Dataset Information

Dynamic antibody response in SARS-CoV-2 infected patients and COVID-19 vaccine recipients alongside vaccine effectiveness in comorbid and multimorbid groups.

ABSTRACT:

Objectives

Underlying medical conditions are critical risk factors for COVID-19 susceptibility and its rapid clinical manifestation. Therefore, the preexisting burden of non-communicable diseases (NCDs) makes the preparedness for COVID-19 more challenging for low- and middle-income countries (LMICs). These countries have relied on vaccination campaigns as an effective measure to tackle COVID-19. In this study, we investigated the impact of comorbidities on humoral antibody responses against the specific receptor-binding domain (RBD) of SARS-CoV2.

Methods

A total of 1005 patients were selected for the SARS-CoV-2 specific immunoglobulin G (IgG1, IgG2, IgG3, and IgG4 subclasses) and total antibody (TAb) tests (IgG and IgM), of which 912 serum samples were ultimately selected based on the specimen cutoff analyte value. Patients with multimorbidity (N = 60) were recruited for follow-up studies from the initial cohort, and their immune response (IgG and TAb) was measured at multiple time points after the second dose of vaccination. Siemens Dimension Vista SARS-CoV-2 IgG (CV2G) and SARS-CoV-2 TAb assay (CV2T) were used to carry out the serology test.

Results

Out of a total of 912 participants, vaccinated individuals (N = 711) had detectable antibody responses up to 7-8 months. The synergistic effect of natural infection and vaccine response was also studied. Participants with breakthrough infections (N = 49) mounted a greater antibody response compared to individuals with normal vaccination response (N = 397) and those who were naturally infected before receiving the second dose of vaccine (N = 132). Investigation of the impact of comorbidities revealed that diabetes mellitus (DM) (N = 117) and kidney disease (N = 50) had a significant negative impact on the decline of the humoral antibody response against SARS-CoV-2. IgG and TAb declined more rapidly in diabetic and kidney disease patients compared to the other four comorbid groups. Follow-up studies demonstrated that antibody response rapidly declined within 4 months after receiving the second dose.

Conclusion

The generalized immunization schedule for COVID-19 needs to be adjusted for high-risk comorbid groups, and a booster dose must be administered early within 4 months after receiving the second dose.

SUBMITTER: Das D

PROVIDER: S-EPMC10199753 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Dynamic antibody response in SARS-CoV-2 infected patients and COVID-19 vaccine recipients alongside vaccine effectiveness in comorbid and multimorbid groups.

Das Depro D Raha Fahmida Khanam FK Adnan Khondekar Mustaq KM Siraj Md Rubayet MR Shapla Mariam Jamila MJ Shumy Farzana F Haque Md Emdadul ME Khan Monwar Hasanat MH Sanyal Susmita S Hosen Md Ismail MI Nabi Ahm Nurun AN Sanyal Mousumi M Chakraborty Sajib S Amin Md Zahid MZ

Heliyon 20230520 5

<h4>Objectives</h4>Underlying medical conditions are critical risk factors for COVID-19 susceptibility and its rapid clinical manifestation. Therefore, the preexisting burden of non-communicable diseases (NCDs) makes the preparedness for COVID-19 more challenging for low- and middle-income countries (LMICs). These countries have relied on vaccination campaigns as an effective measure to tackle COVID-19. In this study, we investigated the impact of comorbidities on humoral antibody responses agai ...[more]

PMID: 37251854

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Project description:IntroductionThe vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues.ObjectivesThis research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.MethodsThe methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.Results(1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.ConclusionThe findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

Project description:The long-term immunoglobulin responses of COVID-19 vaccinations is important to determine the efficacy of these vaccinations. This study aimed to investigate and compare the long-term immunoglobulin response of COVID-19 vaccination recipients, using anti-S IgG, anti-N IgG, and IgM titer levels. This study included 267 participants, comprising individuals who tested positive for COVID-19 through PCR testing (n = 125), and those who received the Pfizer (n = 133), Sinopharm (n = 112), AstraZeneca (n = 20), or Sputnik (n = 2) vaccines. Female participants comprised the largest share of this study (n = 147, 55.1%). This study found that most participants had positive IgG antibodies, with 96.3% having anti-S IgG and 75.7% having anti-N IgG. Most participants (90.3%) tested negative for anti-N IgM antibodies. Sinopharm-vaccinated individuals exhibited a notably lower rate of positive anti-S IgG (93.8%) and a significantly higher rate of positive anti-N IgG antibodies (91%). Anti-N IgG levels were significantly correlated with the number of prior COVID-19 infections (p = 0.015). Specifically, individuals with a history of four COVID-19 infections had higher anti-N IgG titers (14.1 ± 1.4) than those with only one experience of COVID-19 infection (9.4 ± 7.2). Individuals who were infected with COVID-19 after receiving the vaccine demonstrated higher levels of anti-N IgG, exhibiting a 25% increase in mean titer levels compared to those who were infected prior to vaccination. There was a statistically significant association between anti-N IgG positivity with age (p = 0.034), and smoking status (p = 0.006) of participants. Participants younger than 20 and older than 60 showed the highest positivity rate of anti-N (>90%). Smokers had a low positivity rate of anti-N (68.8%) compared to nonsmokers (83.6%). In conclusion, this study demonstrated that most COVID-19 vaccination recipients had positive IgG antibodies, with differences in the long-term immunoglobulin response depending on the type of vaccine administered and occurrence of COVID-19 infection.

Dataset Information

Dynamic antibody response in SARS-CoV-2 infected patients and COVID-19 vaccine recipients alongside vaccine effectiveness in comorbid and multimorbid groups.

Objectives

Methods

Results

Conclusion

Publications

Dynamic antibody response in SARS-CoV-2 infected patients and COVID-19 vaccine recipients alongside vaccine effectiveness in comorbid and multimorbid groups.

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