Distinct blood inflammatory biomarker clusters stratify host phenotypes during the middle phase of COVID-19

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The associations between clinical phenotypes of coronavirus disease 2019 (COVID-19) and the host inflammatory response during the transition from peak illness to convalescence are not yet well understood. Blood plasma samples were collected from 129 adult SARS-CoV-2 positive inpatient and outpatient participants between April 2020 and January 2021, in a multi-center prospective cohort study at 8 military hospitals across the United States. Plasma inflammatory protein biomarkers were measured in samples from 15 to 28 days post symptom onset. Topological Data Analysis (TDA) was used to identify patterns of inflammation, and associations with peak severity (outpatient, hospitalized, ICU admission or death), Charlson Comorbidity Index (CCI), and body mass index (BMI) were evaluated using logistic regression. The study population (n = 129, 33.3% female, median 41.3 years of age) included 77 outpatient, 31 inpatient, 16 ICU-level, and 5 fatal cases. Three distinct inflammatory biomarker clusters were identified and were associated with significant differences in peak disease severity (p < 0.001), age (p < 0.001), BMI (p < 0.001), and CCI (p = 0.001). Host-biomarker profiles stratified a heterogeneous population of COVID-19 patients during the transition from peak illness to convalescence, and these distinct inflammatory patterns were associated with comorbid disease and severe illness due to COVID-19.

Related collections

Most cited references 32

Record: found
Abstract: found
Article: not found

The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

Jeffrey Leek, W Johnson, Hilary Parker … (2012)

Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects-when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.

0 comments Cited 1885 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study

Matthew Cummings, Matthew Baldwin, Darryl Abrams … (2020)

Summary Background Over 40 000 patients with COVID-19 have been hospitalised in New York City (NY, USA) as of April 28, 2020. Data on the epidemiology, clinical course, and outcomes of critically ill patients with COVID-19 in this setting are needed. Methods This prospective observational cohort study took place at two NewYork-Presbyterian hospitals affiliated with Columbia University Irving Medical Center in northern Manhattan. We prospectively identified adult patients (aged ≥18 years) admitted to both hospitals from March 2 to April 1, 2020, who were diagnosed with laboratory-confirmed COVID-19 and were critically ill with acute hypoxaemic respiratory failure, and collected clinical, biomarker, and treatment data. The primary outcome was the rate of in-hospital death. Secondary outcomes included frequency and duration of invasive mechanical ventilation, frequency of vasopressor use and renal replacement therapy, and time to in-hospital clinical deterioration following admission. The relation between clinical risk factors, biomarkers, and in-hospital mortality was modelled using Cox proportional hazards regression. Follow-up time was right-censored on April 28, 2020 so that each patient had at least 28 days of observation. Findings Between March 2 and April 1, 2020, 1150 adults were admitted to both hospitals with laboratory-confirmed COVID-19, of which 257 (22%) were critically ill. The median age of patients was 62 years (IQR 51–72), 171 (67%) were men. 212 (82%) patients had at least one chronic illness, the most common of which were hypertension (162 [63%]) and diabetes (92 [36%]). 119 (46%) patients had obesity. As of April 28, 2020, 101 (39%) patients had died and 94 (37%) remained hospitalised. 203 (79%) patients received invasive mechanical ventilation for a median of 18 days (IQR 9–28), 170 (66%) of 257 patients received vasopressors and 79 (31%) received renal replacement therapy. The median time to in-hospital deterioration was 3 days (IQR 1–6). In the multivariable Cox model, older age (adjusted hazard ratio [aHR] 1·31 [1·09–1·57] per 10-year increase), chronic cardiac disease (aHR 1·76 [1·08–2·86]), chronic pulmonary disease (aHR 2·94 [1·48–5·84]), higher concentrations of interleukin-6 (aHR 1·11 [95%CI 1·02–1·20] per decile increase), and higher concentrations of D-dimer (aHR 1·10 [1·01–1·19] per decile increase) were independently associated with in-hospital mortality. Interpretation Critical illness among patients hospitalised with COVID-19 in New York City is common and associated with a high frequency of invasive mechanical ventilation, extrapulmonary organ dysfunction, and substantial in-hospital mortality. Funding National Institute of Allergy and Infectious Diseases and the National Center for Advancing Translational Sciences, National Institutes of Health, and the Columbia University Irving Institute for Clinical and Translational Research.

0 comments Cited 1136 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Longitudinal analyses reveal immunological misfiring in severe COVID-19

Carolina Lucas, Patrick Wong, Jon Klein … (2020)

Recent studies have provided insights into the pathogenesis of coronavirus disease 2019 (COVID-19) 1–4 . However, the longitudinal immunological correlates of disease outcome remain unclear. Here we serially analysed immune responses in 113 patients with moderate or severe COVID-19. Immune profiling revealed an overall increase in innate cell lineages, with a concomitant reduction in T cell number. An early elevation in cytokine levels was associated with worse disease outcomes. Following an early increase in cytokines, patients with moderate COVID-19 displayed a progressive reduction in type 1 (antiviral) and type 3 (antifungal) responses. By contrast, patients with severe COVID-19 maintained these elevated responses throughout the course of the disease. Moreover, severe COVID-19 was accompanied by an increase in multiple type 2 (anti-helminths) effectors, including interleukin-5 (IL-5), IL-13, immunoglobulin E and eosinophils. Unsupervised clustering analysis identified four immune signatures, representing growth factors (A), type-2/3 cytokines (B), mixed type-1/2/3 cytokines (C), and chemokines (D) that correlated with three distinct disease trajectories. The immune profiles of patients who recovered from moderate COVID-19 were enriched in tissue reparative growth factor signature A, whereas the profiles of those with who developed severe disease had elevated levels of all four signatures. Thus, we have identified a maladapted immune response profile associated with severe COVID-19 and poor clinical outcome, as well as early immune signatures that correlate with divergent disease trajectories.

0 comments Cited 1130 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Paul W. Blair: pblair@aceso-sepsis.org

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 28 December 2022

Publication date PMC-release: 28 December 2022

Publication date Collection: 2022

Volume: 12

Electronic Location Identifier: 22471

Affiliations

[1 ]GRID grid.201075.1, ISNI 0000 0004 0614 9826, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., ; 6720A Rockledge Dr, Bethesda, MD 20817 USA

[2 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Department of Pathology, , Uniformed Services University of the Health Sciences, ; Bethesda, MD USA

[3 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, , Uniformed Services University of the Health Sciences, ; Bethesda, MD USA

[4 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Department of Medicine, , Uniformed Services University of the Health Sciences, ; Bethesda, MD USA

[5 ]GRID grid.461685.8, ISNI 0000 0004 0467 8038, Brooke Army Medical Center, , Joint Base San Antonio-Ft Sam Houston, ; San Antonio, TX USA

[6 ]GRID grid.241167.7, ISNI 0000 0001 2185 3318, Departments of Internal Medicine and Anesthesiology, , Wake Forest School of Medicine, ; Winston-Salem, NC USA

[7 ]GRID grid.415879.6, ISNI 0000 0001 0639 7318, Naval Medical Center San Diego, ; San Diego, CA USA

[8 ]GRID grid.413661.7, ISNI 0000 0004 0595 1323, Fort Belvoir Community Hospital, ; Fort Belvoir, VA USA

[9 ]GRID grid.416237.5, ISNI 0000 0004 0418 9357, Madigan Army Medical Center, , Joint Base Lewis-McChord, ; Tacoma, WA USA

[10 ]GRID grid.414467.4, ISNI 0000 0001 0560 6544, Walter Reed National Military Medical Center, ; Bethesda, MD USA

[11 ]GRID grid.415882.2, ISNI 0000 0000 9013 4774, Naval Medical Center Portsmouth, ; Portsmouth, VA USA

[12 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Department of Pediatrics, , Uniformed Services University of the Health Sciences, ; Bethesda, MD USA

[13 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Department of Pharmacology & Molecular Therapeutics, , Uniformed Services University of the Health Sciences, ; Bethesda, MD USA

[14 ]GRID grid.415913.b, ISNI 0000 0004 0587 8664, Biological Defense Research Directorate, , Naval Medical Research Center-Frederick, ; Ft. Detrick, MD USA

[15 ]GRID grid.417301.0, ISNI 0000 0004 0474 295X, Tripler Army Medical Center, ; Honolulu, HI USA

[16 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Uniformed Services University of the Health Sciences, ; Bethesda, MD USA

[17 ]GRID grid.453002.0, ISNI 0000 0001 2331 3497, United States Air Force School of Aerospace Medicine, ; Dayton, OH USA

[18 ]GRID grid.417180.b, ISNI 0000 0004 0418 8549, Womack Army Medical Center, ; Fort Bragg, NC USA

[19 ]GRID grid.417114.6, ISNI 0000 0004 0418 8848, William Beaumont Army Medical Center, ; El Paso, TX USA

Article

Publisher ID: 26965

DOI: 10.1038/s41598-022-26965-7

PMC ID: 9795438

PubMed ID: 36577783

SO-VID: b7d37e81-fec6-4280-8afa-cf71cfc69ca8

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 14 March 2022

Date accepted : 22 December 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100017443, Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense;

Award ID: W911QY-20-9-0004

Funded by: National Institute of Allergy and Infectious Diseases, National Institutes of Health

Award ID: Y1-AI-5072

Funded by: Defense Health Program, U.S. DoD

Award ID: HU0001190002

Funded by: Defense Health Agency, U.S. DoD

Award ID: HU00012020070

Award ID: W911QY-20-9-0006

Custom metadata

ScienceOpen disciplines: Uncategorized

Keywords: biomarkers,computational biology and bioinformatics,cytokines,inflammation,viral infection

Data availability:

ScienceOpen disciplines: Uncategorized

Keywords: biomarkers, computational biology and bioinformatics, cytokines, inflammation, viral infection

Comments

Comment on this article

scite_

Cited by 1

Sepsis endotypes identified by host gene expression across global cohorts
Authors: Josh G. Chenoweth, Joost Brandsma, Deborah A. Striegel …

See all cited by

Most referenced authors 1,705

See all reference authors

Distinct blood inflammatory biomarker clusters stratify host phenotypes during the middle phase of COVID-19

Read this article at

Abstract

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 32

The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study

Longitudinal analyses reveal immunological misfiring in severe COVID-19

Author and article information

Contributors

Journal

Affiliations

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 59

Cited by 1

Most referenced authors 1,705