A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients

Abstract

Liver fibrosis must be evaluated in patients with hepatitis C virus (HCV) after liver transplantation because its severity affects their prognosis and the recurrence of HCV. Since invasive biopsy is still the gold standard to identify patients at risk of graft loss from rapid fibrosis progression, it becomes crucial the development of new accurate, non-invasive methods that allow repetitive examination of the patients. Therefore, we have developed a non-invasive, accurate model to distinguish those patients with different liver fibrosis stages. Two hundred and three patients with HCV were histologically classified (METAVIR) into five categories of fibrosis one year after liver transplantation. In this cross-sectional study, patients at fibrosis stages F0-F1 (n = 134) were categorised as "slow fibrosers" and F2-F4 (n = 69) as "rapid fibrosers". Chloroform/methanol serum extracts were analysed by reverse ultra-high performance liquid chromatography coupled to mass spectrometry. A diagnostic model was built through linear discriminant analyses. An algorithm consisting of two sphingomyelins and two phosphatidylcholines accurately classifies rapid and slow fibrosers after transplantation. The proposed model yielded an AUROC of 0.92, 71% sensitivity, 85% specificity, and 84% accuracy. Moreover, specific bile acids and sphingomyelins increased notably along with liver fibrosis severity, differentiating between rapid and slow fibrosers.

DOI: 10.1038/s41598-017-10807-y

7 Figures and Tables

Cite this paper

@inproceedings{Cano2017AMS, title={A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients}, author={Ainara Cano and Zoe Mari{\~n}o and {\'O}scar Millet and Ibon Mart{\'i}nez-Arranz and Miquel Angel Navasa and Juan M. Falcon-Perez and Miriam P{\'e}rez-Cormenzana and Joan Caballer{\'i}a and Nieves Embade and Xavier Forns and Jaume Martin Bosch and Azucena Castro and Jose M. Mato}, booktitle={Scientific reports}, year={2017} }