The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients.

BACKGROUND: Persistence of antibodies to SARS-CoV-2 viral infection may depend on several factors and may be related to the severity of disease or to the different symptoms.
METHODS: We evaluated the antibody response to SARS-CoV-2 in personnel from 9 healthcare facilities and an international medical school and its association with individuals' characteristics and COVID-19 symptoms in an observational cohort study. We enrolled 4735 subjects (corresponding to 80% of all personnel) for three time points over a period of 8-10 months. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach.
RESULTS: Here we show in individuals positive for IgG (≥12 AU/mL) at the beginning of the study an increase [p = 0.0002] in antibody response in paucisymptomatic or symptomatic subjects, particularly with loss of taste or smell (anosmia/dysgeusia: OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis in the first three months. The antibody response persists for at least 8-10 months.
CONCLUSIONS: SARS-CoV-2 infection induces a long lasting antibody response that increases in the first months, particularly in individuals with anosmia/dysgeusia. This may be linked to the lingering of SARS-CoV-2 in the olfactory bulb.

Introduction

It is becoming clear that the antibody response to SARS-CoV-2 can last at least 6 months in symptomatic patients[1], but it seems to decline in asymptomatics[2], including children[3,4]. Similarly, a reduction of antibody response in asymptomatic individuals was shown in a study with a fewer number of individuals (n = 37)[5]. In another study with a small number of subjects (n = 68), both IgG and IgA were shown to remain remarkably high for a 4 month period of time[6]. The antibody response in COVID-19 patients is associated with the establishment of a memory B cell response which is higher at 6 months[1], however, it is not clear whether there are features that correlate with this sustained B cell response. In addition, the antibody response to the spike protein might last for at least 7 months, while that to nucleocapsid wanes over time[7] and this correlates with a reduction in the IFN-γ producing CD8 + T cell response to the nucleocapsid[8]. The quality of an antibody response is fundamental to avoid long-term effects of COVID-19, indeed individuals with low or waning antibody response are more subject to long-COVID[9]. Hence, it is imperative to investigate the association between the antibody response, its duration, and the type of symptoms. We previously showed that an anti-SARS-CoV-2 serological analysis allowed us to follow the diffusion of the virus within healthcare facilities in areas differently hit by the virus[10]. At 8–10 months of distance, we analyzed the duration of this antibody response and evaluated whether there were features correlating with maintenance, reduction, or increase of the antibody response. We found that SARS-CoV-2 antibody levels increase in symptomatic subjects (particularly in individuals with anosmia/dysgeusia) in the first 3 months of the study. Moreover, this antibody response lasts for at least 8–10 months in all people with SARS-CoV-2 antibodies at the start of the study.

Methods

Study design

This observational cohort study aimed at determining the anti-SARS-CoV-2 IgG plasma levels in nearly 4000 employees of nine healthcare facilities and an international medical school located in northern Italy. It has been approved by the institutional review board of Istituto Clinico Humanitas (ICH) for all participating institutes (ID:1374 IgG COVID-19 Humanitas and registered on clinicaltrial.gov NCT04387929). We have adhered to the STROBE reporting guidelines for observational studies. The serological determination was offered to all the employees of the involved sites and the anticipated refusal rate was assumed to be 10–15%. The overall IgG positivity was assumed to be around 10–15%. The primary endpoint was the number of test positive subjects. Given the study size, the study was able to estimate the overall positivity with a width of 95% confidence interval equal to 2% and the positivity for subgroups of at least 200 patients with a width of 95% confidence interval equal to 9%. No power analysis was performed to calculate the sample size. No randomization was performed. Accrual was on a voluntary basis (individuals aged ≥18 years; Humanitas group employees): it started on April 28th 2020 and more than 80% of personnel participated (n = 4735). The study foresees four blood collections every 3/4 months. Ten different centers participate: ICH, Rozzano (MI); Humanitas Gavazzeni, Bergamo; Humanitas Castelli, Bergamo; Humanitas Mater Domini (HMD), Castellanza (VA); Humanitas Medical Center (HMC), Varese; Humanitas University, Pieve Emanuele (MI); Humanitas San Pio X, Milano; Humanitas Cellini, Torino; Humanitas Gradenigo, Torino; Clinica Fornaca, Torino. This study was conducted following the Helsinki principles of good clinical practice and all participants signed informed consent and filled an anamnestic questionnaire before blood collection. In order to be tested, subjects had to fill in the questionnaire. Only after filling the questionnaire in its entirety, would the individuals be scheduled for blood sampling. We analyzed 93 features (72 categorical and 17 numerical and four temporal) including, age, sex, location, professional role, the time between sample collections, COVID-19 symptoms (fever, sore throat, cough, muscle pain, asthenia, anosmia/dysgeusia (loss of smell and taste), gastrointestinal symptoms, conjunctivitis, dyspnea, chest pain, tachycardia, and pneumonia), home exits and smart-working, comorbidities (diabetes, asthma, neoplasia, autoimmunity, cardiovascular disorders, and hepatic disorders). We considered “asymptomatics” subjects without any symptoms; “paucisymptomatics” individuals that developed 1 or 2 symptoms; “symptomatics” individuals with three or more than three symptoms. None of the participants were enrolled at the time of symptoms. Thus, when the serological test was performed, they were either asymptomatics or the symptoms had disappeared. After excluding for employees that became positive for SARS-CoV-2 IgG (n = 2) during the observation period and those that dropped from phase 1 or for which we were missing at least two features, we analyzed 4534 participants (4.25% drop out) that participated to both phase 1 (April–May 2020) and phase 2 (July–August 2020). Among these, 499 subjects participated also to phase 3 (November–December 2020).

IgG measure

For the determination of IgG anti-SARS-CoV-2, the Liaison SARS-CoV-2 S1/S2 IgG assay (DiaSorin, Saluggia (VC), Italy) was used[11]. The method is an indirect chemiluminescence immunoassay for the determination of anti-S1 and anti-S2 specific antibodies. According to kit manufacturer, the test discriminates among negative (<15 AU/mL; with 3.8 as the limit of IgG detection) and positive (≥15 AU/mL) subjects. We considered positive subjects with IgG plasma levels ≥12 AU/mL rather than those with IgG ≥15 AU/mL, as suggested by the test manufacturer, based on our previous publication showing that these two groups behaved very similarly[10]. In addition, we considered also individuals with IgG comprised between 3.8 and 12 AU/mL (which we called IgG med: 3.8 < IgG <12 AU/mL). Consistency and reproducibility of the antibody test in samples collected in the two time points was confirmed for a limited number of individuals (n = 50) displaying different degrees of IgG positivity. The LIAISON assay’s performance in comparison to a microneutralization assay is shown in Bonelli et al[11]. The LIAISON serological S1/S2 assay can distinguish between neutralization positive and negative samples at cut-offs near 15 AU/mL, and additionally, the data indicate that 92% of the samples with >80 AU/mL had neutralization titers ≥1:80, while 87% of samples with >80 AU/mL had neutralization titers ≥1:160.

As the samples were analyzed in separate batches, we compared the test accuracy on 21 samples from the phase 1 with the detection kits of phase 1 and phase 2 and demonstrated that the tested IgG were almost over-imposable (Supplementary Fig. 3).

Statistical analysis and model

We first cleared the dataset by eliminating data from all of those subjects that did not develop an IgG response over time (IgG ≤3.8 at the beginning and at the end of the examination) (n = 2981). We then analyzed the rate of antibody response defined as:

Positive rates mean increased antibody response, while negative rates indicate reduction of antibody response between the two analyzed time points.

For statistical analysis, we performed both a univariate and multivariate analysis. We applied Wilcoxon–Mann–Whitney statistical nonparametric test to compare the antibody rate distribution between classes of subjects (Tables 1 and 2).

Table 1

Demographic distribution of antibody rates.

		counts	min	25 perc	50 perc	75 perc	max	mean	St.dev.	p valuea
Sex	F	1105	−3.39	−0.04	−0.02	0.02	16.32	0.07	0.61	0.0139
	M	448	−0.88	−0.04	−0.02	0	10.37	0.03	0.53
Age	21–30	300	−3.39	−0.04	−0.02	0.01	4.51	0.03	0.41	0.2644
	31–40	365	−0.92	−0.04	−0.02	0.01	2.26	0.02	0.21	0.2302
	41–50	455	−2.49	−0.04	−0.02	0.03	3.11	0.06	0.37	0.1083
	51–60	309	−0.88	−0.04	−0.01	0.02	16.32	0.1	0.96	0.1442
	60+	124	−0.68	−0.04	−0.02	−0.01	10.37	0.12	0.99	0.0586
BMIb	18.5≤ BMI <25	940	−0.92	−0.04	−0.02	0.01	10.37	0.03	0.41	0.3821
	BMI ≥30	106	−3.39	−0.05	−0.02	0.14	16.32	0.23	1.69	0.2182
	25≤ BMI <30	347	−0.57	−0.04	−0.02	0.01	3.11	0.05	0.29	0.3933
	BMI <18.5	73	−0.22	−0.04	−0.02	0.02	1.27	0.05	0.26	0.3959
IgG class phase 1	IgG ≥12	613	−3.39	−0.08	0.02	0.23	16.32	0.18	0.92	2.1 E-10
	IgG ≤3.8	74	0.01	0.01	0.02	0.03	0.08	0.02	0.01	1.5 E-15
	3.8 < IgG <12	866	−0.13	−0.03	−0.02	−0.01	0.83	−0.02	0.04	8.0 E-22
Role	Otherc	200	−0.57	−0.04	−0.02	−0.01	16.32	0.09	1.17	0.0116
	Anesthesiologist	19	−0.83	−0.04	−0.02	0.01	0.09	−0.05	0.2	0.4305d
	Biologist	18	−0.18	−0.05	−0.02	−0.02	0.03	−0.04	0.05	0.0978d
	Surgeon	67	−0.88	−0.05	−0.02	0.07	0.61	0.02	0.21	0.2653
	Physiotherapist	21	−0.12	−0.04	−0.02	0.01	0.35	0.01	0.1	0.4204
	Nurse	398	−3.39	−0.04	−0.02	0.04	3.11	0.08	0.41	0.0581
	Physician	210	−0.92	−0.04	−0.01	0.02	10.37	0.08	0.75	0.0804
	Healthcare partner operator	149	−2.49	−0.03	−0.01	0.17	1.55	0.1	0.38	0.0009
	Front office (PARC)	108	−0.32	−0.04	−0.02	0.01	2.55	0.03	0.27	0.2892
	Researcher	50	−1.5	−0.03	−0.02	−0.01	4.51	0.06	0.68	0.1514
	Cleaning service	29	−0.65	−0.03	−0.02	0.03	1.72	0.09	0.41	0.2414
	Transport service	14	−0.39	−0.04	−0.01	0.02	2.22	0.13	0.62	0.4359d
	Staff	188	−0.4	−0.04	−0.02	−0.01	0.98	0	0.15	0.0026
	Student	20	−0.19	−0.03	−0.02	−0.01	0.21	−0.02	0.08	0.3705d
	Laboratory technician	31	−0.17	−0.03	−0.01	−0.01	0.51	0.02	0.15	0.3243
	Radiology technician	31	−0.18	−0.03	−0.02	0.01	1.01	0.04	0.22	0.4002
Site	Otherc	21	−0.19	−0.04	−0.02	−0.01	2.22	0.08	0.49	0.2080
	Casa di Cura Cellini	51	−0.17	−0.05	−0.03	−0.01	1.27	0.01	0.22	0.0111
	Clinica Fornaca di Sessant	47	−0.4	−0.05	−0.03	0	0.53	0	0.15	0.0833
	Humanitas Castelli	87	−0.28	−0.04	0.02	0.21	3.11	0.24	0.62	4.7 E-05
	Humanitas Gavazzeni	313	−0.88	−0.04	−0.01	0.15	10.37	0.13	0.68	0.0001
	Humanitas Gradenigo	109	−2.49	−0.04	−0.02	−0.01	0.67	−0.01	0.27	0.0586
	Humanitas Mater Domini	105	−0.17	−0.03	−0.02	−0.01	0.63	0	0.1	0.3412
	Humanitas Medical Care	23	−0.19	−0.04	−0.02	−0.01	0.02	−0.03	0.04	0.0969
	Humanitas Rozzano	667	−3.39	−0.04	−0.02	0	16.32	0.04	0.7	0.0338
	Humanitas San Pio X	98	−0.68	−0.03	−0.02	0	2.39	0.04	0.29	0.2968
	Humanitas University	32	−0.2	−0.04	−0.02	−0.01	0.19	−0.03	0.08	0.0590

aWilcoxon–Mann–Whitney test.

bSome subjects did not indicate their BMI.

cRefers to volunteers and other professionals that operate in several structures.

dMinority class is less or equal to 20 (Wilcoxon–Mann–Whitney test is not reliable).

Table 2

Antibody rates according to symptoms.

			counts	min	25 perc	50 perc	75 perc	max	mean	St. dev	p valuea
Class symptoms phase 1 (subjects with IgG ≥12)	Asymptomatic		91	−1.5	−0.13	−0.05	0.09	2.39	0.04	0.5	0.00003
	Paucisymptomatic		203	−3.39	−0.08	0.02	0.21	4.51	0.14	0.56	0.32865
	Symptomatic		319	−2.49	−0.06	0.07	0.28	16.32	0.24	1.16	0.00057
Symptoms phase 1 (subjects with IgG ≥12)	Fever	No	350	−3.39	−0.09	0.01	0.21	4.51	0.13	0.02725	0.02725
		Yes	263	−2.49	−0.06	0.05	0.25	16.32	0.24
	Low-grade fever	No	481	−3.39	−0.08	0.02	0.23	16.32	0.19	0.17265	0.17265
		Yes	132	−0.39	−0.07	0.05	0.26	2.55	0.14
	Cough	No	372	−3.39	−0.08	0.01	0.19	16.32	0.14	0.01120	0.01120
		Yes	241	−2.49	−0.07	0.07	0.31	10.37	0.24
	Sore throath	No	353	−3.39	−0.09	0.02	0.23	16.32	0.19	0.08309	0.08309
		Yes	260	−2.49	−0.07	0.04	0.25	4.51	0.16
	Muscle pain	No	299	−1.5	−0.1	0	0.2	4.51	0.13	0.00763	0.00763
		Yes	314	−3.39	−0.06	0.06	0.27	16.32	0.22
	Asthenia	No	341	−3.39	−0.1	0	0.21	16.32	0.17	0.00574	0.00574
		Yes	272	−2.49	−0.06	0.07	0.25	10.37	0.19
	Anosmia / dysgeusia	No	313	−3.39	−0.12	−0.01	0.2	16.32	0.14	0.00006	0.00006
		Yes	300	−0.86	−0.05	0.06	0.28	10.37	0.22
	Gastrointestinal symptoms	No	403	−3.39	−0.08	0.03	0.21	10.37	0.17	0.46477	0.46477
		Yes	210	−2.49	−0.08	0.02	0.25	16.32	0.19
	Conjunctivitis	No	517	−3.39	−0.08	0.02	0.21	16.32	0.18	0.16050	0.16050
		Yes	96	−2.49	−0.07	0.04	0.32	1.72	0.14
	Dyspnea	No	493	−3.39	−0.08	0.02	0.23	16.32	0.16	0.34700	0.34700
		Yes	120	−2.49	−0.07	0.05	0.26	10.37	0.24
	Chest pain	No	502	−3.39	−0.08	0.01	0.24	10.37	0.15	0.08088	0.08088
		Yes	111	−0.39	−0.04	0.07	0.22	16.32	0.3
	Tachycardia	No	512	−3.39	−0.08	0.01	0.21	4.51	0.13	0.02353	0.02353
		Yes	101	−2.49	−0.06	0.08	0.32	16.32	0.4
	Pneumonia	No	568	−3.39	−0.08	0.02	0.22	16.32	0.15	0.18692	0.18692
		Yes	45	−0.88	−0.06	0.06	0.38	10.37	0.51	1.69

aWilcoxon–Mann–Whitney test.

We analyzed the distribution of the rate feature and found a high value of kurtosis (461) around the median value of 0.016, hence to perform a multivariate analysis we restricted the data set to subjects with IgG rates either below the 10th percentile or above the 90th percentile to prevent a bias-variance problem in machine learning models and subjected the data to a linear regression analysis between the training and test data sets, where the target variable (rate of antibodies) was standardized using the Yeo–Johnson method[12]. We then applied Chi-squared statistical test to evaluate differences between classes and the rate thresholds described above (Tables 3 and 4). In order to evaluate the possible interactions between features and the rate of antibody response, we developed a multivariate approach to perform a binary classification between subjects who increased or decreased the level of antibodies. A set of seven logistic regressions has been applied on data using a bootstrap procedure (samples are drawn with replacement) and the output of each classifier has been averaged by a Bagging classifier to obtain the final output. The selection of hyperparameters of the machine learning model and the feature selection has been performed with a Bayesian optimization approach based on cross validation (fourfolds, stratified by outcome). The comparisons shown in Fig. 2 and Supplementary Fig. 2 were carried out using a one-tailed Wilcoxon matched-pairs signed-rank test. A probability value of P < 0.05 was considered significant. Data analyses were carried out using GraphPad Prism version 8 and Python version 3.8 with the following libraries: Pandas (version 1.1.4, data wrangling), Scipy (version 1.3.2, statistical analysis), Scikit-Learn (version 0.24.1, LR statistical model).

Table 3

Chi-squared analysis of groups <10th percentile and >90th percentile.

		<10 perc	>90 perc	p value
Sex	F	321	340	0.0627
	M	133	105
Age	21–30	93	83	0.5429
	31–40	108	94	0.3804
	41–50	121	142	0.0970
	51–60	88	98	0.3711
	60+	44	28	0.0793
BMIa	18.5≤ BMI < 25	267	255	0.6963
	BMI ≥ 30	58	72	0.1750
	25≤ BMI < 30	106	93	0.4214
	BMI < 18.5	23	25	0.8261
Role	Otherb	63	37	0.0109
	Anesthesiologist	6	7	0.9701
	Biologist	5	2	0.4640
	Surgeon	25	21	0.7007
	Physiotherapist	6	7	0.9710
	Nurse	113	132	0.1255
	Physician	56	61	0.6082
	Healthcare Partner Operator	38	64	0.0062
	Front office (PARC)	31	28	0.8494
	Researcher	13	9	0.5485
	Cleaning service	7	9	0.7698
	Transport Service	5	5	0.7747c
	Staff	69	44	0.0214
	Student	5	4	0.9759c
	Laboratory Technician	6	7	0.9701
	Radiology Technician	6	8	0.7587
Site	Otherb	6	3	0.5223c
	Casa di Cura Cellini	19	8	0.0573
	Clinica Fornaca di Sessant	18	12	0.3828
	Humanitas Castelli	23	47	0.0032
	Humanitas Gavazzeni	97	142	0.0005
	Humanitas Gradenigo	36	22	0.0918
	Humanitas Mater Domini	20	23	0.7041
	Humanitas Medical Care	8	3	0.2379c
	Humanitas Rozzano	190	160	0.0806
	Humanitas San Pio X	27	21	0.5025
	Humanitas University	10	4	0.1905c

aSome subjects did not indicate their BMI.

bRefers to volunteers and other professionals that operate in several structures.

cMinority class is less or equal to 5 (chi-square test is not reliable).

Table 4

Chi-squared analysis of groups <10th percentile and >90th percentile per symptoms.

		<10 perc	>90 perc	p value	% Yes <10 perca	%Yes >90 perca
Symptoms
Fever	No	360	284	3.9E-07
	Yes	94	161		37	63
Low-grade fever	No	388	359	0.0678
	Yes	66	86		43	57
Cough	No	335	284	0.0016
	Yes	119	161		43	58
Sore throat	No	302	271	0.0923
	Yes	152	174		47	53
Muscle pain	No	310	246	0.0001
	Yes	144	199		42	58
Asthenia	No	340	268	3.7E-06
	Yes	114	177		39	61
Anosmia/dysgeusia	No	364	247	4.0E-15
	Yes	90	198		31	69
Gastrointestinal symptoms	No	336	313	0.2485
	Yes	118	132		47	53
Conjunctivitis	No	400	382	0.3633
	Yes	54	63		46	54
Dyspnea	No	405	375	0.0370
	Yes	49	70		41	59
Chest pain	No	415	367	0.0001
	Yes	39	78		33	67
Tachycardia	No	406	371	0.0107
	Yes	48	74		39	61
Pneumonia	No	440	420	0.0889
	Yes	14	25		36	64
Total of Symptoms in phase 1	Asymptomatic	150	80	3.4E-07	65	35
	Paucisymptomatic	179	158	0.2520	53	47
	Symptomatic	125	207	5.6E-09	38	62
Comorbidities
Chronic obstructive bronchopneumopathy	No	451	444	0.6305b
	Yes	3	1
Asthma	No	430	412	0.2408
	Yes	24	33
Dyslipidemia	No	413	404	0.9834
	Yes	41	41
Past neoplasia	No	431	438	0.0063
	Yes	23	7
Hypertension	No	401	409	0.0915
	Yes	53	36
Past coronaropathies	No	454	443	0.4702b
	Yes	0	2
Atrial fibrillation	No	450	441	0.7439b
	Yes	4	4
Past stroke/ TIA	No	452	445	0.4878b
	Yes	2	0
Steatosis/cyrrosis	No	448	444	0.1359b
	Yes	6	1
Chronic kidney failure	No	453	445	0.9920b
	Yes	1	0
Other liver diseases	No	452	441	0.6641b
	Yes	2	4
Rheumatological diseases	No	445	431	0.3715
	Yes	9	14
Other diseases of the immune system	No	418	409	0.9726
	Yes	36	36
Diabetes mellitus	No	452	443	0.6305b
	Yes	2	2
Gotta	No	452	445	0.4878b
	Yes	2	0

aPercentage of subjects with symptoms (Yes) per rate class.

bMinority class is less or equal to 5 (chi-square test is not reliable).

Fig. 2

Anti-Spike S1/S2 IgG plasma levels.

Anti-Spike S1/S2 IgG plasma levels were measured in asymptomatics (n = 61), paucisymptomatics (n = 163), and symptomatics (n = 275) at three different time points (phase 1–3). Each dot corresponds to an individual subject. Log scale on the Y axis. The box plots show the interquartile range, the horizontal lines show the median values, and the whiskers indicate the minimum-to-maximum range. P values were determined using a one-tailed Wilcoxon matched-pairs signed rank test.