Urine Measurements of the Renin-Angiotensin System-Regulated Proteins Predict Death and Graft Loss in Kidney Transplant Recipients Enrolled in a Ramipril versus Placebo Randomized Controlled Trial.
Sofia Farkona, Max Kotlyar, Kevin Burns, Greg Knoll, Davor Brinc, Igor Jurisica, Ana Konvalinka
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引用次数: 0
Abstract
The renin-angiotensin system (RAS) is involved in kidney fibrosis. We previously identified six RAS-regulated proteins (RHOB, BST1, LYPA1, GLNA, TSP1, and LAMB2) that were increased in the urine of patients with kidney allograft fibrosis, compared to patients without fibrosis. We hypothesized that these urinary RAS-regulated proteins predicted primary outcomes in kidney transplant recipients enrolled in the largest RAS inhibitor randomized controlled trial. Urine excretion of 10 peptides corresponding to the six RAS-regulated proteins was quantified using parallel reaction monitoring mass spectrometry assays (normalized by urine creatinine) in a subset of patients in the trial. Machine learning models predicting outcomes based on urine peptide excretion rates were developed and evaluated. Urine samples (n = 111) from 56 patients were collected at 0, 6, 12, and 24 months. Twenty-four primary outcomes (doubling of serum creatinine, graft loss, or death) occurred in 17 patients. Logistic regression utilizing eight peptides of TSP1, BST1, LAMB2, LYPA1, and RHOB, from the last urine sample prior to outcomes, predicted a graft loss with an AUC of 0.78 (p = 0.00001). A random forest classifier utilizing BST1 and LYPA1 peptides predicted death with an AUC of 0.80 (p = 0.0016). Urine measurements of RAS-regulated proteins may predict outcomes in kidney transplant recipients, although further prospective studies are required.
期刊介绍:
Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".