Evidence map›Paper›PMID 37834869›Full record

ArticleJournal of clinical medicine2023

Multi-Omic Candidate Screening for Markers of Severe Clinical Courses of COVID-19.

Alexander Dutsch, Carsten Uhlig, Matthias Bock, Christian Graesser, Sven Schuchardt, Steffen Uhlig, Heribert Schunkert, Michael Joner, Stefan Holdenrieder, Katharina Lechner

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Alexander DutschDepartment of Cardiology, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.ORCID 0000-0003-1680-5383
Carsten UhligInstitute for Laboratory Medicine, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.ORCID 0000-0002-4853-1893
Matthias BockDepartment of Cardiology, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.
Christian GraesserDepartment of Cardiology, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.ORCID 0000-0002-0021-8793
Sven SchuchardtFraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany.ORCID 0000-0002-4593-9324
Steffen UhligQuoData Gesellschaft für Qualitätsmanagement und Statistik, Fabeckstr. 43, 14195 Berlin, Germany.
Heribert SchunkertDepartment of Cardiology, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.ORCID 0000-0001-6428-3001
Michael JonerDepartment of Cardiology, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.
Stefan HoldenriederInstitute for Laboratory Medicine, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.
Katharina LechnerDepartment of Cardiology, German Heart Centre Munich, Technical University of Munich, Lazarettstraße 36, 80636 Munich, Germany.ORCID 0000-0002-3582-3512
German Centre for Cardiovascular Research · DEDeutsches Herzzentrum München · DEFraunhofer Institute for Toxicology and Experimental Medicine · DEZentrum für Qualität in der Pflege · DE

Funding

Deutsche Herzstiftung COVID-19 grant
6 · The paper itself

Abstract

backgroundSevere coronavirus disease 2019 (COVID-19) disease courses are characterized by immuno-inflammatory, thrombotic, and parenchymal alterations. Prediction of individual COVID-19 disease courses to guide targeted prevention remains challenging. We hypothesized that a distinct serologic signature precedes surges of IL-6/D-dimers in severely affected COVID-19 patients.

methodsWe performed longitudinal plasma profiling, including proteome, metabolome, and routine biochemistry, on seven seropositive, well-phenotyped patients with severe COVID-19 referred to the Intensive Care Unit at the German Heart Center. Patient characteristics were: 65 ± 8 years, 29% female, median CRP 285 ± 127 mg/dL, IL-6 367 ± 231 ng/L, D-dimers 7 ± 10 mg/L, and NT-proBNP 2616 ± 3465 ng/L.

resultsBased on time-series analyses of patient sera, a prediction model employing feature selection and dimensionality reduction through least absolute shrinkage and selection operator (LASSO) revealed a number of candidate proteins preceding hyperinflammatory immune response (denoted ΔIL-6) and COVID-19 coagulopathy (denoted ΔD-dimers) by 24-48 h. These candidates are involved in biological pathways such as oxidative stress/inflammation (e.g., IL-1alpha, IL-13, MMP9, C-C motif chemokine 23), coagulation/thrombosis/immunoadhesion (e.g., P- and E-selectin), tissue repair (e.g., hepatocyte growth factor), and growth factor response/regulatory pathways (e.g., tyrosine-protein kinase receptor UFO and low-density lipoprotein receptor (LDLR)). The latter are host- or co-receptors that promote SARS-CoV-2 entry into cells in the absence of ACE2.

conclusionsOur novel prediction model identified biological and regulatory candidate networks preceding hyperinflammation and coagulopathy, with the most promising group being the proteins that explain changes in D-dimers. These biomarkers need validation. If causal, our work may help predict disease courses and guide personalized treatment for COVID-19.

Indexed as

candidate screeningCOVID-19 coagulopathyCOVID-19 longitudinal disease courseD-dimershyperinflammationIL-6multi-omicspersonalizationpredictive diagnosticstargeted prevention

Identifiers

PMID37834869
PMCPMC10573369
OpenAlexW4387105977

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.