Ellisiv Bøgeberg Mathiesen
Michael W. Rowe
Maja-Lisa Løchen, Australian Catholic UniversityFollow
Kaare Harald Bønaa
Wilsgaard, T., Mathiesen, E. B, Patwardhan, A., Rowe, M. W, Schirmer, H., Løchen, M., Sudduth-Klinger, J., Hamren, S., Bønaa, K. H & Njølstad, I. (2015). Clinically significant novel biomarkers for prediction of first ever myocardial infarction: The Tromsø Study. Circulation: Cardiovascular Genetics,8(2), 363-371. United States: Lippincott Williams & Wilkins. Retrieved from https://doi.org/10.1161/CIRCGENETICS.113.000630
Background: 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. Methods and Results: 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% ( P=0.0002 ), whereas the receiver operating characteristic area increased from 0.757 to 0.791, P=0.0004. Conclusions: 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.
Mary MacKillop Institute for Health Research
Open Access Journal Article