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Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder
Alcohol-related liver disease is a public health care burden globally. Only 10–20% of patients with alcohol use disorder have progressive liver disease. This study aimed to identify lipid biomarkers for the early identification of progressive alcohol-related liver disease, which is a key step for ea...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146183/ https://www.ncbi.nlm.nih.gov/pubmed/35629937 http://dx.doi.org/10.3390/metabo12050433 |
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author | Gao, Bei Zeng, Suling Maccioni, Luca Shi, Xiaochun Armando, Aaron Quehenberger, Oswald Zhang, Xinlian Stärkel, Peter Schnabl, Bernd |
author_facet | Gao, Bei Zeng, Suling Maccioni, Luca Shi, Xiaochun Armando, Aaron Quehenberger, Oswald Zhang, Xinlian Stärkel, Peter Schnabl, Bernd |
author_sort | Gao, Bei |
collection | PubMed |
description | Alcohol-related liver disease is a public health care burden globally. Only 10–20% of patients with alcohol use disorder have progressive liver disease. This study aimed to identify lipid biomarkers for the early identification of progressive alcohol-related liver disease, which is a key step for early intervention. We performed untargeted lipidomics analysis in serum and fecal samples for a cohort of 49 subjects, including 17 non-alcoholic controls, 16 patients with non-progressive alcohol-related liver disease, and 16 patients with progressive alcohol-related liver disease. The serum and fecal lipidome profiles in the two patient groups were different from that in the controls. Nine lipid biomarkers were identified that were significantly different between patients with progressive liver disease and patients with non-progressive liver disease in both serum and fecal samples. We further built a random forest model to predict progressive alcohol-related liver disease using nine lipid biomarkers. Fecal lipids performed better (Area Under the Curve, AUC = 0.90) than serum lipids (AUC = 0.79). The lipid biomarkers identified are promising candidates for the early identification of progressive alcohol-related liver disease. |
format | Online Article Text |
id | pubmed-9146183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91461832022-05-29 Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder Gao, Bei Zeng, Suling Maccioni, Luca Shi, Xiaochun Armando, Aaron Quehenberger, Oswald Zhang, Xinlian Stärkel, Peter Schnabl, Bernd Metabolites Article Alcohol-related liver disease is a public health care burden globally. Only 10–20% of patients with alcohol use disorder have progressive liver disease. This study aimed to identify lipid biomarkers for the early identification of progressive alcohol-related liver disease, which is a key step for early intervention. We performed untargeted lipidomics analysis in serum and fecal samples for a cohort of 49 subjects, including 17 non-alcoholic controls, 16 patients with non-progressive alcohol-related liver disease, and 16 patients with progressive alcohol-related liver disease. The serum and fecal lipidome profiles in the two patient groups were different from that in the controls. Nine lipid biomarkers were identified that were significantly different between patients with progressive liver disease and patients with non-progressive liver disease in both serum and fecal samples. We further built a random forest model to predict progressive alcohol-related liver disease using nine lipid biomarkers. Fecal lipids performed better (Area Under the Curve, AUC = 0.90) than serum lipids (AUC = 0.79). The lipid biomarkers identified are promising candidates for the early identification of progressive alcohol-related liver disease. MDPI 2022-05-11 /pmc/articles/PMC9146183/ /pubmed/35629937 http://dx.doi.org/10.3390/metabo12050433 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Bei Zeng, Suling Maccioni, Luca Shi, Xiaochun Armando, Aaron Quehenberger, Oswald Zhang, Xinlian Stärkel, Peter Schnabl, Bernd Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder |
title | Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder |
title_full | Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder |
title_fullStr | Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder |
title_full_unstemmed | Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder |
title_short | Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder |
title_sort | lipidomics for the prediction of progressive liver disease in patients with alcohol use disorder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146183/ https://www.ncbi.nlm.nih.gov/pubmed/35629937 http://dx.doi.org/10.3390/metabo12050433 |
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