Graduation Date

Summer 8-13-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Programs

Pharmaceutical Sciences

First Advisor

Yazen Alnouti

Abstract

Hepatobilary diseases cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may lead to an unfavorable prognosis. In this study, we compared the urinary BA profile in 257 patients with hepatobilary diseases during a 7-year follow-up period. We investigated the use of the urinary BA profile to develop logistic regression models to predict the prognosis of hepatobiliary diseases in terms of developing disease-related complications, especially for ascites. The urinary BA profile was characterized by calculating BA indices, which quantify the composition, metabolism, hydrophilicity, and toxicity of the BA profile. All patients had high total and individual BA concentrations. The percentages of primary BA (CDCA and LCA) were high, while the percentages of secondary BA (MDCA and DCA) were low in patients. BA indices had lower inter- and intra-individual variability than absolute total and individual BA concentrations. The changes of the BA indices were associated with the probability of developing ascites in the entire liver-patient population using logistic regression analysis. BA indices were proved as prognostic biomarkers for hepatobilary diseases.

We have developed and validated a prognosis model based on BA indices to predict the prognosis of ascites in the entire liver-patient population. Other models, including non-BA, original MELD, and mixed BA and non-BA models, were also developed to compare their performance with our BA model. Overall, the mixed BA and non-BA model was the most accurate based on Akaike information criterion (AIC) and receiver operating characteristic (ROC) analyses. The mixed BA and non-BA had lower AIC values indicating a smaller error of distribution and a better trade-off between goodness of fit vs. degrees of freedom. Moreover, the mixed BA and non-BA model had highest area under the ROC curve (AUC) values indicating higher accuracy than other models. One application of 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, such as liver transplantation. This will assist in supply allocation and physician decisions when treating liver diseases.

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