Prenatal metal(loid) mixtures and birth weight for gestational age: A pooled analysis of three cohorts participating in the ECHO program - PubMed (original) (raw)

doi: 10.1016/j.envint.2022.107102. Epub 2022 Jan 23.

Sara S Nozadi 2, Erika Garcia 3, Thomas G O'Connor 4, Anne P Starling 5, Shohreh F Farzan 3, Brian P Jackson 6, Juliette C Madan 7, Akram N Alshawabkeh 8, José F Cordero 9, Theresa M Bastain 3, John D Meeker 10, Carrie V Breton 3, Margaret R Karagas 1; program collaborators for Environmental Influences on Child Health Outcomes

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Prenatal metal(loid) mixtures and birth weight for gestational age: A pooled analysis of three cohorts participating in the ECHO program

Caitlin G Howe et al. Environ Int. 2022 Mar.

Abstract

Background: A growing number of studies have identified both toxic and essential metals which influence fetal growth. However, most studies have conducted single-cohort analyses, which are often limited by narrow exposure ranges, and evaluated metals individually. The objective of the current study was to conduct an environmental mixture analysis of metal impacts on fetal growth, pooling data from three geographically and demographically diverse cohorts in the United States participating in the Environmental Influences on Child Health Outcomes program.

Methods: The pooled sample (N = 1,002) included participants from the MADRES, NHBCS, and PROTECT cohorts. Associations between seven metals (antimony, cadmium, cobalt, mercury, molybdenum, nickel, tin) measured in maternal urine samples collected during pregnancy (median: 16.0 weeks gestation) and birth weight for gestational age z-scores (BW for GA) were investigated using Bayesian Kernel Machine Regression (BKMR). Models were also stratified by cohort and infant sex to investigate possible heterogeneity. Chromium and uranium concentrations fell below the limits of detection for most participants and were evaluated separately as binary variables using pooled linear regression models.

Results: In the pooled BKMR analysis, antimony, mercury, and tin were inversely and linearly associated with BW for GA, while a positive linear association was identified for nickel. The inverse association between antimony and BW for GA was observed in both males and females and for all three cohorts but was strongest for MADRES, a predominantly low-income Hispanic cohort in Los Angeles. A reverse j-shaped association was identified between cobalt and BW for GA, which was driven by female infants. Pooled associations were null for cadmium, chromium, molybdenum, and uranium, and BKMR did not identify potential interactions between metal pairs.

Conclusions: Findings suggest that antimony, an understudied metalloid, may adversely impact fetal growth. Cohort- and/or sex-dependent associations were identified for many of the metals, which merit additional investigation.

Keywords: BKMR; Fetal growth; Metalloids; Metals; Mixtures; Pooled analysis.

Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

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Figures

Fig. 1.

Fig. 1.

Pearson Correlations between Urinary Metal Pairs for the Pooled Sample (N = 1,002). Positive correlations are indicated in blue and negative correlations in red. Darker shades indicate stronger correlations. Numeric cor-relation coefficients are overlaid on the plot. All pairwise correlations were statistically significant (P < 0.05). Urinary metals were adjusted for specific gravity to account for urine dilution and log2-transformed. Abbreviations: Cd, cadmium; Co, cobalt; Hg, mercury; Mo, molybdenum; Ni, nickel; Sb, antimony; Sn, tin. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2.

Fig. 2.

BKMR Metal-BW for GA Associations (N = 1,002). Each panel shows the association between the speciified metal and BW for GA, holding all other metals in the mixture at their median and adjusting for maternal age, pre-pregnancy BMI, education, parity, gestational age at urine collection, in utero tobacco smoke exposure, and cohort. Abbreviations: BKMR, Bayesian Kernel Ma-chine Regression; BW for GA, birth weight for gesta-tional age; Cd, cadmium; Co, cobalt; Hg, mercury; Mo, molybdenum; Ni, nickel; Sb, antimony; Sn, tin.

Fig. 3.

Fig. 3.

BKMR Cumulative Metal Mixture Association with BW for GA (N = 1,002). The y-axis shows the estimated difference in BW for GA when setting all metals to the quantile specified on the x-axis, compared with setting all metals to their median values. The BKMR model was adjusted for maternal age, pre-pregnancy BMI, education, parity, gestational age at urine collection, in utero tobacco smoke exposure, and cohort. Abbreviations: BW for GA, birth weight for gestational age.

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