Do mRNA vaccines accelerate the progression of chronic kidney disease? For six years experience

Do mRNA vaccines accelerate the progression of chronic kidney disease? For six years experience | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Do mRNA vaccines accelerate the progression of chronic kidney disease? For six years experience Türker EMRE, Fatoş METE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8489731/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: During the COVID-19 pandemic, mRNA vaccines which developed using a novel technology distinct from conventional vaccines, were widely administered. They are known to induce more pronounced immune reactions compared to classical vaccines. We investigated whether mRNA and inactivated vaccines accelerate the progression of chronic kidney disease. Methods: A total of 140 patients with CKD followed at Bursa City Hospital were retrospectively analyzed. Serum creatinine and glomerular filtration rate (GFR) values were recorded over three periods: pre-vaccination (−24 months), vaccination period, and post-vaccination (+24 months). GFR was calculated using the CKD-EPI formula. Due to the dependent nature of pre- and post-vaccination comparisons, the paired t -test was used for parametric data and the Wilcoxon signed-rank test for non-parametric data. Statistical significance was set at p < 0.05. Results: In the overall patient group and in the subgroup receiving only inactivated vaccines, a statistically significant difference in serum creatinine levels was observed between the pre- and post-vaccination periods; however, no statistically significant change was detected in GFR. In the subgroup of patients who received only the BioNTech (mRNA) vaccine, no statistically significant changes were observed in either serum creatinine or GFR. Conclusion: In our study, we concluded that mRNA and inactivated vaccines did not accelerate the progression of chronic kidney disease. Internal Medicine Urology & Nephrology mRNA vaccines COVID-19 vaccines chronic kidney disease Figures Figure 1 Introduction In chronic kidney disease (CKD), prevention and treatment of the underlying etiologic disease, slowing or halting disease progression, and preservation of residual renal function are among the primary therapeutic goals. [ 1 ] Viral infections may directly cause renal failure; additionally, through various immune and inflammatory pathways, they can trigger the activation of kidney diseases and contribute to the progression of CKD. [ 2 ] The COVID-19 pandemic, which emerged at the end of 2019, caused severe macrophage activation syndrome (MAS) [ 3 ] and led to high morbidity and mortality, particularly among individuals with chronic diseases. [ 4 ] In addition, its impact on the progression of chronic kidney disease is of significant clinical importance. The rapid global spread and high mortality rate of the COVID-19 pandemic prompted the exploration of accelerated vaccine development strategies, leading to the development of novel messenger RNA (mRNA) vaccines. Because vaccines produced using conventional methods require longer production times, mRNA vaccines were introduced into clinical use earlier. When it was reported that mRNA vaccines, similar to viral infections, can stimulate the immune system in the post-administration period and induce the production of proinflammatory cytokines, thereby leading to various complications, concerns regarding vaccine safety were raised. [ 5 ] Studies in the literature have predominantly focused on whether they cause nephrotoxicity and acute kidney injury. However, in order to better elucidate the potential long-term effects, we investigated whether mRNA and inactivated vaccines accelerate the progression of chronic kidney disease. Method Between 2019 and 2024, a retrospective study was designed to investigate the impact of COVID-19 vaccines on disease progression in patients with chronic kidney disease (CKD) who were followed at the Nephrology Clinic of XXXXX Hospital . In our country, BioNTech (mRNA vaccine), Sinovac (inactivated vaccine), and Turkovac (inactivated vaccine) were administered. A total of 140 patients were deemed eligible for inclusion in the study. Mean serum creatinine and mean glomerular filtration rate (GFR) values were evaluated across three periods: Pre-vaccination period (2019–2020) Vaccination period (2021–2022) Post-vaccination period (2023–2024) Serum creatinine and GFR measurements were compared longitudinally to assess changes in renal function over time. (Study Design depicted Figure-1.) Patients were enrolled in the study according to predefined inclusion and exclusion criteria. Inclusion criteria: Age between 18 and 80 years Regular follow-up for at least two years both before and after vaccination Baseline pre-vaccination GFR between 30 and 90 mL/min/1.73 m² Receipt of at least one dose of a COVID-19 vaccine (BioNTech, Sinovac, or Turkovac) Exclusion criteria: History of acute kidney injury History of dialysis treatment or kidney transplantation History of COVID-19–related complications requiring hospitalization Use of immunosuppressive medications Data Collection Process Data were retrospectively obtained from the hospital information system (HIS), covering a period of two years prior to vaccination, the year of vaccination, and at least two years following vaccination. Renal function was assessed using serum creatinine levels and glomerular filtration rate (GFR) parameters. Only patients with regular follow-up visits, defined as visits occurring at intervals of no longer than six months, were included in the analysis. GFR calculations were performed using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) formula available in the hospital information system. GFR Calculation Formula GFR = 141 × min(Scr/κ, 1)^α × max(Scr/κ, 1)^−1.209 × 0.993^(Age) × Sex factor Where: Scr: Serum creatinine (mg/dL) κ (kappa): 0.9 for males and 0.7 for females α (alpha): −0.411 for males and − 0.329 for females Sex factor: 1.018 for females and 1.0 for males This formula corresponds to the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation used for estimating glomerular filtration rate. To compare renal function progression before and after vaccination, serum creatinine and GFR values were identified and recorded for each patient during the pre-vaccination period, vaccination period, and post-vaccination period. Mean values were calculated using descriptive statistics. For each patient, the mean serum creatinine and GFR values obtained in the pre- and post-vaccination periods were subtracted from the mean values of the vaccination period to calculate difference values. a. Pre-vaccination Difference in Creatinine: Mean value during the vaccination period − mean value during the pre-vaccination period b. Post-vaccination Difference in Creatinine: Mean value during the post-vaccination period − mean value during the vaccination period c. Pre-vaccination Difference in GFR: Mean value during the pre-vaccination period − mean value during the vaccination period d. Post-vaccination Difference in GFR: Mean value during the vaccination period − mean value during the post-vaccination period Comparisons of pre- and post-vaccination mean serum creatinine and GFR values were performed for the overall cohort, the BioNTech group, and the inactivated vaccine group. Statistical analyses were conducted using SPSS version 26.0. The normality of data distribution was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Because the groups consisted of dependent (paired) observations, the paired-samples t-test was used for normally distributed variables, while the Wilcoxon signed-rank test was applied for non-normally distributed variables. A p-value < 0.05 was considered statistically significant. The study was approved by the XXXX Hospital Scientific Research Ethics Committee (Approval No: 2024-20/2; Date of Approval: November 27, 2024). Results A total of 140 patients with chronic kidney disease included in this study were evaluated with respect to renal function parameters during the pre-vaccination, vaccination, and post-vaccination periods. The mean age of the patients was 63.6 ± 11.2 years; 55% were male (n = 77) and 45% were female (n = 63). Among the participants, 38 patients received only the BioNTech vaccine, 25 patients received only a conventional (inactivated) vaccine, and 77 patients received both vaccine types, resulting in a total study population of 140 patients. Of those included in the study, 115 patients had received at least one dose of the BioNTech vaccine, 102 patients had received at least one dose of Sinovac, and 6 patients had received at least one dose of Turkovac. Because the number of patients vaccinated with Turkovac was small and it is an inactivated vaccine similar to Sinovac, these two vaccines were evaluated together under the same category. Accordingly, the numbers of patients receiving mRNA vaccines and inactivated vaccines were comparable. ( Table-1. Baseline Characteristics) For each patient, the mean serum creatinine and GFR values obtained during the pre-vaccination, vaccination, and post-vaccination periods were calculated separately (Table 2). Subsequently, using the vaccination-period mean values as the reference, the mean differences for the pre-vaccination period and the mean differences for the post-vaccination period were calculated for both serum creatinine and GFR (Table 3). The distribution of data for the 140 patients was found to be non-normal. Therefore, a nonparametric test (Wilcoxon signed-rank test) was applied to compare pre- and post-vaccination serum creatinine and GFR values for all patients (Table 4). In the overall cohort, the change in serum creatinine between the pre- and post-vaccination periods was statistically significant (p = 0.014), whereas the change in GFR was not statistically significant (p = 0.99). For the 38 patients who received only the BioNTech vaccine, a normality test was first performed. As the data were considered to be normally distributed, a parametric test (paired-samples t-test) was applied (Tables 5 and 6). According to the results of the paired-samples t-test, no statistically significant changes were observed in either serum creatinine or GFR between the pre- and post-vaccination periods among patients who received the BioNTech vaccine (p = 0.11 and p = 0.77, respectively). For the 25 patients who received only inactivated vaccines, normality testing was performed. Because the distribution of pre- and post-vaccination serum creatinine values was non-normal, the Wilcoxon signed-rank test was applied (Table 7). In contrast, the distribution of pre- and post-vaccination GFR values was normal; therefore, a parametric paired-samples t-test was used (Table 8). According to the results of the Wilcoxon signed-rank test, among patients who received inactivated vaccines, the change in serum creatinine between the pre- and post-vaccination periods was found to be statistically significant (p = 0.007) (Table 7). However, in the comparison of pre- and post-vaccination GFR values, no statistically significant change was observed (p = 0.23) (Table 8). In summary, a statistically significant difference in serum creatinine was observed in the overall cohort and in the inactivated vaccine group when comparing pre- and post-vaccination values, whereas no statistically significant change in GFR was detected. In the group of patients who received only the BioNTech vaccine, no statistically significant changes were identified in either serum creatinine or GFR. Discussion The results were similar in the overall cohort and the inactivated vaccine group. In both groups, a statistically significant difference was observed in the comparison of mean serum creatinine levels between the pre- and post-vaccination periods, whereas no statistically significant difference was detected in mean GFR values. To help explain this finding, a graph illustrating the relationship between GFR and serum creatinine may be informative. Specifically, serum creatinine levels increase only minimally until they reach the upper limit of the normal range, while the slope representing the decline in GFR is relatively steep during this phase. For this reason, it is well recognized that biomarkers other than serum creatinine have been investigated to better reflect renal impairment. As renal dysfunction progresses, serum creatinine increases proportionally much more compared with its previous values, whereas the GFR decline curve tends to follow a more horizontal trajectory. The observation that changes in serum creatinine reached statistical significance while changes in GFR did not may be explained by this nonlinear relationship On the other hand, in the BioNTech-only group, the absence of statistically significant differences in both mean serum creatinine and mean GFR when comparing the pre-vaccination differences with the post-vaccination differences helps to alleviate concerns regarding the potential renal effects of BioNTech vaccines. The findings reported in the literature are consistent with those of our study. Wang et al. [ 6 ] reported that COVID-19 vaccination did not adversely affect renal function in individuals with a history of glomerular disease and that pre- and post-vaccination GFR slopes were similar. Chen et al. [ 7 ] likewise reported that although a mild decline in GFR may be observed in the short term, this difference disappears during long-term follow-up. These findings are in line with the GFR stability observed in our study. Additionally, Nagatsuji et al. [ 8 ] reported transient fluctuations in renal function following vaccination in patients with IgA nephropathy, while Li et al. [ 9 ] similarly demonstrated that renal events occurring after COVID-19 vaccination are generally associated with a favorable prognosis and are reversible. Baskoro and Pranawa [ 5 ] reported that mRNA vaccines induce a proinflammatory cytokine response through Toll-like receptor (TLR) activation, which may lead to transient changes in renal function in some susceptible patients. In addition, Pethő et al. [ 10 ] emphasized that mRNA vaccines may rarely trigger autoimmune glomerulonephritis following immune activation; however, these effects are mostly transient and respond well to treatment. Zhang et al. [ 11 ] reported that COVID-19 vaccination preserves renal function and reduces mortality in patients undergoing hemodialysis, while Ma et al. [ 12 ] demonstrated a high level of vaccine safety. However, some studies have reported modest changes following vaccination: Chuang et al. [ 13 ] observed a small but statistically significant increase in GFR after vaccination, whereas Sun et al. [ 14 ] noted a transient increase in proteinuria levels. Overall, there is strong consensus in the literature that COVID-19 vaccines do not cause severe or permanent deterioration of renal function in patients with chronic kidney disease; on the contrary, renal stability is generally maintained [ 6 – 9 , 11 , 13 , 14 ]. In our study, rather than investigating whether it causes harm, we examined whether it accelerates the progression of the disease. Although the aim of our study is different, the findings reported in the literature support our results. The differing results reported in some studies may be explained by variations in patient characteristics, comorbidity burden, and duration of follow-up. A major strength of our study is that each patient served as their own control, allowing for a comparison of pre- and post-vaccination data within the same individuals. Although retrospective in design, the study includes a relatively long follow-up period of approximately 5–6 years, which enhances the robustness of the findings. The limitations of this study include the retrospective design, which precludes the establishment of a direct causal relationship; the lack of data on proteinuria, inflammatory markers, and immune response parameters; and the absence of post-vaccination renal biopsy data. Conclusion In our study, we concluded that mRNA and inactivated vaccines did not accelerate the progression of chronic kidney disease. Before and after vaccination with mRNA and inactivated vaccines, no difference was detected between the two types of vaccines. Declarations Disclosure The authors declare that they have no conflicts of interest related to this study. References Stevens PE, Levin A, Carrero JJ et al (2024) Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int 105(4S):S117–S314. 10.1016/j.kint.2023.10.018 Bruggeman LA (2019) Common Mechanisms of Viral Injury to the Kidney. Adv Chronic Kidney Dis. ;26(3):164-170.10.1053/j.ackd.2018.12.002 Afrin LB, Weinstock LB, Molderings GJ (2020) Covid-19 hyperinflammation and post-Covid-19 illness may be rooted in mast cell activation syndrome. Int J Infect Dis. ;100:327-332.10.1016/j.ijid.2020.09.016 Péterfi A, Mészáros Á, Szarvas Z et al (2022) Comorbidities and increased mortality of COVID-19 among the elderly: A systematic review. Physiol Int. ;109(2):163-176.10.1556/2060.2022.00206 Baskoro A, Pranawa Y (2023) Immune-mediated kidney responses after COVID-19 vaccination: a narrative review. J Nephropathol. ;12(4):e57.10.4103/ijcm.ijcm_654_22 Wang X, Zhang Y, Li W et al (2023) COVID-19 vaccination and kidney function: longitudinal analysis in chronic kidney disease patients. Nephrol (Carlton) 28(4):275–282 Chen R, Lee M, Cho H et al (2023) Long-term renal function following mRNA COVID-19 vaccination in chronic kidney disease patients. Int J Infect Dis 127:35–41 Nagatsuji M, Fukasawa H, Yamamoto T et al (2024) Transient IgA nephropathy flare after mRNA COVID-19 vaccination: case series and review. BMC Nephrol 25(1):72 Li J, Zhao H, Chen X et al (2024) Renal outcomes after COVID-19 vaccination in patients with preexisting glomerular disease: A retrospective study. J Nephrol 37(1):15–23 Pethő Z, Nagy E, Kovács G et al (2025) Immune activation and renal autoimmunity following mRNA vaccination: mechanisms and clinical implications. Front Immunol 16:1241–1250 Zhang L, Sun J, Liu Y et al (2023) Effectiveness and safety of COVID-19 vaccination in hemodialysis patients: A multicenter study. Clin Nephrol 99(2):104–112 Ma BM, Tam AR, Chan KW, Ma MKM, Hung IFN et al (2022) Immunogenicity and safety of COVID-19 vaccines in patients receiving renal replacement therapy: A systematic review and meta-analysis. Front Med (Lausanne) 9:827859 Chuang P, Lin C, Hsu Y et al (2022) Impact of COVID-19 vaccination on renal outcomes in CKD: a cohort analysis. Vaccine 40(52):7564–7570 Sun J, Hu Y, Zhang S et al (2024) Proteinuria following COVID-19 vaccination: transient change or immune-mediated injury? Clin Exp Nephrol 28(2):161–169 Tables Table-1. Table 1. Baseline Demographic and Clinical Characteristics of the Study Population Characteristic Value Age (years) 63.6 ± 11.2 (range: 22–80) Sex Male: 77 (55%) Female: 63 (45%) Diabetes status Diabetic: 53 (37.9%) Non-diabetic: 87 (62.1%) Mean HbA1c (%) Diabetic: 7.96 Non-diabetic: 5.68 Baseline serum creatinine (mg/dL) 1.226 ± 0.268 (median: 1.200) Baseline GFR (mL/min/1.73 m²) 60.3 ± 15.4 (median: 59.0) Distribution of vaccine types BioNTech: 115 Sinovac: 102 Turkovac: 6 Pure vaccination groups (n) BioNTech only: 38 Inactivated vaccines only: 25 Mixed vaccination: 77 Table 2. Mean Serum Creatinine and GFR Values During the Pre-Vaccination, Vaccination, and Post-Vaccination Periods Period Serum creatinine (mg/dL), mean (range) GFR (mL/min/1.73 m²), mean (range) Pre-vaccination 1.2246 (0.70–2.04) 60.2734 (31.49–104.00) Vaccination period 1.3839 (0.80–2.20) 51.1404 (30.01–89.28) Post-vaccination 1.6025 (0.85–3.21) 42.2404 (15.90–80.70) Table 3. Changes in Serum Creatinine and GFR Between Vaccination Periods Comparison Parameter Mean difference (range) Vaccination – Pre-vaccination Serum creatinine (mg/dL) 0.1593 (−0.32 to 0.81) Post-vaccination – Vaccination Serum creatinine (mg/dL) 0.2186 (−0.54 to 1.38) Pre-vaccination – Vaccination GFR (mL/min/1.73 m²) 9.1330 (−13.22 to 44.00) Vaccination – Post-vaccination GFR (mL/min/1.73 m²) 8.9000 (−25.10 to 41.70) Table 4. Wilcoxon Signed-Rank Test Results for Pre- and Post-Vaccination Changes in Serum Creatinine and GFR (All Patients) Variable Z value p value (2-tailed) Serum creatinine (Post–Pre) −2.469 0.014 Glomerular filtration rate (GFR) (Post–Pre) −0.007 0.994 Table 5. Paired t -Test Results of Renal Function Parameters in Patients Vaccinated Only with BioNTech (mRNA Vaccine) Parameter Mean N Standard Deviation Standard Error Mean Serum Creatinine Pre-vaccination 0.1695 38 0.19113 0.03100 Post-vaccination 0.2474 38 0.24036 0.03899 Glomerular Filtration Rate (GFR) Pre-vaccination 10.7274 38 11.04150 1.79117 Post-vaccination 11.4837 38 8.70730 1.41251 Table 6. Paired t -Test Results for Pre- and Post-Vaccination Renal Function Parameters in Patients Receiving Only the BioNTech (mRNA) Vaccine Variable Mean Difference (Post–Pre) SD SE 95% CI (Lower–Upper) t df p Serum creatinine −0.0779 0.2974 0.0483 −0.1757 to 0.0199 −1.615 37 0.115 Glomerular filtration rate (GFR) −0.7563 16.1808 2.6249 −6.0748 to 4.5622 −0.288 37 0.775 Table 7. Wilcoxon Signed-Rank Test Results for Pre- and Post-Vaccination Serum Creatinine Levels in the Inactivated Vaccine Group Ranks Comparison (Post–Pre) N Mean Rank Sum of Ranks Negative ranks (Post Pre) 19 13.82 262.50 Ties 0 – – Total 25 Test Statistics Parameter Value Z value −2.691 p value (2-tailed) 0.007 Table 8. Paired t -Test Results for Pre- and Post-Vaccination Glomerular Filtration Rate (GFR) in the Inactivated Vaccine Group Descriptive Statistics Time point Mean N SD SE Pre-vaccination GFR 8.0888 25 8.9771 1.7954 Post-vaccination GFR 11.2768 25 11.6013 2.3203 Paired t -Test Mean Difference (Pre–Post) SD SE 95% CI t df p −3.1880 13.1573 2.6315 −8.6191 to 2.2431 −1.211 24 0.237 Additional Declarations The authors declare no competing interests. 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1","display":"","copyAsset":false,"role":"figure","size":111736,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Design and Timeline of Renal Function Assessment\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8489731/v1/5544f50af629d83d2906d9e0.png"},{"id":99804174,"identity":"f9323ffc-340c-420f-8e63-a7a75f46adfb","added_by":"auto","created_at":"2026-01-08 14:12:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":490667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8489731/v1/d9e5e720-fe26-4d57-b681-45ceaab07777.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDo mRNA vaccines accelerate the progression of chronic kidney disease?\u003c/p\u003e\n\u003cp\u003eFor six years experience\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn chronic kidney disease (CKD), prevention and treatment of the underlying etiologic disease, slowing or halting disease progression, and preservation of residual renal function are among the primary therapeutic goals. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eViral infections may directly cause renal failure; additionally, through various immune and inflammatory pathways, they can trigger the activation of kidney diseases and contribute to the progression of CKD. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic, which emerged at the end of 2019, caused severe macrophage activation syndrome (MAS) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and led to high morbidity and mortality, particularly among individuals with chronic diseases. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] In addition, its impact on the progression of chronic kidney disease is of significant clinical importance.\u003c/p\u003e \u003cp\u003eThe rapid global spread and high mortality rate of the COVID-19 pandemic prompted the exploration of accelerated vaccine development strategies, leading to the development of novel messenger RNA (mRNA) vaccines. Because vaccines produced using conventional methods require longer production times, mRNA vaccines were introduced into clinical use earlier.\u003c/p\u003e \u003cp\u003eWhen it was reported that mRNA vaccines, similar to viral infections, can stimulate the immune system in the post-administration period and induce the production of proinflammatory cytokines, thereby leading to various complications, concerns regarding vaccine safety were raised. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eStudies in the literature have predominantly focused on whether they cause nephrotoxicity and acute kidney injury. However, in order to better elucidate the potential long-term effects, we investigated whether mRNA and inactivated vaccines accelerate the progression of chronic kidney disease.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eBetween 2019 and 2024, a retrospective study was designed to investigate the impact of COVID-19 vaccines on disease progression in patients with chronic kidney disease (CKD) who were followed at the Nephrology Clinic of \u003cb\u003eXXXXX Hospital\u003c/b\u003e. In our country, BioNTech (mRNA vaccine), Sinovac (inactivated vaccine), and Turkovac (inactivated vaccine) were administered. A total of 140 patients were deemed eligible for inclusion in the study.\u003c/p\u003e \u003cp\u003eMean serum creatinine and mean glomerular filtration rate (GFR) values were evaluated across three periods:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePre-vaccination period (2019\u0026ndash;2020)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVaccination period (2021\u0026ndash;2022)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePost-vaccination period (2023\u0026ndash;2024)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSerum creatinine and GFR measurements were compared longitudinally to assess changes in renal function over time. (Study Design depicted Figure-1.)\u003c/p\u003e \u003cp\u003ePatients were enrolled in the study according to predefined inclusion and exclusion criteria.\u003c/p\u003e \u003cp\u003eInclusion criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge between 18 and 80 years\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRegular follow-up for at least two years both before and after vaccination\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBaseline pre-vaccination GFR between 30 and 90 mL/min/1.73 m\u0026sup2;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eReceipt of at least one dose of a COVID-19 vaccine (BioNTech, Sinovac, or Turkovac)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eExclusion criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHistory of acute kidney injury\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHistory of dialysis treatment or kidney transplantation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHistory of COVID-19\u0026ndash;related complications requiring hospitalization\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse of immunosuppressive medications\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eData Collection Process\u003c/p\u003e \u003cp\u003eData were retrospectively obtained from the hospital information system (HIS), covering a period of two years prior to vaccination, the year of vaccination, and at least two years following vaccination. Renal function was assessed using serum creatinine levels and glomerular filtration rate (GFR) parameters. Only patients with regular follow-up visits, defined as visits occurring at intervals of no longer than six months, were included in the analysis. GFR calculations were performed using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) formula available in the hospital information system.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGFR Calculation Formula\u003c/p\u003e\u003cp\u003eGFR\u0026thinsp;=\u0026thinsp;141 \u0026times; min(Scr/κ, 1)^α\u0026thinsp;\u0026times;\u0026thinsp;max(Scr/κ, 1)^\u0026minus;1.209 \u0026times; 0.993^(Age) \u0026times; Sex factor\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eScr: Serum creatinine (mg/dL)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eκ (kappa): 0.9 for males and 0.7 for females\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eα (alpha): \u0026minus;0.411 for males and \u0026minus;\u0026thinsp;0.329 for females\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSex factor: 1.018 for females and 1.0 for males\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis formula corresponds to the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation used for estimating glomerular filtration rate.\u003c/p\u003e \u003cp\u003eTo compare renal function progression before and after vaccination, serum creatinine and GFR values were identified and recorded for each patient during the pre-vaccination period, vaccination period, and post-vaccination period. Mean values were calculated using descriptive statistics. For each patient, the mean serum creatinine and GFR values obtained in the pre- and post-vaccination periods were subtracted from the mean values of the vaccination period to calculate difference values.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ea. Pre-vaccination Difference in Creatinine: Mean value during the vaccination period\u0026thinsp;\u0026minus;\u0026thinsp;mean value during the pre-vaccination period\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eb. Post-vaccination Difference in Creatinine: Mean value during the post-vaccination period\u0026thinsp;\u0026minus;\u0026thinsp;mean value during the vaccination period\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ec. Pre-vaccination Difference in GFR: Mean value during the pre-vaccination period\u0026thinsp;\u0026minus;\u0026thinsp;mean value during the vaccination period\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ed. Post-vaccination Difference in GFR: Mean value during the vaccination period\u0026thinsp;\u0026minus;\u0026thinsp;mean value during the post-vaccination period\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eComparisons of pre- and post-vaccination mean serum creatinine and GFR values were performed for the overall cohort, the BioNTech group, and the inactivated vaccine group.\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted using SPSS version 26.0. The normality of data distribution was assessed using the Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests. Because the groups consisted of dependent (paired) observations, the paired-samples t-test was used for normally distributed variables, while the Wilcoxon signed-rank test was applied for non-normally distributed variables. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e The study was approved by the XXXX Hospital Scientific Research Ethics Committee (Approval No: 2024-20/2; Date of Approval: November 27, 2024).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 140 patients with chronic kidney disease included in this study were evaluated with respect to renal function parameters during the pre-vaccination, vaccination, and post-vaccination periods.\u003c/p\u003e\n\u003cp\u003eThe mean age of the patients was 63.6 \u0026plusmn; 11.2 years; 55% were male (n = 77) and 45% were female (n = 63).\u003c/p\u003e\n\u003cp\u003eAmong the participants, 38 patients received only the BioNTech vaccine, 25 patients received only a conventional (inactivated) vaccine, and 77 patients received both vaccine types, resulting in a total study population of 140 patients.\u003c/p\u003e\n\u003cp\u003eOf those included in the study, 115 patients had received at least one dose of the BioNTech vaccine, 102 patients had received at least one dose of Sinovac, and 6 patients had received at least one dose of Turkovac. Because the number of patients vaccinated with Turkovac was small and it is an inactivated vaccine similar to Sinovac, these two vaccines were evaluated together under the same category. Accordingly, the numbers of patients receiving mRNA vaccines and inactivated vaccines were comparable. ( Table-1. Baseline \u0026nbsp;Characteristics)\u003c/p\u003e\n\u003cp\u003eFor each patient, the mean serum creatinine and GFR values obtained during the pre-vaccination, vaccination, and post-vaccination periods were calculated separately (Table 2).\u003c/p\u003e\n\u003cp\u003eSubsequently, using the vaccination-period mean values as the reference, the mean differences for the pre-vaccination period and the mean differences for the post-vaccination period were calculated for both serum creatinine and GFR (Table 3).\u003c/p\u003e\n\u003cp\u003eThe distribution of data for the 140 patients was found to be non-normal. Therefore, a nonparametric test (Wilcoxon signed-rank test) was applied to compare pre- and post-vaccination serum creatinine and GFR values for all patients (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the overall cohort, the change in serum creatinine between the pre- and post-vaccination periods was statistically significant (p = 0.014), whereas the change in GFR was not statistically significant (p = 0.99).\u003c/p\u003e\n\u003cp\u003eFor the 38 patients who received only the BioNTech vaccine, a normality test was first performed. As the data were considered to be normally distributed, a parametric test (paired-samples t-test) was applied (Tables 5 and 6).\u003c/p\u003e\n\u003cp\u003eAccording to the results of the paired-samples t-test, no statistically significant changes were observed in either serum creatinine or GFR between the pre- and post-vaccination periods among patients who received the BioNTech vaccine (p = 0.11 and p = 0.77, respectively).\u003c/p\u003e\n\u003cp\u003eFor the 25 patients who received only inactivated vaccines, normality testing was performed. Because the distribution of pre- and post-vaccination serum creatinine values was non-normal, the Wilcoxon signed-rank test was applied (Table 7).\u003cbr\u003e\u0026nbsp;In contrast, the distribution of pre- and post-vaccination GFR values was normal; therefore, a parametric paired-samples t-test was used (Table 8).\u003c/p\u003e\n\u003cp\u003eAccording to the results of the Wilcoxon signed-rank test, among patients who received inactivated vaccines, the change in serum creatinine between the pre- and post-vaccination periods was found to be statistically significant (p = 0.007) (Table 7).\u003c/p\u003e\n\u003cp\u003eHowever, in the comparison of pre- and post-vaccination GFR values, no statistically significant change was observed (p = 0.23) (Table 8).\u003c/p\u003e\n\u003cp\u003eIn summary, a statistically significant difference in serum creatinine was observed in the overall cohort and in the inactivated vaccine group when comparing pre- and post-vaccination values, whereas no statistically significant change in GFR was detected.\u003c/p\u003e\n\u003cp\u003eIn the group of patients who received only the BioNTech vaccine, no statistically significant changes were identified in either serum creatinine or GFR.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results were similar in the overall cohort and the inactivated vaccine group. In both groups, a statistically significant difference was observed in the comparison of mean serum creatinine levels between the pre- and post-vaccination periods, whereas no statistically significant difference was detected in mean GFR values.\u003c/p\u003e \u003cp\u003eTo help explain this finding, a graph illustrating the relationship between GFR and serum creatinine may be informative. Specifically, serum creatinine levels increase only minimally until they reach the upper limit of the normal range, while the slope representing the decline in GFR is relatively steep during this phase. For this reason, it is well recognized that biomarkers other than serum creatinine have been investigated to better reflect renal impairment. As renal dysfunction progresses, serum creatinine increases proportionally much more compared with its previous values, whereas the GFR decline curve tends to follow a more horizontal trajectory. The observation that changes in serum creatinine reached statistical significance while changes in GFR did not may be explained by this nonlinear relationship\u003c/p\u003e \u003cp\u003eOn the other hand, in the BioNTech-only group, the absence of statistically significant differences in both mean serum creatinine and mean GFR when comparing the pre-vaccination differences with the post-vaccination differences helps to alleviate concerns regarding the potential renal effects of BioNTech vaccines.\u003c/p\u003e \u003cp\u003eThe findings reported in the literature are consistent with those of our study. Wang et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that COVID-19 vaccination did not adversely affect renal function in individuals with a history of glomerular disease and that pre- and post-vaccination GFR slopes were similar. Chen et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] likewise reported that although a mild decline in GFR may be observed in the short term, this difference disappears during long-term follow-up. These findings are in line with the GFR stability observed in our study.\u003c/p\u003e \u003cp\u003eAdditionally, Nagatsuji et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported transient fluctuations in renal function following vaccination in patients with IgA nephropathy, while Li et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] similarly demonstrated that renal events occurring after COVID-19 vaccination are generally associated with a favorable prognosis and are reversible.\u003c/p\u003e \u003cp\u003eBaskoro and Pranawa [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] reported that mRNA vaccines induce a proinflammatory cytokine response through Toll-like receptor (TLR) activation, which may lead to transient changes in renal function in some susceptible patients.\u003c/p\u003e \u003cp\u003eIn addition, Pethő et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] emphasized that mRNA vaccines may rarely trigger autoimmune glomerulonephritis following immune activation; however, these effects are mostly transient and respond well to treatment.\u003c/p\u003e \u003cp\u003eZhang et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported that COVID-19 vaccination preserves renal function and reduces mortality in patients undergoing hemodialysis, while Ma et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated a high level of vaccine safety.\u003c/p\u003e \u003cp\u003eHowever, some studies have reported modest changes following vaccination: Chuang et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] observed a small but statistically significant increase in GFR after vaccination, whereas Sun et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] noted a transient increase in proteinuria levels.\u003c/p\u003e \u003cp\u003eOverall, there is strong consensus in the literature that COVID-19 vaccines do not cause severe or permanent deterioration of renal function in patients with chronic kidney disease; on the contrary, renal stability is generally maintained [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, rather than investigating whether it causes harm, we examined whether it accelerates the progression of the disease. Although the aim of our study is different, the findings reported in the literature support our results.\u003c/p\u003e \u003cp\u003eThe differing results reported in some studies may be explained by variations in patient characteristics, comorbidity burden, and duration of follow-up.\u003c/p\u003e \u003cp\u003eA major strength of our study is that each patient served as their own control, allowing for a comparison of pre- and post-vaccination data within the same individuals. Although retrospective in design, the study includes a relatively long follow-up period of approximately 5\u0026ndash;6 years, which enhances the robustness of the findings.\u003c/p\u003e \u003cp\u003eThe limitations of this study include the retrospective design, which precludes the establishment of a direct causal relationship; the lack of data on proteinuria, inflammatory markers, and immune response parameters; and the absence of post-vaccination renal biopsy data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn our study, we concluded that mRNA and inactivated vaccines did not accelerate the progression of chronic kidney disease.\u003c/p\u003e \u003cp\u003eBefore and after vaccination with mRNA and inactivated vaccines, no difference was detected between the two types of vaccines.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eDisclosure\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest related to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eStevens PE, Levin A, Carrero JJ et al (2024) Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int 105(4S):S117\u0026ndash;S314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.kint.2023.10.018\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBruggeman LA (2019) Common Mechanisms of Viral Injury to the Kidney. Adv Chronic Kidney Dis. ;26(3):164-170.10.1053/j.ackd.2018.12.002\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAfrin LB, Weinstock LB, Molderings GJ (2020) Covid-19 hyperinflammation and post-Covid-19 illness may be rooted in mast cell activation syndrome. Int J Infect Dis. ;100:327-332.10.1016/j.ijid.2020.09.016\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eP\u0026eacute;terfi A, M\u0026eacute;sz\u0026aacute;ros \u0026Aacute;, Szarvas Z et al (2022) Comorbidities and increased mortality of COVID-19 among the elderly: A systematic review. Physiol Int. ;109(2):163-176.10.1556/2060.2022.00206\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBaskoro A, Pranawa Y (2023) Immune-mediated kidney responses after COVID-19 vaccination: a narrative review. \u003cem\u003eJ Nephropathol.\u003c/em\u003e ;12(4):e57.10.4103/ijcm.ijcm_654_22\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWang X, Zhang Y, Li W et al (2023) COVID-19 vaccination and kidney function: longitudinal analysis in chronic kidney disease patients. Nephrol (Carlton) 28(4):275\u0026ndash;282\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChen R, Lee M, Cho H et al (2023) Long-term renal function following mRNA COVID-19 vaccination in chronic kidney disease patients. Int J Infect Dis 127:35\u0026ndash;41\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNagatsuji M, Fukasawa H, Yamamoto T et al (2024) Transient IgA nephropathy flare after mRNA COVID-19 vaccination: case series and review. BMC Nephrol 25(1):72\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLi J, Zhao H, Chen X et al (2024) Renal outcomes after COVID-19 vaccination in patients with preexisting glomerular disease: A retrospective study. J Nephrol 37(1):15\u0026ndash;23\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePethő Z, Nagy E, Kov\u0026aacute;cs G et al (2025) Immune activation and renal autoimmunity following mRNA vaccination: mechanisms and clinical implications. Front Immunol 16:1241\u0026ndash;1250\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhang L, Sun J, Liu Y et al (2023) Effectiveness and safety of COVID-19 vaccination in hemodialysis patients: A multicenter study. Clin Nephrol 99(2):104\u0026ndash;112\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMa BM, Tam AR, Chan KW, Ma MKM, Hung IFN et al (2022) Immunogenicity and safety of COVID-19 vaccines in patients receiving renal replacement therapy: A systematic review and meta-analysis. Front Med (Lausanne) 9:827859\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChuang P, Lin C, Hsu Y et al (2022) Impact of COVID-19 vaccination on renal outcomes in CKD: a cohort analysis. Vaccine 40(52):7564\u0026ndash;7570\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSun J, Hu Y, Zhang S et al (2024) Proteinuria following COVID-19 vaccination: transient change or immune-mediated injury? Clin Exp Nephrol 28(2):161\u0026ndash;169\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable-1. Table 1. Baseline Demographic and Clinical Characteristics of the Study Population\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.6 \u0026plusmn; 11.2 (range: 22\u0026ndash;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale: 77 (55%) Female: 63 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetic: 53 (37.9%) Non-diabetic: 87 (62.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMean HbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetic: 7.96 Non-diabetic: 5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBaseline serum creatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.226 \u0026plusmn; 0.268 (median: 1.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBaseline GFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.3 \u0026plusmn; 15.4 (median: 59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDistribution of vaccine types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBioNTech: 115 Sinovac: 102 Turkovac: 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePure vaccination groups (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBioNTech only: 38 \u0026nbsp;Inactivated vaccines only: 25 Mixed vaccination: 77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Mean Serum Creatinine and GFR Values During the Pre-Vaccination, Vaccination, and Post-Vaccination Periods\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum creatinine (mg/dL), mean (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGFR (mL/min/1.73 m\u0026sup2;), mean (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2246 (0.70\u0026ndash;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.2734 (31.49\u0026ndash;104.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVaccination period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3839 (0.80\u0026ndash;2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.1404 (30.01\u0026ndash;89.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePost-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6025 (0.85\u0026ndash;3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.2404 (15.90\u0026ndash;80.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Changes in Serum Creatinine and GFR Between Vaccination Periods\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean difference (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVaccination \u0026ndash; Pre-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1593 (\u0026minus;0.32 to 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePost-vaccination \u0026ndash; Vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2186 (\u0026minus;0.54 to 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-vaccination \u0026ndash; Vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.1330 (\u0026minus;13.22 to 44.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVaccination \u0026ndash; Post-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.9000 (\u0026minus;25.10 to 41.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Wilcoxon Signed-Rank Test Results for Pre- and Post-Vaccination Changes in Serum Creatinine and GFR (All Patients)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum creatinine (Post\u0026ndash;Pre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;2.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlomerular filtration rate (GFR) (Post\u0026ndash;Pre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5. Paired \u003cem\u003et\u003c/em\u003e-Test Results of Renal Function Parameters in Patients Vaccinated Only with BioNTech (mRNA Vaccine)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Error Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum Creatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePost-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlomerular Filtration Rate (GFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.7274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.04150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.79117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePost-vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.4837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.70730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.41251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Paired \u003cem\u003et\u003c/em\u003e-Test Results for Pre- and Post-Vaccination Renal Function Parameters in Patients Receiving Only the BioNTech (mRNA) Vaccine\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Difference (Post\u0026ndash;Pre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI (Lower\u0026ndash;Upper)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum creatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.1757 to 0.0199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlomerular filtration rate (GFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.7563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;6.0748 to 4.5622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7. Wilcoxon Signed-Rank Test Results for Pre- and Post-Vaccination Serum Creatinine Levels in the Inactivated Vaccine Group\u003c/p\u003e\n\u003cp\u003eRanks\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComparison (Post\u0026ndash;Pre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Rank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Ranks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNegative ranks (Post \u0026lt; Pre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePositive ranks (Post \u0026gt; Pre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e262.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTest Statistics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;2.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 8. Paired \u003cem\u003et\u003c/em\u003e-Test Results for Pre- and Post-Vaccination Glomerular Filtration Rate (GFR) in the Inactivated Vaccine Group\u003c/p\u003e\n\u003cp\u003eDescriptive Statistics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTime point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-vaccination GFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.0888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.9771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePost-vaccination GFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.2768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.6013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePaired \u003cem\u003et\u003c/em\u003e-Test\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Difference (Pre\u0026ndash;Post)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;3.1880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.1573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;8.6191 to 2.2431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mRNA vaccines, COVID-19 vaccines, chronic kidney disease","lastPublishedDoi":"10.21203/rs.3.rs-8489731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8489731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the COVID-19 pandemic, mRNA vaccines which developed using a novel technology distinct from conventional vaccines, were widely administered. They are known to induce more pronounced immune reactions compared to classical vaccines.\u003c/p\u003e\n\u003cp\u003eWe investigated whether mRNA and inactivated vaccines accelerate the progression of chronic kidney disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003cbr\u003e\n \u003c/strong\u003eA total of 140 patients with CKD followed at Bursa City Hospital were retrospectively analyzed. Serum creatinine and glomerular filtration rate (GFR) values were recorded over three periods: pre-vaccination (−24 months), vaccination period, and post-vaccination (+24 months). GFR was calculated using the CKD-EPI formula. Due to the dependent nature of pre- and post-vaccination comparisons, the paired \u003cem\u003et\u003c/em\u003e-test was used for parametric data and the Wilcoxon signed-rank test for non-parametric data. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003cbr\u003e\n \u003c/strong\u003eIn the overall patient group and in the subgroup receiving only inactivated vaccines, a statistically significant difference in serum creatinine levels was observed between the pre- and post-vaccination periods; however, no statistically significant change was detected in GFR. In the subgroup of patients who received only the BioNTech (mRNA) vaccine, no statistically significant changes were observed in either serum creatinine or GFR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003cbr\u003e\n \u003c/strong\u003e\u0026nbsp;In our study, we concluded that mRNA and inactivated vaccines did not accelerate the progression of chronic kidney disease.\u003c/p\u003e","manuscriptTitle":"Do mRNA vaccines accelerate the progression of chronic kidney disease?\nFor six years experience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 15:06:36","doi":"10.21203/rs.3.rs-8489731/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1f4360b-cb48-454b-a617-2905fa50058e","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60425653,"name":"Internal Medicine"},{"id":60425654,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2026-01-06T15:06:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 15:06:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8489731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8489731","identity":"rs-8489731","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}