Association between Alzheimer’s disease and COVID-19: A bidirectional Mendelian randomization
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Abstract
Background
In observational studies, Alzheimer’s disease (AD) has been associated with an increased risk of Coronavirus disease 2019 (COVID-19), and the prognosis of COVID-19 can affect nervous systems. However, the causality between these conditions remains to be determined.
Methods
This study sought to investigate the bidirectional causal relations of AD with COVID-19 using two-sample Mendelian randomization (MR) analysis.
Results
We found that genetically predicted AD was significantly associated with higher risk of severe COVID-19 (odds ratio [OR], 3.329; 95% confidence interval [CI], 1.139-9.725; P =0.028). It’s interesting that genetically predicted severe COVID-19 was also significantly associated with higher risk of AD (OR, 1.004; 95% CI, 1.001-1.007; P =0.018). In addition, the two strong genetic variants associated with severe COVID-19 was associated with higher AD risk (OR, 1.018; 95% CI, 1.003-1.034; P =0.018). There is no evidence to support that genetically predicted AD was significantly associated with COVID-19 susceptibility, and vice versa. No obvious pleiotropy bias and heterogeneity were observed.
Conclusion
Overall, AD may causally affect severe COVID-19, and vice versa, performing bidirectional regulation through independent biological pathways.
Article activity feed
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SciScore for 10.1101/2020.07.27.20163212: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We should notice some limitations. First of all, stratified analyses or analyses adjusted for other covariates were not possible on the account of using available summary statistics datasets. The GWAS of Severe COVID-19 with respiratory failure and the COVID-19 host genetics initiative from UK Biobank individuals include small sample …
SciScore for 10.1101/2020.07.27.20163212: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We should notice some limitations. First of all, stratified analyses or analyses adjusted for other covariates were not possible on the account of using available summary statistics datasets. The GWAS of Severe COVID-19 with respiratory failure and the COVID-19 host genetics initiative from UK Biobank individuals include small sample size, which may lead to small effect for the MR estimate and limit the IVs for COVID-19 for reverse MR analysis. The annotated genes by genetic variants were only a preliminary exploration of the possible mechanisms, and gene expression quantitative trait loci analysis need to be validated in the future studies. Last, our findings are based on European cohort make it difficult to represent the universal conclusions for other ethnic groups. In conclusion, our study showed that AD may causally affect severe COVID-19, which were of clinical significance for particular attention to individuals with AD during this pandemic. Conversely, severe COVID-19 may induce AD, indicating that the future study should pay attention to the prognosis of COVID-19.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.07.27.20163212: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
We should notice some limitations. First of all, stratified analyses or analyses adjusted for other covariates were not possible on the account of using available summary statistics datasets. The GWAS of Severe COVID-19 with respiratory failure and the COVID-19 host genetics initiative from UK Biobank individuals include small sample …
SciScore for 10.1101/2020.07.27.20163212: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
We should notice some limitations. First of all, stratified analyses or analyses adjusted for other covariates were not possible on the account of using available summary statistics datasets. The GWAS of Severe COVID-19 with respiratory failure and the COVID-19 host genetics initiative from UK Biobank individuals include small sample size, which may lead to small effect for the MR estimate and limit the IVs for COVID-19 for reverse MR analysis. The annotated genes by genetic variants were only a preliminary exploration of the possible mechanisms, and gene expression quantitative trait loci analysis need to be validated in the future studies. Last, our findings are based on European cohort make it difficult to represent the universal conclusions for other ethnic groups. In conclusion, our study showed that AD may causally affect severe COVID-19, which were of clinical significance for particular attention to individuals with AD during this pandemic. Conversely, severe COVID-19 may induce AD, indicating that the future study should pay attention to the prognosis of COVID-19. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Disclosure of Potential Conflicts of Interest All authors have approved the manuscript and its submission. No potential conflicts of interest were disclosed by the authors. Funding/Support The study was supported by grants from the National Key R&D Program of China (2017YFE0118800)—European Commission Horizon 2020 (779238-PRODEMOS), the National Natural Science Foundation of China (81872682 and 81773527), and the China-Australian Collaborative Grant (NSFC 81561128020-NHMRC APP1112767). Di Liu was supported by China Scholarship Council (CSC 201908110339). Role of the Funder/Sponsor The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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SciScore for 10.1101/2020.07.27.20163212: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
We should notice some limitations. First of all, stratified analyses or analyses adjusted for other covariates were not possible on the account of using available summary statistics datasets. The GWAS of Severe COVID-19 with respiratory failure and the COVID-19 host genetics initiative from UK Biobank individuals include small sample …
SciScore for 10.1101/2020.07.27.20163212: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
We should notice some limitations. First of all, stratified analyses or analyses adjusted for other covariates were not possible on the account of using available summary statistics datasets. The GWAS of Severe COVID-19 with respiratory failure and the COVID-19 host genetics initiative from UK Biobank individuals include small sample size, which may lead to small effect for the MR estimate and limit the IVs for COVID-19 for reverse MR analysis. The annotated genes by genetic variants were only a preliminary exploration of the possible mechanisms, and gene expression quantitative trait loci analysis need to be validated in the future studies. Last, our findings are based on European cohort make it difficult to represent the universal conclusions for other ethnic groups. In conclusion, our study showed that AD may causally affect severe COVID-19, which were of clinical significance for particular attention to individuals with AD during this pandemic. Conversely, severe COVID-19 may induce AD, indicating that the future study should pay attention to the prognosis of COVID-19. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Disclosure of Potential Conflicts of Interest All authors have approved the manuscript and its submission. No potential conflicts of interest were disclosed by the authors. Funding/Support The study was supported by grants from the National Key R&D Program of China (2017YFE0118800)—European Commission Horizon 2020 (779238-PRODEMOS), the National Natural Science Foundation of China (81872682 and 81773527), and the China-Australian Collaborative Grant (NSFC 81561128020-NHMRC APP1112767). Di Liu was supported by China Scholarship Council (CSC 201908110339). Role of the Funder/Sponsor The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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