The relationship between biomarkers of oxidative DNA damage, polycyclic aromatic hydrocarbon DNA adducts, antioxidant status and genetic susceptibility following exposure to environmental air pollution in humans

Polycyclic aromatic hydrocarbons (PAHs) appear to be significant contributors to the genotoxicity and carcinogenicity of air pollution present in the urban environment for humans. Populations exposed to environmental air pollution show increased levels of PAH DNA adducts and it has been postulated that another contributing cause of carcinogenicity by environmental air pollution may be the production of reactive oxygen species following oxidative stress leading to oxidative DNA damage. The antioxidant status as well as the genetic profile of an individual should in theory govern the amount of protection afforded against the deleterious effects associated with exposure to environmental air pollution. In this study we investigated the formation of total PAH (bulky) and B[a]P DNA adducts following exposure of individuals to environmental air pollution in three metropolitan cities and the effect on endogenously derived oxidative DNA damage. Furthermore the influence of antioxidant status (vitamin levels) and genetic susceptibility of individuals with regard to DNA damage was also investigated. There was no significant correlation for individuals between the levels of vitamin A, vitamin E, vitamin C and folate with M1dG and 8-oxodG adducts as well as M1dG adducts with total PAH (bulky) or B[a]P DNA adducts. The interesting find from this study was the significant negative correlation between the level of 8-oxodG adducts and the level of total PAH (bulky) and B[a]P DNA adducts implying the that the repair of oxidative DNA damage may be enhanced. This correlation was most significant for those individuals that were non smokers or those unexposed to environmental air pollution. Furthermore the significant inverse correlation between 8-oxodG and B[a]P DNA adducts was confined to individuals carrying the wild type genotype for both the GSTM1 and the GSTT1 gene (separately and interacting). This effect was not observed for individuals carrying the null variant

Association Estimates of Urinary Biomarkers, SPMA and t,t-MA, on Repeated-element and Gene-Specific Percent Methylation using OLS Regression.

<p>Abbreviations: SPMA, S-phenylmercapturic acid; t,t-MA, Trans-trans-muconic acid; AIC, Akaike’s information criterion.</p>*<p><i>P</i><0.05.</p>a<p>Model adjusted for age, sex, smoking history, education, ETS hours.</p>b<p>Coefficient refers to the change in methylation % per IQR change in exposure variable.</p>c<p>Adjusted R^2.</p

Characteristics of 158 Petrochemical Workers and 50 Controls, Bulgaria from 1999– 2000.

<p>Abbreviations: ETS, environmental tobacco smoke.</p>*<p><i>P</i><0.05.</p>a<p>Categorical variables are expressed as n (%), and continuous variables are expressed as mean (SD).</p>b<p>P-values were obtained from Pearson’s chi-square test for categorical variables and Welch two-sample t-test for continuous variables and Wilcoxon rank-sum test for non-normally distribution variables.</p

Association of S-phenylmercapturic acid (SPMA) with DNA methylation in Alu, LINE-1, <i>MAGE</i> and <i>p15</i> methylation.

<p>Fitted beta regression models of repeated-element and gene-specific methylation % versus log(SPMA), adjusted for potential confounders as described in the text. Lines correspond to fitted mean trajectories from beta regression models using the logit link, evaluated for hypothetical individuals with sample mean covariate values (petrochemical workers, non-smoking male, age: 40, ETS: 5.3 hours, education: middle-school). P-values shown correspond to main associations of SPMA in each model.</p

Association Estimates of Urinary Biomarkers, SPMA and t,t-MA, on Repeated-element and Gene-Specific Percent Methylation using Beta-regression.

<p>Abbreviations: SPMA, S-phenylmercapturic acid; t,t-MA, Trans-trans-muconic acid; AIC, Akaike’s information criterion.</p>*<p><i>P</i><0.05.</p>a<p>Model adjusted for age, sex, smoking history, education, ETS hours.</p>b<p>Coefficient refers to the change in methylation % per IQR change in exposure variable.</p>c<p>Pseudo-adjusted R^2.</p