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CN 22-1108/R
Volume 41 Issue 4
Apr.  2025
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Article Navigation >  Journal of Clinical Hepatology  > 2025  >  41(4): 670-676

Value of FibroScan， gamma-glutamyl transpeptidase-to-platelet ratio， S index， interleukin-6， and tumor necrosis factor-α in the diagnosis of HBeAg-positive chronic hepatitis B liver fibrosis

DOI: 10.12449/JCH250411
  • 1.

    Yunnan Provincial Clinical Medical Center for Infectious Diseases，The Third People’s Hospital of Kunming，Kunming 650041，China

  • 2.

    School of Public Health，Dali University，Dali，Yunnan 371000，China

Research funding:

Health Research Project of Kunming Municipal Health Commission (2023-03-08-001)

More Information
  • Corresponding author: LIU Li， liuli197210@163.com （ORCID： 0000-0001-7712-4931）; LIU Chunyun， 751440760@qq.com （ORCID： 0000-0001-5343-5305）
  • Received Date: 2024-08-30
  • Accepted Date: 2024-10-28
  • Published Date: 2025-04-25
    Abstract
  •   Objective  To investigate the value of noninvasive imaging detection （FibroScan）， two serological models of gamma-glutamyl transpeptidase-to-platelet ratio （GPR） score and S index， and two inflammatory factors of interleukin-6 （IL-6） and tumor necrosis factor-α （TNF-α） in predicting liver fibrosis in patients with HBeAg-positive chronic hepatitis B （CHB）， as well as the consistency of liver biopsy in pathological staging， and to provide early warning for early intervention of CHB.  Methods  A retrospective analysis was performed for 131 HBeAg-positive CHB patients who underwent liver biopsy in The Third People’s Hospital of Kunming from January 2019 to December 2023. The results of liver biopsy were collected from all patients， and related examinations were performed before liver biopsy， including total bilirubin， alanine aminotransferase， platelet count， gamma-glutamyl transpeptidase， albumin， IL-6， TNF-α， liver stiffness measurement （LSM）， and abdominal ultrasound. An analysis of variance was used for comparison of normally distributed continuous data between groups， and the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A Kappa analysis was used to investigate the consistency between LSM noninvasive histological staging and pathological staging based on liver biopsy， and the Spearman analysis was used to investigate the correlation between each variable and FibroScan in the diagnosis of liver fibrosis stage. The Logistic regression analysis was used to construct joint predictive factors. The receiver operating characteristic （ROC） curve was used to evaluate the value of each indicator alone and the joint predictive model in the diagnosis of liver fibrosis， and the Delong test was used for comparison of the area under the ROC curve （AUC）.  Results  In the consistency check， inflammation degree based on liver biopsy had a Kappa value of 0.807 （P<0.001）， and liver fibrosis degree based on liver biopsy had a Kappa value of 0.827 （P<0.001）， suggesting that FibroScan noninvasive histological staging and liver biopsy showed good consistency in assessing inflammation degree and liver fibrosis stage. Age was positively correlated with LSM， GPR score， S index， IL-6， and TNF-α （all P<0.05）， and GPR score， S index， IL-6， and TNF-α were positively correlated with LSM （all P<0.05）. GPR score， S index， IL-6， and TNF-α were all independent risk factors for diagnosing significant liver fibrosis （≥S2） and progressive liver fibrosis （≥S3） （all P<0.05）. As for each indicator alone， GPR score had the highest value in the diagnosis of significant liver fibrosis （≥S2）， followed by S index， IL-6， and TNF-α， while S index had the highest value in the diagnosis of progressive liver fibrosis （≥S3）， followed by GPR score， TNF-α， and IL-6. The joint model had a higher predictive value than each indicator alone （all P<0.05）.  Conclusion  There is a good consistency between FibroScan noninvasive histological staging and pathological staging based on liver biopsy. GPR score， S index， IL-6， and TNF-α are independent risk factors for evaluating different degree of liver fibrosis in CHB， and the combined prediction model established by them can better diagnose liver fibrosis.

     

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