Characteristics of peripheral lymphocyte subsets and antibodies in COVID-19-infected kidney transplantation recipients

Background

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.

Methods

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.

Results

Except for the increase of CD4⁺HLA-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× (CD4⁺HLA-DR⁺/ CD4⁺ T cells (%)) + (−0.040) × (CD14⁺HLA-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 CD4⁺HLA-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 CD14⁺HLA-DR⁺ monocytes among CD14⁺ monocytes acted as protective factors. COVID-19 antibodies were negatively correlated with CD8⁺CD45RA⁺CD27^- (Terminally Differentiated Effector Memory T Cells, TEMRA), CD8⁺CD28^-, CD8⁺CD38⁺ and CD4⁺CD38⁺ T cells, while positively correlated with CD8⁺CD45RA^-CD27^- (Effector Memory T cells, T8EM), CD8⁺CD45RA^-CD27⁺ (Central Memory T cells, T8CM) and CD8⁺CD28⁺ T cells.

Conclusion

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.