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1 Cardiovascular Medicine, Stanford University, Palo Alto, California, United States
2 Prevention Research Center, Stanford University, Palo Alto, California, United States
3 Division of Research, Kaiser Permanente, Oakland, California, United States
4 Department of Health Research and Policy, Stanford University, Palo Alto, California, United States
5 Aviir Inc., Palo Alto, California, United States
6 Cardiovascular Medicine, Stanford University Medical Center, Stanford, California, United States
7 Cardiovascular Medicine, Stanford University, Stanford, California, United States; Aviir Inc., Palo Alto, California, United States
8 Cardiology, Stanford University, Stanford, California, United States
* To whom correspondence should be addressed. E-mail: ray.tabibiazar{at}aviir.com.
Background: Serum inflammatory markers correlate with outcome and response to therapy in subjects with cardiovascular disease. However, current individual markers lack specificity for the diagnosis of coronary artery disease. We hypothesize that a multi-marker proteomic approach measuring serum levels of vascular derived inflammatory biomarkers could reveal a 'signature of disease' that can serve as an accurate diagnostic tool for the presence of coronary atherosclerosis. Methods: We simultaneously measured serum levels of seven chemokines in 48 subjects with clinically significant CAD ('cases') and 44 controls from the ADVANCE Study. We applied three different classification algorithms to identify the combination of variables that would best predict case-control status and assessed the diagnostic performance of these models with ROC Curves. Results: The serum levels of six chemokines were significantly higher in cases compared with controls (p <0.05). All three classification algorithms entered 3 chemokines in their final model and only logistic regression selected clinical variables. Logistic regression produced the highest ROC of the three classification algorithms (AUC=0.95; SE=0.03), which was markedly better than the AUC for the logistic regression model of traditional risk factors of CAD without (AUC=0.67; SE=0.06) or with CRP (AUC=0.68; SE=0.06). Conclusions: A combination of serum levels of multiple chemokines identifies subjects with active disease with a very high degree of accuracy. These results need to be replicated in larger cross sectional studies and their prognostic value explored in prospective studies. Multimarker approach utilizing informed biomarkers may ultimately lead to improved screening, diagnosis, and monitoring of cardiovascular disease.
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