Abstract
Background
Metabolomics is a promising method to investigate physiological effects of chemical exposures during pregnancy, with the potential to clarify toxicological mechanisms, suggest sensitive endpoints, and identify novel biomarkers of exposures.
Objective
Investigate the influence of chemical exposures on the maternal plasma metabolome during pregnancy.
Methods
Data were obtained from participants (n = 177) in the New Hampshire Birth Cohort Study, a prospective pregnancy cohort. Chemical exposures were assessed via silicone wristbands worn for one week at ~13 gestational weeks. Metabolomic features were assessed in plasma samples obtained at ~24–28 gestational weeks via the Biocrates AbsoluteIDQ® p180 kit and nuclear magnetic resonance (NMR) spectroscopy. Associations between chemical exposures and plasma metabolomics were investigated using multivariate modeling.
Results
Chemical exposures predicted 11 (of 226) and 23 (of 125) metabolomic features in Biocrates and NMR, respectively. The joint chemical exposures did not significantly predict pathway enrichment, though some individual chemicals were associated with certain amino acids and related metabolic pathways. For example, N,N-diethyl-m-toluamide was associated with the amino acids glycine, L-glutamic acid, L-asparagine, and L-aspartic acid and enrichment of the ammonia recycling pathway.
Significance
This study contributes evidence to the potential effects of chemical exposures during pregnancy upon the endogenous maternal plasma metabolome.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 6 print issues and online access
$259.00 per year
only $43.17 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Larsen WJ. Human embryology. Philadelphia, PA: Churchill Livingstone; 2001.
Rice D, Barone S Jr. Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models. Environ health Perspect. 2000;108:511–33.
Selevan SG, Kimmel CA, Mendola P. Identifying critical windows of exposure for children’s health. Environ health Perspect. 2000;108:451–5.
ACOG. Exposure to toxic environmental agents. Fertil Steril. 2013;100:931–4.
Stillerman KP, Mattison DR, Giudice LC, Woodruff TJ. Environmental exposures and adverse pregnancy outcomes: a review of the science. Reprod Sci. 2008;15:631–50.
Wigle DT, Arbuckle TE, Turner MC, Bérubé A, Yang Q, Liu S, et al. Epidemiologic evidence of relationships between reproductive and child health outcomes and environmental chemical contaminants. J Toxicol Environ Health Part B. 2008;11:373–517.
Braun JM. Early-life exposure to EDCs: role in childhood obesity and neurodevelopment. Nat Rev Endocrinol. 2017;13:161.
Kortenkamp A. Low dose mixture effects of endocrine disrupters and their implications for regulatory thresholds in chemical risk assessment. Curr Opin Pharmacol. 2014;19:105–11.
Silva E, Rajapakse N, Kortenkamp A. Something from “nothing”− eight weak estrogenic chemicals combined at concentrations below NOECs produce significant mixture effects. Environ Sci Technol. 2002;36:1751–6.
Kaddurah-Daouk R, Kristal BS, Weinshilboum RM. Metabolomics: a global biochemical approach to drug response and disease. Annu Rev Pharmacol Toxicol. 2008;48:653–83.
Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134:714–7.
Smart RC, Hodgson E. Molecular and biochemical toxicology. Hoboken, NJ: John Wiley & Sons; 2018.
Deng P, Li X, Petriello MC, Wang C, Morris AJ, Hennig B. Application of metabolomics to characterize environmental pollutant toxicity and disease risks. Rev Environ Health. 2019;34:251–9.
Cai Y, Vollmar AKR, Johnson CH. Analyzing metabolomics data for environmental health and exposome research. Computational methods and data analysis for metabolomics. New York, NY: Springer; 2020. p. 447-67.
Mazzella M, Sumner SJ, Gao S, Su L, Diao N, Mostofa G, et al. Quantitative methods for metabolomic analyses evaluated in the Children’s Health Exposure Analysis Resource (CHEAR). J Exposure Sci Environ Epidemiol. 2020;30:16–27.
Bonvallot N, Tremblay-Franco M, Chevrier C, Canlet C, Debrauwer L, Cravedi J-P, et al. Potential input from metabolomics for exploring and understanding the links between environment and health. J Toxicol Environ Health, Part B. 2014;17:21–44.
Hu X, Li S, Cirillo PM, Krigbaum NY, Tran V, Jones DP, et al. Metabolome wide association study of serum poly and perfluoroalkyl substances (PFASs) in pregnancy and early postpartum. Reprod Toxicol. 2019;87:70–8.
Li H, Wang M, Liang Q, Jin S, Sun X, Jiang Y, et al. Urinary metabolomics revealed arsenic exposure related to metabolic alterations in general Chinese pregnant women. J Chromatogr A. 2017;1479:145–52.
Maitre L, Robinson O, Martinez D, Toledano MB, Ibarluzea J, Marina LS, et al. Urine metabolic signatures of multiple environmental pollutants in pregnant women: an exposome approach. Environ Sci Technol. 2018;52:13469–80.
Yang X, Zhang M, Lu T, Chen S, Sun X, Guan Y, et al. Metabolomics study and meta-analysis on the association between maternal pesticide exposome and birth outcomes. Environ Res. 2020;182:109087.
Zhou M, Ford B, Lee D, Tindula G, Huen K, Tran V, et al. Metabolomic markers of phthalate exposure in plasma and urine of pregnant women. Front Public Health. 2018;6:298.
Doherty BT, Pearce JL, Anderson KA, Karagas MR, Romano ME. Assessment of multipollutant exposures during pregnancy using silicone wristbands. Front Public Health. 2020;8:570.
Gilbert-Diamond D, Emond JA, Baker ER, Korrick SA, Karagas MR. Relation between in utero arsenic exposure and birth outcomes in a cohort of mothers and their newborns from New Hampshire. Environ Health Perspect. 2016;124:1299–307.
Davis MA, Li Z, Gilbert-Diamond D, Mackenzie TA, Cottingham KL, Jackson BP, et al. Infant toenails as a biomarker of in utero arsenic exposure. J Exposure Sci Environ Epidemiol. 2014;24:467–73.
Gilbert-Diamond D, Cottingham KL, Gruber JF, Punshon T, Sayarath V, Gandolfi AJ, et al. Rice consumption contributes to arsenic exposure in US women. Proc Natl Acad Sci. 2011;108:20656–60.
O’Connell SG, McCartney MA, Paulik LB, Allan SE, Tidwell LG, Wilson G, et al. Improvements in pollutant monitoring: optimizing silicone for co-deployment with polyethylene passive sampling devices. Environ Pollut. 2014;193:71–8.
Anderson KA, Points GL, Donald CE, Dixon HM, Scott RP, Wilson G, et al. Preparation and performance features of wristband samplers and considerations for chemical exposure assessment. J Exposure Sci Environ Epidemiol. 2017;27:551–9.
Bergmann AJ, Scott RP, Wilson G, Anderson KA. Development of quantitative screen for 1550 chemicals with GC-MS. Anal Bioanal Chem. 2018;410:3101–10.
Dona AC, Jiménez B, Schäfer H, Humpfer E, Spraul M, Lewis MR, et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem. 2014;86:9887–94.
Leek J, Johnson W, Parker H, Fertig E, Jaffe A, Zhang Y, et al. sva; Surrogate variable analysis; R package version 3.36.0. 2020.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.
Buuren SV, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. J Stat Softw. 2010;45:1–68.
Rohart F, Gautier B, Singh A, Le Cao K. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752.
González I, Lê Cao K-A, Davis MJ, Déjean S. Visualising associations between paired ‘omics’ data sets. BioData Min. 2012;5:19.
Team RC. R: A language and enviornment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc: Ser B (Methodol). 1995;57:289–300.
Wu G. Amino acids: metabolism, functions, and nutrition. Amino acids. 2009;37:1–17.
Wu G. Functional amino acids in nutrition and health. Amino Acids. 2013;45:407–11.
Gao H. Amino acids in reproductive nutrition and health. amino acids in nutrition and health. Cham, Switzerland: Springer; 2020. p. 111-31.
Manta-Vogli PD, Schulpis KH, Dotsikas Y, Loukas YL. The significant role of amino acids during pregnancy: nutritional support. J Matern-Fetal Neonatal Med. 2020;33:334–40.
Vaughan O, Rosario F, Powell T, Jansson T. Regulation of placental amino acid transport and fetal growth. Prog Mol Biol Transl Sci. 2017;145:217–51.
Fernstrom JD. Large neutral amino acids: dietary effects on brain neurochemistry and function. Amino acids. 2013;45:419–30.
Brini CM, Tremblay GC. Reversible inhibition of the urea cycle and gluconeogenesis by N, N-diethyl-m-toluamide. Biochemical biophysical Res Commun. 1991;179:1264–8.
Heick H, Peterson R, Dalpe-Scott M, Qureshi I. Insect repellent, N, N-diethyl-m-toluamide, effect on ammonia metabolism. Pediatrics 1988;82:373–6.
Dai Y, Huo X, Cheng Z, Faas MM, Xu X. Early-life exposure to widespread environmental toxicants and maternal-fetal health risk: a focus on metabolomic biomarkers. Sci Total Environ. 2020;739:139626.
Braun JM, Just AC, Williams PL, Smith KW, Calafat AM, Hauser R. Personal care product use and urinary phthalate metabolite and paraben concentrations during pregnancy among women from a fertility clinic. J Exposure Sci Environ Epidemiol. 2014;24:459–66.
Koo HJ, Lee BM. Estimated exposure to phthalates in cosmetics and risk assessment. J Toxicol Environ Health, Part A. 2004;67:1901–14.
Charles A, Darbre P. Oestrogenic activity of benzyl salicylate, benzyl benzoate and butylphenylmethylpropional (Lilial) in MCF7 human breast cancer cells in vitro. J Appl Toxicol. 2009;29:422–34.
Sarantis H, Naidenko OV, Gray S, Houlihan J, Malkan S. Not So Sexy: The Health Risks of Secret Chemicals in Fragrance. Campaign for Safe Cosmetics; 2010. Available from: https://www.safecosmetics.org/wp-content/uploads/2015/02/Not-So-Sexy-report.pdf. Jointly Published by Breast Cancer Fund, Commonweal and Environmental Working Group.
Acknowledgements
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We wish to thank our ECHO colleagues, the medical, nursing and program staff, as well as the children and families participating in the ECHO cohorts. We acknowledge the contribution of the following ECHO program collaborators: ECHO Coordinating Center: Duke Clinical Research Institute, Durham, North Carolina: Benjamin DK, Smith PB, Newby KL (see Appendix for full listing). We would also like to acknowledge and thank the following: Richard Scott, Clarisa Caballero-Ignacio, Michael Barton, Jessica Scotten, Holly Dixon, Kaci Graber, Caoilinn Haggerty, Michelle Schreiner, and Erika Dade.
Funding
This research was support by Children’s Environmental Health and Disease Prevention Research Center at Dartmouth via that National Institute of Environmental Health Sciences (NIEHS, P01 ES022832), Dartmouth Center for Molecular Epidemiology and Centers of Biomedical Research Excellence (COBRE) via National Institute of General Medical Sciences (NIGMS, P20 GM104416), the RTI Children’s Health Exposure Analysis Resource (CHEAR) Exposure Assessment Hub (NIEHS, U2CES026544, Fennell PI), and the National Library of Medicine (R01LM012723). Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) program, Office of The Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), and UH3 OD023275, and NIH NIEHS P42ES007373. BTD was supported by the Training Program for Quantitative Population Sciences in Cancer via the National Cancer Institute (NCI, R25 CA134286). KAA was supported by the National Institute of Environmental Health Sciences (NIEHS, P42 ES016465 and P30 ES030287).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study design, laboratory or statistical analysis plan, and critically revised and reviewed the manuscript. BTD conducted the statistical analyses and drafted the initial manuscript under the supervision of MER and MRK. JCM, AGH, and MRK designed the metabolomics assessment strategy in collaboration with SJS and all provided critical feedback on early versions of the manuscript. SLM, WWP, DAS, and DAK performed the metabolomics laboratory work, assisted in cleaning, analyzing, and interpreting the metabolomics data, and drafted portions of the methods under the supervision of SJS. KAA assisted in interpreting data related to the silicone wristbands and revised portions of the methods to clarify the exposure assessment methods. JG and SLM refined the statistical analysis and assisted with interpretation of results. MER supervised all aspects of the study and drafted portions of the introduction and discussion.
Corresponding author
Ethics declarations
Competing interests
KA, an author of this research, discloses a financial interest in MyExposome, Inc., which is marketing products related to the research being reported. The terms of this arrangement have been reviewed and approved by OSU in accordance with its policy on research conflicts of interest.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
About this article
Cite this article
Doherty, B.T., McRitchie, S.L., Pathmasiri, W.W. et al. Chemical exposures assessed via silicone wristbands and endogenous plasma metabolomics during pregnancy. J Expo Sci Environ Epidemiol 32, 259–267 (2022). https://doi.org/10.1038/s41370-021-00394-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41370-021-00394-6
Keywords
- Exposome
- Metabolome
- Silicone wristband
- Pregnancy
- Exposure
- Multipollutant