Issue 10, 2023

Maternal hair segments reveal metal(loid) levels over the course of pregnancy: a preliminary study in Southern China

Abstract

Characterization of metal(loid) variation during pregnancy and identification of the affecting factors are important for assessing pregnancy exposures in epidemiological studies. In this study, maternal hair was collected in three segments (each 3 cm) from pregnant women in Guangzhou, China. Ten metal(loid)s, including six essential trace metal(loid)s and four toxic trace metal(loid)s, were analyzed to investigate the levels of various metal(loid)s during pregnancy and the factors that influence them. Strong pairwise correlations were observed between manganese (Mn), cobalt (Co), and vanadium (V), between selenium (Se), arsenic (As), and antimony (Sb), and between cadmium (Cd) and lead (Pb). All metal(loid)s except for Se, Mn, and Co showed strong correlations among the three hair segments, and most of the metal(loid)s had good reproducibility, with intraclass correlation coefficients (ICCs) ranging from 0.510 to 0.931, except for As (ICC = 0.334), Mn (ICC = 0.231), and Co (ICC = 0.235). Zn levels decreased, while Sb increased, in maternal hair during pregnancy. Maternal sociodemographic characteristics and dietary intake affected metal(loid) levels in maternal hair. These results provide foundational data for using maternal hair segmental analysis to evaluate exposure variation to metal(loid)s during pregnancy and the potential factors associated with them.

Graphical abstract: Maternal hair segments reveal metal(loid) levels over the course of pregnancy: a preliminary study in Southern China

Supplementary files

Article information

Article type
Paper
Submitted
03 Jul 2023
Accepted
01 Aug 2023
First published
17 Aug 2023

Environ. Sci.: Processes Impacts, 2023,25, 1684-1693

Maternal hair segments reveal metal(loid) levels over the course of pregnancy: a preliminary study in Southern China

B. Liu, F. Cai, B. Tang, J. Li, X. Yan, D. Du, J. Zheng, M. Ren and Y. Yu, Environ. Sci.: Processes Impacts, 2023, 25, 1684 DOI: 10.1039/D3EM00279A

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