Protein Biomarkers of Cardiovascular Disease and Mortality in the Community.

BACKGROUND

The discovery of novel and highly predictive biomarkers of cardiovascular disease (CVD) has the potential to improve risk-stratification methods and may be informative regarding biological pathways contributing to disease.

METHODS AND RESULTS

We used a discovery proteomic platform that targeted high-value proteins for CVD to ascertain 85 circulating protein biomarkers in 3523 Framingham Heart Study participants (mean age, 62 years; 53% women). Using multivariable-adjusted Cox models to account for clinical variables, we found 8 biomarkers associated with incident atherosclerotic CVD, 18 with incident heart failure, 38 with all-cause mortality, and 35 with CVD death (false discovery rate, q<0.05 for all; -value ranges, 9.8×10 to 3.6×10). Notably, a number of regulators of metabolic and adipocyte homeostasis were associated with cardiovascular events, including insulin-like growth factor 1 (IGF1), insulin-like growth factor binding protein 1 (IGFBP1), insulin-like growth factor binding protein 2 (IGFBP2), leptin, and adipsin. In a multimarker approach that accounted for clinical factors, growth differentiation factor 15 (GDF15) was associated with all outcomes. In addition, N-terminal pro-b-type natriuretic peptide, C-reactive protein, and leptin were associated with incident heart failure, and C-type lectin domain family 3 member B (CLEC3B; tetranectin), N-terminal pro-b-type natriuretic peptide, arabinogalactan protein 1 (AGP1), soluble receptor for advanced glycation end products (sRAGE), peripheral myelin protein 2 (PMP2), uncarboxylated matrix Gla protein (UCMGP), kallikrein B1 (KLKB1), IGFBP2, IGF1, leptin receptor, and cystatin-C were associated with all-cause mortality in a multimarker model.

CONCLUSIONS

We identified numerous protein biomarkers that predicted cardiovascular outcomes and all-cause mortality, including biomarkers representing regulators of metabolic homeostasis and inflammatory pathways. Further studies are needed to validate our findings and define clinical utility, with the ultimate goal of improving strategies for CVD prevention.