Abstract
BACKGROUND: While previous reports have shown that hepatitis B virus (HBV) infection affects lipid metabolism and vice versa, the impact of dyslipidemia on the functional cure of HBV infection following peginterferon alfa (PegIFNα) therapy remains unknown. Hence, this study aimed to investigate the effect of dyslipidemia on hepatitis B surface antigen (HBsAg) clearance and develop a nomogram model for predicting patients for whom PegIFNα therapy is indicated. METHODS: A total of 160 nucleos(t)ide analogues (NAs)- experienced chronic hepatitis B (CHB) patients treated with PegIFNα (180 µg/week) were enrolled in this study. The relationship between serum lipid and HBsAg clearance was analysed. Univariate and multivariate COX analyses were used to construct and plot the nomogram model. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to evaluate the discrimination and calibration of the model, respectively. RESULTS: After 48 weeks of PegIFNα therapy, a total of 33 patients in the cohort achieved HBsAg clearance. Univariate and multivariate COX analyses indicated that dyslipidemia was significantly associated with HBsAg clearance and was an independent predictor of HBsAg clearance (HR = 0.243, P = 0.001). Kaplan-Meier survival analyses show that cumulative HBsAg clearance was significantly higher in the normolipidemic group than in the dyslipidemia group (log-rank test, P = 0.007). During the treatment, triglyceride showed an increasing trend, while the levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, apolipoprotein A1 and apolipoprotein B decreased. Dyslipidemia and other indicators independently associated with HBsAg clearance were used to construct the nomogram model. The AUC of the model at 36-week and 48-week were 0.879 and 0.856, and the model demonstrated good discrimination and calibration. CONCLUSION: Dyslipidemia can affect the antiviral efficacy of PegIFNα in NAs-experienced CHB patients. Our findings suggest that the nomogram model constructed using serum lipid has good predictive power and may help physicians to identify the superior patients for PegIFNα therapy.