title: Evaluation of urinary biomarkers for coronary artery disease, diabetes, and diabetic kidney disease.
abstract: BACKGROUND
  In this study we sought to validate urinary biomarkers for diabetes and two common complications, coronary artery disease (CAD) and diabetic nephropathy (DN).
  
  
  METHODS
  A CAD score calculated by summing the product of a classification coefficient and signal amplitude of 15 urinary polypeptides was previously developed. Five sequences of biomarkers in the panel were identified as fragments of collagen alpha-1(I) and alpha-1(III). Prospectively collected urine samples available for analysis from 19 out of 20 individuals with CAD (15 with type 1 diabetes [T1D] and four without diabetes) and age-, sex-, and diabetes-matched controls enrolled in the Coronary Artery Calcification in Type 1 Diabetes study were analyzed for the CAD score using capillary electrophoresis and electrospray ionization mass spectrometry. Two panels of biomarkers that were previously defined to distinguish diabetes status were analyzed to determine their relationship to T1D. Three biomarker panels developed to distinguish DN (DNS) and two biomarker panels developed to distinguish renal disease (RDS) were examined to determine their relationship with renal function.
  
  
  RESULTS
  The CAD score was associated with CAD (odds ratio with 95% confidence interval, 2.2 [1.3-5.2]; P = 0.0016) and remained significant when adjusted individually for age, albumin excretion rate (AER), blood pressure, waist circumference, intraabdominal fat, glycosylated hemoglobin, and lipids. DNS and RDS were significantly correlated with AER, cystatin C, and serum creatinine. The biomarker panels for diabetes were both significantly associated with T1D status (P < 0.05 for both).
  
  
  CONCLUSIONS
  We validated a urinary proteome pattern associated with CAD and urinary proteome patterns associated with T1D and DN.
authors.affiliation: Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, 80045, USA. Janet.Snell-Bergeon@uchsc.edu
authors.forename: Janet K
authors.initials: JK
authors.lastname: Snell-Bergeon
authors.forename: David M
authors.initials: DM
authors.lastname: Maahs
authors.forename: Lorraine G
authors.initials: LG
authors.lastname: Ogden
authors.forename: Gregory L
authors.initials: GL
authors.lastname: Kinney
authors.forename: John E
authors.initials: JE
authors.lastname: Hokanson
authors.forename: Eric
authors.initials: E
authors.lastname: Schiffer
authors.forename: Marian
authors.initials: M
authors.lastname: Rewers
authors.forename: Harald
authors.initials: H
authors.lastname: Mischak
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000097:blood / Q000652:urine*; D001794:Blood Pressure; D002784:Cholesterol / Q000097:blood; D015331:Cohort Studies; D003327:Coronary Disease / Q000097:blood / Q000652:urine*; D003920:Diabetes Mellitus / Q000097:blood / Q000652:urine*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000652:urine; D003925:Diabetic Angiopathies / Q000652:urine*; D003928:Diabetic Nephropathies / Q000097:blood / Q000652:urine*; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D011507:Proteinuria
chemical_list: D015415:Biomarkers; D002784:Cholesterol