1. Epidemiology and Global Health
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Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population

  1. Nightingale Health UK Biobank Initiative
  2. Heli Julkunen
  3. Anna Cichońska
  4. P Eline Slagboom
  5. Peter Würtz  Is a corresponding author
  1. Nightingale Health Ltd, Finland
  2. Leiden University Medical Center, Netherlands
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Cite this article as: eLife 2021;10:e63033 doi: 10.7554/eLife.63033


Biomarkers of low-grade inflammation have been associated with susceptibility to a severe infectious disease course, even when measured prior to disease onset. We investigated whether metabolic biomarkers measured by nuclear magnetic resonance (NMR) spectroscopy could be associated with susceptibility to severe pneumonia (2507 hospitalised or fatal cases) and severe COVID-19 (652 hospitalised cases) in 105,146 generally healthy individuals from UK Biobank, with blood samples collected 2007–2010. The overall signature of metabolic biomarker associations was similar for the risk of severe pneumonia and severe COVID-19. A multi-biomarker score, comprised of 25 proteins, fatty acids, amino acids and lipids, was associated equally strongly with enhanced susceptibility to severe COVID-19 (odds ratio 2.9 [95%CI 2.1–3.8] for highest vs lowest quintile) and severe pneumonia events occurring 7–11 years after blood sampling (2.6 [1.7–3.9]). However, the risk for severe pneumonia occurring during the first 2 years after blood sampling for people with elevated levels of the multi-biomarker score was over four times higher than for long-term risk (8.0 [4.1–15.6]). If these hypothesis generating findings on increased susceptibility to severe pneumonia during the first few years after blood sampling extend to severe COVID-19, metabolic biomarker profiling could potentially complement existing tools for identifying individuals at high risk. These results provide novel molecular understanding on how metabolic biomarkers reflect the susceptibility to severe COVID-19 and other infections in the general population.

Data availability

The data are available for approved researchers from UK Biobank. The metabolic biomarker data has been released to the UK Biobank resource in March 2021.

The following previously published data sets were used
    1. Sudlow et al
    (2015) UK Biobank

Article and author information

Author details

  1. Nightingale Health UK Biobank Initiative

  2. Heli Julkunen

    R&D, Nightingale Health Ltd, Helsinki, Finland
    Competing interests
    Heli Julkunen, HJ is employee of Nightingale Health Ltd..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4282-0248
  3. Anna Cichońska

    R&D, Nightingale Health Ltd, Helsinki, Finland
    Competing interests
    Anna Cichońska, AC is employee and hold stock options with Nightingale Health Ltd..
  4. P Eline Slagboom

    Leiden University Medical Center, Leiden, Netherlands
    Competing interests
    No competing interests declared.
  5. Peter Würtz

    R&D, Nightingale Health Ltd, Helsinki, Finland
    For correspondence
    Competing interests
    Peter Würtz, PW is employee and shareholder of Nightingale Health Ltd..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5832-0221


The study was funded by Nightingale Health Plc. Three of the study authors are employees of Nightingale Health Plc.


Human subjects: The UK Biobank recruited 502 639 participants aged 37-70 years in 22 assessment centres across the UK. All participants provided written informed consent and ethical approval was obtained from the North West Multi-Center Research Ethics Committee. Details of the design of the UK Biobank have been reported previously (Sudlow et al PLOS Medicine 2015). The current analysis was approved under UK Biobank Project 30418.

Reviewing Editor

  1. Edward D Janus, University of Melbourne, Australia

Publication history

  1. Received: September 11, 2020
  2. Accepted: May 2, 2021
  3. Accepted Manuscript published: May 4, 2021 (version 1)
  4. Version of Record published: June 2, 2021 (version 2)


© 2021, Julkunen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


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  1. Further reading

Further reading

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    Identifying the key vector and host species that drive the transmission of zoonotic pathogens is notoriously difficult but critical for disease control. We present a nested approach for quantifying the importance of host and vectors that integrates species’ physiological competence with their ecological traits. We apply this framework to a medically important arbovirus, Ross River virus (RRV), in Brisbane, Australia. We find that vertebrate hosts with high physiological competence are not the most important for community transmission; interactions between hosts and vectors largely underpin the importance of host species. For vectors, physiological competence is highly important. Our results identify primary and secondary vectors of RRV and suggest two potential transmission cycles in Brisbane: an enzootic cycle involving birds and an urban cycle involving humans. The framework accounts for uncertainty from each fitted statistical model in estimates of species’ contributions to transmission and has has direct application to other zoonotic pathogens.

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    The virus SARS-CoV-2 can exploit biological vulnerabilities (e.g. host proteins) in susceptible hosts that predispose to the development of severe COVID-19.


    To identify host proteins that may contribute to the risk of severe COVID-19, we undertook proteome-wide genetic colocalisation tests, and polygenic (pan) and cis-Mendelian randomisation analyses leveraging publicly available protein and COVID-19 datasets.


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    Our work provides a prioritised list of host targets potentially exploited by SARS-CoV-2 and is a precursor for further research on CD209 and FAS as therapeutically tractable targets for COVID-19.


    MAK, JSc, JH, AB, DO, MC, EMM, MG, ID were funded by Open Targets. J.Z. and T.R.G were funded by the UK Medical Research Council Integrative Epidemiology Unit (MC_UU_00011/4). JSh and GJW were funded by the Wellcome Trust Grant 206194. This research was funded in part by the Wellcome Trust [Grant 206194]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.