Clinically Significant Novel Biomarkers for Prediction of First Ever Myocardial Infarction. (April 2015)

• Record Type: Journal Article
• Authors: Wilsgaard, Tom; Mathiesen, Ellisiv Bøgeberg; Patwardhan, Anil; Rowe, Michael W.; Schirmer, Henrik; Løchen, Maja-Lisa; Sudduth-Klinger, Julie; Hamren, Sarah; Bønaa, Kaare Harald; Njølstad, Inger
• Is Part Of: Circulation. Volume 8:Number 2(2015)
• Publication Date: 2015-04
• Subjects: Arrhythmia -- Periodicals; Heart -- Electric properties -- Periodicals; 616.1042
• DOI: 10.1161/CIRCGENETICS.113.000630
• Languages: English
• ISSNs: 1942-325X

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <sec> <title>Background—</title> <p>Identification of individuals with high risk for first-ever myocardial infarction (MI) can be improved. The objectives of the study were to survey multiple protein biomarkers for association with the 10-year risk of incident MI and identify a clinically significant risk model that adds information to current common risk models.</p> </sec> <sec> <title>Methods and Results—</title> <p>We used an immunoassay platform that uses a sensitive, sample-efficient molecular counting technology to measure 51 proteins in samples from the fourth survey (1994) in the Tromsø Study, a longitudinal study of men and women in Tromsø, Norway. A case control design was used with 419 first-ever MI cases (169 females/250 males) and 398 controls (244 females/154 males). Of the proteins measured, 17 were predictors of MI when considered individually after adjustment for traditional risk factors either in men, women, or both. The 6 biomarkers adjusted for traditional risk factors that were selected in a multivariable model (odds ratios [OR] per standard deviation) using a stepwise procedure were apolipoprotein B/apolipoprotein A1 ratio (1.40), kallikrein (0.73), lipoprotein a (1.29), matrix metalloproteinase 9 (1.30), the interaction term IP-10/CXCL10×women (0.69), and the interaction term thrombospondin 4×men (1.38). The composite risk of these biomarkers added significantly to the traditional risk factor model with a net reclassification improvement of 14% (<italic>P</italic>=0.0002), whereas the receiver operating characteristic area increased from 0.757 to 0.791, <italic>P</italic>=0.0004.</p> </sec> <sec> <title>Conclusions—</title> <p>Novel protein biomarker models improve identification of 10-year MI risk above and beyond traditional risk factors with 14% better allocation to either high or low risk group.</p> </sec> </abstract>