PMID- 35932350
OWN - NLM
STAT- MEDLINE
DCOM- 20230206
LR  - 20240314
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 30
IP  - 2
DP  - 2023 Jan
TI  - Associations between co-exposure to multiple metals and renal function: a 
      cross-sectional study in Guangxi, China.
PG  - 2637-2648
LID - 10.1007/s11356-022-22352-x [doi]
AB  - The association between co-exposure to multiple metals and renal function is 
      poorly understood. We aimed to evaluate the individual and joint effects of metal 
      exposure on renal function in this study. We performed a cross-sectional study 
      including 5828 participants in Guangxi, China, in 2019. Urine concentrations of 
      17 metals were detected by inductively coupled plasma mass spectrometry (ICP-MS). 
      Logistic regression model and restricted cubic spline (RCS) were applied to 
      investigate the association of individual metal exposure with renal dysfunction. 
      Weighted quantile sum (WQS) regression and Bayesian kernel machine regression 
      (BKMR) were used to assess the co-exposure effects of the metals. Participants 
      with the highest quartile of urinary Cu were at 1.84-fold (95% confidence 
      interval (CI): 1.20-2.87) increased risk of renal dysfunction compared with the 
      lowest quartile. The highest quartiles of urinary Sr, Cs, V, Ba, and Se were 
      associated with 0.27-fold (95% CI: 0.17-0.43), 0.33 (95% CI: 0.19-0.53), 0.41 
      (95% CI: 0.25-0.65), 0.58 (95% CI: 0.36-0.90), and 0.33 (95% CI: 0.19-0.56) 
      decreased risk of renal dysfunction compared with their lowest quartile, 
      respectively. Furthermore, urinary Ba and Cu were non-linearly correlated with 
      renal dysfunction. The WQS analysis showed that mixed metal exposure was 
      inversely associated with renal dysfunction (OR = 0.47, 95% CI: 0.35-0.62), and 
      Sr accounted for the largest weight (52.2%), followed by Cs (32.3%) in the 
      association. Moreover, we observed a potential interaction between Cu, Cs, and Ba 
      for renal dysfunction in BKMR model. Exposure to Se, Sr, Cs, V, and Ba is 
      associated with decreased risk of renal dysfunction, whereas an increased risk is 
      associated with Cu exposure. Co-exposure to these metals is negatively associated 
      with renal dysfunction, and Sr and Cs are the main contributors to the 
      associations.
CI  - (c) 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Luo, Xingxi
AU  - Luo X
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Huang, Dongping
AU  - Huang D
AD  - Department of Sanitary Chemistry, School of Public Health, Guangxi Medical 
      University, Nanning, 530021, Guangxi, China.
FAU - Xiao, Suyang
AU  - Xiao S
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Lei, Lei
AU  - Lei L
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Wu, Kaili
AU  - Wu K
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Yang, Yu
AU  - Yang Y
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Liu, Meiliang
AU  - Liu M
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Qiu, Xiaoqiang
AU  - Qiu X
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Liu, Shun
AU  - Liu S
AD  - Department of Maternal, Child and Adolescent Health, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China.
FAU - Zeng, Xiaoyun
AU  - Zeng X
AUID- ORCID: 0000-0003-0671-2597
AD  - Department of Epidemiology and Health Statistics, School of Public Health, 
      Guangxi Medical University, Nanning, 530021, Guangxi, China. 
      zengxiaoyun@gxmu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20220806
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Metals)
SB  - IM
MH  - Humans
MH  - Cross-Sectional Studies
MH  - *Environmental Exposure/analysis
MH  - Bayes Theorem
MH  - China
MH  - Metals/analysis
MH  - Kidney/physiology/chemistry
MH  - *Kidney Diseases
OTO - NOTNLM
OT  - Bayesian kernel machine regression
OT  - Metals
OT  - Renal dysfunction
OT  - Weighted quantile sum regression
EDAT- 2022/08/07 06:00
MHDA- 2023/02/07 06:00
CRDT- 2022/08/06 11:14
PHST- 2022/04/25 00:00 [received]
PHST- 2022/07/28 00:00 [accepted]
PHST- 2022/08/07 06:00 [pubmed]
PHST- 2023/02/07 06:00 [medline]
PHST- 2022/08/06 11:14 [entrez]
AID - 10.1007/s11356-022-22352-x [pii]
AID - 10.1007/s11356-022-22352-x [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2023 Jan;30(2):2637-2648. doi: 
      10.1007/s11356-022-22352-x. Epub 2022 Aug 6.