Abdominal imaging associates body composition with COVID-19 severity

  1. Nicolas Basty
  2. Elena P. Sorokin
  3. Marjola Thanaj
  4. Ramprakash Srinivasan
  5. Brandon Whitcher
  6. Jimmy D. Bell
  7. Madeleine Cule
  8. E. Louise Thomas

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The main drivers of COVID-19 disease severity and the impact of COVID-19 on long-term health after recovery are yet to be fully understood. Medical imaging studies investigating COVID-19 to date have mostly been limited to small datasets and post-hoc analyses of severe cases. The UK Biobank recruited recovered SARS-CoV-2 positive individuals (n = 967) and matched controls (n = 913) who were extensively imaged prior to the pandemic and underwent follow-up scanning. In this study, we investigated longitudinal changes in body composition, as well as the associations of pre-pandemic image-derived phenotypes with COVID-19 severity. Our longitudinal analysis, in a population of mostly mild cases, associated a decrease in lung volume with SARS-CoV-2 positivity. We also observed that increased visceral adipose tissue and liver fat, and reduced muscle volume, prior to COVID-19, were associated with COVID-19 disease severity. Finally, we trained a machine classifier with demographic, anthropometric and imaging traits, and showed that visceral fat, liver fat and muscle volume have prognostic value for COVID-19 disease severity beyond the standard demographic and anthropometric measurements. This combination of image-derived phenotypes from abdominal MRI scans and ensemble learning to predict risk may have future clinical utility in identifying populations at-risk for a severe COVID-19 outcome.

Article activity feed

  1. Version published to 10.1371/journal.pone.0283506
  2. ScreenIT

    SciScore for 10.1101/2022.02.22.22270091: (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

    Software and Algorithms
    SentencesResources
    More information may be found on the UKBB researchers and resource catalogue pages (https://www.ukbiobank.ac.uk).
    https://www.ukbiobank.ac.uk
    suggested: (UK Biobank, RRID:SCR_012815)
    Visualization was performed using the ggplot2 v3.3.5 package.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    We developed random forest [42] classifiers for COVID-19 severity prediction in python 3.7.2 using the scikit-learn 1.0.2 package [43].
    python
    suggested: (IPython, RRID:SCR_001658)
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)

    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:
    A limitation of the study related to the relative time between scans, which ranged between 1.0-7.3 years (3.2 on average), though we adjusted our models for the difference in age between scans. Although this was a prospective study, subjects were not recruited for baseline MRI scanning in anticipation of the COVID-19 pandemic, therefore changes in the body composition could have arisen from changes in lifestyle or disease progression, unrelated to COVID-19. However, our analysis controlled for multiple confounding factors. Moreover, in a previous separate longitudinal study using a larger UKBB cohort, we reported small but significant changes in several tissues and organs [73], although none of these were further altered by COVID-19 in our current study. Thus, the fact that in our previous longitudinal study, we did not observe changes in lung volume further increases confidence in our current findings. A further limitation of the study is that the UKBB population does not include younger people or children, with initial recruitment in 2007 covering participants aged 40 to 69 of age, therefore it is unclear whether our observations can be extrapolated to other age groups. In conclusion, body composition assessed via MRI and image-derived phenotypes can provide significant insight into the impact of COVID-19 and could help to understand its long-term impact on those suffering its aftermath. Our study showed a significant decrease in lung volume in SARS-CoV-2 infected cases. We...

    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.

    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.

  3. Version published to 10.1101/2022.02.22.22270091 on medRxiv