Cardiac Myosin-Binding Protein C in Suspected Acute Coronary Syndrome: From Sarcomeric Injury Biology to Decision-Grade Risk Stratification

Cardiac myosin-binding protein C (cMyBP-C) is a cardiac-restricted sarcomeric protein; after cardiomyocyte injury, circulating intact cMyBP-C and/or cMyBP-C fragments, collectively referred to here as the cMyC biomarker signal, appear rapidly in blood. In suspected acute coronary syndrome (ACS), its most important potential role is not as another marker of injury but as a decision-enhancing biomarker beyond symptoms, electrocardiography, cardiac troponin T and I concentrations measured with high-sensitivity assays (hs-cTnT and hs-cTnI), time from pain onset, and pre-test probability. This narrative review separates three clinical tasks frequently conflated in the biomarker literature: diagnosis of acute myocardial infarction, emergency-department triage, and prediction of short-term or post-infarction risk. We integrate cMyBP-C sarcomeric architecture, N-terminal regulatory biology, phosphorylation, proteolysis, circulating fragments, assay epitopes, analytical stability, diagnostic algorithms, point-of-care testing, ST-segment elevation myocardial infarction reperfusion biology, and major confounders including renal dysfunction, heart failure, age, sex, and chronic ventricular remodeling. Current evidence supports further evaluation of cMyC as an adjunct in early presenters and accelerated diagnostic pathways. However, diagnostic safety and efficacy have not been consistently reproduced across platforms and populations, and external validation—particularly of rule-out performance—remains insufficient for routine clinical use. Recurrent injury assessment and post-infarction risk phenotyping remain promising but incompletely validated applications. Before guideline adoption, cMyC needs phenotype-specific, multicenter implementation trials demonstrating incremental net benefit, cost-effectiveness, and patient-level safety compared with contemporary hs-cTnT- and hs-cTnI-based clinical decision algorithms.