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1.
Front Immunol ; 13: 790334, 2022.
Article in English | MEDLINE | ID: covidwho-1715001

ABSTRACT

The capacity of pre-existing immunity to human common coronaviruses (HCoV) to cross-protect against de novo COVID-19is yet unknown. In this work, we studied the sera of 175 COVID-19 patients, 76 healthy donors and 3 intravenous immunoglobulins (IVIG) batches. We found that most COVID-19 patients developed anti-SARS-CoV-2 IgG antibodies before IgM. Moreover, the capacity of their IgGs to react to beta-HCoV, was present in the early sera of most patients before the appearance of anti-SARS-CoV-2 IgG. This implied that a recall-type antibody response was generated. In comparison, the patients that mounted an anti-SARS-COV2 IgM response, prior to IgG responses had lower titres of anti-beta-HCoV IgG antibodies. This indicated that pre-existing immunity to beta-HCoV was conducive to the generation of memory type responses to SARS-COV-2. Finally, we also found that pre-COVID-19-era sera and IVIG cross-reacted with SARS-CoV-2 antigens without neutralising SARS-CoV-2 infectivity in vitro. Put together, these results indicate that whilst pre-existing immunity to HCoV is responsible for recall-type IgG responses to SARS-CoV-2, it does not lead to cross-protection against COVID-19.


Subject(s)
Betacoronavirus/physiology , COVID-19/immunology , Common Cold/immunology , Immunoglobulins, Intravenous/therapeutic use , SARS-CoV-2/physiology , Aged , Aged, 80 and over , Antibodies, Neutralizing/metabolism , Antibodies, Viral/metabolism , Antigens, Viral/immunology , COVID-19/mortality , COVID-19/therapy , Cross Reactions , Female , Humans , Immunity, Heterologous , Immunoglobulin G/metabolism , Immunoglobulin M/metabolism , Immunologic Memory , Male , Middle Aged , Survival Analysis
2.
Nutrients ; 13(7)2021 Jun 30.
Article in English | MEDLINE | ID: covidwho-1288968

ABSTRACT

Background & Aims: SARS-CoV2 infection is associated with an increased risk of malnutrition. Although there are numerous screening and nutritional management protocols for malnutrition, only few studies have reported nutritional evolution after COVID-19. The objectives of this study were to describe the evolution of nutritional parameters between admission and 30 days after hospital discharge, and to determine predictive factors of poor nutritional outcome after recovery in adult COVID-19 patients. Methods: In this observational longitudinal study, we report findings after discharge in 91 out of 114 patients initially admitted for COVID-19 who received early nutritional management. Nutritional status was defined using GLIM criteria and compared between admission and day 30 after discharge. Baseline predictors of nutritional status at day 30 were assessed using logistic regression. Results: Thirty days after discharge, 28.6% of patients hospitalized for COVID-19 were malnourished, compared to 42.3% at admission. Half of malnourished patients (53%) at admission recovered a normal nutritional status after discharge. Weight trajectories were heterogeneous and differed if patients had been transferred to an intensive care unit (ICU) during hospitalization (p = 0.025). High oxygen requirement during hospitalization (invasive ventilation p = 0.016 (OR 8.3 [1.6-61.2]) and/or oxygen therapy over 5 L/min p = 0.021 (OR 3.2 [1.2-8.9]) were strong predictors of malnutrition one month after discharge. Conclusions: With early nutritional management, most patients hospitalized for COVID-19 improved nutritional parameters after discharge. These findings emphasize the importance of nutritional care in COVID-19 patients hospitalized in medicine departments, especially in those transferred from ICU.


Subject(s)
COVID-19/diet therapy , Hospitalization , Malnutrition/epidemiology , Nutritional Status , Adult , Aged , Aged, 80 and over , Female , Humans , Intensive Care Units , Logistic Models , Longitudinal Studies , Male , Middle Aged , Nutrition Assessment , Nutrition Therapy/methods , Oxygen Inhalation Therapy/statistics & numerical data , Patient Discharge , Risk Factors , SARS-CoV-2 , Severity of Illness Index
3.
Sci Transl Med ; 13(577)2021 01 20.
Article in English | MEDLINE | ID: covidwho-963895

ABSTRACT

Humoral immune responses are typically characterized by primary IgM antibody responses followed by secondary antibody responses associated with immune memory and composed of IgG, IgA, and IgE. Here, we measured acute humoral responses to SARS-CoV-2, including the frequency of antibody-secreting cells and the presence of SARS-CoV-2-specific neutralizing antibodies in the serum, saliva, and bronchoalveolar fluid of 159 patients with COVID-19. Early SARS-CoV-2-specific humoral responses were dominated by IgA antibodies. Peripheral expansion of IgA plasmablasts with mucosal homing potential was detected shortly after the onset of symptoms and peaked during the third week of the disease. The virus-specific antibody responses included IgG, IgM, and IgA, but IgA contributed to virus neutralization to a greater extent compared with IgG. Specific IgA serum concentrations decreased notably 1 month after the onset of symptoms, but neutralizing IgA remained detectable in saliva for a longer time (days 49 to 73 post-symptoms). These results represent a critical observation given the emerging information as to the types of antibodies associated with optimal protection against reinfection and whether vaccine regimens should consider targeting a potent but potentially short-lived IgA response.


Subject(s)
Antibodies, Neutralizing/blood , Antibodies, Viral/blood , COVID-19/diagnosis , Immunity, Humoral , Immunoglobulin A/blood , SARS-CoV-2/immunology , Biomarkers/blood , Bronchoalveolar Lavage Fluid/immunology , Bronchoalveolar Lavage Fluid/virology , COVID-19/blood , COVID-19/immunology , COVID-19/virology , Case-Control Studies , Host-Pathogen Interactions , Humans , Immunoglobulin G/blood , Immunoglobulin M/blood , Longitudinal Studies , Saliva/immunology , Saliva/virology , Time Factors
4.
Clin Nutr ESPEN ; 40: 214-219, 2020 12.
Article in English | MEDLINE | ID: covidwho-778669

ABSTRACT

BACKGROUND & AIMS: Nutritional knowledge in patients with SARS-Cov2 infection (COVID-19) is limited. Our objectives were: i) to assess malnutrition in hospitalized COVID-19 patients, ii) to investigate the links between malnutrition and disease severity at admission, iii) to study the impact of malnutrition on clinical outcomes such as transfer to an intensive care unit (ICU) or death. METHODS: Consecutive patients hospitalized in a medicine ward at a university hospital were included from March 21st to April 24th 2020 (n = 114, 60.5% males, age: 59.9 ± 15.9 years). Nutritional status was defined using Global Leadership Initiative on Malnutrition (GLIM) criteria. Clinical, radiological and biological characteristics of COVID-19 patients were compared according to the presence of malnutrition. Logistic regression was used to assess associations between nutritional parameters and unfavourable outcomes such as transfer to intensive care unit (ICU) or death. RESULTS: The overall prevalence of malnutrition was 42.1% (moderate: 23.7%, severe: 18.4%). The prevalence of malnutrition reached 66.7% in patients admitted from ICU. No significant association was found between nutritional status and clinical signs of COVID-19. Lower albumin levels were associated with a higher risk of transfer to ICU (for 10 g/l of albumin, OR [95%CI]: 0.31 [0.1; 0.7]; p < 0.01) and this association was independent of age and CRP levels. CONCLUSIONS: COVID-19 in medical units dedicated to non-intensive care is associated with a high prevalence of malnutrition, especially for patients transferred from ICU. These data emphasize the importance of early nutritional screening in these patients to adapt management accordingly.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Hospitalization , Malnutrition/epidemiology , Pneumonia, Viral/complications , Adult , Aged , COVID-19 , Female , France/epidemiology , Hospital Mortality , Humans , Longitudinal Studies , Male , Malnutrition/complications , Middle Aged , Nutrition Assessment , Pandemics , Prevalence , SARS-CoV-2 , Severity of Illness Index
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