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Chemical mixtures and neurobehavior: a review of epidemiologic findings and future directions

  • Ann M. Vuong ORCID logo EMAIL logo , Kimberly Yolton , Joseph M. Braun , Bruce P. Lanphear and Aimin Chen
Published/Copyright: June 29, 2020

Abstract

Background

Epidemiological studies have historically focused on single toxicants, or toxic chemicals, and neurodevelopment, even though the interactions of chemicals and nutrients may result in additive, synergistic, antagonistic, or potentiating effects on neurological endpoints. Investigating the impact of environmentally-relevant chemical mixtures, including heavy metals and endocrine disrupting chemicals (EDCs), is more reflective of human exposures and may result in more refined environmental policies to protect the public.

Objective

In this review, we provide a summary of epidemiological studies that have analyzed chemical mixtures of heavy metals and EDCs and neurobehavior utilizing multi-chemical models, including frequentist and Bayesian methods.

Content

Studies investigating chemicals and neurobehavior have the opportunity to not only examine the impact of chemical mixtures, but they can also identify chemicals from a mixture that may play a key role in neurotoxicity, investigate interactive effects, estimate non-linear dose response, and identify potential windows of susceptibility. The examination of neurobehavioral domains is particularly challenging given that traits emerge and change over time and subclinical nuances of neurobehavior are often unrecognized. To date, only a handful of epidemiological studies examining neurodevelopment have utilized multi-pollutant models in the investigation of heavy metals and EDCs. However, these studies were successful in identifying contaminants of importance from the exposure mixtures.

Summary and Outlook

Investigators are encouraged to broaden their focus to include more environmentally relevant mixtures of chemicals using advanced statistical approaches, particularly to aid in identifying potential mechanisms underlying associations.


Corresponding author: Dr. Ann M. Vuong, Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, 4700 S. Maryland Parkway Suite 335, Las Vegas, NV, 89119-3063, USA. Tel: 702-895-4950, E-mail:

Award Identifier / Grant number: P01R829389

Funding source: National Institute of Environmental Health Sciences

Award Identifier / Grant number: P01 ES11261R01 ES014575R01 ES020349R01 ES024381R01 ES025214R01ES028277

  1. Research Funding: National Institute of Environmental Health Sciences (P01 ES11261, R01 ES020349, R01 ES024381, R01 ES025214, R01 ES014575, R01ES028277) and Environmental Protection Agency (P01 R829389).

  2. Author contribution: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical statements: Ethical statements are mandatory for original research that involved human or animal subjects.

References

1. Rice, D, Barone, SJr. Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models. Environ Health Perspect 2000;108:511–33. https://doi.org/10.2307/3454543.Search in Google Scholar

2. Grandjean, P, Landrigan, PJ. Neurobehavioural effects of developmental toxicity. Lancet Neurol 2014;13:330–8. https://doi.org/10.1016/s1474-4422(13)70278-3.Search in Google Scholar

3. CDC: Centers for Disease Control and Prevention. Fourth national report on human exposure to environmental chemicals. Updated Tables. 2019.Search in Google Scholar

4. Lazarevic, N, Barnett, AG, Sly, PD, Knibbs, LD. Statistical methodology in studies of prenatal exposure to mixtures of endocrine-disrupting chemicals: a review of existing approaches and new alternatives. Environ Health Perspect 2019;127:26001. https://doi.org/10.1289/ehp2207.Search in Google Scholar PubMed PubMed Central

5. Hubaux, R, Becker-Santos, DD, Enfield, KS, Lam, S, Lam, WL, Martinez, VD. Arsenic, asbestos and radon: emerging players in lung tumorigenesis. Environ Health 2012;11:89. https://doi.org/10.1186/1476-069x-11-89.Search in Google Scholar

6. Schmidt, RJ, Kogan, V, Shelton, JF, Delwiche, L, Hansen, RL, Ozonoff, S, et al.. Combined prenatal pesticide exposure and folic acid intake in relation to autism spectrum disorder. Environ Health Perspect 2017;125:097007. https://doi.org/10.1289/ehp604.Search in Google Scholar PubMed PubMed Central

7. Ni, W, Yang, W, Yu, J, Li, Z, Jin, L, Liu, J, et al.. Umbilical cord concentrations of selected heavy metals and risk for orofacial clefts. Environ Sci Technol 2018;52:10787–95. https://doi.org/10.1021/acs.est.8b02404.Search in Google Scholar PubMed

8. Kim, Y, Ha, EH, Park, H, Ha, M, Kim, Y, Hong, YC, et al.. Prenatal lead and cadmium co-exposure and infant neurodevelopment at 6 months of age: the Mothers and Children’s Environmental Health (MOCEH) study. Neurotoxicology 2013;35:15–22. https://doi.org/10.3410/f.726187712.793515206.Search in Google Scholar

9. Freire, C, Amaya, E, Gil, F, Fernandez, MF, Murcia, M, Llop, S, et al.. Prenatal co-exposure to neurotoxic metals and neurodevelopment in preschool children: the Environment and Childhood (INMA) Project. Sci Total Environ 2018;621:340–51. https://doi.org/10.1016/j.scitotenv.2017.11.273.Search in Google Scholar PubMed

10. Rodrigues, EG, Bellinger, DC, Valeri, L, Hasan, MO, Quamruzzaman, Q, Golam, M, et al.. Neurodevelopmental outcomes among 2- to 3-year-old children in Bangladesh with elevated blood lead and exposure to arsenic and manganese in drinking water. Environ Health 2016;15:44. https://doi.org/10.1186/s12940-016-0127-y.Search in Google Scholar PubMed PubMed Central

11. Kim, Y, Kim, BN, Hong, YC, Shin, MS, Yoo, HJ, Kim, JW, et al.. Co-exposure to environmental lead and manganese affects the intelligence of school-aged children. Neurotoxicology 2009;30:564–71. https://doi.org/10.1016/j.neuro.2009.03.012.Search in Google Scholar PubMed

12. Buckley, JP, Hamra, GB, Braun, JM. Statistical approaches for investigating periods of susceptibility in children’s environmental health research. Curr Environ Health Rep 2019;6:1–7. https://doi.org/10.1007/s40572-019-0224-5.Search in Google Scholar PubMed PubMed Central

13. Hamra, GB, Buckley, JP. Environmental exposure mixtures: questions and methods to address them. Curr Epidemiol Rep 2018;5:160–5. https://doi.org/10.1007/s40471-018-0145-0.Search in Google Scholar PubMed PubMed Central

14. Lampa, E, Lind, L, Lind, PM, Bornefalk-Hermansson, A. The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees. Environ Health 2014;13:57. https://doi.org/10.1186/1476-069x-13-57.Search in Google Scholar

15. Howard, GJ, Webster, TF. Contrasting theories of interaction in epidemiology and toxicology. Environ Health Perspect 2013;121:1–6. https://doi.org/10.1289/ehp.1205889.Search in Google Scholar PubMed PubMed Central

16. Bello, GA, Arora, M, Austin, C, Horton, MK, Wright, RO, Gennings, C. Extending the Distributed Lag Model framework to handle chemical mixtures. Environ Res 2017;156:253–64. https://doi.org/10.1016/j.envres.2017.03.031.Search in Google Scholar PubMed PubMed Central

17. Schelldorfer, J, Buhlmann, P, Van De Geer, S. Estimation for high-dimensional linear mixed-effects models using l(1)-penalization. Scand J Stat 2011;38:197–214. https://doi.org/10.1111/j.1467-9469.2011.00740.x.Search in Google Scholar

18. Bobb, JF, Valeri, L, Claus Henn, B, Christiani, DC, Wright, RO, et al.. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics 2015;16:493–508. https://doi.org/10.1093/biostatistics/kxu058.Search in Google Scholar PubMed PubMed Central

19. Liu, SH, Bobb, JF, Lee, KH, Gennings, C, Claus Henn, B, Bellinger, D, et al.. Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures. Biostatistics 2018;19:325–41. https://doi.org/10.1093/biostatistics/kxx036.Search in Google Scholar PubMed PubMed Central

20. Liu, SH, Bobb, JF, Henn, BC, Schnaas, L, Tellez-Rojo, MM, Gennings, C, et al.. Modeling the health effects of time-varying complex environmental mixtures: mean field variational Bayes for lagged kernel machine regression. Environmetrics 2018;29. https://doi.org/10.1002/env.2504.Search in Google Scholar PubMed PubMed Central

21. Liu, SH, Bobb, JF, Claus Henn, B, Gennings, C, Schnaas, L, Tellez-Rojo, M, et al.. Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures. Stat Med 2018;37:4680–94. https://doi.org/10.1002/sim.7947.Search in Google Scholar PubMed PubMed Central

22. Coker, E, Gunier, R, Bradman, A, Harley, K, Kogut, K, Molitor, J, et al.. Association between pesticide profiles used on agricultural fields near maternal residences during pregnancy and IQ at age 7 years. Int J Environ Res Publ Health 2017;14. https://doi.org/10.3390/ijerph14050506.Search in Google Scholar PubMed PubMed Central

23. Furlong, MA, Herring, A, Buckley, JP, Goldman, BD, Daniels, JL, Engel, LS, et al.. Prenatal exposure to organophosphorus pesticides and childhood neurodevelopmental phenotypes. Environ Res 2017;158:737–47. https://doi.org/10.1016/j.envres.2017.07.023.Search in Google Scholar PubMed PubMed Central

24. Lenters, V, Iszatt, N, Forns, J, Cechova, E, Kocan, A, Legler, J, et al.. Early-life exposure to persistent organic pollutants (OCPs, PBDEs, PCBs, PFASs) and attention-deficit/hyperactivity disorder: a multi-pollutant analysis of a Norwegian birth cohort. Environ Int 2019;125:33–42. https://doi.org/10.1016/j.envint.2019.01.020.Search in Google Scholar PubMed

25. Stroustrup, A, Bragg, JB, Andra, SS, Curtin, PC, Spear, EA, Sison, DB, et al.. Neonatal intensive care unit phthalate exposure and preterm infant neurobehavioral performance. PloS One 2018;13:e0193835. https://doi.org/10.1371/journal.pone.0193835.Search in Google Scholar PubMed PubMed Central

26. Valeri, L, Mazumdar, MM, Bobb, JF, Claus Henn, B, Rodrigues, E, Sharif, OIA, et al.. The joint effect of prenatal exposure to metal mixtures on neurodevelopmental outcomes at 20-40 Months of age: evidence from rural Bangladesh. Environ Health Perspect. 2017;125:067015. https://doi.org/10.1289/isee.2016.4331.Search in Google Scholar

27. Wasserman, GA, Liu, X, Parvez, F, Chen, Y, Factor-Litvak, P, LoIacono, NJ, et al.. A cross-sectional study of water arsenic exposure and intellectual function in adolescence in Araihazar, Bangladesh. Environ Int 2018;118:304–13. https://doi.org/10.1016/j.envint.2018.05.037.Search in Google Scholar PubMed PubMed Central

28. Forns, J, Mandal, S, Iszatt, N, Polder, A, Thomsen, C, Lyche, JL, et al.. Novel application of statistical methods for analysis of multiple toxicants identifies DDT as a risk factor for early child behavioral problems. Environ Res 2016;151:91–100. https://doi.org/10.1016/j.envres.2016.07.014.Search in Google Scholar PubMed

29. Braun, JM, Kalkbrenner, AE, Just, AC, Yolton, K, Calafat, AM, Sjodin, A, et al.. Gestational exposure to endocrine-disrupting chemicals and reciprocal social, repetitive, and stereotypic behaviors in 4- and 5-year-old children: the HOME study. Environ Health Perspect 2014;122:513–20. https://doi.org/10.1289/ehp.1307261.Search in Google Scholar PubMed PubMed Central

30. Zhang, H, Yolton, K, Webster, GM, Sjodin, A, Calafat, AM, Dietrich, KN, et al.. Prenatal PBDE and PCB exposures and reading, cognition, and externalizing behavior in children. Environ Health Perspect 2017;125:746–52. https://doi.org/10.1289/ehp478.Search in Google Scholar

31. Kordas, K, Ardoino, G, Coffman, DL, Queirolo, EI, Ciccariello, D, Manay, N, et al.. Patterns of exposure to multiple metals and associations with neurodevelopment of preschool children from Montevideo, Uruguay. J Environ Public Health 2015;2015:493471. https://doi.org/10.1155/2015/493471.Search in Google Scholar PubMed PubMed Central

32. Guo, J, Wu, C, Zhang, J, Qi, X, Lv, S, Jiang, S, Zhou, T, et al.. Prenatal exposure to mixture of heavy metals, pesticides and phenols and IQ in children at 7 years of age: the SMBCS study. Environ Int 2020;139:105692. https://doi.org/10.1016/j.envint.2020.105692.Search in Google Scholar PubMed

33. Hamra, GB, Lyall, K, Windham, GC, Calafat, AM, Sjodin, A, Volk, H, et al.. Prenatal exposure to endocrine-disrupting chemicals in relation to autism spectrum disorder and intellectual disability. Epidemiology 2019;30:418–26. https://doi.org/10.1097/ede.0000000000000983.Search in Google Scholar

34. Tanner, EM, Hallerback, MU, Wikstrom, S, Lindh, C, Kiviranta, H, Gennings, C, et al.. Early prenatal exposure to suspected endocrine disruptor mixtures is associated with lower IQ at age seven. Environ Int 2019;134:105185.10.1016/j.envint.2019.105185Search in Google Scholar PubMed

35. Karri, V, Schuhmacher, M, Kumar, V. Heavy metals (Pb, Cd, as and MeHg) as risk factors for cognitive dysfunction: a general review of metal mixture mechanism in brain. Environ Toxicol Pharmacol 2016;48:203–13. https://doi.org/10.1016/j.etap.2016.09.016.Search in Google Scholar PubMed

36. Gu, C, Chen, S, Xu, X, Zheng, L, Li, Y, Wu, K, et al.. Lead and cadmium synergistically enhance the expression of divalent metal transporter 1 protein in central nervous system of developing rats. Neurochem Res 2009;34:1150–6. https://doi.org/10.1007/s11064-008-9891-6.Search in Google Scholar PubMed

37. Sanders, AP, Claus Henn, B, Wright, RO. Perinatal and childhood exposure to cadmium, manganese, and metal mixtures and effects on cognition and behavior: a review of recent literature. Curr Environ Health Rep 2015;2:284–94. https://doi.org/10.1007/s40572-015-0058-8.Search in Google Scholar PubMed PubMed Central

38. Rai, A, Maurya, SK, Khare, P, Srivastava, A, Bandyopadhyay, S. Characterization of developmental neurotoxicity of As, Cd, and Pb mixture: synergistic action of metal mixture in glial and neuronal functions. Toxicol Sci 2010;118:586–601. https://doi.org/10.1093/toxsci/kfq266.Search in Google Scholar PubMed

39. Ashok, A, Rai, NK, Tripathi, S, Bandyopadhyay, S. Exposure to As-, Cd-, and Pb-mixture induces Abeta, amyloidogenic APP processing and cognitive impairments via oxidative stress-dependent neuroinflammation in young rats. Toxicol Sci 2015;143:64–80. https://doi.org/10.1093/toxsci/kfu208.Search in Google Scholar PubMed

40. Gao, P, He, P, Wang, A, Xia, T, Xu, B, Xu, Z, et al.. Influence of PCB153 on oxidative DNA damage and DNA repair-related gene expression induced by PBDE-47 in human neuroblastoma cells in vitro. Toxicol Sci 2009;107:165–70. https://doi.org/10.1093/toxsci/kfn224.Search in Google Scholar PubMed

41. He, W, Wang, A, Xia, T, Gao, P, Xu, B, Xu, Z, et al.. Cytogenotoxicity induced by PBDE-47 combined with PCB153 treatment in SH-SY5Y cells. Environ Toxicol. 2010;25:564–72. https://doi.org/10.1002/tox.20517.Search in Google Scholar PubMed

42. Borman, ED, Vecchi, N, Pollock, T, deCatanzaro, D. Diethylhexyl phthalate magnifies deposition of (14) C-bisphenol A in reproductive tissues of mice. J Appl Toxicol. 2017;37:1225–31. https://doi.org/10.1002/jat.3484.Search in Google Scholar PubMed

43. Pollock, T, Mantella, L, Reali, V, deCatanzaro, D. Influence of tetrabromobisphenol A, with or without concurrent triclosan, upon bisphenol A and estradiol concentrations in mice. Environ Health Perspect 2017;125:087014. https://doi.org/10.1289/ehp1329.Search in Google Scholar PubMed PubMed Central

44. Pollock, T, Tang, B, deCatanzaro, D. Triclosan exacerbates the presence of 14C-bisphenol A in tissues of female and male mice. Toxicol Appl Pharmacol 2014;278:116–23. https://doi.org/10.1016/j.taap.2014.04.017.Search in Google Scholar PubMed

45. Pollock, T, Weaver, RE, Ghasemi, R, deCatanzaro, D. A mixture of five endocrine-disrupting chemicals modulates concentrations of bisphenol A and estradiol in mice. Chemosphere 2018;193:321–8. https://doi.org/10.1016/j.chemosphere.2017.11.030.Search in Google Scholar PubMed

46. Ferrante, MC, Mattace Raso, G, Esposito, E, Bianco, G, Iacono, A, Clausi, MT, et al.. Effects of non-dioxin-like polychlorinated biphenyl congeners (PCB 101, PCB 153 and PCB 180) alone or mixed on J774A.1 macrophage cell line: modification of apoptotic pathway. Toxicol Lett 2011;202:61–8. https://doi.org/10.1016/j.toxlet.2011.01.023.Search in Google Scholar PubMed

47. Zhu, B, Wang, Q, Shi, X, Guo, Y, Xu, T, Zhou, B. Effect of combined exposure to lead and decabromodiphenyl ether on neurodevelopment of zebrafish larvae. Chemosphere 2016;144:1646–54. https://doi.org/10.1016/j.chemosphere.2015.10.056.Search in Google Scholar PubMed

48. Chen, Y, Liu, S, Xu, H, Zheng, H, Bai, C, Pan, W, et al.. Maternal exposure to low dose BDE209 and Pb mixture induced neurobehavioral anomalies in C57BL/6 male offspring. Toxicology 2019;418:70–80. https://doi.org/10.1016/j.tox.2019.02.016.Search in Google Scholar PubMed

49. Zhao, W, Cheng, J, Gu, J, Liu, Y, Fujimura, M, Wang, W. Assessment of neurotoxic effects and brain region distribution in rat offspring prenatally co-exposed to low doses of BDE-99 and methylmercury. Chemosphere 2014;112:170–6. https://doi.org/10.1016/j.chemosphere.2014.04.011.Search in Google Scholar PubMed

50. Cheng, J, Fujimura, M, Zhao, W, Wang, W. Neurobehavioral effects, c-Fos/Jun expression and tissue distribution in rat offspring prenatally co-exposed to MeHg and PFOA: PFOA impairs Hg retention. Chemosphere 2013;91:758–64. https://doi.org/10.1016/j.chemosphere.2013.02.016.Search in Google Scholar PubMed

51. Braun, JM, Gennings, C, Hauser, R, Webster, TF. What can epidemiological studies tell us about the impact of chemical mixtures on human health? Environ Health Perspect 2016;124:A6–9. https://doi.org/10.1289/ehp.1510569.Search in Google Scholar PubMed PubMed Central

52. Weisskopf, MG, Seals, RM, Webster, TF. Bias amplification in epidemiologic analysis of exposure to mixtures. Environ Health Perspect 2018;126:047003. https://doi.org/10.1289/ehp2450.Search in Google Scholar

53. Zeger, SL, Thomas, D, Dominici, F, Samet, JM, Schwartz, J, Dockery, D, et al.. Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environ Health Perspect. 2000;108:419–26. https://doi.org/10.2307/3454839.Search in Google Scholar

54. Coull, BA, Bobb, JF, Wellenius, GA, Kioumourtzoglou, MA, Mittleman, MA, Koutrakis, P, et al.. Part 1. Statistical learning methods for the effects of multiple air pollution constituents. Res Rep Health Eff Inst 2015: 5–50. PMID: 26333238.Search in Google Scholar

55. Lenters, V, Portengen, L, Smit, LA, Jonsson, BA, Giwercman, A, Rylander, L, et al.. Phthalates, perfluoroalkyl acids, metals and organochlorines and reproductive function: a multipollutant assessment in Greenlandic, Polish and Ukrainian men. Occup Environ Med. 2015;72:385–93. https://doi.org/10.1136/oemed-2014-102264.Search in Google Scholar PubMed

Received: 2020-02-04
Accepted: 2020-04-23
Published Online: 2020-06-29
Published in Print: 2020-09-25

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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