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Biomarkers of Maternal Lead Exposure During Pregnancy Using Micro-Spatial Child Deciduous Dentine Measurements.

Environment international(2022)

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摘要
Background: Lead is a toxic chemical of public health concern, however limited biomarkers are able to recon-struct prior lead exposures in early-life when biospecimens are not collected and stored. Although child tooth dentine measurements accurately assess past child perinatal lead exposure, it has not been established if they reflect maternal exposure in pregnancy.Aim: To assess the prenatal relationship between child tooth dentine and maternal blood lead measurements and to estimate maternal lead exposure during the 2nd and 3rd trimesters of pregnancy from weekly child dentine profiles.Methods: We measured early-life lead exposure in child tooth dentine and maternal blood from 419 child-mother dyads enrolled in the Programming Research in Obesity, Growth, Environment and Social Stress (PROGRESS) cohort. We employed the Super-Learner algorithm to determine the relationship of dentine lead data with maternal blood lead concentrations and to predict maternal lead from child dentine lead data in blinded ana-lyses. We validated and quantified the bias of our results internally.Results: Mothers had moderate blood lead levels (trimesters: 2nd = 29.45 ug/L, 3rd = 31.78 ug/L). Trimester -averaged and weekly child dentine lead measurements were highly correlated with maternal blood levels in the corresponding trimesters. The predicted trimester-specific maternal lead levels were significantly correlated with actual measured blood values (trimesters: 2nd = 0.83; 3rd = 0.88). Biomarkers of maternal lead exposure discriminated women highly exposed to lead (>mean) with 85 % and 96 % specificity in the 2nd and 3rd tri-mesters, respectively, with 80 % sensitivity. Discussion: Weekly child dentine lead levels can serve as biomarkers of past child and maternal lead exposures during pregnancy.
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关键词
Tooth dentine lead levels,Blood lead levels,Prenatal lead exposure,Pregnancy,Machine learning,Super-Learner algorithm
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