Analysis of maternal polymorphisms in arsenic (+3 oxidation state)-methyltransferase AS3MT and fetal sex in relation to arsenic metabolism and infant birth outcomes: Implications for risk analysis

Arsenic (+3 oxidation state) methyltransferase (AS3MT) is the key enzyme in the metabolism of inorganic arsenic (iAs). Polymorphisms of AS3MT influence adverse health effects in adults, but little is known about their role in iAs metabolism in pregnant women and infants. The relationships between seven single nucleotide polymorphisms (SNPs) in AS3MT and urinary concentrations of iAs and its methylated metabolites were assessed in mother-infant pairs of the Biomarkers of Exposure to ARsenic (BEAR) cohort. Maternal alleles for five of the seven SNPs (rs7085104, rs3740400, rs3740393, rs3740390, and rs1046778) were associated with urinary concentrations of iAs metabolites, and alleles for one SNP (rs3740393) were associated with birth outcomes/measures. These associations were strongly dependent upon the male sex of the fetus but independent of fetal genotype for AS3MT. These data highlight a potential sex-dependence of the relationships among maternal genotype, iAs metabolism and infant health outcomes

As "Ómicas" como ferramenta no estudo da Saúde Ambiental

Deaths caused by environmental pollution are agrowing public health issue. Most of the premature deaths related to pollution are caused by non communicable diseases such as chronic obstructive pulmonary disease, type-2 diabetes, cardiovascular disease and cancer. They are considered complex diseases because of their multicausality and the various mechanisms involved in their emergence and evolution.Knowledge of disease-causing mechanismsis increasing and the identification of disease-associated biomarkers improving thanks to technological progress, in particular that of the technologiesthat are applied to the measurement and interpretation of molecular components—the so-called “Omics” technologies. These technologies have allowed the cellular causes of some complex diseases to be identified: genetic variants of susceptibility or protection to pollutants (Genomics), as well as changes in the DNA (Epigenomics) and their effects on the process of transcription of specific genes for repair, on metabolism or on the non-coding RNA associated with diseases (Transcriptomics). In addition, Proteomics and Metabolomics do not cease to provide information on proteins and metabolites involved in disease processes. Bioinformatics has evolved parallel to the development of omics, which has allowed the results of the measurements of hundreds of molecules to be interpreted and organized into networks that show the relationships among them.Omics are mainly used to develop disease risk models based on population studies, but information on genomes, transcriptomes, epigenomes, microbiomes, proteomes and metabolomesis also used to decipher diseases in order to facilitate prognosis and guide patient treatment, thus contributing to personalized, precision medicine. However, their clinical application is still limited by their cost and their technical, regulatory and ethical implications.Las muertes provocadas por la contaminación ambiental son un problema de salud pública en incremento. La mayoría de las muertes prematuras provocadas por la contaminación son enfermedades no transmisibles, como enfermedad pulmonar obstructiva crónica, diabetes tipo 2, enfermedades cardiovasculares y cáncer. Estas son consideradas enfermedades complejas por su multicausalidad y los diversos mecanismos involucrados en su aparición y evolución. El conocimiento del mecanismo de producción de la enfermedad, y la identificación de biomarcadores asociados a enfermedad está avanzando gracias al avance de la tecnología, y específicamente de la tecnología aplicada a medición e interpretación de componentes moleculares: las tecnologías “ÓMICAS”. Estas han permitido identificar causas celulares de algunas enfermedades complejas: variantes genéticas de susceptibilidad o protección a agentes contaminantes (Genómica), así como cambios sobre el ADN (Epigenómica) y sus efectos en el proceso de transcripción de genes específicos de reparación, metabolismo o bien RNA no codificante asociado a enfermedades (Transcriptómica); además la Proteómica y la Metabolómica aportan constante información sobre las proteínas y metabolitos involucrados en los procesos de enfermedad. Paralelo al desarrollo de las tecnologías ómicas ha evolucionado la bioinformática, que ha permitido la interpretación de los resultados de mediciones de cientos de moléculas para organizarlos en redes que traducen las relaciones entre ellas. Las tecnologías ómicas se aplican principalmente para determinar modelos de riesgo de enfermedad en base a estudios poblacionales, pero también la información del genoma, transcriptoma, el epigenoma, el microbioma, el proteoma y el metaboloma se utilizarán para ayudar a descifrar la enfermedad a fin de facilitar el pronóstico y guiar el tratamiento de pacientes, ayudando a la medicina individualizada y medicina de precisión. Sin embargo, su aplicación clínica está aún limitada por el costo y las implicaciones técnicas, regulatorias y éticas.As mortes causadas pela poluição ambiental sãoum problema de saúde pública crescente. A maioria das mortes prematuras causadas por contaminação sãodoençasnãotransmissíveis, como doença pulmonar obstrutiva crónica, diabetes tipo 2, doenças cardiovasculares e cancro. Estas são consideradas doenças complexas pela sua multicausalidade e pelos vários mecanismos envolvidos no seu aparecimento e evolução. O conhecimento do mecanismo de produção da doença e a identificação de biomarcadores associados à doençaestá a avançar graçasao desenvolvimento da tecnologia e, especificamente, à tecnologia aplicada à medição e interpretação de componentes moleculares: as tecnologias “ÓMICAS”. Estas permitiram identificar as causas celulares de algumasdoenças complexas: variantes genéticas de suscetibilidade ouproteção a agentes contaminantes (Genómica), bem como alterações no DNA (Epigenética) e os seusefeitos no processo de transcrição de genes específicos de reparação, metabolismo ou RNAnão-codificanteassociado a doenças (Transcriptómica);acresce a Proteómica e a Metabolómica que fornecem informação sobre as proteínas e metabólitosenvolvidos nos processos de doença. Paralelamente ao desenvolvimento das novas técnicas biotecnológicas, geralmente denominadas por “Ómicas”, evoluiu a bioinformática, o que permitiu a interpretação dos resultados das análises de centenas de moléculas para organizá-las em redes que traduzem as relações entre elas. As tecnologias “Ómicas” aplicam-se principalmente para determinar modelos de risco de doença com base em estudos populacionais, mas igualmente a informação do genoma, do transcriptoma, do epigenoma, do microbioma, do proteoma e do metaboloma será usada para ajudar a decifrar a doença, a fim de facilitar o prognóstico e orientar o tratamento dos pacientes, auxiliado a medicina individualizada e a medicina de precisão. No entanto, a sua aplicação clínica ainda é limitada pelo custo e implicações técnicas, regulamentares e éticas

Omics as Environmental Health study tools

Deaths caused by environmental pollution are agrowing public health issue. Most of the premature deaths related to pollution are caused by non communicable diseases such as chronic obstructive pulmonary disease, type-2 diabetes, cardiovascular disease and cancer. They are considered complex diseases because of their multicausality and the various mechanisms involved in their emergence and evolution.Knowledge of disease-causing mechanismsis increasing and the identification of disease-associated biomarkers improving thanks to technological progress, in particular that of the technologiesthat are applied to the measurement and interpretation of molecular components—the so-called “Omics” technologies. These technologies have allowed the cellular causes of some complex diseases to be identified: genetic variants of susceptibility or protection to pollutants (Genomics), as well as changes in the DNA (Epigenomics) and their effects on the process of transcription of specific genes for repair, on metabolism or on the non-coding RNA associated with diseases (Transcriptomics). In addition, Proteomics and Metabolomics do not cease to provide information on proteins and metabolites involved in disease processes. Bioinformatics has evolved parallel to the development of omics, which has allowed the results of the measurements of hundreds of molecules to be interpreted and organized into networks that show the relationships among them.Omics are mainly used to develop disease risk models based on population studies, but information on genomes, transcriptomes, epigenomes, microbiomes, proteomes and metabolomesis also used to decipher diseases in order to facilitate prognosis and guide patient treatment, thus contributing to personalized, precision medicine. However, their clinical application is still limited by their cost and their technical, regulatory and ethical implications.</p

Environmental exposure to arsenic, AS3MT polymorphism and prevalence of diabetes in Mexico

Exposure to arsenic in drinking water is associated with increased prevalence of diabetes. We previously reported an association of diabetes and urinary concentration of dimethylarsinite (DMAsIII), a toxic product of arsenic methylation by arsenic ( +3 oxidation state) methyltransferase (AS3MT). Here we examine associations between AS3MT polymorphism, arsenic metabolism and diabetes. Fasting blood glucose, oral glucose tolerance and self-reported diagnoses were used to identify diabetic individuals. Inorganic arsenic and its metabolites were measured in urine. Genotyping analysis focused on six polymorphic sites of AS3MT. Individuals with M287T and G4965C polymorphisms had higher levels of urinary DMAsIII and were more frequently diabetic than the respective wild-type carriers, although the excess was not statistically significant. Odds ratios were 11.4 (95% confidence interval (CI) 2.2–58.8) and 8.8 (95% CI 1.6–47.3) for the combined effects of arsenic exposure >75th percentile and 287T and 4965C genotypes, respectively. Carriers of 287T and 4965C may produce more DMAsIII and be more likely to develop diabetes when exposed to arsenic

Speciation of Arsenic in Exfoliated Urinary Bladder Epithelial Cells from Individuals Exposed to Arsenic in Drinking Water

BackgroundThe concentration of arsenic in urine has been used as a marker of exposure to inorganic As (iAs). Relative proportions of urinary metabolites of iAs have been identified as potential biomarkers of susceptibility to iAs toxicity. However, the adverse effects of iAs exposure are ultimately determined by the concentrations of iAs metabolites in target tissues.ObjectiveIn this study we examined the feasibility of analyzing As species in cells that originate in the urinary bladder, a target organ for As-induced cancer in humans.MethodsExfoliated bladder epithelial cells (BECs) were collected from urine of 21 residents of Zimapan, Mexico, who were exposed to iAs in drinking water. We determined concentrations of iAs, methyl-As (MAs), and dimethyl-As (DMAs) in urine using conventional hydride generation-cryotrapping-atomic absorption spectrometry (HG-CT-AAS). We used an optimized HG-CT-AAS technique with detection limits of 12–17 pg As for analysis of As species in BECs.ResultsAll urine samples and 20 of 21 BEC samples contained detectable concentrations of iAs, MAs, and DMAs. Sums of concentrations of these As species in BECs ranged from 0.18 to 11.4 ng As/mg protein and in urine from 4.8 to 1,947 ng As/mL. We found no correlations between the concentrations or ratios of As species in BECs and in urine.ConclusionThese results suggest that urinary levels of iAs metabolites do not necessarily reflect levels of these metabolites in the bladder epithelium. Thus, analysis of As species in BECs may provide a more effective tool for risk assessment of bladder cancer and other urothelial diseases associated with exposures to iAs

Exposure to arsenic in drinking water is associated with increased prevalence of diabetes: a cross-sectional study in the Zimapan and Lagunera Regions in Mexico

Abstract Background Human exposures to inorganic arsenic (iAs) have been linked to an increased risk of diabetes mellitus. Recent laboratory studies showed that methylated trivalent metabolites of iAs may play key roles in the diabetogenic effects of iAs. Our study examined associations between chronic exposure to iAs in drinking water, metabolism of iAs, and prevalence of diabetes in arsenicosis-endemic areas of Mexico. Methods We used fasting blood glucose (FBG), fasting plasma insulin (FPI), oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c), and insulin resistance (HOMA-IR) to characterize diabetic individuals. Arsenic levels in drinking water and urine were determined to estimate exposure to iAs. Urinary concentrations of iAs and its trivalent and pentavalent methylated metabolites were measured to assess iAs metabolism. Associations between diabetes and iAs exposure or urinary metabolites of iAs were estimated by logistic regression with adjustment for age, sex, hypertension and obesity. Results The prevalence of diabetes was positively associated with iAs in drinking water (OR 1.13 per 10 ppb, p < 0.01) and with the concentration of dimethylarsinite (DMAsIII) in urine (OR 1.24 per inter-quartile range, p = 0.05). Notably, FPI and HOMA-IR were negatively associated with iAs exposure (β -2.08 and -1.64, respectively, p < 0.01), suggesting that the mechanisms of iAs-induced diabetes differ from those underlying type-2 diabetes, which is typically characterized by insulin resistance. Conclusions Our study confirms a previously reported, but frequently questioned, association between exposure to iAs and diabetes, and is the first to link the risk of diabetes to the production of one of the most toxic metabolites of iAs, DMAsIII

Association of AS3MT polymorphisms and the risk of premalignant arsenic skin lesions

Exposure to naturally occurring inorganic arsenic (iAs), primarily from contaminated drinking water, is considered one of the top environmental health threats worldwide. Arsenic (+3 oxidation state) methyltransferase (AS3MT) is the key enzyme in the biotransformation pathway of iAs. AS3MT catalyzes the transfer of a methyl group from S-adenosyl-L-methionine to trivalent arsenicals, resulting in the production of methylated (MAs) and dimethylated arsenicals (DMAs). MAs is a susceptibility factor for iAs-induced toxicity. In this study, we evaluated the association of the polymorphism in AS3MT gene with iAs metabolism and with the presence of arsenic (As) premalignant skin lesions. This is a case-control study of 71 cases with skin lesions and 51 controls without skin lesions recruited from a iAs endemic area in Mexico. We measured urinary As metabolites, differentiating the trivalent and pentavalent arsenical species, using the hydride generation atomic absorption spectrometry. In addition, the study subjects were genotyped to analyze three single nucleotide polymorphisms (SNPs), A-477G, T14458C (nonsynonymus SNP; Met287Thr), and T35587C, in the AS3MT gene. We compared the frequencies of the AS3MT alleles, genotypes, and haplotypes in individuals with and without skin lesions. Marginal differences in the frequencies of the Met287Thr genotype were identified between individuals with and without premalignant skin lesions (p=0.055): individuals carrying the C (TC+CC) allele (Thr) were at risk [odds ratio=4.28; 95% confidence interval (1.0–18.5)]. Also, individuals with C allele of Met287Thr displayed greater percentage of MAs in urine and decrease in the percentage of DMAs. These findings indicate that Met287Thr influences the susceptibility to premalignant As skin lesions and might be at increased risk for other adverse health effects of iAs exposure

Chronic Exposure to Arsenic and Markers of Cardiometabolic Risk: A Cross-Sectional Study in Chihuahua, Mexico

BackgroundExposure to arsenic (As) concentrations in drinking water > 150 μg/L has been associated with risk of diabetes and cardiovascular disease, but little is known about the effects of lower exposures.ObjectiveThis study aimed to examine whether moderate As exposure, or indicators of individual As metabolism at these levels of exposure, are associated with cardiometabolic risk.MethodsWe analyzed cross-sectional associations between arsenic exposure and multiple markers of cardiometabolic risk using drinking-water As measurements and urinary As species data obtained from 1,160 adults in Chihuahua, Mexico, who were recruited in 2008–2013. Fasting blood glucose and lipid levels, the results of an oral glucose tolerance test, and blood pressure were used to characterize cardiometabolic risk. Multivariable logistic, multinomial, and linear regression were used to assess associations between cardiometabolic outcomes and water As or the sum of inorganic and methylated As species in urine.ResultsAfter multivariable adjustment, concentrations in the second quartile of water As (25.5 to < 47.9 μg/L) and concentrations of total speciated urinary As (< 55.8 μg/L) below the median were significantly associated with elevated triglycerides, high total cholesterol, and diabetes. However, moderate water and urinary As levels were also positively associated with HDL cholesterol. Associations between arsenic exposure and both dysglycemia and triglyceridemia were higher among individuals with higher proportions of dimethylarsenic in urine.ConclusionsModerate exposure to As may increase cardiometabolic risk, particularly in individuals with high proportions of urinary dimethylarsenic. In this cohort, As exposure was associated with several markers of increased cardiometabolic risk (diabetes, triglyceridemia, and cholesterolemia), but exposure was also associated with higher rather than lower HDL cholesterol.CitationMendez MA, González-Horta C, Sánchez-Ramírez B, Ballinas-Casarrubias L, Hernández Cerón R, Viniegra Morales D, Baeza Terrazas FA, Ishida MC, Gutiérrez-Torres DS, Saunders RJ, Drobná Z, Fry RC, Buse JB, Loomis D, García-Vargas GG, Del Razo LM, Stýblo M. 2016. Chronic exposure to arsenic and markers of cardiometabolic risk: a cross-sectional study in Chihuahua, Mexico. Environ Health Perspect 124:104–111; http://dx.doi.org/10.1289/ehp.140874

Follow-up study on lead exposure in children living in a smelter community in northern Mexico

<p>Abstract</p> <p>Background</p> <p>To study the changes of children lead exposure in the city of Torreon during the last five years, after environmental and public health interventions, using the timeline of lead in blood concentration as the biomarker of exposure and its relation to lead in soil concentrations.</p> <p>Methods</p> <p>This follow-up study started in 2001 and consisted of 232 children living in nine neighborhoods in Torreon. Children were tested at 0, 6, 12 and 60 months. Lead in blood concentrations, Hemoglobin, Zinc-Protoporphyrin, anthropometric measures and socioeconomic status questionnaire was supplied to the parents.</p> <p>Results</p> <p>Median and range of lead in blood concentrations obtained at 0, 6, 12, 60 months were: 10.12 μg/dl (1.9 - 43.8), 8.75 μg/dl (1.85 - 41.45), 8.4 μg/dl (1.7 - 35.8) and 4.4 μg/dl (1.3 - 30.3), respectively. The decrease of lead in blood levels was significantly related to ages 0, 6, 12 and 60 months of the follow-up study. The timeline of B-Pb was associated with the timeline of lead in soil concentrations.</p> <p>Conclusions</p> <p>B-Pb levels have significantly decreased in the group of children studied. This could be explained by a) environmental interventions by authorities and the smelter companies, b) normal changes in hygienic habits as children age and c) lead redistribution from blood to hard tissues.</p

Association Between Variants in Arsenic (+3 Oxidation State) Methyltranserase ( AS3MT ) and Urinary Metabolites of Inorganic Arsenic: Role of Exposure Level

Variants in AS3MT, the gene encoding arsenic (+3 oxidation state) methyltranserase, have been shown to influence patterns of inorganic arsenic (iAs) metabolism. Several studies have suggested that capacity to metabolize iAs may vary depending on levels of iAs exposure. However, it is not known whether the influence of variants in AS3MT on iAs metabolism also vary by level of exposure. We investigated, in a population of Mexican adults exposed to drinking water As, whether associations between 7 candidate variants in AS3MT and urinary iAs metabolites were consistent with prior studies, and whether these associations varied depending on the level of exposure. Overall, associations between urinary iAs metabolites and AS3MT variants were consistent with the literature. Referent genotypes, defined as the genotype previously associated with a higher percentage of urinary dimethylated As (DMAs%), were associated with significant increases in the DMAs% and ratio of DMAs to monomethylated As (MAs), and significant reductions in MAs% and iAs%. For 3 variants, associations between genotypes and iAs metabolism were significantly stronger among subjects exposed to water As >50 versus ≤50 ppb (water As X genotype interaction P < .05). In contrast, for 1 variant (rs17881215), associations were significantly stronger at exposures ≤50 ppb. Results suggest that iAs exposure may influence the extent to which several AS3MT variants affect iAs metabolism. The variants most strongly associated with iAs metabolism—and perhaps with susceptibility to iAs-associated disease—may vary in settings with exposure level