Plasma NGAL levels in stable kidney transplant recipients and the risk of allograft loss.

Jutta S Swolinsky, Ricarda M Hinz, Carolin E Markus,Eugenia Singer,Friederike Bachmann, Fabian Halleck,Susanne Kron, Marcel G Naik,Danilo Schmidt, Martin Obermeier, Pimrapat Gebert,Geraldine Rauch, Siegfried Kropf,Michael Haase, Klemens Budde,Kai-Uwe Eckardt, Timm H Westhoff,Kai M Schmidt-Ott

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association(2024)

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摘要
BACKGROUND:The objective of this study was to investigate the utility of neutrophil gelatinase-associated lipocalin (NGAL) and calprotectin (CPT) to predict long-term graft survival in stable kidney transplant recipients (KTR). METHODS:A total of 709 stable outpatient KTR were enrolled >2 months post-transplant. The utility of plasma and urinary NGAL (pNGAL, uNGAL) and plasma and urinary CPT at enrollment to predict death-censored graft loss was evaluated during a 58-month follow-up. RESULTS:Among biomarkers, pNGAL showed the best predictive ability for graft loss and was the only biomarker with an area under the curve (AUC) > 0.7 for graft loss within 5 years. Patients with graft loss within 5 years (n = 49) had a median pNGAL of 304 [interquartile range (IQR) 235-358] versus 182 (IQR 128-246) ng/mL with surviving grafts (P < .001). Time-dependent receiver operating characteristic analyses at 58 months indicated an AUC for pNGAL of 0.795, serum creatinine-based Chronic Kidney Disease Epidemiology Collaboration estimated glomerular filtration rate (eGFR) had an AUC of 0.866. pNGAL added to a model based on conventional risk factors for graft loss with death as competing risk (age, transplant age, presence of donor-specific antibodies, presence of proteinuria, history of delayed graft function) had a strong independent association with graft loss {subdistribution hazard ratio (sHR) for binary log-transformed pNGAL [log2(pNGAL)] 3.4, 95% confidence interval (CI) 2.24-5.15, P < .0001}. This association was substantially attenuated when eGFR was added to the model [sHR for log2(pNGAL) 1.63, 95% CI 0.92-2.88, P = .095]. Category-free net reclassification improvement of a risk model including log2(pNGAL) in addition to conventional risk factors and eGFR was 54.3% (95% CI 9.2%-99.3%) but C-statistic did not improve significantly. CONCLUSIONS:pNGAL was an independent predictor of renal allograft loss in stable KTR from one transplant center but did not show consistent added value when compared with baseline predictors including the conventional marker eGFR. Future studies in larger cohorts are warranted.
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