Exploring causal relationships between COVID-19 and cardiometabolic disorders: A bi-directional Mendelian randomization study

  1. Yong Xiang
  2. Carlos Kwan-Long Chau
  3. Jinghong Qiu
  4. Shitao Rao
  5. Hon-Cheong So

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Abstract

Background

More than 100 million cases of COVID-19 have been reported worldwide. A number of risk factors for infection or severe infection have been identified, however observational studies were subject to confounding bias. In addition, there is still limited knowledge about the complications or medical consequences of the disease.

Methods

Here we performed bi-directional Mendelian randomization (MR) analysis to evaluate causal relationships between liability to COVID-19 (and severe/critical infection) and a wide range of around 30 cardiometabolic disorders (CMD) or traits. Genetic correlation (rg) was assessed by LD score regression(LDSC). The latest GWAS summary statistics from the COVID-19 Host Genetics Initiative was used, which comprised comparisons of general population controls with critically ill, hospitalized and any infected cases.

Results

Overall we observed evidence that liability to COVID-19 or severe infection may be causally associated with higher risks of type 2 diabetes mellitus(T2DM), chronic kidney disease(CKD), ischemic stroke (especially large artery stroke[LAS]) and heart failure(HF) when compared to the general population. On the other hand, our findings suggested that liability to atrial fibrillation (AF), stroke (especially LAS), obesity, diabetes (T1DM and T2DM), low insulin sensitivity and impaired renal function (low eGFR and diabetic kidney disease) may be causal risk factors for COVID-19 or severe disease. In genetic correlation analysis, T2DM, CAD, obesity, fasting insulin, CKD, gout, stroke and urate showed positive rg with critical or hospitalized infection. All above findings passed multiple testing correction at a false discovery rate (FDR)<0.05.

Conclusions

In summary, this study provides evidence for tentative bi-directional causal associations between liability to COVID-19 and severe disease and a number of CM disorders. Further replications and prospective studies are required to verify the findings.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.20.21254008: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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:
    Limitations: There are several limitations for this study. We have employed the latest and largest GWAS summary statistics to date for COVID-19, however the data is based on meta-analysis of a large number of separate studies, and the samples may be heterogeneous. For example, the baseline clinical features, demographics, comorbid disease patterns etc. of patients or controls may differ across cohorts. The control population were unscreened, and therefore asymptomatic patients or those with mild symptoms may be missed. Hospitalization is in general a reasonably good proxy for moderate or severe illness, but the criteria for hospitalization may still differ across countries and cohorts. Although the sample size of COVID-19 GWAS is already quite large, the number of critically ill and hospitalized cases may still be relatively limited when compared to the current standards. As such, a lack of association may be due to lack of power. The same also applies to GWAS of CM disorders or traits with smaller sample sizes. MR is a highly useful methodology that has been successfully applied in cardiovascular medicine 67 and other fields to evaluate causal relationships. However, it is not without limitations. One concern of MR is horizontal pleiotropy (an instrument associated with the outcome not through the exposure), which we have tried to address with different methodologies of different principles. However, each method has its own assumptions (e.g. InSIDE assumption for MR-Egger, s...

    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.

    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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.03.20.21254008 on medRxiv