Last updated:
ID:
1039744
Start date:
11 October 2025
Project status:
Current
Principal investigator:
Professor Chunmei Cao
Lead institution:
Beijing Friendship Hospital, Capital Medical University, China

Scientific Rationale
Cardiovascular diseases remain the leading global killer, which mechanisms linking vascular dysfunction to cardiac injury are incompletely understood. Aortic stiffness and extracellular matrix remodeling are central to vascular and myocardial disease but lack causal clarification. Despite advances in reperfusion, many STEMI survivors face recurrent events, suggesting hidden genetic, proteomic, and environmental drivers. AD remains one of the deadliest vascular emergencies, with no proven preventive therapies.
The UK Biobank’s integration of imaging, multi-omics, prescription, and outcome data provides a unique platform to tackle these gaps. This project will (i) uncover organ-level mechanisms linking vascular and cardiac pathology, (ii) refine prediction of residual post-STEMI risk, and (iii) identify drug repurposing opportunities for AD. By bridging molecular biology with population epidemiology, the work aims to shift CVD care from reactive treatment to proactive prevention, reducing mortality and improving long-term health outcomes.
Research questions:
1. How does aortic biomechanical dysfunction (stiffness, dilation) causally contribute to cardiac remodeling and heart failure?
2. What genetic, molecular, and environmental factors explain residual cardiovascular risk in post-STEMI patients?
3. Can existing medications (e.g., antihypertensives, statins) reduce the risk of aortic dissection (AD), and through which pathways?
Obejectives:
1. Use cardiovascular MRI, genetics, and serum biomarkers to establish causal links between vascular dysfunction and heart failure.
2. Build a multi-omics risk model for predicting major adverse cardiovascular events (MACE) after STEMI, integrating imaging, genomics, proteomics, metabolomics, and exposures.
3. Evaluate UK Biobank prescription, imaging, and genetic data to assess associations between drug use and AD outcomes.