Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China
Many metals are involved in the pathogenesis of diabetes, but most of existing studies focused on single metals. The study of mixtures represents real-life exposure scenarios and deserves attention. This study aimed to explore the potential relationship of urinary copper (Cu), zinc (Zn), arsenic (As...
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2021
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oai:doaj.org-article:2df7ccf9011b4f57a0de3e9d02373d862021-11-14T04:28:23ZCorrelation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China0147-651310.1016/j.ecoenv.2021.112976https://doaj.org/article/2df7ccf9011b4f57a0de3e9d02373d862021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0147651321010885https://doaj.org/toc/0147-6513Many metals are involved in the pathogenesis of diabetes, but most of existing studies focused on single metals. The study of mixtures represents real-life exposure scenarios and deserves attention. This study aimed to explore the potential relationship of urinary copper (Cu), zinc (Zn), arsenic (As), selenium (Se), and strontium (Sr) contents with fasting plasma glucose (FPG) levels in 2766 participants. The levels of metals in urine were determined by inductively coupled plasma–mass spectrometry. We used linear regression models and the Bayesian kernel machine regression (BKMR) to evaluate the association between metals and FPG levels. In the multiple metals linear regression, Zn (β = 0.434), Se (β = 0.172), and Sr (β = −0.143) showed significant association with FPG levels (all P < 0.05). The BKMR model analysis showed that the results of single metal association were consistent with the multiple metals linear regression. The mixture of five metals had a positive over-all effect on FPG levels, and Zn (PIP = 1.000) contributed the most to the FPG levels. Cu and As were negatively correlated with FPG levels in women. The potential interaction effect between Cu and Sr was observed in participants aged ≥ 60 years old (Pinteraction = 0.035). In summary, our results suggested that multiple metals in urine are associated with FPG levels. Further studies are needed to confirm these findings and clarify the underlying mechanisms.Xiaoting MoJiansheng CaiYinxia LinQiumei LiuMin XuJunling ZhangShuzhen LiuChunmei WeiYanfei WeiShenxiang HuangTingyu MaiDechan TanHuaxiang LuTingyu LuoRuoyu GouZhiyong ZhangJian QinElsevierarticleUrinary metalFasting plasma glucoseDiabetesBayesian kernel machine regressionEnvironmental pollutionTD172-193.5Environmental sciencesGE1-350ENEcotoxicology and Environmental Safety, Vol 228, Iss , Pp 112976- (2021) |
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| language |
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| topic |
Urinary metal Fasting plasma glucose Diabetes Bayesian kernel machine regression Environmental pollution TD172-193.5 Environmental sciences GE1-350 |
| spellingShingle |
Urinary metal Fasting plasma glucose Diabetes Bayesian kernel machine regression Environmental pollution TD172-193.5 Environmental sciences GE1-350 Xiaoting Mo Jiansheng Cai Yinxia Lin Qiumei Liu Min Xu Junling Zhang Shuzhen Liu Chunmei Wei Yanfei Wei Shenxiang Huang Tingyu Mai Dechan Tan Huaxiang Lu Tingyu Luo Ruoyu Gou Zhiyong Zhang Jian Qin Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China |
| description |
Many metals are involved in the pathogenesis of diabetes, but most of existing studies focused on single metals. The study of mixtures represents real-life exposure scenarios and deserves attention. This study aimed to explore the potential relationship of urinary copper (Cu), zinc (Zn), arsenic (As), selenium (Se), and strontium (Sr) contents with fasting plasma glucose (FPG) levels in 2766 participants. The levels of metals in urine were determined by inductively coupled plasma–mass spectrometry. We used linear regression models and the Bayesian kernel machine regression (BKMR) to evaluate the association between metals and FPG levels. In the multiple metals linear regression, Zn (β = 0.434), Se (β = 0.172), and Sr (β = −0.143) showed significant association with FPG levels (all P < 0.05). The BKMR model analysis showed that the results of single metal association were consistent with the multiple metals linear regression. The mixture of five metals had a positive over-all effect on FPG levels, and Zn (PIP = 1.000) contributed the most to the FPG levels. Cu and As were negatively correlated with FPG levels in women. The potential interaction effect between Cu and Sr was observed in participants aged ≥ 60 years old (Pinteraction = 0.035). In summary, our results suggested that multiple metals in urine are associated with FPG levels. Further studies are needed to confirm these findings and clarify the underlying mechanisms. |
| format |
article |
| author |
Xiaoting Mo Jiansheng Cai Yinxia Lin Qiumei Liu Min Xu Junling Zhang Shuzhen Liu Chunmei Wei Yanfei Wei Shenxiang Huang Tingyu Mai Dechan Tan Huaxiang Lu Tingyu Luo Ruoyu Gou Zhiyong Zhang Jian Qin |
| author_facet |
Xiaoting Mo Jiansheng Cai Yinxia Lin Qiumei Liu Min Xu Junling Zhang Shuzhen Liu Chunmei Wei Yanfei Wei Shenxiang Huang Tingyu Mai Dechan Tan Huaxiang Lu Tingyu Luo Ruoyu Gou Zhiyong Zhang Jian Qin |
| author_sort |
Xiaoting Mo |
| title |
Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China |
| title_short |
Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China |
| title_full |
Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China |
| title_fullStr |
Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China |
| title_full_unstemmed |
Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China |
| title_sort |
correlation between urinary contents of some metals and fasting plasma glucose levels: a cross-sectional study in china |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/2df7ccf9011b4f57a0de3e9d02373d86 |
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