Metabolite Profiling and Cardiovascular Event Risk: A Prospective Study of Three 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 Women's 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<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes and medication. When further adjusting for routine lipids, four metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation: 1.18 [95%CI 1.12-1.24]; P=4×10-10) 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.
- Received September 7, 2014.
- Revision received December 10, 2014.
- Accepted January 2, 2015.