Abstract
In this meta-analysis, studies reporting arsenic-induced oxidative damage in mouse models were systematically evaluated to provide a scientific understanding of oxidative stress mechanisms associated with arsenic poisoning. Fifty-eight relevant peer-reviewed publications were identified through exhaustive database searching. Oxidative stress indexes assessed included superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), glutathione peroxidase (GPx), glutathione-s-transferase (GST), glutathione reductase (GR), oxidized glutathione (GSSG), malondialdehyde (MDA), and reactive oxygen species (ROS). Our meta-analysis showed that arsenic exposure generally suppressed measured levels of the antioxidants, SOD, CAT, GSH, GPx, GST, and GR, but increased levels of the oxidants, GSSG, MDA, and ROS. Arsenic valence was important and GR and MDA levels increased to a significantly (P < 0.05) greater extent upon exposure to As3+ than to As5+. Other factors that contributed to a greater overall oxidative effect from arsenic exposure included intervention time, intervention method, dosage, age of animals, and the sample source from which the indexes were estimated. Our meta-analysis effectively summarized a wide range of studies and detected a positive relationship between arsenic exposure and oxidative damage. These data provide a scientific basis for the prevention and treatment of arsenic poisoning.
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Acknowledgments
The authors would like to thank the Department of Public Health, Shihezi University School of Medicine for assistance with this work as well as funding from the “Key Areas of Science and Technology Research Project of Xinjiang Production and Construction Corps (Nos. 2014BA039 and 2015AG014),” the “High-tech Intellectual Project of Shihezi University (No. RCZX201112),” and the “National Natural Science Foundation of China (No. 81560517).”
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Mengchuan Xu and Dongsheng Rui contributed equally to this work.
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Xu, M., Rui, D., Yan, Y. et al. Oxidative Damage Induced by Arsenic in Mice or Rats: A Systematic Review and Meta-Analysis. Biol Trace Elem Res 176, 154–175 (2017). https://doi.org/10.1007/s12011-016-0810-4
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DOI: https://doi.org/10.1007/s12011-016-0810-4