Effectiveness of Pfizer-BioNTech COVID-19 Booster and its Correlation with Vitamin D levels – a Quasi Experiment

Effectiveness of Pfizer-BioNTech COVID-19 Booster and its Correlation with Vitamin D levels – a Quasi Experiment | 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 Effectiveness of Pfizer-BioNTech COVID-19 Booster and its Correlation with Vitamin D levels – a Quasi Experiment Farooq Ahmad, Muhammad Ahrar Bin Naeem, Abdur Rehman Khalid, Smak Ahmed, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9481803/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 Background Vitamin D has immunomodulatory effects, yet its influence on SARS-CoV-2 vaccine–induced antibody responses remain unclear. This study assessed the humoral immunogenicity of a Pfizer–BioNTech COVID-19 booster and whether baseline serum vitamin D levels correlate with post-booster IgG titers. Materials and Methods In this single-arm quasi‐experimental study (ERB #169/06/01/2022/S1 ERB), 54 eligible medical students received a Pfizer–BioNTech booster. Vitamin D levels and IgG titers were assessed on baseline and four weeks post-intervention. Statistical analyses included paired t-tests for pre-post IgG changes, Pearson’s correlation between vitamin D levels and IgG change, and multivariate logistic regression adjusted for gender, baseline vitamin D, BMI, and sunlight exposure. Significance was set at p < 0.05. Results The cohort (n = 54) exhibited a significant overall increase in IgG titers after booster (Δ = 4.7 ± 11.3 AU/mL, p = 0.003). Males showed a significant rise (Δ = 3.7 ± 9.9 AU/mL, p = 0.028), whereas females did not (p = 0.061). No significant correlation was found between baseline vitamin D and IgG change in the total sample (r = 0.185, p = 0.18). Stratified analysis demonstrated a positive correlation in males (r = 0.451, p = 0.05) and a non-significant negative correlation in females (r = − 0.157, p = 0.56). Logistic regression did not identify significant predictors of booster effectiveness. Conclusions The Pfizer–BioNTech booster elicits a robust IgG response in healthy young adults. Baseline vitamin D status did not uniformly influence antibody increases, though a male-specific positive association warrants further investigation. Larger, controlled studies are needed to clarify vitamin D’s role in modulating post-vaccination immunity. Immunology Infectious Diseases Virology Booster dose response COVID-19 humoral immunity Pfizer-BioNTech Vitamin D Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Coronavirus disease (COVID-19) is an infectious illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In most instances, the clinical presentation is comparable to that of influenza, with individuals typically exhibiting mild respiratory symptoms. However, in older adults and individuals with compromised immune systems, the disease is more likely to progress to severe complications, including pneumonia and other life-threatening conditions ( 1 ). On March 11, 2020, the World Health Organization designated COVID-19 as a pandemic. Since this declaration, the virus has not only significantly impacted public health but also fundamentally transformed pre-pandemic global operations. Moreover, COVID-19 has induced substantial economic disruptions, manifesting as reduced income levels and elevated unemployment rates ( 2 ). In response to these challenges, several biopharmaceutical companies initiated the development of a prophylactic COVID-19 vaccine. Although vaccine development typically spans 10 to 15 years, extraordinary efforts by researchers, and clinical trial participants enabled the creation of effective vaccines in under one year ( 3 ). One of the first and most widely used of these was the Pfizer–BioNTech (PBNT) COVID-19 vaccine, a lipid nanoparticle-formulated, nucleoside-modified mRNA vaccine encoding the prefusion-stabilized full-length spike (S) protein of SARS-CoV-2 ( 4 , 5 ). Follow-up through six months demonstrated sustained efficacy of 91.3% (95% CI, 89.0 to 93.2) and a favorable safety profile ( 6 , 7 ). Vitamin D is obtained from dietary sources and synthesized in the skin and converted to its active metabolite calcitriol. Calcitriol modulates calcium homeostasis and exerts immunoregulatory effects by influencing gene expression related to antimicrobial responses ( 8 ). It has shown to prevent and mitigate systemic infectious and inflammatory conditions, suggesting its potential as a therapeutic agent in COVID-19 infection ( 9 ). Multiple observational studies have identified a significant association between low serum vitamin D levels and increased susceptibility to SARS-CoV-2 infection, as well as heightened risks of severe disease progression and mortality ( 10 , 11 ) A systematic review and meta-analysis reported that individuals with deficient vitamin D levels had higher odds of COVID-19 infection, severe disease, and mortality compared to those with sufficient levels ( 12 ). Furthermore, comparative analyses across different populations have revealed that vitamin D deficiency may contribute to disparities in COVID-19 outcomes. For instance, studies have indicated that certain ethnic groups with lower average vitamin D levels, such as Asian populations, experience higher rates of infection severity and mortality compared to populations with higher vitamin D levels ( 13 ). However, the relationship between vitamin D status and antibody responses following COVID-19 vaccination remains underexplored, with limited studies addressing this association. To address these gaps, the present quasi-experimental study was designed with two primary objectives: first, to evaluate the immunogenic effectiveness of the PBNT booster dose in eliciting an enhanced COVID-19 immunoglobulin G (IgG) response; and second, to determine whether baseline vitamin D levels correlate with the magnitude of post-vaccination IgG titer changes, thereby elucidating the potential modulatory role of vitamin D in vaccine-induced humoral immunity. 2. Materials and Methods This study was performed according to the TREND guidelines of non-randomized /quasi-experiment study design ( 14 ). The study was registered and approved by the Institutional Ethical Review Board of Allama Iqbal Medical College (AIMC) and Jinnah Hospital, Lahore (JHL), under ERB number 169/06/01/2022/S1 ERB dated 06/01/2022. All participants gave written informed consent prior to enrollment. 2.1. Subjects: The study participants included young adults who were medical students enrolled at AIMC. Participants were recruited following a detailed interview, during which the study characteristics, design, intervention, and ethical considerations were thoroughly explained. Participants were asked to maintain their usual diet and sunlight exposure throughout the study and to refrain from initiating any new nutritional supplements. Those already taking supplements were instructed to continue their existing regimen to preserve their baseline nutritional status. 2.2. Eligibility criteria: Participants were eligible if they met all of the following: ( 1 ) completion of the primary two-dose COVID-19 vaccination series, regardless of the vaccine type; ( 2 ) completion of the vaccine series at least six months prior to recruitment; ( 2 ) absence of any acute or chronic comorbidities. They were excluded if they met any of the following: ( 1 ) a PCR-confirmed history of COVID-19 infection; ( 2 ) completion of the primary vaccination series fewer than six months before recruitment; ( 3 ) prior receipt of any COVID-19 booster dose; ( 4 ) baseline serum COVID-19 IgG levels below the detection threshold of the test; ( 5 ) documented or suspected acute infection (e.g., common cold, influenza) within one month of recruitment; ( 6 ) any chronic medical condition (such as diabetes, hypertension, or autoimmune disorders); ( 7 ) regular use of immunosuppressive agents (e.g., corticosteroids); ( 8 ) regular use of anti-allergy medications; or ( 9 ) daily tobacco use. 2.3. Study Design: A quasi-experimental design was employed for this study. Using a census sampling strategy, all 1,658 medical students enrolled at AIMC were invited to participate. Recruitment announcements were distributed via posters on departmental notice boards, brief presentations in lecture halls, and a notification sent through the AIMC online student portal. Volunteers were invited for one-on-one interviews, during which the study objectives, procedures, and ethical safeguards were explained and written informed consent was obtained. 2.3.1. Data Collection: Participants were recruited following a detailed interview, during which the study characteristics, design, intervention, and ethical considerations were thoroughly explained. Baseline demographic information collected included age, gender, contact information, blood group, height and weight, vaccination status, history of any supplement use, history of tobacco use, history of previous COVID-19 infection, history of any ongoing acute or chronic infections or diseases, history of allergies, and the history of use of any antiallergy and immunosuppressive drugs. The height and weight of each participant were measured during the interview using the HRS protocol ( 15 ). Ineligible volunteers were informed via email. Eligible volunteers were assigned a unique participant ID number, which was used in all subsequent data collection to ensure confidentiality. Participants were instructed to maintain their habitual dietary and sunlight–exposure patterns and to refrain from initiating any new nutritional supplements. Study staff conducted weekly follow-up reminders to reinforce adherence to these instructions. 2.3.2. Baseline Serology: Baseline venous blood samples were collected from all participants in accordance with WHO best-practice guidelines for phlebotomy ( 16 ). The samples were taken in a separate room allocated to the project in the Pathology department of AIMC by an expert phlebotomist. The blood sample collection of all participants spanned a period of three days. Serology tests for baseline vitamin D and COVID-19 IgG levels were performed once the samples for all the participants were taken. All tests were conducted in the pathology department laboratory of AIMC/JHL. The 25(OH) Bio-active Vitamin D ELISA test kit was used to determine vitamin D levels, and the RatioDiagnostic COVID-19 IgG ELISA test kit was used to determine COVID-19 IgG levels. 2.3.3. Booster dose administration: Once the baseline sampling was done, all participants were given the booster dose for PBNT COVID-19 Vaccine over the course of three days. The booster dose was given in the vaccination center of the JHL by a trained nurse following CDC protocols for vaccine administration ( 17 ). Doses of 0.3mL were administered intramuscularly after dilution as recommended by the guidelines ( 18 ). 2.3.4. Post-intervention serology: Venous blood samples of the participants were taken four weeks after the booster dose, timed to coincide with the expected peak of the humoral response, as broad surveys of antibody kinetics confirm that peak IgG levels typically occur between days 20 and 30 following booster administration ( 19 ). The sample was drawn in the same conditions as the pre-intervention sampling over the course of three days. Similarly, the same COVID-19 IgG test was performed on the collected samples to assess the increase or decrease in the IgG titer. 2.4. Study Timeline: The study commenced on August 15, 2023, and was completed on 22nd December 2023. Participants were recruited over a period of 2 months from August 15 to October 13. The pre-intervention serology was completed between October 16 and October 19. Booster dose was administered in a period of three days beginning on October 25. A 4-week delay period followed to allow the intervention to take effect. The post-intervention samples were drawn beginning November 22 through November 24. 2.5. Study Endpoints: The primary aims of this study were to quantify the humoral response of the PBNT booster by measuring the rise in SARS-CoV-2 spike–specific IgG antibody titers following administration, and to examine whether participants’ serum vitamin D levels were associated with the magnitude of these post-booster IgG responses, thereby assessing the potential modulatory effect of vitamin D on mRNA vaccine–induced humoral immunity. 2.6. Sample Size: No formal sample size calculation was performed for this quasi-experimental study. Instead, all eligible individuals who volunteered were enrolled. Recruitment continued until the end of the designated enrollment period, resulting in the final cohort size being determined by participant availability and willingness to participate. 2.7. Statistical analysis: We used IBM SPSS Statistics 25 to perform the statistical analysis. All collected data and the results of serology testing were recorded on a Microsoft Excel sheet. The descriptive statistics throughout the manuscript are reported as mean and standard deviation unless stated otherwise. All baseline characteristics of the participants are reported separately for males and females. Independent samples t-tests were applied to compare baseline means, and chi-square tests were used to compare baseline proportions between groups. Pearson’s correlation coefficient was calculated for assessing the correlation between average vitamin D levels and change in COVID-19 IgG and was reported separately for males and females. Average of pre and post-intervention vitamin D levels was utilized. A value of zero implies no correlation, a positive value implies a positive correlation, and a negative value suggests a negative correlation among the variables. The higher magnitude of the correlation coefficient the stronger correlation. A pre-post analysis was performed on the COVID-19 IgG titers before and after administration of the booster dose and a paired t-test was applied to determine the statistical significance. A positive value of change in IgG implies an increase in the serum IgG titer after administration of the booster dose and vice versa. A multivariate logistic regression model was also performed to assess the effectiveness of the COVID-19 booster and was adjusted for gender, baseline vitamin D levels, BMI and sunlight exposure time. The effectiveness of the COVID-19 booster was defined as a positive increase in post-intervention IgG antibody titers. Odds ratios (ORs) with a 95% confidence interval were reported for the effectiveness of the booster. An OR of one indicates equal booster effectiveness between groups, whereas an OR greater than 1 suggests greater effectiveness compared to the reference group, and an OR less than 1 indicates lower effectiveness. A p-value of less than 0.05 was considered significant. All graphs were made using Microsoft Excel. 3. Results Out of the total 1658 students, 134 volunteered to participate in the study. 70 volunteers fulfilled our eligibility criteria and were invited for blood sampling and vaccination. Finally, the study reports outcomes for 54 participants with the rest of them being lost during the course of study as illustrated in the flow diagram in Figure 1. Table 1 reports the baseline characteristics of all the participants as well as separate baseline characteristics for males and females. The mean age of all participants was 21.6 (1.6) years. 55% of the participants had sunlight exposure time of 15 minutes or more. Baseline COVID-19 IgG levels and average Vitamin D levels were found to be 34.2 (14.7) AU/ml and 16.9 (4) ng/ml respectively. Almost 85% of the participants had received either Sinovac (37%) or Sinopharm (48%) vaccine previously. The study observed comparable baseline characteristics between males and females including BMI, sunlight exposure, baseline COVID-19 IgG titers, average vitamin D levels and previous vaccine received. However, a statistically significant difference was noted between the age of the two groups ( P = 0.013) as evident in Table 1. The study revealed that upon receiving the booster vaccination, a statistically significant positive change in COVID IgG levels was observed for all participants (mean = 4.7; SD = 11.3; P = 0.003) as well as for males (mean = 3.7; SD = 9.9; P = 0.028) describing an effective response. However, for females, the change in IgG levels was found to be statistically insignificant ( P = 0.061) as shown in Table 2. Multivariate logistic regression was performed to identify the baseline factors independently predicting the effectiveness of the booster dose. Gender, BMI, sunlight exposure and prior vaccine type were taken as independent predictors. Although the results were statistically insignificant for the whole model as well as for independent predictors, some notable ones included almost twice the odds of the booster dose being effective in females as compared to males (adjusted OR = 1.8; 95% CI: 0.40, 8.32; P = 0.44) and almost thrice the odds of booster dose effectiveness in those who had received Pfizer vaccine previously as compared to those who had received Sinovac (adjusted OR = 2.9; 95% CI: 0.26, 33.16; P = 0.390) as shown in Table 3. A linear correlation between change in COVID IgG levels and average vitamin D levels of all participants was examined but was found to be insignificant (r = 0.185; 95% CI: -0.89, 0.43; P = 0.18) as illustrated in Figure 2. Similarly, linear correlation between these two variables was also examined for the two genders separately. We observed a positive linear correlation for males signifying a greater increase in COVID IgG levels after receiving booster dose at higher levels of vitamin D (r = 0.451; 95% CI: 0.15, 0.67; P = 0.05) as shown in Figure 3. For females, however, a slightly negative but statistically insignificant correlation was observed between COVID IgG levels and average vitamin D levels (r = -0.157; 95% CI: -0.6, 0.37; P = 0.561) as illustrated in Figure 4. 4. Discussion This quasi trial aimed at exploring the effectiveness of PBNT booster dose in healthy young adults as well as the relationship between COVID-19 IgG titers and vitamin D levels of the participants. This study demonstrated that the PBNT booster dose effectively increases COVID-19 IgG antibody levels in healthy young adults. We also explored the relationship between serum vitamin D levels, prior vaccine type, and gender, highlighting several nuanced findings. A positive increase in the COVID-19 IgG was demonstrated. While IgG levels are an important marker of humoral immunity, their correlation with functional protection is critical. Neutralizing antibodies, a subset of IgG, have been shown to predict protection against symptomatic SARS-CoV-2 infection ( 20 , 21 ). A study by Khoury et al. demonstrated that neutralization levels strongly correlate with protection, with 50% protection against detectable infection achieved at 20.2% of the mean convalescent neutralization level, and protection from severe disease requiring even lower levels ( 21 ). These findings emphasize the predictive value of antibody titers in determining protection and highlight the importance of sustained antibody levels and functionality to mitigate both infection and severe disease. The relationship between vitamin D and IgG production in our study revealed a positive correlation in males, but no significant effect in females. This is consistent with a randomized controlled trial by Fateh et al., which demonstrated a significant increase in IgG levels following vitamin D supplementation ( 22 ). These findings suggest that vitamin D might play a role in enhancing vaccine-induced immunity, but this role may be influenced by biological and hormonal factors ( 23 ). However, our findings contradict those of Chillon et al., who found no significant difference in the dynamic changes of IgG levels or neutralization potency with respect to vitamin D status ( 24 ), and Jolliffe et al., who reported that vitamin D supplementation did not affect vaccine-induced IgG titers or neutralizing antibody responses ( 25 ). These inconsistencies may stem from variations in baseline vitamin D levels, population characteristics, and immune system variability, underscoring the complex role of vitamin D in modulating immune responses. Interestingly, our data showed a greater IgG response to the booster dose in males, which deviates from the typical trend in vaccine studies where females often exhibit stronger immune responses ( 26 ). This difference may be attributed to the hormonal and genetic factors that influence immune function. Klein et al. found sex-specific differences in immune responses, with females showing more robust activation of adaptive immune cells, including CD4 + T cells and B cells. While this could explain the general trend of stronger responses in females, it does not fully account for our findings of higher IgG levels in males ( 27 ). Moreover, the study by Polack et al. on the safety and efficacy of the Pfizer vaccine reported similar vaccine efficacy in males and females but did not observe sex-based differences in immune responses ( 28 ). This discrepancy suggests that our observations may reflect specific characteristics of the population studied, or perhaps other confounding factors that were not considered in these larger cohort studies. In terms of prior vaccine type, participants who received the PBNT vaccine showed stronger IgG responses to the booster compared to those who had received the Sinovac vaccine, although this difference was not statistically significant. This aligns with the immunological principle that mRNA vaccines, such as PBNT, elicit robust T-cell and memory B-cell responses ( 29 ). Re-exposure to the same vaccine platform is known to enhance these recall responses ( 30 ). Several limitations must be acknowledged. First, the small sample size reduces the statistical power of our findings, particularly for subgroup analyses, limiting our ability to detect subtle differences or interactions between variables. Second, as a single-arm study, the absence of a control group limits our ability to directly compare the effectiveness of the PBNT booster to other vaccine boosters or to no intervention. Finally, despite our efforts to standardize lifestyle factors, residual confounding from unmeasured variables, particularly dietary intake, sunlight exposure, and physical activity, cannot be ruled out as a potential influence on the study outcomes. 4.1. Conclusion In conclusion, our findings highlight the complexity of factors influencing COVID-19 vaccine-induced immunity, including gender, baseline immunity, and prior vaccine type. Future research should further explore these relationships, considering confounding variables such as vitamin D status and lifestyle factors, to provide a clearer understanding of the immunological mechanisms behind vaccine responses. Larger-scale studies with diverse populations and extended follow-up periods are essential to validate these preliminary findings and inform public health strategies. Declarations 5.1. Funding This research was funded by the Research Grant Board of the Allama Iqbal Medical College, Lahore under grant number 10/RGB/AIMC/21 5.2. Conflict of Interest The authors have no conflicts of interest. 5.3. Acknowledgements None. 5.4. Data Availability Statement The research data can be provided upon request to the corresponding author. References Coronavirus [Internet]. [cited 2025 Apr 26]. Available from: https://www.who.int/health-topics/coronavirus#tab=tab_1 Mishra NP, Das SS, Yadav S, Khan W, Afzal M, Alarifi A, et al. Global impacts of pre- and post-COVID-19 pandemic: Focus on socio-economic consequences. Sensors International [Internet]. 2020 Jan 1 [cited 2025 Apr 26];1:100042. 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All participants (n = 54) Males* (n = 38) Females* (n = 16) P -value Age, years 21.64 (1.63) 21.82 (1.55) 20.63 (1.54) 0.013 BMI, kg/m 2 22.79 (3.91) 23.46 (4.13) 21.19 (2.86) 0.051 Sunlight exposure time 0.257 Less than 15 minutes, n (%) 24 (44.4) 9 (56.3) 7 (43.8) 15 minutes and more, n (%) 30 (55.6) 15 (39.5) 23 (60.5) Baseline COVID-19 IgG titer, AU/mL 34.27 (14.70) 35.52 (14.64) 31.3 (14.89) 0.341 Average Vitamin D levels, ng/mL 16.88 (3.99) 17.37 (3.85) 15.71 (4.22) 0.167 Name of Previous Vaccine 0.848 Sinovac 20 (37) 14 (36.8) 6 (37.5) Sinopharm 26 (48.1) 19 (50) 7 (43.8) Pfizer 8 (14.8) 5 (13.2) 3 (18.8) All values are reported in mean and standard deviation until stated otherwise. The significant P-values are bolded. *Independent sample t-test and chi-square test are implied to determine whether the differences among the means and proportions of both genders are significant or not. BMI = Basal metabolic index Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9481803","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626926534,"identity":"9134e6f0-9095-4b47-aee6-71037557fac8","order_by":0,"name":"Farooq Ahmad","email":"","orcid":"https://orcid.org/0000-0002-2494-9734","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Farooq","middleName":"","lastName":"Ahmad","suffix":""},{"id":626926535,"identity":"b9afe3f9-6e7c-4a76-8b9d-c5a1c8f1ed63","order_by":1,"name":"Muhammad Ahrar Bin Naeem","email":"","orcid":"https://orcid.org/0000-0002-5241-5887","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ahrar Bin","lastName":"Naeem","suffix":""},{"id":626926536,"identity":"d9a9dbad-27a9-4f66-be74-fa8aecaf142e","order_by":2,"name":"Abdur Rehman Khalid","email":"","orcid":"https://orcid.org/0000-0002-1536-4144","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Abdur","middleName":"Rehman","lastName":"Khalid","suffix":""},{"id":626926537,"identity":"0f3d1094-9282-4dbe-bbb3-222acd2e52d2","order_by":3,"name":"Smak Ahmed","email":"","orcid":"https://orcid.org/0009-0007-3185-7639","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Smak","middleName":"","lastName":"Ahmed","suffix":""},{"id":626926538,"identity":"3fa9589e-affc-4469-8126-c53955f95906","order_by":4,"name":"Muhammad Fahad Nadeem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie2PsUrDUBSGTwjY5UDXK/YVhJTAASEkD+KScCEuaWfBQq+LXXwAh76Cq/OVC+2SNmugS7NnyK6Ipy1YEJLqJng/7vBz+T9+DoDF8gfpKQQ4PFfrBgP+c+51l4L6SzlLqqdBulPUj5Rd9H0MzD52K73Va1NDOLicaRKYFeHzzPDKJLhuVXAsz+cgkfI4FSLfyJc8YWWRjlSLEkHmXSC4SCUsxPBxI0mz4ijTqmC/9t8Qpqw4DyL5WEsqqhOKyIhXDCuu62nUIZWnVkRNV3NvybekTqVQxlTyStxxC/Yzv6xv7yJaFo15xzCi4qbaNpOgVTngHWOyb8ad9W9EvylbLBbL/+AT0TBfXzyUQ4cAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0007-4599-2671","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Fahad","lastName":"Nadeem","suffix":""},{"id":626926539,"identity":"30939ec5-8aa0-491f-86e8-4c8952208d0c","order_by":5,"name":"Muhammad Usman","email":"","orcid":"https://orcid.org/0000-0002-6875-1885","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Usman","suffix":""},{"id":626926540,"identity":"098975c3-8350-405a-be89-4ffde4be07a3","order_by":6,"name":"Nouman Amjad","email":"","orcid":"https://orcid.org/0009-0009-7163-7483","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Nouman","middleName":"","lastName":"Amjad","suffix":""},{"id":626926541,"identity":"f08f3b0b-0c1a-40c8-bcd6-8169de526ddc","order_by":7,"name":"Maryam Ijaz","email":"","orcid":"https://orcid.org/0000-0003-3663-7719","institution":"Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Ijaz","suffix":""},{"id":626926542,"identity":"6156e5c5-2199-4891-8dfa-d0ae5d2804a3","order_by":8,"name":"Shahid Mahmood","email":"","orcid":"https://orcid.org/0000-0002-9813-5303","institution":"Department of Community Medicine, Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Shahid","middleName":"","lastName":"Mahmood","suffix":""},{"id":626926544,"identity":"23599032-89d2-4c1b-83cb-99737f0ca56f","order_by":9,"name":"Zunaira Kanwal","email":"","orcid":"","institution":"Department of Biochemistry, Allama Iqbal Medical College, Lahore","correspondingAuthor":false,"prefix":"","firstName":"Zunaira","middleName":"","lastName":"Kanwal","suffix":""}],"badges":[],"createdAt":"2026-04-21 09:14:49","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9481803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9481803/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107706175,"identity":"d5f19da2-8ac6-4e3f-92a1-da426cea482c","added_by":"auto","created_at":"2026-04-24 09:17:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant Recruitment and Selection Flowchart – \u003c/strong\u003ethis flowchart illustrates the stepwise enrollment process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9481803/v1/d31b22505e426dc010b496bb.png"},{"id":107531801,"identity":"80489745-bdec-4146-b0ae-474be05185fc","added_by":"auto","created_at":"2026-04-22 10:34:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Between Average Vitamin D and Change in COVID-19 IgG Titers (All Subjects)\u003c/strong\u003e – the scatter plot displays individual participants’ changes in COVID-19 IgG titers four weeks after the Pfizer–BioNTech booster versus their average serum vitamin D levels. Each blue point represents one subject; the orange line is the linear regression fit.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9481803/v1/d1a167c9f396464f90f93836.png"},{"id":107706258,"identity":"421447ac-ef09-4c8f-a054-a8070e913f7f","added_by":"auto","created_at":"2026-04-24 09:17:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Between Average Vitamin D and Change in COVID-19 IgG Titers\u003c/strong\u003e \u003cstrong\u003e(Males) \u003c/strong\u003e– the scatter plot displays male participants’ changes in COVID-19 IgG titers four weeks after the Pfizer–BioNTech booster versus their average serum vitamin D levels. Each blue point represents one subject; the orange line is the linear regression fit.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9481803/v1/fcbd9277c655f436cf88ab20.png"},{"id":107531804,"identity":"339c81a4-9c85-4c88-9b3a-9019d6cc0c8a","added_by":"auto","created_at":"2026-04-22 10:34:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Between Average Vitamin D and Change in COVID-19 IgG Titers\u003c/strong\u003e \u003cstrong\u003e(Females) \u003c/strong\u003e– the scatter plot displays female participants’ changes in COVID-19 IgG titers four weeks after the Pfizer–BioNTech booster versus their average serum vitamin D levels. Each blue point represents one subject; the orange line is the linear regression fit.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9481803/v1/2a1f07a33f1f5c7a62ff9be5.png"},{"id":107708944,"identity":"b58bfd4b-4d40-43a2-8e3f-20ae3f3117e2","added_by":"auto","created_at":"2026-04-24 09:33:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":423120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9481803/v1/ef131709-b73a-4918-b73a-119192be9890.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEffectiveness of Pfizer-BioNTech COVID-19 Booster and its Correlation with Vitamin D levels – a Quasi Experiment\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoronavirus disease (COVID-19) is an infectious illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In most instances, the clinical presentation is comparable to that of influenza, with individuals typically exhibiting mild respiratory symptoms. However, in older adults and individuals with compromised immune systems, the disease is more likely to progress to severe complications, including pneumonia and other life-threatening conditions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn March 11, 2020, the World Health Organization designated COVID-19 as a pandemic. Since this declaration, the virus has not only significantly impacted public health but also fundamentally transformed pre-pandemic global operations. Moreover, COVID-19 has induced substantial economic disruptions, manifesting as reduced income levels and elevated unemployment rates (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In response to these challenges, several biopharmaceutical companies initiated the development of a prophylactic COVID-19 vaccine. Although vaccine development typically spans 10 to 15 years, extraordinary efforts by researchers, and clinical trial participants enabled the creation of effective vaccines in under one year (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). One of the first and most widely used of these was the Pfizer\u0026ndash;BioNTech (PBNT) COVID-19 vaccine, a lipid nanoparticle-formulated, nucleoside-modified mRNA vaccine encoding the prefusion-stabilized full-length spike (S) protein of SARS-CoV-2 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Follow-up through six months demonstrated sustained efficacy of 91.3% (95% CI, 89.0 to 93.2) and a favorable safety profile (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVitamin D is obtained from dietary sources and synthesized in the skin and converted to its active metabolite calcitriol. Calcitriol modulates calcium homeostasis and exerts immunoregulatory effects by influencing gene expression related to antimicrobial responses (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It has shown to prevent and mitigate systemic infectious and inflammatory conditions, suggesting its potential as a therapeutic agent in COVID-19 infection (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple observational studies have identified a significant association between low serum vitamin D levels and increased susceptibility to SARS-CoV-2 infection, as well as heightened risks of severe disease progression and mortality (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eA systematic review and meta-analysis reported that individuals with deficient vitamin D levels had higher odds of COVID-19 infection, severe disease, and mortality compared to those with sufficient levels (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Furthermore, comparative analyses across different populations have revealed that vitamin D deficiency may contribute to disparities in COVID-19 outcomes. For instance, studies have indicated that certain ethnic groups with lower average vitamin D levels, such as Asian populations, experience higher rates of infection severity and mortality compared to populations with higher vitamin D levels (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the relationship between vitamin D status and antibody responses following COVID-19 vaccination remains underexplored, with limited studies addressing this association. To address these gaps, the present quasi-experimental study was designed with two primary objectives: first, to evaluate the immunogenic effectiveness of the PBNT booster dose in eliciting an enhanced COVID-19 immunoglobulin G (IgG) response; and second, to determine whether baseline vitamin D levels correlate with the magnitude of post-vaccination IgG titer changes, thereby elucidating the potential modulatory role of vitamin D in vaccine-induced humoral immunity.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study was performed according to the TREND guidelines of non-randomized /quasi-experiment study design (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The study was registered and approved by the Institutional Ethical Review Board of Allama Iqbal Medical College (AIMC) and Jinnah Hospital, Lahore (JHL), under ERB number 169/06/01/2022/S1 ERB dated 06/01/2022. All participants gave written informed consent prior to enrollment.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Subjects:\u003c/h2\u003e \u003cp\u003eThe study participants included young adults who were medical students enrolled at AIMC. Participants were recruited following a detailed interview, during which the study characteristics, design, intervention, and ethical considerations were thoroughly explained. Participants were asked to maintain their usual diet and sunlight exposure throughout the study and to refrain from initiating any new nutritional supplements. Those already taking supplements were instructed to continue their existing regimen to preserve their baseline nutritional status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Eligibility criteria:\u003c/h2\u003e \u003cp\u003eParticipants were eligible if they met all of the following: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) completion of the primary two-dose COVID-19 vaccination series, regardless of the vaccine type; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) completion of the vaccine series at least six months prior to recruitment; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) absence of any acute or chronic comorbidities.\u003c/p\u003e \u003cp\u003eThey were excluded if they met any of the following: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a PCR-confirmed history of COVID-19 infection; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) completion of the primary vaccination series fewer than six months before recruitment; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) prior receipt of any COVID-19 booster dose; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) baseline serum COVID-19 IgG levels below the detection threshold of the test; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) documented or suspected acute infection (e.g., common cold, influenza) within one month of recruitment; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) any chronic medical condition (such as diabetes, hypertension, or autoimmune disorders); (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) regular use of immunosuppressive agents (e.g., corticosteroids); (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) regular use of anti-allergy medications; or (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) daily tobacco use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Study Design:\u003c/h2\u003e \u003cp\u003eA quasi-experimental design was employed for this study. Using a census sampling strategy, all 1,658 medical students enrolled at AIMC were invited to participate. Recruitment announcements were distributed via posters on departmental notice boards, brief presentations in lecture halls, and a notification sent through the AIMC online student portal. Volunteers were invited for one-on-one interviews, during which the study objectives, procedures, and ethical safeguards were explained and written informed consent was obtained.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Data Collection:\u003c/h2\u003e \u003cp\u003eParticipants were recruited following a detailed interview, during which the study characteristics, design, intervention, and ethical considerations were thoroughly explained. Baseline demographic information collected included age, gender, contact information, blood group, height and weight, vaccination status, history of any supplement use, history of tobacco use, history of previous COVID-19 infection, history of any ongoing acute or chronic infections or diseases, history of allergies, and the history of use of any antiallergy and immunosuppressive drugs. The height and weight of each participant were measured during the interview using the HRS protocol (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIneligible volunteers were informed via email. Eligible volunteers were assigned a unique participant ID number, which was used in all subsequent data collection to ensure confidentiality.\u003c/p\u003e \u003cp\u003eParticipants were instructed to maintain their habitual dietary and sunlight\u0026ndash;exposure patterns and to refrain from initiating any new nutritional supplements. Study staff conducted weekly follow-up reminders to reinforce adherence to these instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Baseline Serology:\u003c/h2\u003e \u003cp\u003eBaseline venous blood samples were collected from all participants in accordance with WHO best-practice guidelines for phlebotomy (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The samples were taken in a separate room allocated to the project in the Pathology department of AIMC by an expert phlebotomist. The blood sample collection of all participants spanned a period of three days.\u003c/p\u003e \u003cp\u003eSerology tests for baseline vitamin D and COVID-19 IgG levels were performed once the samples for all the participants were taken. All tests were conducted in the pathology department laboratory of AIMC/JHL. The 25(OH) Bio-active Vitamin D ELISA test kit was used to determine vitamin D levels, and the RatioDiagnostic COVID-19 IgG ELISA test kit was used to determine COVID-19 IgG levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Booster dose administration:\u003c/h2\u003e \u003cp\u003eOnce the baseline sampling was done, all participants were given the booster dose for PBNT COVID-19 Vaccine over the course of three days. The booster dose was given in the vaccination center of the JHL by a trained nurse following CDC protocols for vaccine administration (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Doses of 0.3mL were administered intramuscularly after dilution as recommended by the guidelines (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Post-intervention serology:\u003c/h2\u003e \u003cp\u003eVenous blood samples of the participants were taken four weeks after the booster dose, timed to coincide with the expected peak of the humoral response, as broad surveys of antibody kinetics confirm that peak IgG levels typically occur between days 20 and 30 following booster administration (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The sample was drawn in the same conditions as the pre-intervention sampling over the course of three days. Similarly, the same COVID-19 IgG test was performed on the collected samples to assess the increase or decrease in the IgG titer.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Study Timeline:\u003c/h2\u003e \u003cp\u003eThe study commenced on August 15, 2023, and was completed on 22nd December 2023. Participants were recruited over a period of 2 months from August 15 to October 13. The pre-intervention serology was completed between October 16 and October 19. Booster dose was administered in a period of three days beginning on October 25. A 4-week delay period followed to allow the intervention to take effect. The post-intervention samples were drawn beginning November 22 through November 24.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Study Endpoints:\u003c/h2\u003e \u003cp\u003eThe primary aims of this study were to quantify the humoral response of the PBNT booster by measuring the rise in SARS-CoV-2 spike\u0026ndash;specific IgG antibody titers following administration, and to examine whether participants\u0026rsquo; serum vitamin D levels were associated with the magnitude of these post-booster IgG responses, thereby assessing the potential modulatory effect of vitamin D on mRNA vaccine\u0026ndash;induced humoral immunity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Sample Size:\u003c/h2\u003e \u003cp\u003eNo formal sample size calculation was performed for this quasi-experimental study. Instead, all eligible individuals who volunteered were enrolled. Recruitment continued until the end of the designated enrollment period, resulting in the final cohort size being determined by participant availability and willingness to participate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis:\u003c/h2\u003e \u003cp\u003eWe used IBM SPSS Statistics 25 to perform the statistical analysis. All collected data and the results of serology testing were recorded on a Microsoft Excel sheet. The descriptive statistics throughout the manuscript are reported as mean and standard deviation unless stated otherwise. All baseline characteristics of the participants are reported separately for males and females. Independent samples t-tests were applied to compare baseline means, and chi-square tests were used to compare baseline proportions between groups. Pearson\u0026rsquo;s correlation coefficient was calculated for assessing the correlation between average vitamin D levels and change in COVID-19 IgG and was reported separately for males and females. Average of pre and post-intervention vitamin D levels was utilized. A value of zero implies no correlation, a positive value implies a positive correlation, and a negative value suggests a negative correlation among the variables. The higher magnitude of the correlation coefficient the stronger correlation.\u003c/p\u003e \u003cp\u003eA pre-post analysis was performed on the COVID-19 IgG titers before and after administration of the booster dose and a paired t-test was applied to determine the statistical significance. A positive value of change in IgG implies an increase in the serum IgG titer after administration of the booster dose and vice versa. A multivariate logistic regression model was also performed to assess the effectiveness of the COVID-19 booster and was adjusted for gender, baseline vitamin D levels, BMI and sunlight exposure time. The effectiveness of the COVID-19 booster was defined as a positive increase in post-intervention IgG antibody titers. Odds ratios (ORs) with a 95% confidence interval were reported for the effectiveness of the booster. An OR of one indicates equal booster effectiveness between groups, whereas an OR greater than 1 suggests greater effectiveness compared to the reference group, and an OR less than 1 indicates lower effectiveness. A p-value of less than 0.05 was considered significant. All graphs were made using Microsoft Excel.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOut of the total 1658 students, 134 volunteered to participate in the study. 70 volunteers fulfilled our eligibility criteria and were invited for blood sampling and vaccination. Finally, the study reports outcomes for 54 participants with the rest of them being lost during the course of study as illustrated in the flow diagram in Figure 1.\u003c/p\u003e\n\u003cp\u003eTable 1 reports the baseline characteristics of all the participants as well as separate baseline characteristics for males and females. The mean age of all participants was 21.6 (1.6) years. 55% of the participants had sunlight exposure time of 15 minutes or more. Baseline COVID-19 IgG levels and average Vitamin D levels were found to be 34.2 (14.7) AU/ml and 16.9 (4) ng/ml respectively. Almost 85% of the participants had received either Sinovac (37%) or Sinopharm (48%) vaccine previously. The study observed comparable baseline characteristics between males and females including BMI, sunlight exposure, baseline COVID-19 IgG titers, average vitamin D levels and previous vaccine received. However, a statistically significant difference was noted between the age of the two groups (\u003cem\u003eP\u003c/em\u003e = 0.013) as evident in Table 1.\u003c/p\u003e\n\u003cp\u003eThe study revealed that upon receiving the booster vaccination, a statistically significant positive change in COVID IgG levels was observed for all participants (mean = 4.7; SD = 11.3; \u003cem\u003eP\u003c/em\u003e = 0.003) as well as for males (mean = 3.7; SD = 9.9; \u003cem\u003eP\u003c/em\u003e = 0.028) describing an effective response. However, for females, the change in IgG levels was found to be statistically insignificant (\u003cem\u003eP\u003c/em\u003e = 0.061) as shown in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression was performed to identify the baseline factors independently predicting the effectiveness of the booster dose. Gender, BMI, sunlight exposure and prior vaccine type were taken as independent predictors. Although the results were statistically insignificant for the whole model as well as for independent predictors, some notable ones included almost twice the odds of the booster dose being effective in females as compared to males (adjusted OR = 1.8; 95% CI: 0.40, 8.32; \u003cem\u003eP\u003c/em\u003e = 0.44) and almost thrice the odds of booster dose effectiveness in those who had received Pfizer vaccine previously as compared to those who had received Sinovac (adjusted OR = 2.9; 95% CI: 0.26, 33.16; \u003cem\u003eP\u003c/em\u003e = 0.390) as shown in Table 3.\u003c/p\u003e\n\u003cp\u003eA linear correlation between change in COVID IgG levels and average vitamin D levels of all participants was examined but was found to be insignificant (r = 0.185; 95% CI: -0.89, 0.43; \u003cem\u003eP\u003c/em\u003e = 0.18) as illustrated in Figure 2. Similarly, linear correlation between these two variables was also examined for the two genders separately. We observed a positive linear correlation for males signifying a greater increase in COVID IgG levels after receiving booster dose at higher levels of vitamin D (r = 0.451; 95% CI: 0.15, 0.67; \u003cem\u003eP\u003c/em\u003e = 0.05) as shown in Figure 3. For females, however, a slightly negative but statistically insignificant correlation was observed between COVID IgG levels and average vitamin D levels (r = -0.157; 95% CI: -0.6, 0.37; \u003cem\u003eP\u003c/em\u003e = 0.561) as illustrated in Figure 4.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis quasi trial aimed at exploring the effectiveness of PBNT booster dose in healthy young adults as well as the relationship between COVID-19 IgG titers and vitamin D levels of the participants. This study demonstrated that the PBNT booster dose effectively increases COVID-19 IgG antibody levels in healthy young adults. We also explored the relationship between serum vitamin D levels, prior vaccine type, and gender, highlighting several nuanced findings.\u003c/p\u003e \u003cp\u003eA positive increase in the COVID-19 IgG was demonstrated. While IgG levels are an important marker of humoral immunity, their correlation with functional protection is critical. Neutralizing antibodies, a subset of IgG, have been shown to predict protection against symptomatic SARS-CoV-2 infection (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A study by Khoury et al. demonstrated that neutralization levels strongly correlate with protection, with 50% protection against detectable infection achieved at 20.2% of the mean convalescent neutralization level, and protection from severe disease requiring even lower levels (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These findings emphasize the predictive value of antibody titers in determining protection and highlight the importance of sustained antibody levels and functionality to mitigate both infection and severe disease.\u003c/p\u003e \u003cp\u003eThe relationship between vitamin D and IgG production in our study revealed a positive correlation in males, but no significant effect in females. This is consistent with a randomized controlled trial by Fateh et al., which demonstrated a significant increase in IgG levels following vitamin D supplementation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These findings suggest that vitamin D might play a role in enhancing vaccine-induced immunity, but this role may be influenced by biological and hormonal factors (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, our findings contradict those of Chillon et al., who found no significant difference in the dynamic changes of IgG levels or neutralization potency with respect to vitamin D status (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and Jolliffe et al., who reported that vitamin D supplementation did not affect vaccine-induced IgG titers or neutralizing antibody responses (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These inconsistencies may stem from variations in baseline vitamin D levels, population characteristics, and immune system variability, underscoring the complex role of vitamin D in modulating immune responses.\u003c/p\u003e \u003cp\u003eInterestingly, our data showed a greater IgG response to the booster dose in males, which deviates from the typical trend in vaccine studies where females often exhibit stronger immune responses (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This difference may be attributed to the hormonal and genetic factors that influence immune function. Klein et al. found sex-specific differences in immune responses, with females showing more robust activation of adaptive immune cells, including CD4\u0026thinsp;+\u0026thinsp;T cells and B cells. While this could explain the general trend of stronger responses in females, it does not fully account for our findings of higher IgG levels in males (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Moreover, the study by Polack et al. on the safety and efficacy of the Pfizer vaccine reported similar vaccine efficacy in males and females but did not observe sex-based differences in immune responses (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This discrepancy suggests that our observations may reflect specific characteristics of the population studied, or perhaps other confounding factors that were not considered in these larger cohort studies.\u003c/p\u003e \u003cp\u003eIn terms of prior vaccine type, participants who received the PBNT vaccine showed stronger IgG responses to the booster compared to those who had received the Sinovac vaccine, although this difference was not statistically significant. This aligns with the immunological principle that mRNA vaccines, such as PBNT, elicit robust T-cell and memory B-cell responses (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Re-exposure to the same vaccine platform is known to enhance these recall responses (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Several limitations must be acknowledged. First, the small sample size reduces the statistical power of our findings, particularly for subgroup analyses, limiting our ability to detect subtle differences or interactions between variables. Second, as a single-arm study, the absence of a control group limits our ability to directly compare the effectiveness of the PBNT booster to other vaccine boosters or to no intervention. Finally, despite our efforts to standardize lifestyle factors, residual confounding from unmeasured variables, particularly dietary intake, sunlight exposure, and physical activity, cannot be ruled out as a potential influence on the study outcomes.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Conclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, our findings highlight the complexity of factors influencing COVID-19 vaccine-induced immunity, including gender, baseline immunity, and prior vaccine type. Future research should further explore these relationships, considering confounding variables such as vitamin D status and lifestyle factors, to provide a clearer understanding of the immunological mechanisms behind vaccine responses. Larger-scale studies with diverse populations and extended follow-up periods are essential to validate these preliminary findings and inform public health strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e5.1. Funding\u003c/h2\u003e\n\u003cp\u003eThis research was funded by the Research Grant Board of the Allama Iqbal Medical College, Lahore under grant number 10/RGB/AIMC/21\u003c/p\u003e\n\u003ch2\u003e5.2. Conflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors have no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003e5.3. Acknowledgements\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003e5.4. Data Availability Statement\u003c/h2\u003e\n\u003cp\u003eThe research data can be provided upon request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCoronavirus [Internet]. [cited 2025 Apr 26]. 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The Effect of Vit-D Supplementation on the Side Effect of BioNTech, Pfizer Vaccination and Immunoglobulin G Response Against SARS-CoV-2 in the Individuals Tested Positive for COVID-19: A Randomized Control Trial. Clin Nutr Res [Internet]. 2023 [cited 2025 Apr 27];12(4):269. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10641329/\u003c/li\u003e\n\u003cli\u003eAranow C. Vitamin D and the Immune System. J Investig Med [Internet]. 2011 [cited 2025 Apr 27];59(6):881. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3166406/\u003c/li\u003e\n\u003cli\u003eChillon TS, Demircan K, Heller RA, Hirschbil-Bremer IM, Diegmann J, Bachmann M, et al. Relationship between Vitamin D Status and Antibody Response to COVID-19 mRNA Vaccination in Healthy Adults. Biomedicines [Internet]. 2021 Nov 1 [cited 2025 Apr 27];9(11):1714. 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Available from: https://www.nature.com/articles/nri.2016.90\u003c/li\u003e\n\u003cli\u003ePolack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N Engl J Med [Internet]. 2020 Dec 31 [cited 2025 Apr 27];383(27):NEJMoa2034577. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7745181/\u003c/li\u003e\n\u003cli\u003eYu Y, Esposito D, Kang Z, Lu J, Remaley AT, De Giorgi V, et al. mRNA vaccine-induced antibodies more effective than natural immunity in neutralizing SARS-CoV-2 and its high affinity variants. Scientific Reports 2022 12:1 [Internet]. 2022 Feb 16 [cited 2025 Apr 27];12(1):1\u0026ndash;10. Available from: https://www.nature.com/articles/s41598-022-06629-2\u003c/li\u003e\n\u003cli\u003eCharles A Janeway J, Travers P, Walport M, Shlomchik MJ. Immunological memory. 2001 [cited 2025 Apr 27]; Available from: https://www.ncbi.nlm.nih.gov/books/NBK27158/\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBaseline characteristics of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eall\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eparticipants\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;stratified by gender.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll participants\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMales*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge,\u0026nbsp;\u003c/strong\u003eyears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e21.64 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e21.82 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e20.63 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI,\u0026nbsp;\u003c/strong\u003e\u003cem\u003ekg/m\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e22.79 (3.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e23.46 (4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e21.19 (2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.051\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSunlight exposure time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.257\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLess than 15 minutes, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e24 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e9 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e7 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e15 minutes and more, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e30 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e15 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e23 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline COVID-19 IgG titer,\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAU/mL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e34.27 (14.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e35.52 (14.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e31.3 (14.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.341\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Vitamin D levels,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eng/mL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e16.88 (3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e17.37 (3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e15.71 (4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.167\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of Previous Vaccine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.848\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSinovac\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e20 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e14 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e6 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSinopharm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e26 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e19 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e7 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePfizer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e8 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e5 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003eAll values are reported in mean and standard deviation until stated otherwise. The significant P-values are bolded.\u003cbr\u003e*Independent sample t-test and chi-square test are implied to determine whether the differences among the means and proportions of both genders are significant or not.\u0026nbsp;\u003cbr\u003eBMI = Basal metabolic index\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Allama Iqbal Medical College","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":"Booster dose response, COVID-19, humoral immunity, Pfizer-BioNTech, Vitamin D","lastPublishedDoi":"10.21203/rs.3.rs-9481803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9481803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eVitamin D has immunomodulatory effects, yet its influence on SARS-CoV-2 vaccine\u0026ndash;induced antibody responses remain unclear. This study assessed the humoral immunogenicity of a Pfizer\u0026ndash;BioNTech COVID-19 booster and whether baseline serum vitamin D levels correlate with post-booster IgG titers.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eIn this single-arm quasi‐experimental study (ERB #169/06/01/2022/S1 ERB), 54 eligible medical students received a Pfizer\u0026ndash;BioNTech booster. Vitamin D levels and IgG titers were assessed on baseline and four weeks post-intervention. Statistical analyses included paired t-tests for pre-post IgG changes, Pearson\u0026rsquo;s correlation between vitamin D levels and IgG change, and multivariate logistic regression adjusted for gender, baseline vitamin D, BMI, and sunlight exposure. Significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe cohort (n\u0026thinsp;=\u0026thinsp;54) exhibited a significant overall increase in IgG titers after booster (Δ\u0026thinsp;=\u0026thinsp;4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 AU/mL, p\u0026thinsp;=\u0026thinsp;0.003). Males showed a significant rise (Δ\u0026thinsp;=\u0026thinsp;3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 AU/mL, p\u0026thinsp;=\u0026thinsp;0.028), whereas females did not (p\u0026thinsp;=\u0026thinsp;0.061). No significant correlation was found between baseline vitamin D and IgG change in the total sample (r\u0026thinsp;=\u0026thinsp;0.185, p\u0026thinsp;=\u0026thinsp;0.18). Stratified analysis demonstrated a positive correlation in males (r\u0026thinsp;=\u0026thinsp;0.451, p\u0026thinsp;=\u0026thinsp;0.05) and a non-significant negative correlation in females (r = \u0026minus;\u0026thinsp;0.157, p\u0026thinsp;=\u0026thinsp;0.56). Logistic regression did not identify significant predictors of booster effectiveness.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe Pfizer\u0026ndash;BioNTech booster elicits a robust IgG response in healthy young adults. Baseline vitamin D status did not uniformly influence antibody increases, though a male-specific positive association warrants further investigation. Larger, controlled studies are needed to clarify vitamin D\u0026rsquo;s role in modulating post-vaccination immunity.\u003c/p\u003e","manuscriptTitle":"Effectiveness of Pfizer-BioNTech COVID-19 Booster and its Correlation with Vitamin D levels – a Quasi Experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 10:34:51","doi":"10.21203/rs.3.rs-9481803/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":"f1e354c4-efe5-4292-9d63-9252c51b4865","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66805713,"name":"Immunology"},{"id":66805714,"name":"Infectious Diseases"},{"id":66805715,"name":"Virology"}],"tags":[],"updatedAt":"2026-04-22T10:34:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 10:34:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9481803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9481803","identity":"rs-9481803","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}