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Cheung KS, Lam LK, Hui RWH, Mao X, Zhang RR, Chan KH, Hung IF, Seto WK, Yuen MF. Effect of moderate-to-severe hepatic steatosis on neutralising antibody response among BNT162b2 and CoronaVac recipients. Clin Mol Hepatol 2022; 28:553-564. [PMID: 35545127 PMCID: PMC9293606 DOI: 10.3350/cmh.2022.0082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 05/06/2022] [Indexed: 11/05/2022] Open
Abstract
Aim Studies of hepatic steatosis (HS) effect on COVID-19 vaccine immunogenicity are lacking. We aimed to compare immunogenicity of BNT162b2 and CoronaVac among moderate/severe HS and control subjects. Patients and Methods 295 subjects who received BNT162b2 or CoronaVac vaccines from five vaccination centers were categorized into moderate/severe HS (controlled attenuation parameter ≥268 dB/m on transient elastography) (n=74) or control (n=221) groups. Primary outcomes were seroconversion rates of neutralising antibody by live virus Microneutralization (vMN) assay (titer ≥10) at day 21 (BNT162b2) or day28 (CoronaVac) and day56 (both). Secondary outcome was highest-tier titer response (top 25% of vMN titer; cutoff: 160 [BNT162b2] and 20 [CoronaVac]) at day 56. Results For BNT162b2 (n=228 [77.3%]), there was no statistical differences in seroconversion rates (71.7% vs 76.6% [day21]; 100% vs 100% [day56]) or vMN GMT (13.2 vs 13.3, [day21]; 91.9 vs 101.4, [day56]) among moderate/severe HS and control groups respectively. However, lower proportion of moderate/severe HS patients had highest-tier response (5.0% vs 15.5%; p=0.037 [day56]). For CoronaVac (n=67 [22.7%]), there was no statistical differences in seroconversion rates (7.1% vs 15.1%, [day21]; 64.3% vs 83.0%, [day56]) or vMN GMT (5.3 vs 5.8,) at day 28. However, moderate/severe HS patients had lower vMN GMT (9.1 vs 14.8, p=0.021) at day 56 with lower proportion having highest-tier response (21.4% vs 52.8%, p=0.036). Conclusion While there was no difference in seroconversion rate between moderate/severe HS and control groups after two doses of vaccine, a lower proportion of moderate/severe HS patients achieved highest-tier response for either BNT162b2 or CoronaVac.
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Affiliation(s)
- Ka Shing Cheung
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong.,Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Lok Ka Lam
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong
| | - Rex Wan Hin Hui
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong
| | - Xianhua Mao
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong
| | - Ruiqi R Zhang
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong
| | - Kwok Hung Chan
- Department of Microbiology, The University of Hong Kong, Queen Mary Hospital, Hong Kong
| | - Ivan Fn Hung
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong
| | - Wai Kay Seto
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong.,Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.,State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong
| | - Man Fung Yuen
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong.,State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong
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Liu D, Zheng Y, Kang J, Wang D, Bai L, Mao Y, Zha G, Tang H, Zhang R. Not Only High Number and Specific Comorbidities but Also Age Are Closely Related to Progression and Poor Prognosis in Patients With COVID-19. Front Med (Lausanne) 2022; 8:736109. [PMID: 35071254 PMCID: PMC8782432 DOI: 10.3389/fmed.2021.736109] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 12/02/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Some patients with comorbidities and rapid disease progression have a poor prognosis. Aim: We aimed to investigate the characteristics of comorbidities and their relationship with disease progression and outcomes of COVID-19 patients. Methods: A total of 718 COVID-19 patients were divided into five clinical type groups and eight age-interval groups. The characteristics of comorbidities were compared between the different clinical type groups and between the different age-interval groups, and their relationships with disease progression and outcomes of COVID-19 patients were assessed. Results: Approximately 91.23% (655/718) of COVID-19 patients were younger than 60 years old. Approximately 64.76% (465/718) had one or more comorbidities, and common comorbidities included non-alcoholic fatty liver disease (NAFLD), hyperlipidaemia, hypertension, diabetes mellitus (DM), chronic hepatitis B (CHB), hyperuricaemia, and gout. COVID-19 patients with comorbidities were older, especially those with chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD). Hypertension, DM, COPD, chronic kidney disease (CKD) and CVD were mainly found in severe COVID-19 patients. According to spearman correlation analysis the number of comorbidities was correlated positively with disease severity, the number of comorbidities and NAFLD were correlated positively with virus negative conversion time, hypertension, CKD and CVD were primarily associated with those who died, and the above-mentioned correlation existed independently of age. Risk factors included age, the number of comorbidities and hyperlipidaemia for disease severity, the number of comorbidities, hyperlipidaemia, NAFLD and COPD for the virus negative conversion time, and the number of comorbidities and CKD for prognosis. Number of comorbidities and age played a predictive role in disease progression and outcomes. Conclusion: Not only high number and specific comorbidities but also age are closely related to progression and poor prognosis in patients with COVID-19. These findings provide a reference for clinicians to focus on not only the number and specific comorbidities but also age in COVID-19 patients to predict disease progression and prognosis. Clinical Trial Registry: Chinese Clinical Trial Register ChiCTR2000034563.
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Affiliation(s)
- Dafeng Liu
- Department of Internal Medicine, The Public and Health Clinic Centre of Chengdu, Chengdu, China.,The Public and Health Clinic Centre of Chengdu Substation, Chengdu New Emergent Infectious Disease Prevention and Control Workstation, Chengdu, China
| | - Yongli Zheng
- Department of Internal Medicine, The Public and Health Clinic Centre of Chengdu, Chengdu, China.,The Public and Health Clinic Centre of Chengdu Substation, Chengdu New Emergent Infectious Disease Prevention and Control Workstation, Chengdu, China
| | - Jun Kang
- Department of Internal Medicine, The Public and Health Clinic Centre of Chengdu, Chengdu, China.,The Public and Health Clinic Centre of Chengdu Substation, Chengdu New Emergent Infectious Disease Prevention and Control Workstation, Chengdu, China
| | - Dongmei Wang
- Department of Internal Medicine, The Public and Health Clinic Centre of Chengdu, Chengdu, China.,The Public and Health Clinic Centre of Chengdu Substation, Chengdu New Emergent Infectious Disease Prevention and Control Workstation, Chengdu, China
| | - Lang Bai
- Center of Infectious Diseases, Sichuan University West China Hospital, Chengdu, China
| | - Yi Mao
- Department of Internal Medicine, The Public and Health Clinic Centre of Chengdu, Chengdu, China.,The Public and Health Clinic Centre of Chengdu Substation, Chengdu New Emergent Infectious Disease Prevention and Control Workstation, Chengdu, China
| | - Guifang Zha
- Department of Internal Medicine, The Public and Health Clinic Centre of Chengdu, Chengdu, China.,The Public and Health Clinic Centre of Chengdu Substation, Chengdu New Emergent Infectious Disease Prevention and Control Workstation, Chengdu, China
| | - Hong Tang
- Center of Infectious Diseases, Sichuan University West China Hospital, Chengdu, China
| | - Renqing Zhang
- Department of Internal Medicine, The Public and Health Clinic Centre of Chengdu, Chengdu, China.,The Public and Health Clinic Centre of Chengdu Substation, Chengdu New Emergent Infectious Disease Prevention and Control Workstation, Chengdu, China
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