Literature DB >> 22624806

Urinary metabolite variation is associated with pathological progression of the post-hepatitis B cirrhosis patients.

Xiaoning Wang1, Xiaoyan Wang, Guoxiang Xie, Mingmei Zhou, Huan Yu, Yan Lin, Guangli Du, Guoan Luo, Wei Jia, Ping Liu.   

Abstract

Cirrhosis is a common and terminal outcome of many chronic liver conditions. A urinary metabonomic study using gas chromatography-mass spectrometry (GC-MS) and ultra performance liquid chromatography time-of-flight mass spectrometry (UPLC-TOFMS) was carried out to elucidate the pathophysiological basis of posthepatitis B cirrhosis in 63 posthepatitis B cirrhosis patients and 31 health controls. Urinary metabolic profile and corresponding differential metabolites associated with Child-Pugh (CP) grading of liver function were characterized, in addition to the blood routine, liver, and renal function tests. Multivariate statistical tools including principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were employed in the metabolite analysis along with a univariate statistical method, Wilcoxon-Mann-Whitney test. The alterations of differential metabolites contributing to the intergroup variation between healthy controls and cirrhotic patients, and among cirrhosis of CP grade A, B and C were also investigated. Six metabolites, α-hydroxyhippurate, tyrosine-betaxanthin, 3-hydroxyisovalerate, canavaninosuccinate, estrone, and glycoursodeoxycholate, were significantly altered among cirrhotic patients with CP A, B, and C, reflecting abnormal metabolism of amino acid, bile acids, hormones, and intestinal microbial metabolism. The results show that dynamic alteration of urinary metabolome, characterized by the changes of a panel of the differential metabolite markers, is indicative of an exacerbated liver function, highlighting their diagnostic and prognostic potential for the liver cirrhosis development.

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Year:  2012        PMID: 22624806     DOI: 10.1021/pr300337s

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  12 in total

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10.  Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning.

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