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Choe K, Park HY, Ikram M, Lee HJ, Park TJ, Ullah R, Kim MO. Systematic Review of the Common Pathophysiological Mechanisms in COVID-19 and Neurodegeneration: The Role of Bioactive Compounds and Natural Antioxidants. Cells 2022; 11:cells11081298. [PMID: 35455977 PMCID: PMC9031507 DOI: 10.3390/cells11081298] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/08/2022] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
The novel coronavirus (2019-nCoVCOVID-19) belongs to the Beta coronavirus family, which contains MERS-CoV (Middle East respiratory syndrome coronavirus) and SARS-CoV (severe acute respiratory syndrome coronavirus). SARS-CoV-2 activates the innate immune system, thereby activating the inflammatory mechanism, causing the release of inflammatory cytokines. Moreover, it has been suggested that COVID-19 may penetrate the central nervous system, and release inflammatory cytokines in the brains, inducing neuroinflammation and neurodegeneration. Several links connect COVID-19 with Alzheimer’s disease (AD), such as elevated oxidative stress, uncontrolled release of the inflammatory cytokines, and mitochondrial apoptosis. There are severe concerns that excessive immune cell activation in COVID-19 may aggravate the neurodegeneration and amyloid-beta pathology of AD. Here, we have collected the evidence, showing the links between the two diseases. The focus has been made to collect the information on the activation of the inflammation, its contributors, and shared therapeutic targets. Furthermore, we have given future perspectives, research gaps, and overlapping pathological bases of the two diseases. Lastly, we have given the short touch to the drugs that have equally shown rescuing effects against both diseases. Although there is limited information available regarding the exact links between COVID-19 and neuroinflammation, we have insight into the pathological contributors of the diseases. Based on the shared pathological features and therapeutic targets, we hypothesize that the activation of the immune system may induce neurological disorders by triggering oxidative stress and neuroinflammation.
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Affiliation(s)
- Kyonghwan Choe
- Division of Life Science and Applied Life Science (BK21 FOUR), College of Natural Sciences, Gyeongsang National University, Jinju 52828, Korea; (K.C.); (M.I.); (H.J.L.); (R.U.)
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Hyun Young Park
- Department of Pediatrics, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands;
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNS), Maastricht Medical Center, 6229 ER Maastricht, The Netherlands
| | - Muhammad Ikram
- Division of Life Science and Applied Life Science (BK21 FOUR), College of Natural Sciences, Gyeongsang National University, Jinju 52828, Korea; (K.C.); (M.I.); (H.J.L.); (R.U.)
| | - Hyeon Jin Lee
- Division of Life Science and Applied Life Science (BK21 FOUR), College of Natural Sciences, Gyeongsang National University, Jinju 52828, Korea; (K.C.); (M.I.); (H.J.L.); (R.U.)
| | - Tae Ju Park
- Haemato-Oncology/Systems Medicine Group, Paul O’Gorman Leukaemia Research Centre, Institute of Cancer Sciences, College of Medical, Veterinary & Life Sciences (MVLS), University of Glasgow, Glasgow G12 0ZD, UK;
| | - Rahat Ullah
- Division of Life Science and Applied Life Science (BK21 FOUR), College of Natural Sciences, Gyeongsang National University, Jinju 52828, Korea; (K.C.); (M.I.); (H.J.L.); (R.U.)
| | - Myeong Ok Kim
- Division of Life Science and Applied Life Science (BK21 FOUR), College of Natural Sciences, Gyeongsang National University, Jinju 52828, Korea; (K.C.); (M.I.); (H.J.L.); (R.U.)
- Alz-Dementia Korea Co., Jinju 52828, Korea
- Correspondence: ; Tel.: +82-55-772-1345; Fax: +82-55-772-2656
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Burns J, Movsisyan A, Stratil JM, Biallas RL, Coenen M, Emmert-Fees KM, Geffert K, Hoffmann S, Horstick O, Laxy M, Klinger C, Kratzer S, Litwin T, Norris S, Pfadenhauer LM, von Philipsborn P, Sell K, Stadelmaier J, Verboom B, Voss S, Wabnitz K, Rehfuess E. International travel-related control measures to contain the COVID-19 pandemic: a rapid review. Cochrane Database Syst Rev 2021; 3:CD013717. [PMID: 33763851 PMCID: PMC8406796 DOI: 10.1002/14651858.cd013717.pub2] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND In late 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers. OBJECTIVES To assess the effectiveness of international travel-related control measures during the COVID-19 pandemic on infectious disease transmission and screening-related outcomes. SEARCH METHODS We searched MEDLINE, Embase and COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO Global Database on COVID-19 Research to 13 November 2020. SELECTION CRITERIA We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across international borders during the COVID-19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID-19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome. DATA COLLECTION AND ANALYSIS Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements. MAIN RESULTS Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission. Most studies compared travel-related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects. Below we outline the results for each intervention category by illustrating the findings from selected outcomes. Travel restrictions reducing or stopping cross-border travel (31 modelling studies) The studies assessed cases avoided and shift in epidemic development. We found very low-certainty evidence for a reduction in COVID-19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect. There was very low-certainty evidence that cross-border travel controls can slow the spread of COVID-19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure. Screening at borders (13 modelling studies; 13 observational studies) Screening measures covered symptom/exposure-based screening or test-based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely. For symptom/exposure-based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate-certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low-certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low-certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low-certainty evidence), although all but one study observed this proportion to be less than 54%. For test-based screening, one modelling study provided very low-certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low-certainty evidence). Quarantine (12 modelling studies) The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low- to low-certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low-certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance. Quarantine and screening at borders (7 modelling studies; 4 observational studies) The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low- to low-certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low-certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine. AUTHORS' CONCLUSIONS With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross-border travel and quarantine of travellers, there is a lack of 'real-world' evidence. The certainty of the evidence for most travel-related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure-based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure-based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel-related control measures from a societal perspective.
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Affiliation(s)
- Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ani Movsisyan
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jan M Stratil
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Renke Lars Biallas
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Michaela Coenen
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Karl Mf Emmert-Fees
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
| | - Karin Geffert
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Sabine Hoffmann
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Olaf Horstick
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Michael Laxy
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Carmen Klinger
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Suzie Kratzer
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Tim Litwin
- Institute for Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Susan Norris
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Oregon Health & Science University, Portland, OR, USA
| | - Lisa M Pfadenhauer
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Peter von Philipsborn
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Kerstin Sell
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ben Verboom
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Stephan Voss
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Katharina Wabnitz
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Eva Rehfuess
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
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Burns J, Movsisyan A, Stratil JM, Coenen M, Emmert-Fees KM, Geffert K, Hoffmann S, Horstick O, Laxy M, Pfadenhauer LM, von Philipsborn P, Sell K, Voss S, Rehfuess E. Travel-related control measures to contain the COVID-19 pandemic: a rapid review. Cochrane Database Syst Rev 2020; 10:CD013717. [PMID: 33502002 DOI: 10.1002/14651858.cd013717] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND In late 2019, first cases of coronavirus disease 2019, or COVID-19, caused by the novel coronavirus SARS-CoV-2, were reported in Wuhan, China. Subsequently COVID-19 spread rapidly around the world. To contain the ensuing pandemic, numerous countries have implemented control measures related to international travel, including border closures, partial travel restrictions, entry or exit screening, and quarantine of travellers. OBJECTIVES To assess the effectiveness of travel-related control measures during the COVID-19 pandemic on infectious disease and screening-related outcomes. SEARCH METHODS We searched MEDLINE, Embase and COVID-19-specific databases, including the WHO Global Database on COVID-19 Research, the Cochrane COVID-19 Study Register, and the CDC COVID-19 Research Database on 26 June 2020. We also conducted backward-citation searches with existing reviews. SELECTION CRITERIA We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across national borders during the COVID-19 pandemic. We also included studies concerned with severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) as indirect evidence. Primary outcomes were cases avoided, cases detected and a shift in epidemic development due to the measures. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome. DATA COLLECTION AND ANALYSIS One review author screened titles and abstracts; all excluded abstracts were screened in duplicate. Two review authors independently screened full texts. One review author extracted data, assessed risk of bias and appraised study quality. At least one additional review author checked for correctness of all data reported in the 'Risk of bias' assessment, quality appraisal and data synthesis. For assessing the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, ROBINS-I for observational ecological studies and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed certainty of evidence with GRADE, and the review author team discussed ratings. MAIN RESULTS We included 40 records reporting on 36 unique studies. We found 17 modelling studies, 7 observational screening studies and one observational ecological study on COVID-19, four modelling and six observational studies on SARS, and one modelling study on SARS and MERS, covering a variety of settings and epidemic stages. Most studies compared travel-related control measures against a counterfactual scenario in which the intervention measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of travel restrictions, or a combination of measures. There were concerns with the quality of many modelling studies and the risk of bias of observational studies. Many modelling studies used potentially inappropriate assumptions about the structure and input parameters of models, and failed to adequately assess uncertainty. Concerns with observational screening studies commonly related to the reference test and the flow of the screening process. Studies on COVID-19 Travel restrictions reducing cross-border travel Eleven studies employed models to simulate a reduction in travel volume; one observational ecological study assessed travel restrictions in response to the COVID-19 pandemic. Very low-certainty evidence from modelling studies suggests that when implemented at the beginning of the outbreak, cross-border travel restrictions may lead to a reduction in the number of new cases of between 26% to 90% (4 studies), the number of deaths (1 study), the time to outbreak of between 2 and 26 days (2 studies), the risk of outbreak of between 1% to 37% (2 studies), and the effective reproduction number (1 modelling and 1 observational ecological study). Low-certainty evidence from modelling studies suggests a reduction in the number of imported or exported cases of between 70% to 81% (5 studies), and in the growth acceleration of epidemic progression (1 study). Screening at borders with or without quarantine Evidence from three modelling studies of entry and exit symptom screening without quarantine suggests delays in the time to outbreak of between 1 to 183 days (very low-certainty evidence) and a detection rate of infected travellers of between 10% to 53% (low-certainty evidence). Six observational studies of entry and exit screening were conducted in specific settings such as evacuation flights and cruise ship outbreaks. Screening approaches varied but followed a similar structure, involving symptom screening of all individuals at departure or upon arrival, followed by quarantine, and different procedures for observation and PCR testing over a period of at least 14 days. The proportion of cases detected ranged from 0% to 91% (depending on the screening approach), and the positive predictive value ranged from 0% to 100% (very low-certainty evidence). The outcomes, however, should be interpreted in relation to both the screening approach used and the prevalence of infection among the travellers screened; for example, symptom-based screening alone generally performed worse than a combination of symptom-based and PCR screening with subsequent observation during quarantine. Quarantine of travellers Evidence from one modelling study simulating a 14-day quarantine suggests a reduction in the number of cases seeded by imported cases; larger reductions were seen with increasing levels of quarantine compliance ranging from 277 to 19 cases with rates of compliance modelled between 70% to 100% (very low-certainty evidence). AUTHORS' CONCLUSIONS With much of the evidence deriving from modelling studies, notably for travel restrictions reducing cross-border travel and quarantine of travellers, there is a lack of 'real-life' evidence for many of these measures. The certainty of the evidence for most travel-related control measures is very low and the true effects may be substantially different from those reported here. Nevertheless, some travel-related control measures during the COVID-19 pandemic may have a positive impact on infectious disease outcomes. Broadly, travel restrictions may limit the spread of disease across national borders. Entry and exit symptom screening measures on their own are not likely to be effective in detecting a meaningful proportion of cases to prevent seeding new cases within the protected region; combined with subsequent quarantine, observation and PCR testing, the effectiveness is likely to improve. There was insufficient evidence to draw firm conclusions about the effectiveness of travel-related quarantine on its own. Some of the included studies suggest that effects are likely to depend on factors such as the stage of the epidemic, the interconnectedness of countries, local measures undertaken to contain community transmission, and the extent of implementation and adherence.
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Affiliation(s)
- Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ani Movsisyan
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jan M Stratil
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Michaela Coenen
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Karl Mf Emmert-Fees
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
| | - Karin Geffert
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Sabine Hoffmann
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Olaf Horstick
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Michael Laxy
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
| | - Lisa M Pfadenhauer
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Peter von Philipsborn
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Kerstin Sell
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Stephan Voss
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Eva Rehfuess
- Institute for Medical Information Processing, Biometry and Epidemiology, IBE, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
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