Abstract 4990: Multi-Marker Approach Using Novel Biomarkers Improves Prediction of Subclinical Atherosclerosis: Observations from the Dallas Heart Study
Background: Although many novel markers are independently associated with cardiovascular risk, few significantly improve predictive performance. We hypothesized that combining multiple novel biomarkers would improve prediction of subclinical atherosclerosis over standard methods.
Methods: We measured coronary calcium (CAC) by electron beam CT (n=2440), as well as hsCRP, NT-proBNP, and four novel biomarkers including soluble receptor for advanced glycation end products (sRAGE), peptidoglycan recognition protein-1 (PGLYRP-1), soluble endothelial cell-selective adhesion molecule (sESAM), and osteoprotegerin (OPG) in the Dallas Heart Study, a multi-ethnic probability-based population sample age 30 – 65. An integer score was created based on the number of biomarkers that were elevated >= 95th percentile for sex and race. The incremental value of the biomarker score over a model including Framingham risk categories in predicting CAC was analyzed.
Results: Increasing biomarker score was associated with increased CAC prevalence (59% for score >= 3 vs. 17% for score = 0, figure⇓). Compared with the Framingham risk model alone, the addition of the biomarker score significantly increased the C index (0.705 vs. 0.749, p<0.0001), lowered the Bayesian Information Criterion (2232 vs. 2195), and also improved reclassification into higher and lower risk categories (Net Reclassification Index = 14.2%, p<0.0001; Integrated Discrimination Index =2.4%, p<0.0001).
Conclusion: Use of a multi-marker score including established and novel biomarkers improves prediction of CAC beyond the Framingham risk categories using multiple different statistical metrics.