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Using Structural Equation Model to Explore the Effects of Prenatal Metal Exposure on Toddler Neurodevelopment
structural equation model (SEM),arsenic,mercury,neurodevelopment,newborn,pregnancy,
|Publication Year :||2010|
材料與方法: 研究對象選取自2004年4月至2005年1月間收案的台灣出生世代先驅研究，共計106名孕婦及其新生兒被納入本研究。母親血液和新生兒臍帶血液中的金屬濃度作為反映出生前暴露的指標。以嬰幼兒綜合發展測驗 (The Comprehensive Developmental Inventory for Infants and Toddlers)評估孩童兩歲大時之神經行為發展。
結果:母親血液中的總砷和總汞濃度分別為6.7 ± 2.0 ( ug/L ) and 4.6 ± 1.7 ( ug/L )，臍帶血中的總砷和總汞濃度則為13.9 ± 1.8 ( ug/L ) and 4.5 ± 1.6 ( ug/L )。結構方程模式顯示，其模型適配度卡方值為 38.12 (p值=0.12)，CFI=0.95，RMSEA=0.06。血液中的砷及汞濃度和出生狀態及神經行為發展有關。模型中的出生狀態也可被推定為出生前金屬暴露影響神經行為發展的中介因子。
Background and objectives: Fetuses, infants, and young children are the most susceptible to some of environmental toxicological chemicals which can potentially affect their neurodevelopment. The effects of single metal exposure have been widely discussed for a long time, but few studies described joint exposure of heavy metals. To evaluate the roles of various affecting metals, a structural equation model was applied to estimate the effect from prenatal metal exposure on birth outcomes and the following neurodevelopment.
Methods: A prospective cohort study was conducted from April 2004 to January 2005 in Taiwan and a total of 106 delivering women and their newborns were enrolled in this study. Metal concentrations in maternal and umbilical cord blood samples were obtained to reflect prenatal metal exposure while birth outcomes were observed after delivery. The Comprehensive Developmental Inventory for Infants and Toddlers (CDIIT) was used for assessing toddler’s neurodevelopment at 2 years of age.
Results: Concentration of total arsenic and total mercury were 6.7 ± 2.0 ug/L and 4.6 ± 1.7 ug/L in maternal blood samples and 13.9 ± 1.8 ug/L and 4.5 ± 1.6 ug/L in umbilical blood samples. Results of structural equation model indicated that prenatal arsenic and mercury exposure were inversely associated with birth outcomes (coefficients -0.28 (p=0.05), -0.06 (p=0.05)) and neurodevelopment with model χ2 value of 38.12 (p=0.12), CFI of 0.95 and RMSEA of 0.06 for goodness of fit. This model also indicated that fetal birth outcome could be a mediated factor for prenatal metal exposure to predict following neurodevelopment.
Conclusions: By comparing different exposure pathways, this study suggested prenatal arsenic exposure might play a stronger role, as compared to mercury, affecting the birth outcomes and the following neurodevelopment. Seafood intake during pregnancy was thought as one of the exposure sources for arsenic and mercury exposure, therefore, further study to simultaneously explore to the nutritional benefits of seafood as well as the potential harms should be concerned.
|Appears in Collections:||職業醫學與工業衛生研究所|
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