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  4. Gamma-Glutamyltransferase: A Predictive Biomarker of Cellular Antioxidant Inadequacy and Disease Risk

Gamma-Glutamyltransferase: A Predictive Biomarker of Cellular Antioxidant Inadequacy and Disease Risk

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Author(s)
Koenig, Gerald
•
Seneff, Stephanie
Date Issued
2015
Journal
Disease Markers
Publisher
Hindawi Publishing Corporation
Citation
Gerald Koenig and Stephanie Seneff, “Gamma-Glutamyltransferase: A Predictive Biomarker of Cellular Antioxidant Inadequacy and Disease Risk,” Disease Markers, vol. 2015, Article ID 818570, 18 pages, 2015.
Version
Final published version
Abstract
Gamma-glutamyltransferase (GGT) is a well-established serum marker for alcohol-related liver disease. However, GGT’s predictive utility applies well beyond liver disease: elevated GGT is linked to increased risk to a multitude of diseases and conditions, including cardiovascular disease, diabetes, metabolic syndrome (MetS), and all-cause mortality. The literature from multiple population groups worldwide consistently shows strong predictive power for GGT, even across different gender and ethnic categories. Here, we examine the relationship of GGT to other serum markers such as serum ferritin (SF) levels, and we suggest a link to exposure to environmental and endogenous toxins, resulting in oxidative and nitrosative stress. We observe a general upward trend in population levels of GGT over time, particularly in the US and Korea. Since the late 1970s, both GGT and incident MetS and its related disorders have risen in virtual lockstep. GGT is an early predictive marker for atherosclerosis, heart failure, arterial stiffness and plaque, gestational diabetes, and various liver diseases, including viral hepatitis, other infectious diseases, and several life-threatening cancers. We review literature both from the medical sciences and from life insurance industries demonstrating that serum GGT is a superior marker for future disease risk, when compared against multiple other known mortality risk factors.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
http://creativecommons.org/licenses/by/2.0
Persistent DSpace Link
http://hdl.handle.net/1721.1/99364
DOI of Published Version
https://doi.org/10.1155/2015/818570
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