Metabolite Profiling and Cardiovascular Event Risk A Prospective Study of 3 Population-Based Cohorts

Background —High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors. Methods and Results —We applied quantitative NMR metabolomics to identify biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the SABRE study (n=2622; 573 events) and British Women9s Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P -10 ) and monounsaturated fatty acid levels (1.17 [1.11-1.24]; P=1×10 -8 ) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89 [0.84-0.94]; P=6×10 -5 ) and docosahexaenoic acid levels (0.90 [0.86-0.95]; P=5×10 -5 ) were associated with lower risk. A risk score incorporating these four biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the two validation cohorts (relative integrated discrimination improvement 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5-10% risk range (net reclassification 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289). Conclusions —Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.