Individual and joint effects of metal exposure on metabolic syndrome among Chinese adults

被引:22
作者
Liu, Ling [1 ]
Li, Xiang [1 ]
Wu, Mingyang [2 ]
Yu, Meng [1 ]
Wang, Limei [1 ]
Hu, Liqin [1 ]
Li, Yaping [1 ]
Song, Lulu [2 ]
Wang, Youjie [1 ,2 ]
Mei, Surong [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Environm Hlth Incubat,Tongji Med Co, Key Lab Environm & Hlth Wuhan,Minist Environm Pro, Key Lab Environm & Hlth,Minist Educ,Sch Publ Hlth, 13 Hongkong Rd, Wuhan 430030, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Publ Hlth, Dept Maternal & Child Hlth, Wuhan, Hubei, Peoples R China
关键词
Metals; Metabolic syndrome; Joint effects; Elastic net model; Chinese adults; OXIDATIVE STRESS; VARIABLE SELECTION; PHYSICAL-ACTIVITY; NATIONAL-HEALTH; SELENIUM LEVELS; TRACE-ELEMENTS; SERUM ZINC; US ADULTS; ASSOCIATION; RISK;
D O I
10.1016/j.chemosphere.2021.132295
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Growing evidence suggests that metal exposure contributes to metabolic syndrome (MetS), but little is known about the effects of combined exposure to metal mixtures. This cross-sectional study included 3748 adults who were recruited from the Medical Physical Examination Center of Tongji Hospital, Wuhan, China. The levels of 21 metal(loid)s in urine were measured by inductively coupled plasma mass spectrometry. MetS was diagnosed according to National Cholesterol Education Program's Adult Treatment Panel III recommendations. Multivariate logistic regression model was uesd to explore the effects of single-metal and multi-metal exposures. The elastic net (ENET) regularization with an environmental risk score (ERS) was performed to estimate the joint effects of exposure to metal mixtures. A total of 636 participants (17%) were diagnosed with MetS. In single metal models, MetS was positively associated with zinc (Zn) and negatively associated with nickel (Ni). In multiple metal models, the associations remained significant after adjusting for the other metals. In the joint association analysis, the ENET models selected Zn as the strongest predictor of MetS. Compared to the lowest quartile, the highest quartile of ERS was associated with an elevated risk of MetS (OR = 3.72; 95% CI: 2.77, 5.91;P-trend < 0.001). Overall, we identified that the combined effect of multiple metals was related to an increased MetS risk, with Zn being the major contributor. These findings need further validation in prospective studies.
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页数:10
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