariate regression to elucidate risk-outcome relationships and biological mechanisms using multi-omics data;
c) Developing machine learning models that integrate clinical, psychosocial, and omics data to construct a clinically applicable risk assessment tool.
Scientific Rationale
Chronic diseases such as cancer, heart disease, and diabetes are multifactorial, arising from complex interactions between genetic, metabolic, environmental, and behavioral factors. For example, liver cancer development involves viral hepatitis, metabolic abnormalities, and genetic susceptibility, reflecting high clinical and molecular heterogeneity. Multi-omics technologies combined with machine learning provide unprecedented opportunities to integrate multimodal data, uncover underlying mechanisms, and build predictive models. Leveraging the UK Biobank’s resources, this study aims to systematically identify risk factors, dissect molecular pathways, and develop an integrated predictive model to advance risk stratification and precision prevention, enabling early intervention and personalized management.
