Interaction between manganese and GSTP1 in relation to autism spectrum disorder while controlling for exposure to mixture of lead, mercury, arsenic, and cadmium

Abstract

Background: We previously reported a significant interactive association between polymorphisms of GSTP1 and blood manganese concentrations (BMC) with autism spectrum disorder (ASD) in Jamaican children. In this paper, we investigate the same interactive association with ASD while adjusting for the mixture of four metals (lead, mercury, cadmium, and arsenic). Method: We used data from 163 case-control pairs of children 2–8 years of age from our autism project in Jamaica, in which we collected blood for heavy metals analysis at enrollment. To minimize potential multicollinearity between concentrations of the four metals, we generated a mixture index using generalized weighted quantile sum regression, which was used in conditional logistic regression models to control for the four metals while assessing the interactive association between GSTP1 and BMC with ASD. Results: Similar to the findings we reported previously, we found that in co-dominant and dominant models for GSTP1, among children with the Ile/Ile genotype, those with BMC > 12μg/L had 4.6 and 4.27 times higher odds of ASD compared to those with BMC < 12μg/L (adjusted Matched Odds Ratio (MOR) = 4.6, 95% CI: 1.21 – 17.42 and adjusted MOR = 4.27, 95% CI: 1.15 – 15.85, respectively). In the co-dominant model, for children with the Ile/Val and Val/Val genotypes, the adjusted MORs were 1.26 (95% CI: 0.32, 5.01) and 0.26 (95% CI: 0.05, 1.42), respectively. Conclusions: After adjusting for the mixture of four metals, the interactive association of BMC and GSTP1 with ASD remained significant with similar magnitude of associations. Results should be interpreted cautiously.

A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

“…The contribution of Mn for the other GSTP1 genotypes (Ile/Val and Val/Val) was not strong (i.e., <0.2) for both positive and negative models. This finding is consistent with the results from the analysis where interactive effect between Mn and GSTP1 in relation to ASD was tested without or with adjustment of other four metals in the ERAJ study (Rahbar et al., , ), which showed that higher Mn concentrations were significantly associated with ASD for only the GSTP1 Ile/Ile genotype, but this significant association was not found among children with Ile/Val or Val/Val.…”

“…So far, a significant gene‐environment interaction between GSTP1 and Mn was found when Mn was the only metal that was assessed (Rahbar et al., ). Furthermore, the model with an interaction between GSTP1 and Mn, which controls for a mixture of an additional four metals (Pb, Hg, As, and Cd) as potential confounders, has been recently conducted by investigators and suggested that the interaction effect between GSTP1 and Mn in relation to ASD was still significant after controlling for the mixture score of the other four metals (Rahbar et al., ). They conducted WQS regression to generate a weighted index of the aforementioned four metals, which was then adjusted for as a confounder in a separate conditional logistic model where the interaction between BMC and GSTP1 was assessed, since the current WQS approach (a) does not allow potential interactions of metals within a mixture, and (b) it cannot account for the clustered data in the WQS estimation process.…”

“…One of the main objectives of this research is to establish WQS index for a mixture of all five metals in order to minimize any potential effects of multicollinearity due to possible correlation among metal concentrations in blood, and investigate whether the effect of metals on ASD differs by GST gene polymorphisms. Specifically, based on the previous findings (Rahbar et al., , ), we conducted the proposed method that indicates a potential interaction effect between Mn and GSTP1 in relation to ASD within a mixture, under the conditional logistic regression model framework using in Equation to account for the within‐pair correlations. Suppose there are two dummy variables for three GSTP1 categories, that is, G 2 (=1 if Ile/Val) and G 3 (=1 if Val/Val), interactive model can be written as Other exposure variables (a matrix u ) included demographics and socioeconomic status (SES), parents' education, and potential nutritional exposures.…”

“…() found a significant gene‐environment interaction between one of the glutathione S ‐transferase (GST) family genes, GSTP1 , and blood manganese concentrations (BMC) in relation to ASD in Jamaican children from their matched case‐control study. Since their original analysis did not take into account co‐exposure to other metals, they have recently conducted an analysis to assess whether this interaction effect remains statistically significant after controlling for the mixture of four other metals (lead, mercury, arsenic, and cadmium) (Rahbar et al., ). They used WQS regression to generate a weighted index of the aforementioned four metals, which was then adjusted for as a potential confounder in a separate conditional logistic model where the interaction between BMC and GSTP1 was assessed.…”

Early life metal exposure dysregulates cellular bioenergetics in children with regressive autism spectrum disorder

“…Studies have suggested that Mn exposure is associated with neurocognitive dysfunction and psychosis 14 . Studies have associated higher Mn blood concentration during childhood with more severe ASD symptoms 60 , lower cognitive function 50 , and inflammatory markers 61 . However, using deciduous teeth, Mn concentrations have been shown to be lower in children with the ASD during the prenatal and early postnatal periods in a twin study with higher Mn concentrations correlating with less severe ASD symptoms 11 .…”