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Biomarkers of liver fibrosis

Authors

  • Leon A Adams

    Corresponding author
    1. School of Medicine and Pharmacology, University of Western Australia, Australia
    2. Department of Gastroenterology and Hepatology, Sir Charles Gairdner Hospital, Perth, Australia
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  • Conflict of Interest: Leon Adams is a co-applicant on patents for Hepascore and is employed by the University of Western Australia which has a licensing agreement with Quest Diagnostics Inc. regarding the commercialization of Hepascore.

A/Professor Leon A Adams, School of Medicine and Pharmacology, University of Western Australia, 4th floor, G Block, Sir Charles Gairdner Hospital, Verdun St, Nedlands, WA 6009, Australia. Email: leon.adams@uwa.edu.au

Abstract

Fibrosis prediction is an essential part of the assessment and management of patients with chronic liver disease. Blood-based biomarkers offer a number of advantages over the traditional standard of fibrosis assessment of liver biopsy, including safety, cost-savings and wide spread accessibility. Current biomarker algorithms include indirect surrogate measures of fibrosis, including aminotransaminases and platelet count, or direct measures of fibrinogenesis or fibrinolysis such as hyaluronic acid and tissue inhibitor of metalloproteinase-1. A number of algorithms have now been validated across a range of chronic liver disease including chronic viral hepatitis, alcoholic and non-alcoholic fatty liver disease. Furthermore, several models have been demonstrated to be dynamic to changes in fibrosis over time and are predictive of liver-related survival and overall survival to a greater degree than liver biopsy. Current limitations of biomarker models include a significant indeterminate range, and a predictive ability that is limited to only a few stages of fibrosis. Utilization of these biomarker models requires knowledge of patient co-morbidities which may produce false positive or negative results in a small proportion of individuals. Furthermore, knowledge of the underlying prevalence of fibrosis in the patient population is required for interpretation of the positive or negative predictive values of a test result. Novel proteins identified by proteomic technology and genetic polymorphisms from genome association studies offer the possibility for further refinement and individualization of biomarker fibrosis models in the future.

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