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.

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

Ramoni, Davide;Liberale, Luca;Carbone, Federico;Montecucco, Fabrizio
2025-01-01

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1282616
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