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Bolormaa E, Shim J, Choi YS, Kwon D, Choe YJ, Choe SA. Methodology of comparative studies on the relative effectiveness of COVID-19 vaccines: a systematic review. Osong Public Health Res Perspect 2024; 15:395-408. [PMID: 39511961 DOI: 10.24171/j.phrp.2024.0063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 08/26/2024] [Indexed: 11/15/2024] Open
Abstract
BACKGROUND This study aimed to comprehensively outline the methodological approaches used in published research comparing the vaccine effectiveness (VE) of coronavirus disease 2019 (COVID-19) vaccines. METHODS A systematic search was conducted on June 13, 2024, to identify comparative studies evaluating the effectiveness of mRNA versus non-mRNA and monovalent versus bivalent COVID-19 vaccines. We screened titles, abstracts, and full texts, collecting data on publication year, country, sample size, study population composition, study design, VE estimates, outcomes, and covariates. Studies that reported relative VE (rVE) were analyzed separately from those that did not. RESULTS We identified 25 articles comparing rVE between mRNA and non-mRNA COVID-19 vaccines, as well as between monovalent and bivalent formulations. Among the studies assessing VE by vaccine type, 126 did not provide rVE estimates. Comparative VE studies frequently employed retrospective cohort designs. Among the definitions of rVE used, the most common were hazard ratio and absolute VE, calculated as (1-odds ratio)×100. Studies were most frequently conducted in the United Kingdom and the United States, and the most common outcome was infection. Most targeted the general population and assessed the VE of mRNA vaccines using the AstraZeneca vaccine as a reference. A small proportion, 7.3% (n=11), did not adjust for any variables. Only 3 studies (2.0%) adjusted for all core confounding variables recommended by the World Health Organization. CONCLUSION Few comparative studies of COVID-19 vaccines have incorporated rVE methodologies. Reporting rVE and employing a consistent set of covariates can broaden our understanding of COVID-19 vaccines.
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Affiliation(s)
- Erdenetuya Bolormaa
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jiae Shim
- Division of Epidemiological Investigation Analysis, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Young-Sook Choi
- Division of Epidemiological Investigation Analysis, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Donghyok Kwon
- Division of Epidemiological Investigation Analysis, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea
| | - Young June Choe
- Department of Pediatrics, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Seung-Ah Choe
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea
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2
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Cheung YYH, Lau EHY, Yin G, Lin Y, Jiang J, Cowling BJ, Lam KF. Joint analysis of vaccination effectiveness and antiviral drug effectiveness for COVID-19: a causal inference approach. Int J Infect Dis 2024; 143:107012. [PMID: 38521448 DOI: 10.1016/j.ijid.2024.107012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024] Open
Abstract
OBJECTIVES This study aims to estimate the causal effects of oral antivirals and vaccinations in the prevention of all-cause mortality and progression to severe COVID-19 in an integrative setting with both antivirals and vaccinations considered as interventions. METHODS We identified hospitalized adult patients (i.e. aged 18 or above) in Hong Kong with confirmed SARS-CoV-2 infection between March 16, 2022, and December 31, 2022. An inverse probability-weighted (IPW) Andersen-Gill model with time-dependent predictors was used to address immortal time bias and produce causal estimates for the protection effects of oral antivirals and vaccinations against severe COVID-19. RESULTS Given prescription is made within 5 days of confirmed infection, nirmatrelvir-ritonavir is more effective in providing protection against all-cause mortality and development into severe COVID-19 than molnupiravir. There was no significant difference between CoronaVac and Comirnaty in the effectiveness of reducing all-cause mortality and progression to severe COVID-19. CONCLUSIONS The use of oral antivirals and vaccinations causes lower risks of all-cause mortality and progression to severe COVID-19 for hospitalized SARS-CoV-2 patients.
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Affiliation(s)
- Yue Yat Harrison Cheung
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric Ho Yin Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Mathematics, Imperial College London, London, The United Kingdom
| | - Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jialiang Jiang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin John Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health (D24H) Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Kwok Fai Lam
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong Special Administrative Region, China; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
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Xie J, Mothe B, Alcalde Herraiz M, Li C, Xu Y, Jödicke AM, Gao Y, Wang Y, Feng S, Wei J, Chen Z, Hong S, Wu Y, Su B, Zheng X, Cohet C, Ali R, Wareham N, Alhambra DP. Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes. Nat Commun 2024; 15:4031. [PMID: 38740772 DOI: 10.1038/s41467-024-48339-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
The rapid global distribution of COVID-19 vaccines, with over a billion doses administered, has been unprecedented. However, in comparison to most identified clinical determinants, the implications of individual genetic factors on antibody responses post-COVID-19 vaccination for breakthrough outcomes remain elusive. Here, we conducted a population-based study including 357,806 vaccinated participants with high-resolution HLA genotyping data, and a subset of 175,000 with antibody serology test results. We confirmed prior findings that single nucleotide polymorphisms associated with antibody response are predominantly located in the Major Histocompatibility Complex region, with the expansive HLA-DQB1*06 gene alleles linked to improved antibody responses. However, our results did not support the claim that this mutation alone can significantly reduce COVID-19 risk in the general population. In addition, we discovered and validated six HLA alleles (A*03:01, C*16:01, DQA1*01:02, DQA1*01:01, DRB3*01:01, and DPB1*10:01) that independently influence antibody responses and demonstrated a combined effect across HLA genes on the risk of breakthrough COVID-19 outcomes. Lastly, we estimated that COVID-19 vaccine-induced antibody positivity provides approximately 20% protection against infection and 50% protection against severity. These findings have immediate implications for functional studies on HLA molecules and can inform future personalised vaccination strategies.
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Affiliation(s)
- Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Beatriz Mothe
- Infectious Diseases Department, IrsiCaixa AIDS Research Institute, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
| | - Marta Alcalde Herraiz
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Chunxiao Li
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Annika M Jödicke
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Yaqing Gao
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Yunhe Wang
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Shuo Feng
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Jia Wei
- Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | - Zhuoyao Chen
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Binbin Su
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Catherine Cohet
- Real-World Evidence Workstream, Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, Noord-Holland, The Netherlands
| | - Raghib Ali
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Nick Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Daniel Prieto Alhambra
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK.
- Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands.
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Nakamura N, Kobashi Y, Kim KS, Park H, Tani Y, Shimazu Y, Zhao T, Nishikawa Y, Omata F, Kawashima M, Yoshida M, Abe T, Saito Y, Senoo Y, Nonaka S, Takita M, Yamamoto C, Kawamura T, Sugiyama A, Nakayama A, Kaneko Y, Jeong YD, Tatematsu D, Akao M, Sato Y, Iwanami S, Fujita Y, Wakui M, Aihara K, Kodama T, Shibuya K, Iwami S, Tsubokura M. Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. PLOS DIGITAL HEALTH 2024; 3:e0000497. [PMID: 38701055 PMCID: PMC11068210 DOI: 10.1371/journal.pdig.0000497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/14/2024] [Indexed: 05/05/2024]
Abstract
As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an "antibody score", which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.
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Affiliation(s)
- Naotoshi Nakamura
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yurie Kobashi
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
- Department of General Internal Medicine, Hirata Central Hospital, Fukushima, Japan
| | - Kwang Su Kim
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Science System Simulation, Pukyong National University, Busan, South Korea
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Hyeongki Park
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yuta Tani
- Medical Governance Research Institute, Tokyo, Japan
| | - Yuzo Shimazu
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Tianchen Zhao
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Yoshitaka Nishikawa
- Department of General Internal Medicine, Hirata Central Hospital, Fukushima, Japan
| | - Fumiya Omata
- Department of General Internal Medicine, Hirata Central Hospital, Fukushima, Japan
| | - Moe Kawashima
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
| | | | - Toshiki Abe
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
| | | | - Yuki Senoo
- Medical Governance Research Institute, Tokyo, Japan
| | - Saori Nonaka
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Morihito Takita
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Chika Yamamoto
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Takeshi Kawamura
- Proteomics Laboratory, Isotope Science Center, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Akira Sugiyama
- Proteomics Laboratory, Isotope Science Center, The University of Tokyo, Tokyo, Japan
| | - Aya Nakayama
- Proteomics Laboratory, Isotope Science Center, The University of Tokyo, Tokyo, Japan
| | - Yudai Kaneko
- Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
- Medical & Biological Laboratories Co., Ltd, Tokyo, Japan
| | - Yong Dam Jeong
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Daiki Tatematsu
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Marwa Akao
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yoshitaka Sato
- Department of Virology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shoya Iwanami
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yasuhisa Fujita
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Masatoshi Wakui
- Department of Laboratory Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
| | - Tatsuhiko Kodama
- Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Kenji Shibuya
- Soma Medical Center of Vaccination for COVID-19, Fukushima, Japan
- Tokyo Foundation for Policy Research, Tokyo, Japan
| | - Shingo Iwami
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan
- Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan
- NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan
- Science Groove Inc., Fukuoka, Japan
| | - Masaharu Tsubokura
- Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan
- Department of General Internal Medicine, Hirata Central Hospital, Fukushima, Japan
- Medical Governance Research Institute, Tokyo, Japan
- Minamisoma Municipal General Hospital, Fukushima, Japan
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5
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Català M, Burn E, Rathod-Mistry T, Xie J, Delmestri A, Prieto-Alhambra D, Jödicke AM. Observational methods for COVID-19 vaccine effectiveness research: an empirical evaluation and target trial emulation. Int J Epidemiol 2024; 53:dyad138. [PMID: 37833846 PMCID: PMC10859138 DOI: 10.1093/ije/dyad138] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND There are scarce data on best practices to control for confounding in observational studies assessing vaccine effectiveness to prevent COVID-19. We compared the performance of three well-established methods [overlap weighting, inverse probability treatment weighting and propensity score (PS) matching] to minimize confounding when comparing vaccinated and unvaccinated people. Subsequently, we conducted a target trial emulation to study the ability of these methods to replicate COVID-19 vaccine trials. METHODS We included all individuals aged ≥75 from primary care records from the UK [Clinical Practice Research Datalink (CPRD) AURUM], who were not infected with or vaccinated against SARS-CoV-2 as of 4 January 2021. Vaccination status was then defined based on first COVID-19 vaccine dose exposure between 4 January 2021 and 28 January 2021. Lasso regression was used to calculate PS. Location, age, prior observation time, regional vaccination rates, testing effort and COVID-19 incidence rates at index date were forced into the PS. Following PS weighting and matching, the three methods were compared for remaining covariate imbalance and residual confounding. Last, a target trial emulation comparing COVID-19 at 3 and 12 weeks after first vaccine dose vs unvaccinated was conducted. RESULTS Vaccinated and unvaccinated cohorts comprised 583 813 and 332 315 individuals for weighting, respectively, and 459 000 individuals in the matched cohorts. Overlap weighting performed best in terms of minimizing confounding and systematic error. Overlap weighting successfully replicated estimates from clinical trials for vaccine effectiveness for ChAdOx1 (57%) and BNT162b2 (75%) at 12 weeks. CONCLUSION Overlap weighting performed best in our setting. Our results based on overlap weighting replicate previous pivotal trials for the two first COVID-19 vaccines approved in Europe.
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Affiliation(s)
- Martí Català
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Edward Burn
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Trishna Rathod-Mistry
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Junqing Xie
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Antonella Delmestri
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Annika M Jödicke
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
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Wan EYF, Mathur S, Zhang R, Lam AHY, Wang B, Yan VKC, Chui CSL, Li X, Wong CKH, Lai FTT, Cheung CL, Chan EWY, Tan KCB, Wong ICK. Long-term effects of coronavirus disease 2019 on diabetes complications and mortality in people with diabetes: Two cohorts in the UK and Hong Kong. Diabetes Obes Metab 2023; 25:3807-3816. [PMID: 37735816 DOI: 10.1111/dom.15279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 09/23/2023]
Abstract
AIM To evaluate the long-term associations between coronavirus disease 2019 (COVID-19) and diabetes complications and mortality, in patients with diabetes. MATERIALS AND METHODS People with diabetes diagnosed with COVID-19 infection (exposed group), from 16 March 2020 to 31 May 2021 from the UK Biobank (UKB cohort; n = 2456), and from 1 April 2020 to 31 May 2022 from the electronic health records in Hong Kong (HK cohort; n = 80 546), were recruited. Each patient was randomly matched with participants with diabetes but without COVID-19 (unexposed group), based on age and sex (UKB, n = 41 801; HK, n = 391 849). Patients were followed for up to 18 months until 31 August 2021 for UKB, and up to 28 months until 15 August 2022 for HK. Characteristics between cohorts were further adjusted with Inverse Probability Treatment Weighting. Long-term association of COVID-19 with multi-organ disease complications and all-cause mortality after 21 days of diagnosis was evaluated by Cox regression. RESULTS Compared with uninfected participants, patients with COVID-19 infection with diabetes were consistently associated with higher risks of cardiovascular diseases (coronary heart disease [CHD]: hazard ratio [HR] [UKB]: 1.6 [95% confidence interval {CI}: 1.0, 2.4], HR [HK]: 1.2 [95% CI: 1.0, 1.5]; and stroke: HR [UKB]: 2.0 [95% CI: 1.1, 3.6], HR [HK]: 1.5 [95% CI: 1.3, 1.8]), microvascular disease (end stage renal disease: HR [UKB]: 2.1 [95% CI: 1.1, 4.0], HR [HK]: 1.2 [95% CI: 1.1, 1.4]) and all-cause mortality (HR [UKB]: 4.6 [95% CI: 3.8, 5.5], HR [HK]: 2.6 [95% CI: 2.5, 2.8]), in both cohorts. CONCLUSIONS COVID-19 infection is associated with long-term increased risks of diabetes complications (especially cardiovascular complications, and mortality) in people with diabetes. Monitoring for signs/symptoms of developing these long-term complications post-COVID-19 infection in the infected patient population of people with diabetes may be beneficial in minimizing their morbidity and mortality.
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Affiliation(s)
- Eric Yuk Fai Wan
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sukriti Mathur
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ran Zhang
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Athene Hoi Ying Lam
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Boyuan Wang
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Vincent Ka Chun Yan
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Celine Sze Ling Chui
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Xue Li
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Carlos King Ho Wong
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Francisco Tsz Tsun Lai
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
| | - Ching Lung Cheung
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
| | - Esther Wai Yin Chan
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
- Department of Pharmacy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China
| | - Kathryn Choon Beng Tan
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ian Chi Kei Wong
- Centre for Safe Medication Practice and research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong, China
- The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China
- Aston Pharmacy School, Aston University, Birmingham, UK
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7
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Liu Y, Sánchez-Ovando S, Carolan L, Dowson L, Khvorov A, Jessica Hadiprodjo A, Tseng YY, Delahunty C, Khatami A, Macnish M, Dougherty S, Hagenauer M, Riley KE, Jadhav A, Harvey J, Kaiser M, Mathew S, Hodgson D, Leung V, Subbarao K, Cheng AC, Macartney K, Koirala A, Marshall H, Clark J, Blyth CC, Wark P, Kucharski AJ, Sullivan SG, Fox A. Superior immunogenicity of mRNA over adenoviral vectored COVID-19 vaccines reflects B cell dynamics independent of anti-vector immunity: Implications for future pandemic vaccines. Vaccine 2023; 41:7192-7200. [PMID: 37903679 DOI: 10.1016/j.vaccine.2023.10.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/22/2023] [Accepted: 10/13/2023] [Indexed: 11/01/2023]
Abstract
Both vector and mRNA vaccines were an important part of the response to the COVID-19 pandemic and may be required in future outbreaks and pandemics. The aim of this study was to validate whether immunogenicity differs for adenoviral vectored (AdV) versus mRNA vaccines against SARS-CoV-2, and to investigate how anti-vector immunity and B cell dynamics modulate immunogenicity. We enrolled SARS-CoV-2 infection-naïve health care workers who had received two doses of either AdV AZD1222 (n = 184) or mRNA BNT162b2 vaccine (n = 274) between April and October 2021. Blood was collected at least once, 10-48 days after vaccine dose 2 for antibody and B cell analyses. Median ages were 42 and 39 years, for AdV and mRNA vaccinees, respectively. Surrogate virus neutralization test (sVNT) and spike binding antibody titres were a median of 4.2 and 2.2 times lower, respectively, for AdV compared to mRNA vaccinees (p < 0.001). Median percentages of memory B cells that recognized fluorescent-tagged spike and RBD were 2.9 and 8.3 times lower, respectively for AdV compared to mRNA vaccinees. Titres of IgG reactive with human adenovirus type 5 hexon protein rose a median of 2.2-fold after AdV vaccination but were not correlated with anti-spike antibody titres. Together the results show that mRNA induced substantially more sVNT antibody than AdV vaccine, which reflected greater B cell expansion and targeting of the RBD rather than an attenuating effect of anti-vector antibodies. ClinicalTrials.gov Identifier: NCT05110911.
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Affiliation(s)
- Yi Liu
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Stephany Sánchez-Ovando
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Louise Carolan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Leslie Dowson
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Arseniy Khvorov
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - A Jessica Hadiprodjo
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Yeu Yang Tseng
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Catherine Delahunty
- Immune Health Program, Hunter Medical Research Institute and School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Ameneh Khatami
- The Children's Hospital at Westmead, Sydney Children's Hospital Network, National Centre for Immunisation Research and Surveillance, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia
| | - Marion Macnish
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia
| | - Sonia Dougherty
- Queensland Children's Hospital, Children's Health Queensland Hospital and Health Service, and University of Queensland, Brisbane, Australia
| | - Michelle Hagenauer
- Alfred Health, Monash Health and Monash University, Melbourne, Australia
| | - Kathryn E Riley
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, Australia; Division of Paediatric Medicine, Women's and Children's Health Network, Adelaide, Australia
| | - Ajay Jadhav
- The Children's Hospital at Westmead, Sydney Children's Hospital Network, National Centre for Immunisation Research and Surveillance, Sydney, Australia
| | - Joanne Harvey
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia
| | - Marti Kaiser
- Alfred Health, Monash Health and Monash University, Melbourne, Australia
| | - Suja Mathew
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, Australia; Division of Paediatric Medicine, Women's and Children's Health Network, Adelaide, Australia
| | - David Hodgson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vivian Leung
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Kanta Subbarao
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Allen C Cheng
- Alfred Health, Monash Health and Monash University, Melbourne, Australia
| | - Kristine Macartney
- The Children's Hospital at Westmead, Sydney Children's Hospital Network, National Centre for Immunisation Research and Surveillance, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia
| | - Archana Koirala
- The Children's Hospital at Westmead, Sydney Children's Hospital Network, National Centre for Immunisation Research and Surveillance, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia
| | - Helen Marshall
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, Australia; Division of Paediatric Medicine, Women's and Children's Health Network, Adelaide, Australia
| | - Julia Clark
- Queensland Children's Hospital, Children's Health Queensland Hospital and Health Service, and University of Queensland, Brisbane, Australia
| | - Christopher C Blyth
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia; School of Medicine, University of Western Australia, Perth Children's Hospital, and Department of Microbiology, PathWest Laboratory Medicine, QEII medical centre, Perth, Australia
| | - Peter Wark
- Immune Health Program, Hunter Medical Research Institute and School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Adam J Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Sheena G Sullivan
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; Department of Epidemiology, University of California, Los Angeles, USA
| | - Annette Fox
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia.
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8
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Liu Y, Sánchez-Ovando S, Carolan L, Dowson L, Khvorov A, Hadiprodjo J, Tseng YY, Delahunty C, Khatami A, Macnish M, Dougherty S, Hagenauer M, Riley KE, Jadhav A, Harvey J, Kaiser M, Mathew S, Hodgson D, Leung V, Subbarao K, Cheng AC, Macartney K, Koirala A, Marshall H, Clark J, Blyth CC, Wark P, Kucharski AJ, Sullivan SG, Fox A. Comparative B cell and antibody responses induced by adenoviral vectored and mRNA vaccines against COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.02.23290871. [PMID: 37333329 PMCID: PMC10275006 DOI: 10.1101/2023.06.02.23290871] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Both vector and mRNA vaccines were an important part of the response to the COVID-19 pandemic and may be required in future outbreaks and pandemics. However, adenoviral vectored (AdV) vaccines may be less immunogenic than mRNA vaccines against SARS-CoV-2. We assessed anti-spike and anti-vector immunity among infection-naïve Health Care Workers (HCW) following two doses of AdV (AZD1222) versus mRNA (BNT162b2) vaccine. 183 AdV and 274 mRNA vaccinees enrolled between April and October 2021. Median ages were 42 and 39 years, respectively. Blood was collected at least once, 10-48 days after vaccine dose 2. Surrogate virus neutralization test (sVNT) and spike binding antibody titres were a median of 4.2 and 2.2 times lower, respectively, for AdV compared to mRNA vaccinees (p<0.001). Median percentages of memory B cells that recognized fluorescent-tagged spike and RBD were 2.9 and 8.3 times lower, respectively for AdV compared to mRNA vaccinees. Titres of IgG reactive with human Adenovirus type 5 hexon protein rose a median of 2.2-fold after AdV vaccination but were not correlated with anti-spike antibody titres. Together the results show that mRNA induced substantially more sVNT antibody than AdV vaccine due to greater B cell expansion and targeting of the RBD. Pre-existing AdV vector cross-reactive antibodies were boosted following AdV vaccination but had no detectable effect on immunogenicity.
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Affiliation(s)
- Yi Liu
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Stephany Sánchez-Ovando
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Louise Carolan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Leslie Dowson
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Arseniy Khvorov
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Jessica Hadiprodjo
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Yeu Yang Tseng
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Catherine Delahunty
- Immune Health Program, Hunter Medical Research Institute and School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Ameneh Khatami
- The Children’s Hospital at Westmead; Sydney Children’s Hospital Network; National Centre for Immunisation Research and Surveillance, Sydney, Australia
- The University of Sydney; and National Centre for Immunisation Research and Surveillance, Sydney, Australia
| | - Marion Macnish
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia
| | - Sonia Dougherty
- Queensland Children’s Hospital, Children’s Health Queensland Hospital and Health Service; and University of Queensland, Brisbane, Australia
| | | | - Kathryn E. Riley
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, Australia
- Division of Paediatric Medicine, Women’s and Children’s Health Network, Adelaide, Australia
| | - Ajay Jadhav
- The Children’s Hospital at Westmead; Sydney Children’s Hospital Network; National Centre for Immunisation Research and Surveillance, Sydney, Australia
| | - Joanne Harvey
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia
| | - Marti Kaiser
- Alfred Health and Monash University, Melbourne, Australia
| | - Suja Mathew
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, Australia
- Division of Paediatric Medicine, Women’s and Children’s Health Network, Adelaide, Australia
| | - David Hodgson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vivian Leung
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Kanta Subbarao
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Allen C. Cheng
- Alfred Health and Monash University, Melbourne, Australia
| | - Kristine Macartney
- The Children’s Hospital at Westmead; Sydney Children’s Hospital Network; National Centre for Immunisation Research and Surveillance, Sydney, Australia
- The University of Sydney; and National Centre for Immunisation Research and Surveillance, Sydney, Australia
| | - Archana Koirala
- The Children’s Hospital at Westmead; Sydney Children’s Hospital Network; National Centre for Immunisation Research and Surveillance, Sydney, Australia
- The University of Sydney; and National Centre for Immunisation Research and Surveillance, Sydney, Australia
| | - Helen Marshall
- Adelaide Medical School and Robinson Research Institute, The University of Adelaide, Adelaide, Australia
- Division of Paediatric Medicine, Women’s and Children’s Health Network, Adelaide, Australia
| | - Julia Clark
- Queensland Children’s Hospital, Children’s Health Queensland Hospital and Health Service; and University of Queensland, Brisbane, Australia
| | - Christopher C. Blyth
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Australia
- School of Medicine, University of Western Australia; Perth Children’s Hospital; and Department of Microbiology, PathWest Laboratory Medicine, QEII medical centre, Perth, Australia
| | - Peter Wark
- Immune Health Program, Hunter Medical Research Institute and School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Adam J. Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Sheena G. Sullivan
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Epidemiology, University of California, Los Angeles, USA
| | - Annette Fox
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
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Choi SH, Park JY, Kweon OJ, Park JH, Kim MC, Lim Y, Chung JW. Immune Responses After Vaccination With Primary 2-Dose ChAdOx1 Plus a Booster of BNT162b2 or Vaccination With Primary 2-Dose BNT162b2 Plus a Booster of BNT162b2 and the Occurrence of Omicron Breakthrough Infection. J Korean Med Sci 2023; 38:e155. [PMID: 37218354 DOI: 10.3346/jkms.2023.38.e155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/05/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Before the omicron era, health care workers were usually vaccinated with either the primary 2-dose ChAdOx1 nCoV-19 (Oxford-AstraZeneca) series plus a booster dose of BNT162b2 (Pfizer-BioNTech) (CCB group) or the primary 2-dose BNT162b2 series plus a booster dose of BNT162b2 (BBB group) in Korea. METHODS The two groups were compared using quantification of the surrogate virus neutralization test for wild type severe acute respiratory syndrome coronavirus 2 (SVNT-WT), the omicron variant (SVNT-O), spike-specific IgG, and interferon-gamma (IFN-γ), as well as the omicron breakthrough infection cases. RESULTS There were 113 participants enrolled in the CCB group and 51 enrolled in the BBB group. Before and after booster vaccination, the median SVNT-WT and SVNT-O values were lower in the CCB (SVNT-WT [before-after]: 72.02-97.61%, SVNT-O: 15.18-42.29%) group than in the BBB group (SVNT-WT: 89.19-98.11%, SVNT-O: 23.58-68.56%; all P < 0.001). Although the median IgG concentrations were different between the CCB and BBB groups after the primary series (2.677 vs. 4.700 AU/mL, respectively, P < 0.001), they were not different between the two groups after the booster vaccination (7.246 vs. 7.979 AU/mL, respectively, P = 0.108). In addition, the median IFN-γ concentration was higher in the BBB group than in the CCB group (550.5 and 387.5 mIU/mL, respectively, P = 0.014). There was also a difference in the cumulative incidence curves over time (CCB group 50.0% vs. BBB group 41.8%; P = 0.045), indicating that breakthrough infection occurred faster in the CCB group. CONCLUSION The cellular and humoral immune responses were low in the CCB group so that the breakthrough infection occurred faster in the CCB group than in the BBB group.
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Affiliation(s)
- Seong-Ho Choi
- Division of Infectious Diseases, Department of Internal Medicine, Chung-Ang University Hospital, Seoul, Korea
| | - Ji Young Park
- Department of Pediatrics, Chung-Ang University Hospital, Seoul, Korea
| | - Oh Joo Kweon
- Department of Laboratory Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Korea
| | - Joung Ha Park
- Division of Infectious Diseases, Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Korea
| | - Min-Chul Kim
- Division of Infectious Diseases, Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Korea
| | - Yaeji Lim
- Department of Applied Statistics, Chung-Ang University, Seoul, Korea
| | - Jin-Won Chung
- Division of Infectious Diseases, Department of Internal Medicine, Chung-Ang University Hospital, Seoul, Korea.
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Fan X, Han J, Zhao E, Fang J, Wang D, Cheng Y, Shi Y, Wang Z, Yao Z, Lu P, Liu T, Li Q, Poulsen KL, Yuan Z, Song Y, Zhao J. The effects of obesity and metabolic abnormalities on severe COVID-19-related outcomes after vaccination: A population-based study. Cell Metab 2023; 35:585-600.e5. [PMID: 36931274 PMCID: PMC9974355 DOI: 10.1016/j.cmet.2023.02.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/01/2022] [Accepted: 02/24/2023] [Indexed: 03/05/2023]
Abstract
Breakthrough SARS-CoV-2 infections of vaccinated individuals are being reported globally, resulting in an increased risk of hospitalization and death among such patients. Therefore, it is crucial to identify the modifiable risk factors that may affect the protective efficacy of vaccine use against the development of severe COVID-19 and thus to initiate early medical interventions. Here, in population-based studies using the UK Biobank database and the 2021 National Health Interview Survey (NHIS), we analyzed 20,362 participants aged 50 years or older and 2,588 aged 18 years or older from both databases who tested positive for SARS-COV-2, of whom 33.1% and 67.7% received one or more doses of vaccine, respectively. In the UK Biobank, participants are followed from the vaccination date until October 18, 2021. We found that obesity and metabolic abnormalities (namely, hyperglycemia, hyperlipidemia, and hypertension) were modifiable factors for severe COVID-19 in vaccinated patients (all p < 0.05). When metabolic abnormalities were present, regardless of obesity, the risk of severe COVID-19 was higher than that of metabolically normal individuals (all p < 0.05). Moreover, pharmacological interventions targeting such abnormalities (namely, antihypertensive [adjusted hazard ratio (aHR) 0.64, 95% CI 0.48-0.86; p = 0.003], glucose-lowering [aHR 0.55, 95% CI 0.36-0.83; p = 0.004], and lipid-lowering treatments [aHR 0.50, 95% CI 0.37-0.68; p < 0.001]) were significantly associated with a reduced risk for this outcome. These results show that more proactive health management of patients with obesity and metabolic abnormalities is critical to reduce the incidence of severe COVID-19 after vaccination.
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Affiliation(s)
- Xiude Fan
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Junming Han
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Enfa Zhao
- Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province 230022, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Dawei Wang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Yiping Cheng
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Yingzhou Shi
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Zhen Wang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Zhenyu Yao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Peng Lu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Tianbao Liu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Qihang Li
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Kyle L Poulsen
- Department of Anesthesiology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Yongfeng Song
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China.
| | - Jiajun Zhao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China.
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Chang A, Akhtar A, Lai L, Orellana-Noia VM, Linderman SL, McCook-Veal AA, Switchenko JM, Saini M, Valanparambil RM, Blum KA, Allen PB, Lechowicz MJ, Romancik JT, Ayers A, Leal A, O'Leary CB, Churnetski MC, Baird K, Kives M, Wrammert J, Nooka AK, Koff JL, Dhodapkar MV, Suthar MS, Cohen JB, Ahmed R. Antibody binding and neutralization of live SARS-CoV-2 variants including BA.4/5 following booster vaccination of patients with B-cell malignancies. CANCER RESEARCH COMMUNICATIONS 2022; 2:1684-1692. [PMID: 36644323 PMCID: PMC9833496 DOI: 10.1158/2767-9764.crc-22-0471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Non-Hodgkin lymphoma and chronic lymphocytic leukemia (NHL/CLL) patients elicit inadequate antibody responses after initial SARS-CoV-2 vaccination and remain at high risk of severe COVID-19 disease. We investigated IgG, IgA, and IgM responses after booster vaccination against recent SARS-CoV-2 variants including Omicron BA.5 in 67 patients. Patients had lower fold increase and total anti-spike binding titers after booster than healthy individuals. Antibody responses negatively correlated with recent anti-CD20 therapy and low B cell numbers. Antibodies generated after booster demonstrated similar binding properties against SARS-CoV-2 variants compared to those generated by healthy controls with lower binding against Omicron variants. Importantly, 43% of patients showed anti-Omicron BA.1 neutralizing antibodies after booster and all these patients also had anti-Omicron BA.5 neutralizing antibodies. NHL/CLL patients demonstrated inferior antibody responses after booster vaccination, particularly against Omicron variants. Prioritization of prophylactic and treatment agents and vaccination of patients and close contacts with updated vaccine formulations are essential.
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Affiliation(s)
- Andres Chang
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
| | - Akil Akhtar
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
| | - Lilin Lai
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
- Department of Pediatrics, Emory University Schools of Medicine, Atlanta, Georgia
- Emory National Primate Research Center, Atlanta, Georgia
| | - Victor M. Orellana-Noia
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Susanne L. Linderman
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
| | - Ashley A. McCook-Veal
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Jeffrey M. Switchenko
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Manpreet Saini
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
- International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Rajesh M. Valanparambil
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
| | - Kristie A. Blum
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Pamela B. Allen
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Mary Jo Lechowicz
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Jason T. Romancik
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Amy Ayers
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alyssa Leal
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Colin B. O'Leary
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Michael C. Churnetski
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Katelin Baird
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Melissa Kives
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Jens Wrammert
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
- Department of Pediatrics, Emory University Schools of Medicine, Atlanta, Georgia
| | - Ajay K. Nooka
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Jean L. Koff
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Madhav V. Dhodapkar
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
| | - Mehul S. Suthar
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
- Department of Pediatrics, Emory University Schools of Medicine, Atlanta, Georgia
| | - Jonathon B. Cohen
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
- Corresponding Authors: Rafi Ahmed, Emory University School of Medicine, Atlanta, GA 30322. Phone: 404-727-4700; Fax: 404-727-3722; E-mail: ; and Jonathon B. Cohen,
| | - Rafi Ahmed
- Emory Vaccine Center, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia
- Corresponding Authors: Rafi Ahmed, Emory University School of Medicine, Atlanta, GA 30322. Phone: 404-727-4700; Fax: 404-727-3722; E-mail: ; and Jonathon B. Cohen,
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Panahi Y, Einollahi B, Beiraghdar F, Darvishi M, Fathi S, Javanbakht M, Shafiee S, Akhavan-Sigari R. Fully understanding the efficacy profile of the COVID-19 vaccination and its associated factors in multiple real-world settings. Front Immunol 2022; 13:947602. [PMID: 36389777 PMCID: PMC9641184 DOI: 10.3389/fimmu.2022.947602] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/25/2022] [Indexed: 09/29/2023] Open
Abstract
We performed a review study according to recent COVID-19 vaccines' real-world data to provide comparisons between COVID-19 vaccines regarding their relative efficacy. Although most vaccine platforms showed comparable effectiveness and efficacy, we highlight critical points and recent developments generated in studies that might affect vaccine efficacy including population-dependent effects of the vaccine (transplantation, adiposity, and specific comorbidities, as well as older age, male sex, ethnicity, and prior infection), vaccine type, variants of concern (VOC), and an extended vaccine schedule. Owing to these factors, community-based trials can be of great importance in determining vaccine effectiveness in a systematic manner; thus, uncertainty remains regarding vaccine efficacy. Long immune protection of vaccination with BNT162b2 or ChAdOx1 nCoV-19 has been demonstrated to be up to 61 months and 5-12 months after the previous infection, and boosting infection-acquired immunity for both the first and second doses of the BNT162b2 and ChAdOx1 nCoV-19 vaccines was correlated with high and durable protection. However, large cohort and longitudinal studies are required for the evaluation of immunity dynamics and longevity in unvaccinated, vaccinated, and infected individuals, as well as vaccinated convalescent individuals in real-world settings. Regarding the likelihood of vaccine escape variants evolving, an ongoing examination of the protection conferred against an evolving virus (new variant) by an extended schedule can be crucial.
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Affiliation(s)
- Yunes Panahi
- Pharmacotherapy Department, Faculty of Pharmacy, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Behzad Einollahi
- Nephrology and Urology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Fatemeh Beiraghdar
- Nephrology and Urology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohammad Darvishi
- Infectious Diseases and Tropical Medicine Research Center (IDTMRC), Department of Aerospace and Subaquatic Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Saeid Fathi
- Department of Parasite Vaccine Research and Production, Razi Vaccine and Serum Research Institute, Agriculture Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Mohammad Javanbakht
- Nephrology and Urology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Sepehr Shafiee
- Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Akhavan-Sigari
- Department of Neurosurgery, University Medical Center Tuebingen, Tuebingen, Germany
- Department of Health Care Management and Clinical Research, Collegium Humanum Warsaw Management University, Warsaw, Poland
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Li Z, Liu S, Li F, Li Y, Li Y, Peng P, Li S, He L, Liu T. Efficacy, immunogenicity and safety of COVID-19 vaccines in older adults: a systematic review and meta-analysis. Front Immunol 2022; 13:965971. [PMID: 36177017 PMCID: PMC9513208 DOI: 10.3389/fimmu.2022.965971] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/24/2022] [Indexed: 01/08/2023] Open
Abstract
BackgroundOlder adults are more susceptible to severe health outcomes for coronavirus disease 2019 (COVID-19). Universal vaccination has become a trend, but there are still doubts and research gaps regarding the COVID-19 vaccination in the elderly. This study aimed to investigate the efficacy, immunogenicity, and safety of COVID-19 vaccines in older people aged ≥ 55 years and their influencing factors.MethodsRandomized controlled trials from inception to April 9, 2022, were systematically searched in PubMed, EMBASE, the Cochrane Library, and Web of Science. We estimated summary relative risk (RR), rates, or standardized mean difference (SMD) with 95% confidence interval (CI) using random-effects meta-analysis. This study was registered with PROSPERO (CRD42022314456).ResultsOf the 32 eligible studies, 9, 21, and 25 were analyzed for efficacy, immunogenicity, and safety, respectively. In older adults, vaccination was efficacious against COVID-19 (79.49%, 95% CI: 60.55−89.34), with excellent seroconversion rate (92.64%, 95% CI: 86.77−96.91) and geometric mean titer (GMT) (SMD 3.56, 95% CI: 2.80−4.31) of neutralizing antibodies, and provided a significant protection rate against severe disease (87.01%, 50.80−96.57). Subgroup and meta-regression analyses consistently found vaccine types and the number of doses to be primary influencing factors for efficacy and immunogenicity. Specifically, mRNA vaccines showed the best efficacy (90.72%, 95% CI: 86.82−93.46), consistent with its highest seroconversion rate (98.52%, 95% CI: 93.45−99.98) and GMT (SMD 6.20, 95% CI: 2.02−10.39). Compared to the control groups, vaccination significantly increased the incidence of total adverse events (AEs) (RR 1.59, 95% CI: 1.38−1.83), including most local and systemic AEs, such as pain, fever, chill, etc. For inactivated and DNA vaccines, the incidence of any AEs was similar between vaccination and control groups (p > 0.1), while mRNA vaccines had the highest risk of most AEs (RR range from 1.74 to 7.22).ConclusionCOVID-19 vaccines showed acceptable efficacy, immunogenicity and safety in older people, especially providing a high protection rate against severe disease. The mRNA vaccine was the most efficacious, but it is worth surveillance for some AEs it caused. Increased booster coverage in older adults is warranted, and additional studies are urgently required for longer follow-up periods and variant strains.
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Affiliation(s)
- Zejun Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shouhuan Liu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Fengming Li
- Ministry of Education Key Laboratory of Child Development and Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Yifeng Li
- College of Pediatrics, Chongqing Medical University, Chongqing, China
| | - Yilin Li
- College of Pediatrics, Chongqing Medical University, Chongqing, China
| | - Pu Peng
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Sai Li
- College of Pediatrics, Chongqing Medical University, Chongqing, China
| | - Li He
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Tieqiao Liu, ; Li He,
| | - Tieqiao Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Tieqiao Liu, ; Li He,
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Intensity of Humoral Immune Responses, Adverse Reactions, and Post-Vaccination Morbidity after Adenovirus Vector-Based and mRNA Anti-COVID-19 Vaccines. Vaccines (Basel) 2022; 10:vaccines10081268. [PMID: 36016156 PMCID: PMC9416671 DOI: 10.3390/vaccines10081268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of the study was to compare mRNA vaccine BNT162b2 with adenovirus vector- based vaccines in terms of presence of adverse reactions, immunogenicity, and protection against COVID-19. A total of 270 individuals were enrolled, of which 135 were vaccinated with adenovirus vector-based vaccines and compared with 135 age- and sex-matched participants who received the BNT162b2 mRNA vaccine. Serum sampling was performed on all participants on days 21, 42, 90, and 180 following the first dose, to evaluate anti-spike IgG and IgA responses. Antibodies were quantified by chemiluminescent microplate and ELISA assays. We demonstrate that both mRNA and adenovirus vector-based vaccines caused mild side-effects and were effective in inducing adequate antibody responses against SARS-CoV-2, although BNT162b2 was superior concerning the intensity of antibody responses and protection against severe COVID-19. Moreover, we identify that IgG and IgA responses depended primarily on both history of previous COVID-19 infection and vaccination platform used, with individuals immunized with a single-dose vaccine having lower antibody titers over time. Lastly, all vaccine platforms had limited side-effects, with the most frequent pain at the injection site. Our results provide useful information regarding antibody responses after vaccination with different vaccine platforms, which can be useful for public health vaccination strategies.
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