Research Questions and Objectives:
Cardiovascular-kidney-metabolic (CKM) syndrome, central nervous system (CNS) disorders, and cancer often coexist, significantly increasing global disease burden. While common risk factors such as aging, obesity, and chronic inflammation are recognized, the biological mechanisms underlying their comorbidity remain unclear. Utilizing the UK Biobank’s extensive multi-omics resources (genomics, proteomics, metabolomics, imaging), lifestyle factors, and clinical records, we will: (1) Identify molecular pathways jointly linked to CKM, major CNS disorders, and cancer; (2) Quantify the contribution of metabolic dysfunction, neuroimmune inflammation, vascular injury, and environmental exposures (e.g., air pollution, diet); (3) Develop artificial intelligence prediction models for early risk stratification and personalized intervention.
Scientific Rationale:
Cross-organ biomarkers-such as circulating proteins, metabolites, and cardiac/brain MRI data-capture the interplay between the heart, kidney, brain, and tumour biology. Evidence suggests CKM traits like insulin resistance and renal impairment may promote neurodegeneration and cancer via lipid peroxidation, blood-brain barrier disruption, and chronic inflammation. Previous studies often used limited omics layers or lacked accurate environmental exposure data. The UK Biobank’s longitudinal, multi-modal dataset-including accelerometer-measured activity and geocoded exposures-enables a holistic investigation. By integrating genomics (SNPs, polygenic risk scores), proteomics (inflammatory/angiogenic markers), metabolomics, and imaging measures , we can dissect cross-disease mechanisms. Causal inference and ensemble machine learning will help distinguish true biomarkers from confounders and maximize prediction accuracy. This research will clarify shared mechanisms driving CKM, CNS disorders, and cancer, providing a foundation for precision prevention and targeted therapies.