| Title: | Multi-Omics and Systemic Factors in Urological Diseases: Risk Prediction, Diagnosis, and Prognosis |
| Lead Institution: | Beijing Hospital |
| Principal investigator: |
Application 665208
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About
Key Research Questions: What genetic, proteomic, and metabolic risk factors contribute to the development of urological diseases, and how can their causal relationships be established? How can multi-omics data and clinical information be integrated to develop biomarkers and predictive tools for the early detection and stratification of urological diseases? To what extent do systemic factors (e.g., metabolic syndrome, obesity) and lifestyle factors influence the risk, progression, and outcomes of urological diseases? Objectives 1. To identify risk factors for urological diseases such as urological cancers and benign prostatic hyperplasia, and evaluate their causal associations. 2. To integrate clinical, genetic, proteomic, and imaging data to develop novel biomarkers and predictive tools for the early detection, risk stratification, and survival prediction of urological diseases. 3. To examine the impact of systemic factors, such as metabolic syndrome, obesity, and cardiovascular health, along with lifestyle factors (e.g., diet and physical activity), on the incidence and progression of urological diseases. Scientific Rationale Urological diseases, including urological cancers, benign prostatic hyperplasia and urinary stone diseases, impose a significant global health burden. Despite their prevalence, the underlying mechanisms, risk factors, and systemic influences remain poorly understood. By leveraging the UK Biobank's comprehensive data, this study integrates multi-omics approaches with clinical and imaging data to identify risk factors, novel biomarkers, and predictive tools. It also explores the impact of systemic and lifestyle factors on disease progression, aiming to enhance early detection, improve risk stratification, and inform precision medicine strategies.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16403 | Prospective cohort study integrating plasma proteomics and machine learning for early risk prediction of prostate cancer | Yongming Chen (+7) | 2025 | International Journal of Surgery |
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