Chapter 11: Biomarkers of Exposure: Oxidative Stress to DNA and Lipids – Relation to Air Pollution
-
Published:19 Oct 2011
-
Special Collection: 2011 ebook collection , 2011 ebook collection , ECCC Environmental eBooks 1968-2022 , 2011-2015 environmental chemistry subject collectionSeries: Issues in Toxicology
S. Loft and P. Møller, in Biomarkers and Human Biomonitoring Volume 2: Selected Biomarkers of Current Interest, ed. L. Knudsen, D. F. Merlo, L. Knudsen, and D. F. Merlo, The Royal Society of Chemistry, 2011, vol. 2, ch. 11, pp. 160-173.
Download citation file:
Oxidative stress can be caused by many different environmental exposures and it is involved in the pathogenesis of multiple important diseases, in the causative pathways and/or in the progression or complications. Thus, biomarkers of exposure to relevant biologically effective oxidative stress are important. So far, most focus has been on biomarkers of oxidative damage to DNA and lipids measured in cells, tissues or a range of biological fluids. A series of European research projects have developed and validated, in particular, the analytical issues regarding biomarkers of oxidative damage to DNA and nucleotides measured in cells and in urine. Exposure to air pollution has been validated with respect to biomarkers of oxidative damage to DNA and lipids in systematic reviews including meta-analysis. Oxidative stress is a very important mechanistic aspect of the health effects of air pollution, especially particulate matter, and the major outcomes, including cancer and airway and cardiovascular diseases. However, it should also be recognized that biomarkers of oxidative stress to DNA and lipids are not specific for any exposure or for any disease outcome. Moreover, systematic reviews indicate that a diet rich in some fruits and vegetables and particular supplements can reduce the levels of oxidative damage to DNA and lipids. Furthermore, physiological processes and repair mechanisms and kinetics can influence the levels of damage, and these need to be taken into account in the interpretation of data.