Mohammad H Rahbar1,2,3, Maureen Samms-Vaughan4, MinJae Lee5,6, Jing Zhang3,7, Manouchehr Hessabi3, Jan Bressler1,8, MacKinsey A Bach1, Megan L Grove1,8, Sydonnie Shakespeare-Pellington4, Compton Beecher9, Wayne McLaughlin9,10, Katherine A Loveland11. 1. Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 2. Division of Clinical and Translational Sciences, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 3. Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 4. Department of Child & Adolescent Health, The University of the West Indies (UWI), Mona Campus, Kingston, Jamaica. 5. Division of Biostatistics, Department of Population & Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. 6. Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. 7. Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 8. Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 9. Department of Basic Medical Sciences, The University of the West Indies, Mona Campus, Kingston, Jamaica. 10. Caribbean Genetics (CARIGEN), The University of the West Indies, Mona Campus, Kingston, Jamaica. 11. Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA.
Abstract
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.
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.
Entities:
Keywords:
Autism Spectrum Disorder; Generalized weighted quantile sum regression (gWQS); Heavy metals; Jamaica; Mixture analysis; glutathione S-transferase (GST) genes (GSTP1, GSTT1, and GSTM1)
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