1. Integration of Multi-Omics Data and Exploration of Kidney Disease Mechanisms:
A comprehensive multi-omics analysis leveraging brain MRI, carotid ultrasound, and proteomic, genomic, and metabolomic profiles to identify risk factors and predictive markers for kidney disease and cardio-cerebrovascular events, with an emphasis on the pathophysiology of cardiovascular-kidney-metabolic (CKM) syndrome.
2. Investigating Genetic and Environmental Risk Factors for Kidney Diseases:
Combine UK Biobank genomic data (whole-genome sequencing, genotyping) with environmental exposure data (air pollution, lifestyle factors, etc.) to analyze how gene-environment interactions impact the development of kidney diseases, such as chronic kidney disease and acute kidney injury.
3. Develop an Early Warning Model for Kidney Disease:
Utilize longitudinal data from UK Biobank (including biochemical markers and imaging data) along with electronic health records (linked via the NHS) to build risk prediction models based on machine learning or deep learning techniques.
4. Evaluate the Long-Term Effects of Medications and Treatment Strategies:
Analyze medication records and follow-up data from UK Biobank to assess the impact of renin-angiotensin system inhibitors (ACEI/ARB) and novel targeted therapies (e.g., rituximab) on the progression of kidney diseases.
