Circulating endothelial progenitor cells predict coronary artery disease severity.

BACKGROUND: Circulating endothelial progenitor cells (EPCs) may play an important role in the body's defense against atherosclerosis. Previous studies have shown an association between EPC numbers and the presence of traditional coronary artery disease (CAD) risk factors. The relationship between EPC numbers and the severity of atherosclerosis is, however, not known. METHODS: EPC counts were measured by quantitative cell culture in 122 patients undergoing diagnostic cardiac catheterization. The association between patients' EPC count and the presence of multivessel CAD and traditional cardiac risk factors was assessed using logistic regression analysis. RESULTS: The median age of the study population was 58 years; 37% had multivessel CAD, 29% had diabetes, and 14% had myocardial infarction this admission. EPC counts did not vary significantly with most established cardiac risk factors but were lower in diabetics versus nondiabetics and trended toward lower numbers in older patients. EPC count was the second strongest predictor of multivessel CAD, after patient age. Patients with multivessel disease had significantly lower EPC counts than those without (median, 3 vs 13; P < .0088). For every 10 colony forming unit increase in EPCs, a patient's likelihood for multivessel CAD declined by 20% (P < .001). CONCLUSION: This study demonstrates an inverse relationship between circulating EPCs and CAD severity, independent of traditional risk factors. If confirmed in ongoing studies, this may represent an important new diagnostic and therapeutic target for coronary disease treatment.

Gene expression phenotypes of atherosclerosis.

OBJECTIVE: Fulfilling the promise of personalized medicine by developing individualized diagnostic and therapeutic strategies for atherosclerosis will depend on a detailed understanding of the genes and gene variants that contribute to disease susceptibility and progression. To that end, our group has developed a nonbiased approach congruent with the multigenic concept of complex diseases by identifying gene expression patterns highly associated with disease states in human target tissues. METHODS AND RESULTS: We have analyzed a collection of human aorta samples with varying degrees of atherosclerosis to identify gene expression patterns that predict a disease state or potential susceptibility. We find gene expression signatures that relate to each of these disease measures and are reliable and robust in predicting the classification for new samples with >93% in each analysis. The genes that provide the predictive power include many previously suspected to play a role in atherosclerosis and additional genes without prior association with atherosclerosis. CONCLUSIONS: Hence, we are reporting a novel method for generating a molecular phenotype of disease and then identifying genes whose discriminatory capability strongly implicates their potential roles in human atherosclerosis.