Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases

Author:

Li Wenkuan1ORCID,Alamoudi Jawaher Abdullah1ORCID,Gautam Nagsen1ORCID,Kumar Devendra1ORCID,Olivera Macro2ORCID,Gwon Yeongjin3ORCID,Mukgerjee Sandeep4ORCID,Alnouti Yazen1ORCID

Affiliation:

1. Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE 68198, USA

2. Department of Internal Medicine, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA

3. Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA

4. Department of Internal Medicine, College of Medicine, Creighton University Medical Center, Omaha, NE, USA

Abstract

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.

Funder

Society of American Gastrointestinal and Endoscopic Surgeons

Publisher

Hindawi Limited

Subject

Hepatology

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1. Regulation of MAIT cells through host-derived antigens;Frontiers in Immunology;2024-06-24

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