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Analysis of risk factors for kidney stones in patients with hyperuricemia: a cross-sectional study based on NHANES
Xiaojing Liu1, Jingjing Zhou1, Conghui Liu1
1Department of Nephrology, Beijing LuHe Hospital, Capital Medical University, Beijing, China.
Background:
Hyperuricemia is a growing public health concern and a well-established risk factor for nephrolithiasis. However, the specific interplay of demographic, clinical, and biochemical factors that contribute to kidney stone formation within this high-risk population is not fully elucidated, particularly in a large, nationally representative cohort. This study aimed to identify the demographic, clinical, and biochemical risk factors for kidney stones in patients with hyperuricemia using a large, nationally representative U.S. cohort.
Methods:
This cross-sectional study included data from 661 patients with hyperuricemia from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. The presence of kidney stones was determined based on the questionnaire item #KIQ026. Multivariable logistic regression analysis was performed to identify significant risk factors. Restricted cubic splines (RCS) were used to model the dose-response relationships. A receiver operating characteristic (ROC) curve was constructed to evaluate the combined predictive value of the identified risk factors.
Results:
Among the 661 patients with hyperuricemia, 78 (11.8%) reported a history of kidney stones. The multivariable logistic regression analysis indicated that comorbid hypertension [odds ratio (OR) =2.25, 95% confidence interval (CI): 1.29-4.21], total serum calcium (OR =0.04, 95% CI: 0.003-0.68), and blood urea nitrogen (BUN) (OR =1.12, 95% CI: 1.05-1.20) were significantly associated with kidney stones. The dose-response analysis revealed a negative linear relationship between total serum calcium and the risk of kidney stones (P for non-linearity =0.18) and a positive linear relationship between BUN and the risk (P for non-linearity =0.68). The ROC curve based on these factors yielded an area under the curve (AUC) of 0.692 (95% CI: 0.651-0.730), indicating moderate predictive ability, with a sensitivity of 82.1% and a specificity of 51.3%.
Conclusions:
The prevalence of kidney stones in patients with hyperuricemia is influenced by comorbid hypertension, total serum calcium, and BUN. For patients with hyperuricemia, strategies such as controlling blood pressure, maintaining a balanced calcium intake, and optimizing protein intake could be beneficial and warrant further evaluation in preventing the occurrence of kidney stones. These findings require further validation in future studies.
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