Linking Neutrophil Extracellular Traps and Platelet Activation: A Composite Biomarker Score for Predicting Outcomes after Acute Myocardial Infarction

中性粒细胞胞外陷阱 狼牙棒 血小板活化 医学 生物标志物 心肌梗塞 血小板 内科学 中性粒细胞弹性蛋白酶 免疫学 心脏病学 炎症 生物 经皮冠状动脉介入治疗 生物化学
作者
Kathryn Hally,Olivia M. Parker,M. Brunton-O’Sullivan,S. Harding,Peter Larsen
出处
期刊:Thrombosis and Haemostasis [Thieme Medical Publishers (Germany)]
卷期号:121 (12): 1637-1649 被引量:39
标识
DOI:10.1055/s-0041-1728763
摘要

Activation of both platelets and neutrophils can contribute to the risk of major adverse cardiovascular events (MACE) following acute myocardial infarction (AMI). Neutrophil extracellular traps (NETs) are an important product of the platelet-neutrophil axis and exaggerate vascular damage in cardiovascular disease. Additionally, activated platelets can drive NETosis and are directly linked to thromboembolic risk. Investigating the combined effect of biomarkers for NETosis and platelet activation represents a novel approach to risk prediction post-AMI. Here, we examined the utility of a composite biomarker score, inclusive of both pathways, for predicting MACE post-AMI. In a case-control design, 100 case patients who experienced MACE within 1 year of index admission were matched in a 1:2 ratio with control patients. Serum levels of myeloperoxidase-DNA, neutrophil elastase-DNA, and citrullinated histone H3 were assayed by enzyme-linked immunosorbent assay (ELISA) as markers of NET burden. To measure platelet activation, soluble P-selectin was assayed by ELISA in parallel. Platelet and neutrophil counts were also recorded. Composite biomarker scores, inclusive of biomarkers for NETosis and platelet activation, were assessed using multivariate regression modeling. These composite biomarker scores were independent predictors of 1-year MACE. The strongest association with MACE was observed using a composite of platelet count, soluble P-selectin, and all NET markers (odds ratio: 1.94; 1.16-3.25). Here, we demonstrate the importance of combining biomarkers of NETosis and platelet activation for risk prediction in patients with AMI. Combining biomarkers from closely linked, but distinct, biological pathways was more effective than utilizing either type of biomarker alone.
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