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Disease Markers
Volume 28 (2010), Issue 5, Pages 273-280

Decreased IP-10 and Elevated TGFβ1 Levels are Associated with Viral Clearance Following Therapy in Patients with Hepatitis C Virus

Silvia Lee,1,2 Julius Varano,2 James P. Flexman,1 Wendy Cheng,3 Mark W. Watson,4 Enrico Rossi,5 Leon A. Adams,6 Max Bulsara,7 and Patricia Price2,4

1Department of Microbiology and Infectious Disease, Royal Perth Hospital, Perth, Australia
2School of Pathology and Laboratory Medicine, University of Western Australia, Perth, Australia
3Department of Gastroenterology and Hepatology, Royal Perth Hospital, Perth, Australia
4Clinical Immunology and Immunogenetics, Royal Perth Hospital, Perth, Australia
5PathWest, Queen Elizabeth II Medical Centre, Nedlands, Australia
6School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
7School of Population Health, University of Western Australia, Perth, Australia

Received 25 June 2010; Accepted 25 June 2010

Copyright © 2010 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The role of pro-fibrogenic cytokines in the outcome of infections with hepatitis C virus (HCV) and the response to treatment with pegylated interferon-alpha (pegIFNα) and ribavirin remains unclear. To address this issue, we assessed hepatic fibrosis and plasma markers pertinent to T-cell mediated fibrogenesis and inflammation at the start of treatment. Levels of soluble (s)CD30, interleukin-13 receptor alpha 2 (IL-13Rα2), total and active transforming growth factor-beta 1 (TGFβ1), interleukin-18 (IL-18) and interferon-gamma inducible protein-10 (IP-10, CXCL10) were correlated with the severity of fibrosis and with treatment outcome using multiple logistic regression modelling. The Hepascore algorithm was confirmed as a marker of fibrosis, but was a poor predictor of treatment outcome. Inclusion of all immunological markers improved prediction based on Hepascore alone (p = 0.045), but optimal prediction was achieved with an algorithm (“TIPscore”) based on TGFβ1 (total), IP-10, age, sex and HCV genotype (p = 0.003 relative to Hepascore). Whilst this was only marginally more effective than predictions based on HCV genotype age and sex (p = 0.07), it associates high TGFβ1 and low IP-10 levels with a failure of therapy.