Violán et al. BMC Infectious Diseases (2022) 22:721 https://doi.org/10.1186/s12879-022-07696-6 RESEARCH Open Access Kinetics of humoral immune response over 17 months of COVID‑19 pandemic in a large cohort of healthcare workers in Spain: the ProHEpiC‑19 study Concepción Violán1,2,3,4*†, Pere Torán‑Monserrat1,2,3,5,6†, Bibiana Quirant3,7,8, Noemi Lamonja‑Vicente1,2, Lucía A. Carrasco‑Ribelles9, Carla Chacón1,2, Josep Maria Manresa‑Dominguez1,2,8,10, Francesc Ramos‑Roure1,11, Rosalia Dacosta‑Aguayo1,3, Cristina Palacios‑Fernández1,2, Albert Roso‑Llorach9,12, Aleix Pujol1,3, Dan Ouchi9,13, Mónica Monteagudo9, Pilar Montero‑Alia1,2,5,14, Rosa Garcia‑Sierra1,5,10, Fernando Arméstar4,15, Maria Doladé16, Nuria Prat2, Josep Maria Bonet2, Bonaventura Clotet3,13,17,18, Ignacio Blanco19,20, Marc Boigues‑Pons3,7,8, Nemesio Moreno‑Millán2, Julia G. Prado3,13,21†, Eva María Martínez Cáceres3,7,8,22†for the ProHEpiC-19 study Abstract Background: Understanding the immune response to the SARS-CoV-2 virus is critical for efficient monitoring and control strategies. The ProHEpic-19 cohort provides a fine-grained description of the kinetics of antibodies after SARS- CoV-2 infection with an exceptional resolution over 17 months. Methods: We established a cohort of 769 healthcare workers including healthy and infected with SARS-CoV-2 in northern Barcelona to determine the kinetics of the IgM against the nucleocapsid (N) and the IgG against the N and spike (S) of SARS-CoV-2 in infected healthcare workers. The study period was from 5 May 2020 to 11 November 2021.We used non-linear mixed models to investigate the kinetics of IgG and IgM measured at nine time points over 17 months from the date of diagnosis. The model included factors of time, gender, and disease severity (asympto‑ matic, mild-moderate, severe-critical) to assess their effects and their interactions. Findings: 474 of the 769 participants (61.6%) became infected with SARS-CoV-2. Significant effects of gender and disease severity were found for the levels of all three antibodies. Median IgM(N) levels were already below the positiv‑ ity threshold in patients with asymptomatic and mild-moderate disease at day 270 after the diagnosis, while IgG(N and S) levels remained positive at least until days 450 and 270, respectively. Kinetic modelling showed a general rise † Concepción Violán and Pere Torán-Monserrat contributed equally to this article † Julia G. Prado and Eva María Martínez Cáceres last shared authorship *Correspondence: cviolanf.mn.ics@gencat.cat 1 Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Unitat de Suport a la Recerca Metropolitana Nord, Mare de Déu de Guadalupe 2, Planta 1ª, Mataro, 08303 Barcelona, Spain Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​ mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Violán et al. BMC Infectious Diseases (2022) 22:721 Page 2 of 13 in both IgM(N) and IgG(N) levels up to day 30, followed by a decay with a rate depending on disease severity. IgG(S) levels remained relatively constant from day 15 over time. Interpretation: IgM(N) and IgG(N, S) SARS-CoV-2 antibodies showed a heterogeneous kinetics over the 17 months. Only the IgG(S) showed a stable increase, and the levels and the kinetics of antibodies varied according to disease severity. The kinetics of IgM and IgG observed over a year also varied by clinical spectrum can be very useful for public health policies around vaccination criteria in adult population. Funding: Regional Ministry of Health of the Generalitat de Catalunya (Call COVID19-PoC SLT16_04; NCT04885478). Keywords: SARS-CoV-2, COVID-19, Antibodies, IgG, IgM, Seroprevalence, Kinetics, Humoral immunity, Clinical spectrum, Health care workers, Cohort, Non-linear mixed models Background immunofluorescence assays (IFA) with standardized rea- Infection with the SARS-CoV-2 can be detected by meas- gents [12]. Using reagents in compliance with WHO’s uring the level of virus-specific antibodies, reflecting an international standards for anti-SARS-CoV-2 antibodies immune response against a recent or previous infection allows to evaluate vaccine efficacy and federate and com- [1]. Several studies described the rapid increase (Igs) of pare epidemiological and immunological surveillance various immunoglobulin isotypes (IgA, IgM, IgG) against studies. At the beginning of the pandemic, antibody lev- the epitopes of the spike (S) glycoprotein as well as to the els were determined using qualitative assays; while using nucleocapsid (N) protein in SARS-CoV-2 infection [2–5]. internationally standardized enzyme immunoassays for COVID-19, the disease caused by the SARS-CoV-2, the quantitative detection of specific IgG antibodies is has a broad clinical spectrum, including various forms more recent [13]. Studies with more sample points and of clinical presentation ranging between asymptomatic more accurate statistics analysis provide a better assess- infections and critical illness. Likewise, the antibody ment of the disease burden and transmission to better response to the SARS-CoV-2 infection is also heteroge- inform public health efforts against COVID-19 [9, 14]. neous [6]. Few longitudinal studies have performed sero- The aim of this study is to describe the kinetics of IgM logical follow-up across the clinical spectrum, and with (N) and IgG (N, S) antibodies against SARS-CoV-2 and a limited study duration between 80 to 270 days [2–4]. assess their relationship with the clinical spectrum and An early study reported a rapid rise and subsequent fall other factors. Our findings provide a detailed picture of of antibodies, which stabilized at later time points, indi- the immune response against the virus in a population cating that immunity against SARS-CoV-2 may last for which plays the most crucial role in fighting for the pan- at least 120 days after infection [7]. Two later studies demic. Our results can guide public health policies to suggested this protection for up to at least 180 days [2, develop more efficient strategies for monitoring, treat- 8] while other recent reports claim that it persists for at ment, and prevention of COVID-19. least a year [9, 10]. However, knowledge on the kinetics and the immune response to SARS-CoV-2 infection is Methods still limited, and larger and more detailed longitudinal Study design and ethics studies are needed to define the half-life of antibodies ProHEpiC-19 is a prospective, longitudinal study, against SARS-CoV-2. involving two groups of healthcare workers (healthy Of the few studies investigating the kinetics of the anti- and infected) in the Northern Metropolitan Area of bodies only one have studied IgM antibody [10]. None of Barcelona (Spain). The ethics committees of the Foun- these studies have more than five measurements [11]. In dation University Institute for Primary Health Care addition, all have made either linear or LOESS regression Research Jordi Gol i Gurina (IDIAPJGol) (ref. 20/067) models, which do not allow to obtain a model that repre- and The Germans Trias i Pujol Research Institute sents the non-linearity and heterogeneity of the antibody (IGTP) (ref.COV20/00660 (PI-20-205)) approved the response, indicated by previous kinetics studies. The use study protocol, which has been published on Clinical- of non-linear models over linear or LOESS models would Trials.gov (NCT04885478, registered on 13/5/21). All allow to obtain a more realistic representation of the anti- participants recruited in the study were fully informed body kinetics that can better explain the behaviour of the about the ProHEpiC-19 protocol and signed informed antibodies. consent to participate. They consented to use their The WHO recommends that a generic population- collected data for research and agreed to the appli- based serological testing should be carried out using cable regulations, privacy policies, and terms of use. enzyme linked immunosorbent assays (ELISA) or Participant data has been anonymised according to a Violán et al. BMC Infectious Diseases (2022) 22:721 Page 3 of 13 numerical coding system based on order and stored in SARS‑CoV‑2 detection and quantification of IgM and IgG a database securely. The database will be maintained for RT-PCR was used as the primary diagnostic test. RNA a period of 15 years after the completion of the study. for RT-PCR testing was extracted from fresh samples No participants or members of the public were using the STARMag 2019-nCoV kit by means of a liquid- directly involved in the design or analysis of the dispensing robot. RNA detection was performed using reported data. The funders of the study had no role in the Allplex SARS-CoV-2 assay, a multiplex RT-PCR study design, data collection, data analysis, data inter- assay able to detect four SARS-CoV-2 target genes in a pretation, or writing of the report. The corresponding single tube. For the antibody tests, we conducted a pre- author (CV) had full access to all data, while LACR, validation study with six different commercially available ARLL, DO, JMMD, and AP; had access to the raw data. and IVD-CE-approved ELISA tests and selected an anti- SARS-CoV-2 IgG(N) and IgM(N) enzyme immunoassay (ELISA) kits based on their performance. Infected par- Participant recruitment, follow‑up ticipants were also tested for and measured the level of Healthcare workers (physicians, nurses, nursing assis- antibodies against the spike (S) subunit of SARS-CoV-2 tant, and other essential workers) in direct contact by means of the DECOV1901 ELISA kit, which allows with patients during the first, second or third wave quantitative measurement of IgG. Please, see study pro- of COVID-19 were recruited between 3 March 2020 tocol NCT04885478 (also attached as Additional file 1) to and 22 March 2021. Inclusion criteria were agreeing more details. to participate and confirming their availability for the follow-up sessions 7, 15, 30, 60, 90, 180, 270, and 360, Data sources and 450 days and, 18, 24,30, and 36 months after their We used two data sources: (1) A database has been cre- first visit (baseline). The first follow-up was on 5 May ated for the ProHEpiC-19 cohort. The database is stored, 2020 and the last on 11 November 2021. Participants curated, and validated in a centralized data warehouse with less than two visits were considered to be a drop- at the Catalan Health Institute, and it is suitable to man- out. At baseline, participants were categorized to their age the demographic, social, and clinical data of profes- groups (infected or uninfected) based on RT-PCR, as sionals; (2) results of the SARS-COV-2 antibody tests; well as IgM(N) and IgG(N) antibody tests: If any of (3) results of the RT-PCR test. (4) We also implemented the two tests was positive, the participant was consid- a Teleform-based data collection specific for the study, ered infected. If an uninfected participant got infected, which includes the unique numeric patient identifier and they started again the study as an infected participant, the results of the serological and immunological tests. repeating the serological tests on the days said before The unique numeric patient identifier allows to link the specified above. The analyses reported in this work two data sources. included only participants with positive SARS-CoV-2 antibodies, as considered infected participants. Sample size Participants completed several clinical questionnaires Sample size and power estimations can be found in the and were examined for COVID-19-specific symptoms supplementary protocol. The one-way ANOVA of the during the baseline visit, when the RT-PCR test with total collected sample of 478 infected participants (72, nasal and oropharyngeal swab and the antibody tests 367, and 39 from the three subgroups) was estimated to were performed. The RT-PCR test was repeated at the achieve a 100% power to detect differences among the second visit, 7 days after the baseline visit. The antibody means versus the alternative of equal means using an tests were repeated at 15, 30, 60, 90, 180, 270, 360, and F test with a 0.05 significance level and an effect size of 450 days following the baseline visit. Day 0 is defined as η2 = 0.06. day 0 from date of diagnosis. The analysis of IgG(S) lev- els stopped as participants were vaccinated, while the Statistical analysis IgM(N) and IgG(N) continued. Infected participants Missing values were found only in in the following soci- were divided into three different disease severity sub- odemographic variables: education (3.5%), marital status groups according to their symptomology: (1) asympto- (4.6%), and nationality (7.5%). matic: no symptoms; (2) mild-moderate: people with one The evolution of antibody test was considered both or more clinical symptoms characteristic of COVID-19 as a categorical (i.e. positive/negative result) and as a and who did not require hospital admission; (3) severe- continuous scalar variable. Descriptive analyses were critical: patients who required hospital admission. The performed to characterise the immune response at characteristics of the whole ProHEpiC-19 cohort, includ- each timepoints with the occurrence and frequency for ing the healthy group, at baseline are described in Table1. Violán et al. BMC Infectious Diseases (2022) 22:721 Page 4 of 13 Table 1 Demographics and PCR testing for the participants according to their disease severity Negative at baseline Asymptomatic Mild-moderate illness Severe-critical illness Total and during follow-up N = 73 (9.5) N = 363 (47.2) N = 38 (4.9) N = 769 N = 295 (38.4) Age (years) 46 (38.5–56) [19–66] 45 (31–52) [19–66] 45 (35–53) [18–66] 56.5 (50–61) [30–66] 46 (36–54) [18–66] Gender assigned at birth Female 238 (80.7) 48 (65.7) 274 (75.5) 18 (47.4) 578 (75.2) Male 57 (19.3) 25 (34.3) 89 (24.5) 20 (52.6) 191 (24.8) Profession Doctor 112 (38.0) 9 (12.3) 76 (20.9) 13 (34.2) 210 (27.3) Nurse 95 (32.2) 19 (26.0) 96 (26.4) 10 (26.3) 220 (28.6) Nurse assistant 13 (4.41) 9 (12.3) 34 (9.37) 4 (10.5) 60 (7.80) Others 75 (25.4) 36 (49.3) 157 (43.3) 11 (28.9) 279 (36.3) Highest educational level attained Higher level vocational 34 (11.8) 6 (8.57) 32 (9.22) 1 (2.78) 73 (9.84) school University 27 (9.34) 29 (41.4) 94 (27.1) 10 (27.8) 160 (21.6) Others 228 (78.9) 35 (50.0) 221 (63.7) 25 (69.4) 509 (68.6) NA 6 3 16 2 27 Marital status Single 44 (15.3) 16 (25.0) 63 (18.2) 3 (8.33) 126 (17.1) Married/cohabitation 196 (68.1) 42 (65.6) 255 (73.5) 29 (80.6) 522 (71.0) Divorced 43 (14.9) 3 (4.69) 22 (6.34) 3 (8.33) 71 (9.66) Widow 5 (1.74) 3 (4.69) 7 (2.02) 1 (2.78) 16 (2.18) NA 7 9 16 2 34 Nationality Spain 270 (95.4) 57 (90.5) 291 (87.7) 35 (100) 653 (91.6) European Union 1 (0.35) 0 (0.00) 1 (0.30) 0 (0.00) 2 (0.28) South America 7 (2.47) 1 (1.59) 23 (6.93) 0 (0.00) 31 (4.35) Others 5 (1.77) 5 (7.94) 17 (5.12) 0 (0.00) 27 (3.79) NA 12 10 31 3 59 Number of symptoms at – 0 (0) [0–0] 7 (4–9) [1–17] 9 (8–11) [2–17] 6 (2–9) [0–17] (*) baseline Days of follow-up 424 (269.5–481) [13–545] 302 (189–365) 350 (271–434.5) 438.5 (357.8–475.5) 363 (269–464) [14–527] [13–537] [156–505] [13–545] Days since first positive – 302 (220.5–374.5) 365 (287–429) [28–617] 492 (341.8–540.8) 370 (286–510) (*) diagnosis test [14–602] [28–582] [14–617] ≥ 1 positive PCR during 0 (0) 20 (27.4) 118 (32.5) 2 (5.3) 204 (26.5) follow-up Categoric variables are described as N (%), and numeric variables as median (IQR) [min, max] NA not available In categories with NA, percentages were calculated excluding these answers. Diagnosis was made based on a positive PCR or IgM(N) or IgG(N) test. *Excludes negative participants. “Others” category in “Profession” includes physiotherapists, management and administration staff, and social workers; “Others” category in “Highest educational level attained” includes primary (5–12 years old) and secondary education (12–16 years old); and “Others” category in “Nationality” includes participants from Morocco, Senegal and Russia categorical variables and median and inter-quartile range to disease severity (Kruskal–Wallis followed by Holm- for numeric variables with non-normal distributions con- adjusted Dunn’s test) and (Mann–Whitney U test). For firmed. The antibody levels were analysed with respect a more detailed description of the antibody kinetics, we to time, disease severity, and gender. We stratified anti- also fitted locally estimated scatterplot smoothing mod- body levels by days since diagnosis, describing them by els (LOESS) and calculated their 95% confidence intervals boxplots and comparing the timepoints using a Kruskal– (CIs). LOESS is an exploratory, nonparametric technique Wallis test, followed by Holm-adjusted Dunn’s test. Then, that is flexible, fast and easily deployed to determine if at each timepoint, differences were assessed according the evolution of the antibody levels is linear or non-linear. Violán et al. BMC Infectious Diseases (2022) 22:721 Page 5 of 13 LOESS is useful for exploration but it does not provide with positive IgM(N) values fell. By day 270, 11.8% of the a regression equation describing the evolution, neither participants were negative for all antibody tests, while does LOESS provides regression coefficients and p-values 88.2% of the participants were positive for at least IgG(N) for making inferences and comparisons between groups. or IgG(S). As Table 2B shows, 68.2% of participants still After LOESS indicated that the antibody evolution was had IgG(N) values over the positivity threshold at day non-linear and because the antibody levels were repeated 360. measures from different time points, it is necessary to fit a regression model using a nonlinear mixed-effects Levels of SARS‑CoV‑2 antibodies stratified by gender model (NLME). Finally, nonlinear mixed effects mod- and disease severity els (NLME) were also fitted to obtain the curves char- We found a statistically significant difference in all anti- acterising the antibody kinetics. In these models, each body levels across clinical conditions (p < 0.001 both parameter was assumed to have a fixed and a random overall and pairwise comparisons). There was also a sig- effect. Both LOESS and NLME models were first fitted nificant difference in antibody levels between men and on data from all patients and then on data stratified by women for all immunoglobulins: IgM(N) (p = 0.01), disease severity and gender. Model diagnostics were per- IgG(N) (p < 0.001), and IgG(S) (p = 0.006). formed based on residual analysis, and goodness-of-fit Figure 1 shows the differences in antibody levels was checked with Akaike and Bayesian Information Cri- between the different time-points. Pairwise comparisons teria (AIC and BIC). For these all analyses except LOESS, show that both IgM(N) and IgG(N) levels from day 270 time from diagnosis was discretised into time bins so that are no longer significantly different from those on day 0 tests performed between the designated time points were (Figs. 1A-B). Both IgM(N) and IgG(N) levels present a considered as performed at the earlier time point. There- rise until and fall from day 30 (Fig. 1A-B), while IgG(S) fore, tests performed in the first 14 days since diagnosis levels remain relatively constant following the first rise are treated as “Day 0” and tests performed between day at day 15 (Fig. 1C). All antibody levels of patients with 450 and 615 (i.e. the last day observed, see Table 1) are severe-critical disease were the highest up to day 180 treated as “Day 450”. For the complete list and description (IgM(N)), day 450 (IgG(N) and day 270 (IgG(S)) (Fig. 1D- of the time bins, see Additional file 1: Table S1. F). Resulting p-values are shown in the figure. Median All tests were two-sided, and statistical probability IgM(N) levels were below the positivity threshold in peo- of p < 0.05 was considered significant. All analyses were ple with asymptomatic and mild-moderate diseases after performed using R version 4.0.4. See Additional file 1 for day 270 from diagnosis (Fig. 1D). However, IgG(N, S) lev- more information about the statistical methods. els still surpassed this threshold at day 360 (Fig. 1E-F). In terms of gender, men showed higher levels than women Results at day 30 for all antibodies (Fig. 1G-I) and days 60 and 90 Participant characteristics for IgG(S) (Fig. 1I). A total of 860 participants were recruited, of whom 769 were eligible; 443 (57.6%) tested positive at baseline, Kinetics of IgM and IgG and 31 (4%) had their first positive SARS-CoV-2 anti- IgG(N) and IgG(S) had a different kinetics. In relation to body test during follow-up (Additional file 1: Fig. S1, the kinetics of the three antibodies stratified by disease Table 1). Women had more often asymptomatic or mild severity, both LOESS (Fig. 2A, C, E) and NLME (Fig. 2B, disease, while men tended to have more severe disease D, F) showed a general rise in both IgM(N) and IgG(N) (p-value = 0.001). Differences in the prevalence of spe- levels up to day 30 followed by a decay, and both the cific clinical symptoms according to disease severity and height of the peak and the rate of decay depended on gender are shown in Additional file 1: Table S2. disease severity. IgG(S) levels increased at day 15 and remained relatively constant over time. Seroprevalence Additional file 1: Table S3 shows the parameters esti- Table 2A presents the frequency of each possible com- mated for each component of the NLME curves. Q-Q bination of antibody test results at each time-point. At plots of each model are available in Additional file 1: Fig. baseline, more than one third of the participants with S2. For both IgM(N) and IgG(N), initial levels were sig- available data (38.7%) tested negative for all antibodies, nificantly higher in severe-critical. IgM(N) levels from but this proportion decreased over time. From day 15 to asymptomatic participants decreased quicker, while day 180 of infection, more than 45% of participants tested those of severe-critical decreased slower than mild- positive for all antibodies. However, this proportion also moderate participants. This slower decline also occurs decreased from day 180 as the number of participants in IgG(N). Similarly to previous analyses, levels of IgG(S) Violán et al. BMC Infectious Diseases (2022) 22:721 Page 6 of 13 Table 2 Description (N, %) of the results of the antibodies tests Each column shows the participants that had one of the combinations of the diagnostic test results at each timepoint. Column shading highlights the most frequent results combinations at each timepoint. For Table 2A, only the records with available results for all three antibodies were used, while for Table 2B all records including (N) antibodies were used. *These percentages have been calculated using the maximum number of available samples per timepoint (i.e. (N) samples). This information is available at Table increased with disease severity for all timepoints, and the Discussion levels were practically constant from day 30 in all groups. Our study draws strength from using longitudinal data Figure 3 demonstrates the kinetics stratified by gender. from a large, representative, well characterized, and strati- The evolution patterns of the three antibodies are practi- fied sample of healthcare workers. The immune response cally identical except for the peak in men on day 30. Cor- to SARS-CoV-2 differs across disease severity. Our results respondingly, the initial increase rates are also higher in show that antibody response starts within 15 days of the men. The parameters of these NLME curves can be also infection for the three studied isotypes. Thereafter, their found in Additional file 1: Table S3. behaviour diverges according to disease severity. We found Violán et al. BMC Infectious Diseases (2022) 22:721 Page 7 of 13 A B C 30 1500 30 20 IgM(N) (index units) IgG(N) (index units) 1000 IgG(S) (IU/mL) 20 10 500 10 0 0 0 0 15 30 60 90 180 270 360 0 15 30 60 90 180 270 360 450 0 15 30 60 90 180 270 Time from diagnosis (days) Time from diagnosis (days) Time from diagnosis (days) Disease severity Asymptomatic Mild−moderate Severe−critical D E F 1500 15 15 1000 IgM(N) (index units) IgG(N) (index units) 10 IgG(S) (IU/mL) 10 500 5 5 0 0 0 0 15 30 60 90 180 270 360 0 15 30 60 90 180 270 360 450 0 15 30 60 90 180 270 Time from diagnosis (days) Time from diagnosis (days) Time from diagnosis (days) Sex Women Men G H I 1500 15 10 1000 IgM(N) (index units) IgG(N) (index units) IgG(S) (IU/mL) 10 5 500 5 0 0 0 0 15 30 60 90 180 270 360 0 15 30 60 90 180 270 360 450 0 15 30 60 90 180 270 Time from diagnosis (days) Time from diagnosis (days) Time from diagnosis (days) Fig. 1 IgM (N), IgG (N) ang IgG(S) levels, by days since diagnosis. Antibody levels are represented with a boxplot together with a dot and text describing their mean value. The dashed and the solid horizontal lines represent the uncertainty and positivity thresholds, respectively. A–C show the significant differences in the median antibody levels between days. D–F show significant differences in the median antibody levels across disease severity at each timepoint. Finally, G–I show the significant differences in antibody levels between genders at each timepoint. Significance levels were reported as: * for p-value ≤ 0.05; ** for p-value ≤ 0.01; *** for p-value ≤ 0.001; and **** for p-value ≤ 0.0001 (See figure on next page.) Fig. 2 Kinetics of IgM(N), IgG(N) and IgG(S) levels since diagnosis in the total sample and stratified by disease severity. A, C and E show the LOESS regression models, connecting datapoints belonging to the same participants. B, D and F show the estimated non-linear mixed-effect (NLME) model curves. Each point corresponds to the mean value at each time point. The bars correspond to the standard deviation. The dashed and the solid horizontal lines represent the uncertainty and positivity thresholds, respectively Violán et al. BMC Infectious Diseases (2022) 22:721 Page 8 of 13 Disease severity All Asymptomatic Mild/Moderate Severe−critical A B 15 10 10 IgM(N) (index units) IgM(N) (index units) 5 5 0 0 0 1530 60 90 180 270 360 0 15 30 60 90 180 270 360 Time from diagnosis (days) Time from diagnosis (days) C D 15 15 10 IgG(N) (index units) IgG(N) (index units) 10 5 5 0 0 01530 60 90 180 270 360 450 540 0 1530 60 90 180 270 360 450 Time from diagnosis (days) Time from diagnosis (days) E F 1000 1000 IgG(S) (IU/mL) IgG(S) (IU/mL) 500 500 0 0 0 1530 60 90 180 270 360 15 30 60 90 180 270 Time from diagnosis (days) Time from diagnosis (days) Fig. 2 (See legend on previous page.) Violán et al. BMC Infectious Diseases (2022) 22:721 Page 9 of 13 a higher level of antibodies in patients with severe versus To date, our study provides the most detailed and asymptomatic infections [11]. Our results also corroborate comparative description of the kinetics of IgG(N) and the early appearance of IgG(N) and (S) [6, 14] IgG(S) production after SARS-CoV-2 infection. IgG(N) Asymptomatic individuals maintain antibody lev- levels show a rapid rise followed by a decay with a slope els above the threshold for IgM(N) up to 180 days, while depending on disease severity, and it stabilizes between antibodies for IgG(N) and (S) remain above the positivity day 270 and 360. In contrast, IgG(S) kinetics shows a pla- threshold until day 270. The long-term presence of IgG(N) teau from day 30 post-infection. This difference has also antibodies and their protective efficacy, however, needs been reported in another study [19] and may be attribut- further research [15, 16] Compared with asymptomatic able to the differences between the S and N proteins of cases, participants with mild-moderate COVID-19 pre- SARS-COV-2 in their molecular structure, their abun- sented higher IgG antibody levels, and their IgM(N) levels dance in the viral particle, and their specific functions. also remained above the threshold for the entire follow-up. While the former facilitates the entry of the virus into These findings are similar to those reported at 180 days the host cell, the latter has a role in viral genomic pack- in a longitudinal study of IgM(S) and can be explained by aging [20]. Monitoring of both IgG N and S antibodies the differentiation of B cells to IgM memory plasma cells can help identify stimulation of memory cells triggered that continue to produce the IgM isotype antibodies for at by the two different antigens. Turner et al. [17] analysed least a year [17]. Another recent study also found positive the frequency of S-specific plasma cells in bone marrow IgM levels for up to one year, although the isotypes IgM(S) of convalescent individuals, and observed that infection rather than IgM(N). Severe-critical participants had higher with SARS-CoV-2 is able to induce resting memory B antibody levels than the other groups in the first 30 days of cells and long-lived bone marrow plasma cells, support- follow-up and maintained the highest levels for all three ing the existence of a robust humoral immune memory isotypes throughout the entire follow-up period. This is in infected individuals. Cohen KW et al. confirmed the consistent with the results of other studies conducted over former results and also analysed specific T-cells to SARS a shorter period of time [9, 18] COV-2 [21]. Interestingly, they found that CD4 + T cell The LOESS models describe the trajectories of the anti- responses equally target several SARS-CoV-2 proteins, body levels, while the NLME allows to compare the strati- whereas CD8 + T cells preferentially target the N pro- fied trajectories while accounting for the non-linearity of tein, highlighting the potential importance of including the antibody level kinetics. Our study further elaborates this antigen in future vaccines [21]. In addition, the fact the kinetics of the humoral immune response while appro- that the IgG(N) response is persistent over time sup- priately accounting for its characteristics and influenc- ports that this antigen could be monitored among other ing factors as justified by the model selection. Our results parameters (Specific T Cell and Neutralysing antibodies) are better equipped to inform social distancing and (re) to define subgroups of people who develop an efficient vaccination policies. Kinetic antibodies results are criti- immune response [22–24].The maintenance of an IgG(N) cally important in the design and implementation of epi- and IgG(S) as well as specific T and B immune responses demiological models and public health measures such as may protect against possible reinfections and render (re) social distancing policy and vaccination models. However, vaccination redundant. Further in-depth immunological additional information regarding the value of the antibody studies will be necessary to address their role in protec- kinetics, and protection from infection or disease sever- tion from severe cases—including death—, and in rein- ity should increment the impact for decision making in fection after both, infection and vaccination [25] public health policies. Furthermore, our analyses can be Humoral immune response seems to have a very similar easily adapted for estimating antibody levels measured in trajectory in men and women, although we found higher other units by applying the corresponding conversion fac- IgG(N) or IgG(S) levels in men at all timepoints. Other tors. Likewise, they can be also employed to investigate the studies with an eight-month follow-up indicate that gen- kinetics after vaccination, thus assessing the effectiveness der and disease severity is associated due to differences of the vaccines in comparison with the immunisation due in immune memory to SARS-CoV-2. However, most of to infection and informing the criteria for revaccination. the heterogeneity in immune memory to SARS-CoV-2 is (See figure on next page.) Fig. 3 Kinetics of IgM(N), IgG(N) and IgG(S) levels since diagnosis in the total sample and stratified by gender. A, C and E show the LOESS regression models, connecting datapoints belonging to the same participants. B, D and F show the estimated non-linear mixed-effect (NLME) model curves. Each point corresponds to the mean value at each time point. The bars correspond to the standard deviation. The dashed and the solid horizontal lines represent the uncertainty and positivity thresholds, respectively Violán et al. BMC Infectious Diseases (2022) 22:721 Page 10 of 13 Sex All Men Women A B 8 15 6 10 IgM(N) (index units) IgM(N) (index units) 4 2 5 0 0 0 15 30 60 90 180 270 360 0 15 30 60 90 180 270 360 Time from diagnosis (days) Time from diagnosis (days) C D 15 10 10 IgG(N) (index units) IgG(N) (index units) 5 5 0 0 01530 60 90 180 270 360 450 540 0 1530 60 90 180 270 360 450 Time from diagnosis (days) Time from diagnosis (days) E F 600 1000 400 IgG(S) (index units) IgG(S) (IU/mL) 200 500 0 0 0 1530 60 90 180 270 360 0 15 30 60 90 180 270 Time from diagnosis (days) Time from diagnosis (days) Fig. 3 (See legend on previous page.) Violán et al. BMC Infectious Diseases (2022) 22:721 Page 11 of 13 still unexplained, and further investigation on the role of fighting against the pandemic, larger studies with a more cellular immunity is also needed, especially in vulnerable heterogeneous sample and longer follow-up period can groups, (non-seroconvertors, people with immunodefi- provide better generalisability and potentially elaborate ciencies, and autoimmune disorders) [2, 26] on further effects. Even though the fitted estimating Anti-SARS-CoV2 antibodies were determined by equations for the evolution of each antibody levels are ELISA techniques. The maximum levels of IgM(N) and provided in Additional file 1, NMLE models should be IgG(N) antibodies tested could be even higher than considered as a method to study the temporal evolution, reported, as the technique used was semi-quantitative instead of a punctual estimator. Due to the standardised due to the limited availability of quantitative in vitro quantitative assessment of IgG(S), our kinetic models can diagnostic techniques at the beginning of the pandemic. serve as a reference for future studies and inform social Although, the WHO’s international standard for quan- distancing and vaccination strategies. tification of IgG(S) antibodies was established after the study start, we retrospectively analysed the samples for Supplementary Information IgG(S) using a quantitative technique. The follow-up of The online version contains supplementary material available at https://​doi.​ IgG(S) levels stopped as participants were vaccinated, org/​10.​1186/​s12879-​022-​07696-6. while the IgM(N) and IgG(N) continued. Additional file 1: Table S1. Number of available samples of each SARS- Interpretation of these results should consider the CoV-2 antibody per assessment timepoint. Table S2. Description (N, %) of limitations of the study. First, the majority of the Pro- the main symptoms in participants according to disease severity and sex HEpiC-19 sample are of white European origin, so find- assigned at birth. Table S3. Parameter estimation for SARS-CoV-2 antibod‑ ies (IgM(N), IgG(N), IgG(S)) NLME models. Figure S1. Flow chart of the ings might not be generalizable to other ethnic groups. ProHEpiC-19 study participants, including the recruitment procedure and Second, differences due to gender or disease severity the type of relationship with Sars-CoV-2. The analysis considered the “posi‑ were considered in the analysis. In addition to these, the tive at baseline or during follow-up” participants. Figure S2. Q-Q plots of the non-linear mixed-effects models. study could be extended to include many other factors such as age, nationality, socioeconomic status or comor- Acknowledgments bidities, but the homogeneity of the sample in these The authors would like to sincerely thank the participants for their effort and aspects did not allow sufficient statistical power to do so. selfless involvement in the ProHEpiC-19 cohort study. Also, they want to thank Future work could broaden the sample recruitment to the Management Department, Primary Care Directorate, and the Directorate of the Clinical Laboratory of the Metropolitan North for the facilities they people more diverse in these respects to extend the gen- have given for the project. In addition, we deeply thank the technical staff of erality of the findings. IrsiCaixa for processing the samples. We thank “CERCA Programme/Generalitat As more and more people are getting vaccinated, it is de Catalunya” for institutional support. Disclosure of results before publication: A previous version of this work has been important to understand whether and how the immu- published in MedRxiv https://​www.​medrx​iv.​org/​conte​nt/​10.​1101/​2021.​09.​10.​ nity due to vaccination is different from that due to the 21262​527v1 infection. Are the kinetics and the durations of the anti- Study protocol: Protocol and informed consent are published on ClinicalTrials. gov (NCT04885478, registered on 13/05/21) body levels similar? Epidemiological modelling studies, ProHEpiC-19 Investigators: Marta Soldevilla ­Garcia1, Ester Moral ­Roldan1, especially long-term immunity monitoring, are focusing Magda Alemany ­Costa1, Eva Olivares ­Ortega2, Alba Pachón ­Camacho1,2, Marta on the SARS-CoV-2 nowadays, however, one must also Bujalance ­Devesa1, Mariella Soto ­Espinoza1, Antonio Negrete ­Palma1, Mariana Martinez de San José1, Ester Lucas ­Varas3,7, Ester Badia ­Perich2, Mónica Piña evaluate the interactions between SARS-CoV-2 and other ­Rodriguez2, Elena Domenech G ­ raells2, Eduard Moreno G­ abriel1,2,6,22, Victòria coronaviruses for potential cross-immunisation [25, 27]. Sabaté ­Cintas2, Mª Jose Argerich González2, Asumció Vazquez D ­ uran2, Alex Moreover, epidemiological modelling of SARS-CoV-2 Ortega ­Roca2, Anna Devesa ­Pradells2, Athina ­Kielpilanen13, Oscar Blanch ­Lombarte13, Miguel Angel Marin ­Lopez13, Julieta ­Carabelli13, Ruth Peña can also benefit from that of other viruses, such as flu Poderós13, Esther Jimenez ­Moyano13, Eulalia Grau ­Segura13, Laia Bernard and HIV-1; and vice versa [28, 29]. Second, the code to ­Rosa13, Raul Pérez C­ aballero13, Felipe Rodriguez ­Lozano13, Gema Fernández specify and fit the NLME is available, and one can exploit ­Rivas22, Sonia Molinos ­Abos22, Jaume Barallat Martinez de ­Osaba16, Lorena Tello ­Trigo15, Cristina Perez ­Cano23, Juan Matllo ­Aguilar23, Anabel López Mar‑ and extend as they see fit, and the results of this study tínez23, Inmaculada Agüera Iglesias.23 will inform the clinical practice guidelines to assess the 1 Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari SARS-COV-2 seroprevalence. d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Mataró, Spain. 2 Direcció d’Atenció Primària Metropolitana Nord Institut Català de Salut. To conclude, we monitored three types of antibod- Barcelona, Spain. 3Germans Trias i Pujol Research Institute (IGTP), Badalona, ies for 17 months, and analysed their levels and kinetics Spain. 4Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain. while also considering the effect of gender and disease 5 Department of Medicine, Faculty of Medicine, Universitat de Girona, 17003 Girona, Spain. 6Multidisciplinary Research Group in Health and Society severity. NLME models allowed a more detailed under- GREMSAS (2017 SGR 917), 08007 Barcelona, Spain 7 Cell Biology, Physiology, standing of the trajectories, confirming that infected Immunology Department. FOCIS Center of Excellence- Universitat Autònoma people can maintain immunity due to the prolonged de Barcelona, Cerdanyola del Vallès, Spain. 8Immunology Division. Laboratori clinic Metropolitana Nord (LCMN). Hospital Universitari Germans Trias i Pujol, seroprevalence of IgG isotypes. While our study focused Badalona, Spain. 9Fundació Institut Universitari d’Investigació en Atenció on the healthcare workers due to their importance in Primària Jordi Gol (IDIAP Jordi Gol), Barcelona, Spain. 10Department of Nursing, Violán et al. BMC Infectious Diseases (2022) 22:721 Page 12 of 13 Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain. 11Depart‑ Author details 1 ment of Medicine, Faculty of Medicine, Universitat Autónoma de Barcelona, Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi 08193 Bellaterra, Spain. 12Departament de Pediatria, d’Obstetrícia i Ginecologia Gol), Unitat de Suport a la Recerca Metropolitana Nord, Mare de Déu de i de Medicina Preventiva, Universitat Autónoma de Barcelona, 08193 Bellaterra, Guadalupe 2, Planta 1ª, Mataro, 08303 Barcelona, Spain. 2 Direcció d’Atenció Spain. 13AIDS Research Institute Irsicaixa, Badalona, Spain. 14Centre d’Atenció Primària Metropolitana Nord Institut Català de Salut, Barcelona, Spain. 3 Ger‑ Primària La Riera (Mataró 1), Institut Català de la Salut, Barcelona, Spain 15Inten‑ mans Trias i Pujol Research Institute (IGTP), Camí de les Escoles, S/N, Badalona, sive Care Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Spain. 08916 Barcelona, Spain. 4 Universitat Autònoma de Barcelona, Cerdanyola 16 Clinical and Biochemical Analysis Division. Laboratori clinic Metropolitana del Vallès, Spain. 5 Department of Medicine, Faculty of Medicine, Universitat Nord (LCMN). Hospital Universitari Germans Trias i Pujol, Badalona, Spain. de Girona, 17003 Girona, Spain. 6 Multidisciplinary Research Group in Health 17 Lluita contra la SIDA Foundation, Hospital Universitari Germans Trias i Pujol, and Society GREMSAS (2017 SGR 917), 08007 Barcelona, Spain. 7 Cell Biology, Badalona, Spain. 18University of Vic-Central University of Catalonia (UVic-UCC), Physiology, Immunology Department, FOCIS Center of Excellence-Universitat 08500 Vic, Spain; 19Hospital Universitari Germans Trias i Pujol, Badalona, Spain; Autònoma de Barcelona, Cerdanyola del Vallès, Spain. 8 Immunology Division, Gerència Territorial Metropolitana Nord, Institut Català de la Salut, Barcelona, Laboratori Clinic Metropolitana Nord (LCMN), Hospital Universitari Germans Spain. 20Centro de Investigación Biomédica en Red de Enfermedades Infeccio‑ Trias i Pujol, Badalona, Spain. 9 Fundació Institut Universitari d’Investigació en sas, (CIBERINF), Instituto de Salud Carlos III (ISCIII). 21Department of Medicine, Atenció Primària Jordi Gol (IDIAP Jordi Gol), Barcelona, Spain. 10 Department Faculty of Medicine, Universitat Autònoma de Barcelona, 08193 Bellaterra, of Nursing, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain. Spain 22Department of Social Psychology, Universitat Autònoma de Barcelona, 11 Department of Medicine, Faculty of Medicine, Universitat Autónoma de Cerdanyola de Vallès, Bellaterra, Spain. 8 EDUARD MORENO) 22Microbiology Barcelona, 08193 Bellaterra, Spain. 12 Departament de Pediatria, d’Obstetrícia Department, Laboratori Clinic Metropolitana Nord (LCMN), Hospital Universi‑ i Ginecologia i de Medicina Preventiva, Universitat Autónoma de Barcelona, tari Germans Trias i Pujol, 08916 Badalona, Spain. 23Basic Prevention Unit, Met‑ 08193 Bellaterra, Spain. 13 AIDS Research Institute Irsicaixa, Badalona, Spain. 14 ropolitana Nord Hospital Universitari Germans Trias i Pujol, 08916 Badalona. Centre d’Atenció Primària La Riera (Mataró 1), Institut Català de la Salut, Barcelona, Spain. 15 Intensive Care Unit, Hospital Universitari Germans Trias i Author contributions Pujol, Badalona, Spain. 16 Clinical and Biochemical Analysis Division, Laboratori CV, PT-M, JG and EM-C developed the study concept and design. BQ, NL-V, Clinic Metropolitana Nord (LCMN), Hospital Universitari Germans Trias i Pujol, RD-A, CCh, FR-R, FP-F, MM, PM, RG-S, FA, CP MB-P and ProHEpiC-19 investiga‑ Badalona, Spain. 17 Lluita contra la SIDA Foundation, Hospital Universitari tors contributed to the data acquisition. LC-R-B, JM-D, AR-LL, AP NM-M, and Germans Trias i Pujol, Badalona, Spain. 18 University of Vic-Central University DO contributed to the data analysis. CV, PT, JGP, and E M-C compiled the of Catalonia (UVic-UCC), 08500 Vic, Spain. 19 Hospital Universitari Germans Trias initial draft of the manuscript. All authors contributed to the interpretation of i Pujol, Badalona, Spain. 20 Gerència Territorial Metropolitana Nord, Institut Cat‑ the results and the critical revision of the manuscript. MD, NP, JB, BC, and IB alà de la Salut, Barcelona, Spain. 21 Centro de Investigación Biomédica en Red contributed to the study supervision. All authors read and approved the final de Enfermedades Infecciosas (CIBERINF), Instituto de Salud Carlos III (ISCIII), manuscript. Madrid, Spain. 22 Department of Medicine, Faculty of Medicine, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain. Funding This project has been funding by the regional Ministry of Health of the Received: 15 February 2022 Accepted: 17 August 2022 Generalitat de Catalunya (Call COVID19-PoC SLT16_04) the Carlos III Health Institute (Ministry of Economy and Competitiveness, Spain) through the RETICS RD16/0007/0001 and RETIC RD16/0025/0041), the European Union European Regional Development Fund (ERDF) funds, the Catalan Government (Grant Number AGAUR 2017 SGR 445), and thePI19 Contratos predoctorales de References formación en investigación en salud (AES 2019). 1. Galit and Seder RGA. The power of antibody-based surveillance. N Engl J Med. 2020;383:1782–4. https://​doi.​org/​10.​1056/​nejme​20295​32. Availability of data and materials 2. Dan JM, Mateus J, Kato Y, Hastie KM, Yu ED, Faliti CE, et al. Immunological The datasets generated and/or analysed during the current study are not pub‑ memory to SARS-CoV-2 assessed for up to 8 months after infection. 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Kilpeläinen A, Jimenez-Moyano E, Blanch-Lombarte O, Ouchi D, Quirant- • gold Open Access which fosters wider collaboration and increased citations Sanchez B, Chamorro A, et al. Highly functional cellular immunity in • maximum visibility for your research: over 100M website views per year SARS-CoV-2 non-seroconvertors is associated with immune protection 5. BioRxiv 2021:2021.05.04.438781. At BMC, research is always in progress. 27. Abela IA, Pasin C, Schwarzmüller M, Epp S, Sickmann ME, Schanz MM, et al. Multifactorial seroprofiling dissects the contribution of pre-existing Learn more biomedcentral.com/submissions