Zhu Wei, Zhang Xin, Zhou Zhen, Sun Yin, Zhang Guangyuan, Duan Xiaolu, Huang Zhicong, Ai Guoyao, Liu Yang, Zhao Zhijian, Zhong Wen, Zeng Guohua
Guangdong Key Laboratory of Urology, Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA.
Clin Kidney J. 2023 May 25;16(11):2205-2215. doi: 10.1093/ckj/sfad119. eCollection 2023 Nov.
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.
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.
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.
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.
基因变异与肾结石形成有关。然而,单核苷酸多态性(SNP)与结石复发的关联尚未得到充分研究。本研究旨在识别与肾结石复发相关的基因变异,并构建一个使用SNP和临床特征来预测肾结石复发风险的预测列线图模型。
我们对2012年1月1日至12月31日期间接受手术取石的1001例患者的49个SNP进行了基因分型。所有患者经CT扫描证实结石清除,随后接受至少5年的随访。通过Cox比例风险模型分析SNP与结石复发的关联。利用LASSO Cox回归建立了一个使用SNP和临床特征来预测肾结石复发风险的预测列线图模型。
3年、5年、7年的复发率分别为46.8%、71.2%和78.4%。确定了5个与肾结石复发风险相关的SNP。我们使用计算机生成的随机数将其中500例患者分配到训练队列,501例患者分配到验证队列。构建了一个将基于14个SNP的分类器与临床风险因素相结合的列线图。该列线图在训练队列中3年、5年和7年的曲线下面积(AUC)分别为0.645、0.723和0.75,在验证队列中分别为0.631、0.708和0.727。结果表明,该列线图的预测准确性高于单独的SNP分类器或临床因素。
SNP与肾结石复发显著相关,应为用于评估肾结石复发的传统临床风险因素增加预后价值。一个使用临床和基因变量来预测肾结石复发的列线图已显示出其在未来作为肾结石患者随访期间评估工具的潜力。