Prediction of diabetic nephropathy using urine proteomic profiling 10 years prior to development of nephropathy
ABSTRACT 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.
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.
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.
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.
SourceAvailable from: Salvatore Di PaoloNephrology Dialysis Transplantation 12/2014; DOI:10.1093/ndt/gfu372 · 3.49 Impact Factor
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ABSTRACT: Neural tube defects (NTDs) are common birth defects, whose specific biomarkers are needed. The purpose of this pilot study is to determine whether protein profiling in NTD-mothers differ from normal controls using SELDI-TOF-MS. ProteinChip Biomarker System was used to evaluate 82 maternal serum samples, 78 urine samples and 76 amniotic fluid samples. The validity of classification tree was then challenged with a blind test set including another 20 NTD-mothers and 18 controls in serum samples, and another 19 NTD-mothers and 17 controls in urine samples, and another 20 NTD-mothers and 17 controls in amniotic fluid samples. Eight proteins detected in serum samples were up-regulated and four proteins were down-regulated in the NTD group. Four proteins detected in urine samples were up-regulated and one protein was down-regulated in the NTD group. Six proteins detected in amniotic fluid samples were up-regulated and one protein was down-regulated in the NTD group. The classification tree for serum samples separated NTDs from healthy individuals, achieving a sensitivity of 91% and a specificity of 97% in the training set, and achieving a sensitivity of 90% and a specificity of 97% and a positive predictive value of 95% in the test set. The classification tree for urine samples separated NTDs from controls, achieving a sensitivity of 95% and a specificity of 94% in the training set, and achieving a sensitivity of 89% and a specificity of 82% and a positive predictive value of 85% in the test set. The classification tree for amniotic fluid samples separated NTDs from controls, achieving a sensitivity of 93% and a specificity of 89% in the training set, and achieving a sensitivity of 90% and a specificity of 88% and a positive predictive value of 90% in the test set. These suggest that SELDI-TOF-MS is an additional method for NTDs pregnancies detection.PLoS ONE 07/2014; 9(7):e103276. DOI:10.1371/journal.pone.0103276 · 3.53 Impact Factor
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