title: High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis.
abstract: OBJECTIVE
  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.
  
  
  RESULTS
  Circulating lipids, lipoprotein subclasses, and small molecules were assayed by NMR for 1595 individuals aged 24-39 years from the population-based Cardiovascular Risk in Young Finns Study. Carotid intima-media 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 (≥ 90 th percentile) or plaque. The best prediction of high intima-media 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: 0.67-0.98; P = 0.007). Tyrosine (1.33; 1.10-1.60; P = 0.003) and glutamine (1.38; 1.13-1.68; 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.
  
  
  CONCLUSIONS
  High-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.
authors.affiliation: Computational Medicine,  Institute of Clinical Medicine, University of Oulu, PO Box 5000, 90014 Oulu,  Finland.
authors.forename: Peter
authors.initials: P
authors.lastname: Würtz
authors.forename: Juho R
authors.initials: JR
authors.lastname: Raiko
authors.forename: Costan G
authors.initials: CG
authors.lastname: Magnussen
authors.forename: Pasi
authors.initials: P
authors.lastname: Soininen
authors.forename: Antti J
authors.initials: AJ
authors.lastname: Kangas
authors.forename: Tuulia
authors.initials: T
authors.lastname: Tynkkynen
authors.forename: Russell
authors.initials: R
authors.lastname: Thomson
authors.forename: Reino
authors.initials: R
authors.lastname: Laatikainen
authors.forename: Markku J
authors.initials: MJ
authors.lastname: Savolainen
authors.forename: Jari
authors.initials: J
authors.lastname: Laurikka
authors.forename: Pekka
authors.initials: P
authors.lastname: Kuukasjärvi
authors.forename: Matti
authors.initials: M
authors.lastname: Tarkka
authors.forename: Pekka J
authors.initials: PJ
authors.lastname: Karhunen
authors.forename: Antti
authors.initials: A
authors.lastname: Jula
authors.forename: Jorma S
authors.initials: JS
authors.lastname: Viikari
authors.forename: Mika
authors.initials: M
authors.lastname: Kähönen
authors.forename: Terho
authors.initials: T
authors.lastname: Lehtimäki
authors.forename: Markus
authors.initials: M
authors.lastname: Juonala
authors.forename: Mika
authors.initials: M
authors.lastname: Ala-Korpela
authors.forename: Olli T
authors.initials: OT
authors.lastname: Raitakari
mesh_terms: D000328:Adult; D050197:Atherosclerosis / Q000175:diagnosis*; D015415:Biomarkers / Q000378:metabolism*; D059168:Carotid Intima-Media Thickness; D005260:Female; D006801:Humans; D050356:Lipid Metabolism / Q000502:physiology*; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D058226:Plaque, Atherosclerotic / Q000175:diagnosis; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D055815:Young Adult
chemical_list: D015415:Biomarkers