High Liver Fat Associates with Higher Risk of Developing Symptomatic COVID-19 Infection - Initial UK Biobank Observations

  1. A Roca-Fernández
  2. A Dennis
  3. R Nicolls
  4. J McGonigle
  5. M Kelly
  6. R Banerjee

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Abstract

Background

A high proportion of COVID-19 patients develop acute liver dysfunction. Early research has suggested that pre-existing fatty liver disease may be a significant risk factor for hospitalisation. Liver fat, in particular, is a modifiable parameter and can be a target for public health policy and individual patient plans. In this study we aimed to assess pre-existing liver disease as a risk factor for developing symptomatic COVID-19.

Methods

From 502,506 participants from the UK Biobank, 42,146 underwent MRI (aged 45–82), and had measures of liver fat, liver fibroinflammatory disease and liver iron. Patients were censored on May 28th to determine how many had tested for COVID-19 with symptomatic disease. UK testing was restricted to those with symptoms in hospital. COVID-19 symptoms included fever, dry cough, sore throat, diarrhoea and fatigue. Univariate analysis was performed on liver phenotypic biomarkers to determine if these variables increased risk of symptomatic COVID-19, and compared to previously described risk factors associated with severe COVID-19, including to age, ethnicity, gender and obesity,

Findings

Increased liver fat was associated with a higher risk for symptomatic confirmed COVID-19 in this population in univariate analysis(OR:1.85, p = 0.03). In obese participants, only those with concomitant fatty liver(≥10%) were at increased risk(OR:2.96, p = 0.02), with those having normal liver fat (< 5%) showing no increased risk(OR:0.36, p = 0.09).

Conclusions

UK Biobank data demonstrated an association between pre-existing liver disease and obesity with severe COVID-19, with higher proportions of liver fat in obese individuals a likely risk factor for symptomatic disease and severity.

Public policy measures to protect patients with liver disease who may have almost double the risk of the general population should be considered, especially as dietary and pharmacological strategies to reduce body weight and liver fat already exist.

Funding

University of Oxford, Innovate UK, UK Biobank. Authors are employees of Perspectum Ltd.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.04.20122457: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The UKB has approval from the North West Multi-Centre Research Ethics Committee (MREC) and obtained written informed consent from all participants prior to the study.
    Consent: The UKB has approval from the North West Multi-Centre Research Ethics Committee (MREC) and obtained written informed consent from all participants prior to the study.
    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:
    The main limitation of our study is the relatively small proportion of UKB participants for which testing results are available. This analysis was performed on the second release of COVID-19 testing data and subsequent analyses as more data are released will provide additional power to investigate a larger number of exposures. This limitation is largely a result of UK policy at the time to only test those patients requiring hospitalisation; however, one benefit of this is policy is the ability to infer that those participants who tested positive had severe COVID-19 disease requiring hospitalisation. This inference will not be possible in future releases of COVID-19 testing data following changes in the UK testing policy to support widespread testing.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.1101/2020.06.04.20122457v1 on medRxiv