PDF(Pubmed)
Abstract：
Environmental exposure to toxic elements contributes to the pathogenesis of chronic kidney disease (CKD). Few studies focus on the association of urinary metals and metalloids concentrations with the urinary albumin/creatinine ratio (UACR) among elderly, especially in areas and seasons with severe air pollution.
We aimed to evaluate the associations of urinary metals and metalloids concentration with UACR, which is an early and sensitive indicator of CKD.
We conducted a cross-sectional study among 275 elderly people in Beijing from November to December 2016, which has experienced the most severe air pollution in China. We measured 15 urinary metals and metalloids concentration and estimated their association with UACR using a generalized linear model (GLM). Bayesian kernel machine regression (BKMR) and quantile g-computation (qgcomp) models were also conducted to evaluate the combined effect of metal and metalloid mixtures concentration.
Of the 275 elderly people included in the analysis, we found that higher urinary Cu concentration was positively associated with UACR using GLM (β = 0.36, 95% CI: 0.25, 0.46). Using the BKMR model, we found that the change in UACR was positively associated with a change in urinary Cu concentration from its 25th to 75th percentile value with all other metals and metalloids concentration fixed at their 25th, 50th, or 75th percentile levels. Urinary Cu concentration had the most significant positive contribution (59.15%) in the qgcomp model. Our finding was largely robust in three mixture modeling approaches: GLM, qgcomp, and BKMR.
This finding suggests that urinary Cu concentration was strongly positively associated with UACR. Further analyses in cohort studies are required to corroborate this finding.
We aimed to evaluate the associations of urinary metals and metalloids concentration with UACR, which is an early and sensitive indicator of CKD.
We conducted a cross-sectional study among 275 elderly people in Beijing from November to December 2016, which has experienced the most severe air pollution in China. We measured 15 urinary metals and metalloids concentration and estimated their association with UACR using a generalized linear model (GLM). Bayesian kernel machine regression (BKMR) and quantile g-computation (qgcomp) models were also conducted to evaluate the combined effect of metal and metalloid mixtures concentration.
Of the 275 elderly people included in the analysis, we found that higher urinary Cu concentration was positively associated with UACR using GLM (β = 0.36, 95% CI: 0.25, 0.46). Using the BKMR model, we found that the change in UACR was positively associated with a change in urinary Cu concentration from its 25th to 75th percentile value with all other metals and metalloids concentration fixed at their 25th, 50th, or 75th percentile levels. Urinary Cu concentration had the most significant positive contribution (59.15%) in the qgcomp model. Our finding was largely robust in three mixture modeling approaches: GLM, qgcomp, and BKMR.
This finding suggests that urinary Cu concentration was strongly positively associated with UACR. Further analyses in cohort studies are required to corroborate this finding.
摘要：
环境暴露于有毒元素有助于慢性肾脏病（CKD）的发病机理。很少有研究关注老年人尿中金属和类金属浓度与尿白蛋白/肌酐比（UACR）的关联。特别是在空气污染严重的地区和季节。
我们旨在评估尿金属和类金属浓度与UACR的关联，这是CKD的早期和敏感指标。
我们于2016年11月至12月在北京的275名老年人中进行了一项横断面研究，该研究经历了中国最严重的空气污染。我们测量了15种尿中金属和准金属的浓度，并使用广义线性模型（GLM）估计了它们与UACR的关联。还进行了贝叶斯核机回归（BKMR）和分位数g计算（qgcomp）模型，以评估金属和准金属混合物浓度的综合影响。
在纳入分析的275名老年人中，我们发现，较高的尿铜浓度与使用GLM的UACR呈正相关（β=0.36，95％CI：0.25，0.46）。使用BKMR模型，我们发现，UACR的变化与尿铜浓度从第25百分位数到第75百分位数的变化呈正相关，所有其他金属和类金属浓度固定在第25位，50岁，或第75百分位数水平。尿Cu浓度在qgcomp模型中具有最显著的正贡献(59.15%)。我们的发现在三种混合建模方法中基本上是稳健的：GLM，qgcomp,BKMR
这一发现表明尿Cu浓度与UACR呈强烈正相关。需要在队列研究中进行进一步的分析来证实这一发现。
我们旨在评估尿金属和类金属浓度与UACR的关联，这是CKD的早期和敏感指标。
我们于2016年11月至12月在北京的275名老年人中进行了一项横断面研究，该研究经历了中国最严重的空气污染。我们测量了15种尿中金属和准金属的浓度，并使用广义线性模型（GLM）估计了它们与UACR的关联。还进行了贝叶斯核机回归（BKMR）和分位数g计算（qgcomp）模型，以评估金属和准金属混合物浓度的综合影响。
在纳入分析的275名老年人中，我们发现，较高的尿铜浓度与使用GLM的UACR呈正相关（β=0.36，95％CI：0.25，0.46）。使用BKMR模型，我们发现，UACR的变化与尿铜浓度从第25百分位数到第75百分位数的变化呈正相关，所有其他金属和类金属浓度固定在第25位，50岁，或第75百分位数水平。尿Cu浓度在qgcomp模型中具有最显著的正贡献(59.15%)。我们的发现在三种混合建模方法中基本上是稳健的：GLM，qgcomp,BKMR
这一发现表明尿Cu浓度与UACR呈强烈正相关。需要在队列研究中进行进一步的分析来证实这一发现。
