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Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease.

Publication ,  Journal Article
Shah, SH; Sun, J-L; Stevens, RD; Bain, JR; Muehlbauer, MJ; Pieper, KS; Haynes, C; Hauser, ER; Kraus, WE; Granger, CB; Newgard, CB; Califf, RM ...
Published in: Am Heart J
May 2012

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.

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Published In

Am Heart J

DOI

EISSN

1097-6744

Publication Date

May 2012

Volume

163

Issue

5

Start / End Page

844 / 850.e1

Location

United States

Related Subject Headings

  • Survival Analysis
  • Stroke Volume
  • Sex Distribution
  • Severity of Illness Index
  • Risk Assessment
  • Retrospective Studies
  • Prognosis
  • Prevalence
  • Predictive Value of Tests
  • Multivariate Analysis
 

Citation

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Shah, S. H., Sun, J.-L., Stevens, R. D., Bain, J. R., Muehlbauer, M. J., Pieper, K. S., … Newby, L. K. (2012). Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am Heart J, 163(5), 844-850.e1. https://doi.org/10.1016/j.ahj.2012.02.005
Shah, Svati H., Jie-Lena Sun, Robert D. Stevens, James R. Bain, Michael J. Muehlbauer, Karen S. Pieper, Carol Haynes, et al. “Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease.Am Heart J 163, no. 5 (May 2012): 844-850.e1. https://doi.org/10.1016/j.ahj.2012.02.005.
Shah SH, Sun J-L, Stevens RD, Bain JR, Muehlbauer MJ, Pieper KS, et al. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am Heart J. 2012 May;163(5):844-850.e1.
Shah, Svati H., et al. “Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease.Am Heart J, vol. 163, no. 5, May 2012, pp. 844-850.e1. Pubmed, doi:10.1016/j.ahj.2012.02.005.
Shah SH, Sun J-L, Stevens RD, Bain JR, Muehlbauer MJ, Pieper KS, Haynes C, Hauser ER, Kraus WE, Granger CB, Newgard CB, Califf RM, Newby LK. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am Heart J. 2012 May;163(5):844-850.e1.
Journal cover image

Published In

Am Heart J

DOI

EISSN

1097-6744

Publication Date

May 2012

Volume

163

Issue

5

Start / End Page

844 / 850.e1

Location

United States

Related Subject Headings

  • Survival Analysis
  • Stroke Volume
  • Sex Distribution
  • Severity of Illness Index
  • Risk Assessment
  • Retrospective Studies
  • Prognosis
  • Prevalence
  • Predictive Value of Tests
  • Multivariate Analysis