key: cord-0694002-z99djm4o
authors: Štěpánek, Ladislav; Janošíková, Magdaléna; Štěpánek, Lubomír; Nakládalová, Marie; Boriková, Alena
title: The kinetics and predictors of anti‐SARS‐CoV‐2 antibodies up to 8 months after symptomatic COVID‐19: A Czech cross‐sectional study
date: 2022-04-22
journal: J Med Virol
DOI: 10.1002/jmv.27784
sha: 32ea8139c805beec712dea09567a0efbc8438c06
doc_id: 694002
cord_uid: z99djm4o

The presence of neutralizing SARS‐CoV‐2‐specific antibodies indicates protection against (re)infection, however, the knowledge of their long‐term kinetics is limited. This study analyzed the presence of COVID‐19‐induced antibodies in unvaccinated healthcare workers (HCWs) over the period of 1–8 months post symptom onset (SO) and explored the determinants of persisting immunoglobulin (Ig) seropositivity. Six hundred sixty‐two HCWs were interviewed for anamnestic data and tested for IgG targeting the spike protein (S1 and S2) and IgM targeting the receptor‐binding domain. A Cox regression model was used to explore potential predictors of seropositivity with respect to the time lapse between SO and serology testing. 82.9% and 44.7% of HCWs demonstrated IgG and IgM seropositivity, respectively, with a mean interval of 83 days between SARS‐CoV‐2 detection and serology testing. On average, HCWs reported seven symptoms in the acute phase lasting 20 days. IgG seropositivity rates among HCWs decreased gradually to 80%, 50%, and 35% at 3, 6, and 8 months after SO, while IgM seropositivity fell rapidly to 60%, 15%, and 0% over the same time intervals. The number of symptoms was the only predictor of persisting IgG seropositivity (odds ratio [OR] 1.096, 95% confidence interval [CI] 1.003–1.199, p = 0.043) and symptom duration a predictor of IgM seropositivity (OR 1.011, 95% CI 1.004–1.017, p = 0.002). Infection‐induced anti‐SARS‐CoV‐2 IgG rates drop to a third in seropositive participants over the course of 8 months. Symptom count and duration in the acute phase of COVID‐19 are both relevant to the subsequent kinetics of antibody responses.

and B cells evolve and, consequently, plasma cells begin to produce virus-specific antibodies targeting the nucleocapsid (N) or the spike (S) protein of SARS-CoV-2. This usually happens a few days up to weeks after infection, depending on the immunoglobulin (Ig) subclass. 3 The ability to defend a cell from an infectious particle by neutralizing the particle's biological effects, that is, the neutralizing capacity, is an essential characteristic of antibodies. Given their mechanism of action, neutralizing antibodies play a crucial role in the prevention of COVID-19 (re)infection. While antibodies targeting the N protein are unlikely to directly neutralize SARS-CoV-2, those targeting the S protein (specifically, the receptor-binding domain (RBD) of the S1 subunit) are considered to be the main neutralizers. [4] [5] [6] As the natural immune response to develops in the infected individual, IgM acts as the first antibody response and a powerful suppressor of SARS-CoV-2, while IgG appears later and remains present in the human body for months. 1, 7 Neutralizing antibodies against SARS-CoV-2 provide the best current indication of being protected against reinfection (in previously infected subjects) or breakthrough infection (in vaccinated subjects). In other words, neutralizing antibodies are the most reliable markers indicating immunity to COVID-19 known to date. 6, 8 The long-term characteristics of anti-SARS-CoV-2 antibodies, which include the persistence of antibodies and the duration of the immune protection, remain largely unclear, although some follow-up studies have revealed relatively stable IgG titers over several months following COVID-19 infection. 1, 3, 8 However, results vary depending on the sample size, population, comorbidities, treatment, and type of antibody detection assays. The immune characteristics of natural infection play a key and referenced role in estimating antibody effects after vaccination, and thus in creating vaccination strategies. 1 The present study follows up on the pilot work of the same authors dealing with natural, infection-induced antibody responses after the acute phase of COVID-19. 9 The aim of the follow-up study was to analyze the kinetics of antibody responses in unvaccinated healthcare workers (HCWs) after COVID-19 in the period of 1-8 months post symptom onset (PSO) and to determine predictors of persistent IgG and IgM seropositivity.

The study sample consisted of all HCWs (n = 662) from the Olomouc Region, Czech Republic, who requested to have their COVID-19 recognized as an occupational disease (OD) at the OD Center of the Department of Occupational Medicine, Olomouc University Hospital, between November 2020 and September 2021. These HCWs had their viral ribonucleic acid collected by a nasopharyngeal swab and detected using a reverse transcription-polymerase chain reaction (RT-PCR) test in the acute phase of the disease. The HCWs also brought a report about the course of their disease from their general practitioner (GP). They were examined at the OD Center according to a uniform protocol and submitted a blood sample for one-time serology testing. The examination took place at least 30 days and at the latest 8 months after their diagnostic RT-PCR test. All included cases were symptomatic and previously SARS-CoV-2 naïve. HCWs with SARS-CoV-2 reinfection and those who reported suspected COVID-19 symptoms after their recovery were excluded. Exclusion criteria also comprised vaccination against COVID-19 at the time of the examination and an insufficient medical report from the GP.

During the examination, the HCWs were asked about all symptoms of COVID-19 listed by the World Health Organization and the Centers for Disease Control and Prevention and their duration. 10, 11 The presence of each particular symptom was determined by a yes/no question, and the total symptom duration was calculated by subtracting the symptom onset (SO) date from the symptom recovery date. Information provided by the participants was validated against the GP's report. In case of a discrepancy between the anamnestic data provided by the participant and the GP's report, the data were repeatedly verified (through other medical reports if available) and the data from the participant was finally taken into account. Disease severity was assessed according to the National Institutes of Health (NIH). 12 Epidemiological data showed that in the period in which HCWs became infected with SARS-CoV-2, the wild-type variant of the virus was gradually replaced by the delta variant in the Czech Republic. 13 All participants signed an informed consent form regarding the anonymous use of their data. The study was approved by the Ethics 

The presence of antibodies was determined using SARS-CoV-2 chemiluminescence immunoassays by DiaSorin-Liaison SARS-CoV-2 S1/S2 IgG and Liaison SARS-CoV-2 IgM performed on the Liaison XL analyzer (DiaSorin S.p.A.). The automated IgG assay detects antibodies against the S1 and S2 subunits of the S protein, whereas the IgM assay detects antibodies targeting the RBD. For the diagnostic assays, DiaSorin guarantees clinical sensitivity and specificity above 95%, as well as excellent detection of neutralizing antibodies (94.4% positive agreement with the plaque reduction neutralization test). 14 Recent studies have proved that the performance of DiaSorin assays is comparable to other commercial immunoassays and opens the possibility of their application in epidemiological studies. [15] [16] [17] The level of IgG antibodies was considered negative at <15 AU/ml, positive at ≥15 AU/ml (i.e., 

Statistical analyses were conducted in the R software environment (R Foundation for Statistical Computing; http://www.r-project.org/). All numerical variables were characterized with descriptive statistics.

The studied variables, especially antibody levels, showed a rightskewed distribution, as evidenced by the mean/median index ≫1 (Table 1) . We quantified the correlations of numerical variables with Spearman's correlation coefficient (r) and used regression analysis methods to explore the dependence of the serology status (seropositivity or seronegativity, inversely, as a disjunct event) on personal, anthropometric, and anamnestic data.

Taking into account the natural changes in antibody levels over time and the cross-sectional nature of the study with uneven intervals between the participants' SO and serology testing, we opted for a proportional hazards regression model (time-to-event analysis with right-censoring/Cox regression) to eliminate the effect of the intervals' irregularity. This means the model predicted the serology status bound to a specific interval from SO to serology testing (=the response variable consisted of two components). Other examined variables served as independent explanatory variables (predictors).

Kaplan-Meier curves to specify statistically significant predictors.

The theoretical assumption of the model used is 100% seropositivity at the beginning of the interval between SO or PCR test, respectively, and serology testing, which is justified by the available knowledge of antibody development 3, 18, 19 and the minimum interval of 30 days required for inclusion in the study. The level of statistical significance was set at p = 0.05.

The study population consisted of 662 HCWs aged 44 years on average, with a predominance of women (n = 541) over men (n = 121; Table 1 ). Both the mean and median concentrations of anti-SARS-CoV-2 IgG were above the seropositivity cutoff point. Specifically, 549 (82.9%) participants were IgG seropositive at the time of serology testing.

According to the median anti-SARS-CoV-2 IgM level, most of the HCWs were seronegative, but due to a right-skewed distribution of antibody values, the mean IgM value was above the immunoassay cutoff point. Two hundred ninety-six (44.7%) HCWs were IgM seropositive. The interval between SARS-CoV-2 detection and serology testing averaged 81 days, which was 2 days less than the average interval between SO and serology testing. In other words, 

An inverse relationship was noted between the interval from SO to serology testing and both IgG and IgM seropositivity rates (Figure 1 ).

The portion of participants with IgG levels above the cutoff point fell below 80% if serology testing was done more than 3 months PSO, below 60% if more than 6 months PSO, and below 35% at 8 months PSO. The decrease in the portion of IgM seropositive HCWs was even more radical. Less than 50% of them manifested IgM levels above the cutoff point 3 months PSO, and if serology testing was performed more than 6 months PSO, the proportion of IgM seropositive participants did not exceed 15%.

The only statistically significant predictor of IgG with respect to the time interval between COVID-19 SO and serology testing in the Cox regression was the number of symptoms present in the acute phase of the disease (Table 3) Only one variable, symptom duration, significantly predicted maintaining IgM levels above the cutoff point (Table 3) . Each day of symptom duration increased the probability of IgM seropositivity 1.011 times (p = 0.002), and inversely, decreased the probability of seronegativity by 0.989 times. The log-rank test showed that the most significant difference in IgM seropositivity occurrence appeared when the sample was divided into subgroups with symptom duration shorter than 21 days and exceeding or equal to 21 days (p < 0.001; Figure 3 ). and IgM no longer achieved concentrations above the cutoff point ( Figure 1) .

The results of a serology examination of IgG after a long time has passed since COVID-19 infection greatly depend on which specific parts of SARS-CoV-2 the antibodies are detected against. Many studies consistently observe that IgG to the N protein, found inside the virus or infected cells, decays faster than IgG to the S protein, and as such is a marker of a more recent infection. It is, however, less sensitive for assessing population seroprevalence. 5 Titers of IgG targeting the RBD appear to be very robust over time, as shown by a longitudinal study from Switzerland with a progressive increase in titers at 1, 3, and 6 months PSO. 28 An Austrian study found that even after 12 months, antibodies against the RBD persisted in all cases with increasing concentrations. 29 Our study involved the detection of IgG directed nonspecifically against various epitopes of the S1 and S2

subunits of the S protein.

The kinetics of SARS-CoV-2-specific antibodies are multifactorial, with a number of identified factors influencing Ig persistence. Moreover, data on particular factors is still limited or even conflicting. 30 Available studies most often report a positive association of disease severity with both antibody levels 24,25,31 and seropositivity persistence 24 in the months following the acute phase.

In other words, the more severe the course of COVID-19 is, the later phase. An Italian 10-month longitudinal study using multivariate linear regression analysis showed that older age, the number of symptoms in the acute phase, and disease severity were all independent predictors of long-term immunity for both IgG and IgM. 33 An Estonian cross-sectional study showed a higher number of symptoms of the acute phase of COVID-19 (median 6) in participants with persisting IgG seropositivity several months after COVID-19, compared to seronegative participants (median 5). 34 In our study, the numbers of recorded symptoms in the same subgroups were one unit higher (medians 7 and 6). Finally, a cross-sectional study from the United States among participants who examined 2-3 months PSO proved decreasing seronegativity rates inversely to an increasing number of symptoms reported in the acute phase. 35 Most symptoms are organ-specific, so a higher number of symptoms usually means a higher number of affected body systems, which can be reflected in subsequent IgG antibody responses, as in the case of documented lung involvement. 36 T A B L E 3 Proportional hazards (Cox) regression expressing the chance of seronegativity (or seropositivity as an inverse value) with respect to the interval between symptom onset and serology testing. cannot be recognized as an OD, which is a study limitation. Stepanek: Methodology; formal analysis. Marie Nakladalova: Conceptualization; supervision. Alena Borikova: Data curation.

The study was supported by the University Hospital Olomouc Fund (RVO 00098892) and the Palacký University Olomouc Fund (RVO 61989592).

The authors declare no conflicts of interest.

The data that support the findings of this study are available from the corresponding author (Ladislav Stepanek) upon reasonable request.

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