定量抗 HBc 联合定量 HBsAg 可预测 PEG-IFN-α 序贯联合治疗中 NA

StephenW

定量抗 HBc 联合定量 HBsAg 可预测 PEG-IFN-α 序贯联合治疗中 NA 抑制的慢性乙型肝炎患者的 HBsAg 清除率
王文新 1 2 3 , 贾瑞 2 3 , 高莹莹 2 , 刘家业 2 , 栾君清 2 , 飞乔 2 , 刘丽敏 2 , 张晓宁 2 , 福生王 1 2 3 , 付俊良 1 2 3
隶属关系
隶属关系

    1
    北京大学302临床医学院，北京，中国。
    2
    中国人民解放军总医院第五医学中心传染病高级科，国家传染病临床研究中心，北京，中国。
    3
    中国人民解放军（PLA）医学院，北京，中国。

    PMID：35958609 PMCID：PMC9360425 DOI：10.3389/fimmu.2022.894410

抽象的

背景和目的：慢性乙型肝炎患者在核苷（酸）类似物（NA）和聚乙二醇干扰素-α（PEG-IFN-α）联合治疗下，缺乏准确的乙型肝炎表面抗原（HBsAg）清除预测因子。乙肝）。本研究旨在确定定量抗乙型肝炎核心抗体 (qAnti-HBc) 和定量乙型肝炎核心相关抗原 (qHBcrAg) 作为 NA 抑制的接受 PEG-IFN-α 附加的 CHB 患者 HBsAg 清除的预测因子治疗。

方法：纳入 74 名在 NA 治疗≥1 年后实现 HBV DNA 抑制（HBV DNA < 20 IU/ml）和定量 HBsAg (qHBsAg) < 1,500 IU/ml 的 CHB 患者。 15 名患者继续接受 NA 单药治疗，而 59 名患者接受 PEG-IFN-α 附加治疗。对于附加组和单独使用 NA 组，每 12 周或 24 周分别检测一次血清 qAnti-HBc 和 qHBcrAg 水平。

结果：基线时血清 qAnti-HBc 而不是 qHBcrAg 水平与先前 NA 治疗的持续时间呈负相关。治疗 48 周后，qAnti-HBc 和 qHBcrAg 水平进一步下降，附加组和 NA 组分别有 17/59 (28.81%) 和 0/15 (0%) 达到 HBsAg 清除。在附加组中，基线 qAnti-HBc < 0.1 IU/ml (52.63%) 的患者 HBsAg 清除率明显更高。逻辑回归分析确定了基线 qAnti-HBc 但未确定 qHBcrAg，后者是 HBsAg 消失的独立预测因子。接受者操作特征曲线分析表明，qAnti-HBc 和 qHBsAg 的组合对 HBsAg 清除具有更好的预测价值。

结论：qHBsAg 和基线 qAnti-HBc 水平的组合可能是更好的预测 NA 抑制 CHB 患者接受 PEG-IFN-α 附加治疗的 HBsAg 清除的策略。

关键词：HBcrAg； HBsAg 清除； PEG-IFN-α；抗-HBc；慢性乙型肝炎；联合治疗。

版权所有 © 2022 王嘉高刘、栾、乔刘、张、王、付。

StephenW

Quantitative anti-HBc combined with quantitative HBsAg can predict HBsAg clearance in sequential combination therapy with PEG-IFN-α in NA-suppressed chronic hepatitis B patients
Wen-Xin Wang  1   2   3 , Rui Jia  2   3 , Ying-Ying Gao  2 , Jia-Ye Liu  2 , Jun-Qing Luan  2 , Fei Qiao  2 , Li-Min Liu  2 , Xiao-Ning Zhang  2 , Fu-Sheng Wang  1   2   3 , Junliang Fu  1   2   3
Affiliations
Affiliations

    1
    Peking University 302 Clinical Medical School, Beijing, China.
    2
    Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese People's Liberation Army (PLA) General Hospital, National Clinical Research Center for Infectious Diseases, Beijing, China.
    3
    Medical School of Chinese People's Liberation Army (PLA), Beijing, China.

    PMID: 35958609 PMCID: PMC9360425 DOI: 10.3389/fimmu.2022.894410

Abstract

Background and aims: Precise predictors are lacking for hepatitis B surface antigen (HBsAg) clearance under the combination therapy of nucleos(t)ide analogs (NA) and pegylated interferon-alpha (PEG-IFN-α) in patients with chronic hepatitis B (CHB). This study aimed to determine the quantitative anti-hepatitis B core antibody (qAnti-HBc) and quantitative hepatitis B core-related antigen (qHBcrAg) as predictors for HBsAg clearance in NA-suppressed patients with CHB receiving PEG-IFN-α add-on therapy.

Methods: Seventy-four CHB patients who achieved HBV DNA suppression (HBV DNA < 20 IU/ml) and quantitative HBsAg (qHBsAg) < 1,500 IU/ml after ≥1 year of NA treatment were enrolled. Fifteen patients continued on NA monotherapy, while 59 patients received PEG-IFN-α add-on therapy. Serum qAnti-HBc and qHBcrAg levels were detected every 12 or 24 weeks for add-on and NA-alone groups, respectively.

Results: Serum qAnti-HBc but not qHBcrAg levels at baseline were negatively correlated with the duration of prior NA therapy. After 48-week treatment, both qAnti-HBc and qHBcrAg levels declined further, and 17/59 (28.81%) and 0/15 (0%) achieved HBsAg clearance in add-on and NA groups, respectively. In the add-on group, the rate of HBsAg clearance was significantly higher in patients with baseline qAnti-HBc < 0.1 IU/ml (52.63%). Logistic regression analysis identified baseline qAnti-HBc but not qHBcrAg, which was an independent predictor for HBsAg loss. Receiver operating characteristic curve analysis showed that the combination of qAnti-HBc and qHBsAg had a better predictive value for HBsAg clearance.

Conclusions: A combination of qHBsAg and baseline qAnti-HBc levels may be a better prediction strategy for HBsAg clearance in NA-suppressed CHB patients receiving PEG-IFN-α add-on therapy.

Keywords: HBcrAg; HBsAg clearance; PEG-IFN-α; anti-HBc; chronic hepatitis B; combined treatment.

Copyright © 2022 Wang, Jia, Gao, Liu, Luan, Qiao, Liu, Zhang, Wang and Fu.

StephenW

https://www.frontiersin.org/arti ... mmu.2022.894410/pdf

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