Research question and Objective
Kidney diseases, including glomerulonephritis, diabetic nephropathy, lupus nephritis, renal failure, dialysis, kidney transplantation, nephrotic syndrome, and metabolic kidney diseases, are a growing global health burden with significant morbidity and mortality. Despite advances in understanding genetic predispositions and lifestyle influences, the interplay between environmental exposures (e.g., air pollution, heavy metals), molecular mechanisms , and disease progression remains underexplored. This study aims to:
Identify novel risk factors and biomarkers for kidney diseases by integrating multi-omics and environmental data.
Investigate causal relationships between environmental exposures, molecular changes, and disease progression using Mendelian Randomization (MR) and machine learning approaches.
Develop predictive models to stratify individuals at high risk of kidney diseases, enabling early intervention and personalized treatment strategies.
Scientific Rationale
Kidney diseases are complex disorders influenced by genetic, environmental, and lifestyle factors. Circulating proteins, metabolites, and genomic profiles provide critical insights into disease mechanisms, reflecting the integration of genetic, environmental, and behavioral influences. Previous studies have linked air pollution, heavy metal exposure, and metabolic dysregulation to kidney dysfunction. However, these findings are often limited by single-omics approaches or observational designs. By combining multi-omics data (genomics, proteomics, metabolomics) with detailed environmental and lifestyle information from the UK Biobank, we can uncover novel biomarkers, clarify causal pathways, and address gaps in traditional research. This integrated approach will enable a comprehensive exploration of kidney disease etiology, providing a robust foundation for prevention and treatment strategies.