Research ArticleKidney transplant

A urine score for noninvasive accurate diagnosis and prediction of kidney transplant rejection

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Science Translational Medicine  18 Mar 2020:
Vol. 12, Issue 535, eaba2501
DOI: 10.1126/scitranslmed.aba2501
  • Fig. 1 Study design.

    In this multicenter, prospective study, a total of 601 patient samples were assessed. Urine samples (364) belonged to renal transplant recipients, where every urine sample was paired with a renal transplant biopsy for phenotype classification into the following diagnoses: stable (STA; n = 170), acute rejection (AR) (n = 103), borderline AR (bAR; n = 50), and BK virus nephropathy (BKVN; n = 9). An additional 32 patients with AR had urine samples collected before the rejection episode; these samples were also paired with biopsies. In the context of a stable kidney transplant, we compared patients’ urine scores with a scores run on urine samples from healthy controls without any renal insufficiency (n = 54). In addition, we compared the urine scores from stable kidney transplant patients against the scores from patients with early CKD (stages 1 and 2; n = 183). The selected urinary biomarkers were measured on all urine samples collected, and statistical analysis and modeling were performed on a subset of patients, defined as STA or AR, and split out randomly as a training set (n = 111) for modeling the urine score. The fixed Q score model from the training set was then applied to two separate validation sets consisting of STA and AR samples, with set sizes of 103 and 59 for validation set 1 and validation set 2, respectively. The fixed urine score was next applied to samples with other transplant injuries (bAR/BKVN; n = 59) and for prediction of rejection in the cohort of patients (preAR samples; n = 32), with urine samples collected within 8 months of a biopsy-confirmed AR episode. The preAR samples were collected at a time where the paired biopsy was histologically stable, and there was no graft dysfunction.

  • Fig. 2 The selected urinary biomarkers could segregate nonrejection patients from those with AR.

    (A) A urine score model was trained on 111 samples consisting of 72 STA and 39 AR samples to generate a scaled Q score ranging from 0 to 100. The distribution of the STA and AR groups is depicted in the figure. (B) The quiescence threshold was set at 32 with a corresponding sensitivity of 94.9% and specificity of 100%. The AUC of the ROC curve was 0.99 (P < 0.0001). (C) The urine score model was applied to a set of 103 independent samples consisting of 71 STA and 32 AR samples. The median and 95% CI for the STA and AR group were 13.14 (8.75 to 17.94) and 45.16 (40.77 to 57.87), respectively (P < 0.0001). (D) At the predetermined quiescence threshold, the sensitivity was 90.6% and the specificity was 91.6%. The AUC of the ROC curve was 0.98 (P < 0.0001). (E) The fixed urine score model was applied to a set of 59 independent samples consisting of 27 STA and 32 AR. The median and 95% CI for the STA and AR groups were 16.21 (8.16 to 26.22) and 67.25 (60.46 to 78.73), respectively (P < 0.0001). (F) At the predetermined quiescence threshold, the sensitivity was 100.0% and the specificity was 96.3%. The AUC of the ROC curve was 1.00 (P < 0.0001).

  • Fig. 3 Application of the urine score to other types of injuries.

    (A) The urine score model was applied to a set of samples consisting of all the previously identified STA and AR along with bAR and BKVN samples. The median and 95% CI for the bAR and BKVN group were 38.57 (34.71 to 40.80) and 23.70 (10.48 to 53.38), respectively (P < 0.0001 for STA versus AR and STA versus BKVN; P < 0.01 for AR versus bAR and AR versus BKVN). (B) The AUC of the ROC curve for AR versus all other outcomes was 0.96 (P < 0.0001).

  • Fig. 4 Patterns of the urine score in kidney injuries.

    The urine scores were aggregated for different outcomes. Healthy control (HC), 10.80 (7.90 to 12.20); CKD stage 1 (CKD1), 24.27 (23.10 to 25.12); CKD stage 2 (CKD2), 34.24 (32.17 to 37.12); STA, 12.19 (9.75 to 14.39); bAR, 38.47 (34.71 to 40.80); AR, 58.76 (54.95 to 63.40). Data are given as median and 95% CI.

  • Fig. 5 Comparison of the urine score performance.

    (A) The urine score performed better than eGFR or the urinary protein/creatinine ratio in discriminating AR versus STA. For the entire set of 170 STA and 103 AR, the Q score, eGFR, and protein/creatinine (Pr/Cr) ratio were plotted and the AUC was calculated. The AUCs were 0.99, 0.86, and 0.76, respectively. (B) Similar performance of the urine score for noninvasive diagnosis of biopsy-confirmed pediatric and adult AR. The urine score performed well for diagnosis of AR in children ≤18 years old as well as adult recipients >18 years old: pediatric AUC = 0.95 (no AR 30, AR 21); adult AUC = 0.99 (no AR 138, AR 82).

  • Fig. 6 The urine score can measure and distinguish between clinically relevant transplant parameters.

    (A) Patients with AR were split into those with ABMR (n = 38) and TCMR (n = 65). There was no significant difference between these two groups (P = 0.776). ns, not significant. (B) Patients with AR were split into increasing histology score as per the Banff histology classification. One-way ANOVA with test for linear trend indicated a significant difference (P = 0.0004). (C) The urine scores were no different in patients with biopsy-confirmed AR, whether the AR was diagnosed on a for-cause (n = 47) or protocol (n = 18) (P = 0.395) biopsy. (D and E) The urine score correlates with the paired biopsy inflammation (i) score (P < 0.0001) (D) and tubulitis (t) score (P < 0.0001) (E). (F) When the Q score was applied to patients with samples collected before confirmed episodes of AR, 38% had scores above threshold for preAR at variable times up to 8 months before the rejection episode.

  • Fig. 7 The urine score can measure and distinguish between clinically relevant transplant parameters.

    (A) In the total cohort of 364 transplant samples, there were 160 clinically indicated biopsies, of which 90 had a urine score above the rejection threshold and 70 below the threshold. Of the 204 protocol biopsies, 71 had a score above the rejection threshold and 133 below. The breakdown of different biopsy diagnoses paired with the sample is shown in the figure as percentages. (B) Accuracy tables for biopsy (for-cause versus protocol) and the Q score (score < 32 versus score ≥ 32) for AR and STA classification.

  • Table 1 Demographics and characteristics.

    Values are reported in the given units with SD in parentheses, and all comparisons between groups were nonsignificant. Recipients are both adult (>18 years) and pediatric (≤18 years) in each cohort. Clinical pathology data are based on the day of biopsy, which is timed with the urine collection, which occurred before performance of the biopsy. ABMR/TCMR split is of AR-confirmed patients, based on the Banff allograft pathology classification (17). All other demographic and clinical information is based on the day of the kidney transplant.

    Phenotype characteristicTraining
    (n = 111)
    Validation #1
    (n = 103)
    Validation #2
    (n = 59)
    Recipient
      Recipient age, year (SD)29 (13.4)32 (14.2)32 (11.7)
      Recipient male/female (%)48/5249/5142/58
    Donor
      Donor age, year (SD)35 (12.4)36 (14.4)39 (10.0)
      Donor male/female (%)43/5744/5640/69
    Ethnicity (%)
      Caucasian353948
      Hispanic322521
      Asian262724
      African American322
      Other475
    Donor source (%)
      Deceased49.442.455%
    Clinical pathology
      AR/STA39/7232/7132/27
      ABMR/TCMR (of AR)24/159/235/27
    Cause of ESRD (%)
      Obstructive545
      Diabetes202218
      Hypertension433742
      Glomerulonephritis877
      Immune-mediated202324
      Congenital542
      Other152

Supplementary Materials

  • stm.sciencemag.org/cgi/content/full/12/535/eaba2501/DC1

    Fig. S1. Correlation matrix of biomarkers.

    Table S1. Multivariate logistic regression of AR status as assessed by individual biomarkers.

    Table S2. Biomarker log-likelihood ratios for the urine score.

    Table S3. Multivariate regression of clinical and demographic variables with the urine score.

    Data file S1. Urine score data.

  • The PDF file includes:

    • Fig. S1. Correlation matrix of biomarkers.
    • Table S1. Multivariate logistic regression of AR status as assessed by individual biomarkers.
    • Table S2. Biomarker log-likelihood ratios for the urine score.
    • Table S3. Multivariate regression of clinical and demographic variables with the urine score.

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

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