Ye Z X, Qiao Y, Zhang Y S, Liu G H, Zhou J M, Dong J, Zhao Y, Ji Z G, Xiao H
Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100730, China.
Zhonghua Yi Xue Za Zhi. 2020 Jul 14;100(26):2036-2039. doi: 10.3760/cma.j.cn112137-20191026-02321.
To establish the metabolic evaluation database of urolithiasis, perform metabolic evaluation, and provide instructions for treatment and prevention of urolithiasis. This metabolic evaluation database was developed by JAVA and was established by Oracle11g database and Browser/Server framework. We extracted the clinical data of all patients who had complete information, and analyzed their risk factors of stone formation, stone-related medical history, blood and urine tests results and 24-hour urine analysis. A total of 360 patients diagnosed as urolithiasis were included in this research. Male to female ratio was 1.9∶1, and the urolithiasis was first diagnosed at (35.5±13.5) years old. Family history was positive in 39.7% of patients. Metabolic syndrome occurred in 35.0% of patients. Overweight or obesity occurred in 73.2% and 50.0% of male patients, respectively. Abdominal obesity in 62.3% and 56.1% of male and female patients, respectively. Among all patients, 67.5% had high urine sodium, 53.6% had hypercalciuria, 41.1% had hypocitraturia, 29.7% had hyperuricosuria, 22.5% had hypomagnesuria, 15.8% had hyperoxaluria, 11.7% had hyperphosphoraturia, and 36.4% had low urinary volume. The prevalence of overweight or obesity, abdominal obesity, hypertension, diabetes, and metabolic syndrome in stone patients were significantly higher than those in general population. The number of 24-hour urinary abnormalities was positively associated with body mass index. The interventions on high urinary sodium, low urinary volume, obesity and metabolic syndrome were important to the treatment of urolithiasis. This database would facilitate the metabolic evaluation, provide evidence for the treatment and prevention of urolithiasis, and lay foundation for finding important controllable risk factors of urinary stone.
建立尿石症代谢评估数据库，进行代谢评估，并为尿石症的治疗和预防提供指导。该代谢评估数据库由JAVA开发，采用Oracle11g数据库和浏览器/服务器框架建立。我们提取了所有信息完整的患者的临床资料，分析了他们的结石形成危险因素、结石相关病史、血液和尿液检测结果以及24小时尿液分析。本研究共纳入360例诊断为尿石症的患者。男女比例为1.9∶1，尿石症首次诊断年龄为（35.5±13.5）岁。39.7%的患者有家族史。35.0%的患者发生代谢综合征。男性患者超重或肥胖发生率分别为73.2%和50.0%。男性和女性患者腹部肥胖发生率分别为62.3%和56.1%。所有患者中，67.5%尿钠高，53.6%高钙尿症，41.1%低枸橼酸尿症，29.7%高尿酸尿症，22.5%低镁尿症，15.8%高草酸尿症，11.7%高磷尿症，36.4%尿量低。结石患者中超重或肥胖、腹部肥胖、高血压、糖尿病和代谢综合征的患病率显著高于普通人群。24小时尿液异常数量与体重指数呈正相关。对高尿钠、低尿量、肥胖和代谢综合征的干预对尿石症的治疗很重要。该数据库将有助于代谢评估，为尿石症的治疗和预防提供证据，并为寻找尿路结石重要的可控危险因素奠定基础。
