| Title: | The Impact of Uric Acid Levels on Body Metabolism and Multisystem Diseases, and Interventions for Management |
| Lead Institution: | Jilin University |
| Principal investigator: |
Application 569503
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About
This study investigates the relationship between blood uric acid levels and aging-related diseases. The research aims to explore several key areas: Cohort Study: This will examine how blood uric acid levels correlate with metabolic indicators (such as blood glucose and lipids) and the function of key organs (cardiovascular system, liver, kidneys, nervous system, and musculoskeletal system). The study will also analyze the association between uric acid levels and metabolic diseases (e.g., hypertension, diabetes, hyperlipidemia) as well as kidney damage, cardiovascular diseases, and neurological disorders across different age groups during the aging process. Omics Analysis: The study will explore the mechanisms behind aging-related hyperuricemia, gout, and comorbidities, focusing on the circulatory and digestive systems. Specifically, it will investigate the interactions between high uric acid, metabolism, immune regulation, and inflammation, aiming to identify biomarkers related to uric acid changes and their role in aging and disease. Treatment and Management: The study will also look into the relationship between uric acid levels and mental health or cognitive function, particularly how this relationship varies with age. Furthermore, it will explore the influence of lifestyle factors-such as diet, exercise, and sleep-on the management of blood uric acid levels and its effects on uric acid generation and excretion. By combining cohort research and omics analysis, the study aims to provide new insights into how uric acid affects metabolism and organ function across different age groups. The findings will contribute to better understanding of the mechanisms behind age-related changes and help develop strategies for managing blood uric acid levels to prevent and treat related diseases.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 20441 | Processed meat intake and incident gout: Integrated evidence from epidemiology, plasma proteomics, and machine learning | Zhengda Wang (+8) | 2026 | Journal of Advanced Research |
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