Serum ceruloplasmin can predict liver fibrosis in hepatitis B virus-infected patients

Autor: Yueyong Zhu, Meng-Xin Lin, Yu-Rui Liu, Na-Ling Kang, Zu-Xiong Huang, Jie-Min Zhang, Da-Wu Zeng, Xu-Dong Chen
Rok vydání: 2020
Předmět:
Zdroj: World Journal of Gastroenterology
ISSN: 1007-9327
DOI: 10.3748/wjg.v26.i27.3952
Popis: Background The presence of significant liver fibrosis in hepatitis B virus (HBV)-infected individuals with persistently normal serum alanine aminotransferase (PNALT) levels is a strong indicator for initiating antiviral therapy. Serum ceruloplasmin (CP) is negatively correlated with liver fibrosis in HBV-infected individuals. Aim To examine the potential value of serum CP and develop a noninvasive index including CP to assess significant fibrosis among HBV-infected individuals with PNALT. Methods Two hundred and seventy-five HBV-infected individuals with PNALT were retrospectively evaluated. The association between CP and fibrotic stages was statistically analyzed. A predictive index including CP [Ceruloplasmin hepatitis B virus (CPHBV)] was constructed to predict significant fibrosis and compared to previously reported models. Results Serum CP had an inverse correlation with liver fibrosis (r = -0.600). Using CP, the areas under the curves (AUCs) to predict significant fibrosis, advanced fibrosis, and cirrhosis were 0.774, 0.812, and 0.853, respectively. The CPHBV model was developed using CP, platelets (PLT), and HBsAg levels to predict significant fibrosis. The AUCs of this model to predict significant fibrosis, advanced fibrosis, and cirrhosis were 0.842, 0.920, and 0.904, respectively. CPHBV was superior to previous models like the aspartate aminotransferase (AST)-to-PLT ratio index, Fibrosis-4 score, gamma-glutamyl transpeptidase-to-PLT ratio, Forn's score, and S-index in predicting significant fibrosis in HBV-infected individuals with PNALT. Conclusion CPHBV could accurately predict liver fibrosis in HBV-infected individuals with PNALT. Therefore, CPHBV can be a valuable tool for antiviral treatment decisions.
Databáze: OpenAIRE