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Interindividual Variations in DNA Adduct Levels Assessed by Analysis of Multiple Genetic Polymorphisms in Smokers

Departments of ¹ Health Risk Analysis and Toxicology and ² Population Genetics, Genomics and Bioinformatics, Maastricht University, the Netherlands and ³ Centre for Human Genetics, University of Leuven, Leuven, Belgium

Requests for reprints: Joost H.M. van Delft, Department of Health Risk Analysis and Toxicology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands. Phone: 31-43-3881092; Fax: 31-43-3884146. E-mail: j.vandelft{at}grat.unimaas.nl

Genetic polymorphisms in genes involved in processes that affect DNA damage may explain part of the large interindividual variation in DNA adduct levels in smokers. We investigated the effect of 19 polymorphisms in 12 genes involved in carcinogen metabolism, DNA repair, and oxidant metabolism on DNA adduct levels (determined by ³²P post-labeling) in lymphocytes of 63 healthy Caucasian smokers. The total number of alleles that were categorized as putatively high-risk alleles seemed associated with bulky DNA adduct levels (P = 0.001). Subsequently, to investigate which polymorphisms may have the highest contribution to DNA adduct levels in these smokers, discriminant analysis was done. In the investigated set of polymorphisms, GSTM1*0 (P < 0.001), mEH*2 (P = 0.001), and GPX1*1 (P < 0.001) in combination with the level of exposure (P < 0.001) were found to be key effectors. DNA adduct levels in subjects with a relatively high number of risk alleles of these three genes were >2-fold higher than in individuals not having these risk alleles. Noteworthy, all three genes are involved in deactivation of reactive carcinogenic metabolites. This study shows that analysis of multiple genetic polymorphisms may predict the interindividual variation in DNA adduct levels upon exposure to cigarette smoke. It is concluded that discriminant analysis presents an important statistical tool for analyzing the effect of multiple genotypes on molecular biomarkers. (Cancer Epidemiol Biomarkers Prev 2006;15(4):624–9)

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