TY - JOUR
T1 - High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis
AU - Wurtz, Peter
AU - Raiko, Juho R.
AU - Magnussen, Costan G.
AU - Soininen, Pasi
AU - Kangas, Antti J.
AU - Tynkkynen, Tuulia
AU - Thomson, Russell J.
AU - Laatikainen, Reino
AU - Savolainen, Markku J.
AU - [and eleven others], null
PY - 2012
Y1 - 2012
N2 - Aims High-throughput metabolite quantification holds promise for cardiovascular risk assessment. Here, we evaluated whether metabolite quantification by nuclear magnetic resonance (NMR) improves prediction of subclinical atherosclerosis in comparison to conventional lipid testing. Methods and resultsCirculating lipids, lipoprotein subclasses, and small molecules were assayed by NMR for 1595 individuals aged 2439 years from the population-based Cardiovascular Risk in Young Finns Study. Carotid intimamedia thickness (IMT), a marker of subclinical atherosclerosis, was measured in 2001 and 2007. Baseline conventional risk factors and systemic metabolites were used to predict 6-year incidence of high IMT (<90th percentile) or plaque. The best prediction of high intimamedia thickness was achieved when total and HDL cholesterol were replaced by NMR-determined LDL cholesterol and medium HDL, docosahexaenoic acid, and tyrosine in prediction models with risk factors from the Framingham risk score. The extended prediction model improved risk stratification beyond established risk factors alone; area under the receiver operating characteristic curve 0.764 vs. 0.737, P=0.02, and net reclassification index 17.6, P=0.0008. Higher docosahexaenoic acid levels were associated with decreased risk for incident high IMT (odds ratio: 0.74; 95 confidence interval: 0.670.98; P=0.007). Tyrosine (1.33; 1.101.60; P=0.003) and glutamine (1.38; 1.131.68; P=0.001) levels were associated with 6-year incident high IMT independent of lipid measures. Furthermore, these amino acids were cross-sectionally associated with carotid IMT and the presence of angiographically ascertained coronary artery disease in independent populations. ConclusionHigh-throughput metabolite quantification, with new systemic biomarkers, improved risk stratification for subclinical atherosclerosis in comparison to conventional lipids and could potentially be useful for early cardiovascular risk assessment.
AB - Aims High-throughput metabolite quantification holds promise for cardiovascular risk assessment. Here, we evaluated whether metabolite quantification by nuclear magnetic resonance (NMR) improves prediction of subclinical atherosclerosis in comparison to conventional lipid testing. Methods and resultsCirculating lipids, lipoprotein subclasses, and small molecules were assayed by NMR for 1595 individuals aged 2439 years from the population-based Cardiovascular Risk in Young Finns Study. Carotid intimamedia thickness (IMT), a marker of subclinical atherosclerosis, was measured in 2001 and 2007. Baseline conventional risk factors and systemic metabolites were used to predict 6-year incidence of high IMT (<90th percentile) or plaque. The best prediction of high intimamedia thickness was achieved when total and HDL cholesterol were replaced by NMR-determined LDL cholesterol and medium HDL, docosahexaenoic acid, and tyrosine in prediction models with risk factors from the Framingham risk score. The extended prediction model improved risk stratification beyond established risk factors alone; area under the receiver operating characteristic curve 0.764 vs. 0.737, P=0.02, and net reclassification index 17.6, P=0.0008. Higher docosahexaenoic acid levels were associated with decreased risk for incident high IMT (odds ratio: 0.74; 95 confidence interval: 0.670.98; P=0.007). Tyrosine (1.33; 1.101.60; P=0.003) and glutamine (1.38; 1.131.68; P=0.001) levels were associated with 6-year incident high IMT independent of lipid measures. Furthermore, these amino acids were cross-sectionally associated with carotid IMT and the presence of angiographically ascertained coronary artery disease in independent populations. ConclusionHigh-throughput metabolite quantification, with new systemic biomarkers, improved risk stratification for subclinical atherosclerosis in comparison to conventional lipids and could potentially be useful for early cardiovascular risk assessment.
KW - lipoproteins
KW - metabolomics
KW - risk factors
KW - tyrosine
UR - http://handle.uws.edu.au:8081/1959.7/uws:35834
U2 - 10.1093/eurheartj/ehs020
DO - 10.1093/eurheartj/ehs020
M3 - Article
SN - 1522-9645
SN - 0195-668X
VL - 33
SP - 2307
EP - 2316
JO - European Heart Journal
JF - European Heart Journal
IS - 18
ER -