Factors associated with humoral response after BNT162b2 mRNA COVID-19 vaccination in kidney transplant patients

Abstract

Kidney transplant recipients (KTR) are at increased risk of severe coronavirus disease 2019 (COVID-19) infection [1]. Although these patients are obviously at high-priority for vaccination, there are some concerns concerning the efficacy of vaccines. Recent reports confirmed reduced seroresponse of KTR to COVID-19 vaccine [2]. Less than 50% of KTR exhibit antibody positivity after two injections of vaccine [2]. Nevertheless, factors associated with poor response are not well described. We studied 153 KTR COVID-19 naïve (no clinical history, negative serology) KTR. All the patients received two doses of the BNT162b2 mRNA COVID-19 vaccine. Humoral response [SARS-Cov-2 immunoassay, Abbott® designed to detect IgG antibodies to the receptor-binding domain (RBD) of the S1 subunit of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] and factors associated with this response were studied. As a relevant threshold cannot be determined, we used that provided by the manufacturer, and one more empirically defined by a value above 660 UA/mL (upper quartile of antibody titers). We also analysed a subgroup of patients without any detectable response after vaccine. Among 153 patients, 81 (53%) had an antibody titer ≥50 UA/mL and 38 (25%) >660 UA/mL (high responders), mean ± SD 2.5 ± 1.2 months after the second dose. Among 72 patients (47%) with an antibody titer <50 UA/mL, 32 (21%) had no detectable antibodies. Age and gender did not differ between responders and non-responders (Table 1). Transplant duration was significantly longer in responders [median (interquartile range) 158 (60–194) versus 28 (15–84) months, P < 0.001]. Clinical and biological characteristics of responders and non-responders patients (thresholds 50 UA/mL, 2.5 ± 1.2 months after the second dose) For continuous variables, median and interquartiles 25–75. aPer mm3. Clinical and biological characteristics of responders and non-responders patients (thresholds 50 UA/mL, 2.5 ± 1.2 months after the second dose) For continuous variables, median and interquartiles 25–75. aPer mm3. Estimated glomerular filtration rate (eGFR) was lower in non-responders [48 (32–58) versus 62 (38–81) mL/min/1.73 m2, P < 0.001]. Antibody titers were strongly related to eGFR (r = 0.314, P < 0.001). Seventy-four percent of patients with satisfactory renal function (eGFR >60 mL/min/1.73 m2) achieved significant humoral response, whereas only 37% of those with either intermediate (eGFR 30–60 mL/min/1.73 m2) or low (eGFR <30 mL/min/1.73 m2) renal function did. Non-responders were more frequently under mycophenolic acid (MPA) (76% versus 32%, P < 0.001), whereas responders were more likely to receive mTOR inhibitors (mTORi) (36% versus 6%, P < 0.001). There was a trend towards fewer use of belatacept in responders (0% versus 6%, P = 0.047). Calcineurin inhibitors (CNIs) use was similar in responders and non-responders (Table 1). CD3, CD4 and CD8 counts were lower in non-responders (Table 1). Using multiple logistic regression, shorter transplant duration (P = 0.003), lower eGFR (P < 0.001), MPA use (P < 0.001) and lower CD4 count (P = 0.016) were all associated with lack of response to vaccine (Table 2). This model strongly predicts vaccine response [area under the curve 0.89 (0.82–0.93), P < 0.001] (Figure 1). Receiver operating characteristic curve illustrating the ability of a multiple logistic regression model predicting response to COVID-19 vaccine (thresholds 50 UA/mL, 2.5 ± 1.2 months after the second dose). AUC, area under the curve. Factors associated with response to BNT162b2 mRNA COVID-19 vaccine (logistic regression) (thresholds 50 UA/mL, 2.5 ± 1.2 months after the second dose) CD3, CD4 and CD8 cell counts were closely related. Trivariate stepwise analysis including the three T lymphocytes subsets only retains CD4 cell count as associated with vaccine response. As a consequence, only CD4 cell count was maintained in the model. a/50 mm3. OR, odds ratio; CI, confidence interval; NK, natural killer. Factors associated with response to BNT162b2 mRNA COVID-19 vaccine (logistic regression) (thresholds 50 UA/mL, 2.5 ± 1.2 months after the second dose) CD3, CD4 and CD8 cell counts were closely related. Trivariate stepwise analysis including the three T lymphocytes subsets only retains CD4 cell count as associated with vaccine response. As a consequence, only CD4 cell count was maintained in the model. a/50 mm3. OR, odds ratio; CI, confidence interval; NK, natural killer. Complete absence of response to vaccine was associated with low CD4 count (P = 0.049) and reduced eGFR (P = 0.006). Robust vaccine response was associated with better eGFR (P < 0.001), longer transplant duration (P < 0.001) and no MPA use (<0.001). We report in a single-centre study that only half of KTR have a vaccine response after two doses of BNT162b2 mRNA COVID-19 vaccine. We observed that some factors are strongly associated with vaccine response. Patients receiving MPA had a lower rate of vaccine response. MPA inhibits both T- and B-cell proliferation. Previous studies reported similar results [3, 4]. Reduced response to other vaccines has been also reported in patients under MPA when compared with those receiving CNI [5–7]. Reduced renal function diminishes the likelihood of achieving seroprotection after complete vaccine. Similar results have been observed for H1N1 vaccination [7]. The mechanisms are not very clear, since significant humoral response is reported in about 90% of dialysis patients [8]. Low CD4 T-cell counts were associated with reduced response to vaccine. Post-transplant low CD4 counts indicate accelerated immune senescence and are associated with increased risk of cancer and infection [9]. We showed that four risk factors explain 90% of response likelihood to BNT162b2 mRNA COVID-19 vaccine in KTR. These factors are not modifiable except for MPA use. Better strategies of vaccination as well as reinforced barrier measures are required in KTR. None declared.

Topics & Concepts