细胞因子失调在危重症COVID-19患者中的预后意义
Prognostic significance of cytokine dysregulation in critically ill COVID-19 patients.
作者信息
Lemos Fabian Fellipe Bueno, Lopes Luana Weber, Brito Gabriel Carvalho, Viana Airton Idalecio Sousa, de Castro Caroline Tianeze, Luz Marcel Silva, Gonçalves André Pereira, Dórea Rafael Santos Dantas Miranda, da Silva Filipe Antônio França, de Brito Breno Bittencourt, Santos Maria Luísa Cordeiro, Júnior Geovani Moreno Santos, de Lorenzo Barcia Maria Teresa Araújo, de Amorim Marques Renata, Botelho André Bezerra, Dantas Anna Carolina Saúde, Pinheiro Fillipe Dantas, Teixeira Adriano Fernandes, Souza Cláudio Lima, Oliveira Márcio Vasconcelos, de Magalhães Queiroz Dulciene Maria, de Melo Fabrício Freire
机构信息
Multidisciplinary Health Institute, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil.
Collective Health Institute, Federal University of Bahia, Salvador 40110-040, Bahia, Brazil.
出版信息
Cytokine. 2025 Mar;187:156867. doi: 10.1016/j.cyto.2025.156867. Epub 2025 Jan 27.
BACKGROUND
Understanding the immunopathogenesis of COVID-19 has yielded valuable insights into predicting adverse outcomes-particularly mortality. However, significant gaps persist in our comprehension of the complex interplay among the proposed pathophysiological mechanisms. Here, we aim to investigate the immunological factors associated with mortality in critically ill, unvaccinated COVID-19 patients admitted to the intensive care unit (ICU).
METHODS
We conducted a single-center, prospective study involving 56 unvaccinated COVID-19 patients admitted to the ICU. Plasma cytokine levels at admission were quantified using enzyme-linked immunosorbent assay (ELISA). Continuous variables were presented as median (IQR), and categorical variables as frequencies and percentages. Non-parametric tests assessed group differences. Logistic regression and receiver operating characteristic (ROC) curve analyses identified predictors of mortality, with bootstrapping (1000 re-samplings; 95 % BCa CI) applied for model validation.
RESULTS
Deceased patients exhibited significantly higher levels of interleukin (IL)-1β, IL-2, IL-6, transforming growth factor (TGF)-β, and interferon (IFN)-γ compared to survivors. Conversely, IL-10 and IL-27 were associated with favorable outcomes. Logistic regression modeling identified elevated IL-2 and IFN-γ levels as significant predictors of mortality. Notably, individual ROC curve analyses demonstrated that IL-1β and TGF-β had excellent discriminatory ability for mortality, while IFN-γ, IL-2, and IL-27 showed very good to excellent discriminatory capacity.
CONCLUSION
Our results indicate that distinct cytokine profiles differentiate survivors from non-survivors in critically ill, unvaccinated COVID-19 patients. These findings highlight the importance of cytokine dysregulation in severe COVID-19 cases and suggest potential targets for prognostic approaches. Further research is warranted to validate these results and translate them into effective clinical management strategies.
背景
了解新型冠状病毒肺炎（COVID-19）的免疫发病机制有助于深入预测不良结局，尤其是死亡率。然而，我们对所提出的病理生理机制之间复杂相互作用的理解仍存在重大差距。在此，我们旨在研究入住重症监护病房（ICU）的未接种疫苗的重症COVID-19患者中与死亡率相关的免疫因素。
方法
我们开展了一项单中心前瞻性研究，纳入了56例入住ICU的未接种疫苗的COVID-19患者。入院时使用酶联免疫吸附测定（ELISA）对血浆细胞因子水平进行定量。连续变量以中位数（四分位间距）表示，分类变量以频率和百分比表示。采用非参数检验评估组间差异。通过逻辑回归和受试者工作特征（ROC）曲线分析确定死亡率的预测因素，并应用自抽样法（1000次重新抽样；95%偏差校正置信区间）进行模型验证。
结果
与幸存者相比，死亡患者的白细胞介素（IL）-1β、IL-2、IL-6、转化生长因子（TGF）-β和干扰素（IFN）-γ水平显著更高。相反，IL-10和IL-27与良好结局相关。逻辑回归模型确定IL-2和IFN-γ水平升高是死亡率的显著预测因素。值得注意的是，个体ROC曲线分析表明，IL-1β和TGF-β对死亡率具有出色的鉴别能力，而IFN-γ、IL-2和IL-27显示出非常好至出色的鉴别能力。
结论
我们的结果表明，在未接种疫苗的重症COVID-19患者中，不同的细胞因子谱可区分幸存者和非幸存者。这些发现凸显了细胞因子失调在重症COVID-19病例中的重要性，并提示了预后方法的潜在靶点。有必要进一步开展研究以验证这些结果并将其转化为有效的临床管理策略。
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