Long Honghui, Tai Yunze, Fan Jiwen, Ou Xiaoqi, Yan Lin, Fan Yu, Feng Weihua, Chen Jie, Li Yi
Department of Transfusion Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.
Int Immunopharmacol. 2025 Jan 3;145:113755. doi: 10.1016/j.intimp.2024.113755. Epub 2024 Dec 12.
Peripheral lymphocyte subsets play vital roles in various disease conditions. However, the roles of kidney transplant recipients (KTRs) with novel coronavirus pneumonia (COVID-19) are still unclear. In this research, we investigated the predictive value of peripheral blood lymphocyte subsets on the severity of KTRs with COVID-19 and the correlation between antibodies and lymphocyte levels.
84 patients with kidney transplantation combined with COVID-19 admitted from December 2022 to February 2023 were included. On the basis of the severity of COVID-19, they were categorized into a mild (n = 49) and a severe group (n = 35). The logistic regression method was utilized to build the critical risk prediction model for KTRs complicated with COVID-19. The receiver operator characteristic curve (ROC), calibration plot and clinical decision curve analysis (DCA) were applied to assess the discrimination, calibration and clinical application value of this model. The Spearman correlation test was applied to examine the connection between antibodies and various immune indexes.
Except for the increase of CD4HLA-DR T cells, the number of CD3, CD4, and CD8 T cell subsets in severe was lower than that in mild (P < 0.05). IL-6 in severe was higher than mild (P < 0.05). After screening variables, we established a regression equation prediction model, logit (P) = 4.965+ (-0.038) × (CD3/lymphocytes (%)) + 0.064× (CD4HLA-DR/ CD4 T cells (%)) + (-0.040) × (CD14HLA-DR/monocytes (%)). The area under the ROC curve (AUC) of the prediction model was 0.779 (95 % CI 0.679-0.879, P = 0.001). The cut-off value was 0.308, with a prediction sensitivity of 0.829 (95 % CI 0.657-0.928) and a specificity of 0.653 (95 % CI 0.503-0.779). Logistic regression analysis showed the increase in the percentage of CD4HLA-DR T cells among CD4 T cells was a risk factor for COVID-19 severity among kidney transplant recipients, while the increase in the percentage of CD3 T cells among lymphocytes and CD14HLA-DR monocytes among CD14 monocytes acted as protective factors. COVID-19 antibodies were negatively correlated with CD8CD45RACD27 (Terminally Differentiated Effector Memory T Cells, TEMRA), CD8CD28, CD8CD38 and CD4CD38 T cells, while positively correlated with CD8CD45RACD27 (Effector Memory T cells, T8EM), CD8CD45RACD27 (Central Memory T cells, T8CM) and CD8CD28 T cells.
A predictive model was developed and validated for predicting the severe form of COVID-19 in KTRs. The model showed good predictive ability, concordance, and potential clinical utility.
外周血淋巴细胞亚群在多种疾病状态中发挥着重要作用。然而，新型冠状病毒肺炎（COVID-19）肾移植受者（KTRs）的情况仍不清楚。在本研究中，我们调查了外周血淋巴细胞亚群对COVID-19肾移植受者病情严重程度的预测价值以及抗体与淋巴细胞水平之间的相关性。
纳入2022年12月至2023年2月收治的84例肾移植合并COVID-19患者。根据COVID-19的严重程度，将他们分为轻症组（n = 49）和重症组（n = 35）。采用逻辑回归方法建立COVID-19肾移植受者的重症风险预测模型。应用受试者工作特征曲线（ROC）、校准图和临床决策曲线分析（DCA）来评估该模型的区分度、校准度和临床应用价值。采用Spearman相关性检验来检验抗体与各种免疫指标之间的联系。
除CD4HLA-DR T细胞数量增加外，重症组CD3、CD4和CD8 T细胞亚群数量均低于轻症组（P < 0.05）。重症组IL-6高于轻症组（P < 0.05）。筛选变量后，我们建立了回归方程预测模型，logit（P）= 4.965 + （-0.038）×（CD3/淋巴细胞（%）） + 0.064×（CD4HLA-DR/CD4 T细胞（%）） + （-0.040）×（CD14HLA-DR/单核细胞（%））。预测模型的ROC曲线下面积（AUC）为0.779（95%CI 0.679 - 0.879，P = 0.001）。截断值为0.308，预测敏感性为0.829（95%CI 0.657 - 0.928），特异性为0.653（95%CI 0.503 - 0.779）。逻辑回归分析显示，CD4 T细胞中CD4HLA-DR T细胞百分比增加是肾移植受者COVID-19病情严重程度的危险因素，而淋巴细胞中CD3 T细胞百分比增加以及CD14单核细胞中CD14HLA-DR单核细胞百分比增加则为保护因素。COVID-19抗体与CD8CD45RACD27（终末分化效应记忆T细胞，TEMRA）、CD8CD28、CD8CD38和CD4CD38 T细胞呈负相关，而与CD8CD45RACD27（效应记忆T细胞，T8EM）、CD8CD45RACD27（中央记忆T细胞，T8CM）和CD8CD28 T细胞呈正相关。
建立并验证了一个预测模型，用于预测肾移植受者COVID-19的重症形式。该模型显示出良好的预测能力、一致性和潜在的临床应用价值。
