PMID- 37578585
OWN - NLM
STAT- MEDLINE
DCOM- 20230913
LR  - 20230923
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 30
IP  - 43
DP  - 2023 Sep
TI  - Associations between multiple metals during early pregnancy and gestational 
      diabetes mellitus under four statistical models.
PG  - 96689-96700
LID - 10.1007/s11356-023-29121-4 [doi]
AB  - Gestational diabetes mellitus (GDM) is one of the most common complications of 
      pregnancy. Metal exposure is an emerging factor affecting the risk of GDM. 
      However, the effects of metal mixture on GDM and key metals within the mixture 
      remain unclear. This study was aimed at investigating the association between 
      metal mixture during early pregnancy and the risk of GDM using four statistical 
      methods and further at identifying the key metals within the mixture associated 
      with GDM. A nested case-control study including 128 GDM cases and 318 controls 
      was conducted in Beijing, China. Urine samples were collected before 13 
      gestational weeks and the concentrations of 13 metals were measured. Single-metal 
      analysis (unconditional logistic regression) and mixture analyses (Bayesian 
      kernel machine regression (BKMR), quantile g-computation, and elastic-net 
      regression (ENET) models) were applied to estimate the associations between 
      exposure to multiple metals and GDM. Single-metal analysis showed that Ni was 
      associated with lower risk of GDM, while positive associations of Sr and Sb with 
      GDM were observed. Compared with the lowest quartile of Ni, the ORs of GDM in the 
      highest quartiles were 0.49 (95% CI 0.24, 0.98). In mixture analyses, Ni and Mg 
      showed negative associations with GDM, while Co and Sb were positively associated 
      with GDM in BKMR and quantile g-computation models. No significant joint effect 
      of metal mixture on GDM was observed. However, interestingly, Ni was identified 
      as a key metal within the mixture associated with decreased risk of GDM by all 
      three mixture methods. Our study emphasized that metal exposure during early 
      pregnancy was associated with GDM, and Ni might have important association with 
      decreased GDM risk.
CI  - (c) 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Li, Luyi
AU  - Li L
AD  - Department of Occupational and Environmental Health Sciences, School of Public 
      Health, Peking University, Beijing, 100191, China.
FAU - Xu, Jialin
AU  - Xu J
AD  - Department of Environmental Health, T.H. Chan School of Public Health, Harvard 
      University, Boston, MA, 02115, USA.
FAU - Zhang, Wenlou
AU  - Zhang W
AD  - Department of Occupational and Environmental Health Sciences, School of Public 
      Health, Peking University, Beijing, 100191, China.
FAU - Wang, Zhaokun
AU  - Wang Z
AD  - Department of Occupational and Environmental Health Sciences, School of Public 
      Health, Peking University, Beijing, 100191, China.
FAU - Liu, Shan
AU  - Liu S
AD  - Department of Occupational and Environmental Health Sciences, School of Public 
      Health, Peking University, Beijing, 100191, China.
FAU - Jin, Lei
AU  - Jin L
AD  - Institute of Reproductive and Child Health, Department of Epidemiology and 
      Biostatistics, School of Public Health, Peking University, Beijing, 100191, 
      China.
FAU - Wang, Qi
AU  - Wang Q
AD  - Department of Toxicology, School of Public Health, Peking University, Beijing, 
      100191, China.
FAU - Wu, Shaowei
AU  - Wu S
AD  - Department of Occupational and Environmental Health, School of Public Health, 
      Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, China.
AD  - Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi 
      Province, Xi'an, 710061, Shaanxi, China.
FAU - Shang, Xuejun
AU  - Shang X
AD  - Department of Andrology, Jinling Hospital, School of Medicine, Nanjing 
      University, Nanjing, 210002, China.
FAU - Guo, Xinbiao
AU  - Guo X
AD  - Department of Occupational and Environmental Health Sciences, School of Public 
      Health, Peking University, Beijing, 100191, China.
FAU - Huang, Qingyu
AU  - Huang Q
AD  - Key Laboratory of Urban Environment and Health, Institute of Urban Environment, 
      Chinese Academy of Sciences, 1799 Jimei Road, Xiamen, 361021, China. 
      qyhuang@iue.ac.cn.
FAU - Deng, Furong
AU  - Deng F
AD  - Department of Occupational and Environmental Health Sciences, School of Public 
      Health, Peking University, Beijing, 100191, China.
LA  - eng
GR  - 2022YFC3702704/National Key Research and Development Program of China/
GR  - 2018YFC1004302/National Key Research and Development Program of China/
PT  - Journal Article
DEP - 20230814
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Metals)
SB  - IM
MH  - Pregnancy
MH  - Female
MH  - Humans
MH  - *Diabetes, Gestational/chemically induced/epidemiology
MH  - Case-Control Studies
MH  - Bayes Theorem
MH  - Metals
MH  - Logistic Models
OTO - NOTNLM
OT  - Bayesian kernel machine regression
OT  - Elastic-net regression
OT  - Gestational diabetes mellitus
OT  - Metal
OT  - Nested case-control study
OT  - Quantile g-computation
EDAT- 2023/08/14 12:41
MHDA- 2023/09/13 06:42
CRDT- 2023/08/14 11:25
PHST- 2022/11/28 00:00 [received]
PHST- 2023/07/29 00:00 [accepted]
PHST- 2023/09/13 06:42 [medline]
PHST- 2023/08/14 12:41 [pubmed]
PHST- 2023/08/14 11:25 [entrez]
AID - 10.1007/s11356-023-29121-4 [pii]
AID - 10.1007/s11356-023-29121-4 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2023 Sep;30(43):96689-96700. doi: 
      10.1007/s11356-023-29121-4. Epub 2023 Aug 14.