A two-step gas chromatography-tandem mass spectrometry method for measurement of multiple environmental pollutants in hu
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RESEARCH ARTICLE
A two-step gas chromatography-tandem mass spectrometry method for measurement of multiple environmental pollutants in human plasma Caitlin L. Johnson 1 & Elisa Jazan 1 & Sek Won Kong 2,3 & Kurt D. Pennell 4 Received: 10 June 2020 / Accepted: 1 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Individuals are exposed to a wide variety of chemicals over their lifetime, yet current understanding of mixture toxicology is still limited. We present a two-step analytical method using a gas chromatograph-triple quadrupole mass spectrometer that requires less than 1 mL of sample. The method is applied to 183 plasma samples from a study population of children with autism spectrum disorder, their parents, and unrelated neurotypical children. We selected 156 environmental chemical compounds and ruled out chemicals with detection rates less than 20% of our study cohort (n = 61), as well as ones not amenable to the selected extraction and analytical methods (n = 34). The targeted method then focused on remaining chemicals (n = 61) plus 8 additional polychlorinated biphenyls (PCBs). Persistent pollutants, such as p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE) and PCB congeners 118 and 180, were detected at high frequencies and several previously unreported chemicals, including 2,4,6trichlorophenol, isosafrole, and hexachlorobutadiene, were frequently detected in our study cohort. This work highlights the benefits of employing a multi-step analytical method in exposure studies and demonstrates the efficacy of such methods for reporting novel information on previously unstudied pollutant exposures. Keywords Gas chromatography-tandem mass spectrometry . GC-MS/MS . Chemical mixture . Biomonitoring . Pesticides . Persistent pollutants . environmental toxicants . polychlorinated biphenyls . p,p′-Dichlorodiphenyltrichloroethane . Human plasma
Introduction Responsible Editor: Ester Heath Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11356-020-10702-6) contains supplementary material, which is available to authorized users. * Sek Won Kong [email protected] * Kurt D. Pennell [email protected] 1
Department of Civil and Environmental Engineering, Tufts University, Medford, MA 02155, USA
2
Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA 02115, USA
3
Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
4
School of Engineering, Brown University, Box D, 184 Hope Street, Providence, RI 02912, USA
The assessment of risks from exposure to chemical mixtures is an important research and regulatory priority (Meek et al. 2011; Carlin et al. 2013; Kortenkamp and Faust 2018). Humans are exposed to a large number of chemicals over their lifetime, exposures which are often low level and transient but which may nevertheless represent significant contributions to health risk (Patel et al. 2010; Nachman 2011). Despite this, the majority of exposure studies assess a limited nu
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