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Distribution characteristics of SARS-CoV-2 IgM/IgG in false-positive results detected by chemiluminescent immunoassay

  • Yan Lei , Xiaolan Lu , Daiyong Mou , Qin Du , Guangrong Wang EMAIL logo and Qiang Wang EMAIL logo
Published/Copyright: November 14, 2022
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Open Life Sciences
From the journal Open Life Sciences Volume 17 Issue 1

Abstract

There have been several false-positive results in the antibody detection of COVID-19. This study aimed to analyze the distribution characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin M (IgM) and immunoglobulin G (IgG) in false-positive results using chemiluminescent immunoassay. The characteristics of false-positive results in SARS-CoV-2 IgM and IgG tests were analyzed. The false-positive proportion of single SARS-CoV-2 IgM-positive results was 95.88%, which was higher than those of single SARS-CoV-2 IgG-positive results (71.05%; p < 0.001) and SARS-CoV-2 IgM- and IgG-positive results (39.39%; p < 0.001). The S/CO ratios of SARS-CoV-2 IgM and IgG in false-positive results ranged from 1.0 to 50.0. The false-positive probability of SARS-CoV-2 IgM in the ratios of specimen signals to the cutoff value (S/CO) range (1.0–3.0) was 95.06% (77/81), and the probability of false-positive results of SARS-CoV-2 IgG in the S/CO range (1.0–2.0) was 85.71% (24/28). Dynamic monitoring showed that the S/CO values of IgM in false-positive results decreased or remained unchanged, whereas the S/CO values of IgG in false-positive results decreased. The possibility of false-positive single SARS-CoV-2 IgM-positive and single SARS-CoV-2 IgG-positive results was high. As the value of S/CO ratios decreased, the probability of false-positives consequently increased, especially among the single SARS-CoV-2 IgM-positive results.

Keywords: SARS-CoV-2; COVID-19; antibody; chemiluminescent immunoassay; immunoglobulin M; immunoglobulin G; false positive

1 Introduction

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a catastrophic effect on the world’s population, becoming the most consequential global health crisis since the influenza pandemic of 1918 [1,2,3]. A range of nucleic acid-based and antigen/antibody-based tests are available to detect SARS-CoV-2 infection [4]. While nucleic acid-based or antigen detection tests are used for diagnostic purposes, antibody detection tests may be used to assess exposure to the virus or conduct serosurveillance of populations [4,5]. In the early stages, the detection of novel coronavirus-specific antibodies in individuals who are not vaccinated with the novel coronavirus vaccine can be used as a reference for diagnosis [6].

Currently, serological testing products for COVID-19 include chemiluminescent immunoassay (CLIA), gold immunochromatography assay, enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence assay (ECLIA), lateral flow immunoassay (LFI) and so on [7,8,9,10,11]. CLIA is a central laboratory-based method that can provide high-throughput testing using serum/plasma. SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies with CLIA assays have high sensitivity and specificity, but false-positive results occur regularly [12,13]. Our previously published work showed that false-positive SARS-CoV-2 IgM results could be caused by a moderate to a high concentration of rheumatoid factor IgM in the patient’s serum [14]. There have been some interferences resulting in false-positive results of SARS-CoV-2 serological tests, such as certain pathological factors, biological factors, other endogenous interference factors, other viral infections, and cross-reactions [15,16]

Despite the wide spread of SARS-CoV-2, most areas around the world still have an overall low seroprevalence in the unvaccinated population [17,18]. The predictive value of a positive test will be lower in individuals with a low background risk of infection, especially in low-prevalence settings [19]. Interpretation of a positive test in a patient with a low pretest probability should be performed with caution, as false-positives could lead to potential harm to the patient.

Based on the earlier, there is a need to distinguish false-positive results from true-positive results in serological tests. Moreover, there are limited published data about false-positive results in SARS-CoV-2 IgM and IgG testing. Therefore, we analyzed the characteristics of false-positive results in SARS-CoV-2 IgM and IgG testing using the CLIA method. We further propose an alternative strategy for COVID-19 false-positive serological tests, enabling the best use of serological tests in immunological and seroprevalence studies of SARS-CoV-2.

2 Materials and methods

2.1 Participants and case definition

We conducted this study in two academic hospitals in Nanchong (population of 7.15 million), located in northeastern Sichuan Province in China. In this study, the cases were from two cohorts of non-SARS-CoV-2 and SARS-CoV-2 patients collected in Nanchong. The first cohort included 14,673 patients admitted to the Affiliated Hospital of North Sichuan Medical College and Nanchong Central Hospital, Nanchong, China from March 2020 to September 2020. All these patients in the first cohort were unvaccinated and had no epidemiological risk of contracting SARS-CoV-2 infection, including recent travel history to a country with a community outbreak of COVID-19 or contact with symptomatic people who had traveled back from a country with a community outbreak of COVID-19. In the second cohort, we recruited only 35 COVID-19 patients from March 2020 to December 2020 in Nanchong, where a total of 39 confirmed COVID-19 cases were reported. The confirmed number was maintained and did not increase throughout 2021.

A confirmed COVID-19 case was defined based on the Diagnosis and Treatment Protocol for COVID-19 (Tentative 8th Edition) released by the National Health Commission of the People’s Republic of China [6]. Briefly, a confirmed case had to meet three criteria: (1) fever and/or respiratory symptoms; (2) abnormal lung imaging findings; and (3) a positive SARS-CoV-2 nucleic acid test result.

  1. Informed consent: Informed consent has been obtained from all individuals included in this study.

  2. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance with the tenets of the Helsinki Declaration, and has been approved by the ethics committee of the Affiliated Hospital of North Sichuan Medical College (NO. 2020ER008-1).

2.2 Sample collection

Paired throat swabs and blood samples were taken from each patient. Residual blood samples were analyzed after the requested standard-of-care laboratory testing. In a subset of participants, blood samples from SARS-CoV-2 IgM false-positive cases and true-positive cases were obtained 30 days after the first positive serological tests.

2.3 Measurement of SARS-CoV-2 IgM and IgG

SARS-CoV-2 IgM and IgG tests were both performed for these cases. SARS-CoV-2 IgM and IgG serum levels were measured using an automatic CLIA system (Bioscience Biotechnology Co., Ltd.) with reagents, including SARS-CoV-2 antibody (IgM and IgG) detection kits (Bioscience Biotechnology Co., Ltd., lot number for IgM: G202002415, IgG: G202002414). Briefly, IgG and IgM antibody detection was developed based on CLIA, which uses recombinant antigens containing the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, acting as the conjugated antigen. The antibody levels were expressed by the chemiluminescence signal. The commercially available assay used in this study has been evaluated to be sufficiently sensitive and specific for detecting SARS-CoV-2 IgM and IgG in clinical specimens [20]. According to the manufacturer’s instructions and standard operating procedures, daily maintenance was performed before sample testing, and both internal quality control and sample testing were carried out afterward.

All the samples that showed initial positive antibody test results were retested and confirmed by another SARS-CoV-2 antibody (IgM and IgG) detection kit (Sichuan Orienter Biotechnology Co., Ltd.) using an automatic CLIA system.

2.4 SARS-CoV-2 RNA testing

Nasal and pharyngeal swabs (Baso, Zhuhai, Guangdong, China) were collected from all enrolled patients. According to the manufacturer’s instructions, RNA was extracted using a nucleic acid automated extraction system (Zhijiang, Shanghai, China). A commercial multiplex real-time PCR kit (Maccura2019-nCoV Assay, Chengdu, Sichuan, China) was then used to detect the ORF1ab, N, and E genes of SARS-CoV-2. The results were considered positive when two or three genes were identified.

2.5 Result judgment

SARS-CoV-2 IgM and IgG results detected by CLIA were given in the form of the ratios of specimen signals to the cutoff values (S/CO), which were nonreactive (negative) if the S/CO ratio was < 1.0 and reactive (positive) if the S/CO ratio was ≥ 1.0 by the manufacturer. Moreover, a “false-positive” case was defined as a positive antibody result from a non-COVID-19-infected patient. A “true-positive” case was defined as a positive antibody result from a COVID-19 patient.

2.6 Statistical analysis

Measurement data were displayed as mean ± standard deviation (SD), and count data are expressed as percentages. The chi-square test was used to compare enumeration data. All statistical analyses were performed using SPSS version 23.0 (SPSS Co., Inc., Chicago, IL), and statistical significance was defined as p < 0.05, as determined using two-tailed tests.

3 Results

3.1 Comparison of the proportion of false-positives in single SARS-CoV-2 IgM-, single SARS-CoV-2 IgG-, and SARS-CoV-2 IgM- & IgG- positive results

The IgM and IgG of 14,708 patients were tested for SARS-CoV-2 in this study, excluding cases with duplicated patients. A total of 168 (1.19%) patients with positive results (S/CO ratio ≥ 1.0) were included for further analysis. Among these 168 cases, 35 (20.83%) were defined as “true positives,” and the remaining 133 (79.17%) were defined as “false-positives.”

Ninety-seven of 168 (57.74%) cases had single SARS-CoV-2 IgM-positive results: 4/97 (4.12%) were true positives, and 93/97 (95.88%) were false-positives. Thirty-eight of 168 (22.62%) cases had single SARS-CoV-2 IgG-positive results: 11/38 (28.95%) were true positives, and 27/38 (71.05%) were false-positives. Thirty-three of 168 (19.64%) cases had SARS-CoV-2 IgM- and IgG-positive results: 20/33 (60.61%) were true positives, and 13/33 (39.39%) were false positives. The false-positive proportion of single SARS-CoV-2 IgM-positive results (95.88%) was significantly higher than those of single SARS-CoV-2 IgG-positive results (71.05%; p < 0.001) and SARS-CoV-2 IgM- & IgG-positive results (39.39%; p < 0.001 [Table 1]).

Table 1

Comparison of the proportion of true-positives and false-positives in single SARS-CoV-2 IgM-, single SARS-CoV-2 IgG-, and SARS-CoV-2 IgM- and IgG- positive results

n SARS-CoV-2
True-positive (n [%]) Fasle-positive (n [%])
Single IgM (+) 97 4 (4.12%) 93 (95.88%)
Single IgG (+) 38 11 (28.95%) 27 (71.05%)*
IgM & IgG (+) 33 20 (60.61%) 13 (39.39%)*#
χ 2 49.589
p-Value <0.0001

Note: *p < 0.001, when compared with the single SARS-CoV-2 IgM positive. # p < 0.01, when compared with the single SARS-CoV-2 IgG positive.

3.2 Comparison of the false-positive proportion of single SARS-CoV-2 IgM-, single SARS-CoV-2 IgG-, and SARS-CoV-2 IgM- and IgG-positive results in different sexes and ages

In this study, 69 of 133 (51.88%) false-positives were observed among males: 43/69 (62.32%) had a single SARS-CoV-2 IgM-positive result, 18/69 (26.09%) had a single SARS-CoV-2 IgG-positive result, and 8/69 (11.59%) had SARS-CoV-2 IgM- & IgG-positive results. A total of 64 of 133 (48.12%) false-positives were observed among females: 50/64 (78.13%) had a single SARS-CoV-2 IgM-positive result, 9/64 (14.06%) had a single SARS-CoV-2 IgG-positive result, and 5/64 (7.81%) had SARS-CoV-2 IgM- and IgG-positive results. Based on these results, no significant difference was found in the false-positive proportion of the three result patterns between sexes (p > 0.05; Table 2). Similarly, no significant difference was found in the false-positive rates of the three result patterns in different age groups (p > 0.05; Table 3).

Table 2

Comparison of the false-positives proportion of single SARS-CoV-2 IgM-, single SARS-CoV-2 IgG-, and SARS-CoV-2 IgM- and IgG- positive results in different sexes

Single IgM (+) Single IgG (+) IgM and IgG (+)
Male 62.32% (43/69) 26.09% (18/69) 11.59% (8/69)
Female 78.13% (50/64) 14.06% (9/64) 7.81% (5/64)
χ2 3.229 2.967 0.538
p-Value 0.072 0.085 0.463
Table 3

Comparison of the false-positives proportion of single SARS-CoV-2 IgM-, single SARS-CoV-2 IgG-, and SARS-CoV-2 IgM- and IgG- positive results in different ages

Age (years) Single IgM (+) Single IgG (+) IgM and IgG (+)
≤20 54.55% (12/22) 40.91% (9/22) 4.55% (1/22)
21∼ 69.23% (9/13) 15.38% (2/13) 15.38% (2/13)
31∼ 72.00% (18/25) 20.00% (5/25) 8.00% (2/25)
41∼ 68.42% (13/19) 10.53% (2/19) 21.05% (4/19)
51∼ 65.22% (15/23) 17.39% (4/23) 17.39% (4/23)
61∼ 90.91% (10/11) 9.09% (1/11) 0.00% (0/11)
>70 80.00% (16/20) 20.00% (4/20) 0.00% (0/20)
χ 2/Fisher 6.060 6.802 7.912
p-Value 0.426 0.327 0.169

3.3 Distribution of SARS-CoV-2 IgM S/CO values in false-positive and positive results

Among the cases in our study, 130 had SARS-CoV-2 IgM S/CO values greater than or equal to 1.0. A total of 97/130 (74.62%) cases had single SARS-CoV-2 IgM-positive results. Figure 1 shows the distribution of SARS-CoV-2 IgM S/CO values in the false-positive and true-positive results. The S/CO values of IgM false-positives ranged from 1.0 to 32.0, with most of them concentrated between 1.0 and 3.0 (Figure 1a and b). The S/CO values of the SARS-CoV-2 IgM true-positive cases ranged from 1.0 to 180.0, with most of them between 3.0 and 10.0 (Figure 1a), whereas the S/CO values of single SARS-CoV-2 IgM true-positive cases were all more than 100.0 (Figure 1b). The overlapping range of S/CO values for SARS-CoV-2 IgM false-positive and true-positive cases was mainly from 1.0 to 50.0 (Figure 1a). Notably, neither SARS-CoV-2 IgM nor single SARS-CoV-2 IgM in false-positive cases showed S/CO values greater than 40.0 (Figure 1a and b).

Figure 1 Distribution of SARS-CoV-2 IgM S/CO results greater than or equal to 1.0. (a) Distribution of S/CO values of SARS-CoV-2 IgM in false-positive and positive results. (b) Distribution of S/CO values of single SARS-CoV-2 IgM in false-positive and positive results. S/CO = ratios of specimen signals to the cut-off values.
Figure 1

Distribution of SARS-CoV-2 IgM S/CO results greater than or equal to 1.0. (a) Distribution of S/CO values of SARS-CoV-2 IgM in false-positive and positive results. (b) Distribution of S/CO values of single SARS-CoV-2 IgM in false-positive and positive results. S/CO = ratios of specimen signals to the cut-off values.

As shown in Table 4, when the S/CO values of SARS-CoV-2 IgM were in the ranges of 1.0–3.0, 3.0–5.0, 5.0–10.0, 10.0–50.0, and greater than 50.0, the probability of SARS-CoV-2 IgM false-positives was 95.06, 66.67, 50.00, 75.00, and 0.00%, respectively. Meanwhile, when the S/CO values of single SARS-CoV-2 IgM were in the ranges of 1.0–50.0 and greater than 50.0, the probability of a single SARS-CoV-2 IgM false-positive result was 100.00 and 0.00%, respectively.

Table 4

Probability of the false-positive results in SARS-CoV-2 IgM and single SARS-CoV-2 IgM positive results

S/CO SARS-CoV-2 IgM Single SARS-CoV-2 IgM
False-positive True-positive False-positive True-positive
1.0–3.0 95.06% (77/81)* 4.94% (4/81) 100.00% (70/70)# 0.00% (0/70)
3.0–5.0 66.67% (10/15)* 33.33% (5/15) 100.00% (8/8)# 0.00% (0/8)
5.0–10.0 50.00% (7/14)* 50.00% (7/14) 100.00% (5/5)# 0.00% (0/5)
10.0–50.0 75.00% (12/16)* 25.00% (4/16) 100.00% (10/10)# 0.00% (0/10)
>50.0 0.00% (0/4) 100.00% (4/4) 0.00% (0/4) 100.00% (4/4)
Fisher 34.775 28.635
p-Value <0.0001 <0.0001

Note: S/CO = ratios of specimen signals to the cut-off values. * p < 0.001, when compared with the SARS-CoV-2 IgM in ranges greater than 50.0. # p < 0.001, when compared with the single SARS-CoV-2 IgM in ranges greater than 50.0.

3.4 Distribution of SARS-CoV-2 IgG S/CO values in false-positive and positive results

Of the 71 cases with S/CO values of SARS-CoV-2 IgG greater than or equal to 1.0, 38/71 (53.52%) cases had single SARS-CoV-2 IgG-positive results. Figure 2 shows the distribution of SARS-CoV-2 IgG S/CO values in false-positive and true-positive results. The S/CO values of false-positives ranged from 1.0 to 40.0, with most concentrated between 1.0 and 2.0, whereas the S/CO values of true positives ranged from 1.0 to 360.0, with most above 10.0. Moreover, the overlapping range of S/CO values for SARS-CoV-2 IgG-positive and false-positive cases was mainly from 1.0 to 50.0 (Figure 2a and b). Interestingly, S/CO values above 40.0 indicated no false-positive SARS-CoV-2 IgG results (Figure 2a and b).

Figure 2 Distribution of SARS-CoV-2 IgG S/CO values greater than or equal to 1.0. (a) Distribution of S/CO values of SARS-CoV-2 IgG in false-positive and positive results. (b) Distribution of S/CO values of single SARS-CoV-2 IgG in false-positive and positive results. S/CO = ratios of specimen signals to the cut-off values.
Figure 2

Distribution of SARS-CoV-2 IgG S/CO values greater than or equal to 1.0. (a) Distribution of S/CO values of SARS-CoV-2 IgG in false-positive and positive results. (b) Distribution of S/CO values of single SARS-CoV-2 IgG in false-positive and positive results. S/CO = ratios of specimen signals to the cut-off values.

When the S/CO values of SARS-CoV-2 IgG were in the ranges of 1.0–2.0, 2.0–5.0, 5.0–10.0, 10.0–50.0, and greater than 50.0, the probability of SARS-CoV-2 IgG false-positives was 85.71, 50.00, 62.5, 23.08, and 0.00%, respectively. When the S/CO values of single SARS-CoV-2 IgG were in the ranges of 1.0–2.0, 2.0–5.0, 5.0–10.0, and 10.0–50.0, the probability of false-positive single SARS-CoV-2 IgG results was 94.74, 57.14, 75.00, and 25.00%, respectively (Table 5).

Table 5

Probability of the false-positive results in SARS-CoV-2 IgG and single SARS-CoV-2 IgG-positive results

S/CO SARS-CoV-2 IgG Single SARS-CoV-2 IgG
False-positive Positive False-positive Positive
1.0–2.0 85.71% (24/28)*& 14.29% (4/28) 94.74% (18/19)# 5.26% (1/19)
2.0–5.0 50.00% (8/16)*& 50.00% (8/16) 57.14% (4/7)# 42.84% (3/7)
5.0–10.0 62.50% (5/8)*& 37.50% (3/8) 75.00% (3/4)# 25.00% (1/4)
10.0–50.0 23.08% (3/13)* 76.92% (10/13) 25.00% (2/8) 75.00% (6/8)
>50.0 0.00% (0/6) 100.00% (6/6) — —
Fisher 24.416 13.892
p-Value <0.0001 0.001

Note: S/CO = ratios of specimen signals to the cut-off values. * p < 0.001, when compared with the SARS-CoV-2 IgG in ranges of greater than 50.0; & p < 0.01, when compared with the SARS-CoV-2 IgG in ranges of 10.0–50.0. # p < 0.01, when compared with the single SARS-CoV-2 IgG in ranges of 10.0–50.0.

3.5 Dynamic changes in SARS-CoV-2 IgM and IgG in false-positive cases

Eighteen SARS-CoV-2 IgM false-positive cases and four true-positive cases were dynamically monitored at different time points. Of the 18 false-positive cases, 10 cases turned negative, and the remaining eight cases did not. The median time of conversion to seronegative status in the 10 cases was 9 days. Compared with the dynamic change trend of true-positive cases, the changing trend in SARS-CoV-2 IgM false-positive cases was substantially lower. On the other hand, among the eight false-positive cases that were not monitored to become seronegative, three cases showed a downward trend, and five remained unchanged (Figure 3a).

Figure 3 Dynamic changes in SARS-CoV-2 specific antibody in false-positive or positive results. (a) Dynamic changes of SARS-CoV-2 IgM in false-positive results. (b) Dynamic changes of SARS-CoV-2 IgG in false-positive results. The left vertical axis was suitable for the false-positive results, and the right vertical axis was suitable for the positive results. The axis “Days” represents the days after the first test.
Figure 3

Dynamic changes in SARS-CoV-2 specific antibody in false-positive or positive results. (a) Dynamic changes of SARS-CoV-2 IgM in false-positive results. (b) Dynamic changes of SARS-CoV-2 IgG in false-positive results. The left vertical axis was suitable for the false-positive results, and the right vertical axis was suitable for the positive results. The axis “Days” represents the days after the first test.

Seven SARS-CoV-2 IgG false-positive cases and four true-positive cases were dynamically monitored at different time points. Of the seven false-positive cases, six became seronegative, and only one case did not. The median time of conversion to seronegative status of the six cases was 5 days. Compared with the dynamic change trend of SARS-CoV-2 IgG in true-positive cases, the changing trend in false-positive cases was substantially lower. Notably, only one false-positive case, which was not monitored as seronegative, showed a downward trend (Figure 3b).

4 Discussion

During the COVID-19 pandemic, assay kits for detecting IgM and IgG antibodies against SARS-CoV-2 proteins have expanded our measures for COVID-19 detection [13,21]. Antibodies for SARS-CoV-2 are produced within the range of 5–14 days after the onset of disease symptoms [22,23]. Almost all COVID-19 patients develop detectable IgG and IgM antibodies within several weeks of symptom onset [13,21,24]. The low cost, ease of performing and interpreting, and fast turn-around time make serological tests attractive for quick diagnosis and mass screening [25]. Furthermore, detecting specific antibodies has been proven to show public health and clinical utility for pandemic monitoring and response and managing affected patients [5,26].

In our study, we used the CLIA method based on the recombinant SARS-CoV-2 S-RBD protein to detect serum IgG and IgM. Its analytical performance was successfully evaluated by Wan et al. They reported the performance verification of SARS-CoV-2 IgM (82% sensitivity and 93.85% specificity) and SARS-CoV-2 IgG (86% sensitivity and 96.92% specificity) detection kits among COVID-19 patients [20]. Due to the problems of immunological detection methods, some interferences resulted in false-positive results of SARS-CoV-2 serological tests that were inconsistent with clinical manifestations and epidemiological characteristics [14,15,16]. False-positive signals were detected in serological tests using CLIA and reported in other immunoassay analytical techniques, such as ELISA, ECLIA, and LFI [11]. Additionally, some false-positive cases likely did not result from problems with the sample, procedure, or other random factors, which was supported by obtaining repeated positive results with similar S/CO values on repeat testing (data not shown) in our study, making a transient response to antigens less likely. Although we can use electronic medical records and laboratory results, including nucleic acid-based tests, as a source to determine true anti-SARS-CoV-2 status, the procedure is labor-intensive and time-consuming. Therefore, we sought an effective strategy to solve such problems when confronted with false-positive results in SARS-CoV-2 antibody screening tests.

To elucidate these issues, we analyzed the false-positive cases of SARS-CoV-2 IgM and IgG detected using CLIA among non-SARS-CoV-2-infected patients. This study showed that the false-positive proportion of single SARS-CoV-2 IgM-positive results was 95.88%, substantially higher than those of single SARS-CoV-2 IgG-positive results (71.05%) and SARS-CoV-2 IgM- and IgG-positive results (39.39%). Other studies have also reported that most false-positive signals were detected in SARS-CoV-2 IgM assays [14,15,27]. These false-positive results of SARS-CoV-2 IgM and IgG antibodies may be caused by autoantibodies, heterophilic antibodies, and other factors [12,14,15,16]. Therefore, we concluded that the possibility of false-positive single SARS-CoV-2 IgM-positive and single SARS-CoV-2 IgG-positive results was high. The false-positive rate of IgM and IgG together is lower than when examined individually. In this study, we also observed true-positive cases of SARS-CoV-2 IgM and IgG in confirmed COVID-19 patients. The true-positive proportions of single SARS-CoV-2 IgM-, single SARS-CoV-2 IgG- and SARS-CoV-2 IgM- & IgG-positive results were 4.12, 28.95, and 60.61%, respectively. The combined detection of SARS-CoV-2 IgM and IgG antibodies is an effective tool for improving diagnostic sensitivity and specificity [12]. Thus, the combined detection of SARS-CoV-2 IgM and IgG antibodies should be given high priority in its implementation as the standard serological test in clinical and public health practice during the pandemic.

In this study, we found that the S/CO values of the IgM false-positive results were mainly between 1.0 and 3.0, whereas the S/CO values of the IgG false-positive results were mainly between 1.0 and 2.0. These results indicated that the SARS-CoV-2 IgM and IgG false-positive results detected by CLIA primarily existed in the low-value area. The false-positive results, which were low positives or low values, needed further confirmation. Therefore, the S/CO value may be a helpful indicator in differentiating false positives from true positives. Aside from the S/CO values, we also compared the false-positive proportions of single SARS-CoV-2 IgM-, single SARS-CoV-2 IgG-, and SARS-CoV-2 IgM- and IgG-positive results in different sexes and ages. Our study showed no significant false-positive proportion differences between sexes and ages.

After SARS-CoV-2 invades the human body, the time and duration of producing IgM and IgG antibodies are different. Due to the dynamic change in specific antibodies, cases with a single positive SARS-CoV-2 IgM/IgG antibody test can be determined by dynamically monitoring them over time [6]. Despite this, the question of how long the supposed antibody level can last in false-positive cases remains unknown. To investigate this, we analyzed the subsequent results of four true-positive cases and 25 false-positive cases, including 18 cases with a SARS-CoV-2 IgM-positive result and seven cases with a SARS-CoV-2 IgG-positive result, and dynamic monitoring of the serum antibody levels after the first test. The time of conversion to seronegativity in IgM false-positive cases was 4–19 days, with a median time of 9 days. Moreover, the antibody duration in the IgM false-positive cases was substantially shorter than that in COVID-19 patients with a duration of 2–6 months, as reported in other studies [28,29]. Meanwhile, the conversion time to seronegativity in IgG false-positive cases was 2–8 days, with a median seroconversion time of 5 days. Compared with previous studies, the antibody duration in the IgG false-positive cases was also substantially shorter than the duration of serum IgG antibodies in COVID-19 patients (6 months) [28,29]. For other cases observed during the monitoring period and those that did not become seronegative, IgM antibody levels showed a downward trend or remained unchanged. Similarly, IgG antibody levels in those cases also showed a downward trend. Due to the lack of blood samples collected from the false-positive cases in the later stage, the time of conversion in their antibodies remained unknown. In this study, the results suggest that the dynamic monitoring of serum antibody levels was also of practical value in distinguishing between true-positive and false-positive results.

To summarize, these findings should be comprehensively judged when confronted with positive results in SARS-CoV-2-specific antibody testing. First, it is crucial to observe the antibody pattern. If it is a single SARS-CoV-2 IgM-positive pattern, the probability of a false-positive result is higher than that of a single SARS-CoV-2 IgG-positive pattern. Moreover, if it is a SARS-CoV-2 IgM- and IgG-positive pattern, the probability of a true-positive result is higher than that of a single positive antibody result. Second, one must observe the S/CO value distribution of the antibody results as follows: (1) if the S/CO value of a single SARS-CoV-2 IgG-positive result is between 1.0 and 2.0, the probability of a false-positive is approximately 94.74% (Table 5); (2) if the S/CO value of a single SARS-CoV-2 IgM-positive result is between 1.0 and 3.0, the probability of a false-positive is approximately 100.0% (Table 4); (3) if a single SARS-CoV-2 IgM test has a positive result, and the S/CO value is very high (>20.0), the result needs to be judged in combination with the S/CO value of the SARS-CoV-2 IgG result; (3a) if the S/CO value of the SARS-CoV-2 IgG result approaches 0, there is a high possibility of a false-positive result; and (3b) if the S/CO value of the SARS-CoV-2 IgG result is close to 1.0, it is more likely to be a true-positive result. Last, antibody level changes should be observed dynamically. In cases of a single SARS-CoV-2 IgM-positive status, IgM antibody increases, and IgG antibody becomes positive (i.e., IgM- and IgG-positive status), with which we can judge if the patient truly has a SARS-CoV-2 infection. Otherwise, it is a false-positive result. On the other hand, in the dynamic monitoring of SARS-CoV-2-positive IgM and positive IgG status, the IgM antibody is the first to increase and subsequently decrease, whereas the IgG antibody titer has a 4-fold increase, with which we can judge if the patient truly has a SARS-CoV-2 infection [6]. Otherwise, these are false-positive results. Furthermore, the single SARS-CoV-2 IgG-positive status shows a rapidly decreasing trend in dynamic monitoring, which may indicate a false-positive result.

Despite the findings of our study, several limitations were noted. (1) Due to the inadequate conditions of our laboratory, we failed to determine the definite interferences or factors causing false-positive results. (2) The disease prevalence of the population is not known. The false-positive rate is highly dependent on the disease prevalence. (3) Since most of the SARS-COV-2-specific antibody detection was performed using the CLIA platform due to its high throughput, our research analysis focused only on CLIA. Thus, this study’s characteristics of false-positive results may not apply to other SARS-COV-2 antibody detection methods. Regardless, this study can still provide a reference strategy for other researchers.

5 Conclusion

The results of our study confirm that separate positive SARS-COV-2 IgM and IgG antibody tests would be an unconvincing result. We proposed that the possible usage of the SARS-COV-2 IgM and IgG antibody patterns, S/CO values or ranges, and dynamic changes in antibody levels are useful for screening positive antibody results. This study aimed to assist clinicians or technicians in developing timely and effective diagnostic strategies to distinguish false-positive results from true-positive results. However, regardless of the method, laboratories should develop approaches to identify analytical false-positive results wherever possible, particularly when screening low-prevalence populations.

# These authors contributed equally to this work

  1. Funding information: This research was supported by the Science and Technology Project of Nanchong(20YFZJ0095, 20YFZJ0111), and the Nanchong City’s S&T strategic cooperation projects between the city and the university (19SXHZ0198, 20SXQT0337).

  2. Author contributions: Y.L. and X.L. carried out the studies, participated in collecting data, and drafted the manuscript. D.M. and Q.D. participated in collecting data. Q.W. and G.W. performed the statistical analysis and participated in its design. All authors read and approved the final version of the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-06-19
Revised: 2022-08-29
Accepted: 2022-09-14
Published Online: 2022-11-14

© 2022 Yan Lei et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/biol-2022-0512
Keywords for this article
SARS-CoV-2; COVID-19; antibody; chemiluminescent immunoassay; immunoglobulin M; immunoglobulin G; false positive
Creative Commons
BY 4.0

Articles in the same Issue

  1. Biomedical Sciences
  2. Effects of direct oral anticoagulants dabigatran and rivaroxaban on the blood coagulation function in rabbits
  3. The mother of all battles: Viruses vs humans. Can humans avoid extinction in 50–100 years?
  4. Knockdown of G1P3 inhibits cell proliferation and enhances the cytotoxicity of dexamethasone in acute lymphoblastic leukemia
  5. LINC00665 regulates hepatocellular carcinoma by modulating mRNA via the m6A enzyme
  6. Association study of CLDN14 variations in patients with kidney stones
  7. Concanavalin A-induced autoimmune hepatitis model in mice: Mechanisms and future outlook
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  10. Swimming attenuates tumor growth in CT-26 tumor-bearing mice and suppresses angiogenesis by mediating the HIF-1α/VEGFA pathway
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  12. Functional conservation and divergence in plant-specific GRF gene family revealed by sequences and expression analysis
  13. Application of the FLP/LoxP-FRT recombination system to switch the eGFP expression in a model prokaryote
  14. Biomedical evaluation of antioxidant properties of lamb meat enriched with iodine and selenium
  15. Intravenous infusion of the exosomes derived from human umbilical cord mesenchymal stem cells enhance neurological recovery after traumatic brain injury via suppressing the NF-κB pathway
  16. Effect of dietary pattern on pregnant women with gestational diabetes mellitus and its clinical significance
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  18. Effect of the genetic mutant G71R in uridine diphosphate-glucuronosyltransferase 1A1 on the conjugation of bilirubin
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  20. Nutrition intervention in the management of novel coronavirus pneumonia patients
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  22. Bmi-1 directly upregulates glucose transporter 1 in human gastric adenocarcinoma
  23. Lacunar infarction aggravates the cognitive deficit in the elderly with white matter lesion
  24. Hydroxysafflor yellow A improved retinopathy via Nrf2/HO-1 pathway in rats
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  26. Elevated IL-35 level and iTr35 subset increase the bacterial burden and lung lesions in Mycobacterium tuberculosis-infected mice
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  31. Analysis of genetic characteristics of 436 children with dysplasia and detailed analysis of rare karyotype
  32. Bioinformatics network analyses of growth differentiation factor 11
  33. NR4A1 inhibits the epithelial–mesenchymal transition of hepatic stellate cells: Involvement of TGF-β–Smad2/3/4–ZEB signaling
  34. Expression of Zeb1 in the differentiation of mouse embryonic stem cell
  35. Study on the genetic damage caused by cadmium sulfide quantum dots in human lymphocytes
  36. Association between single-nucleotide polymorphisms of NKX2.5 and congenital heart disease in Chinese population: A meta-analysis
  37. Assessment of the anesthetic effect of modified pentothal sodium solution on Sprague-Dawley rats
  38. Genetic susceptibility to high myopia in Han Chinese population
  39. Potential biomarkers and molecular mechanisms in preeclampsia progression
  40. Silencing circular RNA-friend leukemia virus integration 1 restrained malignancy of CC cells and oxaliplatin resistance by disturbing dyskeratosis congenita 1
  41. Endostar plus pembrolizumab combined with a platinum-based dual chemotherapy regime for advanced pulmonary large-cell neuroendocrine carcinoma as a first-line treatment: A case report
  42. The significance of PAK4 in signaling and clinicopathology: A review
  43. Sorafenib inhibits ovarian cancer cell proliferation and mobility and induces radiosensitivity by targeting the tumor cell epithelial–mesenchymal transition
  44. Characterization of rabbit polyclonal antibody against camel recombinant nanobodies
  45. Active legumain promotes invasion and migration of neuroblastoma by regulating epithelial-mesenchymal transition
  46. Effect of cell receptors in the pathogenesis of osteoarthritis: Current insights
  47. MT-12 inhibits the proliferation of bladder cells in vitro and in vivo by enhancing autophagy through mitochondrial dysfunction
  48. Study of hsa_circRNA_000121 and hsa_circRNA_004183 in papillary thyroid microcarcinoma
  49. BuyangHuanwu Decoction attenuates cerebral vasospasm caused by subarachnoid hemorrhage in rats via PI3K/AKT/eNOS axis
  50. Effects of the interaction of Notch and TLR4 pathways on inflammation and heart function in septic heart
  51. Monosodium iodoacetate-induced subchondral bone microstructure and inflammatory changes in an animal model of osteoarthritis
  52. A rare presentation of type II Abernethy malformation and nephrotic syndrome: Case report and review
  53. Rapid death due to pulmonary epithelioid haemangioendothelioma in several weeks: A case report
  54. Hepatoprotective role of peroxisome proliferator-activated receptor-α in non-cancerous hepatic tissues following transcatheter arterial embolization
  55. Correlation between peripheral blood lymphocyte subpopulations and primary systemic lupus erythematosus
  56. A novel SLC8A1-ALK fusion in lung adenocarcinoma confers sensitivity to alectinib: A case report
  57. β-Hydroxybutyrate upregulates FGF21 expression through inhibition of histone deacetylases in hepatocytes
  58. Identification of metabolic genes for the prediction of prognosis and tumor microenvironment infiltration in early-stage non-small cell lung cancer
  59. BTBD10 inhibits glioma tumorigenesis by downregulating cyclin D1 and p-Akt
  60. Mucormycosis co-infection in COVID-19 patients: An update
  61. Metagenomic next-generation sequencing in diagnosing Pneumocystis jirovecii pneumonia: A case report
  62. Long non-coding RNA HOXB-AS1 is a prognostic marker and promotes hepatocellular carcinoma cells’ proliferation and invasion
  63. Preparation and evaluation of LA-PEG-SPION, a targeted MRI contrast agent for liver cancer
  64. Proteomic analysis of the liver regulating lipid metabolism in Chaohu ducks using two-dimensional electrophoresis
  65. Nasopharyngeal tuberculosis: A case report
  66. Characterization and evaluation of anti-Salmonella enteritidis activity of indigenous probiotic lactobacilli in mice
  67. Aberrant pulmonary immune response of obese mice to periodontal infection
  68. Bacteriospermia – A formidable player in male subfertility
  69. In silico and in vivo analysis of TIPE1 expression in diffuse large B cell lymphoma
  70. Effects of KCa channels on biological behavior of trophoblasts
  71. Interleukin-17A influences the vulnerability rather than the size of established atherosclerotic plaques in apolipoprotein E-deficient mice
  72. Multiple organ failure and death caused by Staphylococcus aureus hip infection: A case report
  73. Prognostic signature related to the immune environment of oral squamous cell carcinoma
  74. Primary and metastatic squamous cell carcinoma of the thyroid gland: Two case reports
  75. Neuroprotective effects of crocin and crocin-loaded niosomes against the paraquat-induced oxidative brain damage in rats
  76. Role of MMP-2 and CD147 in kidney fibrosis
  77. Geometric basis of action potential of skeletal muscle cells and neurons
  78. Babesia microti-induced fulminant sepsis in an immunocompromised host: A case report and the case-specific literature review
  79. Role of cerebellar cortex in associative learning and memory in guinea pigs
  80. Application of metagenomic next-generation sequencing technique for diagnosing a specific case of necrotizing meningoencephalitis caused by human herpesvirus 2
  81. Case report: Quadruple primary malignant neoplasms including esophageal, ureteral, and lung in an elderly male
  82. Long non-coding RNA NEAT1 promotes angiogenesis in hepatoma carcinoma via the miR-125a-5p/VEGF pathway
  83. Osteogenic differentiation of periodontal membrane stem cells in inflammatory environments
  84. Knockdown of SHMT2 enhances the sensitivity of gastric cancer cells to radiotherapy through the Wnt/β-catenin pathway
  85. Continuous renal replacement therapy combined with double filtration plasmapheresis in the treatment of severe lupus complicated by serious bacterial infections in children: A case report
  86. Simultaneous triple primary malignancies, including bladder cancer, lymphoma, and lung cancer, in an elderly male: A case report
  87. Preclinical immunogenicity assessment of a cell-based inactivated whole-virion H5N1 influenza vaccine
  88. One case of iodine-125 therapy – A new minimally invasive treatment of intrahepatic cholangiocarcinoma
  89. S1P promotes corneal trigeminal neuron differentiation and corneal nerve repair via upregulating nerve growth factor expression in a mouse model
  90. Early cancer detection by a targeted methylation assay of circulating tumor DNA in plasma
  91. Calcifying nanoparticles initiate the calcification process of mesenchymal stem cells in vitro through the activation of the TGF-β1/Smad signaling pathway and promote the decay of echinococcosis
  92. Evaluation of prognostic markers in patients infected with SARS-CoV-2
  93. N6-Methyladenosine-related alternative splicing events play a role in bladder cancer
  94. Characterization of the structural, oxidative, and immunological features of testis tissue from Zucker diabetic fatty rats
  95. Effects of glucose and osmotic pressure on the proliferation and cell cycle of human chorionic trophoblast cells
  96. Investigation of genotype diversity of 7,804 norovirus sequences in humans and animals of China
  97. Characteristics and karyotype analysis of a patient with turner syndrome complicated with multiple-site tumors: A case report
  98. Aggravated renal fibrosis is positively associated with the activation of HMGB1-TLR2/4 signaling in STZ-induced diabetic mice
  99. Distribution characteristics of SARS-CoV-2 IgM/IgG in false-positive results detected by chemiluminescent immunoassay
  100. SRPX2 attenuated oxygen–glucose deprivation and reperfusion-induced injury in cardiomyocytes via alleviating endoplasmic reticulum stress-induced apoptosis through targeting PI3K/Akt/mTOR axis
  101. Aquaporin-8 overexpression is involved in vascular structure and function changes in placentas of gestational diabetes mellitus patients
  102. Relationship between CRP gene polymorphisms and ischemic stroke risk: A systematic review and meta-analysis
  103. Effects of growth hormone on lipid metabolism and sexual development in pubertal obese male rats
  104. Cloning and identification of the CTLA-4IgV gene and functional application of vaccine in Xinjiang sheep
  105. Antitumor activity of RUNX3: Upregulation of E-cadherin and downregulation of the epithelial–mesenchymal transition in clear-cell renal cell carcinoma
  106. PHF8 promotes osteogenic differentiation of BMSCs in old rat with osteoporosis by regulating Wnt/β-catenin pathway
  107. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
  108. Bilateral dacryoadenitis in adult-onset Still’s disease: A case report
  109. A novel association between Bmi-1 protein expression and the SUVmax obtained by 18F-FDG PET/CT in patients with gastric adenocarcinoma
  110. The role of erythrocytes and erythroid progenitor cells in tumors
  111. Relationship between platelet activation markers and spontaneous abortion: A meta-analysis
  112. Abnormal methylation caused by folic acid deficiency in neural tube defects
  113. Silencing TLR4 using an ultrasound-targeted microbubble destruction-based shRNA system reduces ischemia-induced seizures in hyperglycemic rats
  114. Plant Sciences
  115. Seasonal succession of bacterial communities in cultured Caulerpa lentillifera detected by high-throughput sequencing
  116. Cloning and prokaryotic expression of WRKY48 from Caragana intermedia
  117. Novel Brassica hybrids with different resistance to Leptosphaeria maculans reveal unbalanced rDNA signal patterns
  118. Application of exogenous auxin and gibberellin regulates the bolting of lettuce (Lactuca sativa L.)
  119. Phytoremediation of pollutants from wastewater: A concise review
  120. Genome-wide identification and characterization of NBS-encoding genes in the sweet potato wild ancestor Ipomoea trifida (H.B.K.)
  121. Alleviative effects of magnetic Fe3O4 nanoparticles on the physiological toxicity of 3-nitrophenol to rice (Oryza sativa L.) seedlings
  122. Selection and functional identification of Dof genes expressed in response to nitrogen in Populus simonii × Populus nigra
  123. Study on pecan seed germination influenced by seed endocarp
  124. Identification of active compounds in Ophiopogonis Radix from different geographical origins by UPLC-Q/TOF-MS combined with GC-MS approaches
  125. The entire chloroplast genome sequence of Asparagus cochinchinensis and genetic comparison to Asparagus species
  126. Genome-wide identification of MAPK family genes and their response to abiotic stresses in tea plant (Camellia sinensis)
  127. Selection and validation of reference genes for RT-qPCR analysis of different organs at various development stages in Caragana intermedia
  128. Cloning and expression analysis of SERK1 gene in Diospyros lotus
  129. Integrated metabolomic and transcriptomic profiling revealed coping mechanisms of the edible and medicinal homologous plant Plantago asiatica L. cadmium resistance
  130. A missense variant in NCF1 is associated with susceptibility to unexplained recurrent spontaneous abortion
  131. Assessment of drought tolerance indices in faba bean genotypes under different irrigation regimes
  132. The entire chloroplast genome sequence of Asparagus setaceus (Kunth) Jessop: Genome structure, gene composition, and phylogenetic analysis in Asparagaceae
  133. Food Science
  134. Dietary food additive monosodium glutamate with or without high-lipid diet induces spleen anomaly: A mechanistic approach on rat model
  135. Binge eating disorder during COVID-19
  136. Potential of honey against the onset of autoimmune diabetes and its associated nephropathy, pancreatitis, and retinopathy in type 1 diabetic animal model
  137. FTO gene expression in diet-induced obesity is downregulated by Solanum fruit supplementation
  138. Physical activity enhances fecal lactobacilli in rats chronically drinking sweetened cola beverage
  139. Supercritical CO2 extraction, chemical composition, and antioxidant effects of Coreopsis tinctoria Nutt. oleoresin
  140. Functional constituents of plant-based foods boost immunity against acute and chronic disorders
  141. Effect of selenium and methods of protein extraction on the proteomic profile of Saccharomyces yeast
  142. Microbial diversity of milk ghee in southern Gansu and its effect on the formation of ghee flavor compounds
  143. Ecology and Environmental Sciences
  144. Effects of heavy metals on bacterial community surrounding Bijiashan mining area located in northwest China
  145. Microorganism community composition analysis coupling with 15N tracer experiments reveals the nitrification rate and N2O emissions in low pH soils in Southern China
  146. Genetic diversity and population structure of Cinnamomum balansae Lecomte inferred by microsatellites
  147. Preliminary screening of microplastic contamination in different marine fish species of Taif market, Saudi Arabia
  148. Plant volatile organic compounds attractive to Lygus pratensis
  149. Effects of organic materials on soil bacterial community structure in long-term continuous cropping of tomato in greenhouse
  150. Effects of soil treated fungicide fluopimomide on tomato (Solanum lycopersicum L.) disease control and plant growth
  151. Prevalence of Yersinia pestis among rodents captured in a semi-arid tropical ecosystem of south-western Zimbabwe
  152. Effects of irrigation and nitrogen fertilization on mitigating salt-induced Na+ toxicity and sustaining sea rice growth
  153. Bioengineering and Biotechnology
  154. Poly-l-lysine-caused cell adhesion induces pyroptosis in THP-1 monocytes
  155. Development of alkaline phosphatase-scFv and its use for one-step enzyme-linked immunosorbent assay for His-tagged protein detection
  156. Development and validation of a predictive model for immune-related genes in patients with tongue squamous cell carcinoma
  157. Agriculture
  158. Effects of chemical-based fertilizer replacement with biochar-based fertilizer on albic soil nutrient content and maize yield
  159. Genome-wide identification and expression analysis of CPP-like gene family in Triticum aestivum L. under different hormone and stress conditions
  160. Agronomic and economic performance of mung bean (Vigna radiata L.) varieties in response to rates of blended NPS fertilizer in Kindo Koysha district, Southern Ethiopia
  161. Influence of furrow irrigation regime on the yield and water consumption indicators of winter wheat based on a multi-level fuzzy comprehensive evaluation
  162. Discovery of exercise-related genes and pathway analysis based on comparative genomes of Mongolian originated Abaga and Wushen horse
  163. Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review
  164. Evolution trend of soil fertility in tobacco-planting area of Chenzhou, Hunan Province, China
  165. Animal Sciences
  166. Morphological and molecular characterization of Tatera indica Hardwicke 1807 (Rodentia: Muridae) from Pothwar, Pakistan
  167. Research on meat quality of Qianhua Mutton Merino sheep and Small-tail Han sheep
  168. SI: A Scientific Memoir
  169. Suggestions on leading an academic research laboratory group
  170. My scientific genealogy and the Toronto ACDC Laboratory, 1988–2022
  171. Erratum
  172. Erratum to “Changes of immune cells in patients with hepatocellular carcinoma treated by radiofrequency ablation and hepatectomy, a pilot study”
  173. Erratum to “A two-microRNA signature predicts the progression of male thyroid cancer”
  174. Retraction
  175. Retraction of “Lidocaine has antitumor effect on hepatocellular carcinoma via the circ_DYNC1H1/miR-520a-3p/USP14 axis”
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