Biomarkers of atherosclerosis: Clinical applications
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Corresponding author Vera Bittner, MD, MSPH Preventive Cardiology, Division of Cardiovascular Disease, University of Alabama at Birmingham, 701 19th Street South – LHRB 310, Birmingham, AL 35294, USA. E-mail: [email protected] Current Cardiovascular Risk Reports 2009, 3:23–30 Current Medicine Group LLC ISSN 1932-9520 Copyright © 2009 by Current Medicine Group LLC
article, we review the most recently published literature investigating blood-based biomarkers used individually or as part of multimarker risk prediction models for the risk stratification of individuals with known or suspected CVD. Additionally, we examine whether incorporating these biomarkers assists with clinical decision making.
Assessment of a New Biomarker’s Predictive Ability Current cardiovascular risk prediction models incorporate traditional risk factors to estimate 10year cardiovascular risk. Numerous blood-based biomarkers have been identified that are associated with increased cardiovascular risk after adjusting for traditional risk factors. Many of these biomarkers, alone or in combination, have been incorporated into risk prediction models to determine whether their addition increases the model’s predictive ability. We review the recently published literature on blood-based biomarkers and examine whether incorporating these markers may improve clinical decision making.
Introduction Traditionally, risk prediction models incorporate demographic and clinical variables such as age, gender, blood pressure, cholesterol levels, diabetes status, and smoking status to risk stratify individuals with known or suspected cardiovascular disease (CVD) [1,2]. Although these models perform well on a population basis, they misclassify some individuals and, having been designed with a 10-year time horizon, underestimate long-term cardiovascular risk [3]. Therefore, identifying new variables that, when used in addition to traditional risk factors, could improve the risk stratification of those with known or suspected CVD is of interest. Many biomarkers independently predict cardiovascular events when added individually to models containing traditional demographic and clinical variables. Recently, investigators have attempted to construct multimarker risk prediction models, incorporating traditional risk factors and multiple biomarkers simultaneously. In this
Before reviewing the data on individual biomarkers, we summarize the recent controversy about assessing the use of a new biomarker. Traditionally, most investigators have used multivariable modeling to determine whether a biomarker is independently associated with cardiovascular events after adjusting for traditional risk factors. Once this association is demonstrated, the next logical step is to investigate the degree to which this biomarker improves the model’s overall predictive ability. This is usually accomplished by calculating the area under the receiver operating characteristic (ROC) curve, also known as the C statistic, for the model containing only traditional cardiovascular risk factors and
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