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Related Concept Videos

Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...

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High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease
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Published on: October 31, 2012

Predictive diagnostic of chronic allograft dysfunction using urinary proteomics analysis.

Rachel Tetaz1, Candice Trocmé, Mathieu Roustit

  • 1GREPI - AGIM - FRE3405 - CNRS, Université Joseph Fourier, EPHE, Paris, France. rtetaz@chu-grenoble.fr

Annals of Transplantation
|September 29, 2012
PubMed
Summary

New urinary biomarkers predict Chronic Allograft Dysfunction (CAD) in kidney transplant patients early. These non-invasive markers, identified three months post-transplant, enable timely intervention to prevent long-term graft failure.

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Area of Science:

  • Nephrology
  • Proteomics
  • Transplant Immunology

Background:

  • Chronic Allograft Dysfunction (CAD) is a primary cause of long-term kidney transplant failure.
  • Current diagnosis via biopsy occurs late, after irreversible damage.
  • Early identification of CAD is crucial for effective management.

Purpose of the Study:

  • To identify predictive urinary biomarkers for CAD before renal lesions manifest.
  • To analyze urine proteomic profiles for early CAD detection.
  • To improve long-term kidney allograft survival rates.

Main Methods:

  • Analysis of 29 urinary samples collected three months post-transplant using SELDI-TOF technology.
  • Evaluation of CAD development through serum creatinine levels and allograft biopsy.
  • Comparison of protein profiles between CAD and non-CAD patient groups.

Main Results:

  • Identified 18 urinary biomarkers predictive of CAD occurrence.
  • A specific 8860 Da protein showed high diagnostic performance (93% sensitivity, 65% specificity).
  • A multivariate algorithm using three biomarkers confirmed predictive relevance in an independent cohort.

Conclusions:

  • Non-invasive urinary biomarkers detected three months post-transplant can predict CAD up to four years later.
  • Early identification of at-risk patients allows for improved immunosuppressive therapy management.
  • Systematic biomarker measurement can enhance kidney transplant patient care and outcomes.