HBeAg Negativity Is Associated With More Advanced Liver Fibrosis in Patients With Chronic Hepatitis B: A Propensity Score-Matching Analysis

Clinical characteristics of patients with hepatitis and cirrhosis and the construction of a prediction model

BACKGROUND

Hepatitis B-associated cirrhosis is an important disease burden in China. However, there is a lack of effective predictors in clinical practice to drive delivery and enable early treatment to delay disease progression.

AIM

To analyzing the clinical characteristics of patients with hepatitis and cirrhosis, the nomogram model was established and validated.

METHODS

The clinical data of 1070 patients with hepatitis B who were treated in our hospital from October 2015 to July 2022 were collected. In a 7:3 ratio, 749 cases were divided into training cohorts and 321 cases were divided into validation cohorts. In addition, the training cohort and validation cohort were further divided into hepatitis group and hepatitis B-related cirrhosis group based on whether the patient progressed to cirrhosis. Binary logistic regression was used to analyze the influencing factors of hepatitis progression to cirrhosis. A roadmap prediction model was established, and the predictive effect of the model was evaluated by patient-subject receiver operating characteristic curve (ROC), and the effectiveness of the model was evaluated by decision curve analysis.

RESULTS

Binary logistic regression analysis was performed using hepatitis B-related cirrhosis = 1 and hepatitis = 0 as dependent variables, and univariate analysis of serological indicators was used as covariates. The results showed that glutamic oxaloacetate aminotransferase/glutamate acetone aminotransferase levels, prothrombin time activity, and hepatitis B e antigen levels were all contributing factors to the progression of hepatitis to cirrhosis. The area under the ROC curve was 0.693 [95% confidence interval (CI): 0.631 to 0.756] for the training cohort and 0.675 (95%CI: 0.561 to 0.790) for the validation cohort. In addition, the decision analysis curves of the prediction models of both the training cohort and the validation cohort confirmed the effectiveness of the nomogram prediction model.

CONCLUSION

Three independent factors influencing the progression to cirrhosis in patients with hepatitis B were identified. The construction of a nomogram prediction model from hepatitis to cirrhosis has high application value as a tool for predicting the occurrence of liver cirrhosis in hepatitis B patients.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

Liver fibrosis is closely related to metabolic factors in metabolic associated fatty liver disease with hepatitis B virus infection

This case-control study aimed to identify the clinical characteristics and explore the risk factors for liver fibrosis in metabolic associated fatty liver disease (MAFLD) patients with hepatitis B virus (HBV) infection. The patients were grouped into MAFLD + HBV and MAFLD (without HBV infection). Propensity score matching (PSM) was used to match baseline features between the groups. We included 401 patients with biopsy-proven MAFLD, 179 of whom had HBV infection. A total of 83 pairs were successfully matched via PSM, and steatosis scores and ballooning in the MAFLD + HBV group were lower than those in the MAFLD group, while the inflammation scores and liver fibrosis stages were higher. After adjusted for confounding factors, HBV infection was associated with a higher risk of significant liver fibrosis in patients with MAFLD [odds ratio (OR): 3.140, P = 0.003]. Overall, 43.58% (78/179) of patients in the MAFLD + HBV group had significant liver fibrosis. Further multivariate regression analysis, hypertension (OR: 2.640; P = 0.031), type 2 diabetes (OR: 4.939; P = 0.035), and elevated glutamyl-transferase levels (OR: 3.980; P = 0.001) were risk factors for liver fibrosis in the MAFLD + HBV group. This suggests metabolic rather than viral factors are more closely associated with liver fibrosis in MAFLD patients with HBV infection.

MAFLD is Associated with the Risk of Liver Fibrosis and Inflammatory Activity in HBeAg-Negative CHB Patients

PURPOSE

Chronic hepatitis B (CHB) and metabolic associated fatty liver disease (MAFLD) are both important public health problems. The effect of concomitant MAFLD on patients with CHB is still unclear. This study aimed to explore the influence of MAFLD on liver fibrosis and inflammation in CHB patients with different hepatitis B e antigen (HBeAg) status.

PATIENTS AND METHODS

We retrospectively collected the clinical data of 399 treatment-naïve CHB patients who underwent liver biopsy. All patients were divided into two groups (HBeAg± group). Logistic regression analysis was used to identify factors associated with liver inflammatory activity and significant fibrosis in patients with CHB. Multivariable logistic regressions were repeated in subgroups stratified by HBeAg status.

RESULTS

In patients with CHB, MAFLD was independently associated with a risk of moderate-to-severe liver activity and significant fibrosis (P <0.05). In the HBeAg-negative group, patients with MAFLD had significantly higher levels of alanine aminotransferase (ALT) (P <0.05) and more severe liver inflammatory activity and fibrosis (P <0.05) compared to those without MAFLD. MAFLD was independently associated with a risk of moderate-to-severe liver activity (A ≥3: OR 3.97, 95% CI 1.71-9.22, P =0.001) and significant fibrosis (F ≥2: OR 2.02, 95% CI 1.09-3.73, P =0.026). In the HBeAg-positive group, MAFLD was found to be independently associated with moderate-to-severe liver activity (OR 2.44, 95% CI 1.03-5.79, P =0.044) but not fibrosis (P =0.618).

CONCLUSION

MAFLD is associated with the risk of liver fibrosis and inflammatory activity in HBeAg-negative CHB patients. Sufficient attention should be paid to the prevention and treatment of MAFLD in patients with CHB, especially in HBeAg-negative patients.