Research Questions:
1. Investigate the prevalence of and factors influencing CKM in the UK Biobank population.
2. Assess whether genomic data/biomarkers can improve the predictive power of models like SCORE2 for CKM.
3. Identify effective predictors readily available in community healthcare settings.
4. Conduct cross-cohort analysis using UKB and CKB to develop and validate a precise CKM risk model for Asian populations, compare East-West differences, and build dynamic prediction tools.
Research Objectives:
1. Identify genetic, biomarker, and lifestyle factors associated with CKM.
2. Construct and validate prognostic models for CKM.
3. Discern cross-population and population-specific risk factors.
4. Model Optimization: Develop an integrated model in UKB and perform external validation and optimization in CKB.
5. Develop a dynamic prediction model and risk score integrating multi-omics data and Asian-specific factors.
6. Translation: Develop a simplified risk tool suitable for primary care.
Scientific Rationale:
CKM emphasizes the interconnections among cardiovascular, renal, and metabolic diseases. The UK Biobank provides an ideal platform for systematically studying its multifactorial etiology and early prediction. Existing models (e.g., SCORE2) have limitations such as being static, Eurocentric, and insufficient for predicting composite outcomes. Leveraging UKB’s rich multi-omics data to build advanced models, followed by validation and optimization in the CKB Asian cohort, is a key pathway for developing equitable and precise CKM risk prediction tools, enabling precision prevention for Asian populations.
