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Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases
Hepatobiliary diseases and their complications cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may exacerbate the underlying condition and lead to unfavorable prognosis. To develop and validate prognostic biomarkers for the prediction of complications of...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986411/ https://www.ncbi.nlm.nih.gov/pubmed/35402050 http://dx.doi.org/10.1155/2022/5473752 |
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author | Li, Wenkuan Alamoudi, Jawaher Abdullah Gautam, Nagsen Kumar, Devendra Olivera, Macro Gwon, Yeongjin Mukgerjee, Sandeep Alnouti, Yazen |
author_facet | Li, Wenkuan Alamoudi, Jawaher Abdullah Gautam, Nagsen Kumar, Devendra Olivera, Macro Gwon, Yeongjin Mukgerjee, Sandeep Alnouti, Yazen |
author_sort | Li, Wenkuan |
collection | PubMed |
description | Hepatobiliary diseases and their complications cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may exacerbate the underlying condition and lead to unfavorable prognosis. To develop and validate prognostic biomarkers for the prediction of complications of cholestatic liver disease based on urinary BA indices, liquid chromatography-tandem mass spectrometry was used to analyze urine samples from 257 patients with cholestatic liver diseases during a 7-year follow-up period. The urinary BA profile and non-BA parameters were monitored, and logistic regression models were used to predict the prognosis of hepatobiliary disease-related complications. Urinary BA indices were applied to quantify the composition, metabolism, hydrophilicity, and toxicity of the BA profile. We have developed and validated the bile-acid liver disease complication (BALDC) model based on BA indices using logistic regression model, to predict the prognosis of cholestatic liver disease complications including ascites. The mixed BA and non-BA model was the most accurate and provided higher area under the receiver operating characteristic (ROC) and smaller akaike information criterion (AIC) values compared to both non-BA and MELD (models for end stage liver disease) models. Therefore, the mixed BA and non-BA model could be used to predict the development of ascites in patients diagnosed with liver disease at early stages of intervention. This will help physicians to make a better decision when treating hepatobiliary disease-related ascites. |
format | Online Article Text |
id | pubmed-8986411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89864112022-04-07 Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases Li, Wenkuan Alamoudi, Jawaher Abdullah Gautam, Nagsen Kumar, Devendra Olivera, Macro Gwon, Yeongjin Mukgerjee, Sandeep Alnouti, Yazen Int J Hepatol Research Article Hepatobiliary diseases and their complications cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may exacerbate the underlying condition and lead to unfavorable prognosis. To develop and validate prognostic biomarkers for the prediction of complications of cholestatic liver disease based on urinary BA indices, liquid chromatography-tandem mass spectrometry was used to analyze urine samples from 257 patients with cholestatic liver diseases during a 7-year follow-up period. The urinary BA profile and non-BA parameters were monitored, and logistic regression models were used to predict the prognosis of hepatobiliary disease-related complications. Urinary BA indices were applied to quantify the composition, metabolism, hydrophilicity, and toxicity of the BA profile. We have developed and validated the bile-acid liver disease complication (BALDC) model based on BA indices using logistic regression model, to predict the prognosis of cholestatic liver disease complications including ascites. The mixed BA and non-BA model was the most accurate and provided higher area under the receiver operating characteristic (ROC) and smaller akaike information criterion (AIC) values compared to both non-BA and MELD (models for end stage liver disease) models. Therefore, the mixed BA and non-BA model could be used to predict the development of ascites in patients diagnosed with liver disease at early stages of intervention. This will help physicians to make a better decision when treating hepatobiliary disease-related ascites. Hindawi 2022-03-30 /pmc/articles/PMC8986411/ /pubmed/35402050 http://dx.doi.org/10.1155/2022/5473752 Text en Copyright © 2022 Wenkuan Li et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Li, Wenkuan Alamoudi, Jawaher Abdullah Gautam, Nagsen Kumar, Devendra Olivera, Macro Gwon, Yeongjin Mukgerjee, Sandeep Alnouti, Yazen Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases |
title | Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases |
title_full | Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases |
title_fullStr | Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases |
title_full_unstemmed | Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases |
title_short | Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases |
title_sort | urinary ba indices as prognostic biomarkers for complications associated with liver diseases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986411/ https://www.ncbi.nlm.nih.gov/pubmed/35402050 http://dx.doi.org/10.1155/2022/5473752 |
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