| Title: | An Integrated Analysis of Genetic, Environmental, Behavioral, and Microecological Factors in Kidney Disease and Multimorbidity. |
| Lead Institution: | Chongqing University |
| Principal investigator: |
Application 1092882
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About
Kidney diseases, including chronic kidney disease (CKD) and acute kidney injury (AKI), are major public health burdens with rising prevalence and significant multimorbidity. The pathogenesis involves genetic susceptibility, environmental exposures, lifestyle factors, socioeconomic status, and gut-kidney axis interactions. Kidney diseases are systemically linked to cardiovascular, metabolic, immune, hematologic, skeletal, and neuropsychiatric disorders. For instance, CKD often coexists with hypertension, diabetes, anemia, and bone mineral disease, driven by shared pathways such as chronic inflammation, oxidative stress, and uremic toxicity. Large-scale biobanks like UK Biobank offer comprehensive phenotypic, genetic, and clinical data to investigate these complex interactions. We will employ genome-wide association studies (GWAS) to identify genetic variants and molecular mechanisms underlying kidney disease and its comorbidities. Machine learning models will be used for risk prediction, causal inference, and stratification of high-risk populations. Research Questions: 1. Which genetic, environmental, behavioral, and microecological factors predict kidney disease onset and progression? 2. How do these factors interact to drive systemic complications? 3. How does kidney disease interact with cardiovascular, metabolic, and other systems to promote multimorbidity? 4. What are the modifiable and genetic determinants of kidney disease and its comorbidities? Research Objectives: Identify predictive factors, elucidate relative mechanisms, assess multimorbidity across systems, infer causal relationships, uncover genetic variants and correlations, and develop prognostic models. This study will advance understanding of kidney disease complexity and support precision medicine approaches.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 21538 | Serum uric acid and incident sepsis risk: sex-specific nonlinear associations modified by kidney function in the UK Biobank | Zongjun Hu (+2) | 2026 | Scientific Reports |
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