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Association between Serum Uric Acid and the Early Marker of Kidney Function Decline among Chinese Middle-Aged and Older Population: Evidence from the China Health and Retirement Longitudinal Study

摘要Objective To evaluate the association between serum uric acid (SUA) and kidney function decline. Methods Data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older population for analysis. The kidney function decline was defined as an annual estimated glomerular filtration rate (eGFR) decrease by > 3 mL/min per 1.73 m2. Multivariable logistic regression was applied to determine the association between SUA and kidney function decline. The shape of the association was investigated by restricted cubic splines. Results A total of 7,346 participants were included, of which 1,004 individuals (13.67%) developed kidney function decline during the follow-up of 4 years. A significant dose-response relation was recorded between SUA and the kidney function decline (OR 1.14, 95% CI 1.03–1.27), as the risk of kidney function decline increased by 14% per 1 mg/dL increase in SUA. In the subgroup analyses, such a relation was only recorded among women (OR 1.22, 95% CI 1.03–1.45), those aged < 60 years (OR 1.22, 95% CI 1.05–1.42), and those without hypertension and without diabetes (OR 1.22, 95% CI 1.06–1.41). Although the dose-response relation was not observed in men, the high level of SUA was related to kidney function decline (OR 1.83, 95% CI 1.05–3.17). The restricted cubic spline analysis indicated that SUA > 5 mg/dL was associated with a significantly higher risk of kidney function decline. Conclusion The SUA level was associated with kidney function decline. An elevation of SUA should therefore be addressed to prevent possible kidney impairment and dysfunction.

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作者 TANG Xu [1] XU Lu [2] MENG Ruo Gu [3] DU Yi Qing [1] LIU Shi Jun [1] ZHAN Si Yan [4] XU Tao [1] 学术成果认领
作者单位 Department of Urology,Peking University People's Hospital,Beijing 100044,China [1] Research Center of Clinical Epidemiology,Peking University Third Hospital,Beijing 100191,China [2] National Institute of Health Data Science,Center for Data Science in Health and Medicine,Peking University,Beijing 100191,China [3] Research Center of Clinical Epidemiology,Peking University Third Hospital,Beijing 100191,China;Department of Epidemiology and Biostatistics,School of Public Health,Peking University,Beijing 100191,China;Center for Intelligent Public Health,Institute for Artificial Intelligence,Peking University,Beijing 100191,China [4]
栏目名称 Original Articles
DOI 10.3967/bes2023.026
发布时间 2023-04-14
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