Urological diseases (urinary tract infections, kidney stones, chronic kidney disease, etc.) pose major public health challenges. The increasing severity of environmental pollution and population aging exacerbate the disease burden. Although genetic and lifestyle factors have been partially elucidated, the interplay between environmental exposures (air pollution, heavy metals, etc.), metabolic alterations, and molecular mechanisms (genomics, proteomics, metabolomics) remains unclear. Leveraging the UK Biobank’s multi-omics data, environmental records, and lifestyle metrics, this study aims to:
Identify novel risk factors/biomarkers through integrated omics-environment analysis
Elucidate causal relationships between environmental exposures, molecular changes, and disease progression
Develop machine learning models for early risk prediction and personalized intervention
Scientific Rationale
Specific proteins and metabolites circulating in the blood can not only serve as potential biomarkers for urological diseases but also provide deep insights into disease onset and progression. Research indicates that long-term exposure to environmental stressors and unhealthy dietary habits may increase the risk of urological diseases by disrupting oxidative stress balance, inflammatory responses, and cellular metabolic pathways. However, existing studies are often limited to single-omics data or lack precise environmental exposure assessments.
By integrating multi-dimensional omics analyses (genomics, proteomics, metabolomics), refined environmental exposure monitoring data, and objectively measured physical activity levels, we will uncover new biological pathways, identify key risk factors, and provide a scientific foundation for the prevention and treatment of urological diseases. Furthermore, we will apply advanced statistical methods and machine learning techniques to ensure that the developed predictive models achieve high accuracy and reliability.