Scott D. Bringans , Jun Ito , Thomas Stoll , Kaye Winfield , Michael Phillips , Kirsten Peters , Wendy A. Davis , Timothy M.E. Davis , Richard J. Lipscombe
{"title":"Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease","authors":"Scott D. Bringans , Jun Ito , Thomas Stoll , Kaye Winfield , Michael Phillips , Kirsten Peters , Wendy A. Davis , Timothy M.E. Davis , Richard J. Lipscombe","doi":"10.1016/j.euprot.2016.12.001","DOIUrl":null,"url":null,"abstract":"<div><p>A protein biomarker discovery workflow was applied to plasma samples from patients at different stages of diabetic kidney disease. The proteomics platform produced a panel of significant plasma biomarkers that were statistically scrutinised against the current gold standard tests on an analysis of 572 patients. Five proteins were significantly associated with diabetic kidney disease defined by albuminuria, renal impairment (eGFR) and chronic kidney disease staging (CKD Stage ≥1, ROC curve of 0.77). The results prove the suitability and efficacy of the process used, and introduce a biomarker panel with the potential to improve diagnosis of diabetic kidney disease.</p></div>","PeriodicalId":38260,"journal":{"name":"EuPA Open Proteomics","volume":"14 ","pages":"Pages 1-10"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.euprot.2016.12.001","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EuPA Open Proteomics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212968516300393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 28
Abstract
A protein biomarker discovery workflow was applied to plasma samples from patients at different stages of diabetic kidney disease. The proteomics platform produced a panel of significant plasma biomarkers that were statistically scrutinised against the current gold standard tests on an analysis of 572 patients. Five proteins were significantly associated with diabetic kidney disease defined by albuminuria, renal impairment (eGFR) and chronic kidney disease staging (CKD Stage ≥1, ROC curve of 0.77). The results prove the suitability and efficacy of the process used, and introduce a biomarker panel with the potential to improve diagnosis of diabetic kidney disease.
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