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A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C
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 metabol...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990529/ https://www.ncbi.nlm.nih.gov/pubmed/27539580 http://dx.doi.org/10.1186/s40169-016-0109-2 |
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author | Sarfaraz, M. Omair Myers, Robert P. Coffin, Carla S. Gao, Zu-Hua Shaheen, Abdel Aziz M. Crotty, Pam M. Zhang, Ping Vogel, Hans J. Weljie, Aalim M. |
author_facet | Sarfaraz, M. Omair Myers, Robert P. Coffin, Carla S. Gao, Zu-Hua Shaheen, Abdel Aziz M. Crotty, Pam M. Zhang, Ping Vogel, Hans J. Weljie, Aalim M. |
author_sort | Sarfaraz, M. Omair |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40169-016-0109-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4990529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-49905292016-09-01 A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C Sarfaraz, M. Omair Myers, Robert P. Coffin, Carla S. Gao, Zu-Hua Shaheen, Abdel Aziz M. Crotty, Pam M. Zhang, Ping Vogel, Hans J. Weljie, Aalim M. Clin Transl Med Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40169-016-0109-2) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2016-08-18 /pmc/articles/PMC4990529/ /pubmed/27539580 http://dx.doi.org/10.1186/s40169-016-0109-2 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Sarfaraz, M. Omair Myers, Robert P. Coffin, Carla S. Gao, Zu-Hua Shaheen, Abdel Aziz M. Crotty, Pam M. Zhang, Ping Vogel, Hans J. Weljie, Aalim M. A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C |
title | A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C |
title_full | A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C |
title_fullStr | A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C |
title_full_unstemmed | A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C |
title_short | A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C |
title_sort | quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis c |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990529/ https://www.ncbi.nlm.nih.gov/pubmed/27539580 http://dx.doi.org/10.1186/s40169-016-0109-2 |
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