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Wang J, Zhang Z, Zhu L, Zhang Q, Zhang S, Pan Y, Liu J, Cao F, Fan T, Xiong Y, Yin S, Yan X, Chen Y, Zhu C, Li J, Liu X, Wu C, Huang R. Association of hepatitis B core antibody level and hepatitis B surface antigen clearance in HBeAg-negative patients with chronic hepatitis B. Virulence 2024; 15:2404965. [PMID: 39317345 PMCID: PMC11423664 DOI: 10.1080/21505594.2024.2404965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 08/15/2024] [Accepted: 09/05/2024] [Indexed: 09/26/2024] Open
Abstract
Predicting hepatitis B surface antigen (HBsAg) clearance is important for chronic hepatitis B (CHB) patients receiving pegylated interferon-alfa (Peg-IFN) therapy. We aimed to determine the predictive value of serum hepatitis B core antibody (anti-HBc) for HBsAg clearance. A total of 189 HBeAg-negative CHB patients who received Peg-IFN based therapy were retrospectively included and classified into two groups: nucleos(t)ide analogues (NAs) add-on Peg-IFN group (add-on group, n = 94) and Peg-IFN combined with NAs or Peg-IFN monotherapy group (combination or monotherapy group, n = 95). After 48 weeks of treatment, 27.5% (52/189) and 15.9% (30/189) of patients achieved HBsAg clearance and seroconversion, respectively. Patients in the combination or monotherapy group tended to achieve relatively higher HBsAg clearance (31.6% vs. 23.4%, p = 0.208) and seroconversion (21.1% vs. 10.6%, p = 0.050) rates than those in the add-on group. In combination or monotherapy group, anti-HBc levels at week 12 were lower in patients with HBsAg clearance (9.0 S/CO vs. 9.9 S/CO, p < 0.001) and seroconversion (8.8 S/CO vs. 9.8 S/CO, p < 0.001) than those without. Anti-HBc level at week 12 was an independent predictor of HBsAg clearance and seroconversion. Patients with lower anti-HBc levels at week 12 showed a more significant decline in HBsAg levels during treatment. Combination of anti-HBc at week 12 and baseline HBsAg could identify over 70% of patients who achieved HBsAg clearance after 48 weeks of treatment. In addition to HBsAg, anti-HBc level could be used as a promising marker for selecting HBeAg-negative CHB patients who are more likely to respond to Peg-IFN-based therapy.
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Affiliation(s)
- Jian Wang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Zhiyi Zhang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Li Zhu
- Department of Infectious Diseases, The Affiliated Infectious Diseases Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Qing Zhang
- Department of Infectious Diseases, Huai’an No. 4 People’s Hospital, Huai’an, Jiangsu, China
| | - Shaoqiu Zhang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Yifan Pan
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiacheng Liu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Fei Cao
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Tao Fan
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Ye Xiong
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Shengxia Yin
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Xiaomin Yan
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Yuxin Chen
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Chuanwu Zhu
- Department of Infectious Diseases, The Affiliated Infectious Diseases Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Jie Li
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Xingxiang Liu
- Department of Clinical Laboratory, Huai’an No. 4 People’s Hospital, Huai’an, Jiangsu, China
| | - Chao Wu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Rui Huang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
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Cao X, Hu Q, Xu W, Li Q, Zhang J, Chen L, Huang Y, Qi X. Kinetics changes in total cholesterol predict HBeAg seroconversion in chronic hepatitis B patients treated with pegylated interferon-alfa. J Viral Hepat 2023; 30:310-318. [PMID: 36529685 DOI: 10.1111/jvh.13787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/24/2022] [Accepted: 12/10/2022] [Indexed: 01/07/2023]
Abstract
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.
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Affiliation(s)
- Xiongyue Cao
- Department of Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Qiankun Hu
- Department of Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Wei Xu
- Department of Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Qiang Li
- Department of Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jiming Zhang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Liang Chen
- Department of Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Yuxian Huang
- Department of Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Xun Qi
- Department of Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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Shang H, Hu Y, Guo H, Lai R, Fu Y, Xu S, Zeng Y, Xun Z, Liu C, Wu W, Guo J, Ou Q, Chen T. Using machine learning models to predict HBeAg seroconversion in CHB patients receiving pegylated interferon-α monotherapy. J Clin Lab Anal 2022; 36:e24667. [PMID: 36181316 DOI: 10.1002/jcla.24667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 11/11/2022] Open
Abstract
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.
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Affiliation(s)
- Hongyan Shang
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yuhai Hu
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Hongyan Guo
- The School of Public Health, Fujian Medical University, Fuzhou, China
| | - Ruimin Lai
- Department of the Center of Liver Diseases, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ya Fu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Siyi Xu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yongbin Zeng
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Zhen Xun
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Can Liu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Wennan Wu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jianhui Guo
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Qishui Ou
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Tianbin Chen
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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Xia Z, Zheng J, Zheng L, Zheng E, Zou Z, Sheng X, Wu J. Effects of dyslipidemia on E antigen seroconversion of patients with chronic hepatitis B treated by nucleoside (acid) analogs. Lipids Health Dis 2021; 20:148. [PMID: 34717643 PMCID: PMC8557562 DOI: 10.1186/s12944-021-01582-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/18/2021] [Indexed: 11/24/2022] Open
Abstract
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.
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Affiliation(s)
- Ziqiang Xia
- Department of Gastroenterology, Wenzhou People's Hospital, Wenzhou, 325000, China
| | - Juzeng Zheng
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Liang Zheng
- Department of Gastroenterology, Wenzhou People's Hospital, Wenzhou, 325000, China
| | - Endian Zheng
- Department of Gastroenterology, Wenzhou People's Hospital, Wenzhou, 325000, China
| | - Zhuolin Zou
- Department of Infectious Diseases, The First Affiliated Hospital of Jiaxing College, Jiaxing, 314000, China
- Department of Infectious Diseases, The First Hospital of Jiaxing, Jiaxing, 314000, China
| | - Xiong Sheng
- Department of Infectious Diseases, The First Affiliated Hospital of Jiaxing College, Jiaxing, 314000, China.
- Department of Infectious Diseases, The First Hospital of Jiaxing, Jiaxing, 314000, China.
| | - Jinming Wu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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