Rodosthenis S Rodosthenous1, Heather H Burris2, Katherine Svensson3, Chitra J Amarasiriwardena4, Alejandra Cantoral5, Lourdes Schnaas6, Adriana Mercado-García7, Brent A Coull8, Robert O Wright9, Martha M Téllez-Rojo10, Andrea A Baccarelli11. 1. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: rrodosth@hsph.harvard.edu. 2. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Neonatology, Beth Israel Deaconess Medical Center and Division of Newborn Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: heburris@bidmc.harvard.edu. 3. Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: Katherine.svensson@mssm.edu. 4. Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: Chitra.amarasiriwardena@mssm.edu. 5. Division of Research in Community Interventions, National Institute of Perinatology, Mexico City, Mexico. Electronic address: alejandra.cantoral@insp.mx. 6. Division of Research in Community Interventions, National Institute of Perinatology, Mexico City, Mexico. Electronic address: lschnaas@hotmail.com. 7. Division of Research in Community Interventions, National Institute of Perinatology, Mexico City, Mexico. Electronic address: adrianam@insp.mx. 8. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: bcoull@hsph.harvard.edu. 9. Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: Robert.wright@mssm.edu. 10. Division of Research in Community Interventions, National Institute of Perinatology, Mexico City, Mexico. Electronic address: mmtellez@insp.mx. 11. Laboratory of Precision Environmental Biosciences, Columbia University Mailman School of Public Health, New York, NY, USA. Electronic address: andrea.baccarelli@columbia.edu.
Abstract
BACKGROUND: As population lead levels decrease, the toxic effects of lead may be distributed to more sensitive populations, such as infants with poor fetal growth. OBJECTIVES: To determine the association of prenatal lead exposure and fetal growth; and to evaluate whether infants with poor fetal growth are more susceptible to lead toxicity than those with normal fetal growth. METHODS: We examined the association of second trimester maternal blood lead levels (BLL) with birthweight-for-gestational age (BWGA) z-score in 944 mother-infant participants of the PROGRESS cohort. We determined the association between maternal BLL and BWGA z-score by using both linear and quantile regression. We estimated odds ratios for small-for-gestational age (SGA) infants between maternal BLL quartiles using logistic regression. Maternal age, body mass index, socioeconomic status, parity, household smoking exposure, hemoglobin levels, and infant sex were included as confounders. RESULTS: While linear regression showed a negative association between maternal BLL and BWGA z-score (β=-0.06 z-score units per log2 BLL increase; 95% CI: -0.13, 0.003; P=0.06), quantile regression revealed larger magnitudes of this association in the <30th percentiles of BWGA z-score (β range [-0.08, -0.13] z-score units per log2 BLL increase; all P values<0.05). Mothers in the highest BLL quartile had an odds ratio of 1.62 (95% CI: 0.99-2.65) for having a SGA infant compared to the lowest BLL quartile. CONCLUSIONS: While both linear and quantile regression showed a negative association between prenatal lead exposure and birthweight, quantile regression revealed that smaller infants may represent a more susceptible subpopulation.
BACKGROUND: As population lead levels decrease, the toxic effects of lead may be distributed to more sensitive populations, such as infants with poor fetal growth. OBJECTIVES: To determine the association of prenatal lead exposure and fetal growth; and to evaluate whether infants with poor fetal growth are more susceptible to lead toxicity than those with normal fetal growth. METHODS: We examined the association of second trimester maternal blood lead levels (BLL) with birthweight-for-gestational age (BWGA) z-score in 944 mother-infantparticipants of the PROGRESS cohort. We determined the association between maternal BLL and BWGA z-score by using both linear and quantile regression. We estimated odds ratios for small-for-gestational age (SGA) infants between maternal BLL quartiles using logistic regression. Maternal age, body mass index, socioeconomic status, parity, household smoking exposure, hemoglobin levels, and infant sex were included as confounders. RESULTS: While linear regression showed a negative association between maternal BLL and BWGA z-score (β=-0.06 z-score units per log2 BLL increase; 95% CI: -0.13, 0.003; P=0.06), quantile regression revealed larger magnitudes of this association in the <30th percentiles of BWGA z-score (β range [-0.08, -0.13] z-score units per log2 BLL increase; all P values<0.05). Mothers in the highest BLL quartile had an odds ratio of 1.62 (95% CI: 0.99-2.65) for having a SGA infant compared to the lowest BLL quartile. CONCLUSIONS: While both linear and quantile regression showed a negative association between prenatal lead exposure and birthweight, quantile regression revealed that smaller infants may represent a more susceptible subpopulation.
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