title: Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease.
abstract: BACKGROUND
  Cardiovascular risk models remain incomplete. Small-molecule metabolites may reflect underlying disease and, as such, serve as novel biomarkers of cardiovascular risk.
  
  
  METHODS
  We studied 2,023 consecutive patients undergoing cardiac catheterization. Mass spectrometry profiling of 69 metabolites and lipid assessments were performed in fasting plasma. Principal component analysis reduced metabolites to a smaller number of uncorrelated factors. Independent relationships between factors and time-to-clinical events were assessed using Cox modeling. Clinical and metabolomic models were compared using log-likelihood and reclassification analyses.
  
  
  RESULTS
  At median follow-up of 3.1 years, there were 232 deaths and 294 death/myocardial infarction (MI) events. Five of 13 metabolite factors were independently associated with mortality: factor 1 (medium-chain acylcarnitines: hazard ratio [HR] 1.12 [95% CI, 1.04-1.21], P = .005), factor 2 (short-chain dicarboxylacylcarnitines: HR 1.17 [1.05-1.31], P = .005), factor 3 (long-chain dicarboxylacylcarnitines: HR 1.14 [1.05-1.25], P = .002); factor 6 (branched-chain amino acids: HR 0.86 [0.75-0.99], P = .03), and factor 12 (fatty acids: HR 1.19 [1.06-1.35], P = .004). Three factors independently predicted death/MI: factor 2 (HR 1.11 [1.01-1.23], P = .04), factor 3 (HR 1.13 [1.04-1.22], P = .005), and factor 12 (HR 1.18 [1.05-1.32], P = .004). For mortality, 27% of intermediate-risk patients were correctly reclassified (net reclassification improvement 8.8%, integrated discrimination index 0.017); for death/MI model, 11% were correctly reclassified (net reclassification improvement 3.9%, integrated discrimination index 0.012).
  
  
  CONCLUSIONS
  Metabolic profiles predict cardiovascular events independently of standard predictors.
authors.affiliation: Division of Cardiovascular Medicine, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA. svati.shah@duke.edu
authors.forename: Svati H
authors.initials: SH
authors.lastname: Shah
authors.forename: Jie-Lena
authors.initials: JL
authors.lastname: Sun
authors.forename: Robert D
authors.initials: RD
authors.lastname: Stevens
authors.forename: James R
authors.initials: JR
authors.lastname: Bain
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Muehlbauer
authors.forename: Karen S
authors.initials: KS
authors.lastname: Pieper
authors.forename: Carol
authors.initials: C
authors.lastname: Haynes
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Hauser
authors.forename: William E
authors.initials: WE
authors.lastname: Kraus
authors.forename: Christopher B
authors.initials: CB
authors.lastname: Granger
authors.forename: Christopher B
authors.initials: CB
authors.lastname: Newgard
authors.forename: Robert M
authors.initials: RM
authors.lastname: Califf
authors.forename: L Kristin
authors.initials: LK
authors.lastname: Newby
mesh_terms: D000328:Adult; D017677:Age Distribution; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D001774:Blood Chemical Analysis; D006328:Cardiac Catheterization / Q000379:methods / Q000706:statistics & numerical data; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology / Q000378:metabolism*; D015331:Cohort Studies; D016001:Confidence Intervals; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000453:epidemiology* / Q000378:metabolism*; D005260:Female; D006801:Humans; D008055:Lipids / Q000097:blood; D008297:Male; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D015995:Prevalence; D011379:Prognosis; D012189:Retrospective Studies; D018570:Risk Assessment; D012720:Severity of Illness Index; D017678:Sex Distribution; D013318:Stroke Volume; D016019:Survival Analysis
chemical_list: D015415:Biomarkers; D008055:Lipids