Predictive value of single-nucleotide polymorphism signature for nephrolithiasis recurrence: a 5-year prospective study

Wei Zhu; Xin Zhang; Zhen Zhou; Yin Sun; Guangyuan Zhang; Xiaolu Duan; Zhicong Huang; Guoyao Ai; Yang Liu; Zhijian Zhao; Wen Zhong; Guohua Zeng

doi:10.1093/ckj/sfad119

Predictive value of single-nucleotide polymorphism signature for nephrolithiasis recurrence: a 5-year prospective study

Clin Kidney J. 2023 May 25;16(11):2205-2215. doi: 10.1093/ckj/sfad119. eCollection 2023 Nov.

Authors

Wei Zhu¹, Xin Zhang¹, Zhen Zhou¹, Yin Sun^{1

2}, Guangyuan Zhang³, Xiaolu Duan¹, Zhicong Huang¹, Guoyao Ai¹, Yang Liu¹, Zhijian Zhao¹, Wen Zhong¹, Guohua Zeng¹

Affiliations

¹ Guangdong Key Laboratory of Urology, Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
² Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA.
³ Department of Urology, Zhongda Hospital Southeast University, Nanjing, Jiangsu, China.

Abstract

Background: Genetic variations are linked to kidney stone formation. However, the association of single nucleotide polymorphism (SNPs) and stone recurrence has not been well studied. This study aims to identify genetic variants associated with kidney stone recurrences and to construct a predictive nomogram model using SNPs and clinical features to predict the recurrence risk of kidney stones.

Methods: We genotyped 49 SNPs in 1001 patients who received surgical stone removal between Jan 1 and Dec 31 of 2012. All patients were confirmed stone-free by CT scan and then received follow-up at least 5 years. SNP associations with stone recurrence were analyzed by Cox proportion hazard model. A predictive nomogram model using SNPs and clinical features to predict the recurrence risk of kidney stones was developed by use of LASSO Cox regression.

Results: The recurrence rate at 3, 5, 7 years were 46.8%, 71.2%, and 78.4%, respectively. 5 SNPs were identified that had association with kidney stone recurrence risk. We used computer-generated random numbers to assign 500 of these patients to the training cohort and 501 patients to the validation cohort. A nomogram that combined the 14-SNPs-based classifier with the clinical risk factors was constructed. The areas under the curve (AUCs) at 3, 5 and 7 years of this nomogram was 0.645, 0.723, and 0.75 in training cohort, and was 0.631, 0.708, and 0.727 in validation cohort, respectively. Results show that the nomogram presented a higher predictive accuracy than those of the SNP classifier or clinical factors alone.

Conclusion: SNPs are significantly associated with kidney stone recurrence and should add prognostic value to the traditional clinical risk factors used to assess the kidney stone recurrence. A nomogram using clinical and genetic variables to predict kidney stone recurrence has revealed its potential in the future as an assessment tool during the follow-up of kidney stone patients.

Keywords: kidney stone; recurrence; single-nucleotide polymorphism signature.