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Updated: Feb 10, 2026

Robot-Assisted Kidney Transplantation
Published on: July 19, 2021
Urinary metabolites predict prolonged duration of delayed graft function in DCD kidney transplant recipients
S Kostidis1, J R Bank2, D Soonawala2
1Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Abstract:
Extending kidney donor criteria, including donation after circulatory death (DCD), has resulted in increased rates of delayed graft function (DGF) and primary nonfunction. Here, we used Nuclear Magnetic Resonance (NMR) spectroscopy to analyze the urinary metabolome of DCD transplant recipients at multiple time points (days 10, 42, 180, and 360 after transplantation). The aim was to identify markers that predict prolonged duration of functional DGF (fDGF). Forty-seven metabolites were quantified and their levels were evaluated in relation to fDGF. Samples obtained at day 10 had a different profile than samples obtained at the other time points. Furthermore, at day 10 there was a statistically significant increase in eight metabolites and a decrease in six metabolites in the group with fDGF (N = 53) vis-à-vis the group without fDGF (N = 22). In those with prolonged fDGF (≥21 days) (N = 17) urine lactate was significantly higher and pyroglutamate lower than in those with limited fDGF (<21 days) (N = 36). In order to further distinguish prolonged fDGF from limited fDGF, the ratios of all metabolites were analyzed. In a logistic regression analysis, the sum of branched-chain amino acids (BCAAs) over pyroglutamate and lactate over fumarate, predicted prolonged fDGF with an AUC of 0.85. In conclusion, kidney transplant recipients with fDGF can be identified based on their altered urinary metabolome. Furthermore, two ratios of urinary metabolites, lactate/fumarate and BCAAs/pyroglutamate, adequately predict prolonged duration of fDGF.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

