Urinary Proteomics in Diabetes and CKD

Rossing, Kasper; Mischak, Harald; Dakna, Mohammed; Zürbig, Petra; Novák, Jan; Julian, Bruce A.; Good, David W.; Coon, Joshua J.; Tarnow, Lise; Rossing, Peter

doi:10.1681/asn.2007091025

Cited by 258 publications

(267 citation statements)

References 29 publications

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“…One report has identified a 12-peak proteomic mass spectrometer signature that could predict cases of diabetic nephropathy with 76% specificity (29). Similarly, a more complex panel of 65 biomarkers has been shown to predict cases of diabetic nephropathy with 97% specificity and differentiate from other renal diseases with 91% specificity (30). In this latter study, it was noted that many of the urine biomarkers identified were fragments of collagen type I that were reduced in diabetic patients.…”

Section: Novel Biomarkers Which Predict the Clinical Progression Of Dmentioning

confidence: 76%

Successes Achieved and Challenges Ahead in Translating Biomarkers into Clinical Applications

Tesch

Amur

Schousboe

et al. 2010

AAPS J

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Abstract. Biomarkers are important tools for identifying and stratifying diseases, predicting their progression and determining the effectiveness, safety, and doses of therapeutic interventions. This is important for common chronic diseases such as diabetic nephropathy, osteoporosis, and rheumatoid arthritis which affect large numbers of patients worldwide. This article summarizes the current knowledge of established and novel biomarkers for each of these diseases as presented at the 2008 AAPS/ACCP joint symposium "Success Achieved and Challenges Ahead in Translating Biomarkers into Clinical Applications," in Atlanta, Georgia. The advantages and disadvantages of various proteomic, metabolomic, genomic, and imaging biomarkers are discussed in relation to disease diagnosis and stratification, prognosis, drug development, and potential clinical applications. The use of biomarkers as a means to determine therapeutic interventions is also considered. In addition, we show that biomarkers may be useful for adapting therapies for individual needs by allowing the selection of patients who are most likely to respond or react adversely to a particular treatment. They may also be used to determine whether the development of a novel therapy is worth pursuing by informing crucial go/no go decisions around safety and efficacy. Indeed, regulatory bodies now suggest that effective integration of biomarkers into clinical drug development programs is likely to promote the development of novel therapeutics and more personalized medicine.

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Section: Novel Biomarkers Which Predict the Clinical Progression Of Dmentioning

confidence: 76%

Successes Achieved and Challenges Ahead in Translating Biomarkers into Clinical Applications

Tesch

Amur

Schousboe

et al. 2010

AAPS J

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show abstract

“…Urinary peptidome and proteome also suggested that collagen fragments are potential biomarkers for renal fibrosis (407)(408)(409). Global analyses of urinary proteins and peptides could be superior to analyses of single markers (407)(408)(409)(410)(411), and a number of studies have used this approach (412)(413)(414). At the moment, proteomic analyses are not yet feasible for broad clinical application.…”

Section: Serum and Urine Biomarkersmentioning

confidence: 99%

Renal Allograft Fibrosis: Biology and Therapeutic Targets

Floege

2015

American Journal of Transplantation

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“…13 Hence, CE-MS has been used to analyze urine samples from patients with various renal and nonrenal diseases. 14 CE-MS was used for the identification of a panel of 273 urinary peptide biomarkers of CKD (CKD273 classifier) by comparison of the urinary proteome of 379 healthy participants and 230 participants with CKD originating from a variety of renal diseases 15,16 followed by the validation of these findings in independent cohorts. 17,18 Small-scale studies in patients with diabetes showed that this classifier is able to predict the progression from normoalbuminuria to macroalbuminuria as well.…”

mentioning

confidence: 99%

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

Schanstra

Zürbig

Alkhalaf

et al. 2015

Journal of the American Society of Nephrology

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Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303620.065; P,0.001) and integrated discrimination improvement (0.05860.014; P,0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

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Urinary Proteomics in Diabetes and CKD

Cited by 258 publications

References 29 publications

Successes Achieved and Challenges Ahead in Translating Biomarkers into Clinical Applications

Successes Achieved and Challenges Ahead in Translating Biomarkers into Clinical Applications

Renal Allograft Fibrosis: Biology and Therapeutic Targets

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

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