广西某地区老年男性尿中金属浓度与高尿酸血症的相关性研究

Correlation between urinary metal and metalloid concentrations and hyperuricemia in elderly men in a place of Guangxi Zhuang Autonomous Region, China

目的 研究老年男性尿中金属浓度与高尿酸血症的相关性。方法 采用横断面设计,于2016年8月—2017年7月在广西某地区抽取老年男性进行健康体检、尿样采集和问卷调查,采用电感耦合等离子体质谱检测晨尿中锰、钛、钒、铁、钴、镍、铜、锌、砷、钼、镉、铅、硒、铷、锶、钡和铊17种金属元素的浓度。采用Wilcoxon秩和检验、多因素Logistic回归和限制性立方样条(RCS)函数等方法探究老年男性尿中金属浓度与高尿酸血症的相关性。结果 研究最终纳入375名老年男性,共有高尿酸血症患者86名(占22.9%)。高尿酸症患者尿中钒、铁、砷、铅、铷、锶和铊浓度均低于非高尿酸血症人群,而锌浓度高于非高尿酸血症人群,差异均有统计学意义(P<0.05)。RCS分析结果显示尿铜和尿锌与血尿酸关系曲线分别呈“U”型和“倒U”型,尿铁、钴、铷和锶与血尿酸均呈负相关(P<0.05),且尿铷与血尿酸的关系曲线呈现先快速降低后缓慢降低的下降趋势(P<0.05)。根据尿中金属元素浓度三分位数对应的低、中、高水平将研究对象分为T1、T2和T3三组,多因素Logistic回归单金属模型结果显示,尿锌、锶和铷T3浓度组研究对象高尿酸血症的患病风险分别是T1浓度组的3.74(95%CI:1.64~8.50)、0.42(95%CI:0.16~0.93)和0.35(95%CI:0.13~0.99)倍;多金属联合作用模型显示,尿锌T3浓度组研究对象高尿酸血症的患病风险是T1浓度组的4.96(95%CI:2.09~11.81)倍。RCS函数显示校正混杂因素后尿铷和锶与高尿酸血症患病风险呈负向剂量-反应关系(P<0.05,P_(非线性)> 0.05)。结论 尿中锌、铷和锶水平可能与广西某地区老年男性高尿酸血症有关,其中尿锌与高尿酸血症发生风险呈正相关,而尿铷和锶与其呈负相关。

Objective To investigate the association between urinary metal concentrations and hyperuricemia in elderly men. Methods In this cross-sectional study, elderly men were selected from a place in Guangxi from August 2016 to July 2017, and physical examination, urinary sample collection, and a questionnaire survey were performed. Inductively coupled plasma mass spectrometry was used to measure the concentrations of 17 metal elements in morning urinary samples, i.e., manganese, titanium, vanadium, iron, cobalt, nickel, copper, zinc, arsenic, molybdenum, cadmium, lead, selenium, rubidium, strontium, barium, and thallium. The Wilcoxon rank-sum test, the multivariate logistic regression model, and restricted cubic spline(RCS) functions were used to explore the association of urinary metal concentrations with hyperuricemia. Results A total of 375 elderly male subjects were enrolled, among whom 86(22.9%) were diagnosed with hyperuricemia. Compared with the non-hyperuricemia group, the hyperuricemia group had significantly lower urinary concentrations of vanadium, iron, arsenic, lead, rubidium, strontium, and thallium and a significantly higher urinary concentration of zinc(P<0.05). RCS analysis showed a U-shaped curve for the correlation between urinary copper and serum uric acid and an inverted U-shaped curve for the correlation between urinary zinc and serum uric acid. Urinary iron, cobalt, rubidium, and strontium were negatively correlated with serum uric acid(P<0.05), and the curve for the correlation between urinary rubidium and serum uric acid showed a downward trend, with a rapid reduction followed by a slow reduction(P<0.05). The participants were divided into three groups according to the tertiles of urinary metal concentrations: T1(low concentration), T2(medium concentration), and T3(high concentration) groups. The single-metal multivariate logistic regression model showed that the risk of hyperuricemia in the groups with T3 concentrations of zinc, strontium, and rubidium were 3.74 times(95% confidence interval [CI]: 1.64-8.50), 0.42 times(95% CI: 0.16-0.93), and 0.35 times(95% CI: 0.13-0.99), respectively, that in the groups with T1 concentrations, and the multi-metal model showed that the risk of hyperuricemia in the zinc T3 concentration group was 4.96 times(95% CI: 2.09-11.81) that in the zinc T1 concentration group. The RCS function showed a negative dose-response relationship between urinary rubidium/strontium and hyperuricemia after adjustment for confounding factors(P<0.05, P_(nonlinear)>0.05). Conclusion Urinary levels of zinc, rubidium, and strontium may be associated with hyperuricemia in elderly men in this place of Guangxi. Urinary zinc is positively correlated with the risk of hyperuricemia, while urinary rubidium and strontium are negatively correlated with the risk of hyperuricemia.