Discovery and Validation of a Biomarker Model (PRESERVE) Predictive of Renal Outcomes After Liver Transplantation

Abstract

Background and aims: A high proportion of patients develop chronic kidney disease (CKD) after liver transplantation (LT). We aimed to develop clinical/protein models to predict future glomerular filtration rate (GFR) deterioration in this population.

Approach and results: In independent multicenter discovery (CTOT14) and single-center validation (BUMC) cohorts, we analyzed kidney injury proteins in serum/plasma samples at month 3 after LT in recipients with preserved GFR who demonstrated subsequent GFR deterioration versus 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 β-2 microglobulin and CD40 antigen and presence of hepatitis C virus (HCV) infection predicted early (year 1) GFR deterioration (area under the curve [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 β-2 microglobulin and CD40, correlated with GFR changes over the first year.

Conclusions: We have validated a clinical/protein model (PRESERVE) that early after LT can predict future renal deterioration versus preservation with high accuracy. This model may help select recipients at higher risk for subsequent CKD for early, proactive renal sparing strategies.

FIG. 1.

Percentage change in eGFR from baseline month 3 to year 1 in the preserved (CTOT-P) versus diminished (CTOT-D) GFR groups. The horizontal dashed line represents a 10% decline from baseline. Individual subject trajectories are shown in gray (preserved) and light red (diminished) with the mean predicted lines (black for preserved, red for diminished) overlaid. The green bands reflect the associated 95% confidence interval.

FIG. 2.

(A) Receiver operating curve for the PRESERVE step-wise logistic regression model predicting GFR deterioration at 1 year post-LT in the initial CTOT14 plasma discovery cohort. Variables included in the PRESERVE model: HCV⁺ (yes/no), β−2 microglobulin protein level, and CD40 protein level. (B) Receiver operating curve for the same PRESERVE step-wise logistic regression model predicting GFR deterioration at both 1 and 5 years post-LT in the BUMC serum validation cohort.

FIG. 3.

This figure highlights the specific plasma proteins that significantly correlated with serial eGFR changes over the first year following LT: CTOT14 cohort. Abbreviations: a1micro, α−1 microglobulin; b2m, β−2 microglobulin; cor, correlation; cystatc, cystatin C; tff3, trefoil factor-3; thp, Tamm-Horsfall protein; thrombo, thrombomodulin.