#5548 THE PROGNOSTIC VALUE OF LYMPHOCYTE SUBSETS IN ANTIBODY RESPONSE AFTER SARS-COV2 VACCINATION IN DIALYSIS PATIENTS AND KIDNEY TRANSPLANT RECIPIENTS
Authors: Mallioras Ioannis , Georgopoulos Christos , Duni Anila , Markopoulos Georgios , Pappas Efthymios , Baxevanos Gerasimos , Gartzonika Konstantina , Christaki Eirini , Milionis Haralampos , Ntounousi Evangelia 
Published: June 2023

Background and Aims

Mortality due to SARS-COV-2 infection in hemodialysis (HD) patients and kidney transplant recipients (KTRs) is high. Despite intensive vaccination programs in these vulnerable populations, the adequacy of the respective generated immune responses is significantly lower than the general population and additional booster doses have been recommended by multiple health systems and the World Health Organization. The humoral and cellular immune responses to SARS-COV-2 vaccination remain still under further study in HD patients and KTRs. The aim of our study was to determine the predictive value of lymphocyte subpopulations in the production of antibodies against SARS-CoV-2 after the second dose of the vaccine.

Method

The cohort of this prospective study (ClinicalTrials.gov, NCT04932876) included 34 HD patients and 54 KTRs who received two doses of the BNT162b2 (Pfizer–BioNTech). Lymphocyte subpopulations, including B cells, CD4+ and CD8+ T cells as well as naïve and memory T lymphocytes subpopulations among others were analyzed by flow cytometry at three time points, before vaccination (T0), before the 2nd dose (T1), 2 weeks after the 2nd dose (T2). Exclusion criteria included previous infection by SARS-CoV2 as well as infection by SARS-CoV2 during study follow-up. The anti-SARS-CoV2 antibody (Ab) response was assessed by using the ARCHITECT IgG II Quant test (Abbott). Titers &gt;50 arbitrary units (AU)/ml were considered positive for seroconversion at T1 and at T2. A multiple linear regression model was applied separately to the two subgroups of patients.

Results

The mean age of the kidney transplanted recipients was 58.5 years of age while the mean age of HD patients was 68.5 years of age. The analysis of KTRs revealed that the populations of CD19+ lymphocytes, CD3+CD16+56+ cells and CD4+CD45RO lymphocytes can predict antibody formation (p-ANOVA&lt;0.001) based on the multiple regression model: Ab = 4869+519*CD19-226*CD3+CD16+56-139* CD4+CD45RO. The analysis of HD patients revealed that the populations of CD19+ lymphocytes, CD45RA+CD45RO lymphocytes, CD4 to CD8 ratio, CD3-CD16+56+ cells and CD4+CD45RO lymphocytes can predict antibody formation (p-ANOVA&lt;0.001) based on the multiple regression model: Ab = 20267+835.3*CD19-286*CD45RA+CD45RO-375.2*CD4+ CD45RO+851*CD4/CD8-187.3*CD3-CD16+56+. The two regression models explain the variation of the dependent variable (Ab), according to the adjusted ${{\rm{R}}^2}$ index, at a rate of 24% and 67% respectively. The 2 models were analyzed for possible residual autocorrelation (DW statistic &gt;DU&gt;DL in both models). No multicollinearity was observed (All VIF&lt; 1). Normality of the residuals and homoscedasticity also met the criteria for both regression models.

Conclusion

Quantification of lymphocyte subpopulations by flow cytometry appears to have a significant prognostic value regarding development of antibodies after vaccination against SARS-CoV-2, especially in KTRs. Of particular interest are the significant differences in lymphocyte populations related to antibody production between HD patients and KTRs, as more sub-populations appear to influence antibody generation in HD patients compared to KTRs. The above models can predict patients' response to vaccination based on specific lymphocyte subpopulations. More studies are needed to validate these predictive models.