Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease
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 5...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-03-01
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| Series: | EuPA Open Proteomics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212968516300393 |
| _version_ | 1818000733202546688 |
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| author | Scott D. Bringans Jun Ito Thomas Stoll Kaye Winfield Michael Phillips Kirsten Peters Wendy A. Davis Timothy M.E. Davis Richard J. Lipscombe |
| author_facet | Scott D. Bringans Jun Ito Thomas Stoll Kaye Winfield Michael Phillips Kirsten Peters Wendy A. Davis Timothy M.E. Davis Richard J. Lipscombe |
| author_sort | Scott D. Bringans |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:25:29Z |
| format | Article |
| id | doaj.art-b2b96be2a95e4206a7c3d1303adb53d9 |
| institution | Directory Open Access Journal |
| issn | 2212-9685 |
| language | English |
| last_indexed | 2024-04-14T03:25:29Z |
| publishDate | 2017-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EuPA Open Proteomics |
| spelling | doaj.art-b2b96be2a95e4206a7c3d1303adb53d92022-12-22T02:15:10ZengElsevierEuPA Open Proteomics2212-96852017-03-0114C11010.1016/j.euprot.2016.12.001Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney diseaseScott D. Bringans0Jun Ito1Thomas Stoll2Kaye Winfield3Michael Phillips4Kirsten Peters5Wendy A. Davis6Timothy M.E. Davis7Richard J. Lipscombe8Proteomics International, PO Box 3008, Broadway, Nedlands, Perth, Western Australia, WA 6009, AustraliaProteomics International, PO Box 3008, Broadway, Nedlands, Perth, Western Australia, WA 6009, AustraliaProteomics International, PO Box 3008, Broadway, Nedlands, Perth, Western Australia, WA 6009, AustraliaProteomics International, PO Box 3008, Broadway, Nedlands, Perth, Western Australia, WA 6009, AustraliaHarry Perkins Institute of Medial Research, QQ Block, QEII Medical Centre 6 Verdun Street, Nedlands, Perth, Western Australia, WA 6009, AustraliaProteomics International, PO Box 3008, Broadway, Nedlands, Perth, Western Australia, WA 6009, AustraliaUniversity of Western Australia, 35 Stirling Highway, Crawley, Perth, Western Australia, WA 6009, AustraliaUniversity of Western Australia, 35 Stirling Highway, Crawley, Perth, Western Australia, WA 6009, AustraliaProteomics International, PO Box 3008, Broadway, Nedlands, Perth, Western Australia, WA 6009, AustraliaA 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.http://www.sciencedirect.com/science/article/pii/S2212968516300393BiomarkerDiabetic kidney diseaseMRMiTRAQDiabetes |
| spellingShingle | Scott D. Bringans Jun Ito Thomas Stoll Kaye Winfield Michael Phillips Kirsten Peters Wendy A. Davis Timothy M.E. Davis Richard J. Lipscombe Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease EuPA Open Proteomics Biomarker Diabetic kidney disease MRM iTRAQ Diabetes |
| title | Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease |
| title_full | Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease |
| title_fullStr | Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease |
| title_full_unstemmed | Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease |
| title_short | Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease |
| title_sort | comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease |
| topic | Biomarker Diabetic kidney disease MRM iTRAQ Diabetes |
| url | http://www.sciencedirect.com/science/article/pii/S2212968516300393 |
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