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Updated: Feb 2, 2026

In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Novel biomarkers of cardiovascular disease: Applications in clinical practice
Magnus Nakrem Lyngbakken1,2, Peder Langeland Myhre1,2, Helge Røsjø1,2
1a Division of Medicine , Akershus University Hospital , Lørenskog , Norway.
Abstract:
Measurement of biomarkers has revolutionized the work-up of patients with suspected cardiovascular disease. The most widely used contemporary cardiovascular biomarkers are the natriuretic peptides in the diagnosis and prognosis of heart failure and cardiac troponins in the diagnosis of acute myocardial infarction. Numerous other biomarkers pertaining to diagnosis, prognosis, and risk prediction have been identified, but few have made their way to clinical practice. In this review, we will initially describe the fundamental approach to evaluate a novel biomarker. Before implementation of a biomarker into clinical practice, several stringent criteria related to its clinical utility are required. Essential statistical metrics such as discrimination, calibration, and reclassification are required to properly evaluate prediction models. We will then discuss the biomarkers according to main groups of cardiovascular pathology:1. myocardial injury (cardiac troponins, heart-type fatty acid-binding protein, cardiac myosin binding protein-C);2. myocardial stress (A-type and B-type natriuretic peptides, mid-regional pro-adrenomedullin, copeptin); 3. inflammation (C-reactive protein, interleukin 6, growth differentiation factor 15, soluble suppressor of tumorigenicity 2, galectin-3);4. platelet activation (soluble CD40 ligand, P-selectin);5. plaque instability (lipoprotein-associated phospholipase A2, matrix metalloproteinase-9);6. systemic stress (catecholamines, granin proteins);7. calcium homeostasis (secretoneurin). Finally, we will discuss novel applications of cardiovascular biomarkers, more specifically prediction of ventricular arrhythmias, and the use of biomarkers in composite risk prediction models.
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