Assessment of the levels of antispike SARS-CoV-2 IgG antibodies and their association with clinical characteristics in cohort of patients in Saudi Arabia

ABSTRACT

Background:

Coronavirus disease 2019 (COVID-19) has caused a global public health crisis. The disease is known to be caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, but the detailed characteristics of the immune response to this novel virus have not been fully elucidated yet. In this study, we aimed to determine the level of immunoglobulin G (IgG) antibodies and their correlation with clinical features at three time points postinfection in a group of patients in Saudi Arabia.

Method:

In this prospective observational study, we collected the demographic and clinical data from 43 polymerase chain reaction (PCR)-confirmed patients and measured the COVID-19 antispike IgG levels at three different visits.

Result:

The seroconversion rate after COVID-19 infection was 88.4% in the study participants, with no significant changes in the IgG levels through the three visits. The duration of shortness of breath had a significant positive correlation with the IgG level of the patients. Using the logistic regression model, participants having coughs were found to be 12.48 times more likely to develop positive IgG. The IgG levels were less in smokers than nonsmokers [Odds ratio = 6.42 (95% CI 2.11–19.48); P = 0.001].

Conclusion:

Positive IgG levels have been developed in most COVID-19 patients and did not significantly change over 3 months following the diagnosis. The level of IgG antibodies was found to be significantly associated with the presence of cough, duration of shortness of breath, and the smoking habit of the patients. These findings have clinical and public health significance and need to be validated in larger studies in different populations.

Introduction

The burden of the coronavirus disease 2019 (COVID-19) disease has increased as several investigations reported that the virus can affect many organs of the human body, which further complicates the management of the affected patients.[1,2] Therefore, the essential management of COVID-19 depends on the identification of the symptoms and instantly provides a suitable way for managing it. The early identification and management of COVID-19 cases are crucial, not only to reduce the burden of infection on the affected cases but also to reduce the possible transmission of the disease. The identification of COVID-19 cases can be exhausting with financial implication as some cases might be asymptomatic and still spread the infection. However, mass screening might be the most efficacious approach to contain these cases. It is recommended that many factors should be considered before adopting such approach, which might be inapplicable in some countries. COVID-19 infection primarily occurs through direct contact with infected patients as transmission occurs through respiratory droplets.[3,4] For detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), reverse transcriptase-polymerase chain reaction (RT-PCR) has been used as the gold standard modality, being able to identify the potential viral nucleocapsid, envelope genes, and RNA-dependent RNA polymerase.[5]

However, many considerations should be made for this modality to be effective. These include the presence of a good viral load, the adequacy, and representation of the collected sample, and the way to collect it,[6,7] which might lead to severe complications.[8] In addition, COVID-19 infection gives rise to unique serological features, including an elevated likelihood of immunoglobulin G (IgG).[9] Serology is regarded as a cost-effective approach that has been previously used for detecting many infections.[10] For instance, Alandijany et al.[11] reported 100% and 98.4% rates for the sensitivity and specificity, respectively, of the ability of the enzyme-linked immunoassay (ELISA) to detect the IgG antibodies of SARS-CoV-2 within the serum of COVID-19 patients. There have been many concerns about the correlation between COVID-19 symptoms and IgG levels. According to many studies, the seroconversion rate is associated with the severity of the symptoms. While IgG antibodies develop within 2 weeks of the onset of symptoms in patients with moderate to severe COVID-19, these antibodies are not detected in as much as 20% of asymptomatic and mild COVID-19 patients.[12,13] Of the structural proteins of the virus, most serological tests are designed to detect the nucleocapsid (N) protein, and the spike (S) protein.[14] Over the past 2 years, the scientific community has been very interested in studying the antibody response against SARS-CoV-2 antibodies not only to better understand the immunopathology of COVID-19 but also because they could be a surrogate measure of protection against reinfection.[15] It is already reported that the spike protein of the virus is highly immunogenic and could be a potential target for neutralizing antibodies.[16] Thereby, measuring the IgG level from the spike protein could be a potential biomarker and correlate of immunity.[17] As in many other viral diseases, the therapeutic role of the neutralizing SARS-CoV-2-specific antibodies have been explored in many studies to study the role of convalescent plasma in the treatment and postexposure prophylaxis in high-risk individuals.[18] However, there has been no consensus among physicians to use this treatment modality due to the contradictory findings of the conducted studies.[19]

In this study, we aimed to assess the level of antispike IgG level at three time points and to see whether there are significant changes over time and investigate the possible correlation of with relevant patients’ characteristics.

Subjects and Methods

Study setting

This study is conducted in the Polyclinics Health Centre of the Ministry of Interior (MOI Clinics), Security Forces Hospital (SFH), Riyadh, Saudi Arabia.

Study duration

The study is conducted during the period from September to November, 2020.

Study design

This is a prospective observational, noninterventional study

Sample size and sampling

Forty-three patients with PCR-confirmed SARS-CoV-2 infection were included. We used convenient sampling to recruit the patients according to the following selecting criteria: 1) adults (aged 18 years and older), 2) patients who gave written consent and attended all three visits.

Data collection

About 129 blood samples were collected in three different visits after infection. The first visit was on September 30^th, the second visit was on October 30^th, and the third visit was on November 30^th, 2020. The collected blood samples were used for serological analysis of IgG antibodies. The recruited patients presented with different severity of symptoms. In the first visit, a self-administered questionnaire was used to obtain the patients’ demographic variables, their blood groups, the severity of their symptoms (expressed in days), and their history of any chronic disease. The blood samples were withdrawn from patients in the MOI Clinics and then appropriately processed, stored in a container with a temperature of 2°C–8°C, and transported to the laboratory for serological analysis within 2 h of the blood collection. The collected serum samples were processed on the LIAISON®XL analyser for the quantitative determination of IgG anti-S1- and IgG anti-S2-specific antibodies to SARS-CoV-2, using a chemiluminescence immunoassay (CLIA). Following the manufacturer’s recommendations, the cut-off value was <12.0 AU/mL for a negative result, and ≥12.0 AU/mL for a positive result.

Statistical analysis

All analyses were done using Statistical Package for the Social Sciences (SPSS) V26 (IBM Statistics, Armonk, NY, USA), and a two-tailed P value <0.05 was considered significant for all tests. Normally distributed continuous variables were represented as means ± standard deviation (SD) while nominal variables were presented as counts and percentages. Antibody’s IgG (AU/mL) levels were expressed as geometric means titters (GMT) with 95% confidence intervals. Skewness and kurtosis tests were used to evaluate the normal distribution of the continuous variables, and logarithmic transformations were used for the analysis of non-normal distribution variables. Repeated-measures analysis of variance (ANOVA) was adopted to evaluate the changes observed in IgG level over all three points. Bonferroni correction was used in the case of multiple testing to control for type I error.

In addition, we constructed a linear regression model to test the relation between IgG (AU/mL) level and the duration of different COVID-19 symptoms. Moreover, binary logistic regression was conducted to estimate the Odds ratio of producing IgG after COVID-19 infection (positive and negative IgG). Logistic regression was carried out to assess the effect of smoking status and symptoms on the likelihood of producing positive IgG antibodies.

Ethical approval

Ethical approval to conduct this study was obtained from Security Forces Hospital (SFH). Signed informed consent forms were obtained from all patients before conducting this study.

Results

A total of 43 patients were included in the final analysis. Baseline characteristics, including age, blood group, smoking, and pre-existing chronic diseases are described in [Table 1]. Most of the participants (88.4%) developed positive IgG levels after being infected by COVID-19. The GMT IgG level (95% CI value) for all patients at the first visit was 42.36 (30.80–58.27) AU/mL, 42.85 (31.18–58.89) AU/mL at the second visit, and 41.28 (28.82–59.12) AU/mL at the third one [Table 2].

Table 1.

Age and baseline characteristics of the included patients (n=43)

Variables	Count	%
Age (years); mean±SD	38.9±9.9
Blood Group
A−	1	2.3
A+	13	30.2
AB+	4	9.3
B−	1	2.3
B+	5	11.6
O-	2	4.7
O+	17	39.5
Smoking
No	28	65.1
Yes	15	34.9
Diabetes
No	35	81.4
Yes	8	18.6
Hypertension
No	41	95.3
Yes	2	4.7
Asthma
No	41	95.3
Yes	2	4.7

SD: standard deviation

Table 2.

Descriptive statistics for IgG level over three visits (n=43)

Visit	IgG level (AU/mL); n=43
	Positive (≥12.0 AU/mL); n=38		Negative (<12.0 AU/mL); n=5		All IgG (AU/mL); n=43
	Median (IQR)	GMT (95% Cl)	Median (IQR)	GMT (95% Cl)	Median (IQR)	GMT (95% Cl)
Visit #1	50.4 (36.5-98.95)	55.10 (42.82-70.90)	6.10 (3.80-8.57)	5.75 (3.41-9.68)	42.5 (28.7-76.7)	42.36 (30.80-58.27)
Visit #2	46.95 (32.35-111)	55.15 (42.56-71.47)	6.67 (3.80-10.22)	6.29 (3.38-11.71)	42.8 (23.7-86.2)	42.85 (31.18-58.89)
Visit #3	52.40 (38.6-106)	62.48 (46.95-83.16)	8.21 (3.99-9.34)	6.73 (4.66-9.72)	44.8 (21.3-84.8)	41.28 (28.82-59.12)

A repeated-measures ANOVA analysis showed no significant changes between mean IgG levels through all three visits, Wilks’ Lambda = 0.977, F (2,41) = 0.455, P = 0.637. Post hoc analysis with a Bonferroni adjustment revealed that there were no statistically significant differences in mean IgG levels from 1^st visit to 2^nd visit (0.99 [95% CI, 0.92–1.06]; P > 0.05), from 2^nd visit to 3^rd visit (1.04 [95% CI, 0.92–1.17]; P > 0.05), and from 1^st visit to 3^rd visit (1.03 [95% CI, 0.88–1.20]; P > 0.05). Figure 1 shows the nonsignificant differences between mean log IgG through three visits.

Figure 1.

Differences between mean log IgG (AU/mL) through three visits for the included COVID-19 patients (n = 43)

The duration of different COVID-19 symptoms among the included patients was highly variable. The mean duration for any COVID-19 symptom was 11.3 ± 12.8 days. Loss of taste was the most long-lasting symptom (6.3 ± 10.5 days), followed by loss of smell (5.8 ± 10.4 days), fatigue (5.1 ± 5.9 days), and cough (3.3 ± 9.5 days), respectively [Figure 2]. There was a significant association between the duration of shortness of breath and IgG levels, where every one unit increase in IgG (AU/mL) was associated with a 0.3-day increase in shortness of breath duration (b = 0.3; P value = 0.033) [Table 3].

Figure 2.

Duration in days of different COVID-19 symptoms among the included COVID-19 patients (n = 43)

Table 3.

Regression analysis of IgG (AU/mL) level and the duration of different COVID-19 symptoms (n=43)

Predictor	Estimate	SE	t	P	Standardized Estimate	95% Confidence Interval
						Lower	Upper
Fever	5.4	3.0	1.8	0.079	0.3	0.0	0.6
Cough	1.1	1.2	0.9	0.367	0.1	−0.2	0.5
Diarrhea	3.2	4.5	0.7	0.484	0.1	−0.2	0.4
Fatigue	0.4	1.9	0.2	0.820	0.0	−0.3	0.4
Loss of Taste	−0.8	1.1	−0.8	0.455	−0.1	−0.4	0.2
Loss of Smell	−1.0	1.1	−0.9	0.376	−0.1	−0.5	0.2
SOB	4.0	1.8	2.2	0.033*	0.3	0.0	0.6
Headache	0.0	2.7	0.0	0.987	0.0	−0.3	0.3

SE: standard error; SOB: shortness of breath

The nonsmoker participants were more likely to have positive IgG than smoker participants (Odds ratio = 6.42 [95% CI 2.11–19.48]; P = 0.001). The logistic regression models also showed that all the symptoms were statistically nonsignificant to affect the model (P > 0.05) except for the cough symptom, which was statistically significant (Odds ratio = 12.48 [95% CI 1.61–97.14], P = 0.016). This means that participants who had a cough during COVID-19 infection were 12.48 times more likely to develop positive IgG. Linear regression also showed a significant relationship between IgG levels and age (P < 0.001). The IgG level increases by 0.1 AU/mL for each extra year of age. The adjusted R2 value was 0.102 so 10.2% of the variation in IgG level can be explained by the model containing only age.

Discussion

Antibody-mediated immune response to the SARS-CoV-2 infection has been the subject of many studies since the outset of this pandemic. This is due to its potential diagnostic, prognostic, and therapeutic implications, in addition to its role in the development of vaccines and guiding the vaccine strategies.[20] Previous investigations showed that the humoral response against COVID-19 acute infections rapidly forms and peaks at 2 or 3 weeks following the onset of symptoms. However, Prévost et al.[21] showed that the beneficial neutralization effects rapidly declined within the 3^rd and 6^th weeks after the onset of symptoms in COVID-19 patients. In the same context, Beaudoin-Bussières et al.[22] also reported that IgG, IgA, and IgM antibodies significantly decreased after 6 to 10 weeks following the onset of symptoms. Moreover, the authors reported that IgG levels persisted more than IgA and IgM levels, which indicated that IgG levels are associated with better neutralizing abilities. The same findings were also supported by previous studies.[23,24]

In the current study, we aimed to determine the level of antispike IgG antibodies at three time points after the infection and to investigate the possible correlation with demographic and clinical features in a group of patients in Saudi Arabia. Interestingly, we observed that the GMT level of IgG increased from 1^st visit to 2^nd visit after getting the infection, before decreasing again at the 3^rd visit. However, a repeated-measure ANOVA analysis revealed that the differences in the mean IgG level did not differ significantly between the visits. In different studies, the level of IgG varies based on the severity of symptoms and several other factors such as sex and age.[25,26] The 88.4% seroconversion rate in this study is comparable with some previous studies conducted elsewhere.[12,13] The relative stability of the mean IgG levels over the study period is consistent with the results of other studies. According to a study conducted early on during the pandemic, the half-life of IgG antibodies was approximately 36 days over a 3-month observation period of the study, which involved 34 recovered patients.[27] However, in a large study conducted in the United Kingdom using representative data from 7,256 study participants, the estimated half-life of antispike IgG was 184 days after the infection.[28] This is very interesting considering that a growing body of evidence is now indicating that binding and neutralizing antibodies are correlated with each other and are increasingly being considered as correlates of protection against COVID-19.[29]

According to our results, the IgG level tends to increase with age. This is consistent with other studies, in which older age has been shown to be associated with higher IgG levels and longer half-lives.[30] This could indicate that the elderly people depend mainly on their humoral immune system to fight respiratory viruses while the younger could deal better with the infection, thanks to their better innate and cellular immunity, which can often tackle the viruses early on at the mucosal surfaces, without much need for the systemic antibody responses. Therefore, the young populations tend to have milder COVID-19, as has been shown in many studies.

Among the reported symptoms, our analysis showed that the duration of shortness of breath is significantly correlated with having high titters of IgG antibodies and the logistic regression analysis indicated that participants who had a cough during COVID-19 infection were 12.48 times more likely to develop positive IgG. Because shortness of breath is one of the signs of severity identified in many studies,[31] our results are consistent with many serological studies that found higher levels of IgG antibodies in the moderate to severe, compared with mild cases of COVID-19. However, our results contrast with what was reported by Augustin et al.[32] that lower serum IgG is associated with several symptoms, including shortness of breath.

Regarding other symptoms, we did not find a significant association with the positivity or the level of IgG antibodies. This contrasts with other studies, which found a significant association with some symptoms such as loss of smell and diarrhea.[32,33] This can be attributed in part to possible differences in the underlying conditions and severity of the infection among our patients and their genetic structure in responding to infection. This may raise the need for further population studies to assess the immunological response and clinical correlation among different populations in Saudi Arabia.

It is well-known that smoking has a major impact on health-related issues worldwide and smoking has many established associations with several pathways related to diseases. Smoking was also shown to be associated with high risk of COVID-19 complications and can increase risk of severity of COVID-19 infection.[34] Importantly, smoking was also shown to be associated with lower IgG titters in response to COVID-19 vaccine.[35] Hence, we studied the effect of smoking on the IgG level after COVID-19 infection. Our analysis corroborated the findings of the previous studies that the IgG levels of smokers are significantly less than that of nonsmokers.[36]

Limitations

In addition to the small sample size and the relatively short follow-up period, some limitations should be noted in this study. For instance, the IgM and IgG against other viral proteins were not evaluated and the neutralization assays were not performed.

Conclusion

This study showed that positive IgG levels have been developed in most COVID-19 patients and did not significantly change over 3 months following the diagnosis. The level of IgG antibodies was found to be significantly associated with the presence of cough, duration of shortness of breath, and the smoking habit of the patients. The findings of this study could have a great significance from both the clinical and public health perspectives. Therefore, conducting a larger study, with longer follow-up time, that includes more potential antibody classes and assessing for neutralization will validate the results of this study by providing a better characterization of the acquired humoral immunity and its correlations.

Ethics approval and consent to participate

Informed consent was obtained from each participant prior to commencement of the study to take part in this study and for any publication with nonidentifiable quotes.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.