PMID- 35490746
OWN - NLM
STAT- MEDLINE
DCOM- 20220608
LR  - 20220608
IS  - 1879-1298 (Electronic)
IS  - 0045-6535 (Linking)
VI  - 301
DP  - 2022 Aug
TI  - Combined exposure to multiple metals on serum uric acid in NHANES under three 
      statistical models.
PG  - 134416
LID - S0045-6535(22)00909-2 [pii]
LID - 10.1016/j.chemosphere.2022.134416 [doi]
AB  - BACKGROUND: There are rare researches on the correlations between metals exposure 
      and serum uric acid (SUA), and existing research has only investigated the single 
      metal effect. This study aimed to investigate the combined effects of metal 
      mixtures on SUA and hyperuricemia using three statistical models. METHODS: In 
      this study, the data were extracted from three cycle years of the National Health 
      and Nutrition Examination Survey (NHANES). Subsequently, generalized linear 
      regression, weighted quantile regression (WQS) and Bayesian kernel machine 
      regression (BKMR) models were fitted to evaluate the correlations between metal 
      mixtures and both SUA and hyperuricemia. RESULTS: Of 3926 participants included, 
      19.13% participants had hyperuricemia. It was found using multi-metals 
      generalized linear regression models that there were positive correlations of 
      arsenic and cadmium with both outcomes. The negative correlations were identified 
      in cobalt, iodine, and manganese with SUA concentration, whereas only cobalt was 
      negatively correlated with hyperuricemia. Based on the WQS regression model 
      fitted in positive direction, it was suggested that the WQS indices were 
      significantly correlated with SUA (beta = 6.64, 95% CI: 3.14-10.13) and 
      hyperuricemia (OR = 1.25, 95% CI: 1.08-1.44); however, the result achieved by 
      using the model fitted in negative direction indicated that the WQS indices were 
      only significantly correlated with SUA (beta = -5.29, 95%CI: 8.02  approximately  -2.56). With the 
      use of the BKMR model, a significant increasing trend between metal mixtures and 
      hyperuricemia was found, while no significant overall effect of metal mixtures on 
      SUA was identified. The predominant roles of arsenic, cadmium, and cobalt in the 
      change of SUA and hyperuricemia risk were found using all three models. 
      CONCLUSION: The finding of this study revealed that metal mixtures might have a 
      positive combined effect on hyperuricemia. The mutual verification of two 
      outcomes using the three different models provided strong public health 
      implications for protecting people from heavy metal pollution and preventing 
      hyperuricemia.
CI  - Copyright (c) 2022. Published by Elsevier Ltd.
FAU - Ma, Yudiyang
AU  - Ma Y
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Wuhan 
      University, No. 115, Dong-hu Road, Wuhan 430071, China.
FAU - Hu, Qian
AU  - Hu Q
AD  - Department of Public Health, Tongji Medical College, Huazhong University of 
      Science and Technology, China.
FAU - Yang, Donghui
AU  - Yang D
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Wuhan 
      University, No. 115, Dong-hu Road, Wuhan 430071, China.
FAU - Zhao, Yudi
AU  - Zhao Y
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Wuhan 
      University, No. 115, Dong-hu Road, Wuhan 430071, China.
FAU - Bai, Jianjun
AU  - Bai J
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Wuhan 
      University, No. 115, Dong-hu Road, Wuhan 430071, China.
FAU - Mubarik, Sumaira
AU  - Mubarik S
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Wuhan 
      University, No. 115, Dong-hu Road, Wuhan 430071, China.
FAU - Yu, Chuanhua
AU  - Yu C
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Wuhan 
      University, No. 115, Dong-hu Road, Wuhan 430071, China. Electronic address: 
      YuCHua@whu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20220428
PL  - England
TA  - Chemosphere
JT  - Chemosphere
JID - 0320657
RN  - 0 (Metals, Heavy)
RN  - 00BH33GNGH (Cadmium)
RN  - 268B43MJ25 (Uric Acid)
RN  - 3G0H8C9362 (Cobalt)
RN  - N712M78A8G (Arsenic)
SB  - IM
MH  - *Arsenic
MH  - Bayes Theorem
MH  - Cadmium
MH  - Cobalt
MH  - Humans
MH  - *Hyperuricemia/epidemiology
MH  - *Metals, Heavy
MH  - Models, Statistical
MH  - Nutrition Surveys
MH  - Uric Acid
OTO - NOTNLM
OT  - Bayesian kernel machine regression model
OT  - Generalized linear regression model
OT  - Heavy metals
OT  - Serum uric acid
OT  - Weighted quantile regression model
EDAT- 2022/05/02 06:00
MHDA- 2022/06/09 06:00
CRDT- 2022/05/01 19:23
PHST- 2021/12/03 00:00 [received]
PHST- 2022/03/21 00:00 [revised]
PHST- 2022/03/22 00:00 [accepted]
PHST- 2022/05/02 06:00 [pubmed]
PHST- 2022/06/09 06:00 [medline]
PHST- 2022/05/01 19:23 [entrez]
AID - S0045-6535(22)00909-2 [pii]
AID - 10.1016/j.chemosphere.2022.134416 [doi]
PST - ppublish
SO  - Chemosphere. 2022 Aug;301:134416. doi: 10.1016/j.chemosphere.2022.134416. Epub 
      2022 Apr 28.