Associations of urinary metal concentrations with anemia: A cross-sectional study of Chinese community-dwelling elderly

被引:4
|
作者
Qiao, Guojie [1 ,5 ]
Shen, Zhuoheng [2 ,3 ]
Duan, Siyu [2 ,3 ]
Wang, Rui [2 ,3 ]
He, Pei [2 ,3 ]
Zhang, Zhongyuan [2 ,3 ]
Dai, Yuqing [2 ,3 ]
Li, Meiyan [2 ,3 ]
Chen, Yue [2 ,3 ]
Li, Xiaoyu [2 ,3 ]
Zhao, Yi [2 ,3 ]
Liu, Zhihong [2 ,3 ]
Yang, Huifang [2 ,3 ]
Zhang, Rui [2 ,3 ,4 ]
Guan, Suzhen [2 ,3 ,6 ]
Sun, Jian [2 ,3 ,6 ]
机构
[1] Shaanxi Prov Peoples Hosp, Radioimmun Ctr, Xian 710069, Shaanxi, Peoples R China
[2] Ningxia Med Univ, Sch Publ Hlth, Yinchuan 750004, Ningxia, Peoples R China
[3] Key Lab Environm Factors & Chron Dis Control, Yinchuan 750004, Ningxia, Peoples R China
[4] Ningxia Med Univ, Ningxia Key Lab Cerebrocranial Dis, Incubat Base Natl Key Lab, Yinchuan 750004, Ningxia, Peoples R China
[5] 256 Youyi West Rd, Xian 710069, Shaanxi, Peoples R China
[6] 1160 Shengli St, Yinchuan 750004, Ningxia, Peoples R China
关键词
Anemia; Urinary metals; Cross-sectional study; Older people; Metal mixture; BURDEN;
D O I
10.1016/j.ecoenv.2023.115828
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Background: Anemia seriously affects the health and quality of life of the older adult population and may be influenced by various types of environmental metal exposure. Current studies on metals and anemia are mainly limited to single metals, and the association between polymetals and their mixtures and anemia remains unclear. Methods: We determined 11 urinary metal concentrations and hemoglobin levels in 3781 participants. Binary logistic regression and restricted cubic spline (RCS) model were used to estimate the association of individual metals with anemia. We used Bayesian kernel machine regression (BKMR) and Quantile g-computation (Q-g) regression to assess the overall association between metal mixtures and anemia and identify the major contributing elements. Stratified analyses were used to explore the association of different metals with anemia in different populations. Results: In a single-metal model, nine urinary metals significantly associated with anemia. RCS analysis further showed that the association of arsenic (As) and copper (Cu) with anemia was linear, while cobalt, molybdenum, thallium, and zinc were non-linear. The BKMR model revealed a significant positive association between the concentration of metal mixtures and anemia. Combined Q-g regression analysis suggested that metals such as Cu, As, and tellurium (Te) were positively associated with anemia, with Te as the most significant contributor. Stratified analyses showed that the association of different metals with anemia varied among people of different sexes, obesity levels, lifestyle habits, and blood pressure levels. Conclusions: Multiple metals are associated with anemia in the older adult population. A significant positive association was observed between metal mixture concentrations and anemia, with Te being the most important factor. The association between urinary metal concentrations and anemia is more sensitive in the nonhypertensive populations.
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页数:8
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