| Title: | Multi-Omics Study of Cardiovascular-Kidney-Metabolic (CKM) Syndrome and Cancer Comorbidity Mechanisms Based on UK Biobank Data |
| Lead Institution: | Beijing University of Chinese Medicine |
| Principal investigator: |
Application 906426
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About
Research Questions and Objectives: Cardiovascular-kidney-metabolic syndrome (CKM) and tumors are major global health burdens that often coexist and significantly worsen patient outcomes. Although common risk factors such as aging, obesity, and chronic inflammation are known, the mechanisms underlying their co-morbidity remain unclear. This study leverages the UK Biobank's multi-omics data (genomics, proteomics, metabolomics, and imaging), lifestyle indicators, and clinical records to reveal the co-morbidity-related molecules of CKM and tumors through multi-omics integration analysis, investigate the roles of metabolic disorders, immune-inflammatory pathways, and environmental factors (e.g., air pollution and diet), and develop AI prediction models for early risk stratification and personalized interventions. Scientific Rationale: Circulating proteins and metabolites can reflect systemic changes in CKM-tumor co-morbidity and reveal shared molecular pathways. Existing evidence suggests that CKM components (e.g., insulin resistance and chronic kidney disease) may promote tumor development through oxidative stress, lipid metabolism disorders, and chronic inflammation. However, previous studies have been limited to single-omics or lacked precise assessment of environmental exposures. The UK Biobank's unique resources, including longitudinal multi-omics data, accelerometer-measured physical activity, and geocoded environmental exposure data, support systematic research. This study will integrate genomics (SNPs, polygenic risk scores), proteomics (inflammatory/angiogenesis markers), metabolomics (lipid/glucose intermediates), and imaging (body composition, organ fat) data to elucidate cross-disease pathways. Causal inference (Mendelian randomization) and machine learning (ensemble models) will be applied to distinguish biomarkers from confounders and optimize predictive performance. Results will be validated through nested case-control designs and external cohorts.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 21254 | Association of accelerometer-measured physical activity and the risk of incident cardiovascular diseases within the AHA cardiovascular-kidney-metabolic syndrome 0-4 framework: a prospective cohort of the UK Biobank | Jun-Qiao An (+7) | 2026 | Frontiers in Endocrinology |
| 21220 | Cardiovascular-kidney-metabolic health, genetic susceptibility, and incident cancer risk: a prospective UK biobank cohort and machine learning study | Xue He (+6) | 2026 | Frontiers in Endocrinology |
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