Kidney transplantation is the treatment method of choice for patients with terminal kidney failure [1]. Transplant patients require frequent follow-up examinations to detect potential complications at an early stage. Therefore, biopsies are performed during aftercare. They are generally considered to be safe, but it remains an invasive procedure with a risk for complications, at worst of losing the transplant [2; 3]. A metabolite based, non-invasive test for the detection of acute rejection after transplantation would improve clinical detection of asymptomatic rejection to arrange a control biopsy or to supplement the physician’s assessment of patients with non-specific symptoms such as fever.
Study design:
PARASOL is a multicenter prospective observational (non-interventional) study w/o follow-up
Partners:
Regensburg University Hospital (Prof. Dr. Bernhard Banas); Medical University Vienna (Prof. Dr. Georg Böhmig); Institute for Clinical and Experimental Medicine Prague (Prof. MUDr. Ondřej Viklický, CSc.); Centre Hospitalier Universitaire Grenoble Alpes (Prof. Dr. Lionel Rostaing); Bellvitge University Hospital Barcelona (Dr. Oriol Bestard)
Material:
Urine samples from renal allograft patients ≥14 days after transplantation
Reference standard:
Routine kidney biopsy
Approach:
Metabolomics studies make it possible to analyze the entire metabolite spectrum (metabolome) to detect rising and falling concentrations of endogenous substances and associate them with pathological processes. Previous results from the UMBRELLA study, which was performed in collaboration with Bernhard Banas and co-workers from the Department of Nephrology at the University Hospital Regensburg, showed that it is possible to diagnose acute renal allograft rejection from urine using alanine, citrate, lactate, and urea normalized to urine creatinine. This metabolite constellation is further validated in the PARASOL study in a pan-European multicentre study. The diagnostic accuracy for the metabolite constellations will be determined based on their AUC value, sensitivity, and specificity depending on the cut-off value.
Success:
In 2017, Numares launched the AXINON® renalTX-SCORE-U100®* for use in clinical routine.
References:
1. Suthanthiran, M. and T.B. Strom, Renal transplantation. N Engl J Med, 1994. 331(6): p. 365-76.
2. Schwarz, A., et al., Safety and adequacy of renal transplant protocol biopsies. Am J Transplant, 2005. 5(8): p. 1992-6.
3. Furness, P.N., et al., Protocol biopsy of the stable renal transplant: a multicenter study of methods and complication rates. Transplantation, 2003. 76(6): p. 969-73.
*Numares’ products are not yet available for sale within the United States; they have not yet been approved or cleared by the U.S. Food and Drug Administration.
|Partners
5th - 10th November, 2019
Washington, DC, U.S.
There will be a poster presentation showing latest results about metabolomics based detection of kidney allograft rejection by evaluating a metaboliteconstellation of biomarkers to support non-invasive, thus close monitoring after kidney transplant.
Publications
Banas, M., et al.
A Prospective Multicenter Trial to Evaluate Urinary Metabolomics for Non-invasive Detection of Renal Allograft Rejection (PARASOL): Study Protocol and Patient Recruitment.
Front. Med. (2022)
Ehrich, J., et al.
Serum myo-inositol, dimethyl sulfone, and valine in combination with creatinine allow accurate assessment of renal insufficiency.
Diagnostics (2021)
Stämmler, F., et al.
Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C.
Diagnostics (2021)
Atul K. Sharma Tom D. Blydt-Hansen
To accompany Banas et al., Time for a Paradigm Shift
EBioMedicine (2019)
M. Banas, S. Neuman, P. Pagel, F. J. Putz, G. Boehmig, J. Eiglsperger, et al.
A urinary metabolite constellation to detect acute rejection in kidney allografts
EBioMedicine (2019)
M. Banas, S. Neumann, J. Eiglsperger, E. Schiffer, F. J. Putz, S. Reichelt-Wurm, et al.
Identification of a urine metabolite constellation characteristic for kidney allograft rejection
Metabolomics Off J Metabolomic Soc, 14 (9) (2018), p. 116
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