I. Research Question:
How do genetics, lifestyle, environmental exposures and biomarkers interact in the development of kidney disease?
Are there key molecular mechanisms and pathways that can be used for early screening, staging and targeted therapy?
Can multidimensional risk factors associated with kidney disease and its co-morbidities (e.g. hypertension, metabolic syndrome) be identified and modeled?
II.Research objectives:
To integrate genetic, behavioral, environmental and clinical data from UK Biobank to construct a multidimensional risk map for kidney disease;
To clarify the causal relationship between key risk factors and kidney disease using Mendelian randomization and interaction analysis;
Combining multi-omics data to mine the molecular mechanisms and potential therapeutic targets associated with renal impairment;
Construct co-morbidity prediction models based on machine learning to realize early identification and precise management of high-risk groups.
III. Scientific basis:
As the causes of kidney disease are complex, UK Biobank provides an ideal data platform to support multifactorial analysis;
Combine GWAS and clinical data to identify key genes and non-linear exposure effects;
Combining external databases for mechanism validation to enhance biological explanatory power;
Causal inference and network analysis help to identify intervening factors and serve clinical translation.