Research Question
Cardiovascular-Kidney-Metabolic (CKM) syndrome represents a cluster of interrelated conditions, including cardiovascular, renal, and metabolic diseases (hypertension, diabetes, obesity, dyslipidemia, kidney diseases, and cardiovascular disorders), characterized by strong epidemiological comorbidity and bidirectional causal relationships that pose significant global public health challenges. However, the causal pathways linking genetic susceptibility, metabolic dysregulation, proteomic changes, environmental exposures, and imaging phenotypes to CKM onset and progression remain poorly defined.
Purpose
To dissect the causal architecture of both CKM syndrome and its constituent diseases by integrating genetic, multi-omics, imaging, and epidemiological data from the UK Biobank, enabling early prediction, targeted prevention, and personalized management strategies.
Objectives
1!Comprehensive risk profiling – Quantify the independent and combined effects of polygenic risk scores (PRS), proteomic, metabolomic, imaging, environmental, and lifestyle factors on CKM and each of its component diseases.
2!Molecular driver identification – Use multi-omics quantitative trait locus (QTL) mapping and statistical colocalization to pinpoint proteins, metabolites, and biological pathways that influence CKM and individual disease domains.
3!Causal inference and disease interconnectivity – Apply Mendelian randomization to determine the causal effects of identified biomarkers on CKM and on key endpoints of its components (e.g., myocardial infarction for cardiovascular disease, end-stage kidney disease for renal disorders).
Scientific Rationale
Studying cardiovascular, Renal, and Metabolic Diseases will:
1!Uncover both common and disease-specific risk factors and mechanisms.
2!Map causal networks connecting metabolic, cardiovascular, and renal pathophysiology.
3!Enable identification of early biomarkers for each condition and the overall syndrome.
