Hidaka Yu, Jo Norihide, Kikuchi Osamu, Fukahori Masaru, Sawada Takeshi, Shimazu Yutaka, Yamamoto Masaki, Kometani Kohei, Nagao Miki, Nakajima Takako E, Muto Manabu, Morita Satoshi, Hamazaki Yoko
Department of Biomedical Statistics and Bioinformatics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Department of Life Science Frontiers, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.
Int Immunol. 2025 Jun 17;37(7):403-416. doi: 10.1093/intimm/dxaf013.
Despite the high effectiveness of the coronavirus disease 2019 (COVID-19) mRNA vaccines, both immunogenicity and reactogenicity show substantial interindividual variability. One key challenge is predicting high and low responders using easily measurable parameters. In this study, we performed multivariate linear regression analysis, which allows adjustment for confounding, to explore independent predictive factors for antibody responses. Using data from 216 healthy vaccinated donors aged 23-81 years, we evaluated baseline characteristics, prevaccination blood and T-cell phenotypes, and post-vaccination T-cell responses as variables, with anti-receptor-binding domain (RBD) immunoglobulin G (IgG) titers following two doses of BNT162b2 vaccination as the primary outcome. Consistent with previous reports, higher age, a history of allergic disease, and autoimmune disease were associated with lower peak IgG titers. Additionally, the frequencies of interferon-γ+ spike-specific CD4+ T cells (T-cell response) following the first vaccination strongly correlated with higher IgG responses, while those of pre-existing spike-reactive T cells showed no association with peak IgG titers. Furthermore, we identified lower percentages of naïve CD8+ T cells, lower hemoglobin levels, lower lymphocyte counts, and higher mean corpuscular volume as independent pre-vaccination predictors of lower peak IgG levels. Notably, the frequency of naïve CD8+ T cells showed a positive correlation with the peak IgG levels even in univariate analysis. These findings contribute to the individualized prediction of mRNA vaccine efficacy and may provide insights into the mechanisms underlying individual heterogeneity in immune responses.
尽管2019冠状病毒病(COVID-19)mRNA疫苗具有高效性,但免疫原性和反应原性均表现出显著的个体间差异。一个关键挑战是使用易于测量的参数来预测高反应者和低反应者。在本研究中,我们进行了多变量线性回归分析(该分析允许对混杂因素进行调整),以探索抗体反应的独立预测因素。我们使用了来自216名年龄在23至81岁之间的健康接种者的数据,将基线特征、接种前血液和T细胞表型以及接种后T细胞反应作为变量,以两剂BNT162b2疫苗接种后的抗受体结合域(RBD)免疫球蛋白G(IgG)滴度作为主要结果。与之前的报告一致,年龄较大、有过敏疾病史和自身免疫疾病史与较低的IgG滴度峰值相关。此外,首次接种后干扰素-γ+刺突特异性CD4+T细胞的频率(T细胞反应)与较高的IgG反应密切相关,而预先存在的刺突反应性T细胞的频率与IgG滴度峰值无关。此外,我们确定了较低比例的初始CD8+T细胞、较低的血红蛋白水平、较低的淋巴细胞计数以及较高的平均红细胞体积是接种前IgG峰值水平较低的独立预测因素。值得注意的是,即使在单变量分析中,初始CD8+T细胞的频率与IgG峰值水平也呈正相关。这些发现有助于对mRNA疫苗疗效进行个体化预测,并可能为免疫反应中个体异质性的潜在机制提供见解。