The relationship between BMI and COVID-19: exploring misclassification and selection bias in a two-sample Mendelian randomisation study
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Abstract
Objective
To use the example of the effect of body mass index (BMI) on COVID-19 susceptibility and severity to illustrate methods to explore potential selection and misclassification bias in Mendelian randomisation (MR) of COVID-19 determinants.
Design
Two-sample MR analysis.
Setting
Summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) and COVID-19 Host Genetics Initiative (HGI) consortia.
Participants
681,275 participants in GIANT and more than 2.5 million people from the COVID-19 HGI consortia.
Exposure
Genetically instrumented BMI.
Main outcome measures
Seven case/control definitions for SARS-CoV-2 infection and COVID-19 severity: very severe respiratory confirmed COVID-19 vs not hospitalised COVID-19 (A1) and vs population (those who were never tested, tested negative or had unknown testing status (A2)); hospitalised COVID-19 vs not hospitalised COVID-19 (B1) and vs population (B2); COVID-19 vs lab/self-reported negative (C1) and vs population (C2); and predicted COVID-19 from self-reported symptoms vs predicted or self-reported non-COVID-19 (D1).
Results
With the exception of A1 comparison, genetically higher BMI was associated with higher odds of COVID-19 in all comparison groups, with odds ratios (OR) ranging from 1.11 (95%CI: 0.94, 1.32) for D1 to 1.57 (95%CI: 1.57 (1.39, 1.78) for A2. As a method to assess selection bias, we found no strong evidence of an effect of COVID-19 on BMI in a ‘no-relevance’ analysis, in which COVID-19 was considered the exposure, although measured after BMI. We found evidence of genetic correlation between COVID-19 outcomes and potential predictors of selection determined a priori (smoking, education, and income), which could either indicate selection bias or a causal pathway to infection. Results from multivariable MR adjusting for these predictors of selection yielded similar results to the main analysis, suggesting the latter.
Conclusions
We have proposed a set of analyses for exploring potential selection and misclassification bias in MR studies of risk factors for SARS-CoV-2 infection and COVID-19 and demonstrated this with an illustrative example. Although selection by socioeconomic position and arelated traits is present, MR results are not substantially affected by selection/misclassification bias in our example. We recommend the methods we demonstrate, and provide detailed analytic code for their use, are used in MR studies assessing risk factors for COVID-19, and other MR studies where such biases are likely in the available data.
Summary
What is already known on this topic
Mendelian randomisation (MR) studies have been conducted to investigate the potential causal relationship between body mass index (BMI) and COVID-19 susceptibility and severity.
There are several sources of selection (e.g. when only subgroups with specific characteristics are tested or respond to study questionnaires) and misclassification (e.g. those not tested are assumed not to have COVID-19) that could bias MR studies of risk factors for COVID-19.
Previous MR studies have not explored how selection and misclassification bias in the underlying genome-wide association studies could bias MR results.
What this study adds
Using the most recent release of the COVID-19 Host Genetics Initiative data (with data up to June 2021), we demonstrate a potential causal effect of BMI on susceptibility to detected SARS-CoV-2 infection and on severe COVID-19 disease, and that these results are unlikely to be substantially biased due to selection and misclassification.
This conclusion is based on no evidence of an effect of COVID-19 on BMI (a ‘no-relevance control’ study, as BMI was measured before the COVID-19 pandemic) and finding genetic correlation between predictors of selection (e.g. socioeconomic position) and COVID-19 for which multivariable MR supported a role in causing susceptibility to infection.
We recommend studies use the set of analyses demonstrated here in future MR studies of COVID-19 risk factors, or other examples where selection bias is likely.
Article activity feed
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SciScore for 10.1101/2022.03.03.22271836: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations of the study: To our knowledge this is the first MR study of COVID-19 outcomes to take a systematic approach to explore selection and misclassification bias in this area, including a novel application of ‘no-relevance’ analyses and exploiting time-varying associations. In our illustrative example and in the ‘no-relevance’ analyses we have …
SciScore for 10.1101/2022.03.03.22271836: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations of the study: To our knowledge this is the first MR study of COVID-19 outcomes to take a systematic approach to explore selection and misclassification bias in this area, including a novel application of ‘no-relevance’ analyses and exploiting time-varying associations. In our illustrative example and in the ‘no-relevance’ analyses we have conducted appropriate sensitivity analyses to assess the plausibility of the MR assumptions, including comparing estimates from a BMI GWAS without UK Biobank participants included and a negative control design to explore possible bias due to population stratification (7). The summary statistics used for COVID-19 are from the largest available GWAS, incorporating many global studies with differing selection processes (31). The ‘no-relevance’ analysis results may have been biased by weak instruments, as there were far fewer SNPs for any COVID-19 instruments than for BMI (the exposure in our ‘real’ illustrative example). Weak instrument bias in two-sample MR is expected to bias estimates towards the null. However, the F-statistics for the ‘no-relevance’ COVID-19 instruments varied from 22 to 134, suggesting weak instrument bias is unlikely. For some of the COVID-19 exposures in the ‘no-relevance’ analyses we had to relax the p-value threshold in order to have genetic instruments, and this could result in increased likelihood of horizontal pleiotropy. Consistent results from MR-Egger, weighted median analyses, and the ‘...
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.
Results from scite Reference Check: We found no unreliable references.
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