Abstract：
BACKGROUND: Stroke is the second leading cause of death for all human beings and poses a serious threat to human health. Environmental exposure to a mixture of metals may be associated with the occurrence and development of stroke, but the evidence in the Chinese population is not yet conclusive.
OBJECTIVE: This study evaluated the association between stroke risk and 13 metals METHODS: Metal concentrations in whole blood samples from 100 stroke cases and 100 controls were measured by ICP-MS. The cumulative impact of mixed metal on stroke risk was investigated by using three statistical models, BKMR, WQS and QGC.
RESULTS: The case group had higher concentrations of Mg, Mn, Zn, Se, Sn, and Pb than the control group (p<0.05). BKMR model indicated a correlation between the risk of stroke and exposure to mixed metals. WQS model showed that Mg (27.2 %), Se (25.1 %) and Sn (14.8 %) were positively correlated with stroke risk (OR=1.53; 95 %Cl: 1.03-2.37, p=0.013). The QGC model showed that Mg (49.2 %) was positively correlated with stroke risk, while Ti (31.7 %) was negatively correlated with stroke risk.
CONCLUSIONS: Mg may be the largest contributor to the cumulative effect of mixed metal exposure on stroke risk, and the interaction between metals requires more attention. These findings could provide scientific basis for effectively preventing stroke by managing metals in the environment.
OBJECTIVE: This study evaluated the association between stroke risk and 13 metals METHODS: Metal concentrations in whole blood samples from 100 stroke cases and 100 controls were measured by ICP-MS. The cumulative impact of mixed metal on stroke risk was investigated by using three statistical models, BKMR, WQS and QGC.
RESULTS: The case group had higher concentrations of Mg, Mn, Zn, Se, Sn, and Pb than the control group (p<0.05). BKMR model indicated a correlation between the risk of stroke and exposure to mixed metals. WQS model showed that Mg (27.2 %), Se (25.1 %) and Sn (14.8 %) were positively correlated with stroke risk (OR=1.53; 95 %Cl: 1.03-2.37, p=0.013). The QGC model showed that Mg (49.2 %) was positively correlated with stroke risk, while Ti (31.7 %) was negatively correlated with stroke risk.
CONCLUSIONS: Mg may be the largest contributor to the cumulative effect of mixed metal exposure on stroke risk, and the interaction between metals requires more attention. These findings could provide scientific basis for effectively preventing stroke by managing metals in the environment.
摘要：
背景：中风是所有人类死亡的第二大原因，对人类健康构成严重威胁。环境暴露于金属混合物可能与中风的发生和发展有关，但是中国人口的证据还没有定论。
目的：这项研究评估了中风风险与13种金属之间的关系方法：通过ICP-MS测量了100例中风患者和100例对照的全血样品中的金属浓度。使用三种统计模型研究了混合金属对卒中风险的累积影响。BKMR,WQS和QGC。
结果：病例组的镁浓度较高，Mn,Zn,Se,Sn,和Pb高于对照组(p<0.05)。BKMR模型表明中风风险与接触混合金属之间存在相关性。WQS模型表明，Mg(27.2%)，硒（25.1％）和锡（14.8％）与卒中风险呈正相关（OR=1.53；95％Cl：1.03-2.37，p=0.013）。QGC模型显示Mg(49.2%)与卒中风险呈正相关,而Ti(31.7%)与卒中风险呈负相关。
结论：镁可能是混合金属暴露对卒中风险累积影响的最大因素，金属之间的相互作用需要更多的关注。这些发现可以为通过管理环境中的金属有效预防中风提供科学依据。
目的：这项研究评估了中风风险与13种金属之间的关系方法：通过ICP-MS测量了100例中风患者和100例对照的全血样品中的金属浓度。使用三种统计模型研究了混合金属对卒中风险的累积影响。BKMR,WQS和QGC。
结果：病例组的镁浓度较高，Mn,Zn,Se,Sn,和Pb高于对照组(p<0.05)。BKMR模型表明中风风险与接触混合金属之间存在相关性。WQS模型表明，Mg(27.2%)，硒（25.1％）和锡（14.8％）与卒中风险呈正相关（OR=1.53；95％Cl：1.03-2.37，p=0.013）。QGC模型显示Mg(49.2%)与卒中风险呈正相关,而Ti(31.7%)与卒中风险呈负相关。
结论：镁可能是混合金属暴露对卒中风险累积影响的最大因素，金属之间的相互作用需要更多的关注。这些发现可以为通过管理环境中的金属有效预防中风提供科学依据。
