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Article

Interactions between Environmental Factors and Glutathione S-Transferase (GST) Genes with Respect to Detectable Blood Aluminum Concentrations in Jamaican Children

by
Mohammad H. Rahbar
1,2,3,*,
Maureen Samms-Vaughan
4,
Yuansong Zhao
2,5,
Sepideh Saroukhani
2,3,
Jan Bressler
1,6,
Manouchehr Hessabi
2,
Megan L. Grove
1,6,
Sydonnie Shakespeare-Pellington
4 and
Katherine A. Loveland
7
1
Department of Epidemiology, Human Genetics, and Environmental Sciences (EHGES), School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
2
Biostatistics/Epidemiology/Research Design (BERD) Component, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
3
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
4
Department of Child & Adolescent Health, The University of the West Indies (UWI), Mona Campus, Kingston 7, Jamaica
5
Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
6
Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
7
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
*
Author to whom correspondence should be addressed.
Genes 2022, 13(10), 1907; https://doi.org/10.3390/genes13101907
Submission received: 29 July 2022 / Revised: 13 October 2022 / Accepted: 18 October 2022 / Published: 20 October 2022
(This article belongs to the Special Issue Genomics of Neuropsychiatric Disorders)
Versions Notes

Abstract

:
Aluminum (Al) is a metallic toxicant at high concentrations following natural or unnatural exposures. Dietary intake is considered as the main source of aluminum exposure in children. We used data from 366 typically developing (TD) children (ages 2–8 years) who participated as controls in an age- and sex-matched case–control study in Jamaica. We investigated additive and interactive associations among environmental factors and children’s genotypes for glutathione S-transferase (GST) genes (GSTT1, GSTM1, GSTP1), in relation to having a detectable blood aluminum concentration (BAlC) of >5.0 μg/L, using multivariable logistic regression models. Findings from interactive models revealed that the odds of having a detectable BAlC was significantly higher among children who ate string beans (p ≤ 0.01), whereas about 40% lower odds of having a detectable BAlC was observed in children with higher parental education level, (p = 0.02). A significant interaction between consumption of saltwater fish and GSTP1 in relation to having a detectable BAlC using either co-dominant or dominant genetic models (overall interaction p = 0.02 for both models) indicated that consumption of saltwater fish was associated with higher odds of having a detectable BAlC only among children with the GSTP1 Ile105Val Ile/Ile genotype using either co-dominant or dominant models [OR (95% CI) = 2.73 (1.07, 6.96), p = 0.04; and OR (95% CI) = 2.74 (1.08, 6.99), p = 0.03]. Since this is the first study from Jamaica that reports such findings, replication in other populations is warranted.

1. Introduction

Aluminum (Al) is one of the most plentiful elements after oxygen and silicon in the surface of the Earth and is about 8% by mass [1,2,3]. Even though Al is not required for any biological process in humans and animals, it can be a metallic toxicant at high concentrations after natural or unnatural exposure [4,5]. Exposure to Al has been linked to several adverse health effects, such as asthma [6,7], bone disease [8,9,10], immunotoxicity [11,12], congenital malformations [13], and reproductive toxicity in laboratory animals [14,15]. For example, children with early life exposure to a high level of Al had lower lumbar spine bone mass and lower hip bone mass [10]. Moreover, Al is associated with neurological diseases, including Alzheimer’s dementia and multiple sclerosis [16,17], and autism spectrum disorder (ASD) [18]. Specifically, several studies have shown that compared to people without recognizable neurological diseases, people with such disease had a significantly higher content of Al in brain tissue [16].
Major sources of human exposure to Al include food, while minor exposures to Al may occur through drinking water, occupational inhalation of ambient air, skin absorption of cosmetic products, parenteral nutrition solutions, and pharmaceutical products [19,20,21,22,23,24]. In addition to food items that naturally contain Al by uptake from the geologic surroundings during growth, the use of food additives such as firming, coloring, or anticaking agent that contain Al has obviously increased Al exposure in humans [24,25,26]. Many foods or food products have been reported to have high Al contents. For example, raw tea, which is made from young leaves, contains a reasonably high content of Al, and fermented tea has an even higher Al content [27]. A recent study in an Italian population suggests that legumes, sweets (mainly in chocolate-based products), cereals and cereal products, and leafy vegetables have high Al contents [26]. Furthermore, fish and shellfish are often considered as sources of dietary exposure to Al. The Second French Total Diet Study (TDS2) that was conducted by the French Food Safety Agency (AFSSA) reported that fish and fish products were the food groups that had the highest mean contents of Al [28]. As a result of industrialization, food packaging materials made from Al, such as Al cans and Al foil can also increase Al exposure in humans [26,29,30]. For example, even from the same dairy plant, processed cheese wrapped in Al foil had higher Al content than cheese packed in non-Al material (0.034 to 5.718 compared to 0.077 to 2.939 mg/kg) [30]. In its report in 2011, the Joint Food and Agriculture Organization of the United Nations/World Health Organization Expert Committee on Food Additives (JECFA) had proposed a provisional tolerable weekly intake (PTWI) of 2 mg/kg body weight (bw) that applies to all Al compounds in food, including food additives [31]. When expressed as body weight, the food intake of children is generally higher than that of adults, and therefore children are more susceptible to potential exposure to Al through diet. In fact, several studies suggest that dietary Al exposures are likely to be exceeded to a large extent in children in different countries and regions [32,33,34,35]. In Shenzhen, China, although the average dietary exposure to Al of the whole population is lower than the PTWI (1.263 mg/kg bw per week), children aged from 3–13 years have an Al intake of 3.248 mg/kg bw per week, which is 60% more than the PTWI suggested by JECFA [34]. Similarly, a study from Japan reported that the mean dietary intake of Al through food among children (2.85 mg/kg bw per week) was 40% higher than the PTWI in comparison with adults (1.37 mg/kg bw per week) [32]. Considering that Jamaica is one of the world’s major exporters of bauxite, and has a high per capita fish consumption, higher exposure to Al through dietary intake can pose a health risk in Jamaican children [36,37,38,39].
The glutathione S-transferase (GST) superfamily includes six genes of which GST pi 1 (GSTP1), GST mu 1 (GSTM1), and GST theta 1 (GSTT1) are phase II enzymes and have been known for their critical role in detoxification and excretory mechanisms [40]. These enzymes catalyze and promote excretion after conjugation of glutathione with numerous xenobiotics (e.g., heavy metals including Al) [41,42,43]. In several animal studies, significantly decreased GST activities and reduced GSH levels were found following Al exposure [44,45]. A similar result was obtained in a study by Halatek et al. of industrial workers in which the authors noted that people with higher concentrations of Al in urine (>40 μg/L) had a lower GST activity [46]. Moreover, several findings suggested that the differential susceptibility to heavy metals can be explained by the polymorphisms of GST genes, for instance, null alleles of GSTT1 and GSTM1 were associated with a deficiency of enzymatic activity which was related to decreased detoxification and increased oxidative stress [43,47]. According to a recent study in Egypt, children with null GSTM1 and GSTT1 genotypes had a significantly lower level of GST activity compared to other combinations of genotypes, suggesting a poor aluminum detoxification ability [48]. All of these reports indicate that genetic variation can provide an explanation for differences in Al concentrations in a population.
In our previous paper, we used data from 116 age- and sex-matched pairs (ASD vs. typically developing controls (TD)) (232 children) of Jamaican children 2–8 years old. We observed that TD children with GSTP1 Ile/Ile or Val/Val genotype had a significantly higher geometric mean blood Al concentration (BAlC) than those with Ile/Val genotypes (23.75 μg/L vs. 14.57 μg/L, p < 0.03). Furthermore, none of the additive effects of food consumption were statistically significantly associated with log-transformed BAlC (all p > 0.06) [49]. In the present study, we evaluated the additive and interactive association between environmental factors and genotypes of three GST genes, as well as the possible pairwise gene-gene interactions of these genes in relation to a detectable BAlC (>5.0 μg/L) in Jamaican TD children.

2. Materials and Methods

2.1. Study Population

This study was conducted using data from 366 TD control children, between 2–8 years old, who were enrolled in the Epidemiological Research on Autism in Jamaica (ERAJ) studies between December 2009 and September 2017. Detailed information regarding the enrollment and assessment of TD controls has been reported earlier [50,51,52]. Relevant to the research objectives here, the age- and sex-matched TD controls (within six months of the matched ASD case) were identified from schools, churches, and well-child clinics at the University of the West Indies (UWI) and the Social Communication Questionnaire (SCQ) [53] was used to rule out developmental disorders (SCQ score of 0–6) in the TD control children. [49] We collected information about parents/guardians’ sociodemographic characteristics as well as children’s weekly food intake through questionnaires [49], and about 5 mL of whole blood was drawn from each child to assess exposure to the heavy metals including Al and to determine genotypes for the three GST genes. This study was approved by the Institutional Review Boards of The University of Texas Health Science Center in Houston (UTHealth), Michigan Department of Health and Human Services (MDHHS), and the University of the West Indies, Mona campus, in Kingston, Jamaica (HSC-SPH-09-0059).

2.2. Assessment of Al Exposure

In this study, we assessed BAlCs to measure Al exposure in children. BAlCs were assessed at the Trace Metals Lab at the MDHHS in Lansing, MI, USA. We have previously reported details on sample processing and storage [49,51,54]. MDHHS followed a fully authenticated protocol for analyzing Al in blood samples with a limit of detection (LoD) of 5.0 μg/L, and 37.1% (136 out of 366) of children in this study had an undetectable BAlC because it was below the LoD.

2.3. Statistical and Genetic Analysis

We conducted descriptive analyses to assess socioeconomic status (SES) characteristics, and frequencies of the GSTP1, GSTT1, and GSTM1 genotypes for the TD children. Since more than one-third (37%) of BAlCs were below the LoD, we used 5.0 μg/L as the cutoff point and converted BAlCs to a binary variable. The choice of cutoff point reflects the LoD in the ERAJ studies.
Assessment of the children’s genotypes for the GSTP1 Ile105Val (rs1695) polymorphism and insertion deletion polymorphisms in GSTT1 and GSTM1 was carried out as previously described [50,55]. Choice of the genetic models that were used to test their additive and interactive associations with environmental factors was based on differences in the types of polymorphisms. Because there are 3 possible genotypes for GSTP1 rs1695 and 2 possible genotypes for GSTT1 and GSTM1 since the homozygote (I/I) and heterozygote (I/D) cannot be distinguished, only the recessive model was selected for GSTT1 and GSTM1 (D/D vs. I/I and I/D) whereas three different genetic models were specified for GSTP1 rs1695 (dominant, co-dominant, and recessive). Similarly, only the GSTP1 Ile105Val polymorphism was tested for accordance with Hardy–Weinberg equilibrium expectations using the Chi-square test.
Using logistic regression models, we assessed additive association of each independent variable including the three GST genes, sociodemographic characteristics, and consumption of different kinds of vegetables, starches, and seafoods in relation to binary BAlCs (<LoD vs. ≥LoD). Then, we evaluated the potential gene-gene interactions among the three GST genes and possible gene-environment interactions between each of the three GST genes and consumption of various types of food in relation to BAlCs. Subsequently, we developed logistic regression models that contained both additive and interactive effects of GST genes and environmental factors to evaluate the adjusted odds of having a detectable BAlC. To minimize the potential effects of multicollinearity, we only kept one of the correlated variables when the model became unstable by adding both correlated variables. Following the procedure described we used the CONTRAST statement in SAS [56] to access odds ratios and 95% confidence intervals for evaluating the interactive effects in the presence of two-way interactions. All statistical tests were evaluated at 5% level of significance using SAS 9.4 software [57].

3. Results

Demographic information and other characteristics are displayed in Table 1. 81.7% of the 366 TD children were male and 97.3% were Afro-Caribbean. About 25% of them were 72 months or older and 62.6% of the children were born in the Kingston parish. 11.4% of TD children were born to mothers who were at least 35 years old, and 45.5% of the children had at least one parent who attained an education level beyond high school. Moreover, 40.7% of the families owned a car, which represents high SES in Jamaica. The frequencies of null (DD) genotype for GSTM1 and GSTT1 were 26.1% and 24.6%, respectively. In addition, the frequencies of the GSTP1 genotypes were in agreement with Hardy–Weinberg equilibrium expectations (p = 0.67).
In univariable logistic regression analysis (Table 2), we found a significant inverse association between having at least one parent with education level beyond high school and a detectable BAlC in children [OR (95% CI) = 0.52 (0.34, 0.80), p < 0.01]. We also found significant associations between consumption of certain types of food and BAlCs.
Specifically, the odds of having a detectable BAlC in children who ate green banana was significantly lower than in children who never ate such food [OR (95% CI) = 0.59 (0.36, 0.95), p = 0.03]. Our findings were similar for consumption of tuna [OR (95% CI) = 0.64 (0.41, 0.99), p = 0.04], cabbage [OR (95% CI) = 0.50 (0.32, 0.80), p < 0.01], and root vegetables (yam, sweet potato, dasheen, coco) [OR (95% CI) = 0.60 (0.37, 0.97), p = 0.03]] in relation to a detectable BAlC. Furthermore, the odds of having BAlCs above LoD were higher in children who consumed fresh water fish [OR (95% CI) = 1.80 (1.11, 2.91), p = 0.02], cakes/buns [OR (95% CI) = 1.87 (1.05, 3.32), p = 0.03], and lettuce [OR (95% CI) = 1.84 (1.19, 2.84), p < 0.01] than in those who did not eat these foods. Furthermore, children who consumed broad beans [OR (95% CI) = 1.84 (1.19, 2.83), p < 0.01], string beans [OR (95% CI) = 3.27 (2.05, 5.22), p < 0.01], as well as other beans and legumes (red and gungo peas) [OR (95% CI) = 1.96 (1.18, 3.27), p = 0.01], had higher odds of having a detectable BAlC compared to those who never ate such food. We did not find any significant additive associations between BAlCs and genotypes for the three GST genes (all p > 0.08).
Unadjusted multivariable models were used to assess the two-way gene-gene interaction of GST genes in relation to BAlCs (Table 3). Using a dominant genetic model for GSTP1, there was a significant interaction between GSTP1 and GSTM1 with respect to BAlCs (overall interaction p = 0.04) indicating that among children with GSTM1 DD genotype, children with GSTP1 Ile/Val or Val/Val genotype were 68% less likely to have a detectable BAlC than those with the GSTP1 Ile/Ile genotype [OR (95% CI) = 0.32 (0.11, 0.98), p < 0.05].
Additionally, using a co-dominant model for GSTP1, although the interaction between GSTP1 and GSTM1 was marginally significant (overall interaction p = 0.06), we found that among children with GSTM1 DD genotype, the odds of having a detectable BAlC in children with the GSTP1 Val/Val genotype was 0.20 times (or 1/5 times) that of those with the Ile/Ile genotype [OR (95% CI) = 0.20 (0.05, 0.82), p = 0.03]. When we used the recessive genetic model for GSTP1 (overall interaction p = 0.10), we found that (though marginally significant) among children with GSTP1 Val/Val genotype, the odds of having a detectable BAlC in children with the GSTM1 DD genotype was 0.33 times that of those with the I/I or I/D genotype [OR (95% CI) = 0.33 (0.10, 1.07), p = 0.06]. Moreover, although the interaction between GSTM1 and GSTT1 in relation to BAlCs was not statistically significant (overall interaction p = 0.11), we found among children with DD genotype for GSTM1, the odds of having a detectable BAlC was 67% lower in children with DD genotype for GSTT1 than in those with I/I or I/D genotype for GSTT1 [OR (95% CI) = 0.33 (0.13, 0.87), p = 0.03].
In the assessment of the interactive associations of children’s environmental exposures and genotypes for GST genes with respect to detectable BAlCs (Table 4), we identified a significant interaction between consumption of green banana and GSTT1 genotypes in relation to BAlCs (interaction p = 0.04). Specifically, using a recessive genetic model, among children with GSTT1 I/I or I/D genotype, the odds of having a BAlC above LoD in children who ate green banana was 0.45 times that of those who never ate such food [OR (95% CI) = 0.45 (0.25, 0.82), p = 0.01], whereas, no statistically significant associations were found between consumption of green banana and BAlCs among children with GSTT1 DD genotypes [OR (95% CI) = 1.47 (0.55, 3.90), p = 0.44]. In addition, we found a similar interactive association between child’s genotypes for GSTT1 and consumption of porridge, as well as consumption of macaroni in relation to BAlCs (both overall interaction p = 0.03). For example, consumption of porridge or macaroni was associated with about 80% lower odds of having a detectable BAlC among children with GSTT1 I/I or I/D genotypes [OR (95% CI) = 0.27 (0.10, 0.73), p = 0.01, and OR (95% CI) = 0.22 (0.05, 0.97), p < 0.05, respectively], whereas, consumption of porridge or macaroni was not statistically associated with BAlCs among children with GSTT1 DD genotypes [OR (95% CI) = 1.51 (0.46, 4.91), p = 0.49, and OR (95% CI) = 1.97 (0.52, 7.52), p = 0.32, respectively].
Furthermore, there was a significant interaction between consumption of broad beans and GSTM1 genotypes under a recessive genetic model, in relation to BAlCs (interaction p < 0.05). Specifically, among children with GSTM1 null (DD) genotype, the odds of having a detectable BAlC in those who ate broad beans was 3.96 times that of those who never ate such food [OR (95% CI) = 3.96 (1.57, 9.97), p < 0.01], whereas, there was no significant association between consumption of broad beans and BAlCs among children with GSTM1 I/I or I/D genotypes [OR (95% CI) = 1.37 (0.82, 2.27), p = 0.23]. We also identified a significant interaction between consumption of saltwater fish and child’s GSTP1 genotype in relation to a detectable BAlC using either a co-dominant or dominant genetic model (overall interaction p = 0.03, and p = 0.02, respectively). Specifically, among children with GSTP1 Ile/Ile genotypes, the odds of having detectable BAlCs in children who reported eating saltwater fish was 3.36 times that of those who never ate such seafood in both genetic models [OR (95% CI) = 3.36 (1.37, 8.24), p = <0.01 for both models]. Although the overall interaction was marginally significant when using the recessive genetic model (overall interaction p = 0.05), we have found that among children with at least one Ile allele, those who ate saltwater fish had higher odds of having detectable BAlCs than those who never ate such food [OR (95% CI) = 1.76 (1.05, 2.94), p = 0.03]. In a dominant model for GSTP1, we have also found a significant interaction between children’s genotypes for GSTP1 and consumption of white bread in relation to BAlCs (overall p = 0.02). Specifically, among children with GSTP1 Ile/Ile genotype, children who ate white bread were 2.49 times more likely to have detectable BAlCs compared to children who never ate white bread [OR (95% CI) = 2.49 (1.05, 5.90), p = 0.04]. This association was not statistically significant in children with GSTP1 Ile/Val or Val/Val genotypes [OR (95% CI) = 0.75 (0.43, 1.29), p = 0.30]. Furthermore, among children with GSTP1 Ile/Val or Val/Val genotypes, children who ate whole wheat bread were 2.05 times more likely to have detectable BAlCs compared to children who never ate such food [OR (95% CI) = 2.05 (1.22, 3.45), p < 0.01, overall interaction p = 0.02], whereas no statistically significant associations were found between consumption of whole wheat bread and BAlCs among children with GSTP1 Ile/Ile genotype [OR (95% CI) = 0.59 (0.23, 1.52), p = 0.27]. Additional results for the unadjusted associations between genotypes for GST genes and BAlCs by exposure to environmental and dietary factors in TD children are shown in the Supplemental Materials (Table S1).
In the interactive multivariable models that assessed the adjusted associations of children’s GST genotypes, exposure to environmental factors, and their interactions in relation to a detectable BAlC (Table 5), we identified education level of the parents and consumption of string beans as environmental factors that are additively associated with BAlCs in Jamaican TD children (all p ≤ 0.02 in all models). For example, using the co-dominant model for GSTP1 genotype, the odds of having detectable BAlCs in children who consumed string beans was still significantly higher than in those who never ate string beans (OR (95% CI) = 3.07 (1.07, 5.09), p < 0.01), and having at least one parent with education level beyond high school versus up to high school was associated with significantly lower odds of having a BAlC above LoD [OR (95% CI) = 0.57 (0.36, 0.91), p = 0.02]. By using three genetic models for GSTP1 genotype, we have investigated the gene-environment interaction between GSTP1 and consumption of saltwater fish in relation to BAlCs. After holding the aforementioned environmental factors constant, we found similar significant interactions between consumption of saltwater fish and GSTP1 under both co-dominant and dominant genetic models (overall interaction p = 0.02 for both models). Specifically, we found that among children with GSTP1 Ile/Ile genotype, the odds of having detectable BAlCs in children who ate saltwater fish was about 2.7 times that of those who never ate saltwater fish based on both co-dominant or dominant genetic models [OR (95% CI) = 2.73 (1.07, 6.96), p = 0.04; and OR (95% CI) = 2.74 (1.08, 6.99), p = 0.03, respectively]. Though the overall interaction is significant (p = 0.04), no statistically significant associations between saltwater fish consumption and BAlCs by children’s genotypes in GSTP1 was found using the recessive genetic model for GSTP1. In addition, details about adjusted associations between children’s genotypes in GSTP1 and BAlCs by saltwater fish consumption are shown in Table S2.

4. Discussion

Findings from our study suggest that the odds of having a detectable BAlC was about 3 times higher among children who ate string beans, compared to those who did not eat such beans, and 50% times lower in children with at least one parent with education level beyond high school. The association between consumption of saltwater fish and having a detectable BAlC varied by children’s genotype for GSTP1 using either dominant or co-dominant genetic models (overall interaction p = 0.02 for both models), and eating saltwater fish was significantly associated with 3 times higher odds of having a detectable BAlC only among children with GSTP1 Ile/Ile genotype.
Jamaica is known for its abundant and high-quality bauxite for decades. Over 20% of the surface area was covered by bauxite deposits in Jamaica, and a comparatively high level of Al was found in soils [37,59]. Since content of Al in foods varies by species and the soil pH [60], a possible explanation for our finding that consumption of string beans is associated with higher odds of having a detectable BAlC is that legumes including string beans accumulate more Al than others. Filippini et al. [26] conducted a study about dietary intake of Al by obtaining 908 food samples from Italy and measuring the Al content. Legumes were the category of food that had the highest levels of Al (7370.23 μg/kg). In addition, our finding is similar to several studies in China that reported soybeans, a member of the legume family, and bean products had a higher level of dietary Al content [34,61,62].
Our finding indicating 50% lower odds of having a detectable BAlC in children who had parents with higher education levels (at least one of the parents had education beyond high school) is consistent with several previous studies reporting that people from families with a lower level of education were exposed to more heavy metals [54,63,64,65]. In our previous study, we also found that Jamaican children whose parents both had education levels up to high school had 1.82 times the odds of having a detectable blood arsenic concentration (>1.3 μg/L) than children who had at least one parent with an education level beyond high school (p ≤ 0.01) [54]. Jee et al. also demonstrated that a lower level of formal education contributes to significantly higher blood cadmium levels [63]. Another study in Canada reported a significant inverse relationship between education (completed high school or not) and blood mercury levels in pregnant women [65]. A possible reason for our finding is that children from families with a lower level of education tend to be exposed to more fast food that contains a high content of food additives [66]. Moreover, parents with a higher education level may reveal more health-conscious behavior in providing food for their children [67] although they may not be aware that foods such as vegetables, string beans, and lettuce may have high levels of aluminum.
The literature about the association between genetic variation in GST genes and BAlCs in TD children is very limited. Our results support the conclusion that GSTP1 Ile105Val genotype can modify the effect of consuming saltwater fish on BAlCs where only carriage of the Ile/Ile genotype was shown to confer an increased risk of having a level > 5.0 μg/L. This was observed when either a co-dominant or dominant genetic model was used, while there was no significant association between any of the three GST polymorphisms and BAlCs in the additive models. One mechanism that may help to explain this finding is that codon 105 is located in the active site of the enzyme and that GSTP1 Ile105Val has been associated with changes in substrate-specific catalytic activity [68,69,70]. Similar relationships have been reported for another heavy metal. Engström et al. found that variation in the amount of fish intake can influence the level of mercury measured in erythrocytes and that this is dependent on GSTP1 genotype. No significant association with mercury levels was found if fish consumption was low, but individuals with the Ile/Ile genotype had significantly higher mercury levels than those with either the Ile/Val or Val/Val genotype if fish was eaten at least 2.5 times a week [68,71], In addition, previous studies have demonstrated that the GST enzyme itself or glutathione reductase (GR), an enzyme that maintains the supply of the GST substrate reduced glutathione, can be potentially inhibited by heavy metal ions at toxic concentrations [72,73,74]. For example, long term low-level lead exposure in rats has shown significant inhibitory effects (up to 55% inhibition) on GST activity [75]. Cadmium was shown to play a role in the inhibition of GST that was purified from Van Lake fish gills [76]. Since saltwater fish is a source of many heavy metals including arsenic, lead, mercury, and cadmium [77,78], a joint effect of multiple heavy metal exposures through saltwater fish consumption and GST genes is possible in relation to BAlCs. More studies are needed to replicate these relationships.

5. Limitations

We acknowledge that this study has several limitations. First, our participants are more likely to be from the Kingston area. Hence, our findings may not be generalizable to all children in Jamaica. Second, the timing of Al exposure was not available in this study as the BAlCs we used as a biomarker are more likely to reflect recent exposure. Although we used a food frequency questionnaire that reflects the food selection in Jamaica, we cannot exclude the possibility that findings may be confounded by other unmeasured variables, such as the consumption of canned beverages or the use of Al foil that may have a strong association with BAlCs. In addition, since we categorized the frequency of food into binary variables (consumed vs. never consumed), our analysis did not account for the quantity of food intake. Furthermore, to avoid the potential multicollinearity, consumption of several food items including freshwater fish, tuna, cakes/buns, vegetables (broad beans, lettuce, cabbage, and root vegetables) that were significantly associated with BAlCs in the univariable analysis were removed from the multivariable analyses. Furthermore, since SES is associated with parental education level, we choose to use parental education level in the model to avoid any potential for multicollinearity. Therefore, we advise caution in interpretation of these findings.

6. Conclusions

The present work indicated that children in Jamaica may be more susceptible to Al exposures through specific environmental factors as well as variation in GST genes. Our findings from interactive multivariable logistic regression models revealed that consumption of string beans was associated with higher odds of having a detectable BAlC, whereas higher parental education level was associated with lower odds of having a detectable BAlC in TD children. Additionally, we have found that among children with the GSTP1 Ile/Ile genotype, the odds of having a detectable BAlC was higher in children who consumed saltwater fish than in those who did not eat such food under both a co-dominant and dominant genetic model for GSTP1. This finding suggests that GSTP1 rs1695 may serve as an effect modifier for the association between saltwater fish consumption and BAlCs in Jamaican children. Further research is recommended to better understand the biological explanation for these findings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes13101907/s1, Table S1: Associations between children’s genotypes for GST genes and binary detectable blood Al concentrations (BAlCs) by children’s exposure to environmental factors based on logistic regression models that include interaction between GST genes and the main environmental exposure (N = 366); Table S2: Associations between children’s genotypes for GSTP1 and binary detectable blood Al concentrations (BAlCs) by saltwater fish consumption based on logistic regression models that adjusted for parental education level and consumption of string beans (N = 366).

Author Contributions

Conceptualization, M.H.R., M.S.-V., Y.Z. and J.B.; methodology, M.H.R., M.S.-V., Y.Z., J.B. and M.L.G.; validation, M.H.R.; formal analysis, M.H.R., Y.Z. and S.S.; investigation, M.H.R., S.S.-P. and M.S.-V.; resources, M.H.R. and M.S.-V.; data curation, M.S.-V., M.L.G. and S.S.-P.; visualization, M.H. and M.H.R.; writing—original draft preparation, Y.Z., S.S. and M.H.R.; writing—review and editing, M.H.R., Y.Z., S.S., J.B., M.H. and K.A.L.; supervision, M.H.R.; project administration, M.H.R. and M.S.-V.; funding acquisition, M.H.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research is funded by awards to the University of Texas Health Science Center at Houston (UTHealth) by the following organizations or institutions: (1) National Institute of Environmental Health Sciences (NIEHS): R01ES022165 in 2013; (2) Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Institutes of Health Fogarty International Center (NIH-FIC): R21HD057808 in 2009; (3) Translational Science Award (NIH CTSA) grant: UL1 RR024148 in 2006; (4) The National Center for Research Resources (NCRR): UL1 TR000371 in 2012; (5) The National Center for Advancing Translational Sciences (NCATS): UL1 TR003167 in 2019. 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.

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Committee for the Protection of Human Subjects (CPHS) of the University of Texas Health Science Center at Houston (HSC-SPH-09-0059, Primary Investigator: Mohammad H. Rahbar; 20 March 2009).

Informed Consent Statement

Informed consent was obtained from parents/guardians of all children involved in the study. Children’s assents were also obtained if the child was 7–8 years old.

Data Availability Statement

The data analyzed in this study are from two grants (i.e., R21 and R01). The data from R01 are or will be publicly available through the National Database for Autism Research (NDAR). Data from R21 will also be available upon request from the corresponding author.

Acknowledgments

We acknowledge that study data were collected and managed using the REDCap [79] electronic data capture information system hosted at University of Texas Health Science Center at Houston. We also acknowledge contributions for storing the whole blood samples for the assessments of heavy metal concentrations by colleagues in the Analytical Chemistry Lab at MDHHS, under a service contract.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Exley, C. A biogeochemical cycle for aluminium? J. Inorg. Biochem. 2003, 97, 1–7. [Google Scholar] [CrossRef]
  2. Igbokwe, I.O.; Igwenagu, E.; Igbokwe, N.A. Aluminium toxicosis: A review of toxic actions and effects. Interdiscip. Toxicol. 2019, 12, 45–70. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Haynes, W.M. Abundance of Elements in the Earth’s Crust and in the Sea, 95th ed.; CRC Press: Boca Raton, FL, USA, 2016; pp. 14–17. [Google Scholar]
  4. Exley, C. Aluminum in Biological Systems. In Encyclopedia of Metalloproteins; Kretsinger, R.H., Uversky, V.N., Permyakov, E.A., Eds.; Springer: New York, NY, USA, 2013; pp. 33–34. [Google Scholar]
  5. Becaria, A.; Campbell, A.; Bondy, S.C. Aluminum as a toxicant. Toxicol. Ind. Health 2002, 18, 309–320. [Google Scholar] [CrossRef]
  6. Kongerud, J.; Søyseth, V. Respiratory disorders in aluminum smelter workers. J. Occup. Environ. Med. 2014, 56, S60–S70. [Google Scholar] [CrossRef] [Green Version]
  7. Taiwo, O.A.; Sircar, K.D.; Slade, M.D.; Cantley, L.F.; Vegso, S.J.; Rabinowitz, P.M.; Fiellin, M.G.; Cullen, M.R. Incidence of asthma among aluminum workers. J. Occup. Environ. Med. 2006, 48, 275–282. [Google Scholar] [CrossRef] [PubMed]
  8. Chappard, D.; Bizot, P.; Mabilleau, G.; Hubert, L. Aluminum and bone: Review of new clinical circumstances associated with Al(3+) deposition in the calcified matrix of bone. Morphologie 2016, 100, 95–105. [Google Scholar] [CrossRef] [PubMed]
  9. Klein, G.L. Aluminum toxicity to bone: A multisystem effect? Osteoporos. Sarcopenia 2019, 5, 2–5. [Google Scholar] [CrossRef]
  10. Fewtrell, M.S.; Edmonds, C.J.; Isaacs, E.; Bishop, N.J.; Lucas, A. Aluminium exposure from parenteral nutrition in preterm infants and later health outcomes during childhood and adolescence. Proc. Nutr. Soc. 2011, 70, 299–304. [Google Scholar] [CrossRef]
  11. Zhu, Y.; Li, Y.; Miao, L.; Wang, Y.; Liu, Y.; Yan, X.; Cui, X.; Li, H. Immunotoxicity of aluminum. Chemosphere 2014, 104, 1–6. [Google Scholar] [CrossRef]
  12. Zuo, Y.; Lu, X.; Wang, X.; Sooranna, S.R.; Tao, L.; Chen, S.; Li, H.; Huang, D.; Nai, G.; Chen, H.; et al. High-Dose Aluminum Exposure Further Alerts Immune Phenotype in Aplastic Anemia Patients. Biol. Trace Elem. Res. 2021, 199, 1743–1753. [Google Scholar] [CrossRef]
  13. Troisi, J.; Giugliano, L.; Sarno, L.; Landolfi, A.; Richards, S.; Symes, S.; Colucci, A.; Maruotti, G.; Adair, D.; Guida, M.; et al. Serum metallome in pregnant women and the relationship with congenital malformations of the central nervous system: A case-control study. BMC Pregnancy Childbirth 2019, 19, 471. [Google Scholar] [CrossRef] [PubMed]
  14. Mouro, V.G.S.; Menezes, T.P.; Lima, G.D.A.; Domingues, R.R.; Souza, A.C.F.; Oliveira, J.A.; Matta, S.L.P.; Machado-Neves, M. How Bad Is Aluminum Exposure to Reproductive Parameters in Rats? Biol. Trace Elem. Res. 2018, 183, 314–324. [Google Scholar] [CrossRef] [PubMed]
  15. Yokel, R.A. Aluminum reproductive toxicity: A summary and interpretation of scientific reports. Crit. Rev. Toxicol. 2020, 50, 551–593. [Google Scholar] [CrossRef] [PubMed]
  16. Exley, C.; Clarkson, E. Aluminium in human brain tissue from donors without neurodegenerative disease: A comparison with Alzheimer’s disease, multiple sclerosis and autism. Sci. Rep. 2020, 10, 7770. [Google Scholar] [CrossRef] [PubMed]
  17. Kawahara, M.; Kato-Negishi, M. Link between Aluminum and the Pathogenesis of Alzheimer’s Disease: The Integration of the Aluminum and Amyloid Cascade Hypotheses. Int. J. Alzheimers Dis. 2011, 2011, 276393. [Google Scholar] [CrossRef] [Green Version]
  18. Mold, M.; Umar, D.; King, A.; Exley, C. Aluminium in brain tissue in autism. J. Trace Elem. Med. Biol. 2018, 46, 76–82. [Google Scholar] [CrossRef]
  19. Agency for Toxic Substances and Disease Registry (ATSDR), Toxicological Profile for Aluminum; Agency for Toxic Substances and Disease Registry, Department of Health and Human Services, Division of Toxicology and Environmental Medicine: Atlanta, GA, USA, 2008.
  20. Krewski, D.; Yokel, R.A.; Nieboer, E.; Borchelt, D.; Cohen, J.; Harry, J.; Kacew, S.; Lindsay, J.; Mahfouz, A.M.; Rondeau, V. Human health risk assessment for aluminium, aluminium oxide, and aluminium hydroxide. J. Toxicol. Environ. Health Part B 2007, 10, 1–269. [Google Scholar] [CrossRef]
  21. Becker, L.C.; Boyer, I.; Bergfeld, W.F.; Belsito, D.V.; Hill, R.A.; Klaassen, C.D.; Liebler, D.C.; Marks Jr, J.G.; Shank, R.C.; Slaga, T.J. Safety assessment of alumina and aluminum hydroxide as used in cosmetics. Int. J. Toxicol. 2016, 35, 16S–33S. [Google Scholar] [CrossRef]
  22. Advenier, E.; Landry, C.; Colomb, V.; Cognon, C.; Pradeau, D.; Florent, M.; Goulet, O.; Ricour, C.; Corriol, O. Aluminum contamination of parenteral nutrition and aluminum loading in children on long-term parenteral nutrition. J. Pediatr. Gastroenterol. Nutr. 2003, 36, 448–453. [Google Scholar] [CrossRef] [Green Version]
  23. Willhite, C.C.; Karyakina, N.A.; Yokel, R.A.; Yenugadhati, N.; Wisniewski, T.M.; Arnold, I.M.; Momoli, F.; Krewski, D. Systematic review of potential health risks posed by pharmaceutical, occupational and consumer exposures to metallic and nanoscale aluminum, aluminum oxides, aluminum hydroxide and its soluble salts. Crit. Rev. Toxicol. 2014, 44, 1–80. [Google Scholar] [CrossRef]
  24. Stahl, T.; Taschan, H.; Brunn, H. Aluminium content of selected foods and food products. Environ. Sci. Eur. 2011, 23, 37. [Google Scholar] [CrossRef] [Green Version]
  25. Yokel, R.A. Aluminum in food–the nature and contribution of food additives. In Food Additive; El-Samragy, Y., Ed.; Intech: Rijeka, Croatia, 2012; pp. 203–228. [Google Scholar]
  26. Filippini, T.; Tancredi, S.; Malagoli, C.; Cilloni, S.; Malavolti, M.; Violi, F.; Vescovi, L.; Bargellini, A.; Vinceti, M. Aluminum and tin: Food contamination and dietary intake in an Italian population. J. Trace Elem. Med. Biol. 2019, 52, 293–301. [Google Scholar] [CrossRef] [PubMed]
  27. Cao, H.; Qiao, L.; Zhang, H.; Chen, J. Exposure and risk assessment for aluminium and heavy metals in Puerh tea. Sci. Total Environ. 2010, 408, 2777–2784. [Google Scholar] [CrossRef] [PubMed]
  28. Millour, S.; Noël, L.; Kadar, A.; Chekri, R.; Vastel, C.; Sirot, V.; Leblanc, J.C.; Guérin, T. Pb, Hg, Cd, As, Sb and Al levels in foodstuffs from the 2nd French total diet study. Food Chem. 2011, 126, 1787–1799. [Google Scholar] [CrossRef] [PubMed]
  29. Dordevic, D.; Buchtova, H.; Jancikova, S.; Macharackova, B.; Jarosova, M.; Vitez, T.; Kushkevych, I. Aluminum contamination of food during culinary preparation: Case study with aluminum foil and consumers’ preferences. Food Sci. Nutr. 2019, 7, 3349–3360. [Google Scholar] [CrossRef]
  30. Al-Ashmawy, M.A. Prevalence and public health significance of aluminum residues in milk and some dairy products. J. Food Sci. 2011, 76, T73–T76. [Google Scholar] [CrossRef]
  31. World Health Organization; Food Agriculture Organization of the United Nations. Joint FAO/WHO Expert Committee on Food Additives. Evaluation of Certain Food Additives and Contaminants: Seventy-Fourth [74th] Report of the Joint FAO/WHO Expert Committee on Food Additives; World Health Organization: Geneva, Switzerland, 2011. [Google Scholar]
  32. Aung, N.N.; Yoshinaga, J.; Takahashi, J.I. Dietary intake of toxic and essential trace elements by the children and parents living in Tokyo Metropolitan Area, Japan. Food Addit. Contam. 2006, 23, 883–894. [Google Scholar] [CrossRef]
  33. Arnich, N.; Sirot, V.; Rivière, G.; Jean, J.; Noël, L.; Guérin, T.; Leblanc, J.C. Dietary exposure to trace elements and health risk assessment in the 2nd French Total Diet Study. Food Chem. Toxicol. 2012, 50, 2432–2449. [Google Scholar] [CrossRef]
  34. Yang, M.; Jiang, L.; Huang, H.; Zeng, S.; Qiu, F.; Yu, M.; Li, X.; Wei, S. Dietary exposure to aluminium and health risk assessment in the residents of Shenzhen, China. PLoS ONE 2014, 9, e89715. [Google Scholar] [CrossRef]
  35. Rose, M.; Baxter, M.; Brereton, N.; Baskaran, C. Dietary exposure to metals and other elements in the 2006 UK Total Diet Study and some trends over the last 30 years. Food Addit. Contam. 2010, 27, 1380–1404. [Google Scholar] [CrossRef]
  36. Antoine, J.M.R.; Fung, L.A.H.; Grant, C.N. Assessment of the potential health risks associated with the aluminium, arsenic, cadmium and lead content in selected fruits and vegetables grown in Jamaica. Toxicol. Rep. 2017, 4, 181–187. [Google Scholar] [CrossRef] [PubMed]
  37. Lalor, G.C. Geochemical mapping in Jamaica. Environ. Geochem. Health 1996, 18, 89–97. [Google Scholar] [CrossRef] [PubMed]
  38. Food and Agriculturre Organization of the United Nations (FAO). Fishery and Aquaculture Country Profiles, Jamaica. Available online: https://www.fao.org/fishery/en/facp/jam?lang=en (accessed on 12 October 2022).
  39. Hose, H.R. Bauxite Mineralogy. In Essential Readings in Light Metals: Volume 1 Alumina and Bauxite; Donaldson, D., Raahauge, B.E., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 21–29. [Google Scholar]
  40. Hayes, J.D.; Pulford, D.J. The glutathione S-transferase supergene family: Regulation of GST and the contribution of the isoenzymes to cancer chemoprotection and drug resistance. Crit. Rev. Biochem. Mol. Biol. 1995, 30, 445–600. [Google Scholar] [CrossRef] [PubMed]
  41. Whalen, R.; Boyer, T.D. Human glutathione S-transferases. Semin. Liver Dis. 1998, 18, 345–358. [Google Scholar] [CrossRef] [PubMed]
  42. Nebert, D.W.; Vasiliou, V. Analysis of the glutathione S-transferase (GST) gene family. Hum. Genom. 2004, 1, 460–464. [Google Scholar] [CrossRef] [PubMed]
  43. Josephy, P.D. Genetic variations in human glutathione transferase enzymes: Significance for pharmacology and toxicology. Hum. Genom. Proteom. 2010, 2010, 876940. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Zhang, H.; Zhao, W.; Malhotra, A. Efficacy of Curcumin in Ameliorating Aluminum- Induced Neurotoxicity. J. Environ. Pathol. Toxicol. Oncol. 2018, 37, 163–172. [Google Scholar] [CrossRef] [PubMed]
  45. El-Demerdash, F.M.; Baghdadi, H.H.; Ghanem, N.F.; Mhanna, A.B.A. Nephroprotective role of bromelain against oxidative injury induced by aluminium in rats. Environ. Toxicol. Pharmacol. 2020, 80, 103509. [Google Scholar] [CrossRef]
  46. Hałatek, T.; Trzcinka-Ochocka, M.; Matczak, W.; Gruchała, J. Serum Clara cell protein as an indicator of pulmonary impairment in occupational exposure at aluminum foundry. Int. J. Occup. Med. Environ. Health 2006, 19, 211–223. [Google Scholar] [CrossRef] [Green Version]
  47. Autrup, H. Genetic polymorphisms in human xenobiotica metabolizing enzymes as susceptibility factors in toxic response. Mutat. Res. 2000, 464, 65–76. [Google Scholar] [CrossRef]
  48. Said, S.; Moubarz, G.; Awadalla, H.; Sharaf, N.; Hegazy, N.; Elsaied, A.; Abdel Gawad, A.; Elkhafif, M. Role of Glutathione-S-Transferase M1 (GSTM1) and T1 (GSTT1) Genes on Aluminum Concentration and Oxidative Markers among Autistic Children. Egypt. J. Chem. 2021, 64, 7591–7601. [Google Scholar] [CrossRef]
  49. Rahbar, M.H.; Samms-Vaughan, M.; Pitcher, M.R.; Bressler, J.; Hessabi, M.; Loveland, K.A.; Christian, M.A.; Grove, M.L.; Shakespeare-Pellington, S.; Beecher, C.; et al. Role of Metabolic Genes in Blood Aluminum Concentrations of Jamaican Children with and without Autism Spectrum Disorder. Int. J. Environ. Res. Public Health 2016, 13, 1095. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Rahbar, M.H.; Samms-Vaughan, M.; Lee, M.; Christian, M.A.; Bressler, J.; Hessabi, M.; Grove, M.L.; Shakespeare-Pellington, S.; Desai, C.C.; Reece, J.A.; et al. Interaction between manganese and GSTP1 in relation to autism spectrum disorder while controlling for exposure to mixture of lead, mercury, arsenic, and cadmium. Res. Autism. Spectr. Disord. 2018, 55, 50–63. [Google Scholar] [CrossRef] [PubMed]
  51. Rahbar, M.H.; Samms-Vaughan, M.; Lee, M.; Zhang, J.; Hessabi, M.; Bressler, J.; Bach, M.A.; Grove, M.L.; Shakespeare-Pellington, S.; Beecher, C.; et al. Interaction between a Mixture of Heavy Metals (Lead, Mercury, Arsenic, Cadmium, Manganese, Aluminum) and GSTP1, GSTT1, and GSTM1 in Relation to Autism Spectrum Disorder. Res. Autism. Spectr. Disord. 2020, 79, 101681. [Google Scholar] [CrossRef]
  52. Rahbar, M.H.; Samms-Vaughan, M.; Saroukhani, S.; Bressler, J.; Hessabi, M.; Grove, M.L.; Shakspeare-Pellington, S.; Loveland, K.A.; Beecher, C.; McLaughlin, W. Associations of Metabolic Genes (GSTT1, GSTP1, GSTM1) and Blood Mercury Concentrations Differ in Jamaican Children with and without Autism Spectrum Disorder. Int. J. Environ. Res. Public Health 2021, 18, 1377. [Google Scholar] [CrossRef]
  53. Rutter, M.; Bailey, A.; Lord, C. The Social Communication Questionnaire: Manual; Western Psychological Services: Los Angeles, CA, USA, 2003. [Google Scholar]
  54. Rahbar, M.H.; Samms-Vaughan, M.; Zhao, Y.; Saroukhani, S.; Zaman, S.F.; Bressler, J.; Hessabi, M.; Grove, M.L.; Shakspeare-Pellington, S.; Loveland, K.A. Additive and Interactive Associations of Environmental and Sociodemographic Factors with the Genotypes of Three Glutathione S-Transferase Genes in Relation to the Blood Arsenic Concentrations of Children in Jamaica. Int. J. Environ. Res. Public Health 2022, 19, 466. [Google Scholar] [CrossRef]
  55. Rahbar, M.H.; Samms-Vaughan, M.; Hessabi, M.; Bressler, J.; Gillani, S.; Grove, M.L.; Shakespeare-Pellington, S.; Loveland, K.A. Correlation between concentrations of four heavy metals in cord blood and childhood blood of Jamaican children. J. Environ. Sci. Health Part A 2021, 56, 1196–1205. [Google Scholar] [CrossRef]
  56. Kleinbaum, D.G.; Klein, M. Logistic Regression: A Self-Learning Text, 3rd ed.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 602–634. [Google Scholar]
  57. SAS Institute Inc. SAS®, 9.4; SAS Institute Inc.: Cary, NC, USA, 2013. [Google Scholar]
  58. Bach, M.A.; Samms-Vaughan, M.; Hessabi, M.; Bressler, J.; Lee, M.; Zhang, J.; Shakespeare-Pellington, S.; Grove, M.L.; Loveland, K.A.; Rahbar, M.H. Association of Polychlorinated Biphenyls and Organochlorine Pesticides with Autism Spectrum Disorder in Jamaican Children. Res. Autism. Spectr. Disord. 2020, 76, 101587. [Google Scholar] [CrossRef]
  59. Greenberg, W.A.; Wilding, L.P. Pre- and Post-Mined Bauxite Soils of Jamaica: Physical and Chemical Properties. Soil Sci. Soc. Am. J. 2007, 71, 181–188. [Google Scholar] [CrossRef]
  60. Bojórquez-Quintal, E.; Escalante-Magaña, C.; Echevarría-Machado, I.; Martínez-Estévez, M. Aluminum, a Friend or Foe of Higher Plants in Acid Soils. Front. Plant Sci. 2017, 8, 1767. [Google Scholar] [CrossRef]
  61. Xu, G.S.; Jin, R.P.; Zhang, Z.W.; Zhang, W.Q.; Ren, D.L.; Chen, J.; Huang, G.W. Preliminary study on aluminum content of foods and aluminum intake of residents in Tianjin. Biomed. Environ. Sci. 1993, 6, 319–325. [Google Scholar] [PubMed]
  62. Liang, J.; Liang, X.; Cao, P.; Wang, X.; Gao, P.; Ma, N.; Li, N.; Xu, H. A Preliminary Investigation of Naturally Occurring Aluminum in Grains, Vegetables, and Fruits from Some Areas of China and Dietary Intake Assessment. J. Food Sci. 2019, 84, 701–710. [Google Scholar] [CrossRef] [PubMed]
  63. Jee, Y.; Cho, S.I. Associations between socioeconomic status and blood cadmium levels in Korea. Epidemiol. Health 2019, 41, e2019018. [Google Scholar] [CrossRef] [PubMed]
  64. Brailsford, J.M.; Hill, T.D.; Burdette, A.M.; Jorgenson, A.K. Are Socioeconomic Inequalities in Physical Health Mediated by Embodied Environmental Toxins? Socius 2018, 4, 2378023118771462. [Google Scholar] [CrossRef]
  65. Adamou, T.Y.; Riva, M.; Muckle, G.; Laouan-Sidi, E.A.; Ayotte, P. Socio-economic inequalities in blood mercury (Hg) and serum polychlorinated biphenyl (PCB) concentrations among pregnant Inuit women from Nunavik, Canada. Can. J. Public Health 2018, 109, 671–683. [Google Scholar] [CrossRef] [PubMed]
  66. Mölenberg, F.J.M.; Mackenbach, J.D.; Poelman, M.P.; Santos, S.; Burdorf, A.; van Lenthe, F.J. Socioeconomic inequalities in the food environment and body composition among school-aged children: A fixed-effects analysis. Int. J. Obes. 2021, 45, 2554–2561. [Google Scholar] [CrossRef]
  67. Damen, F.W.M.; Luning, P.A.; Fogliano, V.; Steenbekkers, B.L.P.A. What influences mothers’ snack choices for their children aged 2–7? Food Qual. Prefer. 2019, 74, 10–20. [Google Scholar] [CrossRef]
  68. Schläwicke Engström, K.; Strömberg, U.; Lundh, T.; Johansson, I.; Vessby, B.; Hallmans, G.; Skerfving, S.; Broberg, K. Genetic variation in glutathione-related genes and body burden of methylmercury. Environ. Health Perspect. 2008, 116, 734–739. [Google Scholar] [CrossRef] [Green Version]
  69. Hayes, J.D.; Strange, R.C. Glutathione S-transferase polymorphisms and their biological consequences. Pharmacology 2000, 61, 154–166. [Google Scholar] [CrossRef]
  70. Goodrich, J.M.; Wang, Y.; Gillespie, B.; Werner, R.; Franzblau, A.; Basu, N. Glutathione enzyme and selenoprotein polymorphisms associate with mercury biomarker levels in Michigan dental professionals. Toxicol. Appl. Pharmacol. 2011, 257, 301–308. [Google Scholar] [CrossRef]
  71. Custodio, H.M.; Harari, R.; Gerhardsson, L.; Skerfving, S.; Broberg, K. Genetic influences on the retention of inorganic mercury. Arch. Environ. Occup. Health 2005, 60, 17–23. [Google Scholar] [CrossRef] [PubMed]
  72. Dobritzsch, D.; Grancharov, K.; Hermsen, C.; Krauss, G.-J.; Schaumlöffel, D. Inhibitory effect of metals on animal and plant glutathione transferases. J. Trace Elem. Med. Biol. 2020, 57, 48–56. [Google Scholar] [CrossRef] [PubMed]
  73. Kalinina, E.V.; Chernov, N.N.; Novichkova, M.D. Role of glutathione, glutathione transferase, and glutaredoxin in regulation of redox-dependent processes. Biochemistry 2014, 79, 1562–1583. [Google Scholar] [CrossRef] [PubMed]
  74. Rodríguez, V.M.; Del Razo, L.M.; Limón-Pacheco, J.H.; Giordano, M.; Sánchez-Peña, L.C.; Uribe-Querol, E.; Gutiérrez-Ospina, G.; Gonsebatt, M.E. Glutathione reductase inhibition and methylated arsenic distribution in Cd1 mice brain and liver. Toxicol. Sci. 2005, 84, 157–166. [Google Scholar] [CrossRef] [PubMed]
  75. Johnson, A.H.; Lalor, G.C.; Preston, J.; Robotham, H.; Thompson, C.; Vutchkov, M.K. Heavy metals in Jamaican surface soils. Environ. Geochem. Health 1996, 18, 113–121. [Google Scholar] [CrossRef]
  76. Özaslan, M.S.; Demir, Y.; Küfrevioğlu, O.I.; Çiftci, M. Some metals inhibit the glutathione S-transferase from Van Lake fish gills. J. Biochem. Mol. Toxicol. 2017, 31, e21967. [Google Scholar] [CrossRef]
  77. Bosch, A.C.; O’Neill, B.; Sigge, G.O.; Kerwath, S.E.; Hoffman, L.C. Heavy metals in marine fish meat and consumer health: A review. J. Sci. Food Agric. 2016, 96, 32–48. [Google Scholar] [CrossRef]
  78. Ricketts, P.; Voutchkov, M.; Chan, H.M. Risk-Benefit Assessment for Total Mercury, Arsenic, Selenium, and Omega-3 Fatty Acids Exposure from Fish Consumption in Jamaica. Biol. Trace Elem. Res. 2020, 197, 262–270. [Google Scholar] [CrossRef]
  79. Harris, P.A.; Taylor, R.; Thielke, R.; Payne, J.; Gonzalez, N.; Conde, J.G. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 2009, 42, 377–381. [Google Scholar] [CrossRef]
Table
Table 1. Characteristics of typically developing (TD) children and their parents (N = 366).
Table 1. Characteristics of typically developing (TD) children and their parents (N = 366).
Variables Categoriesn (%)
ChildSexMale299 (81.7)
Female67 (18.3)
Age (months)Age < 72275 (75.1)
Age ≥ 7291 (24.9)
RaceAfro-Caribbean356 (97.3)
Placeof birth
(Parish)		Kingston parish229 (62.6)
Other parishes a137 (37.4)
Maternal age (at child’s birth)
(n = 360)		Less than 35319 (88.6)
More than or equal to 3541 (11.4)
Parental education level (n = 356)Both up to high school b194 (54.5)
At least one beyond high school c162 (45.5)
Socioeconomic status (SES)High SES (own a car)149 (40.7)
GSTT1 (n = 348)DD (null alleles)91 (26.1)
Homozygote (I/I) or heterozygote (I/D)257 (73.9)
GSTM1 (n = 349)DD 86 (24.6)
I/I or I/D 263 (75.4)
GSTP1 (n = 351)Ile/Ile95 (27.1)
Ile/Val179 (51.0)
Val/Val77 (21.9)
a Other parishes include all 12 parishes in Jamaica, except for Kingston parish as described previously [58]. b Up to high school education included Primary/Jr. Secondary, and Secondary/High/Technical schools. c Beyond high school education included Vocational, Tertiary College, or University.
Table
Table 2. Univariable associations of environmental factors and genotypes for GST genes with detectable blood Al concentrations (BAlCs) in typically developing (TD) children (N = 366).
Table 2. Univariable associations of environmental factors and genotypes for GST genes with detectable blood Al concentrations (BAlCs) in typically developing (TD) children (N = 366).
Exposure Variables Categories≥LoD
(n = 230)		<LoD
(n = 136)		Odds Ratio
(95% CI)		p Value *
ChildSexMale192 (83.5)107(78.7)0.73 (0.43, 1.25)0.25
Age (months)Age ≥ 7262 (27.0)29 (21.3)1.36 (0.82, 2.25)0.23
RaceAfro-Caribbean223 (97.0)133 (97.8)0.72 (0.18, 2.83)0.64
Place of birth
(Parish)		Kingston parish144 (62.6)85 (62.5)1.01 (0.65, 1.56)0.98
Maternal age in years
(at child’s birth)		More than or equal to 35 26 (11.6) a15 (11.1) b1.05 (0.53, 2.05)0.90
Parental education levelAt least one beyond high school **89 (39.6) c73 (55.7) d0.52 (0.34, 0.80)0.003
Socioeconomic status (SES)High SES (own a car)92 (40.0)57 (41.9)0.92 (0.60, 1.42)0.72
GSTT1 ***
≥LoD (n = 218)
<LoD (n = 130) 		DD 50 (22.9)41 (31.5)0.65 (0.10, 1.05)0.08
I/I or I/D168 (77.1)89 (68.5)(ref)
GSTM1 ***
≥LoD (n = 218)
<LoD (n = 131) 		DD50 (22.9)36 (27.5)0.78 (0.48, 1.29)0.34
I/I or I/D168 (77.1)95(72.5)(ref)
GSTP1
≥LoD (n = 219)
<LoD (n = 132) 		Ile/Ile61 (27.8)34 (25.8)(ref)
Ile/Val111 (50.7)68 (51.5)0.91 (0.54, 1.53)0.72
Val/Val47 (21.5)30 (22.7)0.87 (0.47, 1.62)0.67
Source of Piped waterDrinking199 (94.3) e150 (97.4)0.76 (0.26, 2.22)0.61
Cooking201 (95.3) f152 (98.7)0.55 (0.15, 2.07)0.38
ConsumptionSeafoodSaltwater fish160 (75.5)91 (59.1)1.40 (0.89, 2.20)0.14
Fresh water fish (Pond fish, tilapia)75 (35.4)39 (25.3)1.80 (1.11, 2.91)0.02
Tuna (Canned fish)84 (39.6)48 (31.2)0.64 (0.41, 0.99)0.04
Grain and starchesWhole wheat bread142 (67.0)92 (59.7)1.49 (0.96, 2.31)0.07
Cakes/Buns186 (87.7)124 (80.5)1.87 (1.05, 3.32)0.03
Pasta, macaroni, noodles176 (83.0)141 (91.6)0.50 (0.25, 1.01)0.05
Fruits and vegetablesPeas, beans, nut, legumesRed peas, gungo peas 182 (85.9)108 (70.1)1.96 (1.18, 3.27)0.01
Broad beans151 (71.2)66 (42.9)1.84 (1.19, 2.83)<0.01
String beans112 (52.8)42 (27.3)3.27 (2.05, 5.22)<0.01
Root vegetablesYam, sweet potato, dasheen, coco140 (66.0)113 (73.4)0.60 (0.37, 0.97)0.03
Leafy vegetablesLettuce146 (68.9)81 (52.6)1.84 (1.19, 2.84)<0.01
Callaloo, broccoli, or pakchoi186 (87.7)112 (72.7)1.54 (0.90, 2.62)0.11
Cabbage120 (56.6)108 (70.1)0.50 (0.32, 0.80)<0.01
FruitsTomatoes172 (81.1)100 (64.9)1.36 (0.84, 2.19)0.21
Ackee142 (67.0)110 (71.4)0.66 (0.41, 1.06)0.09
Avocado151 (71.2)71 (46.1)1.51 (0.98, 2.33)0.06
Green banana141 (66.5)117 (76.0)0.59 (0.36, 0.95)0.03
Fried plantain183 (86.3)128 (83.1)0.59 (0.31, 1.11)0.10
* p values are based on the Wald’s test in logistic regression models. ** Beyond high school education included Vocational, Tertiary College, or University. *** DD, I/I, and I/D are defined for GSTT1 and GSTM1 in Table 1. Number of missing data for child’s BAlC ≥ LoD; a = 5, c = 5, e = 1, f = 1. Number of missing data for child’s BAlC < LoD; b = 1, d = 5. (ref) = reference.
Table
Table 3. Associations between GST genes and binary detectable blood Al concentrations (BAlCs) based on logistic regression models that include a two-way gene*gene interaction (N = 366).
Table 3. Associations between GST genes and binary detectable blood Al concentrations (BAlCs) based on logistic regression models that include a two-way gene*gene interaction (N = 366).
ModelsGSTT1 a,b
Genotypes		GSTM1c,d GenotypesGSTP1e,f
Genotypes		OR (95%CI)p Value *Overall Interaction
p Value **
Unadjusted Model for interactive effect between GSTT1 and GSTM1 including the corresponding main effect
RecessiveDDDD vs. I/I or I/D (ref) 0.43 (0.17, 1.11)0.080.11
I/I or I/D 1.08 (0.59, 2.00)0.79
DD vs. I/I or I/D (ref)DD 0.33 (0.13, 0.87)0.03
I/I or I/D 0.84 (0.47,1.48)0.79
Unadjusted Model for interactive effect between GSTM1 and GSTP1 including the corresponding main effect
Co-dominant DD Ile/Val vs. Ile/Ile (ref)0.38 (0.12, 1.19)0.100.06
Val/Val vs. Ile/Ile (ref)0.20 (0.05, 0.82)0.03
Ile/Val vs. Val/Val (ref)1.93 (0.59, 6.28)0.28
I/I or I/D Ile/Val vs. Ile/Ile (ref)1.19 (0.66, 2.15)0.57
Val/Val vs. Ile/Ile (ref)1.32 (0.65, 2.69)0.45
Ile/Val vs. Val/Val (ref)0.90 (0.47, 1.72)0.75
DD vs. I/I or I/D (ref)Ile/Ile2.24 (0.74, 6.74)0.15
Ile/Val0.71 (0.36, 1.40)0.32
Val/Val0.33 (0.10, 1.07)0.06
Dominant DD Val/Val or Ile/Val vs. Ile/Ile (ref) 0.32 (0.11, 0.98)<0.050.04
I/I or I/D 1.23 (0.70, 2.14)0.47
DD vs. I/I or I/D (ref)Val/Val or Ile/Val0.59 (0.33, 1.05)0.07
Ile/Ile2.24 (0.74, 6.74)0.15
Recessive DDVal/Val vs. Ile/Ile or Ile/Val (ref) 0.39 (0.12, 1.23)0.110.10
I/I or I/D1.18 (0.64, 2.17)0.59
DD vs. I/I or I/D (ref)Val/Val0.33 (0.10, 1.07)0.06
Ile/Ile or Ile/Val0.99 (0.57, 1.76)0.99
DD, I/I, and I/D are defined for GSTT1 and GSTM1 in Table 1. * p values are based on the Wald’s test in logistic regression models. ** Overall interaction p values are based on the type 3 effect test in logistic regression models. Number of missing data are for child’s BAlC ≥ LoD; a = 12, c = 12, e = 11. Number of missing data for child’s BAlC < LoD; b = 6, d = 5, f = 4. (ref) = reference.
Table
Table 4. Multivariable logistic regression analysis of associations between children’s exposure to environmental factors and binary detectable blood Al concentrations (BAlCs) by genotypes for GST genes that include gene*environment interaction (N = 366).
Table 4. Multivariable logistic regression analysis of associations between children’s exposure to environmental factors and binary detectable blood Al concentrations (BAlCs) by genotypes for GST genes that include gene*environment interaction (N = 366).
Environmental Factor (Food Consumption)
(Yes vs. No)		GeneModelsGenotypesOR (95% CI)p Value aOverall Interaction
p Value b
PorridgeGSTT1RecessiveDD1.51 (0.46, 4.91)0.490.03
I/I or I/D0.27 (0.10, 0.73)0.01
MacaroniGSTT1RecessiveDD1.97 (0.52, 7.52)0.320.03
I/I or I/D0.22 (0.05, 0.97)<0.05
Green bananaGSTT1RecessiveDD1.47 (0.55, 3.90)0.440.04
I/I or I/D0.45 (0.25, 0.82)0.01
Broad beans
(fava beans)		GSTM1RecessiveDD3.96 (1.57, 9.97)<0.01<0.05
I/I or I/D1.37 (0.82, 2.27)0.23
Saltwater fishGSTP1Co-dominantIle/Ile3.36 (1.37, 8.24)<0.010.03
Ile/Val1.26 (0.67, 2.38)0.47
Val/Val0.53 (0.18, 1.58)0.26
DominantVal/Val or Ile/Val 1.0 (0.58, 1.72)0.990.02
Ile/Ile3.36 (1.37, 8.24)<0.01
RecessiveVal/Val0.53 (0.18, 1.58)0.260.05
Ile/Ile or Ile/Val1.76 (1.05, 2.94)0.03
White breadGSTP1Co-dominantIle/Ile2.49(1.05, 5.90)0.040.06
Ile/Val0.82 (0.43, 1.55)0.53
Val/Val0.59 (0.20, 1.75)0.34
DominantVal/Val or Ile/Val 0.75 (0.43, 1.29)0.300.02
Ile/Ile2.49 (1.05, 5.90)0.04
RecessiveVal/Val0.59 (0.20, 1.75)0.340.24
Ile/Ile or Ile/Val1.20 (0.73, 2.00)0.47
Whole wheat breadGSTP1Co-dominantIle/Ile0.59 (0.23, 1.52)0.270.07
Ile/Val2.19 (1.17, 4.11)0.01
Val/Val1.76 (0.70, 4.48)0.23
DominantVal/Val or Ile/Val 2.05 (1.22, 3.45)<0.01 0.02
Ile/Ile0.59 (0.23, 1.52)0.27
RecessiveVal/Val1.76 (0.70, 4.48)0.230.72
Ile/Ile or Ile/Val1.45 (0.87, 2.42)0.16
Number of missing data are based on numbers reported in Table 3 for GSTT1, GSTM1, and GSTP1. DD, I/I, and I/D are defined for GSTT1 and GSTM1 in Table 1. Results a p values and b Overall interaction p values are described in Table 3.
Table
Table 5. Adjusted associations between exposure to environmental factors and binary detectable blood Al concentrations (BAlCs) by genotypes for GSTP1 genes in typically developing children based on logistic regression models that include gene*environment interaction (N = 366).
Table 5. Adjusted associations between exposure to environmental factors and binary detectable blood Al concentrations (BAlCs) by genotypes for GSTP1 genes in typically developing children based on logistic regression models that include gene*environment interaction (N = 366).
Models
for GSTP1 *		Environmental Factor (EF) CategoryGenotypesOR (95%CI)p Value aOverall Interaction
p Value b
Co-dominantEF1G1 vs. G2-0.57 (0.36, 0.91)0.02-
EF2Yes vs. No-3.07 (1.85, 5.09)<0.01-
EF3Yes vs. NoIle/Ile2.73 (1.07, 6.96)0.040.02
Ile/Val0.98 (0.50, 1.93)0.95
Val/Val0.36 (0.11, 1.14)0.08
DominantEF1G1 vs. G2-0.56 (0.36, 0.90)0.02-
EF2Yes vs. No-2.94 (1.78, 4.83)<0.01-
EF3Yes vs. NoIle/Val or Val/Val0.75 (0.42, 1.35)0.340.02
Ile/Ile2.74 (1.08, 6.99)0.03
RecessiveEF1G1 vs. G2-0.57 (0.36, 0.91)0.02-
EF2Yes vs. No-3.07 (1.85, 5.06)<0.01-
EF3Yes vs. NoVal/Val0.36 (0.11, 1.14)0.080.04
Ile/Ile or Ile/Val1.39 (0.81, 2.41)0.23
* GSTP1 missing data are based on numbers reported in Table 3. EF1 = Parental education level (Parental education level missing data are based on numbers reported in Table 1). EF2 = Consumption of string beans. EF3 = Consumption of saltwater fish. G1 = beyond high school education included Vocational, Tertiary College, or University. G2 = Primary/Jr. Secondary, and Secondary/High/Technical schools. a p and b Overall interaction p values are described in Table 3.
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Rahbar, M.H.; Samms-Vaughan, M.; Zhao, Y.; Saroukhani, S.; Bressler, J.; Hessabi, M.; Grove, M.L.; Shakespeare-Pellington, S.; Loveland, K.A. Interactions between Environmental Factors and Glutathione S-Transferase (GST) Genes with Respect to Detectable Blood Aluminum Concentrations in Jamaican Children. Genes 2022, 13, 1907. https://doi.org/10.3390/genes13101907

AMA Style

Rahbar MH, Samms-Vaughan M, Zhao Y, Saroukhani S, Bressler J, Hessabi M, Grove ML, Shakespeare-Pellington S, Loveland KA. Interactions between Environmental Factors and Glutathione S-Transferase (GST) Genes with Respect to Detectable Blood Aluminum Concentrations in Jamaican Children. Genes. 2022; 13(10):1907. https://doi.org/10.3390/genes13101907

Chicago/Turabian Style

Rahbar, Mohammad H., Maureen Samms-Vaughan, Yuansong Zhao, Sepideh Saroukhani, Jan Bressler, Manouchehr Hessabi, Megan L. Grove, Sydonnie Shakespeare-Pellington, and Katherine A. Loveland. 2022. "Interactions between Environmental Factors and Glutathione S-Transferase (GST) Genes with Respect to Detectable Blood Aluminum Concentrations in Jamaican Children" Genes 13, no. 10: 1907. https://doi.org/10.3390/genes13101907

APA Style

Rahbar, M. H., Samms-Vaughan, M., Zhao, Y., Saroukhani, S., Bressler, J., Hessabi, M., Grove, M. L., Shakespeare-Pellington, S., & Loveland, K. A. (2022). Interactions between Environmental Factors and Glutathione S-Transferase (GST) Genes with Respect to Detectable Blood Aluminum Concentrations in Jamaican Children. Genes, 13(10), 1907. https://doi.org/10.3390/genes13101907

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