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Introduction: Few studies have investigated the role of exposure to metals and metal mixtures on oxidative stress in the general population. Objectives: We evaluated the cross-sectional association of urinary metal and metal mixtures with urinary oxidative stress biomarkers, including oxidized to reduced glutathione ratio (GSSG/GSH), malondialdehyde (MDA), and 8‑oxo‑7,8‑dihydroguanine (8-oxo-dG), in a representative sample of a general population from Spain (Hortega Study). Methods: Urine antimony (Sb), barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), molybdenum (Mo), vanadium (V) and zinc (Zn) were measured by ICPMS in 1440 Hortega Study participants. Results: The geometric mean ratios (GMRs) of GSSG/GSH comparing the 80th to the 20th percentiles of metal distributions were 1.15 (95% confidence intervals [95% CI]: 1.03–1.27) for Mo, 1.17 (1.05–1.31) for Ba, 1.23 (1.04–1.46) for Cr and 1.18 (1.00–1.40) for V. For MDA, the corresponding GMRs (95% CI) were 1.13 (1.03–1.24) for Zn and 1.12 (1.02–1.23) for Cd. In 8-oxo-dG models, the corresponding GMR (95% CI) were 1.12 (1.01–1.23) for Zn and 1.09 (0.99–1.20) for Cd. Cr for GSSG/GSH and Zn for MDA and 8-oxo-dG drove most of the observed associations. Principal component (PC) 1 (largely reflecting non-essential metals) was positively associated with GSSG/GSH. The association of PC2 (largely reflecting essential metals) was positive for GSSG/ GSH but inverse for MDA. Conclusions: Urine Ba, Cd, Cr, Mo, V and Zn were positively associated with oxidative stress measures at metal exposure levels relevant for the general population. The potential health consequences of environmental, including nutritional, exposure to these metals warrants further investigation., This work was supported by the Strategic Action for Research in Health Sciences [CP12/03080, PI10/0082, PI13/01848, PI07/0497 and PI11/00726]; GRUPOS 03/101, PROMETEO/2009/029 and 2005/027, AMP07/075 and ACOMP/2013/039 from the Valencia Government; GRS/279/A/08 from Castilla-Leon Government; European Network of Excellence Ingenious Hypercare (EPSS-037093) from the European Commission; Retics (PREDIMED RD06/0045/0006); CIBER Fisiopatología Obesidad y Nutrición (CIBERobn) [CIBER-02-08-2009, CB06/03 and CB12/03/30016] and CIBER de Diabetes y Enfermedades Metabólicas Relacionadas (CIBERDEM CB07/0/018). The Strategic Action for Research in Health sciences, Retics, CIBEROBN and CIBERDEM are initiatives from Carlos III Health Institute Madrid and the Spanish Ministry of Economy and Competitiveness and co-funded with European Funds for Regional Development (FEDER). The authors declare they have no actual or potential competing financial interests., This work was supported by the Strategic Action for Research in Health Sciences [CP12/03080, PI10/0082, PI13/01848, PI07/0497 and PI11/00726]; GRUPOS 03/101, PROMETEO/2009/029 and 2005/027, AMP07/075 and ACOMP/2013/039 from the Valencia Government; GRS/279/A/08 from Castilla-Leon Government; European Network of Excellence Ingenious Hypercare (EPSS-037093) from the European Commission; Retics (PREDIMED RD06/0045/0006); CIBER Fisiopatologia Obesidad y Nutricion (CIBERobn) [CIBER-02-08-2009, CB06/03 and CB12/03/30016] and CIBER de Diabetes y Enfermedades Metabolicas Relacionadas (CIBERDEM CB07/0/018). The Strategic Action for Research in Health sciences, Retics, CIBEROBN and CIBERDEM are initiatives from Carlos III Health Institute Madrid and the Spanish Ministry of Economy and Competitiveness and co-funded with European Funds for Regional Development (FEDER). The authors declare they have no actual or potential competing financial interests.

Background Selenium and single-nucleotide-polymorphisms in selenoprotein genes have been associated to diabetes. However, the interaction of selenium with genetic variation in diabetes and oxidative stress-related genes has not been evaluated as a potential determinant of diabetes risk. Methods We evaluated the cross-sectional and prospective associations of plasma selenium concentrations with type 2 diabetes, and the interaction of selenium concentrations with genetic variation in candidate polymorphisms, in a representative sample of 1452 men and women aged 18–85 years from Spain. Results The geometric mean of plasma selenium levels in the study sample was 84.2 µg/L. 120 participants had diabetes at baseline. Among diabetes-free participants who were not lost during the follow-up (N=1234), 75 developed diabetes over time. The multivariable adjusted odds ratios (95% confidence interval) for diabetes prevalence comparing the second and third to the first tertiles of plasma selenium levels were 1.80 (1.03, 3.14) and 1.97 (1.14, 3.41), respectively. The corresponding hazard ratios (95% CI) for diabetes incidence were 1.76 (0.96, 3.22) and 1.80 (0.98, 3.31), respectively. In addition, we observed significant interactions between selenium and polymorphisms in PPARGC1A, and in genes encoding mitochondrial proteins, such as BCS1L and SDHA, and suggestive interactions of selenium with other genes related to selenoproteins and redox metabolism. Conclusions Plasma selenium was positively associated with prevalent and incident diabetes. While the statistical interactions of selenium with polymorphisms involved in regulation of redox and insulin signaling pathways provide biological plausibility to the positive associations of selenium with diabetes, further research is needed to elucidate the causal pathways underlying these associations.

Inorganic arsenic exposure may be associated with diabetes, but the evidence at low-moderate levels is not sufficient. Polymorphisms in diabetes-related genes have been involved in diabetes risk. We evaluated the association of inorganic arsenic exposure on diabetes in the Hortega Study, a representative sample of a general population from Valladolid, Spain. Total urine arsenic was measured in 1,451 adults. Urine arsenic speciation was available in 295 randomly selected participants. To account for the confounding introduced by non-toxic seafood arsenicals, we designed a multiple imputation model to predict the missing arsenobetaine levels. The prevalence of diabetes was 8.3%. The geometric mean of total arsenic was 66.0 μg/g. The adjusted odds ratios (95% confidence interval) for diabetes comparing the highest with the lowest tertile of total arsenic were 1.76 (1.01, 3.09) and 2.14 (1.47, 3.11) before and after arsenobetaine adjustment, respectively. Polymorphisms in several genes including IL8RA, TXN, NR3C2, COX5A and GCLC showed suggestive differential associations of urine total arsenic with diabetes. The findings support the role of arsenic on diabetes and the importance of controlling for seafood arsenicals in populations with high seafood intake. Suggestive arsenic-gene interactions require confirmation in larger studies.

Introduction Epidemiological studies report that increasing selenium levels are consistently associated with increasing levels of total cholesterol, low-density lipoprotein (LDL)-cholesterol, and t...

The interaction of selenium, a component of antioxidant selenoproteins, with genetic variation in lipid-related pathways has not been evaluated earlier as a potential determinant of blood lipid levels. We aimed at evaluating the effects of gene-environment interactions between plasma levels of selenium and polymorphisms in lipid metabolic pathways on plasma lipid levels in a study population from Spain (N=1,315). We observed statistically significant associations between plasma selenium and lipid levels (differences in total, low-density lipoprotein [LDL]-cholesterol, and triglycerides comparing the 80th with the 20th percentiles of plasma selenium levels were, respectively, 12.0 (95% confidence interval 6.3, 17.8), 8.9 (3.7, 14.2), and 9.0 (2.9, 15.2) mg/dl). We also found statistically significant interactions at the Bonferroni-corrected significance level (p=0.0008) between selenium and rs2290201 in FABP4 for total and LDL cholesterol levels and rs1800774 in CETP for elevated LDL cholesterol. Other polymorphisms showed statistically significant differential associations of plasma selenium levels and lipids biomarkers at the nominal p-value of 0.05. Reported statistical interactions with genes involved in lipid transport and transfer provide biological support to the positive associations of selenium with lipids shown in cross-sectional studies and lead to the hypothesis that selenium and lipid levels share common biological pathways that need to be elucidated in mechanistic studies.

Background/objectivesObesity has been linked to morbidity and mortality through increased risk for many chronic diseases. Endothelin (EDN) system has been related to endothelial function but it can be involved in lipid metabolism regulation: Receptor type A (EDNRA) activates lipolysis in adipocytes, the two endothelin receptors mediate arsenic-stimulated adipocyte dysfunction, and endothelin system can regulate adiposity by modulating adiponectin activity in different situations and, therefore, influence obesity development. The aim of the present study was to analyze if single nucleotide polymorphisms (SNPs) in the EDN system could be associated with human obesity.Subjects/methodsWe analyzed two samples of general-population-based studies from two different regions of Spain: the VALCAR Study, 468 subjects from the area of Valencia, and the Hortega Study, 1502 subjects from the area of Valladolid. Eighteen SNPs throughout five genes were analyzed using SNPlex.ResultsWe found associations for two polymorphisms of the EDNRB gene which codifies for EDN receptor type B. Genotypes AG and AA of the rs5351 were associated with a lower risk for obesity in the VALCAR sample (p=0.048, OR=0.63) and in the Hortega sample (p=0.001, OR=0.62). Moreover, in the rs3759475 polymorphism, genotypes CT and TT were also associated with lower risk for obesity in the Hortega sample (p=0.0037, OR=0.66) and in the VALCAR sample we found the same tendency (p=0.12, OR=0.70). Furthermore, upon studying the pooled population, we found a stronger association with obesity (p=0.0001, OR=0.61 and p=0.0008, OR=0.66 for rs5351 and rs3759475, respectively). Regarding plasma arsenic levels, we have found a positive association for the two SNPs studied with obesity risk in individuals with higher arsenic levels in plasma: rs5351 (p=0.0054, OR=0.51) and rs3759475 (p=0.009, OR=0.53).ConclusionsOur results support the hypothesis that polymorphisms of the EDNRB gene may influence the susceptibility to obesity and can interact with plasma arsenic levels.

The role of selenium exposure in preventing chronic disease is controversial, especially in selenium-repleted populations. At high concentrations, selenium exposure may increase oxidative stress. Studies evaluating the interaction of genetic variation in genes involved in oxidative stress pathways and selenium are scarce. We evaluated the cross-sectional association of plasma selenium concentrations with oxidative stress levels, measured as oxidized to reduced glutathione ratio (GSSG/GSH), malondialdehyde (MDA), and 8-oxo-7,8-dihydroguanine (8-oxo-dG) in urine, and the interacting role of genetic variation in oxidative stress candidate genes, in a representative sample of 1445 men and women aged 18-85 years from Spain. The geometric mean of plasma selenium levels in the study sample was 84.76 µg/L. In fully adjusted models the geometric mean ratios for oxidative stress biomarker levels comparing the highest to the lowest quintiles of plasma selenium levels were 0.61 (0.50-0.76) for GSSG/GSH, 0.89 (0.79-1.00) for MDA, and 1.06 (0.96-1.18) for 8-oxo-dG. We observed nonlinear dose-responses of selenium exposure and oxidative stress biomarkers, with plasma selenium concentrations above ~110 μg/L being positively associated with 8-oxo-dG, but inversely associated with GSSG/GSH and MDA. In addition, we identified potential risk genotypes associated with increased levels of oxidative stress markers with high selenium levels. Our findings support that high selenium levels increase oxidative stress in some biological processes. More studies are needed to disentangle the complexity of selenium biology and the relevance of potential gene-selenium interactions in relation to health outcomes in human populations.