Diagnosis of subclinical and clinical acute T‐cell‐mediated rejection in renal transplant patients by urinary proteome analysis

Metzger, Jochen; Chatzikyrkou, Christos; Broecker, Verena; Schiffer, Eric; Jaensch, Lothar; Iphoefer, Alexander; Mengel, Michael; Mullen, William; Mischak, Harald; Haller, Hermann; Gwinner, Wilfried

doi:10.1002/prca.201000153

Cited by 64 publications

(52 citation statements)

References 41 publications

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“…These general characteristics are consistent with the concept that inflammatory mechanisms play an important role in the development of SAKI. MMP8 was previously reported as a candidate biomarker for detecting renal allograft rejection (29). Genetic ablation or pharmacologic inhibition of MMP8 delays renal recovery in mice subjected to renal ischemia (30), which is consistent with the MMP8-based decision node in the model.…”

Section: Discussionmentioning

confidence: 99%

A Multibiomarker-Based Model for Estimating the Risk of Septic Acute Kidney Injury

Wong

Cvijanovich

Anas

et al. 2015

Critical Care Medicine

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Objective The development of acute kidney injury in patients with sepsis is associated with worse outcomes. Identifying those at risk for septic acute kidney injury could help to inform clinical decision making. We derived and tested a multibiomarker-based model to estimate the risk of septic acute kidney injury in children with septic shock. Design Candidate serum protein septic acute kidney injury biomarkers were identified from previous transcriptomic studies. Model derivation involved measuring these biomarkers in serum samples from 241 subjects with septic shock obtained during the first 24 hours of admission and then using a Classification and Regression Tree approach to estimate the probability of septic acute kidney injury 3 days after the onset of septic shock, defined as at least two-fold increase from baseline serum creatinine. The model was then tested in a separate cohort of 200 subjects. Setting Multiple PICUs in the United States. Interventions None other than standard care. Measurements and Main Results The decision tree included a first-level decision node based on day 1 septic acute kidney injury status and five subsequent biomarker-based decision nodes. The area under the curve for the tree was 0.95 (CI95, 0.91–0.99), with a sensitivity of 93% and a specificity of 88%. The tree was superior to day 1 septic acute kidney injury status alone for estimating day 3 septic acute kidney injury risk. In the test cohort, the tree had an area under the curve of 0.83 (0.72–0.95), with a sensitivity of 85% and a specificity of 77% and was also superior to day 1 septic acute kidney injury status alone for estimating day 3 septic acute kidney injury risk. Conclusions We have derived and tested a model to estimate the risk of septic acute kidney injury on day 3 of septic shock using a novel panel of biomarkers. The model had very good performance in a test cohort and has test characteristics supporting clinical utility and further prospective evaluation.

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Section: Discussionmentioning

confidence: 99%

A Multibiomarker-Based Model for Estimating the Risk of Septic Acute Kidney Injury

Wong

Cvijanovich

Anas

et al. 2015

Critical Care Medicine

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“…This technology has also successfully been used in clinical trials in transplant patients 25,26 . The data from one of these studies indicated that urine samples from patients with subclinical acute T-cell-mediated tubule-interstitial rejection could be distinguished from non-rejection controls mainly based on altered collagen α(I) and α (III) chain fragments suggesting an involvement of matrix metalloproteinase-8 (MMP-8) 25 . Nevertheless, this CE-MS proteomics assay has mainly been studied in chronic kidney disease patients and its value specifically for transplant patients still remains to be evaluated.…”

Section: Predictive Biomarkers In Transplantation Based On Non-targetmentioning

confidence: 99%

Biomarkers in Transplantation—Proteomics and Metabolomics

Christians

Klawitter

2016

Therapeutic Drug Monitoring

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Modern multi-analyte “omics” technologies allow for the identification of molecular signatures that confer significantly more information than measurement of a single parameter as typically used in current medical diagnostics. Proteomics and metabolomics bioanalytical assays capture a large set of proteins and metabolites in body fluids, cells or tissues and, complementing genomics, assess the phenome. Proteomics and metabolomics contribute to the development of novel predictive clinical biomarkers in transplantation in two ways: They can be used to generate a diagnostic fingerprint or they can be used to discover individual proteins and metabolites of diagnostic potential. Much fewer metabolomics than proteomics biomarker studies in transplant patients have been reported and, in contrast to proteomics discovery studies, new lead metabolite markers have yet to emerge. Most clinical proteomics studies have been discovery studies. Several of these studies have assessed diagnostic sensitivity and specificity. Nevertheless, none of these newly discovered protein biomarkers has yet been implemented in clinical decision making in transplantation. The currently most advanced markers discovered in proteomics studies in transplant patients are the chemokines CXCL-9 and CXCL-10, which have successfully been validated in larger multi-center trials in kidney transplant patients. These chemokines can be measured using standard immunoassay platforms, which should facilitate clinical implementation. Based on the published evidence, it is reasonable to expect that these chemokine markers can help guiding and individualizing immunosuppressive regimens, may be able to predict acute and chronic T cell and anti-body mediated rejection and may be useful tools for risk stratification of kidney transplant patients.

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“…Chronic rejection remains the major limitation of renal transplantation, and circumstantial evidence may indicate a role for MMPs. Numerous studies have collectively identified increases in MMP-2, MMP-7, MMP-8, and MMP-9, as well as TIMP-1 and -2 in cases of allograft rejection (16,27,68,80,104,105,145). Interestingly, polymorphisms in MMP-2 and -9 have been associated with increased allograft survival (116), and the immunosuppressive drug rapamycin inhibits MMP-9 expression while increasing TIMP-1 (94).…”

Section: Mmps In Renal Pathologymentioning

confidence: 99%

Matrix metalloproteinases in kidney homeostasis and diseases

Tan

Liu

2012

American Journal of Physiology-Renal Physiology

215

219

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Diagnosis of subclinical and clinical acute T‐cell‐mediated rejection in renal transplant patients by urinary proteome analysis

Abstract: The established marker set contains peptides related to tubulointerstitial infiltration seen in acute rejection. The set of urinary peptide markers will be used for early diagnosis of acute kidney allograft rejection marker in a multicenter phase III prospective study.

Cited by 64 publications

References 41 publications

A Multibiomarker-Based Model for Estimating the Risk of Septic Acute Kidney Injury

A Multibiomarker-Based Model for Estimating the Risk of Septic Acute Kidney Injury

Biomarkers in Transplantation—Proteomics and Metabolomics

Matrix metalloproteinases in kidney homeostasis and diseases

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