Prenatal Exposure to Chemical Mixtures and Inhibition among Adolescents
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Chemical Exposure Assessment
2.3. Inhibition Assessment
2.4. Covariate Assessment
2.5. Statistical Analysis
3. Results
3.1. Study Population Characteristics
3.2. Chemical Exposure Measures
3.3. Inhibition Measures
3.4. BKMR Analyses of Prenatal Exposure to Five- and Seven-Chemical Mixtures with Inhibition
3.5. Linear Regression Analyses of the Association of Prenatal Exposure to Five Chemicals with Inhibition
3.6. Sex-Stratified Linear Regression Analyses of the Association of Prenatal Exposure to Five Chemicals with Inhibition
3.7. Social Disadvtange-Stratified Linear Regression Analyses of the Association of Prenatal Exposure to Five Chemicals with Inhibition
3.8. Negative Binomial Regression Analyses of the Association of Prenatal Exposure to Five Chemicals with Errors on Inhibition Tasks
3.9. Logistic Regression Analyses of the Association of Prenatal Exposure to Five Chemicals with Overall Performance on Color–Word Inhibition Task
3.10. Secondary Analyses
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Diamond, A. Executive Functions. Annu. Rev. Psychol. 2013, 64, 135–168. [Google Scholar] [CrossRef] [Green Version]
- Lehto, J.E.; Juujärvi, P.; Kooistra, L.; Pulkkinen, L. Dimensions of Executive Functioning: Evidence from Children. Br. J. Dev. Psychol. 2003, 21, 59–80. [Google Scholar] [CrossRef]
- Dumontheil, I. Adolescent Brain Development. Curr. Opin. Behav. Sci. 2016, 10, 39–44. [Google Scholar] [CrossRef] [Green Version]
- Moffitt, T.E.; Arseneault, L.; Belsky, D.; Dickson, N.; Hancox, R.J.; Harrington, H.L.; Houts, R.; Poulton, R.; Roberts, B.W.; Ross, S.; et al. A Gradient of Childhood Self-Control Predicts Health, Wealth, and Public Safety. Proc. Natl. Acad. Sci. USA 2011, 108, 2693–2698. [Google Scholar] [CrossRef] [Green Version]
- Reznick, J.S.; Hegeman, I.M.; Kaufman, E.R.; Woods, S.W.; Jacobs, M. Retrospective and Concurrent Self-Report of Behavioral Inhibition and Their Relation to Adult Mental Health. Dev. Psychopathol. 1992, 4, 301–321. [Google Scholar] [CrossRef]
- Grandjean, P.; Landrigan, P.J. Neurobehavioural Effects of Developmental Toxicity. Lancet Neurol. 2014, 13, 330–338. [Google Scholar] [CrossRef] [Green Version]
- EWG Body Burden: The Pollution in Newborns|EWG. Available online: https://www.ewg.org/research/body-burden-pollution-newborns (accessed on 11 May 2020).
- U.S. Centers for Disease Control and Prevention (CDC). Fourth National Report on Human Exposure to Environmental Chemicals: Updated Tables, January 2019, Volume One; Centers for Disease Control and Prevention: Atlanta, GA, USA, 2019. [Google Scholar]
- Grandjean, P.; Landrigan, P. Developmental Neurotoxicity of Industrial Chemicals. Lancet 2006, 368, 2167–2178. [Google Scholar] [CrossRef]
- Jacobson, J.L.; Jacobson, S.W. Prenatal Exposure to Polychlorinated Biphenyls and Attention at School Age. J. Pediatrics 2003, 143, 780–788. [Google Scholar] [CrossRef]
- Stewart, P.; Fitzgerald, S.; Reihman, J.; Gump, B.; Lonky, E.; Darvill, T.; Pagano, J.; Hauser, P. Prenatal PCB Exposure, the Corpus Callosum, and Response Inhibition. Environ. Health Perspect. 2003, 111, 1670–1677. [Google Scholar] [CrossRef] [Green Version]
- Stewart, P.; Reihman, J.; Gump, B.; Lonky, E.; Darvill, T.; Pagano, J. Response Inhibition at 8 and 9 1/2 Years of Age in Children Prenatally Exposed to PCBs. Neurotoxicol. Teratol. 2005, 27, 771–780. [Google Scholar] [CrossRef]
- Sagiv, S.K.; Thurston, S.W.; Bellinger, D.C.; Altshul, L.M.; Korrick, S.A. Neuropsychological Measures of Attention and Impulse Control among 8-Year-Old Children Exposed Prenatally to Organochlorines. Environ. Health Perspect. 2012, 120, 904–909. [Google Scholar] [CrossRef] [Green Version]
- Longnecker, M.P.; Wolff, M.S.; Gladen, B.C.; Brock, J.W.; Grandjean, P.; Jacobson, J.L.; Korrick, S.A.; Rogan, W.J.; Weisglas-Kuperus, N.; Hertz-Picciotto, I.; et al. Comparison of Polychlorinated Biphenyl Levels across Studies of Human Neurodevelopment. Environ. Health Perspect. 2003, 111, 65–70. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Letz, R. Neurobehav. Evaluation System 2 (NES2) User’s Manual; Version 4.7; Neurobehavioral Systems, Inc.: Atlanta, GA, USA, 1998. [Google Scholar]
- Sanders, A.P.; Claus Henn, B.; Wright, R.O. 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, 284–294. [Google Scholar] [CrossRef] [Green Version]
- Ericson, J.E.; Crinella, F.M.; Clarke-Stewart, K.A.; Allhusen, V.D.; Chan, T.; Robertson, R.T. Prenatal Manganese Levels Linked to Childhood Behavioral Disinhibition. Neurotoxicol. Teratol. 2007, 29, 181–187. [Google Scholar] [CrossRef]
- van Wijngaarden, E.; Thurston, S.W.; Myers, G.J.; Harrington, D.; Cory-Slechta, D.A.; Strain, J.J.; Watson, G.E.; Zareba, G.; Love, T.; Henderson, J.; et al. Methyl Mercury Exposure and Neurodevelopmental Outcomes in the Seychelles Child Development Study Main Cohort at Age 22 and 24 Years. Neurotoxicol. Teratol. 2017, 59, 35–42. [Google Scholar] [CrossRef] [Green Version]
- Carlin, D.J.; Rider, C.V.; Woychik, R.; Birnbaum, L.S. Unraveling the Health Effects of Environmental Mixtures: An NIEHS Priority. Environ. Health Perspect. 2013, 121, A6–A8. [Google Scholar] [CrossRef]
- Carpenter, D.O.; Arcaro, K.; Spink, D.C. Understanding the Human Health Effects of Chemical Mixtures. Environ. Health Perspect. 2002, 110 (Suppl. 1), 25–42. [Google Scholar] [CrossRef] [Green Version]
- Boucher, O.; Burden, M.J.; Muckle, G.; Saint-Amour, D.; Ayotte, P.; Dewailly, É.; Nelson, C.A.; Jacobson, S.W.; Jacobson, J.L. Response Inhibition and Error Monitoring during a Visual Go/No-Go Task in Inuit Children Exposed to Lead, Polychlorinated Biphenyls, and Methylmercury. Environ. Health Perspect. 2012, 120, 608–615. [Google Scholar] [CrossRef] [Green Version]
- Forns, J.; Fort, M.; Casas, M.; Cáceres, A.; Guxens, M.; Gascon, M.; Garcia-Esteban, R.; Julvez, J.; Grimalt, J.O.; Sunyer, J. Exposure to Metals during Pregnancy and Neuropsychological Development at the Age of 4 Years. NeuroToxicology 2014, 40, 16–22. [Google Scholar] [CrossRef]
- Wasserman, G.A.; Liu, X.; Parvez, F.; Factor-Litvak, P.; Ahsan, H.; Levy, D.; Kline, J.; van Geen, A.; Mey, J.; Slavkovich, V.; et al. Arsenic and Manganese Exposure and Children’s Intellectual Function. NeuroToxicology 2011, 32, 450–457. [Google Scholar] [CrossRef] [Green Version]
- Yorifuji, T.; Debes, F.; Weihe, P.; Grandjean, P. Prenatal Exposure to Lead and Cognitive Deficit in 7- and 14-Year-Old Children in the Presence of Concomitant Exposure to Similar Molar Concentration of Methylmercury. Neurotoxicol. Teratol. 2011, 33, 205–211. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Choi, A.L.; Levy, J.I.; Dockery, D.W.; Ryan, L.M.; Tolbert, P.E.; Altshul, L.M.; Korrick, S.A. Does Living near a Superfund Site Contribute to Higher Polychlorinated Biphenyl (PCB) Exposure? Environ. Health Perspect. 2006, 114, 1092–1098. [Google Scholar] [CrossRef] [Green Version]
- Vieira, V.M.; Fabian, M.P.; Webster, T.F.; Levy, J.I.; Korrick, S.A. Spatial Variability in ADHD-Related Behaviors among Children Born to Mothers Residing Near the New Bedford Harbor Superfund Site. Am. J. Epidemiol. 2017, 185, 924–932. [Google Scholar] [CrossRef] [Green Version]
- Korrick, S.A.; Altshul, L.M.; Tolbert, P.E.; Burse, V.W.; Needham, L.L.; Monson, R.R. Measurement of PCBs, DDE, and Hexachlorobenzene in Cord Blood from Infants Born in Towns Adjacent to a PCB-Contaminated Waste Site. J. Expo. Anal. Environ. Epidemiol. 2000, 10, 743–754. [Google Scholar] [CrossRef] [Green Version]
- Sagiv, S.K.; Thurston, S.W.; Bellinger, D.C.; Tolbert, P.E.; Altshul, L.M.; Korrick, S.A. Prenatal Organochlorine Exposure and Behaviors Associated with Attention Deficit Hyperactivity Disorder in School-Aged Children. Am. J. Epidemiol. 2010, 171, 593–601. [Google Scholar] [CrossRef] [Green Version]
- Orenstein, S.T.C.; Thurston, S.W.; Bellinger, D.C.; Schwartz, J.D.; Amarasiriwardena, C.J.; Altshul, L.M.; Korrick, S.A. Prenatal Organochlorine and Methylmercury Exposure and Memory and Learning in School-Age Children in Communities near the New Bedford Harbor Superfund Site, Massachusetts. Environ. Health Perspect. 2014, 122, 1253–1259. [Google Scholar] [CrossRef] [Green Version]
- Myers, G.J.; Davidson, P.W. Prenatal Methylmercury Exposure and Children: Neurologic, Developmental, and Behavioral Research. Environ. Health Perspect. 1998, 106, 841–847. [Google Scholar]
- Oken, E.; Wright, R.O.; Kleinman, K.P.; Bellinger, D.; Amarasiriwardena, C.J.; Hu, H.; Rich-Edwards, J.W.; Gillman, M.W. Maternal Fish Consumption, Hair Mercury, and Infant Cognition in a U.S. Cohort. Environ. Health Perspect. 2005, 113, 1376–1380. [Google Scholar] [CrossRef]
- Amaral, A.F.S.; Porta, M.; Silverman, D.T.; Milne, R.L.; Kogevinas, M.; Rothman, N.; Cantor, K.P.; Jackson, B.P.; Pumarega, J.A.; López, T.; et al. Pancreatic Cancer Risk and Levels of Trace Elements. Gut 2012, 61, 1583–1588. [Google Scholar] [CrossRef] [Green Version]
- Delis, D.; Kaplan, E.; Kramer, J. Delis-Kaplan Executive Function System; Harcourt Assessment, Inc.: San Antonio, TX, USA, 2001. [Google Scholar]
- Caldwell, B.; Bradley, R. Home Observation for Measurement of the Environment; Dorsey: New York, NY, USA, 1985. [Google Scholar]
- Kaufman, A.; Kaufman, N. Kaufman Brief Intelligence Test; American Guidance Service: Circle Pines, MN, USA, 1990. [Google Scholar]
- Bobb, J.F.; Claus Henn, B.; Valeri, L.; Coull, B.A. Statistical Software for Analyzing the Health Effects of Multiple Concurrent Exposures via Bayesian Kernel Machine Regression. Environ. Health: A Glob. Access Sci. Source 2018, 17, 67. [Google Scholar] [CrossRef] [Green Version]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
- Bobb, J.F. bkmr: Bayesian Kernel Machine Regression; R Package Version 0.2.0. 2017. Available online: https://CRAN.R-project.org/package=bkmr (accessed on 11 May 2020).
- Hernán, M.A.; Robins, J.M. How to adjust for selection bias. In Causal Inference: What If; Chapman & Hall/CRC: Boca Raton, FL, USA, 2020; pp. 107–112. [Google Scholar]
- Ettinger, A.S.; Egan, K.B.; Homa, D.M.; Brown, M.J. Blood Lead Levels in U.S. Women of Childbearing Age, 1976–2016. Environ. Health Perspect. 2020, 128, 017012-1–017012-9. [Google Scholar] [CrossRef] [Green Version]
- Arbuckle, T.E.; Liang, C.L.; Morisset, A.S.; Fisher, M.; Weiler, H.; Cirtiu, C.M.; Legrand, M.; Davis, K.; Ettinger, A.S.; Fraser, W.D. Maternal and Fetal Exposure to Cadmium, Lead, Manganese and Mercury: The MIREC Study. Chemosphere 2016, 163, 270–282. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ettinger, A.S.; Arbuckle, T.E.; Fisher, M.; Liang, C.L.; Davis, K.; Cirtiu, C.M.; Bélanger, P.; LeBlanc, A.; Fraser, W.D. Arsenic Levels among Pregnant Women and Newborns in Canada: Results from the Maternal-Infant Research on Environmental Chemicals (MIREC) Cohort. Environ. Res. 2017, 153, 8–16. [Google Scholar] [CrossRef] [Green Version]
- McDowell, M.A.; Dillon, C.F.; Osterloh, J.; Bolger, P.M.; Pellizzari, E.; Fernando, R.; Montes de Oca, R.; Schober, S.E.; Sinks, T.; Jones, R.L.; et al. Hair Mercury Levels in U.S. Children and Women of Childbearing Age: Reference Range Data from NHANES 1999–2000. Environ. Health Perspect. 2004, 112, 1165–1171. [Google Scholar] [CrossRef] [PubMed]
- Carvalho, C.F.; Menezes-Filho, J.A.; Matos, V.P.; de Bessa, J.R.; Coelho-Santos, J.; Viana, G.F.S.; Argollo, N.; Abreu, N. Elevated Airborne Manganese and Low Executive Function in School-Aged Children in Brazil. NeuroToxicology 2014, 45, 301–308. [Google Scholar] [CrossRef]
- Torres-Agustín, R.; Rodríguez-Agudelo, Y.; Schilmann, A.; Solís-Vivanco, R.; Montes, S.; Riojas-Rodríguez, H.; Cortez-Lugo, M.; Ríos, C. Effect of Environmental Manganese Exposure on Verbal Learning and Memory in Mexican Children. Environ. Res. 2013, 121, 39–44. [Google Scholar] [CrossRef]
- Wright, R.O.; Amarasiriwardena, C.; Woolf, A.D.; Jim, R.; Bellinger, D.C. Neuropsychological Correlates of Hair Arsenic, Manganese, and Cadmium Levels in School-Age Children Residing near a Hazardous Waste Site. Neurotoxicology 2005, 27, 210–216. [Google Scholar] [CrossRef] [PubMed]
- Kern, C.H.; Smith, D.R. Preweaning Mn Exposure Leads to Prolonged Astrocyte Activation and Lasting Effects on the Dopaminergic System in Adult Male Rats. Synapse 2011, 65, 532–544. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tran, T.T.; Chowanadisai, W.; Crinella, F.M.; Chicz-DeMet, A.; Lönnerdal, B. Effect of High Dietary Manganese Intake of Neonatal Rats on Tissue Mineral Accumulation, Striatal Dopamine Levels, and Neurodevelopmental Status. NeuroToxicology 2002, 23, 635–643. [Google Scholar] [CrossRef]
- Badgaiyan, R.D.; Wack, D. Evidence of Dopaminergic Processing of Executive Inhibition. PLoS ONE 2011, 6, e28075. [Google Scholar] [CrossRef]
- Chung, S.E.; Cheong, H.K.; Ha, E.H.; Kim, B.N.; Ha, M.; Kim, Y.; Hong, Y.C.; Park, H.; Oh, S.Y. Maternal Blood Manganese and Early Neurodevelopment: The Mothers and Children’s Environmental Health (MOCEH) Study. Environ. Health Perspect. 2015, 123, 717–722. [Google Scholar] [CrossRef] [Green Version]
- Henn, B.C.; Ettinger, A.S.; Schwartz, J.; Téllez-Rojo, M.M.; Lamadrid-Figueroa, H.; Hernández-Avila, M.; Schnaas, L.; Amarasiriwardena, C.; Bellinger, D.C.; Hu, H.; et al. Early Postnatal Blood Manganese Levels and Children’s Neurodevelopment. Epidemiology 2010, 21, 433–439. [Google Scholar] [CrossRef]
- Anglen Bauer, J.; Henn, B.C.; Austin, C.; Zoni, S.; Fedrighi, C.; Cagna, G.; Placidi, D.; White, R.F.; Yang, Q.; Coull, B.A.; et al. Manganese in Teeth and Neurobehavior: Sex-Specific Windows of Susceptibility HHS Public Access. Environ. Int. 2017, 108, 299–308. [Google Scholar] [CrossRef] [PubMed]
- Horning, K.J.; Caito, S.W.; Tipps, K.G.; Bowman, A.B.; Aschner, M. Manganese Is Essential for Neuronal Health. Annu. Rev. Nutr. 2015, 35, 71–108. [Google Scholar] [CrossRef] [PubMed]
- Erikson, K.M.; Thompson, K.; Aschner, J.; Aschner, M. Manganese Neurotoxicity: A Focus on the Neonate. Pharmacol. Ther. 2007, 113, 369–377. [Google Scholar] [CrossRef] [Green Version]
- Agency for Toxic Substances & Disease Registry (ATSDR). Toxicological Profile for Manganese; U.S. Department of Health and Human Services, Public Health Service: Atlanta, GA, USA, 2012. [Google Scholar]
- Stewart, P.W.; Sargent, D.M.; Reihman, J.; Gump, B.B.; Lonky, E.; Darvill, T.; Hicks, H.; Pagano, J. Response Inhibition during Differential Reinforcement of Low Rates (DRL) Schedules May Be Sensitive to Low-Level Polychlorinated Biphenyl, Methylmercury, and Lead Exposure in Children. Environ. Health Perspect. 2006, 114, 1923–1929. [Google Scholar] [CrossRef] [PubMed]
- Kyriklaki, A.; Vafeiadi, M.; Kampouri, M.; Koutra, K.; Roumeliotaki, T.; Chalkiadaki, G.; Anousaki, D.; Rantakokko, P.; Kiviranta, H.; Fthenou, E.; et al. Prenatal Exposure to Persistent Organic Pollutants in Association with Offspring Neuropsychological Development at 4 Years of Age: The Rhea Mother-Child Cohort, Crete, Greece. Environ. Int. 2016, 97, 204–211. [Google Scholar] [CrossRef]
- Choi, A.L.; Cordier, S.; Weihe, P.; Grandjean, P. Negative Confounding in the Evaluation of Toxicity: The Case of Methylmercury in Fish and Seafood. Crit. Rev. Toxicol. 2008, 38, 877–893. [Google Scholar] [CrossRef] [Green Version]
- Fruh, V.; Rifas-Shiman, S.L.; Amarasiriwardena, C.; Cardenas, A.; Bellinger, D.C.; Wise, L.A.; White, R.F.; Wright, R.O.; Oken, E.; Claus Henn, B. Prenatal Lead Exposure and Childhood Executive Function and Behavioral Difficulties in Project Viva. NeuroToxicology 2019, 75, 105–115. [Google Scholar] [CrossRef]
- Stiles, K.M.; Bellinger, D.C. Neuropsychological Correlates of Low-Level Lead Exposure in School-Age Children: A Prospective Study. Neurotoxicol. Teratol. 1993, 15, 27–35. [Google Scholar] [CrossRef]
- Oppenheimer, A.V.; Bellinger, D.C.; Coull, B.A.; Weisskopf, M.A.; Korrick, S.A. Prenatal Exposure to Chemical Mixtures and Working Memory among Adolescents. Environ. Res. 2021, submitted. [Google Scholar]
- Debes, F.; Budtz-Jørgensen, E.; Weihe, P.; White, R.F.; Grandjean, P. Impact of Prenatal Methylmercury Exposure on Neurobehavioral Function at Age 14 Years. Neurotoxicol. Teratol. 2006, 28, 536–547. [Google Scholar] [CrossRef] [Green Version]
- Wasserman, G.A.; Liu, X.; LoIacono, N.J.; Kline, J.; Factor-Litvak, P.; Van Geen, A.; Mey, J.L.; Levy, D.; Abramson, R.; Schwartz, A.; et al. A Cross-Sectional Study of Well Water Arsenic and Child IQ in Maine Schoolchildren. Environ. Health A Glob. Access Sci. Source 2014, 13, 23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wasserman, G.A.; Liu, X.; Parvez, F.; Chen, Y.; Factor-Litvak, P.; LoIacono, N.J.; Levy, D.; Shahriar, H.; Uddin, M.N.; Islam, T.; et al. A Cross-Sectional Study of Water Arsenic Exposure and Intellectual Function in Adolescence in Araihazar, Bangladesh. Environ. Int. 2018, 118, 304–313. [Google Scholar] [CrossRef]
- Wilschefski, S.; Baxter, M. Inductively Coupled Plasma Mass Spectrometry: Introduction to Analytical Aspects. Clin. Biochem. Rev. 2019, 40, 115–133. [Google Scholar] [CrossRef]
- Coetzee, D.J.; McGovern, P.M.; Rao, R.; Harnack, L.J.; Georgieff, M.K.; Stepanov, I. Measuring the Impact of Manganese Exposure on Children’s Neurodevelopment: Advances and Research Gaps in Biomarker-Based Approaches. Environ. Health A Glob. Access Sci. Source 2016, 15, 91. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bauer, J.A.; Devick, K.L.; Bobb, J.F.; Coull, B.A.; Bellinger, D.; Benedetti, C.; Cagna, G.; Fedrighi, C.; Guazzetti, S.; Oppini, M.; et al. Associations of a Metal Mixture Measured in Multiple Biomarkers with IQ: Evidence from Italian Adolescents Living near Ferroalloy Industry. Environ. Health Perspect. 2020, 128, 097002–1–097002-12. [Google Scholar] [CrossRef]
- Gunier, R.B.; Mora, A.M.; Smith, D.; Arora, M.; Austin, C.; Eskenazi, B.; Bradman, A. Biomarkers of Manganese Exposure in Pregnant Women and Children Living in an Agricultural Community in California. Environ. Sci. Technol. 2014, 48, 14695–14702. [Google Scholar] [CrossRef]
Descriptive Characteristic | NBC Participants Included in Main Analysis, n = 373 | NBC Participants not Included in Main Analysis, n = 415 | |||||
---|---|---|---|---|---|---|---|
Inhibition Measures 2 | n(%) | Mean (SD) | Range | n(%) | Mean (SD) | Range | p-Value 3 |
Design Fluency | |||||||
Total number correct scaled score | 373 | 9.6 (2.7) | 2–17 | 155 | 9.3 (3) | 1–19 | 0.3 |
Total number errors raw score | 373 | 2.2 (2.7) | 0–19 | 155 | 2.6 (3.5) | 0–30 | 0.1 |
Color–Word Interference scores | |||||||
Completion time scaled score | 373 | 9.9 (2.8) | 1–16 | 154 | 9.8 (2.7) | 1–14 | 0.6 |
Total number errors raw score | 373 | 2.3 (2.5) | 0–19 | 154 | 2.3 (2.3) | 0–12 | 0.7 |
Overall performance | |||||||
Best performance | 117 (31.4) | 37 (8.9) | 0.1 | ||||
Poor performance | 256 (68.6) | 117 (28.2) | |||||
Missing | 0 | 261 (62.9) | |||||
Exposure Measures 4 | |||||||
Cord serum DDE (ng/g) | 373 | 0.6 (1.2) | 0.02–14.9 | 378 | 0.4 (0.4) | 0.0–4.2 | <0.01 * |
Cord serum HCB (ng/g) | 373 | 0.03 (0.02) | 0.0–0.1 | 378 | 0.03 (0.05) | 0.0–0.7 | 0.1 |
Cord serum ΣPCB4 (ng/g) | 373 | 0.3 (0.3) | 0.01–4.4 | 378 | 0.2 (0.2) | 0.01–1.9 | 0.05 |
Cord blood Pb (μg/dL) | 373 | 1.4 (0.9) | 0–9.4 | 375 | 1.7 (1.7) | 0.0–17.4 | <0.01 * |
Cord blood Mn (µg/dL) | 373 | 4.2 (1.6) | 0.7–14.6 | 335 | 4.3 (2.0) | 0.2–22.1 | 0.6 |
Covariate Measures 5 | |||||||
Child Characteristics | |||||||
Race/Ethnicity | 0.09 | ||||||
Non-Hispanic White | 263 (70.5) | 268 (64.6) | |||||
Hispanic | 33 (8.8) | 56 (13.5) | |||||
Other | 77 (20.6) | 89 (21.4) | |||||
Missing | 0 | 2 (0.5) | |||||
Sex | 0.05 | ||||||
Male | 179 (48.0) | 229 (55.2) | |||||
Female | 194 (52.0) | 186 (44.8) | |||||
Age at Exam | 373 | 15.5 (0.6) | 14.4–17.8 | 155 | 15.7 (0.7) | 13.9–17.9 | <0.01 * |
Home Score | 373 | 43.9 (6.3) | 21–56 | 118 | 42.7 (6.0) | 27–53 | 0.07 |
Year of birth | |||||||
1993–1994 | 100 (26.8) | 159 (38.3) | <0.01 * | ||||
1995–1996 | 153 (41.0) | 147 (35.4) | |||||
1997–1998 | 120 (32.2) | 109 (26.3) | |||||
Maternal Characteristics | |||||||
Marital status at birth | <0.01 * | ||||||
Not married | 136 (36.5) | 195 (47.0) | |||||
Married | 237 (63.5) | 165 (39.8) | |||||
Missing | 0 | 55 (13.3) | |||||
Maternal IQ | 373 | 99.4 (10.4) | 57–124 | 262 | 95.8 (10.2) | 72–126 | <0.01 * |
Seafood during pregnancy (serv/day) | 373 | 0.5 (0.6) | 0–5.3 | 260 | 0.6 (0.7) | 0–6 | 0.6 |
Smoking during pregnancy | 0.1 | ||||||
No | 272 (72.9) | 210 (50.6) | |||||
Yes | 101 (27.1) | 103 (24.8) | |||||
Missing | 0 | 102 (24.6) | |||||
Household Characteristics at Birth | |||||||
Maternal education | <0.01 * | ||||||
≤High School | 190 (50.9) | 231 (55.7) | |||||
>High School | 183 (49.1) | 127 (30.6) | |||||
Missing | 0 | 57 (13.7) | |||||
Paternal Education | <0.01 * | ||||||
≤High School | 246 (66.0) | 266 (64.1) | |||||
>High School | 127 (34.0) | 81 (19.5) | |||||
Missing | 0 | 68 (16.4) | |||||
Annual Household Income | <0.01 * | ||||||
<USD 20,000 | 115 (30.8) | 150 (36.1) | |||||
≥USD 20,000 | 258 (69.2) | 201 (48.4) | |||||
Missing | 0 | 64 (15.4) | |||||
Examination Characteristics | |||||||
Examiner | 0.4 | ||||||
1 | 277 (74.3) | 121 (29.2) | |||||
2 | 96 (25.7) | 34 (8.2) | |||||
Missing | 0 | 260 (62.7) |
Exposure | Design Fluency Total Correct Scaled Score Difference (95% CI) | Color–Word Interference Completion Time Scaled Score Difference (95% CI) |
---|---|---|
Log2 DDE | 0.00 (−0.32, 0.33) | 0.09 (−0.22, 0.40) |
Log2 HCB | 0.04 (−0.28, 0.36) | −0.08 (−0.42, 0.25) |
Log2 ΣPCB4 | −0.15 (−0.49, 0.18) | −0.24 (−0.59, 0.10) |
Log2 Pb | −0.05 (−0.35, 0.26) | 0.07 (−0.25, 0.39) |
Log2 Mn | 0.91 (−0.02, 1.84) | −0.74 (−1.34, −0.14) * |
Log2 Mn2 | −0.59 (−1.49, 0.30) | |
Log2 DDE Log2 Mn | 0.53 (0.05, 1.01) * | |
Log2 DDE Log2 Mn2 | 0.03 (−0.52, 0.58) |
Design Fluency Total Correct Scaled Score | Color–Word Interference Completion Time Scaled Score | |||||
---|---|---|---|---|---|---|
Exposure | Males Difference (95% CI) | Females Difference (95% CI) | p 3 | Males Difference (95% CI) | Females Difference (95% CI) | p 3 |
Log2 DDE | −0.18 (−0.70, 0.34) | −0.17 (−0.66, 0.32) | 0.7 | 0.00 (−0.47, 0.47) | 0.25 (−0.23, 0.72) | 0.4 |
Log2 HCB | −0.19 (−0.64, 0.27) | 0.34 (−0.12, 0.81) | 0.1 | −0.03 (−0.54, 0.48) | −0.11 (−0.59, 0.38) | 0.8 |
Log2 ΣPCB4 | 0.04 (−0.45, 0.53) | −0.25 (−0.78, 0.28) | 1.0 | −0.09 (−0.60, 0.43) | −0.47 (−0.99, 0.04) | 0.5 |
Log2 Pb | 0.28 (−0.25, 0.81) | −0.25 (−0.63, 0.13) | 0.4 | 0.20 (−0.39, 0.79) | −0.05 (−0.44, 0.33) | 0.5 |
Log2 Mn | 1.90 (0.47, 3.34) * | −0.06 (−1.47, 1.36) | 0.1 | −0.80 (−1.69, 0.09) | −0.48 (−1.34, 0.37) | 0.5 |
Log2 Mn2 | 0.07 (−1.22, 1.36) | −0.27 (−1.67, 1.12) | 0.3 | |||
Log2 DDE Log2 Mn | 0.83 (0.06, 1.60) * | 0.10 (−0.64, 0.84) | 0.2 | |||
Log2 DDE Log2 Mn2 | 0.49 (−0.37, 1.35) | 0.03 (−0.74, 0.80) | 0.3 |
Design Fluency Total Correct Scaled Score | Color–Word Interference Completion Time Scaled Score | |||||
---|---|---|---|---|---|---|
Exposure | PNSDI < 3 Difference (95% CI) | PNSDI ≥ 3 Difference (95% CI) | p 4 | PNSDI < 3 Difference (95% CI) | PNSDI ≥ 3 Difference (95% CI) | p4 |
Log2 DDE | −0.07 (−0.48, 0.35) | −0.03 (−0.60, 0.53) | 0.5 | 0.26 (−0.12, 0.64) | −0.18 (−0.79, 0.43) | 0.3 |
Log2 HCB | 0.31 (−0.09, 0.72) | −0.57 (−1.13, 0.00) | 0.01 * | −0.01 (−0.43, 0.42) | −0.26 (−0.86, 0.34) | 0.2 |
Log2 ΣPCB4 | −0.23 (−0.63, 0.17) | 0.21 (−0.47, 0.90) | 0.2 | −0.40 (−0.82, 0.01) | 0.15 (−0.52, 0.83) | 0.2 |
Log2 Pb | −0.32 (−0.73, 0.08) | 0.27 (−0.26, 0.80) | 0.1 | 0.01 (−0.40, 0.43) | 0.07 (−0.51, 0.65) | 1.0 |
Log2 Mn | 1.28 (0.12, 2.45) * | 1.26 (−1.34, 3.87) | 1.0 | −0.76 (−1.52, 0.00) | −0.69 (−1.76, 0.39) | 0.9 |
Log2 Mn2 | 0.44 (−0.77, 1.65) | −2.00 (−4.29, 0.30) | 0.1 | |||
Log2 DDE Log2 Mn | 0.47 (−0.14, 1.08) | 0.81 (−0.38, 1.99) | 0.8 | |||
Log2 DDE Log2 Mn2 | 0.39 (−0.36, 1.15) | −0.24 (−1.30, 0.82) | 0.4 |
Exposure | Design Fluency Total Errors Rate Ratio (95% CI) | Color–Word Interference Total Errors Rate Ratio (95% CI) |
---|---|---|
Log2 DDE | 1.04 (0.92, 1.17) | 0.91 (0.82, 1.02) |
Log2 HCB | 1.08 (0.95, 1.24) | 1.07 (0.95, 1.21) |
Log2 ΣPCB4 | 0.94 (0.82, 1.08) | 1.13 (1.00, 1.28) |
Log2 Pb | 1.00 (0.88, 1.13) | 0.91 (0.82, 1.02) |
Log2 Mn | 0.78 (0.61, 0.99) * | 1.10 (0.88, 1.36) |
Exposure | Color–Word Interference Overall Performance Odds Ratio (95% CI) |
---|---|
Best performance: n = 117 | |
Poor performance: n = 256 | |
Log2 DDE | 0.93 (0.70, 1.22) |
Log2 HCB | 1.22 (0.93, 1.60) |
Log2 ΣPCB4 | 1.16 (0.87, 1.55) |
Log2 Pb | 0.79 (0.59, 1.05) |
Log2 Mn | 1.61 (0.98, 2.64) |
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Oppenheimer, A.V.; Bellinger, D.C.; Coull, B.A.; Weisskopf, M.G.; Zemplenyi, M.; Korrick, S.A. Prenatal Exposure to Chemical Mixtures and Inhibition among Adolescents. Toxics 2021, 9, 311. https://doi.org/10.3390/toxics9110311
Oppenheimer AV, Bellinger DC, Coull BA, Weisskopf MG, Zemplenyi M, Korrick SA. Prenatal Exposure to Chemical Mixtures and Inhibition among Adolescents. Toxics. 2021; 9(11):311. https://doi.org/10.3390/toxics9110311
Chicago/Turabian StyleOppenheimer, Anna V., David C. Bellinger, Brent A. Coull, Marc G. Weisskopf, Michele Zemplenyi, and Susan A. Korrick. 2021. "Prenatal Exposure to Chemical Mixtures and Inhibition among Adolescents" Toxics 9, no. 11: 311. https://doi.org/10.3390/toxics9110311
APA StyleOppenheimer, A. V., Bellinger, D. C., Coull, B. A., Weisskopf, M. G., Zemplenyi, M., & Korrick, S. A. (2021). Prenatal Exposure to Chemical Mixtures and Inhibition among Adolescents. Toxics, 9(11), 311. https://doi.org/10.3390/toxics9110311