In:
Arteriosclerosis, Thrombosis, and Vascular Biology, Ovid Technologies (Wolters Kluwer Health), Vol. 34, No. 4 ( 2014-04), p. 939-945
Kurzfassung:
Incorporation of novel plasma protein biomarkers may improve current models for prediction of atherosclerotic cardiovascular disease (ASCVD) risk. Approach and Results— We used discovery mass spectrometry (MS) to determine plasma concentrations of 861 proteins in 135 myocardial infarction (MI) cases and 135 matched controls. Then, we measured 59 markers by targeted MS in 336 ASCVD case–control pairs. Associations with MI or ASCVD were tested in single-marker and multiple-marker analyses adjusted for established ASCVD risk factors. Twelve single markers from discovery MS were associated with MI incidence (at P 〈 0.01), adjusting for clinical risk factors. Seven proteins in aggregate (cyclophilin A, cluster of differentiation 5 molecule [CD5] antigen-like, cell-surface glycoprotein mucin cell surface associated protein 18 [MUC-18] , collagen-α 1 [XVIII] chain, salivary α-amylase 1, C-reactive protein, and multimerin-2) were highly associated with MI ( P 〈 0.0001) and significantly improved its prediction compared with a model with clinical risk factors alone (C-statistic of 0.71 versus 0.84). Through targeted MS, 12 single proteins were predictors of ASCVD (at P 〈 0.05) after adjusting for established risk factors. In multiple-marker analyses, 4 proteins in combination (α-1–acid glycoprotein 1, paraoxonase 1, tetranectin, and CD5 antigen-like) predicted incident ASCVD ( P 〈 0.0001) and moderately improved the C-statistic from the model with clinical covariates alone (C-statistic of 0.69 versus 0.73). Conclusions— Proteomics profiling identified single- and multiple-marker protein panels that are associated with new-onset ASCVD and may lead to a better understanding of underlying disease mechanisms. Our findings include many novel protein biomarkers that, if externally validated, may improve risk assessment for MI and ASCVD.
Materialart:
Online-Ressource
ISSN:
1079-5642
,
1524-4636
DOI:
10.1161/ATVBAHA.113.302918
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2014
ZDB Id:
1494427-3
