title: Prediction of diabetic nephropathy using urine proteomic profiling 10 years prior to development of nephropathy.
abstract: OBJECTIVE
  We examined whether proteomic technologies identify novel urine proteins associated with subsequent development of diabetic nephropathy in subjects with type 2 diabetes before evidence of microalbuminuria.
  
  
  METHODS
  In a nested case-control study of Pima Indians with type 2 diabetes, baseline (serum creatinine <1.2 mg/dl and urine albumin excretion <30 mg/g) and 10-year urine samples were examined. Case subjects (n = 31) developed diabetic nephropathy (urinary albumin-to-creatinine ratio >300 mg/g) over 10 years. Control subjects (n = 31) were matched to case subjects (1:1) according to diabetes duration, age, sex, and BMI but remained normoalbuminuric (albumin-to-creatinine ratio <30 mg/g) over the same 10 years. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was performed on baseline urine samples, and training (14 cases:14 controls) and validation (17:17) sets were tested.
  
  
  RESULTS
  At baseline, A1C levels differed between case and control subjects. SELDI-TOF MS detected 714 unique urine protein peaks. Of these, a 12-peak proteomic signature correctly predicted 89% of cases of diabetic nephropathy (93% sensitivity, 86% specificity) in the training set. Applying this same signature to the independent validation set yielded an accuracy rate of 74% (71% sensitivity, 76% specificity). In multivariate analyses, the 12-peak signature was independently associated with subsequent diabetic nephropathy when applied to the validation set (odds ratio [OR] 7.9 [95% CI 1.5-43.5], P = 0.017) and the entire dataset (14.5 [3.7-55.6], P = 0.001), and A1C levels were no longer significant.
  
  
  CONCLUSIONS
  Urine proteomic profiling identifies normoalbuminuric subjects with type 2 diabetes who subsequently develop diabetic nephropathy. Further studies are needed to characterize the specific proteins involved in this early prediction.
authors.affiliation: Genomics Center and DF/HCC Cancer Proteomics Core, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Hasan H
authors.initials: HH
authors.lastname: Otu
authors.forename: Handan
authors.initials: H
authors.lastname: Can
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Spentzos
authors.forename: Robert G
authors.initials: RG
authors.lastname: Nelson
authors.forename: Robert L
authors.initials: RL
authors.lastname: Hanson
authors.forename: Helen C
authors.initials: HC
authors.lastname: Looker
authors.forename: William C
authors.initials: WC
authors.lastname: Knowler
authors.forename: Manuel
authors.initials: M
authors.lastname: Monroy
authors.forename: Towia A
authors.initials: TA
authors.lastname: Libermann
authors.forename: S Ananth
authors.initials: SA
authors.lastname: Karumanchi
authors.forename: Ravi
authors.initials: R
authors.lastname: Thadhani
mesh_terms: D000328:Adult; D001794:Blood Pressure; D003404:Creatinine / Q000097:blood; D003924:Diabetes Mellitus, Type 2 / Q000150:complications / Q000235:genetics / Q000652:urine; D003928:Diabetic Nephropathies / Q000453:epidemiology / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D007194:India / Q000453:epidemiology; D008297:Male; D013058:Mass Spectrometry; D008875:Middle Aged; D011237:Predictive Value of Tests; D011507:Proteinuria; D040901:Proteomics; D012016:Reference Values; D015203:Reproducibility of Results; D013997:Time Factors
chemical_list: D003404:Creatinine; D006442:Glycated Hemoglobin