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      Association between COVID‐19 and sensorineural hearing loss: Evidence from a Mendelian randomization study in European and East Asian population

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          Abstract

          Background

          Long coronavirus disease (COVID), characterized by persistent and sometimes debilitating symptoms following a severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection, has garnered increasing attention as a potential public health crisis. Emerging evidence indicates a higher incidence of hearing loss in individuals who have had COVID 2019 (COVID‐19) compared to the general population. However, the conclusions were inconsistent, and the causal relationship between COVID‐19 and sensorineural hearing loss remains unknown.

          Methods

          To addresses this outstanding issue, we performed Mendelian randomization analysis to detect the causal association between COVID‐19 and hearing loss using the largest genome‐wide association study data to date in the European population and confirmed the results in the East Asian population. Comprehensively sensitive analyses were followed, including Cochran's Q test, Mendelian randomization (MR)‐Egger intercept test, MR‐pleiotropy residual sum and outlier, and leave‐one‐out analysis, to validate the robustness of our results.

          Results

          Our results suggested that there is no causal association between COVID‐19 and the risk of hearing loss in the European population. Neither the susceptibility, hospitalization, and severity of COVID‐19 on hearing loss (inverse variance weighted method: odds ratio (OR) = 1.046, 95% confidence interval (CI) = 0.907–1.205, p = .537; OR = 0.995, 95% CI = 0.956–1.036, p = .823; OR = 0.995, 95% CI = 0.967–1.025, p = .76). Replicated analyses in the East Asian population yielded consistent results. No pleiotropy and heterogeneity were found in our results.

          Conclusion

          In conclusion, our MR results do not support a genetically predicted causal relationship between COVID‐19 and sensorineural hearing loss. Thus, the associations observed in prior observational studies may have been influenced by confounding factors rather than a direct cause‐and‐effect relationship. More clinical and mechanism research are needed to further understand this association in the future.

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          An interactive web-based dashboard to track COVID-19 in real time

          Ensheng Dong, Hongru Du, Lauren Gardner (2020)
          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

            Jack Bowden, George Davey Smith, Stephen Burgess (2016)
            Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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              Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

              Neil M. Davies, Michael V. Holmes, George Davey Smith (2019)
              Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence
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                Author and article information

                Contributors
                Qiuyuan Yin: qiuyuan_yin@ynu.edu.cn
                Lei Zhu:
                ORCID: http://orcid.org/0009-0004-3602-1281
                zhulei_evan@126.com
                Journal
                Journal ID (nlm-ta): Immun Inflamm Dis
                Journal ID (iso-abbrev): Immun Inflamm Dis
                Journal ID (doi): 10.1002/(ISSN)2050-4527
                Journal ID (publisher-id): IID3
                Title: Immunity, Inflammation and Disease
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 2050-4527
                Publication date (Electronic): 06 December 2023
                Publication date Collection: December 2023
                Volume: 11
                Issue: 12 ( doiID: 10.1002/iid3.v11.12 )
                Electronic Location Identifier: e1108
                Affiliations
                [ 1 ] Henan Provincial Institute of Medical Genetics, People's Hospital of Zhengzhou University Henan Provincial People's Hospital Zhengzhou China
                [ 2 ] State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, School of Life Sciences Yunnan University Kunming China
                Author notes
                [*] [* ] Correspondence Qiuyuan Yin and Lei Zhu, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, School of Life Sciences, Yunnan University, University Town, Chenggong District, Kunming 650500, China.

                Email: qiuyuan_yin@ 123456ynu.edu.cn and zhulei_evan@ 123456126.com

                Author information
                http://orcid.org/0009-0004-3602-1281
                Article
                Publisher ID: IID31108
                DOI: 10.1002/iid3.1108
                PMC ID: 10698807
                SO-VID: abd80252-aada-4685-8f02-718acfc4dc19
                Copyright © © 2023 The Authors. Immunity, Inflammation and Disease published by John Wiley & Sons Ltd.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Date revision received : 04 November 2023
                Date received : 26 September 2023
                Date accepted : 18 November 2023
                Page count
                Figures: 2, Tables: 0, Pages: 7, Words: 3633
                Funding
                Funded by: Reserve Talents of Young and Middle‐aged Academic and Technical Leaders in Yunnan Province
                Award ID: 202105AC160044
                Categories
                Subject: Original Article
                Subject: Original Articles
                Custom metadata
                source-schema-version-number 2.0
                cover-date December 2023
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.5 mode:remove_FC converted:06.12.2023

                Keywords: causal effect,long covid‐19,mendelian randomization study,severe acute respiratory syndrome coronavirus 2,sensorineural hearing loss
                Data availability:
                Keywords: causal effect, long covid‐19, mendelian randomization study, severe acute respiratory syndrome coronavirus 2, sensorineural hearing loss

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