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Hepatology. 2019 Sep 11. doi: 10.1002/hep.30939. [Epub ahead of print]

Discovery and validation of a biomarker model (preserve) predictive of renal outcomes after liver transplantation.

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Northwestern University Feinberg School of Medicine, Chicago, IL.
Baylor University Medical Center, Dallas, TX.
Mount Sinai Hospital, New York, NY.
Mayo Clinic Arizona, Phoenix, AZ.
Case Western University, Cleveland, OH.
Cleveland Clinic Foundation, Cleveland, OH.
Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, Bethesda, MD.
Rho Federal Systems Division, Chapel Hill, NC.


A high proportion of patients develop chronic kidney disease after liver transplantation. We aimed to develop clinical/protein models to predict future GFR deterioration in this population. In independent multicenter discovery (CTOT14) and single center validation (BUMC) cohorts, we analyzed kidney injury proteins in serum/plasma samples at month 3 after liver transplant in recipients with preserved GFR who demonstrated subsequent GFR deterioration vs. preservation by year 1, and year 5 in the BUMC cohort. In CTOT14, we also examined correlations between serial protein levels and GFR over the first year. A month 3 predictive model was constructed from clinical and protein level variables using the CTOT14 cohort (n=60). Levels of β2-microglobulin and CD40 antigen and presence of HCV infection predicted early (year 1) GFR deterioration (AUC 0.814). We observed excellent validation of this model (AUC 0.801) in the BUMC cohort (n=50) who had both early and late (year 5) GFR deterioration. At an optimal threshold, the model had the following performance characteristics in CTOT14 and BUMC, respectively: accuracy (0.75, 0.8), sensitivity (0.71, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75) and negative predictive value (0.76, 0.82). In the serial CTOT14 analysis, several proteins, including β2-microglobulin and CD40, correlated with GFR changes over the first year. Conclusion: We have validated a clinical/protein model (PRESERVE) that early after liver transplantation can predict future renal deterioration vs. preservation with high accuracy. This model may help select recipients at higher risk for subsequent chronic kidney disease for early, proactive renal sparing strategies.


chronic kidney disease; glomerular filtration rate; plasma; proteomics; serum


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