Interaction between a Mixture of Heavy Metals (Lead, Mercury, Arsenic, Cadmium, Manganese, Aluminum) and GSTP1 in Relation to Autism Spectrum Disorder

Background: Humans are regularly exposed to many environmental chemicals with potentially toxic effects on human health. These exposures often do not occur in isolation, but as a mixture of chemicals; however limited information is published regarding the effects of exposure to mixtures of chemicals on human health, including autism spectrum disorder (ASD). We previously reported on the presence or absence of a significant association of ASD with each of the following six metals: lead (Pb), mercury (Hg), arsenic (As), cadmium (Cd), manganese (Mn), and aluminum (Al). In this study we perform mixture analysis of the six metals in relation to ASD.

Objectives: To investigate the additive or interactive associations between a mixture of the six metals and glutathione S-transferase pi 1 (GSTP1) genotypes in relation to ASD.

Methods: We used data from 266 case-control pairs of children 2-8 years old from our autism project in Jamaica. To minimize potential multicollinearity between concentrations of the six metals, we generated a mixture index using generalized weighted quantile sum regression based on conditional logistic regression models, in which the genotype-specific weights of certain metals are determined to assess its additive or interactive association with GSTP1 genotypes in relation to ASD. Positive and negative overall effects of the six metals on ASD were modeled separately. We also evaluated individual effects of each metal on ASD.

Results: Findings from our univariable negative model indicate that lower overall mixture score was significantly associated with ASD [MOR=0.35, 95% CI=(0.22,0.55), p<0.01]. We also found that association of three metals (Pb, Hg, Mn) with ASD appeared to differ by GSTP1 genotype with a marginally significant interaction effect (p=0.08). After adjusting for potential confounders including maternal age, parental education levels, parish of child’s birth, and consumption of seafood, the overall index effect on ASD was significant [adjusted MOR=0.47, 95% CI=(0.28,0.80), p<0.01] with similar patterns in estimated weights of the metals, but the interaction effect was no longer statistically significant (p=0.21). When the positive overall mixture index was evaluated [unadjusted MOR=1.13, 95% CI=(0.78,1.63), p=0.52; adjusted MOR=1.51, 95% CI=(0.79,2.9), p=0.22), though interaction effects were not statistically significant (p=0.90 for unadjusted model, p=0.52 for adjusted model], we found that higher blood Mn concentrations were associated with ASD for the GSTP1 Ile/Ile genotype (weight for Mn in the positive adjusted model= 0.32), but this association was attenuated among children with Ile/Val (weight for Mn in the positive adjusted model =0.04) or Val/Val (weight for Mn in the positive adjusted model =0.02) genotypes.

Conclusions: Findings from mixture analysis of the six metals in relation to ASD are somewhat similar to our previously reported findings based on analysis of the role of individual metals in additive and interactive models. However, the mixture analysis provides useful information about the positive and negative effects of the mixture index on ASD for both additive and interactive models. The finding of a potential role of GSTP1 as an effect modifier when assessing the role of blood Mn concentration in ASD based on mixture analysis is consistent with our previous reports.