Serum metabolite profiles are associated with the presence of advanced liver fibrosis in Chinese patients with chronic hepatitis B viral infection

Abstract

Background: Accurate and noninvasive diagnosis and staging of liver fibrosis are essential for effective clinical management of chronic liver disease (CLD). We aimed to identify serum metabolite markers that reliably predict the stage of fibrosis in CLD patients. Methods: We quantitatively profiled serum metabolites of participants in 2 independent cohorts. Based on the metabolomics data from cohort 1 (504 HBV associated liver fibrosis patients and 502 normal controls, NC), we selected a panel of 4 predictive metabolite markers. Consequently, we constructed 3 machine learning models with the 4 metabolite markers using random forest (RF), to differentiate CLD patients from normal controls (NC), to differentiate cirrhosis patients from fibrosis patients, and to differentiate advanced fibrosis from early fibrosis, respectively. Results: The panel of 4 metabolite markers consisted of taurocholate, tyrosine, valine, and linoelaidic acid. The RF models of the metabolite panel demonstrated the strongest stratification ability in cohort 1 to diagnose CLD patients from NC (area under the receiver operating characteristic curve (AUROC) = 0.997 and the precision-recall curve (AUPR) = 0.994), to differentiate fibrosis from cirrhosis (0.941, 0.870), and to stage liver fibrosis (0.918, 0.892). The diagnostic accuracy of the models was further validated in an independent cohort 2 consisting of 300 CLD patients with chronic HBV infection and 90 NC. The AUCs of the models were consistently higher than APRI, FIB-4, and AST/ALT ratio, with both greater sensitivity and specificity. Conclusions: Our study showed that this 4-metabolite panel has potential usefulness in clinical assessments of CLD progression in patients with chronic hepatitis B virus infection.

Identification and Validation of Novel Biomarkers for Hepatocellular Carcinoma, Liver Fibrosis/Cirrhosis and Chronic Hepatitis B via Transcriptome Sequencing Technology

“… 6 , 7 Moreover, a number of scoring systems and morphological tests, such as FIB-4 (Fibrosis index based on the 4 factor), APRI (aspartate aminotransferase-to-platelet ratio index), and transient elastography, have been recently used to diagnose and assess liver fibrosis and cirrhosis. 8 However, FIB-4 and APRI are affected by transaminases, while liver elastography is limited by ascites, morbid obesity, and small intercostal space. 9 Besides, alpha-fetoprotein (AFP) is among the most common biomarkers for early detection and monitoring of HCC patients.…”

Predictive value of liver cirrhosis using metabolite biomarkers of bile acid in the blood

“…[21] Bile acids and carnitine were mentioned in a former study to be the promising biomarkers for primary biliary cirrhosis in urine and serum. [22] Some species of bile acids were explored to be capable to distinguish cirrhosis, [23] which the levels between cirrhosis and healthy were tested by different studies. [19,24,25] However, there are still lacking of consistent conclusions in the predictive biomarkers for cirrhosis.…”

Diagnosis of Fibrosis Using Blood Markers and Logistic Regression in Southeast Asian Patients With Non-alcoholic Fatty Liver Disease

“…Using the same four parameters of the famous scoring system FIB-4, our method showed steady and significant improvements in comparison with FIB-4. In addition, we quantitatively profiled 98 serum metabolites in 1,006 participants (including 504 CLD patients and 502 normal controls) and identified four serum metabolite markers, taurocholate, tyrosine, valine, and linoelaidic acid, which can reliably evaluate the stage of fibrosis by jointly using two machine learning methods, least absolute shrinkage and selection operator and random forest (RF) ( 21 ). The prediction models were steadily superior to existing scoring systems, including the APRI, FIB-4, and AST/ALT ratio, with greater sensitivity, specificity, area under the receiver operating characteristic curve (auROC) and area under the precision–recall curve (auPR).…”