Evidence map›Paper›PMID 41479992›Full record

ArticleWorld journal of cardiology2025

Reimagining risk stratification: Dipeptidyl peptidase 3 in the new era of cardiovascular biomarkers.

Davide Ramoni, Luca Liberale, Federico Carbone, Fabrizio Montecucco

Abstract readEditorial
In one paragraph

Article in World journal of cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Davide RamoniDepartment of Internal Medicine, University of Genoa, Genoa 16132, Italy.
Luca LiberaleDepartment of Internal Medicine, University of Genoa, Genoa 16132, Italy.
Federico CarboneDepartment of Internal Medicine, University of Genoa, Genoa 16132, Italy.
Fabrizio MontecuccoDepartment of Internal Medicine, University of Genoa, Genoa 16132, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid evolution of cardiovascular (CV) research demands innovative strategies to enhance risk stratification, diagnosis, and management. While traditional biomarkers, such as natriuretic peptides and troponins, remain essential, they often fall short due to suboptimal sensitivity and specificity, particularly in complex or early-stage cases. Emerging biomarkers are central to advancing personalized medicine by enabling earlier, more accurate detection of CV diseases and enhancing predictive algorithms, including those powered by artificial intelligence and machine learning. Among these novel biomarkers, dipeptidyl peptidase 3 (DPP3) has recently garnered attention as a highly specific indicator of cardiogenic shock, offering both prognostic value and therapeutic target potential. Released during cellular stress, circulating DPP3 (cDPP3) plays a mechanistic role in myocardial depression and blood pressure regulation, positioning it as a compelling candidate for inclusion in multi-marker panels. Its integration into predictive models could further refine therapeutic decision-making and patient stratification in acute cardiac care. This editorial discusses the clinical value of incorporating cDPP3 into CV biomarker research and advocates its inclusion in next-generation predictive algorithms and real-time decision-support tools. Continued exploration of such biomarkers may enable tailored interventions and improve outcomes in complex CV cases.

Indexed as

Acute coronary syndromeCardiogenic shockDipeptidyl peptidase 3Heart failureInflammationPrognostic biomarkersRenin–angiotensin–aldosterone systemRisk stratificationTherapeutic targeting

Identifiers

PMID41479992
PMCPMC12754065

What OpenQuestion 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 OpenQuestion graph.