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Original Clinical Science—General

Uromodulin to Osteopontin Ratio in Deceased Donor Urine Is Associated With Kidney Graft Outcomes

Mansour, Sherry G. DO, MS1,2; Liu, Caroline MHS3; Jia, Yaqi MPH3; Reese, Peter P. MD, MSCE4,5,6; Hall, Isaac E. MD, MS7; El-Achkar, Tarek M. MD8; LaFavers, Kaice A. PhD8; Obeid, Wassim PhD3; Rosenberg, Avi Z. MD, PhD9; Daneshpajouhnejad, Parnaz MD9; Doshi, Mona D. MD10; Akalin, Enver MD11; Bromberg, Jonathan S. MD, PhD12,13; Harhay, Meera N. MD, MSCE14,15,16; Mohan, Sumit MD, MPH17,18; Muthukumar, Thangamani MD19,20; Schröppel, Bernd MD21; Singh, Pooja MD22; El-Khoury, Joe M.1; Weng, Francis L. MD, MSCE23; Thiessen-Philbrook, Heather R. Mmath3; Parikh, Chirag R. MD, PhD3

Author Information
doi: 10.1097/TP.0000000000003299
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Deceased donors undergo extensive biological changes with simultaneous activation of both injury and recovery processes in response to ischemia.1 Neurogenic hypotension and systemic upregulation of proinflammatory markers follow brain death (the predominant process for deceased-donor death in the United States), resulting in direct renal injury secondary to ischemia and reperfusion as well as upregulation of inflammatory pathways.2-4 Along with injury and inflammation, there is initiation of both adaptive and maladaptive processes in the kidney.5,6 We hypothesize that while these adaptive and maladaptive mechanisms are initiated in donor kidneys before implantation, they have an impact on donor kidney injury as well as a durable impact on subsequent graft function with either resolution of parenchymal injury and favorable long-term sequelae or accelerated fibrosis and reduction in graft function, respectively. Interrogating these pathways may aid in predicting recipient graft function, which would help further risk stratify deceased-donor kidneys for appropriate allocation. Markers such as YKL-40 have been shown to have a protective effect in the deceased-donor kidney transplant setting, correlating with reduced graft failure (GF) and improved 6-month graft function.6 These findings indicate the need to investigate other related biological pathways that may affect the trajectory of kidney allograft function following transplantation.

Uromodulin (UMOD) and osteopontin (OPN) are 2 proteins in the kidney frequently evaluated in the setting of acute kidney injury (AKI) and other kidney diseases such as nephrocalcinosis.7,8 The biological effects of these 2 proteins, which are specifically regulated during AKI, range from repair of cells, activating immune cells and crystal formation in the kidneys. We hypothesize that these 2 proteins play an important role in repair pathways contributing to graft outcomes. UMOD, also known as the Tamm-Horsfall protein, is produced exclusively by the kidney, primarily in the epithelial cells of the thick ascending limb.9,10 UMOD has a molecular weight of around 100 kDa.11 It is the major component of hyaline casts and the most abundant protein found in urine.12 UMOD is also released into the renal interstitium and circulation in small amounts that correlate with urinary secretion.13 UMOD appears to have a bidirectional role in immune modulation depending on where it is expressed. UMOD aggregates in the lumen can serve as proinflammatory ligands to help activate the innate immune response and induce an inflammatory cascade with activation of tumor necrosis factor-alpha (TNF-α) and granulocyte recruitment,14-16 but interstitial UMOD has antiinflammatory functions on the renal epithelium and protective immunomodulatory properties in the setting of experimental AKI.17,18 Interstitial UMOD also regulates the number and function of mononuclear phagocytes in the kidney.18 Despite this bidirectional role identified preclinically, most epidemiological associations between UMOD and chronic kidney disease (CKD) progression show that high UMOD levels are associated with decreased risk of CKD and cardiovascular mortality.19-21 Therefore, we hypothesize that UMOD is associated with reduced GF in the deceased-donor kidney transplantation setting.

OPN is a smaller protein with a molecular weight of about 44 kDa.22 OPN is commonly synthesized and concentrated in bone and epithelial tissues but has also been shown to be synthesized in the thick ascending limb and by T cells in the setting of kidney injury.23 OPN may serve as a regulator in a number of metabolic and inflammatory diseases.23 In the kidney, OPN expression is upregulated in injury and recovery processes.24-26 OPN has been shown to have protective effects on kidney function and long-term outcomes in settings such as nephrocalcinosis and vascular calcifications.27,28 Hence, we hypothesize that OPN is also associated with reduced GF in the deceased-donor kidney transplantation setting.

Both UMOD and OPN are synergistic in their effects on the prevention of certain kidney pathologies such as renal calcinosis and stone formation.29,30 In double knockout mice, UMOD exerts its downstream effects on the kidney in conjunction with OPN.8 The deficiency in either protein alone only causes minimal interstitial calcification, but dual blockade of these proteins leads to significant interstitial changes.8

Given this synergistic role in the literature, we set out to identify the association between UMOD and OPN levels in urine of deceased donors and recipient outcomes. Using a multicenter, prospective cohort study of deceased donors, we determined the associations of urinary UMOD and OPN at the time of organ procurement with recipient GF as well as delayed graft function (DGF) and 6-month estimated glomerular filtration rate (eGFR). We also performed preclinical experiments to provide mechanistic insights to the epidemiological associations we identified.


Study Population

The deceased donor study (DDS) is a multicenter, observational, cohort study of deceased donors and their corresponding kidney recipients. DDS includes deceased donors in collaboration with 5 organ procurement organizations (OPOs): Gift of Life Donor Program, Philadelphia, PA; New Jersey Sharing Network, New Providence, NJ; Gift of Life Michigan, Ann Arbor, MI; New York Organ Donor Network, New York, NY; and New England Organ Bank, Waltham, MA. Donor urine samples were collected at the time of organ procurement from May 2010 to December 2013. Inclusion criteria were deceased donors at least aged 16 years with both admission and terminal serum creatinine (SCr). Donors were excluded if both kidneys were discarded or if they were missing urine samples.4 Clinical variables for deceased donors were abstracted from OPO charts, and data for recipients were obtained from the Organ Procurement and Transplantation Network (OPTN). The OPTN data system includes data on all donors, waitlisted candidates, and transplant recipients in the United States, submitted by the members of the OPTN, and has been described elsewhere. The Health Resources and Services Administration, US Department of Health and Human Services provides oversight to the activities of the OPTN contractor. The analyses are based on OPTN data as of July 31, 2017, and may be subject to change due to future data submission or correction by transplant centers. The OPO scientific review committees and the institutional review boards for the participating investigators approved this study.

Operational Definitions of Outcome Variables

The primary outcomes of interest were death-censored GF (dcGF) and all-cause GF defined as all-cause mortality, return to dialysis, or retransplantation. Donor AKI was evaluated as an effect modifier and was defined as a 50% increase in terminal SCr concentration from admission or an absolute increase in SCr of 0.3 mg/dL, irrespective of urine output or time from admission to terminal SCr measurement. Stages of AKI were defined by Acute Kidney Injury Network criteria. Our secondary outcomes of interest were DGF and 6-month eGFR. DGF was defined as any dialysis within the first-week post transplantation, and 6-month eGFR was calculated using the chronic kidney disease epidemiology collaboration equation. For the outcome of dcGF, we had at least 80% power to detect a hazard ratio of at least 1.22 per biomarker SD assuming an event rate of 13%, alpha of 0.05, and the correlation between the biomarker and other covariates was approximately 0.3. PASS v12 was used for all sample size calculations.

Measurement of UMOD and OPN

Upon transfer to the donor operating room, 10 mL of urine was obtained from the catheter tubing and then transported on ice to the OPO, where it was stored at –80°C. Samples were delivered to the Yale University biorepository monthly. Upon arrival to the biorepository, samples underwent a single controlled thaw, where centrifuged at 2000 g for 10 minutes at 4°C, separated into 1-mL aliquots, and immediately stored at −80°C until UMOD and OPN measurements. UMOD and OPN were measured with the Meso Scale Discovery platform (Meso Scale Diagnostics, Gaithersburg, MD), which uses electrochemiluminescence detection combined with patterned arrays. All laboratory personnel were blinded to donor and recipient information.

Statistical Analysis

Continuous variables were reported as mean (SD) or median (interquartile range [IQR]). Categorical variables were reported as frequencies, n (%). Differences in clinical and demographic characteristics were evaluated by the Kruskal-Wallis test or Chi-square test for continuous or categorical variables, respectively. As no clinically accepted cutoffs are available for UMOD and OPN, we evaluated the associations between these 2 markers and outcomes both as continuous (log2-transformed) and categorical (tertiles) variables. We used Cox proportional hazards models to assess the associations of the proteins with dcGF and all-cause GF. The proportional hazards assumption was evaluated by Kolmogorov-type supremum test. The hazard ratios and 95% confidence interval (CIs) of both the univariable and multivariable models are reported. For the secondary outcomes of DGF and 6-month eGFR, we used logistic regression and linear regression models, respectively, to report point estimates and 95% CIs. Since 1 donor may have 1 or 2 recipients, we estimated 95% CIs using a robust sandwich covariance matrix estimator to account for intracluster dependence.31 All inference testing was 2-sided with an alpha of 0.05. Cox proportional hazards, logistic regression, and linear regression models were adjusted for kidney donor risk index (KDRI), urine creatinine, cold ischemia time, and the following recipient characteristics: age, black race, sex, previous kidney transplant, diabetes as the cause of end-stage kidney disease, number of HLA mismatches, panel reactive antibody, body mass index, and preemptive transplant.

We randomly divided our cohort of 2430 recipients into a training dataset and a test dataset with 1215 recipients and their corresponding donors in each dataset. In the training dataset, we explored combinations of UMOD and OPN and the association with dcGF and all-cause GF. Given opposing associations for UMOD and OPN with outcomes in the training dataset as well as some prior reports of opposing associations in the literature,28,32 we evaluated the UMOD:OPN ratio continuously (ratio of log2-transformed UMOD and log2-transformed OPN) and as a categorical variable (tertiles of the ratio) to assess the association of the combined repair markers with dcGF and all-cause GF. Tertile categories were derived from spline plots, and a data-driven cut-point of >3 was established based on the ratio values in the third tertile. Given that there is no established ratio in the literature or a clinically established cutoff, we enhanced the validity of our results by deriving univariate and multivariate Cox proportional hazards in the training dataset and then internally validating our results in the test dataset.

All analyses were conducted on SAS 9.4 software (SAS Institute, Cary, NC) and Stata version 14 (StataCorp LLC).

Immunohistochemical Staining and Quantification

We performed double staining for both UMOD and OPN on 11 deceased-donor kidney tissue samples from a pathology biobank (6 biopsies with acute tubular injury and 4 biopsies without acute tubular injury). Antigen retrieval was performed with citrate (pH 5.8), and endogenous peroxidase and alkaline phosphatase reactions were blocked with levamisole hydrochloride (Abcam, Cambridge, MA) and PolyDetector peroxidase block (BioSB, Santa Barbara, CA) for 10 minutes. Tissue sections were incubated for 60 minutes with mouse monoclonal OPN antibody (1:200, LFMb-14, Santa Cruz Biotechnology, Inc., Dallas, TX) and rabbit polyclonal anti-UMOD antibody (1:1000, MilliporeSigma, St Louis, MO). Detection was performed using horseradish peroxidase polymer antimouse IgG with Emerald green substrate and alkaline phosphatase polymer antirabbit IgG with permanent red substrate (DoubleStain IHC Kit Abcam, Cambridge, MA). OPN was interpreted as positive in green-stained areas, while red stain indicated UMOD positivity. Colocalization was appreciated as follows: blue—OPN expressed at higher concentrations compared with UMOD; purple—UMOD expressed at higher concentrations.

Mouse Bone Marrow Macrophage Isolation and MHCII Expression Assay

Truncated human tUMOD was isolated from urine of normal human donors and purified from endotoxin as described previously.18 Bone marrow macrophages were isolated from Sv129 mice as described in Zhang et al.33 Briefly, mice were euthanized before isolating femurs and tibias. Bone marrow was flushed with cold sterile wash medium (phosphate-buffered saline [PBS] Fetal bovine serum [FBS]) using a 25 gauge needle. Cells were grown on noncoated 100-mm dishes for 6 days in macrophage complete medium (RPMI 1640 with L-glutamine [Corning 10-040-CV], 10% FBS [Gibco 10437-028], 1× penicillin/streptomycin [Gibco 15240062], and 10 ng/mL recombinant mouse M-CSF [Gibco PMC2044]). Cells were harvested on day 6 by treatment with ice-cold PBS/2.5 mmol/L EDTA, pH 8.0, and gentle scraping. Cells were replated in 6 well dishes and treated with 20 ng/mL recombinant mouse interferon-gamma (Millipore Sigma IF005), tUMOD (1 µg/mL in 5% dextrose), or vehicle (5% dextrose) for 18 hours. Cells were then harvested with Accutase (Biolegend 423201) and blocked with Rat antimouse CD16/32 (eBioscience 50-112-9525) before staining with APC Rat antimouse major histocompatibility complex II (MHCII) (eBioscience 17-5321-82). Propidium iodide was used to stain dead cells. Flow cytometry was performed on a Guava easyCyte (EMD Millipore) flow cytometer. FlowJo software was used to analyze the data.


Donor and Recipient Characteristics and Their Relationship to UMOD and OPN

A total of 1298 donors and 2430 recipients met the inclusion criteria (Figure S1, SDC, Donors had a mean (SD) age of 41 (15) years; 784 (60%) were male and 205 (16%) were black (Table 1). Recipients had a mean age of 53 (15) years; 1492 (61%) were male and 956 (39%) were black (Table 1). Donation after neurological determination of death occurred in 1092 (94%) donors. The most frequent comorbidities among donors were hypertension (31%), diabetes (10%), and obesity (32%). For recipients, the most common causes of end-stage kidney disease were diabetes (30%) and hypertension (26%). Mean (SD) kidney donor profile index was 48% (27%). Most donor and recipient characteristics were not significantly different by UMOD or OPN tertiles (Tables S1A and B, SDC, Terminal SCr, however, was lower with increasing UMOD tertiles but greater with increasing OPN tertiles. Cold ischemia time and the number of HLA mismatches were also greater with increasing tertiles of both UMOD and OPN.

TABLE 1. - Donor and recipient characteristics in overall cohort
Donor characteristics N = 1298 Recipient characteristics N = 2430
Age, y 41.44 (14.53) Age, y 52.91 (14.83)
Male 784 (60%) Male 1492 (61%)
Black race 205 (16%) Black race 956 (39%)
Hispanic race 171 (13%) Hispanic race 279 (11%)
BMI, kg/m2 28.42 (7.23) BMI, kg/m2 28.04 (5.76)
Hypertension 399 (31%) Cause of ESKD
Diabetes 130 (10%)  Unknown/other 496 (20%)
Cause of death  Diabetes 726 (30%)
 Head trauma 396 (31%)  Hypertension 643 (26%)
 Anoxia 425 (34%)  Glomerulonephritis 391 (16%)
 Stroke 427 (34%)  Graft failure 174 (7%)
 Other 18 (1%) ESKD duration (mo) 45.85 (38.06)
 Hepatitis C 48 (4%) Preemptive transplant 274 (11%)
 DCD including DND 246 (19%) Previous kidney transplant 315 (13%)
 DCD 206 (16%) Recipient transfusions 438 (18%)
 KDPI based on KDRI 48.23 (27.34) Candidate most recent PRA 21% (35%)
 D from admission to death 5.07 (6.7) Recipient PRA
 Admission SCr, mg/dL 1.1 (0.61)  0% 1545 (64%)
 Terminal SCr, mg/dL 1.17 (0.85)  1%–20% 178 (7%)
AKI stage  21%–80% 326 (13%)
 No AKI 976 (75%)  >80% 381 (16%)
 Stage 1 211 (16%) Kidney biopsied 1117 (46%)
 Stage 2 62 (5%) Kidney pumped 952 (39%)
 Stage 3 49 (4%) Cold ischemia time, h 15.29 (7.1)
Kidneys discarded HLA mismatch level 4.21 (1.52)
 0 1132 (87%)
 1 166 (13%)
Values are means (SD) or n (%).
AKI, acute kidney injury; BMI, body mass index; DCD, donation after cardiac determination of death; DND, donation after neurological determination of death; ESKD, end-stage renal disease; KDPI, kidney donor profile index; KDRI, kidney donor risk index; PRA, panel reactive antibodies; SCr, serum creatinine.

Distribution of UMOD and OPN in Donor AKI

A total of 322 (25%) donors had AKI (Table 1), with the majority having stage 1 AKI (16%), followed by stage 2 (5%) and stage 3 (4%). Donor urine UMOD concentrations were significantly lower with increasing AKI stages, P < 0.001 (Figure 1). This trend remained consistent after indexing UMOD to urine creatinine, P < 0.001 (Table 2). Levels of urine OPN increased with worsening AKI severity (P < 0.001) up to stage 2 (Figure 1) with a consistent pattern after indexing to urine creatinine, P = 0.004 (Table 2).

TABLE 2. - Median (IQR) donor levels of UMOD, OPN, and urine creatinine by AKI status
All donors (N = 1298) AKI P
No AKI (N = 976) Stage 1 (N = 211) Stage 2 (N = 62) Stage 3 (N = 49)
UMOD (ng/mL) 1968 (984, 3819) 2155 (1093, 3983) 1528 (758, 3481) 1350 (720, 3050) 921 (280, 2669) <0.001
OPN (ng/mL) 1438 (534, 3301) 1295 (489, 2981) 1665 (567, 4630) 2729 (1419, 5999) 2011 (593, 3584) <0.001
Urine creatinine (mg/dL) 36 (14, 67) 35 (13, 68) 39 (15, 69) 39 (23, 67) 33 (21, 55) 0.23
Creatinine indexed UMOD (106) 5490 (2655, 13 581) 6082 (2935, 15 760) 4323 (2336, 9259) 3046 (1506, 8732) 2521 (983, 5882) <0.001
Creatinine indexed OPN (106) 4056 (2020, 8625) 3792 (1953, 8071) 4679 (2370, 9085) 7249 (3203, 13 422) 5759 (1891, 10 774) 0.004
AKI, acute kidney injury; IQR, interquartile range; OPN, osteopontin; UMOD, uromodulin.

Median (IQR) levels of donor UMOD and OPN by donor AKI. The above box plots show that UMOD levels measured in the urine of deceased donors at time of organ procurements, decrease with increasing stages of AKI, whereas Osteopontin levels increase with increasing stages of AKI, although decrease slightly from stage 2 to stage 3 AKI. AKI, acute kidney injury; IQR, interquartile range; OPN, osteopontin; UMOD, uromodulin.

Association Between UMOD and OPN and Graft Failure

The mean event rates (95% CI) for dcGF and GF were 33 (29.6–36.9) and 65.7 (60.8–71.1) per 1000 person-years, respectively, over a median (IQR) follow-up time of 4.01 (2.97–5.01) years.

Each doubling of UMOD levels in donor urine was associated with increased risk for dcGF and GF in recipients with adjusted hazard ratios (aHR [95% CI]) of 1.10 (1.01-1.19) and 1.07 (1.01-1.13), respectively, after adjustment for KDRI, donor urine creatinine, and clinical covariates (Table 3). Tertiles of UMOD demonstrated increasing event rates of dcGF and GF, though HRs were not significant for UMOD tertiles. There were no significant interactions by donor AKI status on the relationship between UMOD and dcGF or GF.

TABLE 3. - Association of donor UMOD and OPN with risk of death-censored graft failure and all-cause graft failure
Mean (95% CI) event rate per 1000 person-y HR (95% CI)
Unadjusted Adjusted a
Log2 (n = 2430) 33 (29.6-36.9) 1.11 (1.03-1.19) 1.1 (1.02-1.2)
 Tertile 1 28.6 (23.2-35.2) 1 (ref) 1 (ref)
 Tertile 2 33.9 (28.1-41) 1.19 (0.9-1.57) 1.12 (0.84-1.5)
 Tertile 3 36.4 (30.4-43.6) 1.27 (0.96-1.67) 1.2 (0.89-1.62)
 Log2 (n = 2430) 33 (29.6-36.9) 0.95 (0.89-1) 0.94 (0.88-1)
 Tertile 1 38.6 (32.2-46.2) 1 (ref) 1 (ref)
 Tertile 2 29.9 (24.4-36.5) 0.78 (0.59-1.02) 0.82 (0.61-1.08)
 Tertile 3 31 (25.5-37.7) 0.8 (0.62-1.05) 0.76 (0.56-1.04)
All-cause GF
 Log2 (n = 2430) 65.7 (60.8-71.1) 1.06 (1-1.12) 1.07 (1.01-1.13)
 Tertile 1 60.1 (52.1-69.4) 1 (ref) 1 (ref)
 Tertile 2 66.9 (58.5-76.6) 1.11 (0.91-1.36) 1.12 (0.91-1.37)
 Tertile 3 70 (61.4-79.8) 1.13 (0.93-1.37) 1.16 (0.95-1.43)
 Log2 (n = 2430) 65.7 (60.8-71.1) 0.96 (0.93-1) 0.95 (0.91-1)
 Tertile 1 71.8 (62.9-82) 1 (ref) 1 (ref)
 Tertile 2 64.7 (56.5-74.1) 0.89 (0.74-1.08) 0.86 (0.7-1.05)
 Tertile 3 61 (53-70.1) 0.83 (0.68-1) 0.77 (0.61-0.96)
aAdjusted for urine creatinine, KDRI, and the following clinical covariates: cold ischemia time (22 missing), recipient age (y), race, sex, prior kidney transplant, diabetes as the cause of end-stage kidney disease, number of HLA mismatches, panel reactive antibody (%), body-mass index (1 missing), and preemptive transplant. There were no significant interactions by donor AKI status in the relationship between UMOD and dcGF and GF. Complete case analysis was completed since missing data were rare. Biomarker measurements and outcomes were available in all recipients. Covariate data were complete except for cold ischemia time (0.9% missing), HLA mismatch (0.2% missing), and BMI (0.04% missing). Significant associations are highlighted in light gray.
AKI, acute kidney injury; BMI, body mass index; CI, confidence interval; dcGF, death-censored graft failure; GF, graft failure; HR, hazard ratio; KDRI, kidney donor risk index; OPN, osteopontin; ref, referent; UMOD, uromodulin.

Each doubling of donor urine OPN concentration was independently associated with decreased risk for dcGF (0.95; 0.89-1) and GF (0.96; 0.93-1). A dose-response effect was observed such that the upper tertile showed a significant protective effect against GF compared with the lower tertile of donor urine OPN.

Association Between Uromodulin: Osteopontin Ratio in Training and Test Datasets

In order to capture the opposing associations of UMOD and OPN with graft outcomes and to create a biomarker score for clinical application, we explored the ratio of UMOD to OPN urinary levels at the time of organ procurement in our training dataset. The baseline characteristics of donors and recipients in the training and test datasets are shown in Table S3A and B, SDC, The UMOD:OPN ratio demonstrated independent associations in the training dataset (Table 4). In Figure 2, unadjusted Kaplan-Meier curves showed significantly lower graft survival with UMOD:OPN >3 as compared to ≤3 (log-rank P = 0.0016). In fully adjusted models as shown in Table 4, participants with UMOD:OPN ratio ≤3 had 43% and 26% decreased risk of dcGF and GF, respectively. There were no significant interactions by donor AKI status on the relationship between the UMOD:OPN ratio and dcGF or GF. In the test dataset, the association for GF was confirmed. The association for dcGF lost statistical significance but had a similar estimate.

TABLE 4. - Association of donor UMOD:OPN ratio with risk of all-cause graft failure and death-censored graft failure in the training and test data set
UMOD:OPN ratio Total N Mean event rate (95% CI), per 1000 person-y Unadjusted HR (95% CI) Adjusted HR (95% CI) Mean event rate (95% CI), per 1000 person-y Unadjusted HR (95% CI) Adjusted HR (95% CI)
Training data set
dcGF All-cause GF
>3 387 44.6 (35.0-56.9) 1 (ref) 1 (ref) 76.8 (63.9-92.5) 1 (ref) 1 (ref)
≤3 828 26.5 (21.5-32.7) 0.60 (0.43-0.83) 0.57 (0.41-0.80) 58.4 (50.7-67.3) 0.76 (0.60-0.96) 0.73 (0.57-0.93)
Test data set
dcGF All-cause GF
>3 385 43.1 (33.6-55.1) 1 (ref) 1 (ref) 85.4 (71.7-101.8) 1 (ref) 1 (ref)
≤3 830 29.9 (24.6-36.5) 0.69 (0.51-0.96) 0.73 (0.52-1.02) 59.3 (51.5-68.2) 0.69 (0.55-0.87) 0.70 (0.56-0.88)
Complete case analysis was completed since missing data were rare. Biomarker measurements and outcomes were available in all recipients. Covariate data were complete except for cold ischemia time (0.9% missing), HLA mismatch (0.2% missing), and BMI (0.04% missing). Significant associations are highlighted in light gray.
AKI, acute kidney injury; BMI, body mass index; CI, confidence interval; dcGF, death-censored graft failure; GF, graft failure; HR, hazard ratio; KDRI, kidney donor risk index; OPN, osteopontin; ref, referent; UMOD, uromodulin.

Kaplan-Meier plot of death-censored graft failure by UMOD/OPN ratio in the training dataset. The above survival curve shows that deceased donor kidneys with lower UMOD-to-OPN ratio have better graft survival with a log-rank P value of 0.0016. The numbers below in red and blue show the population at risk at each event time with red representing donor urine with UMOD-to-OPN ratio >3 and blue representing UMOD-to-OPN ratio ≤3, respectively. Primary nonfunction was included as survival time of zero. OPN, osteopontin; UMOD, uromodulin.

Association Between UMOD and OPN and Secondary Outcomes of DGF and 6-Month eGFR

In secondary analysis, we evaluated the relationship between UMOD and OPN with DGF. Higher levels of UMOD were associated with higher DGF, whereas OPN was not associated with DGF (Table 5). A lower UMOD:OPN ratio was significantly associated with 27% lower odds of DGF (aOR, 0.73; 95% CI, 0.6-0.89).

TABLE 5. - Associations between biomarkers and delayed graft function
No of event OR (95% CI)
Unadjusted Adjusted a
 Log2 (n = 2430) 755 (31.07%) 1.09 (1.03-1.16) 1.07 (1-1.14)
 Tertile 1 232 (28.64%) 1 (ref) 1 (ref)
 Tertile 2 233 (28.77%) 1 (0.83-1.22) 1.03 (0.82-1.3)
 Tertile 3 290 (35.8%) 1.33 (1.1-1.62) 1.32 (1.04-1.67)
 Log2 (n = 2430) 755 (31.07%) 1.02 (0.98-1.07) 0.98 (0.93-1.04)
 Tertile 1 245 (30.25%) 1 (ref) 1 (ref)
 Tertile 2 260 (32.1%) 1.08 (0.89-1.31) 1.08 (0.86-1.35)
 Tertile 3 250 (30.86%) 1.02 (0.84-1.25) 0.9 (0.7-1.16)
UMOD:OPN ratio
 Log2 (n = 2430) 755 (31.07%) 1.02 (0.98-1.06) 1.04 (1-1.08)
 >3 270 (34.97%) 1 (ref) 1 (ref)
 ≤3 485 (29.25%) 0.47 (0.4-0.55) 0.73 (0.6-0.89)
aAdjusted for urine creatinine, KDRI, and the following clinical covariates: cold ischemia time (22 missings), recipient age (y), race, sex, prior kidney transplant, diabetes as the cause of end-stage kidney disease, number of HLA mismatches, panel reactive antibody (%), body mass index (1 missing), and preemptive transplant. Complete case analysis was completed since missing data were rare. Biomarker measurements and outcomes were available in all recipients.
CI, confidence interval, KDRI, kidney donor risk index; OPN, osteopontin; OR, odds ratio; ref, referent; UMOD, uromodulin.

We also evaluated the relationship between UMOD and OPN with graft function as measured by recipient 6-month eGFR. Individually, UMOD and OPN were not associated with 6-month eGFR, but a lower UMOD:OPN ratio was associated with higher 6-month eGFR (adjusted β coefficient, 3.19; 95% CI, 1.28-5.11) as shown in Table 6.

TABLE 6. - Associations between biomarkers and 6-mo eGFR
β coefficient (95% CI)
Unadjusted Adjusted a
 Log2 (n = 2430) –0.58 (–1.22-0.07) –0.44 (–1.05-0.18)
 Tertile 1 1 (ref) 1 (ref)
 Tertile 2 –0.72 (–3.12-1.68) –0.08 (–2.28-2.12)
 Tertile 3 –1.10 (–3.49-1.30) –0.44 (–2.73-1.84)
 Log2 (n = 2430) –0.25 (–0.75-0.25) 0.21 (–0.30-0.72)
 Tertile 1 1 (ref) 1 (ref)
 Tertile 2 –0.47 (–2.88-1.92) 0.41 (–1.80-2.62)
 Tertile 3 –1.09 (–3.49-1.31) 1.13 (–1.29-3.55)
Uromodulin:osteopontin ratio
 Log2 (n = 2430) –0.07 (–0.52-0.37) –0.32 (–0.73-0.08)
 >3 1 (ref) 1 (ref)
 ≤3 2.68 (0.58-4.78) 3.19 (1.28-5.11)
aAdjusted for urine creatinine, KDRI, and the following clinical covariates: cold ischemia time (22 missing), recipient age (y), race, sex, prior kidney transplant, diabetes as the cause of end-stage kidney disease, number of HLA mismatches, panel reactive antibody (%), body mass index (1 missing), and preemptive transplant. Complete case analysis was completed since missing data was rare. Biomarker measurements and outcomes were available in all recipients.
CI, confidence interval; eGFR, estimated glomerular filtration rate; KDRI, kidney donor risk index; ref, referent.

UMOD and OPN Immunohistochemical Confirmation

We assessed the staining pattern of UMOD and OPN in human control kidneys compared with those showing histological features of acute tubular injury (clinicopathologic and demographic summary shown in Table S4, SDC, In control tissues, we observed that UMOD staining is limited mostly to the loop of Henle, and no significant staining for OPN was observed (n = 4) (Figure S2A, SDC, By contrast in deceased-donor biopsies with features of acute tubular injury, there was prominent staining of UMOD and OPN in tubular casts and injured tubules including proximal tubules and loop of Henle (n = 6) (Figure S2B, SDC, Thus, we confirm by immunohistochemistry that the increased expression of OPN in injured tubular segments can be observed together with UMOD in the setting of AKI.

UMOD Induces MHCII Expression on Macrophages

To provide mechanistic insight to our epidemiological findings, we examined the effect of a nonpolymerizing truncated human UMOD on the expression of MHCII in bone marrow–derived mouse macrophages (Figure 3). Our results showed that UMOD significantly increased MHCII expression on macrophages as measured by flow cytometry.

Effect of uromodulin on the expression of MHCII in bone marrow–derived mouse macrophages. (A) shows representative histograms for MHCII expression by flow cytometry in macrophages treated with vehicle, UMOD, or IFNγ, the latter used as a positive control. In (B), scatter plots showing all the replicates in each condition are shown. *P < 0.05; **P < 0.01. IFN-γ, interferon-gamma; MHCII, major histocompatibility complex II; UMOD, uromodulin.


In this prospective deceased-donor cohort, donor urine UMOD levels were lower, while OPN levels were greater with increasing severity of donor AKI. UMOD was associated with increased risk of dcGF, while OPN demonstrated a protective association with regard to dcGF. UMOD:OPN ratio ≤3 at the time of organ procurement was associated with lower risk of DGF, higher 6-month eGFR, and lower risk of GF. The ratio of these 2 markers provides a construct that captures their bidirectional associations, which may help identify deceased-donor kidneys at the time of organ procurement that is more likely to have favorable outcomes.

Our study identified that OPN is increased in the setting of donor AKI but was associated with lower recipient GF, whereas UMOD was decreased in the setting of AKI, but was associated with increased risk of DGF, and GF. As our biomarkers were available at a single time point, around the same time as terminal creatinine, it is difficult to apply any temporal relationships between biomarkers and donor AKI. In an effort to interpret this cross-sectional relationship, we postulate that in the immediate phase around AKI, there are fewer healthy nephrons and tubular mass, which is reflected in lower UMOD levels; however, there is also increased production of OPN by T cells in the setting of kidney injury,23 which initiates downstream repair pathways. The identified downstream effects of UMOD and OPN may suggest hypotheses for how they may predict future graft outcomes. In preclinical models, UMOD expression was shown to be decreased in injury, which correlates with our findings of decreased urine UMOD in the setting of deceased donor AKI.34 Although recent literature has shown a protective effect of UMOD on the risk of AKI, CKD and mortality in the ambulatory setting,19-21,35,36 none of the UMOD measurements were assessed in a critical-care/deceased-donor setting similar to our study. Our results suggest that kidney transplantation presents a unique situation, where the immunomodulatory properties of UMOD may be associated with a maladaptive response leading to the observed associations with higher risk of DGF and GF. Interstitial UMOD is known to regulate macrophage number and function in the kidney.15,18 Therefore, it is possible that the maladaptive role of high UMOD production in the setting of transplant could be related to its immunomodulatory properties in the renal interstitium. Our preclinical investigations showed that UMOD increased MHCII expression on macrophages, which may explain the identified association with GF in our cohort as chronic rejection is thought to be 1 of the driving causes of long-term GF.37 Our findings suggest that UMOD can increase the expression of MHCII in macrophages, thereby enhancing their antigen-presenting potential and augmenting the immunostimulatory state in the kidneys. We propose that an ongoing state of immunostimulation in patients with high UMOD production may negatively impact graft function and can partially explain the increased risk of long-term GF associated with high UMOD levels.

On the other hand, OPN expression has been shown to be higher in patients with recovery from AKI28 and has also been shown to be protective against nephrocalcinosis.27 We have shown that OPN is associated with decreased risk of dcGF, which supports our hypothesis. OPN has renoprotective effects through reduction of tubular cell apoptosis, regeneration and repair of tubular cells, and reduction of cell peroxide levels.23,24,38 It has been shown that OPN mediates an antiinflammatory effect in the setting of acute injury via its inhibition of nitric oxide.39 In experimental models, OPN was specifically shown to downregulate nitric oxide production by kidney tubule epithelial cells, which may explain our favorable findings with better graft survival.39,40 Additionally, we postulate that nitric oxide inhibition by OPN may indirectly lead to a reduction in MHCII expression as studies have shown that nitric oxide synthase (catalyst for nitric oxide production) knockout mice have decreased MHCII expression on dendritic cells.41 Furthermore, our biopsy findings show that OPN increases in tandem with UMOD during deceased-donor kidney injury. It is important to note that UMOD staining in casts does not translate into increased expression of UMOD in the kidney per se. It is established that in the setting of AKI, the number of casts increases, and it is possible that increased UMOD staining reflects the increase in casts.

Our cohort-based findings suggest that donor urine OPN was protective against GF, whereas urine UMOD was associated with increased risk of GF, without significant interaction by donor AKI status. Together, the balance of donor urine UMOD and OPN captured in ratio form may not only provide more granular information on graft quality than SCr, but it may also characterize a kidney’s recovery potential after fluctuations in SCr (AKI) before procurement. In our study, UMOD:OPN ratio ≤3 denoted adequate repair potential and was protective against short-term DGF and long-term dcGF and GF. We internally validated these results in a data-driven fashion with similar findings in both the derivation and the validation cohorts.

There are several strengths to our study. The DDS is a large prospective cohort that includes both donor urine measurements and recipient outcomes. This unique design allows us to investigate potential tools to improve kidney allocation decisions. To date, our study is the largest adult cohort evaluating the relationship between UMOD and OPN in the setting of deceased-donor AKI and their potential role in recipient outcomes. Furthermore, we accounted for differing donor urine volumes and dilution by adjusting for urine creatinine in our analyses. Finally, our ratio findings were developed in a training dataset and validated in a test dataset, which suggests that our findings were not due to resubstitution bias or model-selection bias.42

There are also several limitations worth noting. First, both markers were measured at a single time point of organ procurement. This prevents us from assessing any posttransplant trajectories and associations with outcomes. It remains unclear how urine UMOD and OPN levels change after transplantation and what correlations posttransplant recipient levels have with pretransplant donor UMOD and OPN levels. As with all observational studies, our study is subject to unmeasured confounding that could have affected the identified associations. Additionally, although our results were internally validated and showed statistical and clinical significance, we acknowledge that external validation will be necessary to advance these findings to clinical practice. Finally, whether our findings could be applied to living-donor kidneys is unclear. Deceased-donor kidneys undergo significantly more AKI and ischemia-reperfusion injury as compared to living-donor kidneys. Hence, it is possible that the identified associations in our deceased-donor cohort are not generalizable.

In conclusion, our study shows that UMOD:OPN ratio ≤3 is associated with lower risk of DGF, higher 6-month eGFR, and lower risk of GF. Our primary outcome of GF was validated in our test dataset. This ratio may be a clinically meaningful method for capturing the dynamic processes that take place in deceased-donor kidney transplantation and may offer a more timely and accurate way to help allocate donor kidneys than is currently available in clinical practice.


The data reported here have been supplied by the United Network for Organ Sharing (UNOS) as the contractor for the OPTN. The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the OPTN or the US Government.

The content is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. These organizations were not involved in study design, analysis, interpretation, or article creation.


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