title: Urinary proteomic diagnosis of coronary artery disease: identification and clinical validation in 623 individuals.
abstract: OBJECTIVE
  We studied the urinary proteome in a total of 623 individuals with and without coronary artery disease (CAD) in order to characterize multiple biomarkers that enable prediction of the presence of CAD.
  
  
  METHODS
  Urine samples were analyzed by capillary electrophoresis coupled online to micro time-of-flight mass spectrometry.
  
  
  RESULTS
  We defined a pattern of 238 CAD-specific polypeptides from comparison of 586 spot urine samples from 408 individuals. This pattern identified patients with CAD in a blinded cohort of 138 urine samples (71 patients with CAD and 67 healthy individuals) with high sensitivity and specificity (area under the receiver operator characteristic curve 87%, 95% confidence interval 81-92) and was superior to previously developed 15-marker (area under the receiver operator characteristic curve 68%, P < 0.0001) and 17-marker panels (area under the receiver operator characteristic curve 77%, P < 0.0001). The sequences of the discriminatory polypeptides include fragments of alpha-1-antitrypsin, collagen types 1 and 3, granin-like neuroendocrine peptide precursor, membrane-associated progesterone receptor component 1, sodium/potassium-transporting ATPase gamma chain and fibrinogen-alpha chain. Several biomarkers changed significantly toward the healthy signature following 2-year treatment with irbesartan, whereas short-term treatment with irbesartan did not significantly affect the polypeptide pattern.
  
  
  CONCLUSIONS
  Urinary proteomics identifies CAD with high confidence and might also be useful for monitoring the effects of therapeutic interventions.
authors.affiliation: Faculty of Medicine, University of Glasgow, Glasgow, UK.
authors.forename: Christian
authors.initials: C
authors.lastname: Delles
authors.forename: Eric
authors.initials: E
authors.lastname: Schiffer
authors.forename: Constantin
authors.initials: C
authors.lastname: von Zur Muhlen
authors.forename: Karlheinz
authors.initials: K
authors.lastname: Peter
authors.forename: Peter
authors.initials: P
authors.lastname: Rossing
authors.forename: Hans-Henrik
authors.initials: HH
authors.lastname: Parving
authors.forename: Jane A
authors.initials: JA
authors.lastname: Dymott
authors.forename: Ulf
authors.initials: U
authors.lastname: Neisius
authors.forename: Lukas U
authors.initials: LU
authors.lastname: Zimmerli
authors.forename: Janet K
authors.initials: JK
authors.lastname: Snell-Bergeon
authors.forename: David M
authors.initials: DM
authors.lastname: Maahs
authors.forename: Roland E
authors.initials: RE
authors.lastname: Schmieder
authors.forename: Harald
authors.initials: H
authors.lastname: Mischak
authors.forename: Anna F
authors.initials: AF
authors.lastname: Dominiczak
mesh_terms: D000368:Aged; D015415:Biomarkers / Q000737:chemistry; D015331:Cohort Studies; D003324:Coronary Artery Disease / Q000652:urine*; D019075:Electrophoresis, Capillary / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D013058:Mass Spectrometry / Q000379:methods; D008875:Middle Aged; D010455:Peptides / Q000737:chemistry; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers; D010455:Peptides; D020543:Proteome