Open AccessA quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C
Author(s) -
Sarfaraz M. Omair,
Myers Robert P.,
Coffin Carla S.,
Gao ZuHua,
Shaheen Abdel Aziz M.,
Crotty Pam M.,
Zhang Ping,
Vogel Hans J.,
Weljie Aalim M.
Publication year - 2016
Publication title -
clinical and translational medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.125
H-Index - 1
ISSN - 2001-1326
DOI - 10.1186/s40169-016-0109-2
Subject(s) - steatosis , medicine , metabolomics , fibrosis , metabolome , receiver operating characteristic , liver biopsy , metabolite , gastroenterology , pathology , biomarker , biopsy , bioinformatics , biology , biochemistry
Abstract Background High‐throughput technologies have the potential to identify non‐invasive biomarkers of liver pathology and improve our understanding of basic mechanisms of liver injury and repair. A metabolite profiling approach was employed to determine associations between alterations in serum metabolites and liver histology in patients with chronic hepatitis C virus (HCV) infection. Methods Sera from 45 non‐diabetic patients with chronic HCV were quantitatively analyzed using 1 H‐NMR spectroscopy. A metabolite profile of advanced fibrosis (METAVIR F3‐4) was established using orthogonal partial least squares discriminant analysis modeling and validated using seven‐fold cross‐validation and permutation testing. Bioprofiles of moderate to severe steatosis (≥33 %) and necroinflammation (METAVIR A2‐3) were also derived. The classification accuracy of these profiles was determined using areas under the receiver operator curves (AUROCSs) measuring against liver biopsy as the gold standard. Results In total 63 spectral features were profiled, of which a highly significant subset of 21 metabolites were associated with advanced fibrosis (variable importance score >1 in multivariate modeling; R 2 = 0.673 and Q 2 = 0.285). For the identification of F3–4 fibrosis, the metabolite bioprofile had an AUROC of 0.86 (95 % CI 0.74–0.97). The AUROCs for the bioprofiles for moderate to severe steatosis were 0.87 (95 % CI 0.76–0.97) and for grade A2–3 inflammation were 0.73 (0.57–0.89). Conclusion This proof‐of‐principle study demonstrates the utility of a metabolomics profiling approach to non‐invasively identify biomarkers of liver fibrosis, steatosis and inflammation in patients with chronic HCV. Future cohorts are necessary to validate these findings.