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

Wenkuan Li; Jawaher Abdullah Alamoudi; Nagsen Gautam; Devendra Kumar; Macro Olivera; Yeongjin Gwon; Sandeep Mukgerjee; Yazen Alnouti

doi:10.1155/2022/5473752

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

Int J Hepatol. 2022 Mar 30:2022:5473752. doi: 10.1155/2022/5473752. eCollection 2022.

Authors

Wenkuan Li¹, Jawaher Abdullah Alamoudi¹, Nagsen Gautam¹, Devendra Kumar¹, Macro Olivera², Yeongjin Gwon³, Sandeep Mukgerjee⁴, Yazen Alnouti¹

Affiliations

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.