Role of Serum and Urine Neutrophil Gelatinase-associated Lipocalin as Biomarkers for Assessing Graft Function in Kidney Transplant Recipients

Abstract

Objectives: The existing biomarkers used to promptly identify graft dysfunction after kidney transplantation lack consistency. Neutrophil gelatinase-associated lipocalin (NGAL) appears to be a promising biomarker but its levels measured from serum and urine have demonstrated varying predictive values. Our study aimed to explore the potential of NGAL as a biomarker in predicting graft dysfunction in kidney transplant patients, including live and deceased donor recipients. Methods: A single-centered observational cohort study with live and deceased kidney recipients as participants was conducted between 2018 and 2022 at a tertiary care hospital in Southern India. Serum creatinine levels were monitored daily; creatinine reduction on day two and day seven were calculated. The recipients were categorized based on graft recovery into three groups: delayed graft function (DGF), slow graft function (SGF), or immediate graft function (IGF). Analysis of serum and urine NGAL was conducted two hours after the transplant and their predictive values were evaluated by the area under the curves (AUC) method. Results: Of the 40 participants, 34 (85.0%) received their transplant from live-related donors, while six (15.0%) received kidneys from deceased donors. DGF occurred in four (10.0%) patients, SGF in 12 (30.0%), and 24 (60.0%) patients achieved IGF. Serum NGAL demonstrated higher sensitivity compared to urine NGAL. At a cut-off value of 678 ng/mL (AUC = 0.77), serum NGAL showed 90.0% sensitivity and 53.0% specificity. Urine NGAL had 70.0% sensitivity and 74.0% specificity at a cut-off value of 489 ng/mL (AUC = 0.72). Conclusions: Kidney recipients in SGF and DGF categories had elevated levels of serum and urine NGAL compared to those without IGF. Although serum NGAL showed higher sensitivity than urine NGAL in predicting graft dysfunction, both markers lacked the specificity needed for accurate predictions.

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