No abstract

Currently approved COVID-19 vaccines based on mRNA or adenovirus require a first jab followed by recall immunization. There is no indication as to whether individuals who have recovered from COVID-19 should be vaccinated, and if so, if they should receive one or two vaccine doses. Here, we tested the antibody response developed after the first dose of the mRNA based vaccine encoding the SARS-CoV-2 full-length spike protein (BNT162b2) in 124 healthcare professionals of which 57 had a previous history of COVID-19 (ExCOVID). Post-vaccine antibodies in ExCOVID individuals increase exponentially within 7-15 days after the first dose compared to naive subjects (p<0.0001). We developed a multivariate Linear Regression (LR) model with l2 regularization to predict the IgG response for SARS-COV-2 vaccine. We found that the antibody response of ExCOVID patients depends on the IgG pre-vaccine titer and on the symptoms that they developed during the disorder, with anosmia/dysgeusia and gastrointestinal disorders being the most significantly positively correlated in the LR. Thus, one vaccine dose is sufficient to induce a good antibody response in ExCOVID subjects. On the contrary, a second dose might switch-off the immune response due to antigen exhaustion, which occurs in response to several viruses or drive the development of low-affinity antibodies for SARS-CoV-2 which may foster an antibody dependent enhancement (ADE) reaction when re-exposed to the virus. These results question whether a second shot in ExCOVID subjects is indeed required and suggest to post-pone it while monitoring antibody response longevity.

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.

Background and Aims The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. Methods Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. Results We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332 , Candidatus Saccharimonas , and Haemophilus were all underrepresented in the hospitalized population. Conclusion This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

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.

No abstract

An important issue that is often neglected is the difference between male and female genders in response to medical treatments. In the context of COVID-19 vaccine administration, despite identical protocol strategies, it has been observed that females often suffer more adverse consequences than males. Here, we analyzed the adverse events (AEs) of the Comirnaty vaccine in a population of 2385 healthcare workers as a function of age, sex, COVID-19 history and BMI. Using logistic regression analysis, we showed that these variables may contribute to the development of AEs, particularly in young subjects, females and individuals with a BMI below 25 kg/m2. Moreover, partial dependence plots indicate a 50% probability of developing a mild AE for a long period of time (≥7 days) or a severe AE of any duration in women below 40 years old and with a BMI < 20 kg/m2. As this effect is more evident after the second dose of the vaccine, we propose to reduce the amount of vaccine for any additional booster dose in relation to age, sex and BMI. This strategy might reduce adverse events without affecting vaccine efficacy.