TY - JOUR
T1 - Interaction between a mixture of heavy metals (lead, mercury, arsenic, cadmium, manganese, aluminum) and GSTP1, GSTT1, and GSTM1 in relation to autism spectrum disorder
AU - Rahbar, Mohammad H.
AU - Samms-Vaughan, Maureen
AU - Lee, Min Jae
AU - Zhang, Jing
AU - Hessabi, Manouchehr
AU - Bressler, Jan
AU - Bach, MacKinsey A.
AU - Grove, Megan L.
AU - Shakespeare-Pellington, Sydonnie
AU - Beecher, Compton
AU - McLaughlin, Wayne
AU - Loveland, Katherine A.
N1 - Funding Information:
This research is funded by the National Institute of Environmental Health Sciences (NIEHS) by a grant (R01ES022165), as well as the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Institutes of Health Fogarty International Center (NIH-FIC) by a grant (R21HD057808) awarded to The University of Texas Health Science Center at Houston. We also acknowledge the support provided by the Biostatistics/Epidemiology/Research Design (BERD) component of the Center for Clinical and Translational Sciences (CCTS) for this project. CCTS is mainly funded by the NIH Centers for Translational Science Award (NIH CTSA) grant (UL1 RR024148), awarded to University of Texas Health Science Center at Houston in 2006 by the National Center for Research Resources (NCRR) and its renewal (UL1 TR000371) as well as by another grant (UL1 TR003167) by the National Center for Advancing Translational Sciences (NCATS) in 2019. Furthermore, we acknowledge that the management of study data were done using REDCap (Harris et al. 2009). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NICHD, NIH-FIC, NIEHS, NCRR, or NCATS. Finally, we acknowledge contributions by colleagues in the Analytical Chemistry Lab at Michigan Department of Health and Human Services (MDHHS) for analyzing and storing the whole blood samples for the assessments of heavy metal concentrations, under a service contract.
Funding Information:
This research is funded by the National Institute of Environmental Health Sciences (NIEHS) by a grant ( R01ES022165 ), as well as the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Institutes of Health Fogarty International Center (NIH-FIC) by a grant ( R21HD057808 ) awarded to The University of Texas Health Science Center at Houston. We also acknowledge the support provided by the Biostatistics/Epidemiology/Research Design (BERD) component of the Center for Clinical and Translational Sciences (CCTS) for this project. CCTS is mainly funded by the NIH Centers for Translational Science Award (NIH CTSA) grant ( UL1 RR024148 ), awarded to University of Texas Health Science Center at Houston in 2006 by the National Center for Research Resources (NCRR) and its renewal ( UL1 TR000371 ) as well as by another grant ( UL1 TR003167 ) by the National Center for Advancing Translational Sciences (NCATS) in 2019. Furthermore, we acknowledge that the management of study data were done using REDCap ( Harris et al., 2009 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NICHD, NIH-FIC, NIEHS, NCRR, or NCATS. Finally, we acknowledge contributions by colleagues in the Analytical Chemistry Lab at Michigan Department of Health and Human Services (MDHHS) for analyzing and storing the whole blood samples for the assessments of heavy metal concentrations, under a service contract.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/11
Y1 - 2020/11
N2 - Background: Exposure to many environmental chemicals, including metals, often does not occur in isolation, hence requires assessment of the associations between exposure to mixtures of chemicals and human health. Objectives: To investigate associations of a metal mixture of lead (Pb), mercury (Hg), arsenic (As), cadmium (Cd), manganese (Mn), and aluminum (Al) in children with autism spectrum disorder (ASD), additively or interactively with each of three glutathione S-transferase (GST) genes (GSTP1, GSTT1, and GSTM1). Method: Using data from 266 case-control pairs of Jamaican children (2–8 years old), we fitted negative and positive generalized weighted quantile sum (gWQS) regression models to assess the aforementioned associations. Results: Based on additive and interactive negative gWQS models adjusted for maternal age, parental education, child's parish, and seafood consumption, we found inverse associations of the overall mixture score with ASD [MOR (95 % CI) = 0.70 (0.49, 0.99); P < 0.05) and [MOR (95 % CI) = 0.46 (0.25, 0.84); P = 0.01], respectively. In an unadjusted negative gWQS model, we found a marginally significant interaction between GSTP1 and a mixture of three metals (Pb, Hg, and Mn) (P = 0.07) while the association was no longer significant after adjustment for the same covariates (P = 0.24). Conclusions: Differences in diet between ASD and control groups may play a role in the inverse associations we found. The possible interactive association between Mn and GSTP1 in ASD based on gWQS is consistent with our previous reports. However, possible interaction of GSTP1 with Pb and Hg in ASD requires further investigation and replication.
AB - Background: Exposure to many environmental chemicals, including metals, often does not occur in isolation, hence requires assessment of the associations between exposure to mixtures of chemicals and human health. Objectives: To investigate associations of a metal mixture of lead (Pb), mercury (Hg), arsenic (As), cadmium (Cd), manganese (Mn), and aluminum (Al) in children with autism spectrum disorder (ASD), additively or interactively with each of three glutathione S-transferase (GST) genes (GSTP1, GSTT1, and GSTM1). Method: Using data from 266 case-control pairs of Jamaican children (2–8 years old), we fitted negative and positive generalized weighted quantile sum (gWQS) regression models to assess the aforementioned associations. Results: Based on additive and interactive negative gWQS models adjusted for maternal age, parental education, child's parish, and seafood consumption, we found inverse associations of the overall mixture score with ASD [MOR (95 % CI) = 0.70 (0.49, 0.99); P < 0.05) and [MOR (95 % CI) = 0.46 (0.25, 0.84); P = 0.01], respectively. In an unadjusted negative gWQS model, we found a marginally significant interaction between GSTP1 and a mixture of three metals (Pb, Hg, and Mn) (P = 0.07) while the association was no longer significant after adjustment for the same covariates (P = 0.24). Conclusions: Differences in diet between ASD and control groups may play a role in the inverse associations we found. The possible interactive association between Mn and GSTP1 in ASD based on gWQS is consistent with our previous reports. However, possible interaction of GSTP1 with Pb and Hg in ASD requires further investigation and replication.
KW - Autism Spectrum Disorder
KW - Generalized weighted quantile sum regression (gWQS)
KW - Glutathione S-transferase (GST) genes (GSTP1 GSTT1 and GSTM1)
KW - Heavy metals
KW - Jamaica
KW - Mixture analysis
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U2 - 10.1016/j.rasd.2020.101681
DO - 10.1016/j.rasd.2020.101681
M3 - Article
C2 - 33193808
AN - SCOPUS:85093975501
SN - 1750-9467
VL - 79
JO - Research in Autism Spectrum Disorders
JF - Research in Autism Spectrum Disorders
M1 - 101681
ER -