Cardiovascular diseases (CVDs) are a major cause of mortality worldwide and are strongly associated with metabolic risk factors such as dyslipidemia, diabetes, and obesity. Together, these conditions constitute cardiometabolic diseases (CMDs). The global obesity epidemic has intensified the challenge of CMD prevention and management. Owing to complex pathophysiology and the interaction of multiple risk factors, substantial heterogeneity exists in CMD onset and prognosis. Despite prior efforts, including risk equations and the concept of metabolically healthy obesity, approaches to risk stratification and mechanistic understanding remain inadequate.
This project will apply advanced statistical and machine learning methods to develop risk prediction models, identify heterogeneous phenotypes, discover early biomarkers, and explore determinants of CMD development and outcomes. The study aims to address the following key questions:
Which genetic, environmental, and lifestyle factors influence CMD risk and prognosis?
How can risk stratification be refined to support precision medicine?
How to identify the heterogeneous phenotypes related to CMD onset and progression? And how these phenotypes respond to risk factors and interventions, and how do they evolve over time?
What is the role of specific biomarkers in predicting the development of CMDs?
How do CMDs affect long-term outcomes such as severe cardiovascular events, other comorbidities, and mortality?
By integrating clinical, lifestyle, multi-omics, demographic, image, and environmental data, this research will improve CMD risk prediction and phenotype identification, providing evidence to support precision prevention and treatment strategies.
