title: Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events.
abstract: BACKGROUND
  Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events.
  
  
  RESULTS
  We performed mass-spectrometry-based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls ("initial"), and 140 CAD cases and 140 controls ("replication"). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined ("event" group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled ("event-replication" group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis-derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P=0.002, replication P=0.01), and 1 comprising urea cycle metabolites (factor 9, initial P=0.0004, replication P=0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P=0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P=0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P=0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P=0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P=0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P=0.01).
  
  
  CONCLUSIONS
  Metabolite profiles are associated with CAD and subsequent cardiovascular events.
authors.affiliation: Department of Medicine, Duke University, Durham, NC, USA. svati.shah@duke.edu <svati.shah@duke.edu>
authors.forename: Svati H
authors.initials: SH
authors.lastname: Shah
authors.forename: James R
authors.initials: JR
authors.lastname: Bain
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Muehlbauer
authors.forename: Robert D
authors.initials: RD
authors.lastname: Stevens
authors.forename: David R
authors.initials: DR
authors.lastname: Crosslin
authors.forename: Carol
authors.initials: C
authors.lastname: Haynes
authors.forename: Jennifer
authors.initials: J
authors.lastname: Dungan
authors.forename: L Kristin
authors.initials: LK
authors.lastname: Newby
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Hauser
authors.forename: Geoffrey S
authors.initials: GS
authors.lastname: Ginsburg
authors.forename: Christopher B
authors.initials: CB
authors.lastname: Newgard
authors.forename: William E
authors.initials: WE
authors.lastname: Kraus
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000097:blood; D003324:Coronary Artery Disease / Q000235:genetics* / Q000378:metabolism; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D013058:Mass Spectrometry; D055442:Metabolome; D008875:Middle Aged; D009203:Myocardial Infarction / Q000150:complications / Q000235:genetics; D016017:Odds Ratio; D025341:Principal Component Analysis; D016016:Proportional Hazards Models; D012372:ROC Curve; D012307:Risk Factors
chemical_list: D015415:Biomarkers