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. 2023 Aug 25;33:100677. doi: 10.1016/j.bbih.2023.100677

The interplay between previous infection and mental health condition on antibody response to COVID-19 mRNA vaccination

Nicola Grignoli a,b,∗, Serena Petrocchi c, Andrea Polito d, Vanessa Gagliano a, Federica Sallusto e,f, Mariagrazia Uguccioni f,g, Luca Gabutti a,∗∗
PMCID: PMC10493882  PMID: 37701787

Abstract

Increasing evidence has been pointing towards the existence of a bi-directional interplay between mental health condition and immunity. Data collected during the COVID-19 outbreak suggest that depressive symptoms may impact the production of antibodies against SARS-CoV-2, while a previous infection could affect the immune response and cause neuropsychological disturbances. A prospective observational study was designed to investigate the association between mental health conditions and immune response over time. We analyzed the mental health at baseline and the antibodies before and after immunization with the COVID-19 mRNA vaccine in a cohort of healthcare professionals in southern Switzerland. One-hundred and six subjects were enrolled. Anxiety, distress and depression correlated to each other. There were no correlations between the mentioned variables and the vaccine induced IgG antibodies against the receptor binding domain (RBD) of the spike protein. For those who had a previous COVID-19 infection, the antibodies increased according to the grade of depression. For those who did not, the anti-RBD IgG levels remained similar when comparing presence or absence of depression symptoms. Our results show that previous SARS-CoV-2 natural infection in subjects with mental health conditions enhances the immune response to COVID-19 mRNA vaccination. The correlation between immune response to COVID-19 vaccination, a previous exposure to the virus, and symptoms of mood disorders, makes it necessary to explore the direction of the causality between immune response and depressive symptoms.

Keywords: COVID-19 mRNA vaccination, Antibody response, Stress, Anxiety, Depression, Secondary immunity response, Previous infection, Healthcare professionals

1. Introduction

According to the vaccination model immunity and immunosenescence are modulated by chronic or acute stress exposure (Phillips, 2012). As far as chronic stress conditions are concerned, there is comprehensive agreement about their detrimental effects on the cellular and humoral adaptive immunity to natural exposure to pathogens or to vaccination (Powell et al., 2011; Seiler et al. 2019). However, in acute stress conditions, the humoral immunity is preserved and could even be enhanced as an adaptive reaction (Segerstrom and Miller, 2004; Khan et al., 2021). Moreover, a bi-directional influence between psychological factors and immunity emerges from the literature on stress and mental health condition (Dhabhar and Mcewen, 2007; Kamimura et al., 2020; Gentileet al., 2021).

Psychological and behavioral factors are today established by a growing and consistent literature as influencing the individual immune response against SARS-CoV-2 (Madison et al., 2021; Bower et al. 2022). The first empirical data on SARS-CoV-2 tend to verify the negative impact of depressive symptoms on the production of antibodies against SARS-CoV-2 (Kaneko and Tsuboi, 2022) but less is known about the impact of acute stress condition (Peters et al., 2021). Finally, it can be hypothesized that prior exposure to the virus, a frequent condition during the COVID-19 pandemic, which sometimes leads to a Long COVID with neuropsychological disturbances, could in turn modulate the immune response to the vaccine and the patient's mental health (Blomberg et al., 2021; Frere et al., 2022; Davis et al., 2023).

In this prospective pilot study on newly vaccinated healthcare professionals, we have investigated the association between the immune response to a COVID-19 mRNA vaccine over time, measured through antibodies against the receptor binding domain (RBD) of the spike protein, and mental health conditions (such as emotional distress, anxiety, and depression) measured at the baseline with validated psychometric scales in both previously exposed and unexposed subjects to the SARS-CoV-2 infection.

2. Participants and methods

2.1. Data source

Data have been collected in the context of the COVID-19 Vaccine Sub-Study included in a larger investigation named The Analysis of Human Immune System Activity, performed by the Institute for Research in Biomedicine (IRB), Bellinzona, Switzerland. The main research project studied antibodies (IgM, IgG and IgA) and cells of the immune system to SARS-CoV-2 antigens in a cohort of individuals employed by hospitals or working in direct contact with patients in hospitals and in- and outpatient clinics, in the Transfusion Centre of the Swiss Red Cross and in the biomedical research institutes of the Canton of Ticino, Switzerland (Piccoli et al., 2021). A prospective observational sub-study, the COVID-19 Vaccine Sub-study, was designed to understand the immune response to a COVID-19 mRNA vaccine and the vaccine's ability to stimulate blood cells to produce anti-RBD IgG antibodies capable of blocking the SARS-CoV-2 infection. In view of the recognized impact of baseline psychological conditions on vaccine efficacy, an ad hoc questionnaire with validated psychometric scales assessed the level of initial distress, anxiety and depression to be quantified.

2.2. Ethics

This research project was conducted in accordance with the protocol, the Declaration of Helsinki, the principles of Good Clinical Practice, the Human Research Act (HRA) and the Human Research Ordinance (HRO), as well as other locally relevant regulations. The study was approved by the Cantonal Ethical Committee (BASEC number: 2018-02166). Participation was voluntary and data collection was in an anonymous form. Participants received an information sheet and gave their informed consent for participation.

2.3. Procedure

Healthy individuals (age>18 years) employed in Hospitals/Clinics, Fondazione Servizio Trasfusionale CRS della Svizzera Italiana, and the Institute for Research in Biomedicine who received on a volunteer basis vaccination against a COVID-19 with a mRNA vaccine (BNT162b2) were invited to adhere to the study by regular blood sample donation prior to and after the vaccination. For recruitment of Hospital/Clinic employees, a note was sent by the administration to all employees, indicating the type of study to be started. Volunteers could then express their interest in participating and fill in a research questionnaire (Supplementary material 1). Subject signalling in the questionnaire previous to Sars-Cov-2 infection or symptoms or positive PCR test in the 3 months before T0 were excluded from the vaccination study following medical recommendation in effect at that time. The study was performed during Alpha/Delta waves as established by regional epidemiological data. Subjects were enrolled from the end of April to June 2021 and followed until December 2021 (see Table 1 for details on timeline of sampling, vaccination schedule and regional epidemiological data source).

Table 1.

Timeline flowchart of the blood withdrawal for the study with epidemiological data source and vaccination plan reference.

Visit
 1st Visit (T0)
 2nd Visit (T1)
 3 d Visit (T2)
Baseline
 +3 months
 +6 months
April–June 2021 (Alpha/Delta SARS CoV-2 Waves)a From July to September 2021 Until December 2021
Inclusion/exclusion checkb •
Written informed consent •
Pre-vaccination blood for serum sampling (5 mL) •
BNT162b2 vaccinationc • •
Post-vaccination blood for serum sampling (5 mL) • •
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b

Subjects with previous Sars-Cov-2 infection or symptoms or positive PCR test in the 3 months before T0 were excluded from the study following vaccination medical recommendation in effect at that time (Strategie-de-vaccination-COVID-19-OFSP-EKIF.pdf (bag-coronavirus.ch).

Prior to vaccination (T0), 5 mL of blood for the study of antibodies in serum was taken by venous sampling. Three (T1) and 6 months (T2) after completion of the vaccination, again by means of a venous sampling, the same blood withdrawal was repeated. On a volunteer basis with a positivity to SARS CoV-2 antibodies in the initial screening an extra blood sample collection of 20–50 mL for cell isolation and tests (in addition to serum collection) was collected for the study of immune system cells. Previous SARS-CoV-2 infection was tested and assessed for data analysis through the presence of antibody titres in the first blood sample at T0 when subject received the vaccination's first dose. Previous SARS-CoV-2 symptoms were assessed through a self-reporting research questionnaire (See Supplementary material 1). Subjects signalling in the research questionnaire previous Sars-Cov-2 infection or symptoms or positive PCR test in the 3 months before T0 were excluded from the vaccination study following medical recommendation in effect at that time. During the follow-up subjects followed the vaccination plan for BNT162b2 (with second dose at T1).

2.4. Immune response measurement

The measurement of antibody response was used in the early phase of the pandemic as a biological criterion for a personalized indication of vaccination boosters. In particular, the IgG anti-RBD titer has been used as cut-off to decide if specific monoclonal antibodies had to be given to patients. For the present study the proteins present in sera and different subtypes of cells derived from healthy individuals allowed the disclosure or the clarification of different mechanisms at the basis of the organism defence, and allowed the dissection of possible differences between the human response to primary infections or vaccination. Anti-RBD IgG data on T0, T1 (3 months) and T2 (6 months) were analyzed (for detailed methods on antibody measurements see the methods section in the previously published study on the same topic (Piccoli et al., 2021)).

Two different scores have been calculated as follows:

  • •

    Delta T1 (Δt1) = anti-RBD IgG T1 – anti-RBD IgG T0. Higher positive scores indicate that the IgG levels at T1 are higher than at T0. Higher negative scores have the opposite meaning.

  • •

    Delta T2 (Δt2) = anti-RBD IgG T2 – anti-RBD IgG T0. Higher positive scores indicate that the IgG levels at T2 are higher than at T0. Higher negative scores have the opposite meaning.

2.5. Psychometric assessment

Distress was evaluated through the analogic scale of the Distress Thermometer (Donovan et al., 2014); anxiety symptoms were investigated through the Generalized Anxiety Disorder 7-item Scale (GAD-7) (Spitzer et al., 2006); depressive symptoms with the Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001). (see Supplementary material 1).

2.6. Statistical analyses

T-tests were performed to establish whether a previous SARS-CoV-2 virus infection had the expected effect on the anti-RBD IgG levels. A repeated-measures ANOVA was carried out to determine whether there were significant differences in the three-time evaluations of anti-RBD IgG at T0, T1, and T2. Age, gender, and previous SARS-CoV-2 infection were included as covariates in the ANOVA. Results of the Mauchly's test checking the assumption of sphericity were considered and the Greenhouse-Geisser correction was applied in case of violations. Correlation analyses were performed considering distress, GAD, PHQ, and the two deltas.

Finally, a set of moderation analyses was performed with:

  • •

    Distress, GAD, or PHQ as the independent variable

  • •

    Previous SARS-CoV-2 infection as the moderator variable

  • •

    Each delta as the dependent variable

  • •

    Gender, age, and having symptomatic infection of SARS-CoV-2 in the last 9 months as the covariates

A graph has been provided for significant psychological and previous SARS-CoV-2 infection interaction term.

3. Results

3.1. Demographic characteristics

One-hundred and six participants aged from 26 to 62 (M = 43.76, SD = 9.58; 65 women) took part in the research at T0; 89% of them were involved at T1, and 69% at T2.

Non-parametric comparisons between participants who completed both T0 and T1 vs who completed T0 only were performed on age, gender, anti-RBD IgG T0, distress, GAD, and PHQ. The only significant difference was on age: subjects who participated in the second blood sample were younger (M = 37.9, SD = 8.7) than participants who completed T0 only (M = 44.4, SD = 9.48; U = 732.5, p = .029). Similarly, comparisons between participants who completed both T0 and T2 vs who completed T0 only were run. In this case, there were no significant differences.

3.2. Preliminary analysis

3.2.1. Effect of previous infection on IgG levels over time

Having a previous SARS-CoV-2 infection determined significant differences in the anti-RBD IgG levels at T0 and T2 (see Table 2); but did not influence the anti-RBD IgG levels at T1.

Table 2.

Results of the t-tests and descriptive.

 anti-RBD IgG T0
 anti-RBD IgG T1
 anti-RBD IgG T2
M (sd) M (sd) M (sd)
Previous SARS-CoV-2 infection .92 (1.4) 14.91 (10.26) 7.17 (4.33)
No previous SARS-CoV-2 infection .01 (.24) 11.70 (9.87) 3.40 (3.21)
t-test t(20.30) = -2.53, p = .02 t (92) = −1.17, p = .24 t(72) = -3.60, p = .001
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Correlations were calculated between the Deltas and presence of previous SARS-CoV-2 infection for those who had declared to suffer from a symptomatic infection of SARS-CoV-2 in the last 9 months. The correlations considering the Delta RBD IgG levels from T1 to T0 were not significant. Whereas for the sub-group of individuals who suffered from a symptomatic infection of SARS-CoV-2 in the last 9 months, there was a significant correlation between Delta RBD IgG levels from T2 to T0 and the presence of previous SARS-CoV-2 infection (rho = 0.54, p = .003, N = 28).

3.3. Evolution of anti-RBD IgG over time

There was a significant effect of time on anti-RBD IgG, F (1.2; 83.23) = 47.19, p < .001. Planned contrast revealed that anti-RBD IgG at T0 were lower (M = 0.20, SD = 0.35) than at T1 (M = 11.86, SD = 9.08), F (1; 69) = 63.17, p < .001, and at T2 (M = 4.10, SD = 3.72), F (1 69) = 91.11, p < .001. This means that there is an increase of the anti-RBD IgG from T0 to T1 and from T0 to T2, while from T1 to T2 the levels tend to decrease. Age and gender were not significantly related to the anti-RBD IgG levels at the scheduled appointments. A previous SARS-CoV-2 infection was related to the anti-RBD IgG levels but as a trend, F (1; 69) = 3.27, p < .075. The interaction term between anti-RBD IgG levels and previous SARS-CoV-2 infection was not significant, probably due to the small sample size and the low statistical power. Accordingly, the interaction between anti-RBD IgG levels and previous SARS-CoV-2 infection was not significant for the comparisons between anti-RBD IgG at T0 and T1 (d = 0.05), while it was significant for the comparisons between T0 and T2 (F (1 69) = 11.63, p = .001; d = 0.92).

3.4. Anxiety and depression as moderators of previous infection and IgG levels over time

Distress, GAD, and PHQ correlated to each other, as expected. There were no correlations between the variables measuring psychological functioning and the anti-RBD IgG levels. Table 3 reports the results of the moderation analyses. The anti RBD IgG levels increased from T0 to T1 according to the interaction between distress and previous SARS-CoC-2 infection, but as a trend. The previous SARS-CoC-2 infection influenced the RBD IgG levels that increased from T0 to T2 in all the three analyses carried out. There was a significant interaction between the PHQ levels and the previous SARS-CoC-2 infection on the Delta RBD IgG levels from T2 to T0. The interaction between the GAD levels and the previous SARS-CoC-2 infection on the Delta RBD IgG levels from T2 to T0 was a trend.

Table 3.

Results of the moderation analyses.

β SE T Model summary
Model 1 (Δt1)
Distress −.33 .48 −.67 F (5 63) = 1.59, p = .16
Previous SARS-CoV-2 infection −1.58 4.34 −.36
Interaction (distress*infection) −2.09 1.61 −1.84a
Model 2 (Δt1)
GAD −.44 .34 −1.28 F (5 86) = .67, p = .67
Previous SARS-CoV-2 infection .06 4.12 .01
Interaction (GAD*infection) −.49 1.24 −.39
Model 3 (Δt1)
PHQ −.56 .35 −1.57 F (5 86) = .88, p = .50
Previous SARS-CoV-2 infection −1.72 4.15 −.41
Interaction (PHQ*infection) −1.32 1.21 −1.09
Model 1 (Δt2)
Distress .20 .19 1.04 F(5 51) = 3.34 p = .007
Previous SARS-CoV-2 infection 6.12 1.92 3.17**
Interaction (distress*infection) .73 .71 1.03
Model 2 (Δt2)
GAD .044 .14 .31 F(5 67) = 2.81, p = .016
Previous SARS-CoV-2 infection 4.99 1.77 2.8**
Interaction (GAD*infection) .96 .58 1.66b
Model 3 (Δt2)
PHQ .08 .13 .68 F(5 67) = 2.95, p = .012
Previous SARS-CoV-2 infection 4.80 1.48 3.23**
Interaction (PHQ*infection) .84 .42 1.97*
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Notes: a p = .07, b p = .10, *p < .05, **p < .01, ***p < .001.

Graph 1 plots the interaction between PHQ levels and previous SARS-CoV-2 infection on the difference between T0 and T2 in the anti-RBD IgG levels. For those who had a previous SARS-CoV-2 infection (red line in the Graph), the anti-RBD IgG levels increased from T0 to T2 according to the grade of depression. For those who did not have a previous SARS-CoV-2 infection (blue line in the Graph), the anti-RBD IgG levels remained similar when comparing the presence or absence of depression symptoms.

Graph 1.

Graph 1

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Interaction between PHQ, previous COVID infection and deltaT2.

A similar effect was found on Delta anti-RBD IgG levels from T2 to T0 considering the interaction between GAD and previous SARS-CoV-2 infection (even if it was only a trend). A descriptive graph was provided as well (see Graph 2). For those who had a previous SARS-CoV-2 infection (red line in the Graph), the anti-RBD IgG levels increased from T0 to T2 according to the grade of anxiety. For those who did not have a previous SARS-CoV-2 infection (blue line in the Graph), the anti-RBD IgG levels remained similar when comparing the presence or absence of anxiety.

Graph 2.

Graph 2

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Interaction between GAD, previous SARS-CoV-2 infection and Delta T2.

It is possible that participants with high or very high scores on the GAD would have influenced the results of the whole group, in particular the ones with the interaction with previous SARS-CoV-2 infection on Delta anti-RBD IgG levels. Therefore, a sub-analysis has been conducted. Considering the cut-off of 10 (Spitzer et al., 2006), the participants have been divided into two groups according to their scores, GAD+ (participants with scores ≥10, N = 11) and GAD- (participants with scores <10, N = 95). Non-parametric correlations have been carried out between the GAD scores and the anti-RBD IgG levels for the two sub-groups separately considered. These correlations were not significant meaning that GAD + participants did not inflate the results of the whole group.

4. Discussion

Our main finding shows that the sub-group of subjects previously infected and with depressive symptoms had an increased anti-RBD IgG antibody response to the COVID-19 mRNA vaccination. Our results could be explained through a combined effect of the measured variables, where a previous infection induced a physiological or pathological stimulation of the immune system, amplified by stress and modulated by mental health condition.

It has been shown that the protective effect of previous immunization against SARS-CoV-2 tends to decrease after 3 months and that, in some cases, the activation of the innate immune system can lead to a hyperinflammatory secondary response which ultimately causes a more severe course of the disease (Poonia and Kottilil, 2020). The mechanism behind such negative response with deleterious clinical outcomes has been possibly attributed to the antibody-dependent enhancement (ADE) of viral infection (Fu et al. 2020; Darif et al., 2021). The role of ADE in persistent viral replication and secondary immunity disruption for SARS-CoV-2 is a matter of current controversy (Lee et al., 2020; Gan et al., 2022; Okuya et al., 2022). Indeed, evidence should still be found to clearly understand how SARS-CoV-2 infection can lead to the release of proinflammatory cytokines (Nalbandian et al., 2021) that upon reaching the brain risks to generate sickness behaviors (Dantzer et al., 2008; Bouayed and Bohn, 2021). COVID-19 is now recognized as a multi-organ disease with a large spectrum of manifestations (Nalbandian et al., 2021), and there are increasing reports of persistent and prolonged effects after acute SARS-CoV-2 infection. Investigation on pathophysiology of post-COVID syndrome or Long COVID include long-term tissue damages, unresolved inflammation (Yong 2021). Neuropsychiatric manifestations most frequently associated with Long COVID include depression, anxiety, post-traumatic stress disorder, sleep disorders, and fatigue and cognitive deficits (Rogers and Lewis, 2022; Efstathiou et al., 2022; Davis et al., 2023).

Our study subjects were young healthcare professionals working at the hospital/clinic/healthcare centre during data collection and, therefore, they were also exposed to a possible acute stress condition at that time. Clinically advantageous characteristics of the sample (young age, healthy state, physically active) may have determined a positive adaptive response to an acute stress persistent condition. Moreover, the hypothesis that the subjects with more pronounced mental health symptoms were those engaged in the more critical hospital departments facing the pandemic cannot be verified. Furthermore, the enhancement of the immune response observed in our subjects could have been affected by chronic exposure to stress. The observed increase in the antibody levels in subjects who had a mental health condition and were previously infected with SARS-CoV-2, appears to be in line with the literature on secondary immune responses following acute stress (Phillips, 2011; Peters et al., 2021). Last but not least, acute stress has been associated with increased levels of IgA, IgG and IgM in the context of blood donation or university exams (Khan et al., 2021). Subjects included in the study reported low to mild levels of mental health condition (distress, anxiety or depression), which could be related to their hospital activity during the second wave of the pandemic. However, data concerning depression measured through PHQ-9 could have been confounded by somatic symptoms (Vu et al., 2022), particularly in subjects previously affected by SARS-CoV-2 in whom we found a modulation effect on immune response.

Finally, it has been established that mental health conditions can induce an increased IgG antibody response to infection (Guo et al., 2021). The effect of such antibody hyper response on the actual course of SARS-CoV-2 infection is not known in our sample due to limits of the study design, but it can be hypothesized that the observed increase in specific IgG levels could be the expression of a dysregulated immune response against the virus (Jing et al., 2022). Furthermore, in recent years, it has been demonstrated that pro-inflammatory cytokines can induce not only disease related symptoms, but also depressive disorders in somatically ill patients with no history of mental disorders (Dantzer et al., 2008). A study on hospitalized COVID-19 patients suggested that inflammation may in turn contribute to post-Covid depressive symptoms measured one month and three months after discharge (Mazza et al., 2021). The neuroinvasive and neurotrophic properties of SARS-CoV-2, as well as the overproduction of cytokines and the immune cell hyperactivation (i.e., cytokine storm), may all contribute to the neuropsychiatric COVID-19 related symptoms (Efstathiou et al., 2022). Of note, the systemic immune-inflammation index (SII) was higher at the 3-month follow-up in patients reporting depressive symptoms and cognitive impairment, while individuals with lower SII also exhibited a less severe depression (Efstathiou et al., 2022). It would have been interesting to know if the observed increase in the anti-RBD IgG levels was associated with a more pronounced cytokine response in subjects with depressive symptoms, but such an analysis was not performed. The question is potentially relevant, because the observed depressive mood could also be a false positive data related to a Long COVID sickness behavior.

4.1. Strengths and limits

Our study has the merit to be among the first to investigate prospectively the association between mental health condition and immune response to the COVID-19 vaccination in a sample of healthy, young and active healthcare workers exposed to an acute stress condition. The study design allowed us to identify subjects with an asymptomatic previous SARS-CoV-2 infection and to analyse several socio-demographic factors. The study, however, has several limits due to its pilot nature. The most important is that the measurement of mental health condition was performed only at the baseline and thus the correlation with the immune response in the follow-up should be interpreted with caution. Moreover, the effect could have been falsely enhanced by the lack of specificity of the screening for depression which could have overrated somatic symptoms induced by a potential inflammatory response. Finally, data collected are limited to the vaccine's antibody response and no inference on the subjects' actual protection from further SARS-CoV-2 infection or on disease effective course can be made.

4.2. Future perspectives for research and implication for public health

Our results tend to confirm the role of mood as immunomodulator in SARS-CoV-2, but further research is needed to determine the direction of the interplay between previous infection, mental health condition and immunity response. Further biological sources on SARS-CoV-2 immunity response which might be modulated by mental health condition should be integrated in future research protocols. In particular, cytomegalovirus infection (Perera et al., 2023; Burgdorf et al., 2019) and gut microbiome (Ng et al., 2022; Malan-Müller et al., 2023) are currently under investigation and offer new management options. Should the results of this pilot study be confirmed, a precision medicine approach through personalized vaccination programs and follow-up for subjects with previous SARS-CoV-2 infection could be implemented.

5. Conclusion

Immune response to COVID-19 mRNA vaccination, previous exposure to the virus and mood disorders symptoms, correlate. Several hypotheses can be raised: the first is that previous contact with the virus in individuals with depressive symptoms results in a more pronounced immune response, and the second is that individuals who have a more pronounced immune response to COVID-19 antigenic stimulation, tend to develop depressive symptoms. The link, however one interprets it, may not be trivial and suggests the need for additional studies and caution in immune stimulation when depressive symptoms are present.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbih.2023.100677.

Contributor Information

Nicola Grignoli, Email: nicola.grignoli@ti.ch.

Luca Gabutti, Email: luca.gabutti@eoc.ch.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (62.8KB, docx)

Data availability

Data will be made available on request.

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