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
Research Article
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Cite this article as: eLife 2021;10:e63033 doi: 10.7554/eLife.63033
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Abstract

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
    https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779.

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
    peter.wurtz@nightingalehealth.com
    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

Funding

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

Ethics

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)

Copyright

© 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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    2. Genetics and Genomics
    Joshua Batson et al.
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    Mosquitoes are major infectious disease-carrying vectors. Assessment of current and future risks associated with the mosquito population requires knowledge of the full repertoire of pathogens they carry, including novel viruses, as well as their blood meal sources. Unbiased metatranscriptomic sequencing of individual mosquitoes offers a straightforward, rapid and quantitative means to acquire this information. Here, we profile 148 diverse wild-caught mosquitoes collected in California and detect sequences from eukaryotes, prokaryotes, 24 known and 46 novel viral species. Importantly, sequencing individuals greatly enhanced the value of the biological information obtained. It allowed us to a) speciate host mosquito, b) compute the prevalence of each microbe and recognize a high frequency of viral co-infections, c) associate animal pathogens with specific blood meal sources, and d) apply simple co-occurrence methods to recover previously undetected components of highly prevalent segmented viruses. In the context of emerging diseases, where knowledge about vectors, pathogens, and reservoirs is lacking, the approaches described here can provide actionable information for public health surveillance and intervention decisions.

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    Torsten Dahlén et al.
    Research Article

    Background There are multiple known associations between the ABO and RhD blood groups and disease. No systematic population-based studies elucidating associations between a large number of disease categories and blood group have been conducted.

    Methods Using SCANDAT3-S, a comprehensive nationwide blood donation-transfusion database, we modelled outcomes for 1,217 disease categories including 70 million person-years of follow-up, accruing from 5.1 million individuals.

    Results We discovered 49 and 1 associations between a disease and ABO and RhD blood group, respectively, after adjustment for multiple testing. We identified new associations such as kidney stones and blood group B as compared to O. We also expanded previous knowledge on other associations such as pregnancy-induced hypertension and blood group A and AB as compared to O and RhD positive as compared to negative.

    Conclusion Our findings generate strong further support for previously known associations, but also indicate new interesting relations.

    Funding Swedish Research Council.