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Heidarzadeh A, Kazemi S, Amini M. Epidemiological Changes in Serum Virus-Specific IgM/IgG Antibody in SARS-CoV-2 Patients in Guilan Province: A Comparative Study. J Occup Health Epidemiol 2023; 12 (1) :12-17
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1- Associated Prof., Dept. of Community Medicine, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran , abtinh@gmail.com
2- M.Sc in Epidemiology, Dept. of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
3- M.Sc in Health Education, Deputy of Health، Guilan University of Medical Sciences, Rasht, Iran.
2- M.Sc in Epidemiology, Dept. of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
3- M.Sc in Health Education, Deputy of Health، Guilan University of Medical Sciences, Rasht, Iran.
Article history
Received: 2022/04/22
Accepted: 2023/01/25
ePublished: 2023/03/27
Accepted: 2023/01/25
ePublished: 2023/03/27
Subject:
Epidemiology
Keywords: Antibodies [MeSH], Immunoglobulin M [MeSH], Immunoglobulin G [MeSH], SARS-CoV-2 [MeSH], Patients [MeSH]
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Table 1. Demographic and clinical characteristics of individuals
Table 2. Frequency of underlying diseases of individuals
Table 3. Symptoms of individuals
Table 4. Characteristics of individuals had IgG above 1 and IgM was negative
.jpg)
Fig.1. ROC curve for IgM in two periods of different times
.jpg)
Fig.2. ROC curve for IgG in two periods of different times
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Introduction
In December 2019, the first case of Coronavirus Disease 2019 (COVID-19), an illness caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported from Wuhan, Hubei Province in China [1]. On March 11, 2020, the World Health Organization (WHO) declared the outbreak of the novel coronavirus (COVID-19) a s a global pandemic [2]. COVID-19 was identified as viral-induced pneumonia based on clinical symptoms, blood tests, and chest radiography [3]. The frequency of thrombotic reactions due to the severe SARS-CoV-2 infection is increased [4].
The SARS-CoV-2 virus is extremely contagious and spreads quickly [5]. Therefore, it is crucial to identify COVID-19 quickly and treat it. Real-time quantitative polymerase chain reaction (RT-PCR) is used to measure SARS-CoV-2 RNA load to diagnose COVID-19 [6]. Some COVID-19 patients will go undiagnosed if the diagnosis is limited to viral RNA load [7]. On March 3, 2020, SARS-CoV-2-specific IgM and IgG antibody levels were added to the diagnostic and treatment guidelines for Chinese coronavirus pneumonia because viral RNA detection has a high rate of false-negative results. Antibody detection is an alternative method for diagnosing suspicious cases, saving time and money compared to viral RNA load monitoring [8]. Immunoglobulin M (IgM) and Immunoglobulin G (IgG) antibody levels against nuclear and surface protein receptor-binding domains (RBDs) gradually increased after the onset of symptoms [9]. IgM and IgG antigens are mutually exclusive and frequently used in clinical settings [10]. IgM levels peaked at 3 weeks and then started to decline, whereas IgG levels kept increasing and could last for up to 7 weeks [11]. In this study, we investigated the association between previous COVID-19 infection and antibody levels- IgM and IgG- considering the underlying diseases in COVID-19 patients in Guilan province.
Materials and Methods
In this descriptive and comparative study, 212 patients with a COVID-19 history participated in Guilan province. The first time of blood sample collection was about in April 2020. The second blood sample collection was around three months after the first time in August 2020. This study protocol was approved by the Ethics Committee of the Iran University of Medical Sciences (code: IR . IUMS. REC . 1400. 123).
Regardless of age, all persons living in a household were invited through multistage cluster random sampling. We selected clusters from the list of Comprehensive Healthcare Centers (CHCs) and used a simple random sampling method to select households from those covered by CHCs.
Antibody Detection: Two blood samples were obtained from each subject. 2.5 ml of blood was
taken from each person and 2 ml of serum was obtained from this amount. Then 10 µL of serum was used to perform the VivaDiag Rapid test kit. Once on the day, the participants arrived at the CHC and once roughly three months later. The demographics, medical history, COVID-19 symptoms in the previous three months, and history of SARS-CoV-2 exposure were all collected. A VivaDiag Rapid test kit examined samples using an enzyme-linked immunosorbent (ELISA) assay (https://www.vivachek.com/en/index.html).
To determine serum IgM and IgG antibodies against SARS-CoV2, an ELISA test was created and used to coat with SARS-CoV2 recombinant spike protein. Every participant provided their informed permission.
The data were analyzed using the Kolmogorov- Smirnov, Chi-square, and Wilcoxon tests. Based on the Kolmogorov-Smirnov test, quantitative variables (IgM/IgG) did not follow the normal distribution.
(P-value <0.005). Chi-square tests are used to the difference between qualitative-qualitative variables. Using the Wilcoxon test, changes in IgM/IgG were measured in two separate times. Data is analyzed using SPSS-22.
Results
The demographic characteristics of individuals are shown in (Table 1). About one-third of people were between the ages of 31-45 (31 %). The most common underlying diseases were Hypertension (31/212), obesity (23/212), cardiovascular disease (17/212), diabetes (17/212), and chronic obstructive pulmonary disease (COPD), respectively (Table 2). As shown in Table 3, the most common symptoms included fatigue 16.5% (35/212), pain 16% (34/212), cough 15.6% (33/212), and sore throat 15.7% (32/212), respectively.
In December 2019, the first case of Coronavirus Disease 2019 (COVID-19), an illness caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported from Wuhan, Hubei Province in China [1]. On March 11, 2020, the World Health Organization (WHO) declared the outbreak of the novel coronavirus (COVID-19) a s a global pandemic [2]. COVID-19 was identified as viral-induced pneumonia based on clinical symptoms, blood tests, and chest radiography [3]. The frequency of thrombotic reactions due to the severe SARS-CoV-2 infection is increased [4].
The SARS-CoV-2 virus is extremely contagious and spreads quickly [5]. Therefore, it is crucial to identify COVID-19 quickly and treat it. Real-time quantitative polymerase chain reaction (RT-PCR) is used to measure SARS-CoV-2 RNA load to diagnose COVID-19 [6]. Some COVID-19 patients will go undiagnosed if the diagnosis is limited to viral RNA load [7]. On March 3, 2020, SARS-CoV-2-specific IgM and IgG antibody levels were added to the diagnostic and treatment guidelines for Chinese coronavirus pneumonia because viral RNA detection has a high rate of false-negative results. Antibody detection is an alternative method for diagnosing suspicious cases, saving time and money compared to viral RNA load monitoring [8]. Immunoglobulin M (IgM) and Immunoglobulin G (IgG) antibody levels against nuclear and surface protein receptor-binding domains (RBDs) gradually increased after the onset of symptoms [9]. IgM and IgG antigens are mutually exclusive and frequently used in clinical settings [10]. IgM levels peaked at 3 weeks and then started to decline, whereas IgG levels kept increasing and could last for up to 7 weeks [11]. In this study, we investigated the association between previous COVID-19 infection and antibody levels- IgM and IgG- considering the underlying diseases in COVID-19 patients in Guilan province.
Materials and Methods
In this descriptive and comparative study, 212 patients with a COVID-19 history participated in Guilan province. The first time of blood sample collection was about in April 2020. The second blood sample collection was around three months after the first time in August 2020. This study protocol was approved by the Ethics Committee of the Iran University of Medical Sciences (code: IR . IUMS. REC . 1400. 123).
Regardless of age, all persons living in a household were invited through multistage cluster random sampling. We selected clusters from the list of Comprehensive Healthcare Centers (CHCs) and used a simple random sampling method to select households from those covered by CHCs.
Antibody Detection: Two blood samples were obtained from each subject. 2.5 ml of blood was
taken from each person and 2 ml of serum was obtained from this amount. Then 10 µL of serum was used to perform the VivaDiag Rapid test kit. Once on the day, the participants arrived at the CHC and once roughly three months later. The demographics, medical history, COVID-19 symptoms in the previous three months, and history of SARS-CoV-2 exposure were all collected. A VivaDiag Rapid test kit examined samples using an enzyme-linked immunosorbent (ELISA) assay (https://www.vivachek.com/en/index.html).
To determine serum IgM and IgG antibodies against SARS-CoV2, an ELISA test was created and used to coat with SARS-CoV2 recombinant spike protein. Every participant provided their informed permission.
The data were analyzed using the Kolmogorov- Smirnov, Chi-square, and Wilcoxon tests. Based on the Kolmogorov-Smirnov test, quantitative variables (IgM/IgG) did not follow the normal distribution.
(P-value <0.005). Chi-square tests are used to the difference between qualitative-qualitative variables. Using the Wilcoxon test, changes in IgM/IgG were measured in two separate times. Data is analyzed using SPSS-22.
Results
The demographic characteristics of individuals are shown in (Table 1). About one-third of people were between the ages of 31-45 (31 %). The most common underlying diseases were Hypertension (31/212), obesity (23/212), cardiovascular disease (17/212), diabetes (17/212), and chronic obstructive pulmonary disease (COPD), respectively (Table 2). As shown in Table 3, the most common symptoms included fatigue 16.5% (35/212), pain 16% (34/212), cough 15.6% (33/212), and sore throat 15.7% (32/212), respectively.
Table 1. Demographic and clinical characteristics of individuals
| Characteristics | COVID-19 patients (n=212) |
Asymptomatic cases (n=137) |
Symptomatic cases (n=75) |
|
| Age | 0-15 | 29(13.7) | 25(11.8) | 4(1.9) |
| 16-30 | 31(14.6) | 17(8) | 14(6.6) | |
| 31-45 | 66(31.1) | 41(19.3) | 25(11.8) | |
| 46-60 | 51(24.1) | 31(14.6) | 20(9.4) | |
| 61-75 | 29(13.7) | 18(8.5) | 11(5.2) | |
| >76 | 6(2.8) | 5(2.4) | 1(0.5) | |
| Sex | Male | 101(47.6) | 62(29.2) | 39(18.4) |
| Female | 111(52.4) | 75(35.4) | 36(17) | |
Table 2. Frequency of underlying diseases of individuals
| Characteristics | COVID-19 patients (n=212) |
Asymptomatic cases (n=137) |
Symptomatic cases (n=75) |
|
| Smoking | Yes | 12(5.66) | 7(3.3) | 5(2.4) |
| No | 200(94.34) | 130(61.3) | 70(33) | |
| Diabetes | Yes | 17(8.02) | 13(6.1) | 4(1.9) |
| No | 195(91.98) | 124(58.5) | 71(33.5) | |
| Immunodeficiency | Yes | 3(1.41) | 1(0.5) | 2(0.9) |
| No | 209(98.59) | 136(64.2) | 73(34.4) | |
| Obesity | Yes | 23(10.85) | 12(5.7) | 11(5.2) |
| No | 189(89.15) | 125(59) | 64(30.2) | |
| Flu | Yes | 9(4.24) | 5(2.4) | 4(1.9) |
| No | 203(95.76) | 132(62.3) | 71(33.5) | |
| Hemodialysis | Yes | 3(1.41) | 1(0.5) | 2(0.9) |
| No | 209(98.59) | 136(64.2) | 73(34.4) | |
| Liver disease | Yes | 3(1.41) | 0(0) | 3(1.4) |
| No | 209(98.59) | 137(64.6) | 72(34) | |
| Hypertension | Yes | 31(14.62) | 17(8) | 14(6.6) |
| No | 181(85.38) | 120(56.6) | 61(28.8) | |
| Cardiovascular disease | Yes | 17(8.01) | 4(1.9) | 13(6.1) |
| No | 195(91.9) | 133(62.7) | 62(29.2) | |
| COPD | Yes | 13(6.1) | 4(1.9) | 9(4.2) |
| No | 199(93.9) | 133(61.7) | 66(31.1) | |
Table 3. Symptoms of individuals
| Characteristics | COVID-19 patients (n=212) |
Asymptomatic cases (n=137) |
Symptomatic cases (n=75) |
|
| Fever | Yes | 28(13.2) | 0(0) | 28(13.2) |
| No | 184(86.8) | 137(64.6) | 47(22.2) | |
| Chill | Yes | 29(13.7) | 0(0) | 29(13.7) |
| No | 183(86.3) | 137(64.6) | 46(21.7) | |
| Fatigue | Yes | 35(16.5) | 0(0) | 35(16.5) |
| No | 177(83.5) | 137(64.6) | 40(18.9) | |
| Pain | Yes | 34(16) | 0(0) | 34(16) |
| No | 178(84) | 137(64.6) | 41(19.3) | |
| Sore throat | Yes | 32(15.1) | 0(0) | 32(15.1) |
| No | 180(84.9) | 137(64.6) | 43(20.3) | |
| Cough | Yes | 33(15.6) | 0(0) | 33(15.6) |
| No | 179(84.4) | 137(64.6) | 42(19.8) | |
| Runny nose | Yes | 23(10.8) | 0(0) | 23(10.8) |
| No | 189(89.2) | 137(64.6) | 52(24.5) | |
| Dyspnea | Yes | 17(8) | 0(0) | 17(8) |
| No | 195(92) | 137(64.6) | 58(27.4) | |
| Wising | Yes | 11(5.2) | 0(0) | 11(5.2) |
| No | 201(94.8) | 137(64.6) | 64(30.2) | |
| Chest pain | Yes | 9(4.2) | 0(0) | 9(4.2) |
| No | 203(95.8) | 137(64.6) | 66(31.1) | |
| Headache | Yes | 26(12.3) | 0(0) | 26(12.3) |
| No | 186(87.7) | 137(64.6) | 49(23.1) | |
| Vomiting | Yes | 8(3.8) | 0(0) | 8(3.8) |
| No | 204(96.2) | 137(64.6) | 87(31.6) | |
| Abdomen pain | Yes | 10(4.7) | 0(0) | 10(4.7) |
| No | 202(95.3) | 137(64.6) | 65(30.7) | |
| Diarrhea | Yes | 14(6.6) | 0(0) | 14(6.6) |
| No | 198(93.4) | 137(64.6) | 61(28.8) | |
According to our findings, 12.26% (26/212) of individuals had IgG above1.1 after three months of recovery. Most common underlying disease among them was HTN and then COPD, and the IgM of people was negative (Table 4).
Table 4. Characteristics of individuals had IgG above 1 and IgM was negative
| Case no. | Sex | Age | IgM (mg/dL) | IgG (mg/dL) | Underlying diseases |
| 1 | M | 4 | 0.03 | 3.4 | - |
| 2 | F | 6 | 0.02 | 2.1 | - |
| 3 | M | 7 | 0.03 | 2.1 | - |
| 4 | F | 10 | 0.03 | 2.9 | - |
| 5 | M | 14 | 0.02 | 3 | - |
| 6 | F | 15 | 0.3 | 5.2 | Obesity |
| 7 | F | 30 | 0.4 | 4.9 | COPD |
| 8 | F | 32 | 0.01 | 1.5 | - |
| 9 | F | 32 | 0.2 | 1.5 | - |
| 10 | M | 35 | 0. 1 | 2.6 | - |
| 11 | M | 36 | 0.01 | 1.2 | - |
| 12 | M | 36 | 0.02 | 3.2 | - |
| 13 | F | 36 | 0.09 | 1.3 | - |
| 14 | M | 38 | 0.03 | 2.5 | - |
| 15 | F | 40 | 10.3 | 13.6 | - |
| 16 | M | 47 | 0.01 | 1.2 | Liver disease |
| 17 | F | 48 | 0.01 | 7.2 | - |
| 18 | F | 49 | 0.01 | 5.6 | COPD |
| 19 | M | 53 | 0.02 | 2 | CVD/COPD |
| 20 | F | 59 | 0.3 | 11.6 | HTN |
| 21 | F | 60 | 0.05 | 1.8 | Hemodialysis |
| 22 | M | 66 | 0.1 | 6.4 | HTN/Diabetes |
| 23 | M | 66 | 0.04 | 6.2 | - |
| 24 | F | 69 | 0.02 | 3.9 | HTN/Obesity |
| 25 | M | 77 | 0.03 | 4.5 | HTN |
| 26 | M | 78 | 0.02 | 12.8 | HTN |
Conc: Concentration; M: Male; F: Female; COPD: Chronic obstructive pulmonary disease; CVD: Cardiovascular disease; HTN: Hypertension
In addition, changes in IgM/IgG between April and August had a statistically significant difference. (P-value<0.05). We presented the sensitivity and specificity for IgM/IgG at two different times separately (Figures 1 and 2). The test accuracy was 56% with (%0.95 CI: 0.465-0.671) for IgM (p = 0.187). The test accuracy is 82.5% with (%0.95 CI: 0.747-0.903) for IgG (P-value <0.05). Our results showed that IgG remains in most people about three months after exposure to the virus.
Fig.1. ROC curve for IgM in two periods of different times
Fig.2. ROC curve for IgG in two periods of different times
Discussion
Identifying SARS-CoV-2-specific antibodies may be useful for both the profiling of COVID-19 illness and the assessment of whether the host mounted a successful humoral immune response. Our study indicated that the changes of IgM/IgG at two separate times had statistically significant differences and remained in recovered patients for more than 3 months. Xiaoyong et al. (2020) showed IgM was diagnosed earlier than IgG and vanished in most cases 4–6 weeks after symptoms, while IgG may last up to 194 days after the start of symptoms. In contrast to our study, they showed that antibodies remained in recovered patients for more than 6 months [12].
In agreement with our study, Alsayb et al. (2021) revealed an Anti-S IgG antibody in participants 90 days after infection [13]. Similar to our findings, Liu et al. (2020) estimated the lifetime of specific antibodies against SARS-CoV-2 and confirmed that the antibodies disappeared within three months after the onset of symptoms [14]. Chirathaworn et al. (2020) found similar results, which shown 4–12 weeks after disease onset, IgM, IgG, and IgA antibodies to SARS-CoV-2 remained in 13.8 %(30/217), 88.5%(192/217), and 83.4% (181/217) of recovered cases, respectively [15].
Several studies have shown an apparent decrease in antibody response over time, with variable decreasing neutralizing antibody titers [16,17]. Cheng et al. (2021) indicated for the first 5–7 weeks, the proportion of patients with positive IgM antibodies was 100%, then steadily decreased to 50 % during the next 34–42 weeks. In comparison, 34–42 weeks after symptom initiation, the positive proportion of IgG and neutralizing antibodies were 100%. Generally, those results represented that the IgM antibody in the recovered patients faded over time [18]. A similar study conducted by Jiang et al. (2021) demonstrated that 3 – 4 months after infection, most patients had virus-specific T cell responses. All COVID-19 patients had durable IgG responses. However, the IgM antibody decreased quickly [19].
Conclusion
In conclusion, our findings revealed that over time, the level of IgG increased in subjects with previous COVID-19 infections, and the level of antibody IgM decreased.
Acknowledgement
All authors appreciate the support of the Vice Chancellorship of Health of Guilan University of Medical Sciences (GUMS).
Conflict of interest: None declared.
Identifying SARS-CoV-2-specific antibodies may be useful for both the profiling of COVID-19 illness and the assessment of whether the host mounted a successful humoral immune response. Our study indicated that the changes of IgM/IgG at two separate times had statistically significant differences and remained in recovered patients for more than 3 months. Xiaoyong et al. (2020) showed IgM was diagnosed earlier than IgG and vanished in most cases 4–6 weeks after symptoms, while IgG may last up to 194 days after the start of symptoms. In contrast to our study, they showed that antibodies remained in recovered patients for more than 6 months [12].
In agreement with our study, Alsayb et al. (2021) revealed an Anti-S IgG antibody in participants 90 days after infection [13]. Similar to our findings, Liu et al. (2020) estimated the lifetime of specific antibodies against SARS-CoV-2 and confirmed that the antibodies disappeared within three months after the onset of symptoms [14]. Chirathaworn et al. (2020) found similar results, which shown 4–12 weeks after disease onset, IgM, IgG, and IgA antibodies to SARS-CoV-2 remained in 13.8 %(30/217), 88.5%(192/217), and 83.4% (181/217) of recovered cases, respectively [15].
Several studies have shown an apparent decrease in antibody response over time, with variable decreasing neutralizing antibody titers [16,17]. Cheng et al. (2021) indicated for the first 5–7 weeks, the proportion of patients with positive IgM antibodies was 100%, then steadily decreased to 50 % during the next 34–42 weeks. In comparison, 34–42 weeks after symptom initiation, the positive proportion of IgG and neutralizing antibodies were 100%. Generally, those results represented that the IgM antibody in the recovered patients faded over time [18]. A similar study conducted by Jiang et al. (2021) demonstrated that 3 – 4 months after infection, most patients had virus-specific T cell responses. All COVID-19 patients had durable IgG responses. However, the IgM antibody decreased quickly [19].
Conclusion
In conclusion, our findings revealed that over time, the level of IgG increased in subjects with previous COVID-19 infections, and the level of antibody IgM decreased.
Acknowledgement
All authors appreciate the support of the Vice Chancellorship of Health of Guilan University of Medical Sciences (GUMS).
Conflict of interest: None declared.
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