郭楠, 姚雅萍, 宋乐, 王冠珍, 何俊利, 郑宁, 莫琼, 倪文旭, 张波, 黄磊, 王福生. 新型冠状病毒感染后特异性抗体的动态变化[J]. 解放军医学院学报, 2023, 44(12): 1344-1350. DOI: 10.12435/j.issn.2095-5227.2023.085
引用本文: 郭楠, 姚雅萍, 宋乐, 王冠珍, 何俊利, 郑宁, 莫琼, 倪文旭, 张波, 黄磊, 王福生. 新型冠状病毒感染后特异性抗体的动态变化[J]. 解放军医学院学报, 2023, 44(12): 1344-1350. DOI: 10.12435/j.issn.2095-5227.2023.085
GUO Nan, YAO Yaping, SONG Le, WANG Guanzhen, HE Junli, ZHENG Ning, MO Qiong, NI Wenxu, ZHANG Bo, HUANG Lei, WANG Fusheng. Dynamic changes of specific antibodies after COVID-19 infection[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2023, 44(12): 1344-1350. DOI: 10.12435/j.issn.2095-5227.2023.085
Citation: GUO Nan, YAO Yaping, SONG Le, WANG Guanzhen, HE Junli, ZHENG Ning, MO Qiong, NI Wenxu, ZHANG Bo, HUANG Lei, WANG Fusheng. Dynamic changes of specific antibodies after COVID-19 infection[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2023, 44(12): 1344-1350. DOI: 10.12435/j.issn.2095-5227.2023.085

新型冠状病毒感染后特异性抗体的动态变化

Dynamic changes of specific antibodies after COVID-19 infection

  • 摘要:
    背景 COVID-19仍将长期存在,但患者出院后病毒特异性抗体的长期随访研究较少,且现有研究大多受疫苗接种的影响,因此阐述自然感染状态下病毒特异性抗体水平的动态变化,对于预判抗体保护能力、COVID-19患者再感染风险和指导疫苗注射有重要现实价值。
    目的 本研究旨在通过长期多次随访,描述COVID-19康复者出院后无疫苗干扰的情况下病毒特异性抗体的变化。
    方法 对2020年2月15日 - 4月5日武汉某医院出院的COVID-19康复者进行为期12个月的随访。入组患者在出院后6个月、9个月和12个月留取外周血标本进行新型冠状病毒特异性抗体检测。
    结果 COVID-19患者血清总IgM抗体水平在出院后6个月较出院前显著下降,且检测时已呈阴性(<10 AU/mL)。总IgG抗体水平在出院后6个月也显著下降,在6 ~ 9个月保持相对稳定,在9 ~ 12个月显著下降,但在出院后12个月94.44%的患者仍为阳性(>10 AU/mL)。抗核衣壳蛋白IgG抗体在随访期间呈持续性显著下降,在出院后12个月96.08%的患者仍为阳性;抗刺突蛋白IgG抗体、抗受体结合域IgG抗体、中和抗体在出院后6 ~ 9个月维持较稳定的水平,在9 ~ 12个月显著下降,在出院后12个月阳性率分别为99.02%、80.39%和94.12%。年龄、性别、疾病分型和基础疾病对于出院后抗体水平及其变化趋势无显著影响。
    结论 COVID-19感染后各类特异性抗体的水平及下降趋势不同,在出院后12个月COVID-19康复者体内部分抗体仍保持较高的水平。抗体的水平及下降趋势在不同年龄、性别、疾病严重程度及基础疾病的人群之间无统计学差异。

     

    Abstract:
    Background There are few studies on the long-term follow-up of virus-specific antibodies in COVID-19 survivors after discharge, and most results of the existing studies are influenced by vaccination. Therefore, elaborating the dynamic changes of virus-specific antibody levels in the natural infection state is of great practical value for predicting the antibody protective capacity, the risk of reinfection in COVID-19 patients and guiding vaccination.
    Objective To describe the changes in virus-specific antibodies in COVID-19 survivors discharged from the hospital without vaccine interference through long-term and multiple follow-ups.
    Methods We followed up with COVID-19 patients discharged from a hospital in Wuhan from February 15, 2020, to April 5, 2020, for 12 months. At three follow-up time points of 6, 9, and 12 months after discharge, the enrolled patients retained peripheral blood samples for detection of virus-specific antibodies for COVID-19.
    Results Total IgM antibody levels in COVID-19 patients significantly declined at 6 months post-discharge and tested negative (<10 AU/mL). Total IgG antibody levels likewise dramatically declined at 6 months post-discharge, and they were relatively stable from 6 to 9 months, and significantly decreased from 9 to 12 months, but still tested positive at 12 months post-discharge (>10 AU/mL, 94.44%). Anti-nucleocapsid IgG antibody showed a persistent and considerable decline throughout the follow-up period and remained positive at 12 months post-discharge, with a positive rate of 96.08%. The levels of anti-spike protein IgG antibody, anti-receptor binding domain IgG antibody, and neutralizing antibody remained relatively stable from 6 months to 9 months after discharge, but significantly decreased from 9 months to 12 months, and were positive at 12 months after discharge. Age, gender, clinical classification, and underlying disease had no significant effect on antibody levels and their trends after discharge.
    Conclusion The levels and decreasing trends of various types of specific antibodies after COVID-19 infection are different, with some antibodies remaining at high levels in COVID-19 survivors at 12 months after discharge. There are no significant differences in levels and decreasing trends of antibodies between ages, gender, clinical classification of COVID-19, and underlying disease.

     

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