Assessment of renal allograft function via urine-based estimation of glomerular filtration rate
Reuben Sarwal2, Wanzin Yazar2, Nicholas Titzler2, Kavita Chalasani2, Christopher Chin2, Vivienne Gunadhi2, Minnie Sarwal1, Srinka Ghosh2.
1Department of Surgery , UCSF, San Francisco, CA, United States; 2NephroSant, San Mateo, CA, United States
Introduction: The estimation of glomerular filtration (eGFR) remains one of the most important indicators of kidney allograft health post-transplantation. eGFR has been shown to strongly correlate with graft function, and overall graft survival. Evaluation of eGFR early post-transplantation is especially crucial for understanding how well the new allograft is functioning; however, serial monitoring remains a challenge due to the requirement of frequent blood draws, and lab-based visits. We have developed a urine-based eGFR score (ueGFR) in native kidney injury patients, which has a greater than 90% correlation with eGFR formulae and classifies and predicts CKD stages with a 95% accuracy. This multi-center, longitudinal study presents the novel ueGFR score, which relies solely on urinary markers, in the context of kidney transplantation.
Methods: The previously developed ueGFR score, in native kidney injury patients, was applied to 59 unique kidney transplant patients, and 14 healthy controls. Of the 73 patients, 38% (28/73) had biopsy confirmed acute rejection (AR), and the rest had either biopsy-confirmed no-rejection (NR), or another non-rejection related post-transplant complication (ATN, FSGS, or CAN). Spearman correlation analysis was performed between ueGFR scoreand eGFR.non-parametric Mann-Whitney-U with Holm–Bonferroni correction for multiple comparison tests were conducted to determine differences in ueGFR and eGFR values between different phenotypes, and Kruskal-Wallis with Dunn’s post-hoc multiple correction statistical tests were conducted to determine global statistical differences in ueGFR and eGFR values across all different phenotypes.
Results: Non-parametric spearman correlation results showed that the ueGFR score, when applied to the 73 patients, had a statistically significant correlation with eGFR (p-value = 2.28e-13, r2 = 0.80). Furthermore, non-parametric Mann-Whitney-U with Holm–Bonferroni correction for multiple comparison tests, and Kruskal-Wallis with Dunn’s post-hoc multiple correction statistical tests showed statistically significant differences in ueGFR scores by phenotype. Kruskal-Wallis results revealed a global statistically significant difference between phenotypes (p-value = 4.7e-06), and Mann-Whitney-U with Holm–Bonferroni correction for multiple comparison tests revealed a statistically significant difference between healthy controls and acute rejection (p-value = 2.81e-06), and no-rejection and acute rejection (p-value = 0.002).
Conclusions: The positive economic impact of early kidney function detection and its subsequent treatment cannot be underscored, enough. The inclusion of a sensitive non-invasive assay for renal function, to replace invasive eGFR tests early post-transplantation, would result in major socio-economic benefits for these at-risk populations by increasing access to easier and more frequent, low-cost and convenient GFR assessments.
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