Evidence mapPaperPMID 39762268Full record

ArticleScientific reports2025

Application of urinary peptide-biomarkers in trauma patients as a predictive tool for prognostic assessment, treatment and intervention timing.

Gökmen Aktas, Felix Keller, Justyna Siwy, Agnieszka Latosinska, Harald Mischak, Jorge Mayor, Jan Clausen, Michaela Wilhelmi, Vesta Brauckmann, Stephan Sehmisch and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
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

11 authors.

Gökmen Aktas *Department of Trauma Surgery, Hannover Medical School, Carl-Neuberg St. 1, 30625, Hannover, Lower Saxony, Germany. Aktas.Goekmen@mh-hannover.de.
Felix KellerDepartment of Internal Medicine IV (Nephrology and Hypertension), Medical University of Innsbruck, Anich St. 35, 6020, Innsbruck, Austria.
Justyna SiwyMosaiques Diagnostics GmbH, Rotenburger Str 20, 30659, Hannover, Lower Saxony, Germany.
Agnieszka LatosinskaMosaiques Diagnostics GmbH, Rotenburger Str 20, 30659, Hannover, Lower Saxony, Germany.
Harald MischakMosaiques Diagnostics GmbH, Rotenburger Str 20, 30659, Hannover, Lower Saxony, Germany.
Jorge MayorDepartment of Trauma Surgery, Hannover Medical School, Carl-Neuberg St. 1, 30625, Hannover, Lower Saxony, Germany.
Jan ClausenDepartment of Trauma Surgery, Hannover Medical School, Carl-Neuberg St. 1, 30625, Hannover, Lower Saxony, Germany.
Michaela WilhelmiDepartment of Trauma Surgery, Hannover Medical School, Carl-Neuberg St. 1, 30625, Hannover, Lower Saxony, Germany.
Vesta BrauckmannDepartment of Trauma Surgery, Hannover Medical School, Carl-Neuberg St. 1, 30625, Hannover, Lower Saxony, Germany.
Stephan SehmischDepartment of Trauma Surgery, Hannover Medical School, Carl-Neuberg St. 1, 30625, Hannover, Lower Saxony, Germany.
Tarek Omar Pacha *Department of Trauma Surgery, Hannover Medical School, Carl-Neuberg St. 1, 30625, Hannover, Lower Saxony, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment of severely injured patients represents a major challenge, in part due to the unpredictable risk of major adverse events, including death. Preemptive personalized treatment aimed at preventing these events is a crucial objective of patient management; however, the currently available scoring systems provide only moderate guidance. Biomarkers from proteomics/peptidomics studies hold promise for improving the current situation, ultimately enabling precision medicine based on individual molecular profiles. To test the hypothesis that peptide biomarkers could predict patient outcomes in severely injured patients, we initiated a pilot study involving consecutive urine sampling (on days 0, 2, 5, 10, and 14) and subsequent peptidome analysis using capillary electrophoresis coupled to mass spectrometry (CE-MS) of 14 severely injured patients and two additional intensive care unit patients. The urine peptidomes of these patients were compared to those of age- and sex-matched controls. Moreover, previously established urinary peptide-based classifiers, CKD273, AKI204, and Cov50, were applied to the obtained peptidome data, and the association of the classifier's scores with a combined endpoint (death and/or kidney failure and/or respiratory insufficiency) was investigated. CE-MS peptidome analysis identified 191 significantly altered peptides in severely injured patients. A consistent increase in the abundance of peptides from A1AT, AHSG, and HBA1 was observed, while peptides derived from PIGR and UROM were consistently decreased. Most of the significant peptides (adjusted p < 0.05) were from COL1A1, and most were reduced in abundance. Two of the previously defined and validated peptidomic classifiers, CKD273 and AKI204, showed significant associations with the combined endpoint, which was not observed for the routine scores generally applied in the clinics. This prospective pilot study confirmed the hypothesis that urinary peptides provide information on patient outcomes and may guide personalized interventions in severely injured patients based on individual molecular changes. The results obtained allow the planning of a well-powered prospective trial investigating the value of urinary peptides in this context in more detail.

Indexed as

BiomarkersPeptidesWounds and InjuriesAdultAgedElectrophoresis, CapillaryFemaleHumansMaleMass SpectrometryMiddle AgedPilot ProjectsPrognosisProteomicsBiomarkersPeptidesBiomarkerCritical careIntensive carePeptidesPolytraumaPredictionProteomicsTraumaUrine

Identifiers

PMID39762268
PMCPMC11704255

What Socratic holds

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Registered trials

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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 Socratic graph.