Background
Despite advances, gaps remain in Cardiovascular disease(CVD)’s development, the heterogeneity of risk factors on outcomes, its interactions with other conditions, and its variable progression across populations, posing significant challenges to current predictive and preventive strategies.
Objectives
Our objective is to clarify complex CVD risk factors and broaden insights into related conditions to identify novel biomarkers and mechanisms. By leveraging multimodal data and advanced analytics, we will enhance precision in predicting CVD.Through longitudinal analysis, we aim to uncover CVD’s dynamic progression, informing adaptive treatment strategies. Additionally, we will deepen understanding of interdisciplinary connections, disease heterogeneity, and population-specific risks, ultimately advancing precision medicine.
Methods
This study will leverage the multimodal data from UK Biobank to investigate CVD outcomes(including comorbidities)and interdisciplinary outcomes. Advanced analytical technique-such as AI and MR-combined with statistical methods will be used to identify novel biomarkers and mechanisms underlying disease risk and to develop predictive models that enhance early detection and risk stratification of CVD.Longitudinal modeling will capture the dynamic progression of CVD,identifying critical intervention windows and informing personalized treatment strategies. Cross-population and causal inference analyses will further explore disease heterogeneity and population-specific risk profiles, deepening our understanding of interdisciplinary health interactions.
Public Health Impact
This study’s findings have the potential to transform CVD prevention, treatment, and health equity. Insights into the diverse biological mechanisms and risk trajectories of CVD will inform tailored clinical guidelines.
