Long-term dynamics of natural, vaccine-induced, and hybrid immunity to SARS-CoV-2 in a university hospital in Colombia: A cohort study

Long-term dynamics of natural, vaccine-induced, and hybrid immunity to SARS-CoV-2 in a university hospital in Colombia: A cohort study | 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 Article Long-term dynamics of natural, vaccine-induced, and hybrid immunity to SARS-CoV-2 in a university hospital in Colombia: A cohort study Nohemi Caballero, Diana M. Monsalve, Yeny Acosta-Ampudia, Natalia Fajardo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3995124/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 This prospective cohort study aimed to estimate the natural, vaccine-induced, and hybrid immunity to SARS-CoV-2, alongside the immunogenicity of the mRNA-1273 booster after the BNT162b2 primary series in healthcare workers in Colombia. IgG, IgA, and neutralizing antibodies were measured in 110 individuals with SARS-CoV-2 infection or a BNT162b2 primary series. Humoral responses and related factors were explored in a subgroup (n = 36) that received a BNT162b2 primary series followed by a mRNA-1273 booster (2BNT162b2 + 1mRNA-1273), and T-cell responses were evaluated in a subgroup of them (n = 16). For natural immunity, IgG and IgA peaked within three months, declining gradually but remaining detectable up to 283 days post-infection. Neutralizing antibody inhibition post-infection was below positive range (≥ 35%) but exceeded 97% in vaccine-induced and hybrid immunity groups. Following 2BNT162b2 + 1mRNA-1273, IgG peaked 3–4 months post-booster, gradually declining but remaining positive over 10 months, with IgA and neutralizing antibodies stable. Age and blood group were related to IgG response, while obesity and blood type to IgA response post-booster. Autoimmunity and blood type B were associated with lower neutralizing antibody inhibition. There were no differences in T-cell responses according to prior infection. These findings provide long-term insights into the immunity against SARS-CoV-2 and the immunogenicity of mRNA vaccines. SARS-CoV-2 healthcare workers mRNA vaccines humoral response T-cell response immunogenicity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The COVID-19 pandemic has posed significant threats to public health and health systems globally. Various strategies have been adopted worldwide to mitigate the disease burden. These included prevention measures such as social distancing, travel restrictions, and lockdowns 1 , as well as the accelerated spread of safe and effective vaccines and the strengthening of health systems to facilitate the prevention, detection, and treatment of COVID-19 2 . SARS-CoV-2 infections generate an immune response capable of reducing the risk of re-infection 3,4 . However, this response is influenced by various factors, including virus-dependent mechanisms, such as the evolution of antigenically distinct viral variants. For instance, protection from natural immunity against re-infection remains strong for pre-Omicron variants but weakens faster for Omicron and its sublineages 3 . While past infections can trigger a robust immune response, vaccination has played a vital role in mitigating the spread of SARS-CoV-2. The development and implementation of COVID-19 vaccines have led to a progressive decline in morbidity and mortality 5,6 . Multiple vaccines employing different platforms have been made available to the public, and some are still under development. After the application of primary schedules, the emergence of viral variants with higher transmissibility and virulence, and the waning of vaccine effectiveness and immunogenicity over time, led to the implementation of booster doses 7,8 . Vaccine boosters were administered with the same vaccine as the primary schedule (homologous booster) or a different vaccine (heterologous booster). The combination of COVID-19 vaccines is a safe and reassuring alternative 9 , particularly useful in the context of vaccine scarcity, such as in low- and middle-income countries 10,11 . Previous studies addressing the immunogenicity of heterologous boosters showed a significant increase in binding and neutralizing antibody titers that correlated with greater protection against viral variants of concern and a higher T-cell response and IFN-γ secretion, compared to homologous boosters 12–15 . However, there is limited evidence regarding the combination of a 2-dose primary series with BNT162b2 and an mRNA-1273 booster, a combination frequently used among health care workers (HCWs) in Colombia. Immune response to vaccines varies according to multiple factors, including intrinsic (e.g., sex, age), environmental (e.g., preexisting immunity), behavioral (e.g., smoking, alcohol consumption, exercise), and vaccine administration factors (e.g., vaccination dose and schedule) 16 . Therefore, it is important to consider these factors when evaluating the immunogenicity of vaccines. A broader understanding of the long-term protection against SARS-CoV-2 conferred by infection, vaccines, and a combination of both is necessary to aid policymaking and prepare for future emergent diseases. Especially, considering the potential applications of mRNA vaccines to prevent infectious diseases, offering a versatile and rapid response strategy that will likely be effective for various emerging pathogens 17–19 . Thus, we aimed to (i) estimate the natural, vaccine-induced, and hybrid humoral immunity against SARS-CoV-2 in HCWs, and (ii) assess the humoral and cellular responses elicited by the mRNA-1273 booster in HCWs previously vaccinated with two doses of BNT162b2. Materials and methods Setting The study was conducted at the University Hospital Fundación Santa Fe de Bogotá (UHFSFB), a tertiary care hospital in Bogotá, the capital and most populated city of Colombia 20,21 . During the COVID-19 pandemic, UHFSFB served as a referral hospital for COVID-19 medical attention in the city 22 . In February 2021, Colombia gradually started the COVID-19 vaccination process in two phases. The first phase sought to reduce mortality and incidence of severe disease and protect HCWs, while the second sought to reduce infectivity to reach herd immunity 23 . HCWs were among the first to complete their primary vaccination schedule. Thereafter, by November 2021, the government approved a booster dose for adults, that was administered at least 6 months after completing the initial schedule 24 . The vaccines available in the country included BNT162b2 (Pfizer), mRNA-1273 (Moderna), Ad26.COV2.S (Janssen), AZD1222 (AstraZeneca), and CoronaVac (Sinovac) 23 . For the booster dose, HCWs could access a homologous booster or a heterologous booster (using an mRNA or a viral vector-based vaccine), according to their preferences and vaccine availability 24 . Study design and participants In 2020, the UHFSFB, in collaboration with Universidad de los Andes, conducted the CoVIDA-FSFB study 25,26 . The prospective cohort study enrolled a cohort of 420 voluntary adult hospital workers recruited between June 25 and October 30, 2020, who underwent routine SARS-CoV-2 RT-PCR and serological testing over six months, until April 30, 2021 (Fig. 1 ). During the follow-up by March 2021, a subgroup of participants received a two-dose BNT162b2 schedule. Collected serum samples were stored at -70°C until further analysis. To estimate the infection-induced, vaccine-induced, and hybrid humoral immunity against SARS-CoV-2, this analysis focused on a subgroup of the CoVIDA-FSFB participants who met any of the following inclusion criteria (n = 110): (i) RT-PCR-confirmed SARS-CoV-2 infection before study recruitment (n = 29), (ii) RT-PCR-confirmed SARS-CoV-2 infection during the study follow-up (n = 57), (iii) received a primary vaccination schedule with BNT162b2 (n = 24). For this analysis, participants with re-infections, contraindications for phlebotomy, and characteristics that hindered follow-up were excluded (e.g., change of residence, planned long-term travel outside the city). Stored samples of eligible participants were subsequently sent to the Center for Autoimmune Diseases Research (CREA) for analysis. Subsequently, the participants received the first vaccine booster between November 26, 2021, and January 4, 2022. Those who received a booster dose of mRNA-1273 after a two-dose primary schedule of BNT162b2 (2BNT162b2 + 1mRNA-1273 schedule) (n = 36) were invited to participate in an ancillary component to study the humoral immunogenicity of this vaccine combination (Fig. 1 ). The cellular immune response was evaluated in a subset without underlying comorbidities, acute infections, and chronic or acute use of medication (n = 16). Individuals were scheduled a new visit between March 24 and April 11, 2022, to assess eligibility for this study component, and to collect additional information and blood samples to assess humoral and cellular immunogenicity in those who were eligible. Subsequently, they were followed for six additional months, which concluded on October 25, 2022. During this period, blood samples for humoral immunity assessment were collected at 6 and 9 months after the booster, and participants were contacted monthly to identify laboratory-confirmed COVID-19 cases (Fig. 1 ). Outcomes Measurement of IgG, IgA, and neutralizing antibodies The Euroimmun anti-SARS-CoV-2 ELISA (Euroimmun, Luebeck, Germany) was used for serological detection of human IgG and IgA antibodies against the SARS-CoV-2 wild-type S1 structural protein, following the manufacturer’s instructions, as previously described 27 . To evaluate results, a ratio of the OD of the patient sample over the OD of the calibrator was calculated. Ratios < 0.8 were deemed negative, ≥ 0.8 to < 1.1 were considered borderline, and ≥ 1.1 were classified as positive. Antibody positivity was determined using a 1:100 dilution. The anti-S SARS-CoV-2 IgG II Quant assay (S-IgG) (Abbott, Sligo, Ireland) was used to assess the IgG response to the 2BNT162b2 + 1mRNA-1273 schedule. The assay was conducted on the Abbott ARCHITECT i2000SR system according to the manufacturer’s instructions 28 . The assay allows for qualitative and quantitative determination of IgG antibodies against the SARS-CoV-2 glycoprotein receptor binding domain (RBD) in human serum and plasma 29 . The units of the quantitative Abbott anti-S assay, Arbitrary Units per milliliter (AU/mL), were converted to the World Health Organization (WHO) units, Binding Antibody Units per milliliter (BAU/mL), by multiplying by a factor of 0.142, according to the manufacturer’s instructions. To evaluate the neutralizing capacity of anti-SARS-CoV-2 antibodies, the semi-quantitative assay NeutraLISA kits (EUROIMMUN, Lübeck, Germany) was used. This kit detects IgG antibodies capable of neutralizing the S1 subunit where RBD of the SARS-CoV-2 spike protein is located. Results were reported as percent inhibition (%Inhibition) following the manufacturer's instructions, as previously described 30 . Samples were classified as negative (< 20% inhibition), positive (≥ 35% inhibition), or inconclusive (20–34% inhibition). PBMC Isolation and cryopreservation Blood collected in EDTA-anticoagulated tubes was used to isolate peripheral blood mononuclear cells (PBMCs) through a density gradient centrifugation method using Ficoll-Histopaque 1077 (Sigma-Aldrich, St Louis, USA) following the manufacturer's instructions. For cryopreservation, the isolated PBMCs were washed twice with complete RPMI-1640 media (Gibco, NY, USA) and then frozen and stored in fetal bovine serum (FBS) (BioWest, Riverside, USA) containing 10% dimethyl sulfoxide (DMSO) (Sigma-Aldrich, St Louis, USA). Cryovials containing the PBMCs were initially stored at -70°C to allow for a gradual temperature decrease. After 24 hours, these cryovials were transferred to a liquid nitrogen tank, where they were stored until further use. Measurement of SARS-CoV-2 Specific T-cell response To explore the SARS-CoV2-specific T-cell response to the 2BNT162b2 + 1mRNA-1273 schedule, three different peptide pools of SARS-CoV-2 wild type (Mabtech AB, Nacka Strand, Sweden) were used: SARS-CoV-2 S1 scanning pool, which contains 166 peptides from the human SARS-CoV-2 virus; the peptides are 15-mers overlapping with 11 amino acids, covering the S1 domain of the spike protein (amino acid 13–685). SARS-CoV-2 SNMO defined peptide pool that contains 47 synthetic peptides from the human SARS-CoV-2 virus; the peptides are derived from the spike, nucleoprotein, membrane protein, ORF3a and ORF7a. S2 N defined peptide pool, which contains 41 peptides from the human SARS-CoV-2. According to the manufacturer's instructions, each pool of peptides was individually resuspended in DMSO and PBS, resulting in a final stock concentration of 200 µg/mL. Then, cryopreserved PBMCs were thawed in a 37°C water bath, washed twice with RPMI-1640 media pre-warmed to 37°C, and centrifuged at 350g for 10 min. Afterward, cells were analyzed for viability using trypan blue, and seeded at a density of 1 × 106 cells per well in a 96-well plate in RPMI-1640 supplemented with 10% FBS, 100 U/mL Penicillin, 100 µg/mL Streptomycin and 2mM L-glutamine (Gibco, NY, USA). After, cells were rested for 2h and then stimulated with each SARS-CoV-2 peptide pool independently at a final concentration of 2 µg/mL overnight (~ 18h) at 37°C and 5% CO2. As positive control, cells were stimulated with 5 ug/mL of phytohemagglutinin (Sigma Aldrich, St Louis, USA), and as negative control, cells were left unstimulated. All conditions were seeded with Brefeldin A at 10 µg/mL (Sigma Aldrich, St Louis, USA) to inhibit protein transport. The percentage of SARS-CoV-2-specific IFN-γ, IL-2, IL-4 and granzyme B-producing cells were evaluated by flow cytometry. After stimulation with the SARS-CoV-2 peptide pools, cells were harvested and stained with 7AAD-PERCP, anti-CD3-APCH7, anti-CD4-V500 and anti-CD8-APC antibodies (BD Biosciences, CA, USA) at room temperature for 30 min. For intracellular cytokine staining, cells were fixed and permeabilized with BD Cytofix/Cytoperm™ (BD Biosciences, CA, USA), followed by staining with anti-IFN-γ-FITC, anti-IL-2-V450, anti-IL-4-PECy7, and anti-Granzyme B-PE antibodies (BD Biosciences, CA, USA) for 30 min at 4°C in the darkness. Controls for these assays included single-staining and unstained cells, which were used for gating and compensation. Cells were acquired on a FACSCanto II flow cytometer (BD Biosciences) and data were analyzed with FlowJo software version 9 (BD Biosciences). Data Sources During the cohort’s first visit, researchers asked participants about their sociodemographic information and medical history and recorded this information in a medical record and an electronic questionnaire. The medical record was used to document information regarding comorbidities, flu vaccination, and previous viral infections. To gather data about previous viral infections, researchers asked participants whether they had ever been diagnosed with any of the following: dengue, chickenpox, zika, chikungunya, influenza, measles, or hepatitis. These infections were self-reported by the participants, and no specific diagnostic tests conducted to confirm them. For the 2BNT162b + 1mRNA-1273 immunogenicity subgroup, additional data were collected through an electronic questionnaire implemented in REDCap (S2 File in Spanish) 31 . This included data on sociodemographic characteristics (e.g., age, sex, socioeconomic status, city in which they live, and profession), clinical characteristics (e.g., height, weight, Body Mass Index (BMI), blood type, comorbidities, medications, and previous COVID-19 infection), and habits (e.g., physical activity, alcohol, and smoking cigarettes). To avoid inter-interviewer bias, the questionnaire was administered by the same investigator. Information regarding COVID-19 vaccination was obtained from the participant’s vaccination card, which contains information about the vaccination schedule, including the vaccine batch, laboratory, dosage, administration dates, and the health provider institution that administered the vaccine. During the follow-up period, the administration of a second booster dose was approved in Colombia. Participants were asked about receiving this dose, and verification was conducted through the vaccination certificate by the end of the follow-up. Statistical analysis For descriptive analysis qualitative variables were presented as frequencies and proportions, and quantitative variables as means or medians with standard deviations (SD) or interquartile ranges (IQR) depending on their distribution, according to the Shapiro Wilk test. There were no missing data on the independent variables. Missing data on the humoral immunogenicity outcome corresponded to 4.86% (7/144), which were not included in the analysis. IgG and IgA titers were compared before and after a two-dose BNT162b2 primary schedule using the Wilcoxon signed rank test. To compare IgG, IgA, and neutralizing antibodies after the mRNA-1273 booster the Skillings Mack test was used. Differences in IgG, IgA, and neutralizing antibodies according to sociodemographic, clinical variables, and habits were graphically explored. The CMIA kit used to measure anti-spike IgG, for 2BNT162b2 + 1mRNA-1273 immunogenicity assessment, provides values up to 5680 BAU/mL. Values above that threshold were set as equal to the threshold; thus, the data for this variable were right censored. To address this, and given the longitudinal nature of our data, a random effects Tobit model was used to determine factors related to anti-S-RBD IgG antibodies. Regression models were constructed using anti-S-RBD IgG post-booster as the dependent variable. All clinically relevant variables with biological plausibility previously identified through literature search were included as independent variables. Two models were constructed, a bivariate model and a multivariate reduced model with the minimum number of independent variables that best suited the data, using a 0.2 significance level for variable removal from the model 32 . The multivariate model was used to adjust for confounders and detect effect modifiers. All possible interactions between the variables of interest were explored; however, these were not included in the final model as they were not statistically significant. Multicollinearity was assessed using the variance inflation factor (VIF), with a 5.0 cut-off point. Bootstrapping was used to provide more reliable standard errors 33 . The random effects used account for autocorrelation that may arise from within cluster dependencies. The quadrature approximation used in the random-effect estimators was checked, with no relative differences in the coefficients larger than 0.01%. The normal distribution of raw residuals was confirmed. T-cell responses in participants with 2BNT162b2 + 1mRNA-1273, were compared according to whether they previously had COVID-19. Specifically, CD4 + cells producing IFN-γ, IL-2, and IL-4, and CD8 + cells expressing Granzyme B, IFN-γ, IL-2, and IL-4. Responses were evaluated post-stimulation with three distinct peptide pools (S1, SNMO, and S2 N), and the Mann-Whitney U test was used to assess statistical significance. A P value < 0.05 was considered to indicate statistical significance for all statistical tests. Analysis was performed using Stata SE 17.0 34 and visualized in GraphPad Prism version 9 35 . Ethics statement The study protocol was approved by the Fundación Santa Fe de Bogotá Ethics Committee (CCEI-12183-2020, and CCEI-13882-2022). This study was conducted in compliance with Act 008430 − 1993 of the Ministry of Health of Colombia, and classifies as minimal-risk research 36 . All patients provided their written informed consent and were informed about the Colombian data protection law (1581 of 2012). All research was performed in accordance with relevant guidelines and regulations, and in accordance with the Declaration of Helsinki. Results Immunity to SARS-CoV-2 This study included 110 participants: 86 with natural immunity and 24 with vaccine-induced immunity; additionally, 11 participants had hybrid immunity (Fig. 1 ). The median age of participants was 40 years (IQR 33–44 years), most participants were female (81.8%) and from a middle socioeconomic background (66.4%) (Table 1 ). The majority were professional nurses (32.7%), followed by medical doctors and students (15.5%), and more than half had a healthcare position (67.6%). The median BMI was 24.8 (IQR 22.6–26.9 Kg/m 2 ), and 23% had some sort of comorbidity. Table 1 Sociodemographic and clinical characteristics of cohort’s participants (n = 110). Characteristic Total N = 110 Age Median (IQR) 40 ( 33 – 44 ) Sex Male 20 (18.2%) Female 90 (81.8%) Socioeconomic status Low ( 1 – 2 ) 21 (19.1%) Mid ( 3 – 4 ) 73 (66.4%) High ( 5 – 6 ) 16 (14.5%) Occupation Nurse 36 (32.7%) Nurse assistant 14 (12.7%) Medical doctors and students 17 (15.5%) Laboratory workers 13 (11.8%) Administrative assistants 12 (10.9%) Therapists 9 (8.2%) Other 9 (8.2%) Type of position Administrative 23 (21.9%) Blended 11 (10.5%) Healthcare 71 (67.6%) Comorbidities No 85 (77.3%) Yes 25 (22.7%) Active smoking Nonsmoker 82 (74.5%) Previous smoker 23 (20.9%) Current smoker 5 (4.5%) Passive smoking No 78 (72.2%) Yes 30 (27.8%) Previous viral infections * No 72 (65.5%) Yes 38 (34.5%) Influenza vaccine No 18 (16.4%) Yes 92 (83.6%) BMI Median (IQR) 24.8 (22.6–26.9) Underweight 1 (0.9%) Healthy weight 57 (51.8%) Overweight 40 (36.4%) Obesity 12 (10.9%) SARS-CoV-2 infection during follow up No 52 (47.3%) Yes 58 (52.7%) Previous SARS-CoV-2 infection No 81 (73.6%) Yes 29 (26.4%) * Any of the following: dengue, chickenpox, zika, chikungunya, influenza, measles, and hepatitis. BMI: Body Mass Index; IQR: Interquartile range. We assessed the humoral natural immunity up to 283 days post-infection (Fig. 2 A). IgA antibodies peaked earlier, reaching a median ratio of 8.079 on days 8 to 21 post infection, then they began to descend, reaching a median ratio of 3.273 by the end of the follow-up (191 to 283 post infection). On the other hand, IgG antibodies peaked on days 22 to 90, reaching a median ratio of 5.111, and then descended, with a median ratio of 3.352 by days 191 to 283 post infection. We measured neutralizing antibodies in participants with positive IgG anti SARS-CoV-2 antibodies (ratio ≥ 1.1). On days 8 to 21, the mean percentage of inhibition was − 17.85%, followed by 14.86% on days 22 to 90, 12.68% on days 91 to 180 and 22.06% on days 181 to 283 after the infection (Fig. 2 B). In the group with vaccine-induced immunity, for IgG the median ratio increased from 0.205 before vaccination to 8.797 after vaccination (P < 0.0001) (Fig. 2 C). For IgA, the median ratio increased from 0.392 to 9.788 after vaccination (P < 0.0001). The median percentage of inhibition of neutralizing antibodies after vaccination was 97.00% (IQR 94.70%-97.80%). In the group with hybrid immunity, the median ratio for IgG was 9.676 and 8.010 for IgA. As for the neutralizing antibodies, the median percentage of inhibition was 97.61% (IQR 97.25%-98.20%). Humoral immunogenicity of 2BNT162b2 + 1mRNA-1273 A subgroup of 36 participants was included for this analysis. Their mean age was 42 ± 8 years, and most were female (78%) (Table 2 ). Professional nurses accounted for the largest proportion of roles (39%), followed by nursing assistants (22%), and medical doctors (14%). Fifty-three percent of participants had any type of comorbidities, including one participant who had rheumatoid arthritis treated with methotrexate, and one who had ulcerative colitis, treated with azathioprine. Other reported comorbidities included gastritis, migraine, allergies, acne, alopecia, and hypothyroidism. Before baseline, half of the participants had COVID-19, and 14% had been re-infected. During the follow-up period, four laboratory-confirmed SARS-CoV-2 infections occurred. Additionally, three participants received a second booster during the study follow-up. Table 2 Demographics, clinical characteristics, and habits of the subgroup of participants for the assessment of the Humoral Immunogenicity of 2BNT162b2/ 1mRNA-1273 (n = 36). Characteristic Total N = 36 N (%) Sex Female 28 (78) City Bogotá 31 (86) Outside the city 5 ( 14 ) Occupation Professional nurse 14 ( 39 ) Nursing Assistant 8 ( 22 ) Medical doctor 5 ( 14 ) Administrative position 4 ( 11 ) Microbiologist or bacteriologist 4 ( 11 ) Nutritionist 1 ( 3 ) Socioeconomic status Low ( 1 – 2 ) 6 ( 17 ) Mid ( 3 ) 16 ( 44 ) High ( 4 – 6 ) 14 ( 39 ) Previous SARS-CoV-2 infection Yes 18 ( 50 ) Number of previous SARS-CoV-2 infections 0 18 ( 50 ) 1 13 ( 36 ) 2 5 ( 14 ) Comorbidities No 17 ( 47 ) Yes 19 ( 53 ) Autoimmunity No 34 (94) Yes 2 ( 6 ) Use of chronic medications No 34 (94) Yes 2 ( 6 ) Blood group A 14 ( 39 ) B 2 ( 6 ) O 20 ( 56 ) Tobacco smoking Never 33 (92) Past 2 ( 6 ) Current 1 ( 3 ) Moderate alcohol consumption * No 13 ( 36 ) Yes 23 (64) Physical activity ** No 20 ( 56 ) Yes 16 ( 44 ) Second vaccine booster No 33 (92) Yes 3 ( 8 ) * ≤ 2 drinks/day for men and ≤ 1 drink/day for women; ** ≥150 minutes/week of moderate-intensity physical activity or ≥ 75 minutes/week of high-intensity physical activity during the free time Participants’ mean BMI was 25 ± 3 kg/m 2 , and the majority had an O (56%) or A blood type (39%). The majority had never smoked tobacco (93%) and had a moderate alcohol consumption (≤ 2 drinks/day for men and ≤ 1 drink/day for women 37 ) (64%), with a median consumption of two portions (IQR 1–3). More than half of participants (58%) engaged in physical activity regularly, and 44% complied with WHO recommendations for physical activity (≥ 150 minutes/week of moderate intensity physical activity or ≥ 75 minutes/week of high intensity physical activity 38 ). The median time for physical activity was 95 minutes (IQR 0-210) per week. At baseline, the median SARS-CoV-2 anti-S IgG was 3337 BAU/mL (IQR 2060–5489) (Fig. 3 ). On days four to nine after the second dose of BNT162b2 the median anti-S IgG was 3384 BAU/mL (IQR 2090–5666), and on days 29 to 44 days after the second dose it was 2540 BAU/mL (IQR 1642–3341) (S1 Fig). Participants received the booster dose on average 267 days after completing the 2-dose BNT162b2 schedule. The median SARS-CoV-2 anti-S IgG three to four months after receiving the mRNA-1273 booster was 3459 BAU/mL (IQR 988–5680). The difference in medians before the booster and 3–4 months after the booster was not statistically significant (P = 0.6257) (Fig. 3 ). During the follow-up period, SARS-CoV-2 anti-S IgG decreased, with a median of 3306 BAU/mL (IQR 1177–5680) by six to seven months after the booster, and 3188 BAU/mL (IQR 1471–5680) nine to ten months after the booster (P = 0.0173) (Fig. 3 ). The median IgA antibodies ratio 3 to 4 months after the booster was 8.550 (IQR 7.918–8.763). This remained stable over the course of the follow-up, with a median ratio of 8.630 (IQR 7.973–8.803) 6 to 7 months, and 8.610 (IQR 7.840–8.785) 9 to 10 months after the booster. There were no statistically significant differences across these measurements (P = 0.7036) (Fig. 4 A). Regarding neutralizing antibodies, the median percentage of inhibition in months 3 to 4 after the booster was 98.28% (IQR 97.81%-98.69%). There was an increase that then remained stable over the rest of the follow-up (98.57%, IQR 98.21%-98.85% on months 6 to 7, and 98.57%, IQR 98.34%-98.71% on months 9 to 10 after the booster) (P = 0.0340) (Fig. 4 B). Table 3 Factors related to anti-Spike SARS-CoV-2 IgG antibody levels over time after receiving the mRNA-1273 vaccine booster n = 101. Bivariate analysis Multivariate reduced model a Variables Coeff P value 95% CI Coeff P value 95% CI IgG pre-booster -0.149 0.510 -0.594 0.295 -0.321 0.185 -0.796 0.153 Sampling time 3–4 months post-booster Ref 6–7 months post-booster -506.045 0.176 -985.711 312.376 9–10 months post-booster -422.297 0.429 -1468.064 623.4687 Age 145.727 0.001 62.656 228.798 164.564 0.002 60.312 268.815 Sex Male Ref Female 92.634 0.992 -1751.331 1936.600 Socioeconomic status Low ( 1 – 2 ) Ref Mid ( 3 ) -1223.235 0.395 -4039.221 1592.752 High ( 4 – 6 ) -966.225 0.525 -3432.769 1941.230 BMI Normal weight Ref Ref Overweight 723.9461 0.414 -1011.420 2459.312 1237.538 0.186 -596.299 3071.375 Obesity -2133.121 < 0.001 -3195.610 -1070.632 -2008.773 0.099 -4395.815 378.2678 Blood Group A Ref Ref B -2143.335 0.013 -3835.438 -451.232 -5881.276 0.006 -10036.960 -1725.593 O 518.621 0.553 -1193.917 2231.160 -510.676 0.574 -2289.469 1268.117 Autoimmunity No Ref Yes -287.732 0.799 -2498.100 1922.635 Tobacco No Ref Current or previous -1921.504 < 0.001 -2911.658 -931.350 Alcohol consumption * No Ref Yes -241.064 0.786 -1981.401 1499.273 SARS-CoV-2 infection ** No Ref Ref Previous infection -751.919 0.388 -2457.954 954.116 -96.340 0.922 -2029.309 1836.629 Infection during follow-up -1799.949 0.233 -4760.037 1160.139 835.445 0.590 -2199.680 3870.570 Second booster No Ref Ref Yes 2011.721 0.600 -5505.627 9529.070 3663.525 0.350 -1036.802 8363.853 a Wald test P < 0.0001 The multivariate regression model of factors related to anti-spike IgG after the vaccine booster was adjusted for anti-spike IgG levels after the second vaccine dose, age, BMI, blood group, SARS-CoV-2 infection, and a history of receiving a second booster. In this model, for each additional year of age over time, post-booster SARS-CoV-2 anti-spike IgG increased on average 165 BAU/mL (95% CI 60 to 269, P = 0.002) (Table 3 ). Individuals with B blood type had on average 5881 BAU/mL less IgG antibodies post-booster compared to people with group A (95% CI -10037 to -1726, P = 0.006). Additionally, those with obesity and group B blood type had fewer IgA antibodies (P = 0.0278, and 0.0331, respectively) (Fig. 5 ). Similarly, participants with autoimmunity, and those with group B blood type had fewer neutralizing antibodies (P = 0.0158, and 0.0064, respectively) (Fig. 5 ). Cellular immunogenicity For the cellular immunogenicity analysis, a subgroup of 16 participants without acute or chronic diseases or use of medications were included. Their median age was 45 years (IQR 38.5–48.5). The majority were women (75%), 62.5% previously had COVID-19 infection, and their median BMI was 23.98 Kg/m 2 (IQR 22.48–27.73 Kg/m 2 ). There were no statistically significant differences in T-cell responses based on previous SARS-CoV-2 infection (S5 Fig). Discussion Our study provides insights into the long-term humoral immune response to SARS-CoV-2 infection and the humoral and cellular immunogenicity of the mRNA-1273 booster in HCWs previously vaccinated with two doses of BNT162b2. In HCWs with natural immunity, both IgG and IgA responses peaked within the initial three months after infection, remaining positive through follow-up up to 283 days after infection. However, inhibition by neutralizing antibodies was below the positive range (≥ 35%) throughout the follow-up period. Conversely, vaccine-induced and hybrid immunity resulted in a higher percentage of inhibition by neutralizing antibodies, exceeding 97%. After receiving a 2BNT162b2 + 1mRNA-1273 schedule IgG titers decreased over time but remained positive for up to ten months post-booster. IgA and neutralizing antibodies remained stable for the same duration. We identified factors related to humoral response, including age, BMI, autoimmunity, and blood type. After booster administration, an initial increase in antibody levels is expected. A clinical trial conducted in the United States evaluated the humoral immunogenicity of homologous and heterologous schedules up to a month after booster administration 9 . In participants vaccinated with a 2BNT162b2 + 1mRNA-1273 schedule, an increase in antibody levels was evident, peaking by day 15 post-booster 9 . This was also reported after a homologous BNT162b2 booster in HWCs, with IgG and IgA peaking in the third week post-booster 39 . However, prospective studies with longer follow-up periods have shown that humoral responses decrease over time 40,41 . A previous study measured the humoral immune response in a cohort of HWCs after a homologous BNT162b2 booster up to 4 months post-booster and reported a significant decrease in antibody concentration over time, with a reduction of approximately 60% by 150 days after the booster 42 . Yet, studies suggest that despite the decline in IgG titers over time, antibody functions, including neutralizing capacity and Fc-dependent effector functions remain highly relevant for protection against COVID-19 43,44 . In our study, obesity was associated with lower IgA titers after booster administration. Obesity impairs immune responses; studies have reported that BMI is inversely correlated with antibody responses to other vaccines, including hepatitis and influenza 16,45,46 . Furthermore, a systematic review showed that obesity was significantly associated with lower antibody titers after COVID-19 vaccination 47 . Additionally, we identified that blood type B was associated with lower IgG, IgA, and percentage inhibition by neutralizing antibodies. There is scarce and controversial evidence regarding the association between blood type and humoral response. Blood group antigens are important receptors or coreceptors for microorganisms and may influence responses to other vaccines, such as polio 16 . Regarding COVID-19, blood group B was reported to be associated with a higher susceptibility to infection in non-vaccinated individuals 48 . However, ABO-type has been shown to have no association with vaccine effectiveness 49 . A large study on 3,187 convalescent plasma donors found no evidence to confirm that the ABO type influenced the level of SARS-CoV-2 antibody response 50 . Studies with larger sample sizes are required to confirm whether ABO-type influences humoral responses after COVID-19 vaccination. We also identified that older age was associated with higher IgG responses post-booster, likely due to older individuals having greater prior deficits. This aligns with previous research indicating that the response after the vaccine booster was enhanced in older people without prior infection who exhibited lower baseline levels 51 . Monitoring antibody levels over time helps determine how long a response may last. However, immune correlates of protection have not been established; thus, it is not yet clear whether higher levels of antibodies correlate with better outcomes. A randomized clinical trial analyzed the association between antibody levels and SARS-CoV-2 infection, showing that higher levels of all immune markers were correlated with a reduced risk of symptomatic SARS-CoV-2 infection 52 . Nevertheless, infection can still occur in the presence of high levels of antibodies 53 . Correlates of protection after COVID-19 vaccination are probably relative, meaning that most infections are prevented at a particular level of response, but some will occur above that level, likely because of host-dependent factors 53,54 . Previous findings have demonstrated that vaccination combined with natural infection is better than vaccination alone 55 . Although we did not find differences among T-cell responses to peptides pools, likely due to the small sample size for this analysis, other studies have shown higher IFN-γ and IL-2 responses to the S, M, and N proteins in previously infected and vaccinated individuals compared with those in uninfected participants after mRNA vaccines 56 . Moreover, previous studies have suggested that prior SARS-CoV-2 infection enhances immune responses to S, N, and M proteins after vaccination 57 . The strengths of the present study include the longitudinal design with quantitative repeated measures of antibodies over time and the consideration of factors related to humoral response. We analyzed multiple components of the humoral response, including IgG, IgA, and neutralizing antibodies. We prospectively followed participants up to 9.4 months after infection and 10 months after receiving the vaccine booster, providing insights into the kinetics of the humoral response on the long term. To address the longitudinal and censored nature of the CMIA kit data we constructed a random effects Tobit model. Although there are other strategies to address censoring, such as the truncated regression, this may provide inconsistent estimates of the parameters 58 . By considering the within-subject variation in antibody responses over time, the random-effects model accounts for fluctuations in responses that may be due to factors other than the vaccine booster. This study has some limitations. We consecutively included participants without employing probabilistic sampling methods; thus, our study results may only be extrapolated to populations that share similar characteristics. Moreover, the small sample size of each group for cellular immunity comparisons did not allow for the robustness needed to make strong statistical conclusions. In conclusion, our results provide insights into the long-term immune response against SARS-CoV-2. The 2BNT162b2 + 1mRNA-1273 schedule generated a humoral response in HCWs for up to 10 month. Further exploration of factors related to immune responses to vaccines is relevant to tailor efficient vaccination strategies. Declarations Author Contributions NC: Conceptualization, methodology, validation, formal analysis, investigation, data curation, writing original draft, visualization, project administration, funding acquisition); DMM: Methodology, validation, investigation, data curation, writing review and editing; YA: Methodology, validation, investigation, data curation, writing review and editing; NF: Methodology, Investigation, Writing - Original Draft, Visualization; SM: Conceptualization, methodology, formal analysis, writing review and editing; OM: Conceptualization, writing review and editing; CG: Conceptualization, writing review and editing; CR: Conceptualization, methodology, writing original draft, project administration, funding acquisition, supervision; JQ: Conceptualization, methodology, writing review and editing, project administration, funding acquisition, supervision. Additional information The authors declare no competing interests. Acknowledgments The authors would like to express their great appreciation to all health workers who participated in this study. They would also like to acknowledge various people for their contributions to this study: Dr Henry Gallardo, director of Fundación Santa Fe de Bogotá, for supporting the conduction and funding of the study; Dr Dario Londoño, director of Population Health at Fundación Santa Fe de Bogotá for supporting the conduction and funding of the study; Sebastian Cortes Corrales for providing guidance in the statistical analysis; Paula Andrea Rodriguez Urrego and Carolina Reyes Perdomo (Department of Pathology and Laboratory Medicine of Fundación Santa Fe de Bogotá) for overseeing laboratory related activities; Noah Brazer for proofreading assistance. References Girum, T. et al. Optimal strategies for COVID-19 prevention from global evidence achieved through social distancing, stay at home, travel restriction and lockdown: a systematic review. Arch. 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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-3995124","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280896014,"identity":"fbde0d99-7562-42f1-af7c-575cac23cab3","order_by":0,"name":"Nohemi Caballero","email":"","orcid":"","institution":"Population Health, Fundación Santa Fe de Bogotá","correspondingAuthor":false,"prefix":"","firstName":"Nohemi","middleName":"","lastName":"Caballero","suffix":""},{"id":280896017,"identity":"dec8cbf9-fbbe-4455-ac88-c5116f778dbf","order_by":1,"name":"Diana M. Monsalve","email":"","orcid":"","institution":"Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"M.","lastName":"Monsalve","suffix":""},{"id":280896019,"identity":"73cc33eb-753f-4f9f-8d83-1597cf7c316d","order_by":2,"name":"Yeny Acosta-Ampudia","email":"","orcid":"","institution":"Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario","correspondingAuthor":false,"prefix":"","firstName":"Yeny","middleName":"","lastName":"Acosta-Ampudia","suffix":""},{"id":280896021,"identity":"bd565e3d-6a25-41fe-8cd0-4b3a75112bfb","order_by":3,"name":"Natalia Fajardo","email":"","orcid":"","institution":"School of Medicine, Universidad de los Andes","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Fajardo","suffix":""},{"id":280896023,"identity":"0585c40a-6491-4008-b959-5d062355c856","order_by":4,"name":"Sergio Moreno","email":"","orcid":"","institution":"School of Medicine, Universidad de los Andes","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Moreno","suffix":""},{"id":280896026,"identity":"6d101694-5565-4adb-82b5-639a98f29d3f","order_by":5,"name":"Oscar Martínez","email":"","orcid":"","institution":"Department of Pathology and Laboratories, Fundación Santa Fe de Bogotá","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Martínez","suffix":""},{"id":280896028,"identity":"d0ab4179-8412-42ed-ad55-09a7ff37da32","order_by":6,"name":"Catalina González-Uribe","email":"","orcid":"","institution":"School of Medicine, Universidad de los Andes","correspondingAuthor":false,"prefix":"","firstName":"Catalina","middleName":"","lastName":"González-Uribe","suffix":""},{"id":280896032,"identity":"c6fddf8b-2875-4c39-8632-aa8be83f3b7a","order_by":7,"name":"Carolina Ramírez-Santana","email":"","orcid":"","institution":"Center for Autoimmune Diseases Research (CREA), School of Medicine and Health Sciences, Universidad del Rosario","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Ramírez-Santana","suffix":""},{"id":280896034,"identity":"983e5c8f-c92d-4396-bfcd-4649f27154a2","order_by":8,"name":"Juliana Quintero","email":"data:image/png;base64,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","orcid":"","institution":"Population Health, Fundación Santa Fe de Bogotá","correspondingAuthor":true,"prefix":"","firstName":"Juliana","middleName":"","lastName":"Quintero","suffix":""}],"badges":[],"createdAt":"2024-02-28 00:14:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3995124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3995124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53194261,"identity":"105205e0-c335-4ae2-8dcb-25591f4ed9a1","added_by":"auto","created_at":"2024-03-21 18:11:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":844580,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design. \u003c/strong\u003e\u003cem\u003eThe CoVIDA-FSFB cohort included a group of 420 adult hospital workers. A. During the CoVIDA-FSFB project, periodic blood samples were collected over 6 months. Additional samples were collected from participants with RT-PCR-confirmed SARS-CoV-2 infection during the acute phase. B. The samples stored from a\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003esubset of 110 participants of the CoVIDA-FSFB cohort were analyzed, including those who had a SARS-CoV-2 infection before recruitment or during the cohort follow-up time, or who had received a 2-dose BNT162b2 schedule. In this group, the humoral response to SARS-CoV-2 was analyzed, including vaccine-induced (n=24), infection-induced (n=86), and hybrid immunity (n=11), by measuring IgG, IgA, and neutralizing antibodies. Samples were analyzed according to the time after infection or vaccination. C. A subgroup of 36 participants who received a two-dose BNT162b2 primary schedule, and an mRNA-1273 booster continued to be followed. In this group, a blood sample was collected 4-44 days after the 2-dose BNT162b2 schedule and three, six, and nine months after the mRNA-1273 booster to assess the humoral immunogenicity (IgG, IgA, and neutralizing antibodies). On month three after the booster, cellular immunogenicity was analyzed in a subgroup of 16 of them, without underlying comorbidities, acute infections, and chronic or acute use of medication. Figure created with BioRender.com.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/16187327794342977a80e924.png"},{"id":53194260,"identity":"cc10bb0c-eda9-4037-952d-0558c2e83584","added_by":"auto","created_at":"2024-03-21 18:11:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":587503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamic of SARS-CoV-2 immunity. \u003c/strong\u003e\u003cem\u003eLevels of anti-SARS-CoV-2 IgA, IgG, and neutralizing antibodies over time. A and B show the dynamics of humoral immune response in individuals after infection. A. Circles and squares show median IgA and IgG antibodies, respectively. Horizontal lines represent IQR. Ratios equal to or above 1.1 were considered positive. Antibodies were measured before infection and up to 283 days post infection. The number of individuals tested varied according to the time point evaluated. B. Neutralizing antibodies were measured only in individuals with positive IgG antibodies over time. Values above 35% of inhibition were considered positive. The proportion of positive samples over the total samples measured at each time point is displayed above each box. C. IgG and IgA antibodies in naïve individuals before and after vaccination with a two-dose schedule of BNT162b2. Statistical significance was measured using a Wilcoxon signed rank test at a significance level of 5%.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/c87e1ced25405df77f9e6372.png"},{"id":53194264,"identity":"977ebd3e-706e-4ab9-92b9-1f1f8459692f","added_by":"auto","created_at":"2024-03-21 18:11:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":376300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnti-Spike SARS-CoV-2 IgG levels following the BNT162b2 primary schedule and the mRNA-1273 booster. \u003c/strong\u003e\u003cem\u003eIgG antibodies were measured at baseline on days 4 to 44 after the two-dose primary schedule of BNT162b2. There were no statistically significant differences between antibodies before the booster and 3 to 4 months after the booster. After the vaccine booster, antibodies decreased over time. Statistical significance was measured using a Skilling’s-Mack test at a significance level of 5%.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/5dce27a357b8acfed095fa3a.png"},{"id":53194265,"identity":"e4f10af1-54b2-4a3b-a3f5-4d64c1be1a74","added_by":"auto","created_at":"2024-03-21 18:11:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":859042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnti-Spike SARS-CoV-2 IgA and neutralizing antibodies following the mRNA-1273 booster in participants with the BNT162b2 primary schedule. \u003c/strong\u003e\u003cem\u003eIgA and neutralizing antibodies were measured after the booster. Statistical significance was measured using a Skilling’s-Mack test at a significance level of 5%.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/a53f36d625d189b73e69a711.png"},{"id":53194266,"identity":"d04a74a4-23ba-4a8b-8b5d-f1fc3358cac5","added_by":"auto","created_at":"2024-03-21 18:11:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":952768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnti-Spike SARS-CoV-2 IgA and neutralizing antibodies following the mRNA-1273 booster according to participants characteristics. \u003c/strong\u003eI\u003cem\u003egA and neutralizing antibodies were measured according to participants’ sex, autoimmunity, smoking, Body Mass Index (BMI), and alcohol consumption. Statistical significance was measured using the Mann-Whitney test at a significance level of 5%. For IgA antibodies, ratios above 1.1 were considered positive. For neutralizing antibodies, values above 35% of inhibition were considered positive.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/47c1d6e47364f18b321aee01.png"},{"id":64594452,"identity":"1970b89c-4740-468c-bbff-aa4d16e10610","added_by":"auto","created_at":"2024-09-16 10:23:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4349860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/1422e2a7-eb3f-4390-aab0-246bd58d043b.pdf"},{"id":53195247,"identity":"9dae4285-ea80-4c9f-86d4-4cb7b3b73fc1","added_by":"auto","created_at":"2024-03-21 18:19:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":259558,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/853acc2880906db2cff54439.docx"},{"id":53195248,"identity":"1c5f27c1-8dbf-459e-be4e-efb615b9dba9","added_by":"auto","created_at":"2024-03-21 18:19:10","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":106746,"visible":true,"origin":"","legend":"","description":"","filename":"FileS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/e3ef2843eaa3e6dc45bc73c1.pdf"},{"id":53194262,"identity":"9ae2ceb1-611b-427f-8e27-d02e129c8328","added_by":"auto","created_at":"2024-03-21 18:11:09","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":82664,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3995124/v1/088670d9c9f1ecdd780441f7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term dynamics of natural, vaccine-induced, and hybrid immunity to SARS-CoV-2 in a university hospital in Colombia: A cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has posed significant threats to public health and health systems globally. Various strategies have been adopted worldwide to mitigate the disease burden. These included prevention measures such as social distancing, travel restrictions, and lockdowns \u003csup\u003e1\u003c/sup\u003e, as well as the accelerated spread of safe and effective vaccines and the strengthening of health systems to facilitate the prevention, detection, and treatment of COVID-19 \u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSARS-CoV-2 infections generate an immune response capable of reducing the risk of re-infection \u003csup\u003e3,4\u003c/sup\u003e. However, this response is influenced by various factors, including virus-dependent mechanisms, such as the evolution of antigenically distinct viral variants. For instance, protection from natural immunity against re-infection remains strong for pre-Omicron variants but weakens faster for Omicron and its sublineages \u003csup\u003e3\u003c/sup\u003e. While past infections can trigger a robust immune response, vaccination has played a vital role in mitigating the spread of SARS-CoV-2.\u003c/p\u003e \u003cp\u003eThe development and implementation of COVID-19 vaccines have led to a progressive decline in morbidity and mortality \u003csup\u003e5,6\u003c/sup\u003e. Multiple vaccines employing different platforms have been made available to the public, and some are still under development. After the application of primary schedules, the emergence of viral variants with higher transmissibility and virulence, and the waning of vaccine effectiveness and immunogenicity over time, led to the implementation of booster doses \u003csup\u003e7,8\u003c/sup\u003e. Vaccine boosters were administered with the same vaccine as the primary schedule (homologous booster) or a different vaccine (heterologous booster). The combination of COVID-19 vaccines is a safe and reassuring alternative \u003csup\u003e9\u003c/sup\u003e, particularly useful in the context of vaccine scarcity, such as in low- and middle-income countries \u003csup\u003e10,11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies addressing the immunogenicity of heterologous boosters showed a significant increase in binding and neutralizing antibody titers that correlated with greater protection against viral variants of concern and a higher T-cell response and IFN-γ secretion, compared to homologous boosters \u003csup\u003e12\u0026ndash;15\u003c/sup\u003e. However, there is limited evidence regarding the combination of a 2-dose primary series with BNT162b2 and an mRNA-1273 booster, a combination frequently used among health care workers (HCWs) in Colombia.\u003c/p\u003e \u003cp\u003eImmune response to vaccines varies according to multiple factors, including intrinsic (e.g., sex, age), environmental (e.g., preexisting immunity), behavioral (e.g., smoking, alcohol consumption, exercise), and vaccine administration factors (e.g., vaccination dose and schedule) \u003csup\u003e16\u003c/sup\u003e. Therefore, it is important to consider these factors when evaluating the immunogenicity of vaccines.\u003c/p\u003e \u003cp\u003eA broader understanding of the long-term protection against SARS-CoV-2 conferred by infection, vaccines, and a combination of both is necessary to aid policymaking and prepare for future emergent diseases. Especially, considering the potential applications of mRNA vaccines to prevent infectious diseases, offering a versatile and rapid response strategy that will likely be effective for various emerging pathogens \u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. Thus, we aimed to (i) estimate the natural, vaccine-induced, and hybrid humoral immunity against SARS-CoV-2 in HCWs, and (ii) assess the humoral and cellular responses elicited by the mRNA-1273 booster in HCWs previously vaccinated with two doses of BNT162b2.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eSetting\u003c/p\u003e\n\u003cp\u003eThe study was conducted at the University Hospital Fundaci\u0026oacute;n Santa Fe de Bogot\u0026aacute; (UHFSFB), a tertiary care hospital in Bogot\u0026aacute;, the capital and most populated city of Colombia \u003csup\u003e20,21\u003c/sup\u003e. During the COVID-19 pandemic, UHFSFB served as a referral hospital for COVID-19 medical attention in the city \u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn February 2021, Colombia gradually started the COVID-19 vaccination process in two phases. The first phase sought to reduce mortality and incidence of severe disease and protect HCWs, while the second sought to reduce infectivity to reach herd immunity \u003csup\u003e23\u003c/sup\u003e. HCWs were among the first to complete their primary vaccination schedule. Thereafter, by November 2021, the government approved a booster dose for adults, that was administered at least 6 months after completing the initial schedule \u003csup\u003e24\u003c/sup\u003e. The vaccines available in the country included BNT162b2 (Pfizer), mRNA-1273 (Moderna), Ad26.COV2.S (Janssen), AZD1222 (AstraZeneca), and CoronaVac (Sinovac) \u003csup\u003e23\u003c/sup\u003e. For the booster dose, HCWs could access a homologous booster or a heterologous booster (using an mRNA or a viral vector-based vaccine), according to their preferences and vaccine availability \u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eStudy design and participants\u003c/p\u003e\n\u003cp\u003eIn 2020, the UHFSFB, in collaboration with Universidad de los Andes, conducted the CoVIDA-FSFB study \u003csup\u003e25,26\u003c/sup\u003e. The prospective cohort study enrolled a cohort of 420 voluntary adult hospital workers recruited between June 25 and October 30, 2020, who underwent routine SARS-CoV-2 RT-PCR and serological testing over six months, until April 30, 2021 (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). During the follow-up by March 2021, a subgroup of participants received a two-dose BNT162b2 schedule. Collected serum samples were stored at -70\u0026deg;C until further analysis. To estimate the infection-induced, vaccine-induced, and hybrid humoral immunity against SARS-CoV-2, this analysis focused on a subgroup of the CoVIDA-FSFB participants who met any of the following inclusion criteria (n\u0026thinsp;=\u0026thinsp;110): (i) RT-PCR-confirmed SARS-CoV-2 infection before study recruitment (n\u0026thinsp;=\u0026thinsp;29), (ii) RT-PCR-confirmed SARS-CoV-2 infection during the study follow-up (n\u0026thinsp;=\u0026thinsp;57), (iii) received a primary vaccination schedule with BNT162b2 (n\u0026thinsp;=\u0026thinsp;24). For this analysis, participants with re-infections, contraindications for phlebotomy, and characteristics that hindered follow-up were excluded (e.g., change of residence, planned long-term travel outside the city). Stored samples of eligible participants were subsequently sent to the Center for Autoimmune Diseases Research (CREA) for analysis.\u003c/p\u003e\n\u003cp\u003eSubsequently, the participants received the first vaccine booster between November 26, 2021, and January 4, 2022. Those who received a booster dose of mRNA-1273 after a two-dose primary schedule of BNT162b2 (2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273 schedule) (n\u0026thinsp;=\u0026thinsp;36) were invited to participate in an ancillary component to study the humoral immunogenicity of this vaccine combination (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The cellular immune response was evaluated in a subset without underlying comorbidities, acute infections, and chronic or acute use of medication (n\u0026thinsp;=\u0026thinsp;16). Individuals were scheduled a new visit between March 24 and April 11, 2022, to assess eligibility for this study component, and to collect additional information and blood samples to assess humoral and cellular immunogenicity in those who were eligible. Subsequently, they were followed for six additional months, which concluded on October 25, 2022. During this period, blood samples for humoral immunity assessment were collected at 6 and 9 months after the booster, and participants were contacted monthly to identify laboratory-confirmed COVID-19 cases (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of IgG, IgA, and neutralizing antibodies\u003c/h2\u003e \u003cp\u003eThe Euroimmun anti-SARS-CoV-2 ELISA (Euroimmun, Luebeck, Germany) was used for serological detection of human IgG and IgA antibodies against the SARS-CoV-2 wild-type S1 structural protein, following the manufacturer\u0026rsquo;s instructions, as previously described \u003csup\u003e27\u003c/sup\u003e. To evaluate results, a ratio of the OD of the patient sample over the OD of the calibrator was calculated. Ratios\u0026thinsp;\u0026lt;\u0026thinsp;0.8 were deemed negative, \u0026ge;\u0026thinsp;0.8 to \u0026lt;\u0026thinsp;1.1 were considered borderline, and \u0026ge;\u0026thinsp;1.1 were classified as positive. Antibody positivity was determined using a 1:100 dilution.\u003c/p\u003e \u003cp\u003eThe anti-S SARS-CoV-2 IgG II Quant assay (S-IgG) (Abbott, Sligo, Ireland) was used to assess the IgG response to the 2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273 schedule. The assay was conducted on the Abbott ARCHITECT i2000SR system according to the manufacturer\u0026rsquo;s instructions \u003csup\u003e28\u003c/sup\u003e. The assay allows for qualitative and quantitative determination of IgG antibodies against the SARS-CoV-2 glycoprotein receptor binding domain (RBD) in human serum and plasma \u003csup\u003e29\u003c/sup\u003e. The units of the quantitative Abbott anti-S assay, Arbitrary Units per milliliter (AU/mL), were converted to the World Health Organization (WHO) units, Binding Antibody Units per milliliter (BAU/mL), by multiplying by a factor of 0.142, according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003cp\u003eTo evaluate the neutralizing capacity of anti-SARS-CoV-2 antibodies, the semi-quantitative assay NeutraLISA kits (EUROIMMUN, L\u0026uuml;beck, Germany) was used. This kit detects IgG antibodies capable of neutralizing the S1 subunit where RBD of the SARS-CoV-2 spike protein is located. Results were reported as percent inhibition (%Inhibition) following the manufacturer's instructions, as previously described \u003csup\u003e30\u003c/sup\u003e. Samples were classified as negative (\u0026lt;\u0026thinsp;20% inhibition), positive (\u0026ge;\u0026thinsp;35% inhibition), or inconclusive (20\u0026ndash;34% inhibition).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003ePBMC Isolation and cryopreservation\u003c/h2\u003e \u003cp\u003eBlood collected in EDTA-anticoagulated tubes was used to isolate peripheral blood mononuclear cells (PBMCs) through a density gradient centrifugation method using Ficoll-Histopaque 1077 (Sigma-Aldrich, St Louis, USA) following the manufacturer's instructions. For cryopreservation, the isolated PBMCs were washed twice with complete RPMI-1640 media (Gibco, NY, USA) and then frozen and stored in fetal bovine serum (FBS) (BioWest, Riverside, USA) containing 10% dimethyl sulfoxide (DMSO) (Sigma-Aldrich, St Louis, USA). Cryovials containing the PBMCs were initially stored at -70\u0026deg;C to allow for a gradual temperature decrease. After 24 hours, these cryovials were transferred to a liquid nitrogen tank, where they were stored until further use.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of SARS-CoV-2 Specific T-cell response\u003c/h2\u003e \u003cp\u003eTo explore the SARS-CoV2-specific T-cell response to the 2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273 schedule, three different peptide pools of SARS-CoV-2 wild type (Mabtech AB, Nacka Strand, Sweden) were used:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSARS-CoV-2 S1 scanning pool, which contains 166 peptides from the human SARS-CoV-2 virus; the peptides are 15-mers overlapping with 11 amino acids, covering the S1 domain of the spike protein (amino acid 13\u0026ndash;685).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSARS-CoV-2 SNMO defined peptide pool that contains 47 synthetic peptides from the human SARS-CoV-2 virus; the peptides are derived from the spike, nucleoprotein, membrane protein, ORF3a and ORF7a.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eS2 N defined peptide pool, which contains 41 peptides from the human SARS-CoV-2.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAccording to the manufacturer's instructions, each pool of peptides was individually resuspended in DMSO and PBS, resulting in a final stock concentration of 200 \u0026micro;g/mL. Then, cryopreserved PBMCs were thawed in a 37\u0026deg;C water bath, washed twice with RPMI-1640 media pre-warmed to 37\u0026deg;C, and centrifuged at 350g for 10 min. Afterward, cells were analyzed for viability using trypan blue, and seeded at a density of 1 \u0026times; 106 cells per well in a 96-well plate in RPMI-1640 supplemented with 10% FBS, 100 U/mL Penicillin, 100 \u0026micro;g/mL Streptomycin and 2mM L-glutamine (Gibco, NY, USA). After, cells were rested for 2h and then stimulated with each SARS-CoV-2 peptide pool independently at a final concentration of 2 \u0026micro;g/mL overnight (~\u0026thinsp;18h) at 37\u0026deg;C and 5% CO2. As positive control, cells were stimulated with 5 ug/mL of phytohemagglutinin (Sigma Aldrich, St Louis, USA), and as negative control, cells were left unstimulated. All conditions were seeded with Brefeldin A at 10 \u0026micro;g/mL (Sigma Aldrich, St Louis, USA) to inhibit protein transport. The percentage of SARS-CoV-2-specific IFN-γ, IL-2, IL-4 and granzyme B-producing cells were evaluated by flow cytometry. After stimulation with the SARS-CoV-2 peptide pools, cells were harvested and stained with 7AAD-PERCP, anti-CD3-APCH7, anti-CD4-V500 and anti-CD8-APC antibodies (BD Biosciences, CA, USA) at room temperature for 30 min. For intracellular cytokine staining, cells were fixed and permeabilized with BD Cytofix/Cytoperm\u0026trade; (BD Biosciences, CA, USA), followed by staining with anti-IFN-γ-FITC, anti-IL-2-V450, anti-IL-4-PECy7, and anti-Granzyme B-PE antibodies (BD Biosciences, CA, USA) for 30 min at 4\u0026deg;C in the darkness. Controls for these assays included single-staining and unstained cells, which were used for gating and compensation. Cells were acquired on a FACSCanto II flow cytometer (BD Biosciences) and data were analyzed with FlowJo software version 9 (BD Biosciences).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eDuring the cohort\u0026rsquo;s first visit, researchers asked participants about their sociodemographic information and medical history and recorded this information in a medical record and an electronic questionnaire. The medical record was used to document information regarding comorbidities, flu vaccination, and previous viral infections. To gather data about previous viral infections, researchers asked participants whether they had ever been diagnosed with any of the following: dengue, chickenpox, zika, chikungunya, influenza, measles, or hepatitis. These infections were self-reported by the participants, and no specific diagnostic tests conducted to confirm them.\u003c/p\u003e \u003cp\u003eFor the 2BNT162b\u0026thinsp;+\u0026thinsp;1mRNA-1273 immunogenicity subgroup, additional data were collected through an electronic questionnaire implemented in REDCap (S2 File in Spanish) \u003csup\u003e31\u003c/sup\u003e. This included data on sociodemographic characteristics (e.g., age, sex, socioeconomic status, city in which they live, and profession), clinical characteristics (e.g., height, weight, Body Mass Index (BMI), blood type, comorbidities, medications, and previous COVID-19 infection), and habits (e.g., physical activity, alcohol, and smoking cigarettes). To avoid inter-interviewer bias, the questionnaire was administered by the same investigator. Information regarding COVID-19 vaccination was obtained from the participant\u0026rsquo;s vaccination card, which contains information about the vaccination schedule, including the vaccine batch, laboratory, dosage, administration dates, and the health provider institution that administered the vaccine. During the follow-up period, the administration of a second booster dose was approved in Colombia. Participants were asked about receiving this dose, and verification was conducted through the vaccination certificate by the end of the follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor descriptive analysis qualitative variables were presented as frequencies and proportions, and quantitative variables as means or medians with standard deviations (SD) or interquartile ranges (IQR) depending on their distribution, according to the Shapiro Wilk test. There were no missing data on the independent variables. Missing data on the humoral immunogenicity outcome corresponded to 4.86% (7/144), which were not included in the analysis.\u003c/p\u003e \u003cp\u003eIgG and IgA titers were compared before and after a two-dose BNT162b2 primary schedule using the Wilcoxon signed rank test. To compare IgG, IgA, and neutralizing antibodies after the mRNA-1273 booster the Skillings Mack test was used. Differences in IgG, IgA, and neutralizing antibodies according to sociodemographic, clinical variables, and habits were graphically explored.\u003c/p\u003e \u003cp\u003eThe CMIA kit used to measure anti-spike IgG, for 2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273 immunogenicity assessment, provides values up to 5680 BAU/mL. Values above that threshold were set as equal to the threshold; thus, the data for this variable were right censored. To address this, and given the longitudinal nature of our data, a random effects Tobit model was used to determine factors related to anti-S-RBD IgG antibodies. Regression models were constructed using anti-S-RBD IgG post-booster as the dependent variable. All clinically relevant variables with biological plausibility previously identified through literature search were included as independent variables. Two models were constructed, a bivariate model and a multivariate reduced model with the minimum number of independent variables that best suited the data, using a 0.2 significance level for variable removal from the model \u003csup\u003e32\u003c/sup\u003e. The multivariate model was used to adjust for confounders and detect effect modifiers. All possible interactions between the variables of interest were explored; however, these were not included in the final model as they were not statistically significant. Multicollinearity was assessed using the variance inflation factor (VIF), with a 5.0 cut-off point. Bootstrapping was used to provide more reliable standard errors \u003csup\u003e33\u003c/sup\u003e. The random effects used account for autocorrelation that may arise from within cluster dependencies. The quadrature approximation used in the random-effect estimators was checked, with no relative differences in the coefficients larger than 0.01%. The normal distribution of raw residuals was confirmed.\u003c/p\u003e \u003cp\u003eT-cell responses in participants with 2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273, were compared according to whether they previously had COVID-19. Specifically, CD4\u0026thinsp;+\u0026thinsp;cells producing IFN-γ, IL-2, and IL-4, and CD8\u0026thinsp;+\u0026thinsp;cells expressing Granzyme B, IFN-γ, IL-2, and IL-4. Responses were evaluated post-stimulation with three distinct peptide pools (S1, SNMO, and S2 N), and the Mann-Whitney U test was used to assess statistical significance. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance for all statistical tests. Analysis was performed using Stata SE 17.0 \u003csup\u003e34\u003c/sup\u003e and visualized in GraphPad Prism version 9 \u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the Fundaci\u0026oacute;n Santa Fe de Bogot\u0026aacute; Ethics Committee (CCEI-12183-2020, and CCEI-13882-2022). This study was conducted in compliance with Act 008430\u0026thinsp;\u0026minus;\u0026thinsp;1993 of the Ministry of Health of Colombia, and classifies as minimal-risk research \u003csup\u003e36\u003c/sup\u003e. All patients provided their written informed consent and were informed about the Colombian data protection law (1581 of 2012). All research was performed in accordance with relevant guidelines and regulations, and in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eImmunity to SARS-CoV-2\u003c/h2\u003e\n \u003cp\u003eThis study included 110 participants: 86 with natural immunity and 24 with vaccine-induced immunity; additionally, 11 participants had hybrid immunity (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The median age of participants was 40 years (IQR 33\u0026ndash;44 years), most participants were female (81.8%) and from a middle socioeconomic background (66.4%) (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The majority were professional nurses (32.7%), followed by medical doctors and students (15.5%), and more than half had a healthcare position (67.6%). The median BMI was 24.8 (IQR 22.6\u0026ndash;26.9 Kg/m\u003csup\u003e2\u003c/sup\u003e), and 23% had some sort of comorbidity.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSociodemographic and clinical characteristics of cohort\u0026rsquo;s participants (n\u0026thinsp;=\u0026thinsp;110).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026thinsp;=\u0026thinsp;110\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (\u003cspan\u003e33\u003c/span\u003e\u0026ndash;\u003cspan\u003e44\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocioeconomic status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u003cspan\u003e1\u003c/span\u003e\u0026ndash;\u003cspan\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid (\u003cspan\u003e3\u003c/span\u003e\u0026ndash;\u003cspan\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (66.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (\u003cspan\u003e5\u003c/span\u003e\u0026ndash;\u003cspan\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNurse assistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical doctors and students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdministrative assistants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTherapists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdministrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlended\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eActive smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNonsmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (74.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePassive smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious viral infections\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluenza vaccine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (83.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.8 (22.6\u0026ndash;26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSARS-CoV-2 infection during follow up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious SARS-CoV-2 infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (73.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eAny of the following: dengue, chickenpox, zika, chikungunya, influenza, measles, and hepatitis.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBMI: Body Mass Index; IQR: Interquartile range.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWe assessed the humoral natural immunity up to 283 days post-infection (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eA). IgA antibodies peaked earlier, reaching a median ratio of 8.079 on days 8 to 21 post infection, then they began to descend, reaching a median ratio of 3.273 by the end of the follow-up (191 to 283 post infection). On the other hand, IgG antibodies peaked on days 22 to 90, reaching a median ratio of 5.111, and then descended, with a median ratio of 3.352 by days 191 to 283 post infection. We measured neutralizing antibodies in participants with positive IgG anti SARS-CoV-2 antibodies (ratio\u0026thinsp;\u0026ge;\u0026thinsp;1.1). On days 8 to 21, the mean percentage of inhibition was \u0026minus;\u0026thinsp;17.85%, followed by 14.86% on days 22 to 90, 12.68% on days 91 to 180 and 22.06% on days 181 to 283 after the infection (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eIn the group with vaccine-induced immunity, for IgG the median ratio increased from 0.205 before vaccination to 8.797 after vaccination (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eC). For IgA, the median ratio increased from 0.392 to 9.788 after vaccination (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The median percentage of inhibition of neutralizing antibodies after vaccination was 97.00% (IQR 94.70%-97.80%). In the group with hybrid immunity, the median ratio for IgG was 9.676 and 8.010 for IgA. As for the neutralizing antibodies, the median percentage of inhibition was 97.61% (IQR 97.25%-98.20%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eHumoral immunogenicity of 2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273\u003c/h2\u003e\n \u003cp\u003eA subgroup of 36 participants was included for this analysis. Their mean age was 42\u0026thinsp;\u0026plusmn;\u0026thinsp;8 years, and most were female (78%) (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Professional nurses accounted for the largest proportion of roles (39%), followed by nursing assistants (22%), and medical doctors (14%). Fifty-three percent of participants had any type of comorbidities, including one participant who had rheumatoid arthritis treated with methotrexate, and one who had ulcerative colitis, treated with azathioprine. Other reported comorbidities included gastritis, migraine, allergies, acne, alopecia, and hypothyroidism. Before baseline, half of the participants had COVID-19, and 14% had been re-infected. During the follow-up period, four laboratory-confirmed SARS-CoV-2 infections occurred. Additionally, three participants received a second booster during the study follow-up.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDemographics, clinical characteristics, and habits of the subgroup of participants for the assessment of the Humoral Immunogenicity of 2BNT162b2/ 1mRNA-1273 (n\u0026thinsp;=\u0026thinsp;36).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBogot\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutside the city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (\u003cspan\u003e14\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (\u003cspan\u003e39\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNursing Assistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (\u003cspan\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical doctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (\u003cspan\u003e14\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdministrative position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (\u003cspan\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicrobiologist or bacteriologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (\u003cspan\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNutritionist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSocioeconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u003cspan\u003e1\u003c/span\u003e\u0026ndash;\u003cspan\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (\u003cspan\u003e17\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid (\u003cspan\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (\u003cspan\u003e44\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (\u003cspan\u003e4\u003c/span\u003e\u0026ndash;\u003cspan\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (\u003cspan\u003e39\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious SARS-CoV-2 infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (\u003cspan\u003e50\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNumber of previous SARS-CoV-2 infections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (\u003cspan\u003e50\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (\u003cspan\u003e36\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (\u003cspan\u003e14\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (\u003cspan\u003e47\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (\u003cspan\u003e53\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutoimmunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUse of chronic medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBlood group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (\u003cspan\u003e39\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (\u003cspan\u003e56\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTobacco smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eModerate alcohol consumption\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (\u003cspan\u003e36\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePhysical activity\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (\u003cspan\u003e56\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (\u003cspan\u003e44\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond vaccine booster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (\u003cspan\u003e8\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026le; 2 drinks/day for men and \u0026le;\u0026thinsp;1 drink/day for women;\u003c/em\u003e \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e\u0026ge;150 minutes/week of moderate-intensity physical activity or \u0026ge;\u0026thinsp;75 minutes/week of high-intensity physical activity during the free time\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eParticipants\u0026rsquo; mean BMI was 25\u0026thinsp;\u0026plusmn;\u0026thinsp;3 kg/m\u003csup\u003e2\u003c/sup\u003e, and the majority had an O (56%) or A blood type (39%). The majority had never smoked tobacco (93%) and had a moderate alcohol consumption (\u0026le;\u0026thinsp;2 drinks/day for men and \u0026le;\u0026thinsp;1 drink/day for women \u003csup\u003e37\u003c/sup\u003e) (64%), with a median consumption of two portions (IQR 1\u0026ndash;3). More than half of participants (58%) engaged in physical activity regularly, and 44% complied with WHO recommendations for physical activity (\u0026ge;\u0026thinsp;150 minutes/week of moderate intensity physical activity or \u0026ge;\u0026thinsp;75 minutes/week of high intensity physical activity \u003csup\u003e38\u003c/sup\u003e). The median time for physical activity was 95 minutes (IQR 0-210) per week.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cp\u003eAt baseline, the median SARS-CoV-2 anti-S IgG was 3337 BAU/mL (IQR 2060\u0026ndash;5489) (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). On days four to nine after the second dose of BNT162b2 the median anti-S IgG was 3384 BAU/mL (IQR 2090\u0026ndash;5666), and on days 29 to 44 days after the second dose it was 2540 BAU/mL (IQR 1642\u0026ndash;3341) (S1 Fig). Participants received the booster dose on average 267 days after completing the 2-dose BNT162b2 schedule. The median SARS-CoV-2 anti-S IgG three to four months after receiving the mRNA-1273 booster was 3459 BAU/mL (IQR 988\u0026ndash;5680). The difference in medians before the booster and 3\u0026ndash;4 months after the booster was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.6257) (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). During the follow-up period, SARS-CoV-2 anti-S IgG decreased, with a median of 3306 BAU/mL (IQR 1177\u0026ndash;5680) by six to seven months after the booster, and 3188 BAU/mL (IQR 1471\u0026ndash;5680) nine to ten months after the booster (P\u0026thinsp;=\u0026thinsp;0.0173) (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe median IgA antibodies ratio 3 to 4 months after the booster was 8.550 (IQR 7.918\u0026ndash;8.763). This remained stable over the course of the follow-up, with a median ratio of 8.630 (IQR 7.973\u0026ndash;8.803) 6 to 7 months, and 8.610 (IQR 7.840\u0026ndash;8.785) 9 to 10 months after the booster. There were no statistically significant differences across these measurements (P\u0026thinsp;=\u0026thinsp;0.7036) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). Regarding neutralizing antibodies, the median percentage of inhibition in months 3 to 4 after the booster was 98.28% (IQR 97.81%-98.69%). There was an increase that then remained stable over the rest of the follow-up (98.57%, IQR 98.21%-98.85% on months 6 to 7, and 98.57%, IQR 98.34%-98.71% on months 9 to 10 after the booster) (P\u0026thinsp;=\u0026thinsp;0.0340) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFactors related to anti-Spike SARS-CoV-2 IgG antibody levels over time after receiving the mRNA-1273 vaccine booster n\u0026thinsp;=\u0026thinsp;101.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eBivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMultivariate reduced model\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIgG pre-booster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSampling time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;4 months post-booster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;7 months post-booster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-506.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-985.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;10 months post-booster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-422.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1468.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e623.4687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1751.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1936.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocioeconomic status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u003cspan\u003e1\u003c/span\u003e\u0026ndash;\u003cspan\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid (\u003cspan\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1223.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4039.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1592.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (\u003cspan\u003e4\u003c/span\u003e\u0026ndash;\u003cspan\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-966.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3432.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1941.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e723.9461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1011.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2459.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1237.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-596.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3071.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2133.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3195.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1070.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2008.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4395.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e378.2678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2143.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3835.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-451.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5881.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10036.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1725.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1193.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2231.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-510.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2289.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1268.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutoimmunity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-287.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2498.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1922.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTobacco\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent or previous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1921.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2911.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-931.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-241.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1981.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1499.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSARS-CoV-2 infection\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-751.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2457.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e954.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-96.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2029.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1836.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfection during follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1799.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4760.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1160.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e835.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2199.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3870.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecond booster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5505.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9529.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3663.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1036.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8363.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Wald test P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe multivariate regression model of factors related to anti-spike IgG after the vaccine booster was adjusted for anti-spike IgG levels after the second vaccine dose, age, BMI, blood group, SARS-CoV-2 infection, and a history of receiving a second booster. In this model, for each additional year of age over time, post-booster SARS-CoV-2 anti-spike IgG increased on average 165 BAU/mL (95% CI 60 to 269, P\u0026thinsp;=\u0026thinsp;0.002) (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). Individuals with B blood type had on average 5881 BAU/mL less IgG antibodies post-booster compared to people with group A (95% CI -10037 to -1726, P\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\n \u003cp\u003eAdditionally, those with obesity and group B blood type had fewer IgA antibodies (P\u0026thinsp;=\u0026thinsp;0.0278, and 0.0331, respectively) (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e). Similarly, participants with autoimmunity, and those with group B blood type had fewer neutralizing antibodies (P\u0026thinsp;=\u0026thinsp;0.0158, and 0.0064, respectively) (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eCellular immunogenicity\u003c/h2\u003e\n \u003cp\u003eFor the cellular immunogenicity analysis, a subgroup of 16 participants without acute or chronic diseases or use of medications were included. Their median age was 45 years (IQR 38.5\u0026ndash;48.5). The majority were women (75%), 62.5% previously had COVID-19 infection, and their median BMI was 23.98 Kg/m\u003csup\u003e2\u003c/sup\u003e (IQR 22.48\u0026ndash;27.73 Kg/m\u003csup\u003e2\u003c/sup\u003e). There were no statistically significant differences in T-cell responses based on previous SARS-CoV-2 infection (S5 Fig).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides insights into the long-term humoral immune response to SARS-CoV-2 infection and the humoral and cellular immunogenicity of the mRNA-1273 booster in HCWs previously vaccinated with two doses of BNT162b2. In HCWs with natural immunity, both IgG and IgA responses peaked within the initial three months after infection, remaining positive through follow-up up to 283 days after infection. However, inhibition by neutralizing antibodies was below the positive range (≥ 35%) throughout the follow-up period. Conversely, vaccine-induced and hybrid immunity resulted in a higher percentage of inhibition by neutralizing antibodies, exceeding 97%. After receiving a 2BNT162b2 + 1mRNA-1273 schedule IgG titers decreased over time but remained positive for up to ten months post-booster. IgA and neutralizing antibodies remained stable for the same duration. We identified factors related to humoral response, including age, BMI, autoimmunity, and blood type.\u003c/p\u003e \u003cp\u003eAfter booster administration, an initial increase in antibody levels is expected. A clinical trial conducted in the United States evaluated the humoral immunogenicity of homologous and heterologous schedules up to a month after booster administration \u003csup\u003e9\u003c/sup\u003e. In participants vaccinated with a 2BNT162b2 + 1mRNA-1273 schedule, an increase in antibody levels was evident, peaking by day 15 post-booster \u003csup\u003e9\u003c/sup\u003e. This was also reported after a homologous BNT162b2 booster in HWCs, with IgG and IgA peaking in the third week post-booster \u003csup\u003e39\u003c/sup\u003e. However, prospective studies with longer follow-up periods have shown that humoral responses decrease over time \u003csup\u003e40,41\u003c/sup\u003e. A previous study measured the humoral immune response in a cohort of HWCs after a homologous BNT162b2 booster up to 4 months post-booster and reported a significant decrease in antibody concentration over time, with a reduction of approximately 60% by 150 days after the booster \u003csup\u003e42\u003c/sup\u003e. Yet, studies suggest that despite the decline in IgG titers over time, antibody functions, including neutralizing capacity and Fc-dependent effector functions remain highly relevant for protection against COVID-19 \u003csup\u003e43,44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, obesity was associated with lower IgA titers after booster administration. Obesity impairs immune responses; studies have reported that BMI is inversely correlated with antibody responses to other vaccines, including hepatitis and influenza \u003csup\u003e16,45,46\u003c/sup\u003e. Furthermore, a systematic review showed that obesity was significantly associated with lower antibody titers after COVID-19 vaccination \u003csup\u003e47\u003c/sup\u003e. Additionally, we identified that blood type B was associated with lower IgG, IgA, and percentage inhibition by neutralizing antibodies. There is scarce and controversial evidence regarding the association between blood type and humoral response. Blood group antigens are important receptors or coreceptors for microorganisms and may influence responses to other vaccines, such as polio \u003csup\u003e16\u003c/sup\u003e. Regarding COVID-19, blood group B was reported to be associated with a higher susceptibility to infection in non-vaccinated individuals \u003csup\u003e48\u003c/sup\u003e. However, ABO-type has been shown to have no association with vaccine effectiveness \u003csup\u003e49\u003c/sup\u003e. A large study on 3,187 convalescent plasma donors found no evidence to confirm that the ABO type influenced the level of SARS-CoV-2 antibody response \u003csup\u003e50\u003c/sup\u003e. Studies with larger sample sizes are required to confirm whether ABO-type influences humoral responses after COVID-19 vaccination. We also identified that older age was associated with higher IgG responses post-booster, likely due to older individuals having greater prior deficits. This aligns with previous research indicating that the response after the vaccine booster was enhanced in older people without prior infection who exhibited lower baseline levels \u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMonitoring antibody levels over time helps determine how long a response may last. However, immune correlates of protection have not been established; thus, it is not yet clear whether higher levels of antibodies correlate with better outcomes. A randomized clinical trial analyzed the association between antibody levels and SARS-CoV-2 infection, showing that higher levels of all immune markers were correlated with a reduced risk of symptomatic SARS-CoV-2 infection \u003csup\u003e52\u003c/sup\u003e. Nevertheless, infection can still occur in the presence of high levels of antibodies \u003csup\u003e53\u003c/sup\u003e. Correlates of protection after COVID-19 vaccination are probably relative, meaning that most infections are prevented at a particular level of response, but some will occur above that level, likely because of host-dependent factors \u003csup\u003e53,54\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious findings have demonstrated that vaccination combined with natural infection is better than vaccination alone \u003csup\u003e55\u003c/sup\u003e. Although we did not find differences among T-cell responses to peptides pools, likely due to the small sample size for this analysis, other studies have shown higher IFN-γ and IL-2 responses to the S, M, and N proteins in previously infected and vaccinated individuals compared with those in uninfected participants after mRNA vaccines \u003csup\u003e56\u003c/sup\u003e. Moreover, previous studies have suggested that prior SARS-CoV-2 infection enhances immune responses to S, N, and M proteins after vaccination \u003csup\u003e57\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe strengths of the present study include the longitudinal design with quantitative repeated measures of antibodies over time and the consideration of factors related to humoral response. We analyzed multiple components of the humoral response, including IgG, IgA, and neutralizing antibodies. We prospectively followed participants up to 9.4 months after infection and 10 months after receiving the vaccine booster, providing insights into the kinetics of the humoral response on the long term. To address the longitudinal and censored nature of the CMIA kit data we constructed a random effects Tobit model. Although there are other strategies to address censoring, such as the truncated regression, this may provide inconsistent estimates of the parameters \u003csup\u003e58\u003c/sup\u003e. By considering the within-subject variation in antibody responses over time, the random-effects model accounts for fluctuations in responses that may be due to factors other than the vaccine booster. This study has some limitations. We consecutively included participants without employing probabilistic sampling methods; thus, our study results may only be extrapolated to populations that share similar characteristics. Moreover, the small sample size of each group for cellular immunity comparisons did not allow for the robustness needed to make strong statistical conclusions.\u003c/p\u003e \u003cp\u003eIn conclusion, our results provide insights into the long-term immune response against SARS-CoV-2. The 2BNT162b2 + 1mRNA-1273 schedule generated a humoral response in HCWs for up to 10 month. Further exploration of factors related to immune responses to vaccines is relevant to tailor efficient vaccination strategies.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eNC: Conceptualization, methodology, validation, formal analysis, investigation, data curation, writing original draft, visualization, project administration, funding acquisition); DMM: Methodology, validation, investigation, data curation, writing review and editing; YA: Methodology, validation, investigation, data curation, writing review and editing; NF: Methodology, Investigation, Writing - Original Draft, Visualization; SM: Conceptualization, methodology, formal analysis, writing review and editing; OM: Conceptualization, writing review and editing; CG: Conceptualization, writing review and editing; CR: Conceptualization, methodology, writing original draft, project administration, funding acquisition, supervision; JQ: Conceptualization, methodology, writing review and editing, project administration, funding acquisition, supervision.\u003c/p\u003e\n\u003cp\u003eAdditional information\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their great appreciation to all health workers who participated in this study. They would also like to acknowledge various people for their contributions to this study: Dr Henry Gallardo, director of Fundaci\u0026oacute;n Santa Fe de Bogot\u0026aacute;, for supporting the conduction and funding of the study; Dr Dario Londo\u0026ntilde;o, director of Population Health at Fundaci\u0026oacute;n Santa Fe de Bogot\u0026aacute; for supporting the conduction and funding of the study; Sebastian Cortes Corrales for providing guidance in the statistical analysis; Paula Andrea Rodriguez Urrego and Carolina Reyes Perdomo (Department of Pathology and Laboratory Medicine of Fundaci\u0026oacute;n Santa Fe de Bogot\u0026aacute;) for overseeing laboratory related activities; Noah Brazer for proofreading assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGirum, T. \u003cem\u003eet al.\u003c/em\u003e Optimal strategies for COVID-19 prevention from global evidence achieved through social distancing, stay at home, travel restriction and lockdown: a systematic review. \u003cem\u003eArch. 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Regression Models for Categorical and Limited Dependent Variables. 328 (1997).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"SARS-CoV-2, healthcare workers, mRNA vaccines, humoral response, T-cell response, immunogenicity","lastPublishedDoi":"10.21203/rs.3.rs-3995124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3995124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis prospective cohort study aimed to estimate the natural, vaccine-induced, and hybrid immunity to SARS-CoV-2, alongside the immunogenicity of the mRNA-1273 booster after the BNT162b2 primary series in healthcare workers in Colombia. IgG, IgA, and neutralizing antibodies were measured in 110 individuals with SARS-CoV-2 infection or a BNT162b2 primary series. Humoral responses and related factors were explored in a subgroup (n\u0026thinsp;=\u0026thinsp;36) that received a BNT162b2 primary series followed by a mRNA-1273 booster (2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273), and T-cell responses were evaluated in a subgroup of them (n\u0026thinsp;=\u0026thinsp;16). For natural immunity, IgG and IgA peaked within three months, declining gradually but remaining detectable up to 283 days post-infection. Neutralizing antibody inhibition post-infection was below positive range (\u0026ge;\u0026thinsp;35%) but exceeded 97% in vaccine-induced and hybrid immunity groups. Following 2BNT162b2\u0026thinsp;+\u0026thinsp;1mRNA-1273, IgG peaked 3\u0026ndash;4 months post-booster, gradually declining but remaining positive over 10 months, with IgA and neutralizing antibodies stable. Age and blood group were related to IgG response, while obesity and blood type to IgA response post-booster. Autoimmunity and blood type B were associated with lower neutralizing antibody inhibition. There were no differences in T-cell responses according to prior infection. These findings provide long-term insights into the immunity against SARS-CoV-2 and the immunogenicity of mRNA vaccines.\u003c/p\u003e","manuscriptTitle":"Long-term dynamics of natural, vaccine-induced, and hybrid immunity to SARS-CoV-2 in a university hospital in Colombia: A cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 18:11:04","doi":"10.21203/rs.3.rs-3995124/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":"dc810408-c2d5-4642-bc78-505e1665ce76","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T10:15:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 18:11:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3995124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3995124","identity":"rs-3995124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}