Association of serum total cholesterol with pegylated interferon-α treatment in HBeAg-positive chronic hepatitis B patients

Kinetics changes in total cholesterol predict HBeAg seroconversion in chronic hepatitis B patients treated with pegylated interferon-alfa

There is no satisfactory standard for predicting HBeAg seroconversion during Pegylated interferon alpha (PegIFNα) treatment. Studies have shown that IFNα therapy in chronic hepatitis C patients could alter serum lipid profiles. However, there have been no studies on lipid changes that predict the outcome of PegIFNα monotherapy in treated-naive chronic hepatitis B (CHB) patients. This retrospective study included 130 treated-naive HBeAg-positive CHB patients receiving PegIFNα monotherapy. The relationship between serum lipid changes and HBeAg seroconversion was analysed. The TC-ALT-HBsAg-HBeAg-Genotype-Age (CASEGA) model was established to predict HBeAg seroconversion after PegIFN-α monotherapy. Among 130 patients, 33 achieved HBeAg seroconversion (SR) and 97 did not achieve HBeAg seroconversion (NR). The decrease in serum total cholesterol (TC) in the NR group was significantly higher than in the SR group at Week 24 (-9.59% vs. -0.31%, p < 0.001). Multivariate logistic regression analysis showed that the change in TC at Week 24 (odds ratio = 1.065, p = 0.009) was an independent predictor of HBeAg seroconversion. The area under the receiver operating characteristic curve for the CASEGA model was 0.883. The model score at the maximum Youden index was 90, and the specificity, sensitivity, positive predictive value and negative predictive value were 0.727, 0.794, 0.546 and 0.895, respectively. The HBeAg seroconversion rate at Week 72 in patients with scores >90 was significantly higher than that in patients with scores <90 (54.55% vs. 10.47%, p < 0.001). This study indicated that the change in the TC level at 24 weeks in CHB patients treated with PegIFNα was associated with HBeAg seroconversion. The CASEGA prediction model based on the TC change rate of 24 weeks has good predictive efficiency for HBeAg seroconversion.

Using machine learning models to predict HBeAg seroconversion in CHB patients receiving pegylated interferon-α monotherapy

BACKGROUND AND OBJECTIVE

Though there are many advantages of pegylated interferon-α (PegIFN-α) treatment to chronic hepatitis B (CHB) patients, the response rate of PegIFN-α is only 30 ~ 40%. Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN-α.

METHODS

We randomly divided 260 HBeAg-positive CHB patients who were not previously treated and received PegIFN-α monotherapy (180 μg/week) into a training dataset (70%) and testing dataset (30%). The intersect features were extracted from 50 routine laboratory variables using the recursive feature elimination method algorithm, Boruta algorithm, and Least Absolute Shrinkage and Selection Operator Regression algorithm in the training dataset. After that, based on the intersect features, eight machine learning models including Logistic Regression, k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Naïve Bayes were applied to evaluate HBeAg seroconversion in HBeAg-positive CHB patients receiving PegIFN-α monotherapy in the training dataset and testing dataset.

RESULTS

XGBoost model showed the best performance, which had largest AUROC (0.900, 95% CI: 0.85-0.95 and 0.910, 95% CI: 0.84-0.98, in training dataset and testing dataset, respectively), and the best calibration curve performance to predict HBeAg seroconversion. The importance of XGBoost model indicated that treatment time contributed greatest to HBeAg seroconversion, followed by HBV DNA(log), HBeAg, HBeAb, HBcAb, ALT, triglyceride, and ALP.

CONCLUSIONS

XGBoost model based on common laboratory variables had good performance in predicting HBeAg seroconversion in HBeAg-positive CHB patients receiving PegIFN-α monotherapy.

Effects of dyslipidemia on E antigen seroconversion of patients with chronic hepatitis B treated by nucleoside (acid) analogs

BACKGROUND

The prevalence of dyslipidemia in China is increasing annually. Current studies suggest that dyslipidemia affects the antiviral efficacy of hepatitis C virus (HCV) therapies, while recent studies suggest that serum lipids influence the response rates of chronic hepatitis B (CHB) patients receiving PEGylated interferon-alpha (Peg IFN-α) treatment. However, the role of dyslipidemia in the efficacy of nucleoside (acid) analogues (NAs) in CHB patients remains unclear.

METHODS

From January 2010 to December 2013, data from 179 treatment-naive patients with CHB who were hepatitis B e antigen (HBeAg)-positive and had visited the first affiliated hospital of Wenzhou Medical University were assessed. Of these patients, 68 were assigned to the dyslipidemia group (diagnosed with CHB complicated with dyslipidemia) and 111 to the normolipidemic group. The following 3 treatment strategies were performed for all CHB patients over a 5-year period: lamivudine (LAM) plus adefovir dipivoxil (ADV) combination therapy, telbivudine (LdT) monotherapy, and entecavir (ETV) monotherapy. Serum assessments, blood biochemistry, HBV serological markers, HBV DNA before treatment and HBeAg serological conversion and virological responses at different timepoints after treatment were compared between the two groups. Measurement data were compared by τ tests and enumeration data by χ2 tests. Correlation analysis was performed using binary logistic regression analysis.

RESULTS

The rates of HBeAg seroconversion in the dyslipidemia group at years 1, 2, 3, and 4 were 10.3, 13.2, 17.6, and 22.1%, respectively, which were not significantly lower than those of the normolipidemic group (11.7, 16.2, 18.0 and 33.3%; χ2 = 0.085, 0.293, 0.004, and 2.601, respectively; Ρ > 0.05). However, the rates of HBeAg seroconversion in the dyslipidemia group were significantly lower than those in the normolipidemic group at year 5 (27.9% vs. 43.2%, χ2 = 4.216, Ρ < 0.05). Univariate logistic regression analysis revealed significant differences in group, gender, PTA, ALT, AST, CR, and LDL-C between groups with and without seroconversion. Multivariate regression analysis demonstrated that dyslipidemia (OR = 1.993, Ρ = 0.038) and male gender (OR = 2.317, Ρ = 0.029) were risk factors associated with HBeAg seroconversion.

CONCLUSIONS

During antiviral therapy, dyslipidemia affects HBeAg seroconversion in CHB patients treated with NAs, but does not affect the virological response.