Biological responses to COVID-19: Insights from physiological and blood biomarker profiles

Rosita Zakeri; Andrew Pickles; Ewan Carr; Daniel M. Bean; Kevin O'Gallagher; Zeljko Kraljewic; Tom Searle; Anthony Shek; James B. Galloway; James T. H. Teo; Ajay M. Shah; Richard J. B. Dobson; Rebecca Bendayan

doi:10.1016/j.retram.2021.103276

Biological responses to COVID-19: Insights from physiological and blood biomarker profiles

Rosita Zakeri, Andrew Pickles, Ewan Carr, Daniel M. Bean, Kevin O'Gallagher, Zeljko Kraljewic, Tom Searle, Anthony Shek, James B. Galloway, James T. H. Teo, Ajay M. Shah, Richard J. B. Dobson, Rebecca Bendayan

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Background: Understanding the spectrum and course of biological responses to coronavirus disease 2019 (COVID-19) may have important therapeutic implications. We sought to characterise biological responses among patients hospitalised with severe COVID-19 based on serial, routinely collected, physiological and blood biomarker values. Methods and findings: We performed a retrospective cohort study of 1335 patients hospitalised with laboratory-confirmed COVID-19 (median age 70 years, 56 % male), between 1st March and 30th April 2020. Latent profile analysis was performed on serial physiological and blood biomarkers. Patient characteristics, comorbidities and rates of death and admission to intensive care, were compared between the latent classes. A five class solution provided the best fit. Class 1 “Typical response” exhibited a moderately elevated and rising C-reactive protein (CRP), stable lymphopaenia, and the lowest rates of 14-day adverse outcomes. Class 2 “Rapid hyperinflammatory response” comprised older patients, with higher admission white cell and neutrophil counts, which declined over time, accompanied by a very high and rising CRP and platelet count, and exibited the highest mortality risk. Class 3 “Progressive inflammatory response” was similar to the typical response except for a higher and rising CRP, though similar mortality rate. Class 4 “Inflammatory response with kidney injury” had prominent lymphopaenia, moderately elevated (and rising) CRP, and severe renal failure. Class 5 “Hyperinflammatory response with kidney injury” comprised older patients, with a very high and rising CRP, and severe renal failure that attenuated over time. Physiological measures did not substantially vary between classes at baseline or early admission. Conclusions and relevance: Our identification of five distinct classes of biomarker profiles provides empirical evidence for heterogeneous biological responses to COVID-19. Early hyperinflammatory responses and kidney injury may signify unique pathophysiology that requires targeted therapy.

Original language	English
Article number	103276
Journal	Current Research in Translational Medicine
Volume	69
Issue number	2
DOIs	https://doi.org/10.1016/j.retram.2021.103276
Publication status	Published - May 2021

Keywords

Biomarkers
Classes
Inflammation
SARS-CoV-2

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10.1016/j.retram.2021.103276

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Zakeri, R., Pickles, A., Carr, E., Bean, D. M., O'Gallagher, K., Kraljewic, Z., Searle, T., Shek, A., Galloway, J. B., Teo, J. T. H., Shah, A. M., Dobson, R. J. B., & Bendayan, R. (2021). Biological responses to COVID-19: Insights from physiological and blood biomarker profiles. Current Research in Translational Medicine, 69(2), Article 103276. https://doi.org/10.1016/j.retram.2021.103276

@article{5eb840d3373c41e490f055528acef867,

title = "Biological responses to COVID-19: Insights from physiological and blood biomarker profiles",

abstract = "Background: Understanding the spectrum and course of biological responses to coronavirus disease 2019 (COVID-19) may have important therapeutic implications. We sought to characterise biological responses among patients hospitalised with severe COVID-19 based on serial, routinely collected, physiological and blood biomarker values. Methods and findings: We performed a retrospective cohort study of 1335 patients hospitalised with laboratory-confirmed COVID-19 (median age 70 years, 56 % male), between 1st March and 30th April 2020. Latent profile analysis was performed on serial physiological and blood biomarkers. Patient characteristics, comorbidities and rates of death and admission to intensive care, were compared between the latent classes. A five class solution provided the best fit. Class 1 “Typical response” exhibited a moderately elevated and rising C-reactive protein (CRP), stable lymphopaenia, and the lowest rates of 14-day adverse outcomes. Class 2 “Rapid hyperinflammatory response” comprised older patients, with higher admission white cell and neutrophil counts, which declined over time, accompanied by a very high and rising CRP and platelet count, and exibited the highest mortality risk. Class 3 “Progressive inflammatory response” was similar to the typical response except for a higher and rising CRP, though similar mortality rate. Class 4 “Inflammatory response with kidney injury” had prominent lymphopaenia, moderately elevated (and rising) CRP, and severe renal failure. Class 5 “Hyperinflammatory response with kidney injury” comprised older patients, with a very high and rising CRP, and severe renal failure that attenuated over time. Physiological measures did not substantially vary between classes at baseline or early admission. Conclusions and relevance: Our identification of five distinct classes of biomarker profiles provides empirical evidence for heterogeneous biological responses to COVID-19. Early hyperinflammatory responses and kidney injury may signify unique pathophysiology that requires targeted therapy.",

keywords = "Biomarkers, Classes, Inflammation, SARS-CoV-2",

author = "Rosita Zakeri and Andrew Pickles and Ewan Carr and Bean, {Daniel M.} and Kevin O'Gallagher and Zeljko Kraljewic and Tom Searle and Anthony Shek and Galloway, {James B.} and Teo, {James T. H.} and Shah, {Ajay M.} and Dobson, {Richard J. B.} and Rebecca Bendayan",

note = "Funding Information: The authors acknowledge use of the research computing facility at King{\textquoteright}s College London, Rosalind ( https://rosalind.kcl.ac.uk ), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Centres at South London & Maudsley and Guy{\textquoteright}s & St. Thomas{\textquoteright} NHS Foundation Trusts, and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy{\textquoteright}s & St. Thomas{\textquoteright} Charity (TR130505). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, King{\textquoteright}s College London, or the Department of Health and Social Care. Funding Information: RB is funded in part by grant MR/R016372/1 for the King{\textquoteright}s College London MRC Skills Development Fellowship programme funded by the UK Medical Research Council (MRC, https://mrc.ukri.org ) and by grant IS-BRC-1215-20018 for the National Institute for Health Research (NIHR, https://www.nihr.ac.uk ) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London. Funding Information: RJBD is supported by: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London, London, U.K . (2) Health Data Research UK, which is funded by the UK Medical Research Council , Engineering and Physical Sciences Research Council , Economic and Social Research Council , Department of Health and Social Care (England) , Chief Scientist Office of the Scottish Government Health and Social Care Directorates , Health and Social Care Research and Development Division (Welsh Government) , Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust . (3) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union{\textquoteright}s Horizon 2020 research and innovation programme and EFPIA ; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC. (4) The National Institute for Health Research University College London Hospitals Biomedical Research Centre. (5) National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London. (5) The UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare Funding Information: AMS is supported by the British Heart Foundation ( CH/1999001/11735 ), the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy{\textquoteright}s & St Thomas{\textquoteright} NHS Foundation Trust and King{\textquoteright}s College London ( IS-BRC-1215-20006 ), and the Fondation Leducq . Funding Information: JTHT received research support and funding from InnovateUK, Bristol-Myers-Squibb, iRhythm Technologies, and holds shares <£5000 in Glaxo Smithkline and Biogen. All other authors declare that they have no competing interests. Funding Information: RZ is supported by a King{\textquoteright}s Prize Fellowship and the British Heart Foundation Centre for Cardiovascular Research Excellence at King{\textquoteright}s College London . Funding Information: KO{\textquoteright}G is supported by an MRC Clinical Training Fellowship (MR/R017751/1). Funding Information: AS is supported by a King{\textquoteright}s Medical Research Trust studentship . Funding Information: DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 ( https://www.hdruk.ac.uk ). Funding Information: AP is partially supported by NIHR NF-SI-0617-10120 and NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London . Publisher Copyright: {\textcopyright} 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = may,

doi = "10.1016/j.retram.2021.103276",

language = "English",

volume = "69",

journal = "Current Research in Translational Medicine",

issn = "2452-3186",

publisher = "Elsevier Masson",

number = "2",

}

TY - JOUR

T1 - Biological responses to COVID-19: Insights from physiological and blood biomarker profiles

AU - Zakeri, Rosita

AU - Pickles, Andrew

AU - Carr, Ewan

AU - Bean, Daniel M.

AU - O'Gallagher, Kevin

AU - Kraljewic, Zeljko

AU - Searle, Tom

AU - Shek, Anthony

AU - Galloway, James B.

AU - Teo, James T. H.

AU - Shah, Ajay M.

AU - Dobson, Richard J. B.

AU - Bendayan, Rebecca

N1 - Funding Information: The authors acknowledge use of the research computing facility at King’s College London, Rosalind ( https://rosalind.kcl.ac.uk ), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Centres at South London & Maudsley and Guy’s & St. Thomas’ NHS Foundation Trusts, and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy’s & St. Thomas’ Charity (TR130505). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, King’s College London, or the Department of Health and Social Care. Funding Information: RB is funded in part by grant MR/R016372/1 for the King’s College London MRC Skills Development Fellowship programme funded by the UK Medical Research Council (MRC, https://mrc.ukri.org ) and by grant IS-BRC-1215-20018 for the National Institute for Health Research (NIHR, https://www.nihr.ac.uk ) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Funding Information: RJBD is supported by: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, U.K . (2) Health Data Research UK, which is funded by the UK Medical Research Council , Engineering and Physical Sciences Research Council , Economic and Social Research Council , Department of Health and Social Care (England) , Chief Scientist Office of the Scottish Government Health and Social Care Directorates , Health and Social Care Research and Development Division (Welsh Government) , Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust . (3) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA ; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC. (4) The National Institute for Health Research University College London Hospitals Biomedical Research Centre. (5) National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. (5) The UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare Funding Information: AMS is supported by the British Heart Foundation ( CH/1999001/11735 ), the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s & St Thomas’ NHS Foundation Trust and King’s College London ( IS-BRC-1215-20006 ), and the Fondation Leducq . Funding Information: JTHT received research support and funding from InnovateUK, Bristol-Myers-Squibb, iRhythm Technologies, and holds shares <£5000 in Glaxo Smithkline and Biogen. All other authors declare that they have no competing interests. Funding Information: RZ is supported by a King’s Prize Fellowship and the British Heart Foundation Centre for Cardiovascular Research Excellence at King’s College London . Funding Information: KO’G is supported by an MRC Clinical Training Fellowship (MR/R017751/1). Funding Information: AS is supported by a King’s Medical Research Trust studentship . Funding Information: DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 ( https://www.hdruk.ac.uk ). Funding Information: AP is partially supported by NIHR NF-SI-0617-10120 and NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London . Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5

Y1 - 2021/5

N2 - Background: Understanding the spectrum and course of biological responses to coronavirus disease 2019 (COVID-19) may have important therapeutic implications. We sought to characterise biological responses among patients hospitalised with severe COVID-19 based on serial, routinely collected, physiological and blood biomarker values. Methods and findings: We performed a retrospective cohort study of 1335 patients hospitalised with laboratory-confirmed COVID-19 (median age 70 years, 56 % male), between 1st March and 30th April 2020. Latent profile analysis was performed on serial physiological and blood biomarkers. Patient characteristics, comorbidities and rates of death and admission to intensive care, were compared between the latent classes. A five class solution provided the best fit. Class 1 “Typical response” exhibited a moderately elevated and rising C-reactive protein (CRP), stable lymphopaenia, and the lowest rates of 14-day adverse outcomes. Class 2 “Rapid hyperinflammatory response” comprised older patients, with higher admission white cell and neutrophil counts, which declined over time, accompanied by a very high and rising CRP and platelet count, and exibited the highest mortality risk. Class 3 “Progressive inflammatory response” was similar to the typical response except for a higher and rising CRP, though similar mortality rate. Class 4 “Inflammatory response with kidney injury” had prominent lymphopaenia, moderately elevated (and rising) CRP, and severe renal failure. Class 5 “Hyperinflammatory response with kidney injury” comprised older patients, with a very high and rising CRP, and severe renal failure that attenuated over time. Physiological measures did not substantially vary between classes at baseline or early admission. Conclusions and relevance: Our identification of five distinct classes of biomarker profiles provides empirical evidence for heterogeneous biological responses to COVID-19. Early hyperinflammatory responses and kidney injury may signify unique pathophysiology that requires targeted therapy.

AB - Background: Understanding the spectrum and course of biological responses to coronavirus disease 2019 (COVID-19) may have important therapeutic implications. We sought to characterise biological responses among patients hospitalised with severe COVID-19 based on serial, routinely collected, physiological and blood biomarker values. Methods and findings: We performed a retrospective cohort study of 1335 patients hospitalised with laboratory-confirmed COVID-19 (median age 70 years, 56 % male), between 1st March and 30th April 2020. Latent profile analysis was performed on serial physiological and blood biomarkers. Patient characteristics, comorbidities and rates of death and admission to intensive care, were compared between the latent classes. A five class solution provided the best fit. Class 1 “Typical response” exhibited a moderately elevated and rising C-reactive protein (CRP), stable lymphopaenia, and the lowest rates of 14-day adverse outcomes. Class 2 “Rapid hyperinflammatory response” comprised older patients, with higher admission white cell and neutrophil counts, which declined over time, accompanied by a very high and rising CRP and platelet count, and exibited the highest mortality risk. Class 3 “Progressive inflammatory response” was similar to the typical response except for a higher and rising CRP, though similar mortality rate. Class 4 “Inflammatory response with kidney injury” had prominent lymphopaenia, moderately elevated (and rising) CRP, and severe renal failure. Class 5 “Hyperinflammatory response with kidney injury” comprised older patients, with a very high and rising CRP, and severe renal failure that attenuated over time. Physiological measures did not substantially vary between classes at baseline or early admission. Conclusions and relevance: Our identification of five distinct classes of biomarker profiles provides empirical evidence for heterogeneous biological responses to COVID-19. Early hyperinflammatory responses and kidney injury may signify unique pathophysiology that requires targeted therapy.

KW - Biomarkers

KW - Classes

KW - Inflammation

KW - SARS-CoV-2

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U2 - 10.1016/j.retram.2021.103276

DO - 10.1016/j.retram.2021.103276

M3 - Article

SN - 2452-3186

VL - 69

JO - Current Research in Translational Medicine

JF - Current Research in Translational Medicine

IS - 2

M1 - 103276

ER -

Biological responses to COVID-19: Insights from physiological and blood biomarker profiles

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