key: cord-0824094-ab4rx810
authors: Levi, R.; Ubaldi, L.; Pozzi, C.; Angelotti, G.; Sandri, M. T.; Azzolini, E.; Salvatici, M.; Savevski, V.; Mantovani, A.; Rescigno, M.
title: The antibody response to SARS-CoV-2 increases over 5 months in patients with anosmia/dysgeusia
date: 2021-02-08
journal: nan
DOI: 10.1101/2021.02.05.21251219
sha: c71348c88e3041891880ad4724cadf44654e1ae0
doc_id: 824094
cord_uid: ab4rx810

The factors involved in the persistence of antibodies to SARS-CoV-2 are unknown. We evaluated the antibody response to SARS-CoV-2 in personnel from 10 healthcare facilities and its association with individuals' characteristics and COVID-19 symptoms in an observational study. We enrolled 4735 subjects (corresponding to 80% of all personnel) over a period of 5 months when the spreading of the virus was drastically reduced. 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. In individuals positive for IgG (>= 12 AU/mL) at the beginning of the study, we found an increase [p= 0.0002] in antibody response in symptomatic subjects, particularly with anosmia/dysgeusia (OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis. This may be linked to the persistence of SARS-CoV-2 in the olfactory bulb.

It is becoming clear that the antibody response to SARS-CoV-2 can last at least 6 months in 44 symptomatic patients 1 , but it seems to decline in asymptomatics 2 . Similarly, a reduction of antibody 45 response in asymptomatic individuals was shown in a study with a fewer number of individuals (n = 46 37) 3 . The antibody response in COVID-19 patients is associated with the establishment of a memory 47 regression analysis. By contrast the population with past neoplasia or intermediate levels of IgG (3.8 112 Discussion 113

We analyzed the 5-month duration of an antibody response to SARS-CoV-2 in personnel from 9 114 healthcare facilities and an international medical school (Humanitas University) in Northern Italy in 115 areas differently hit by the virus 4 . We show that the antibody response is stable both in symptomatic 116 and asymptomatic/paucisymptomatic individuals and is increased in females and in non-medical 117 healthcare professionals. Previously, it has been shown in a study conducted in the British population 118 that the antibody response declines of nearly 22% in symptomatic individuals and of 64% in 119 asymptomatic individuals 2 . However, this study was based on a prick qualitative test and thus the 120 decline may be related to the sensitivity of the test. We also observed that the antibody response 121 declined when we analyzed the group (3.8 < IgG < 12 AU/mL) with IgG between the limit of 122 detection (3.8 AU/mL) and the threshold of positivity (IgG ≥ 12 AU/mL), as set by manufacturer. 123

Whether this is linked to a noise of the instrument that may change according to the test or to a real 124 reduction in an antibody response that may or may not be specific to SARS-CoV-2, remains to be 125 established. When we analyzed the extremes, i.e. the individuals with higher rates of antibody 126 increase or decrease (< -0.033 and > 0.005 AU/mL*day) we observed that asymptomatics had higher 127 negative rates while symptomatics tended to continue increasing the antibody levels suggesting that 128 extreme changes in rate separate the symptomatics from the asymptomatics. As during the 129 observation time there was very limited viral diffusion in Northern Italy, as confirmed also by the 130 finding that only 2 individuals became IgG positive and 2981 remained IgG negative throughout the 131 study (all excluded from the analysis), we can conclude that the sustained or augmented antibody 132 response may not be linked to a re-exposure to the virus. In an attempt to address what improved the 133 antibody response, we found that several symptoms were associated to increased rates of antibodies, 134 however, in a multivariate logistic analysis only anosmia/dysgeusia and chest pain were linked with 135 the highest regression coefficients. Chest pain and anosmia are long-lasting symptoms in COVID-19 136 patients 8 . In addition, anosmia and/or dysgeusia are very common as they are found in around 50-137 70% of subjects affected by COVID-19 9, 10 . In our cohort (Table 2) , 49% of IgG positive subjects 138 had anosmia/dysgeusia, 28% chest pain and 13.7% both anosmia/dysgeusia and chest pain, 139 suggesting that indeed these two symptoms may, either alone or in combination, associate with IgG 140 increase. We and others previously found that anosmia/dysgeusia together with fever were the 141 symptoms that mostly characterized SARS-CoV-2 exposure 4, 11 . In agreement, anosmia and 142 dysgeusia have been proposed to be used to track SARS-CoV-2 diffusion 12 . Interestingly, SARS-143 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Overall, these data suggest that increased antibody response in patients with anosmia/dysgeusia may 149 be linked to persistence of the virus in the olfactory bulb which through local inflammation and 150 release of antigens, maintains and boosts the antibody response. This study opens new perspectives 151 on the immunity to SARS-CoV-2 and warrants further investigation on the role of anosmia/dysgeusia 152 on antibody response through the design of prospective observational studies coupling the testing of 153 SARS-CoV-2 persistence in the olfactory bulb, loss of smell or taste and antibody titers. Torino; Humanitas Gradenigo, Torino; Clinica Fornaca, Torino. All participants signed an informed 165 consent and filled a questionnaire before blood collection. We analyzed 93 features (72 categorical 166 and 17 numerical and 4 temporal) including, age, sex, location, professional role, time between 167 sample collections, COVID-19 symptoms (fever, sore throat, cough, muscle pain, asthenia, 168 anosmia/dysgeusia (loss of smell and taste), gastrointestinal disorders, conjunctivitis, dyspnea, chest 169 pain, tachycardia, pneumonia), home exits and smart-working, comorbidities (diabetes, asthma, 170 neoplasia, autoimmunity, cardiovascular disorders, hepatic disorders). After excluding for employees 171 that became positive for SARS-CoV-2 IgG (n = 2) during the observation period and those that 172 dropped from phase I or for which we were missing at least two features, we analyzed 4534 173 participants (4.25% drop out). Here we show the results of the end of phase II (second blood 174 sampling). 175 176 IgG measure 177 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.05.21251219 doi: medRxiv preprint immunoassay for the determination of anti-S1 and anti-S2 specific antibodies. According to kit 180 manufacturer, the test discriminates among negative (< 12AU/mL; with 3.8 as the limit of IgG 181 detection) and positive (≥ 12.0 AU/mL) subjects. However, we considered also individuals with IgG 182 comprised between 3.8 and 12 AU/mL (which we called IgG med: 3.8 < IgG < 12.0 AU/mL). For statistical analysis, we performed both a univariate and a multivariate analysis. We applied 197

Wilcoxon-Mann-Whitney statistical non-parametric test to compare the antibody rate distribution 198 between classes of subjects (Table 1 and Table 2 ). 199

We analyzed the distribution of the rate feature and found a high value of kurtosis (461) around the 200 median value of 0.016, hence to perform a multivariate analysis we restricted the data set to subjects 201 with IgG rates either below the 10 th percentile or above the 90 th percentile to prevent a bias-variance 202 problem in machine learning models and subjected the data to a linear regression analysis between 203 the training and test data sets, where the target variable (rate of antibodies) was standardized using 204 the Yeo-Johnson method 6 . We then applied Chi-squared statistical test to evaluate differences 205 between classes and the rate thresholds described above (Tables 3 and 4 ). In order to evaluate the 206 possible interactions between features and the rate of antibody response, we developed a multivariate 207 approach to perform a binary classification between subjects who increased or decreased the level of 208 antibodies. A set of 7 logistic regressions has been applied on data using a bootstrap procedure 209 (samples are drawn with replacement) and the output of each classifier has been averaged by a 210

Bagging classifier to obtain the final output. The selection of hyperparameters of the machine learning 211 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in 

All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.05.21251219 doi: medRxiv preprint

Immunological memory to SARS-CoV-2 assessed for greater than six months 220 after infection. bioRxiv

Declining prevalence of antibody positivity to SARS-CoV-2: a community 222 study of 365,000 adults. medRxiv

Clinical and immunological assessment of asymptomatic SARS-CoV-2 224 infections