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1.  Identification of Diagnostic Urinary Biomarkers for Acute Kidney Injury 
Acute kidney injury (AKI) is an important cause of death among hospitalized patients. The two most common causes of AKI are acute tubular necrosis (ATN) and prerenal azotemia (PRA). Appropriate diagnosis of the disease is important but often difficult. We analyzed urine proteins by 2-DE from 38 patients with AKI. Patients were randomly assigned to a training set, an internal test set or an external validation set. Spot abundances were analyzed by artificial neural networks (ANN) to identify biomarkers which differentiate between ATN and PRA. When the trained neural network algorithm was tested against the training data it identified the diagnosis for 16/18 patients in the training set and all 10 patients in the internal test set. The accuracy was validated in the novel external set of patients where 9/10 subjects were correctly diagnosed including 5/5 with ATN and 4/5 with PRA. Plasma retinol binding protein (PRBP) was identified in one spot and a fragment of albumin and PRBP in the other. These proteins are candidate markers for diagnostic assays of AKI.
doi:10.231/JIM.0b013e3181d473e7
PMCID: PMC2864920  PMID: 20224435
Acute kidney injury; Biomarkers; Diagnosis; Kidney; Urine
2.  Urine Biomarkers Predict the Cause of Glomerular Disease 
Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 × 10−8. Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, α-1 microglobulin, zinc α-2 glycoprotein, α-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.
doi:10.1681/ASN.2006070767
PMCID: PMC2733832  PMID: 17301191

Results 1-2 (2)