Skip to main content
Download PDF

Towards clinical translation of urinary vitronectin for non-invasive detection and monitoring of renal fibrosis in kidney transplant patients

Journal of Translational Medicine volume 22, Article number: 1030 (2024) Cite this article

Abstract

Background

Interstitial fibrosis and tubular atrophy (IFTA) is a critical factor in the prognosis of kidney health. Currently, IFTA quantitation in kidney biopsy samples is crucial for diagnosis and assessing disease severity, but the available non-invasive biomarkers are not satisfactory. Proteomic studies identified urinary vitronectin (VTN) as a potential biomarker for kidney fibrosis. As mass spectrometry techniques are not practical for use in clinical settings, we tested whether evaluation of urinary VTN levels through enzyme-linked immunosorbent assay (ELISA) can help monitor fibrotic changes in kidney transplant recipients and prove the clinical viability of the assay.

Methods

A total of 58 kidney transplant (KTx) patients who underwent renal biopsy were included in the study. Patients were categorized into two groups referred as no fibrosis (0%) or with fibrosis (≥ 5%) based on their histological findings. In a subsequent/follow-up analysis, the time elapsed from transplantation was also considered. The urinary levels of VTN were measured using ELISA.

Results

VTN (p = 0.0180) and VTN normalized by urinary creatinine levels (p = 0.0037), were significantly increased in patients with fibrotic grafts. When focusing on patients with long-term grafts (> 3 years from transplantation, n = 36), VTN exhibited superior potential in identifying fibrotic grafts compared to albuminuria (VTN p = 0.0040 vs. albuminuria p = 0.0132). Importantly, in this group, while albuminuria correctly identified 71% of fibrotic patients, the combination of VTN plus albuminuria correctly classified 89% of fibrotic grafts detected by renal biopsy.

Conclusions

VTN has emerged as a valid indicator of renal fibrosis. Of interest, urinary levels of VTN in combination with conventional clinical parameters (such as albuminuria) significantly improved the non-invasive detection of renal fibrosis in kidney transplant patients.

Background

Kidney fibrosis is widely recognized as the common final pathway and a key factor in the progression of various kidney diseases to end-stage renal failure [1,2,3] Interstitial fibrosis and tubular atrophy (IF/TA) are consistently identified as major contributors to kidney allograft failure and present a significant challenge to long-term allograft survival in kidney transplantation [4, 5]. This progression occurs regardless of the underlying disease, making fibrosis an important therapeutic target [6].

Fibrosis is a complex pathological process characterized by excessive accumulation of extracellular matrix (ECM) in the renal parenchyma, which leads to a progressive loss of kidney function [7]. Underlying pathophysiological mechanisms are diverse, most of them involving chronic injury to renal cells, particularly tubular epithelial cells, that trigger a maladaptive repair response. This may lead to activation and proliferation of myofibroblasts, persistent inflammation and activation of immune inflammatory cells promoting fibrogenic signals [8]. Also, imbalance between matrix metalloproteinases and their inhibitors favours ECM accumulation [9], and microvascular rarefaction leads to chronic hypoxia, further promoting fibrosis [10]. These interconnected mechanisms, among others, create a self-perpetuating cycle of injury, inflammation, and fibrosis that progressively replaces functional renal tissue with scar tissue, ultimately leading to end-stage renal disease [11, 12].

Assessment of kidney fibrosis is still based on kidney biopsy. This technique allows direct visualization of the tissue sample, provide specific diagnoses, quantify fibrosis, and offer detailed cellular information. However, despite recent advances in automation, biopsies are still predominantly based on subjective interpretations from nephropathologists and are invasive, posing risks, patient discomfort, economic burdens, and limitations in representing the entire organ’s status [13]. Thus, efforts are underway to develop objective, non-invasive, and quantitative diagnostic methods [14] while enhancing our understanding of molecular mechanisms to identify potential targeted therapies [3].

In this sense, non-invasive biomarkers for assessing IFTA in kidney transplantation have evolved significantly over the past few decades. From unspecific determinations such as serum creatinine, albuminuria and estimated glomerular filtration rate (eGFR), attention shifted to urinary biomarkers, such as transforming growth factor-β (TGF-β) [15], monocyte chemoattractant protein-1 (MCP-1) [16], matrix metalloproteinase-2 (MMP-2) [17] and α1-microglobulin (A1M) [18] in urine among others. Also, advances in omic technologies allowed the study of complex panels of urinary proteins. In this sense, notable developments include the CKD273 test, which uses a panel of urinary peptides [19, 20] in a clinical setting. Thus, urinary proteomic studies successfully identified several biomarkers aiming to complement or replace invasive kidney biopsies, especially when the latter are contraindicated [21]. Yet, proteomic techniques are hindered by their cost and time requirements, impeding their easy translation to clinical practice [22]. Therefore, there is a pressing need for translating identified biomarkers to facilitate the monitoring of transplanted kidneys in a clinically applicable manner. In this scenario, a previous proteomic study by our group identified vitronectin (VTN) as a potential biomarker for kidney fibrosis [23]. In that study, urine VTN showed differential expression in patients with chronic IFTA lesions, specifically in those with ci and ct mean scores greater than 2 according to Banff criteria.

VTN is a multifunctional glycoprotein that accumulates in the renal interstitium during fibrogenesis [24] and has been identified as one of the most upregulated extracellular matrix proteins in fibrotic kidney tissue scaffolds [25]. While previous studies suggested that VTN may not be essential for fibrogenesis [24], it has been established that VTN interacts with several key proteins involved in fibrosis, including plasminogen activator inhibitor-1 (PAI-1) [26] and integrin receptors among others [27]. Moreover, VTN is primarily expressed and secreted by activated macrophages in chronic kidney disease models and has been recently related to contribute to the fibrotic environment [25]. All these data suggest VTN plays a significant role in the complex process of kidney fibrosis, potentially serving as both a diagnostic marker and a therapeutic target.

Considering the translational limitations of the proteomic analyses, and the potential of VTN to monitor renal fibrosis in a non-invasive manner, we aimed to assess whether urine VTN levels could reflect the findings determined by the gold standard (renal biopsy) using a clinically established technique such ELISA (Enzyme-Linked Immunosorbent Assay). This widely used technique is cost-efficient, and offers higher throughput and easier sample preparation among other advantages [28].

To this end, we conducted a pilot pre-clinical evaluation of urinary VTN levels in kidney transplanted patients [23]. Notably, our cross-sectional study confirmed statistically significant differences between the two examined groups (biopsy-proven non-fibrotic vs. fibrotic patients), highlighting the potential of using a clinically applicable VTN test as a promising approach for non-invasively detecting and monitoring renal fibrosis.

Methods

Patients and study design

This was a single-center, cross-sectional study in which kidney transplanted patients underwent renal biopsy at the Hospital Universitari Germans Trias i Pujol (HUGTiP, Badalona, Spain) between July 2019 and January 2022. The protocol was conducted in accordance with the Declaration of Helsinki and the recommendations of the Guideline for Good Clinical Practice from the “Comitè d’Ètica de la investigació clínica de HUGTiP”, who also approved the protocol (PI-20-083). This protocol did not alter at any time the standard procedure of patient’s medical care. To protect the identity of the participants, an independent arbitrary code was used for sample identification.

Adult kidney transplant recipients from a living or cadaveric donor able to provide written informed consent were evaluated for eligibility and enrolled based on predetermined inclusion criteria. These criteria included patients who had undergone renal transplantation and were referred for biopsy based on clinical grounds (indication biopsy), regardless of whether they presented acute or chronic allograft dysfunction. Also included patients with stable allograft function and donor-specific antibody (DSA) positivity (surveillance biopsy). Lastly, patients undergoing monitoring treatment response or investigation of unexplained symptoms. Notably, none of our recruited patients had been treated with Sodium-Glucose Cotransporter-2 (SGLT2) inhibitors, Endothelin Receptor Antagonists, or Mineralocorticoid Receptor Antagonists for at least 6 months prior to their biopsy. The main exclusion criteria were acute kidney injury (AKI) or urinary tract infection (UTI) at presentation, age < 20 years, diagnosis of Hepatocellular Carcinoma (HCC), HCV infection and unwillingness to participate in the study.

In addition, 14 healthy volunteers were included in some analyses as our control group. These were selected based on a normal estimated glomerular filtration rate (eGFR) (> 90 mL/min/1.73m2) because of ethical considerations related to performing invasive procedures (renal biopsy) on subjects who may not need them. None of these controls had a history of cardiovascular disease, diabetes, UTI or urinary system disease, and their good health status was confirmed through laboratory assessments.

Patient data collection

Our analyses included the following clinical variables: age, sex, diabetes mellitus, hypertension, cardiovascular disease, estimated glomerular filtration rate (GFR), spot urinary protein-to-creatinine ratio (UPCR), serum creatinine, albuminuria, treatment with ACEi, time after transplantation, body mass index (BMI), and immunosuppression regimen. GFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation [29].

Assessment of renal scarring

Allograft kidney biopsies (n = 58) were blindly evaluated by nephropathologists (P.R and L.H) according to the Banff 1997 criteria and their subsequent updates [30]. Specifically, 2-micron thickness histological sections from formalin-fixed paraffin-embedded blocks were made and stained for haematoxylin-eosin, PAS, Jones methenamine silver, and Masson’s trichrome. BenchMark Special Stains System™ (Roche) was used and protocols provided by the manufacturer were followed. The number of glomeruli in each biopsy was over ten. Quantitative evaluation was assessed using visual methods and scoring according to Banff cut-offs for interstitial fibrosis (ci) [ci0: ≤ 5%, ci1: 6–25%, ci2: 26–50%, ci3: > 50%] and tubular atrophy (ct). As high grades of fibrosis > 50% were poorly detected (n = 3), the patients were classified into the absence of interstitial fibrosis (non-fibrotic; ci/ct < 1; 0% fibrosis) or with the presence of interstitial fibrosis (fibrotic; ci/ct≥1; ≥ 5% fibrosis; mean grade of fibrosis detected 20%).

To better capture the relationship between the percentage of renal fibrosis and other variables in our analysis, we used the percentage of interstitial fibrosis as a continuous variable for the subsequent analyses. This was a methodological choice aimed at optimizing the analysis of continuous data and ensuring the accuracy and reliability to assess the impact of different factors on the progression of renal fibrosis and capture the nuances and variations present; this conversion allows for a more precise analysis of the relationship between the percentage of fibrosis and other continuous variables, such as biomarker levels.

Urine sample collection

All urine samples from kidney transplant (KTx) patients were collected up to 8 h before renal biopsy was performed. Samples were maintained at 4ºC before further processing and aliquoting as recommended [31] and for a maximum of 6 h. Approximately, 25 mL of urine from each patient was collected, centrifuged at 600 xg for 15 min to eliminate cells and cell debris, and stored immediately at -80ºC.

Creatinine normalization

As water reabsorption in the kidneys affects urinary solute concentrations, urinary biomarker levels are frequently reported as a ratio to urinary creatinine (U.Creat) to provide a standardized measure for comparison across samples and individuals. This approach often enhances the accuracy of biomarker measurements and their correlation with disease states or kidney function, and it facilitates the practical use of spot urine samples. Consequently, this practice is widely accepted in both clinical and research settings.

Enzyme-linked immunosorbent assay (ELISA)

All urine samples from KTx patients and healthy controls were analyzed using a commercial Vitronectin (VTN) ELISA kit (Cloud-Clone Corporation, USA), following the manufacturer’s recommendations. One milliliter of each urine sample was thawed overnight at 4ºC and concentrated as reported before [23]. The urine concentrate was finally adjusted to 100 µL with the kit’s dilution buffer and analyzed using the ELISA kit.

Statistical analysis

Statistical analyses were performed using GraphPad Prism software (9.0 version). Normal distribution of data was checked by a Shapiro-Wilk test and then the appropriate statistical test for each case was chosen. A Mann-Whitney test (non-parametric) was used to compare two groups of samples. Multiple group comparisons were conducted using the Kruskall-Wallis with Dunn’s multiple comparison test (non-parametric). The correlation of multiple variables was checked using Spearman’s correlation test. Receiver operating characteristic (ROC) curve analyses were performed to calculate the area under the curve and the sensitivity and specificity of each parameter. Finally, Venn diagrams were employed to visually represent the separated or overlapped potential in the identification of fibrotic patients by the two non-invasive biomarkers compared (albuminuria and VTN).

Results

Clinical parameters and patient stratification

Fifty-eight urine samples were collected from kidney transplant (KTx) patients and based on histopathological findings, were classified into two groups, namely “absence of fibrosis” (0%, non-fibrotic) and “presence of fibrosis” (≥ 5%, fibrotic) as described in materials and methods. Table 1 summarizes the relevant clinical parameters, biological variables, and immunosuppressive treatment of these patients.

The comparison of these parameters and variables revealed significant differences between the two groups in the vitronectin (VTN, p = 0.0180) and VTN levels corrected by urinary creatinine (VTN/U.Creat, p = 0.0037) in the fibrotic group, and in the number of patients treated with ACEi/ARBs (p = 0.0279). No significant differences were observed regarding age, sex, cardiovascular disease, diabetes mellitus, body mass index, immunosuppression regimens, serum creatinine, estimated glomerular filtration rate, proteinuria, albuminuria, and “years from transplantation”.

Table 1 Clinical parameters of the patients
Full size table

Correlation of clinical parameters and VTN with fibrosis

To elucidate the association between fibrosis and the monitored parameters, we focused on statistically significant parameters extracted from Table 1, along with other clinical indicators that exhibited a closer relationship to fibrosis, namely proteinuria (p = 0.1594), albuminuria (p = 0.096), and the variable “years from transplantation,” which has been previously established as correlated with allograft fibrosis [32]. Subsequently, a Spearman’s correlation test and the discrimination capacity of these variables between the non-fibrotic and fibrotic groups were assessed.

Global analysis (n = 58) revealed no significant correlation between proteinuria and albuminuria levels and the presence of fibrosis in the biopsies. Notably, a weak yet significant correlation with VTN (r = 0.28, p = 0.036) and a stronger and significant correlation with “years from transplantation” (r = 0.43, p = 0.0008) were observed (Fig. 1A). Further examination of mean differences between the non-fibrotic and fibrotic groups (Fig. 1B-E) demonstrated statistically significant differences only in VTN (p = 0.0180) (Fig. 1D) and normalized VTN levels (p = 0.0037) (Fig. 1E).

Interestingly, the receiver operating characteristic (ROC) curves for VTN and VTN/U.Creat, showed a significant discriminatory potential for the patients with and without fibrosis, with an area under the curve (AUC) values of 0.7094 (Fig. 1H) and 0.7541 (Fig. 1I), respectively. Conversely, “years from transplantation” (Fig. 1F) and albuminuria (Fig. 1G) displayed no significant discriminatory capacity between the two patient groups.

Fig. 1
figure 1

Analysis of all KTx patients. (A) Spearman’s correlation coefficient matrix. Spearman r values are indicated in bold and p-values of the correlation are indicated at the bottom if p-value < 0.2. P-values > 0.2 are indicated as ns. (B) Violin plots showing the descriptive statistics and statistical differences according to the parameter “years from transplantation”, (C) albuminuria levels, (D) VTN levels obtained by ELISA and (E) VTN levels normalized by urinary creatinine, stratified regarding the percentage of fibrosis observed in the kidney biopsies. Statistical differences are indicated for *p < 0.05 and **p < 0.01 by a Mann-Whitney test. (F) ROC curve based on “years from transplantation”, (G) albuminuria (H) VTN, and (I) VTN normalized by urinary creatinine, as a unique biomarker to differentiate the two groups of patients (no fibrosis vs. fibrosis). VTN, Vitronectin; U.Creat, urinary creatinine; mALB, albuminuria

Full size image

Analysis of VTN levels in non-fibrotic and fibrotic patients segregated by “years from transplantation”

Although the parameter “years from transplantation” did not exhibit discriminatory potential in distinguishing the absence or presence of fibrosis, its strong correlation with the percentage of fibrosis prompted us to delve deeper into its impact on VTN as the unique variable that demonstrated such discriminatory capability. For this, we stratified patients based on “years from transplantation” and the absence or presence of fibrosis, and subsequently, we examined the values of VTN levels across these distinct groups.

Our analysis revealed that in recently transplanted patients (< 1 year) and those extending up to the third-year post-transplantation (1–3 years), all individuals exhibited elevated levels of VTN regardless of the observed fibrosis in renal biopsies. From the third year onwards (> 3 years), non-fibrotic patients displayed noticeably reduced VTN levels (Fig. 2), whereas in the fibrotic group, VTN levels remained high. This dichotomous pattern in VTN levels prompted us to categorize patients based on their “years from transplantation” as either “recent” transplanted (≤ 3 years) or “long-term” transplanted (> 3 years).

The “recent” transplanted group, exhibited few discernible differences, except age which was notably higher in the fibrotic group than in the non-fibrotic group. However, none of the other biological variables, clinical parameters, or biomarkers demonstrated the ability to distinguish between the two groups (Supplementary Table 1). The analysis of the “long-term” group is described in the next section.

Fig. 2
figure 2

VTN levels of all the patients stratified by their years from transplantation and fibrosis percentage. Data are shown as mean ± SD in all groups

Full size image

Clinical parameters of the “long-term” transplanted patients

In the analysis of “long-term” transplanted patients, we included 36 individuals from the overall group. Table 2 succinctly outlines the clinical parameters, biological variables, and immunosuppressive treatments for this subset of patients.

Within this time-specific patient group, notable distinctions were detected between non-fibrotic and fibrotic patients. Specifically, significant differences were observed in VTN (p = 0.0040), VTN/U.Creat (p = 0.0037), proteinuria (p = 0.0363), and albuminuria (p = 0.0132). No significant differences were identified in any other parameter, biological variable, or immunosuppression treatment within this context (Table 2).

Table 2 Clinical parameters of the “long-term” (more than 3 years) kidney transplant patients
Full size table

Correlation of clinical parameters and VTN with fibrosis: “long-term” transplanted patients’ analysis

Spearman’s correlation test applied to the parameters deemed statistically significant in Table 2, alongside the “years from transplantation” variable in the long-term transplanted patients, revealed an absence of significant correlation with the percentage of fibrosis for any of the analyzed variables. Notably, VTN still emerged as the parameter with the highest correlation value (r = 0.29, p = 0.0849) (Fig. 3A).

However, the potential significance of these variables became evident when comparing the means of non-fibrotic and fibrotic patients, as all parameters (except “years from transplantation”, Fig. 3B) exhibited significant differences between both groups. Of particular interest, the means of non-fibrotic and fibrotic patients demonstrated statistically greater discrimination in VTN (Fig. 3D) and normalized VTN (Fig. 3E) levels compared with albuminuria levels (Fig. 3C).

Moreover, the receiver operating characteristic (ROC) curves for these three variables underscored their discrimination capacity for fibrotic patients, with VTN/U.Creat exhibiting the highest efficacy (AUC = 0.8438, p = 0.0034, Fig. 3I), followed by VTN (AUC = 0.8259, p = 0.0055, Fig. 3H), and lastly, albuminuria (AUC = 0.7857, p = 0.0149, Fig. 3G).

Fig. 3
figure 3

Analysis of the “long-term” KTx patients. (A) Spearman’s correlation coefficient matrix. Spearman r values are indicated in bold and p-values of the correlation are indicated at the bottom if p-value < 0.2. P-values > 0.2 are indicated as ns. (B) Violin plots showing the descriptive statistics and statistical differences according to the parameter “years from transplantation”, (C) albuminuria levels, (D) VTN levels obtained by ELISA and (E) VTN levels normalized by urinary creatinine, stratified regarding the percentage of fibrosis observed in the kidney biopsies. The statistical differences are indicated for *p < 0.05 and **p < 0.01 by a Mann-Whitney test. (F) ROC curve based on “years from transplantation”, (G) albuminuria (H) VTN, and (I) VTN normalized by urinary creatinine, as a unique biomarker to differentiate the two groups of patients (no fibrosis vs. fibrosis). VTN, Vitronectin; U.Creat, urinary creatinine; mALB, albuminuria

Full size image

Given that VTN, normalized VTN, and albuminuria demonstrated the capacity to differentiate between non-fibrotic and fibrotic patient groups, our subsequent analysis aimed to identify which patients with fibrosis were specifically detected by each parameter or their combinations. To achieve this, we established thresholds for each variable based on their ROC curves (VTN > 12.80 ng/mL; VTN/U.Creat > 0.13; Albuminuria > 95.65 mg/g), designating values above these thresholds as positive. Venn diagrams delineated the number and percentage of fibrotic patients identified by each parameter individually (VTN, VTN/U.Creat, and albuminuria), or in combination (VTN + albuminuria, and VTN/U.Creat + albuminuria) (Fig. 4A and B). In addition, because albuminuria is a widely used clinical parameter, we repeated the analyses using an established clinical threshold (> 300 mg/g) (Fig. 4C and D) [33].

The results showed a consistent pattern, regardless of the albuminuria threshold. Most patients with fibrosis were identified by both VTN (or normalized levels) and albuminuria. However, an important proportion (17–25%) of patients were identified solely by VTN or VTN/U.Creat, and a minimal subset (0-3.6%) by albuminuria alone (Fig. 4).

Fig. 4
figure 4

Analysis of the “long-term” KTx patients. Venn diagrams showing the number and percentage of patients with fibrosis that would be detected by VTN or VTN/U.Creat levels, albuminuria levels or both, without a biopsy. (A and B) VTN, VTN/U.Creat, and albuminuria threshold levels have been stablished based on the ROC curves of these parameters (VTN considered positive > 12.80 ng/mL; VTN/U.Creat considered positive > 0.13; albuminuria considered positive > 95.65 mg/g). (C and D) The clinical albuminuria threshold (> 300 mg/g) was considered for the analysis. VTN, vitronectin; U.Creat, urinary creatinine

Full size image

To confirm that the VTN threshold chosen using the ROC curve was not biased into a sensitivity overestimation due to our specific group of non-fibrotic patients, we also analyzed 14 healthy controls with confirmed healthy eGFR (> 90 mL/min/1.73m2).

The mean ± SD VTN levels obtained from this analysis were 8.70 ± 10.6 ng/mL and the median was 5.16 ng/mL (Fig. 5A and panel), which was much lower than the threshold used for the previous approach (12.80 ng/mL). Furthermore, no statistically significant differences were observed between healthy controls and non-fibrotic KTx patients (Fig. 5A). The discrimination potential of VTN between healthy controls and fibrotic patients was also analyzed (Fig. 5C), leading to an AUC of 0.89, specificity of 82.14%, and sensitivity of 85.71% (Fig. 5D). The same results were observed for normalized VTN levels (Fig. 5B).

Fig. 5
figure 5

(A) VTN levels of healthy controls and “long-term” KTx patients stratified by percentage of fibrosis, with descriptive statistics at the bottom. (B) VTN levels normalized by urinary creatinine of healthy controls and “long-term” KTx patients stratified by percentage of fibrosis, with their descriptive statistics at the bottom. Statistical differences are indicated for *p < 0.05, ***p < 0.001 and ****p < 0.0001 by a Kruskal Wallis test with Dunn’s post-hoc analysis. (C) VTN levels obtained by ELISA stratified regarding the percentage of fibrosis observed in the kidney biopsies. Statistical differences are indicated for ****p < 0.0001 by a Mann-Whitney test. (D) ROC curve based VTN as a unique biomarker to differentiate the two groups of patients (healthy controls vs. fibrotic patients). Cntrl, Healthy controls; VTN, Vitronectin; U.Creat, urinary creatinine

Full size image

Discussion

In this study, we evaluated vitronectin (VTN) as a non-invasive biomarker for detecting and monitoring renal fibrosis in long-term kidney transplant (KTx) patients using an ELISA approach.

In the context of kidney transplantation, fibrosis is a leading cause of allograft loss, particularly in the later stage post-transplantation [34]. The need for early diagnosis and continuous monitoring of fibrosis is critical to surpass the limitations of renal biopsy and prevent the development of chronic allograft nephropathy. But common clinical parameters, such as serum creatinine, eGFR, and proteinuria, are inaccurate in detecting tubulointerstitial scarring at early stages. The sensitivity and specificity limitations/concerns associated with serum creatinine [35] and the glomerular-centric nature of albuminuria [36] highlight the necessity for additional biomarkers.

Biomarkers in renal diseases provide several advantages, including (i) a non-invasive method to assess kidney fibrosis, avoiding the risks and discomfort associated with biopsy procedures, (ii) a more comprehensive assessment of the whole kidney, overcoming the sampling error limitations of biopsies (iii) a more frequent monitoring of disease progression and treatment response, through repeated measurements from blood or urine samples, and (iv) the potential detection of early stages of fibrosis before kidney functional decline is detected [37]. Some recognized biomarkers for kidney fibrosis include transforming growth factor β (TGF-β) [15], monocyte chemoattractant protein-1 (MCP-1) [16], matrix metalloproteinase-2 (MMP-2) [17], kidney injury molecule-1 (KIM-1) [38], neutrophil gelatinase-associated lipocalin (NGAL) [39], and urinary vitronectin (VTN). Additionally, markers of tubule dysfunction like α1-microglobulin (α1M) [40] and uromodulin (UMOD) [41] have shown promise. While these biomarkers show potential, it is important to note that kidney biopsy still remains the gold standard for assessing renal fibrosis due to its ability to directly visualize and quantify fibrotic changes [42]. Thus, a direct comparison to renal biopsy results is required for evaluating the performance of the proposed biomarker. In this sense, our study included a direct comparison of the urinary VTN levels with the results of the timely coincident kidney biopsy.

Analyzing a set of KTX patients (n = 58), our study revealed a significant correlation between VTN and fibrosis, outperforming conventional clinical parameters (Fig. 1). Notably, when analyzing the long-term KTx group, VTN exhibited superior potential in identifying fibrosis compared with albuminuria (Fig. 3). In this specific group, albuminuria was detected in approximately 71% of fibrotic patients, whereas VTN was identified in 86%, and the combination of both correctly classified 89% of fibrotic grafts detected by renal biopsy (Fig. 4). This enhanced efficacy may be attributed to the diverse origins of urinary VTN. Both VTN and albumin share a similar molecular weight. Therefore, both proteins can leak from the bloodstream when glomerular damage appears. However, VTN can also be produced in situ in the kidney [43]. Thus, fibrotic patients with tubulointerstitial scarring but no glomerular damage (no protein leakage) cannot be identified by albuminuria but could be detected by urinary VTN. Importantly, the normalization of VTN by urinary creatinine to reduce the potential bias of urine concentration due to uncontrolled factors did not modify the results (Figs. 3 and 4).

As a potential new biomarker, we established our pilot thresholds based on the values shown by ROC curves; based on this, some of the non-fibrotic patients showed overlapping VTN levels with the fibrotic group. A possible explanation for this observation could be a misclassification of some patients based on biopsy results. Of note, sampling bias during renal biopsy due to a heterogeneous distribution of the histopathological lesions or analysis errors in their further interpretation might be present in up to 20% of cases [44, 45]. Importantly, the reduced levels of urinary VTN found in healthy controls increased the potential of VTN to discriminate fibrosis (AUC = 0.89) (Fig. 5).

An intriguing observation in our study was the lack of statistical differences in VTN levels between the non-fibrotic and fibrotic patients in the “recent” transplanted group (Suppl. Table 1). Our results suggest that high levels of VTN during the first-year post-transplantation are not related to the degree of fibrosis (Fig. 2). This observation could be explained by the pleiotropic participation of VTN in different pathways, such as complement regulation [46], fibrosis [47], and adhesion molecules [48]. Thus, in the complex scenario after a renal transplantation procedure, where an active injury-repair response occurs, including surgical recovery, immunosuppressive treatment, and tissue remodeling, among others, it is probable that VTN could contribute to these complex dynamics, leading to temporarily increased levels. When these processes return to homeostatic values, VTN levels seem to slowly decrease in patients without fibrosis at longer time points (> 3 years) while remaining high in fibrotic patients (Fig. 2). This hypothesis needs to be confirmed in future studies.

In fact, the lack of a longitudinal follow-up of KTx patients is a limitation of this study. It has restricted the capacity to determine the exact time point after transplantation at which VTN would be more useful as an add-on non-invasive biomarker. Additional limitations are the low number of patients without fibrosis in the “long-term” group (n = 8), the rather low number of patients with high fibrosis grades (> 50%) (3/58), and the inclusion of patients based only on clinically indicated biopsies.

Other urinary biomarkers, have been investigated in recent years for their potential to detect kidney fibrosis. Compared to VTN, most of these biomarkers have been evaluated in specific chronic kidney disease (CKD) populations rather than in a broader group of kidney transplant (KTx) recipients. Moreover, not all studies have provided an area under the curve (AUC) value, thus limiting direct comparison of their diagnostic performance [49]. Yet, among those studies reporting AUC values, urinary CTGF has been identified as a biomarker for renal fibrosis in KTx patients, with an AUC of 0.63 at the time of renal biopsy [50]. In lupus nephritis, urinary MCP-1, Hepcidin, L-FABP [16], and TGF-β [15] have been associated with renal fibrosis, displaying AUC values of 0.66, 0.48, 0.60, and 0.90, respectively. Of these biomarkers, only urinary TGF-β exhibits a slightly better AUC than VTN. Nevertheless, in this particular case, patient populations differed from KTx recipients, limiting a broader comparability of these findings.

Early detection and management of fibrosis could significantly reduce the risk of graft loss by preserving and extending graft function. This may be partially tackled through fine tuning of immunosuppressive therapy. Yet, as several anti-fibrotic drugs are on clinical trials [51], new lines of treatment will be available for these patients. Thus, VTN detection may help to early identify those patients susceptible to receiving those treatments, and to non-invasively monitor their response.

Conclusions

VTN has emerged as a valid indicator of renal fibrosis, providing a potential non-invasive biomarker for clinical use. Of interest, the combination of this new biomarker with conventional clinical parameters (such as albuminuria) significantly improves the detection of renal fibrosis in kidney transplant patients. A longitudinal study in a new cohort of patients will help to fully assess the potential of VTN as early diagnostic and prognostic biomarker for renal fibrosis, and its implication in renal function and graft survival.

Abbreviations

ACEi/ARBs:

angiotensin-converting enzyme inhibitor / angiotensin receptor blocker

AUC:

Area under de curve

BMI:

Body mass index

CsA:

Cyclosporine A

CVD:

Cardiovascular disease

DM:

Diabetes Mellitus

eGFR:

Estimated glomerular filtration rate

ELISA:

Enzyme-linked immunosorbent assay

EVE:

Everolimus

HTA:

hypertension

IFTA:

Interstitial fibrosis and tubular atrophy

KTx:

Kidney transplant

mALB:

albuminuria

MPS:

Mycophenolate sodium

PDN:

prednisone

ROC:

Receiver operating characteristics

S.Creat:

Serum creatinine

TAC:

Tacrolimus

U.Creat:

Urinary creatinine

VTN:

Vitronectin

References

  1. Moeller MJ, Kramann R, Lammers T, Hoppe B, Latz E, Ludwig-Portugall I, et al. New aspects of kidney fibrosis-from mechanisms of Injury to Modulation of Disease. Front Med (Lausanne). 2021;8:814497.

    Article  PubMed  Google Scholar 

  2. Li L, Fu H, Liu Y. The fibrogenic niche in kidney fibrosis: components and mechanisms. Nat Rev Nephrol. 2022;18:545–57.

    Article  CAS  PubMed  Google Scholar 

  3. Huang R, Fu P, Ma L. Kidney fibrosis: from mechanisms to therapeutic medicines. Signal Transduct Target Ther. 2023;8:129.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Betjes MGH, Roelen DL, van Agteren M, Kal-van Gestel J. Causes of kidney graft failure in a cohort of recipients with a very long-time Follow-Up after transplantation. Front Med (Lausanne). 2022;9:842419.

    Article  PubMed  Google Scholar 

  5. Saritas T, Kramann R. Kidney allograft fibrosis: diagnostic and therapeutic strategies. Transplantation. 2021;105:e114–30.

    Article  CAS  PubMed  Google Scholar 

  6. Rayego-Mateos S, Valdivielso JM. New therapeutic targets in chronic kidney disease progression and renal fibrosis. Expert Opin Ther Targets. 2020;24:655–70.

    Article  CAS  PubMed  Google Scholar 

  7. Bülow RD, Boor P. Extracellular matrix in kidney fibrosis: more than just a Scaffold. J Histochem Cytochem. 2019;67:643–61.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ferenbach DA, Bonventre JV. Mechanisms of maladaptive repair after AKI leading to accelerated kidney ageing and CKD. Nat Rev Nephrol. 2015;11:264–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chuliá-Peris L, Carreres-Rey C, Gabasa M, Alcaraz J, Carretero J, Pereda J. Matrix metalloproteinases and their inhibitors in pulmonary fibrosis: EMMPRIN/CD147 comes into play. Int J Mol Sci. 2022;23:6894.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sugahara M, Tanaka T, Nangaku M. Hypoxia-inducible factor and Oxygen Biology in the kidney. Kidney360. 2020;1:1021–31.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Panizo S, Martínez-Arias L, Alonso-Montes C, Cannata P, Martín-Carro B, Fernández-Martín JL, et al. Fibrosis in chronic kidney disease: Pathogenesis and consequences. Int J Mol Sci. 2021;22:408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Klinkhammer BM, Boor P. Kidney fibrosis: emerging diagnostic and therapeutic strategies. Mol Aspects Med. 2023;93:101206.

    Article  CAS  PubMed  Google Scholar 

  13. Rende U, Guller A, Goldys EM, Pollock C, Saad S. Diagnostic and prognostic biomarkers for tubulointerstitial fibrosis. J Physiol. 2023;601:2801–26.

    Article  CAS  PubMed  Google Scholar 

  14. Agborbesong E, Bissler J, Li X. Liquid Biopsy at the Frontier of kidney diseases: application of exosomes in Diagnostics and therapeutics. Genes (Basel). 2023;14:1367.

    Article  CAS  PubMed  Google Scholar 

  15. Honkanen E, Teppo AM, Törnroth T, Groop PH, Grönhagen-Riska C. Urinary transforming growth factor-beta 1 in membranous glomerulonephritis. Nephrol Dial Transpl. 1997;12:2562–8.

    Article  CAS  Google Scholar 

  16. Zhang X, Nagaraja HN, Nadasdy T, Song H, McKinley A, Prosek J, et al. A composite urine biomarker reflects interstitial inflammation in lupus nephritis kidney biopsies. Kidney Int. 2012;81:401–6.

    Article  CAS  PubMed  Google Scholar 

  17. Sanders J-SF, Huitema MG, Hanemaaijer R, van Goor H, Kallenberg CGM, Stegeman CA. Urinary matrix metalloproteinases reflect renal damage in anti-neutrophil cytoplasm autoantibody-associated vasculitis. Am J Physiol Ren Physiol. 2007;293:F1927–1934.

    Article  CAS  Google Scholar 

  18. Younes-Ibrahim MS, Younes-Ibrahim M. Biomarkers and kidney diseases: a brief narrative review. J Lab Precision Med. 2022;7.

  19. Argilés À, Siwy J, Duranton F, Gayrard N, Dakna M, Lundin U, et al. CKD273, a New Proteomics Classifier assessing CKD and its prognosis. PLoS ONE. 2013;8:e62837.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Pontillo C, Mischak H. Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease. Clin Kidney J. 2017;10:192–201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lee HK, Jung NH, Lee DE, Lee H, Yang J, Kim YS, et al. Discovery of biomarkers related to interstitial fibrosis and tubular atrophy among kidney transplant recipients by mRNA-Sequencing. J Pers Med. 2023;13:1242.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Khoo A, Liu LY, Nyalwidhe JO, Semmes OJ, Vesprini D, Downes MR, et al. Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry. Nat Rev Urol. 2021;18:707–24.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Carreras-Planella L, Cucchiari D, Cañas L, Juega J, Franquesa M, Bonet J, et al. Urinary vitronectin identifies patients with high levels of fibrosis in kidney grafts. J Nephrol. 2021;34:861–74.

    Article  CAS  PubMed  Google Scholar 

  24. López-Guisa JM, Rassa AC, Cai X, Collins SJ, Eddy AA. Vitronectin accumulates in the interstitium but minimally impacts fibrogenesis in experimental chronic kidney disease. Am J Physiol Ren Physiol. 2011;300:F1244–1254.

    Article  Google Scholar 

  25. Peng Y, Li L, Shang J, Zhu H, Liao J, Hong X, et al. Macrophage promotes fibroblast activation and kidney fibrosis by assembling a vitronectin-enriched microenvironment. Theranostics. 2023;13:3897–913.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhou A, Huntington JA, Pannu NS, Carrell RW, Read RJ. How vitronectin binds PAI-1 to modulate fibrinolysis and cell migration. Nat Struct Mol Biol. 2003;10:541–4.

    Article  CAS  Google Scholar 

  27. Ciregia F, Deroyer C, Cobraiville G, Plener Z, Malaise O, Gillet P, et al. Modulation of αVβ6 integrin in osteoarthritis-related synovitis and the interaction with VTN(381–397 a.a.) competing for TGF-β1 activation. Exp Mol Med. 2021;53:210–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hosseini S, Vázquez-Villegas P, Rito-Palomares M, Martinez-Chapa SO, Advantages. Disadvantages and modifications of Conventional ELISA. In: Hosseini S, Vázquez-Villegas P, Rito-Palomares M, Martinez-Chapa SO, editors. Enzyme-linked Immunosorbent Assay (ELISA): from a to Z. Singapore: Springer; 2018. pp. 67–115.

    Google Scholar 

  29. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Roufosse C, Simmonds N, Clahsen-van Groningen M, Haas M, Henriksen KJ, Horsfield C, et al. A 2018 Reference Guide to the Banff classification of Renal Allograft Pathology. Transplantation. 2018;102:1795–814.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Erdbrügger U, Blijdorp CJ, Bijnsdorp IV, Borràs FE, Burger D, Bussolati B, et al. Urinary extracellular vesicles: a position paper by the Urine Task Force of the International Society for Extracellular Vesicles. J Extracell Vesicles. 2021;10:e12093.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Varma VK, Kajdacsy-Balla A, Akkina S, Setty S, Walsh MJ. Predicting Fibrosis Progression in Renal Transplant recipients using laser-based Infrared Spectroscopic Imaging. Sci Rep. 2018;8:686.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kidney Disease. Improving global outcomes (KDIGO) Glomerular Diseases Work Group. KDIGO 2021 Clinical Practice Guideline for the management of glomerular diseases. Kidney Int. 2021;100:S1–276.

    Article  Google Scholar 

  34. Redondo-Pachón D, Calatayud E, Buxeda A, Pérez-Sáez MJ, Arias-Cabrales C, Gimeno J, et al. Evolution of kidney allograft loss causes over 40 years (1979–2019). Nefrología (English Edition). 2023;43:316–27.

    Article  Google Scholar 

  35. De Rosa S, Greco M, Rauseo M, Annetta MG. The Good, the bad, and the serum creatinine: exploring the effect of muscle Mass and Nutrition. Blood Purif. 2023;52(9–10):775–85.

    Article  PubMed  Google Scholar 

  36. Lin CH, Chang YC, Chuang LM. Early detection of diabetic kidney disease: Present limitations and future perspectives. World J Diabetes. 2016;7(14):290–301.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Duarte-Rojo A, Altamirano JT, Feld JJ. Noninvasive markers of fibrosis: key concepts for improving accuracy in daily clinical practice. Ann Hepatol. 2012;11:426–39.

    Article  PubMed  Google Scholar 

  38. Gohda T, Kamei N, Koshida T, Kubota M, Tanaka K, Yamashita Y, et al. Circulating kidney injury molecule-1 as a biomarker of renal parameters in diabetic kidney disease. J Diabetes Investig. 2020;11:435–40.

    Article  CAS  PubMed  Google Scholar 

  39. Bhavsar NA, Köttgen A, Coresh J, Astor BC. Neutrophil Gelatinase-Associated Lipocalin (NGAL) and kidney Injury Molecule 1 (KIM-1) as predictors of Incident CKD Stage 3: the atherosclerosis risk in communities (ARIC) Study. Am J Kidney Dis. 2012;60:233–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Robles NR, Lopez Gomez J, Garcia Pino G, Valladares J, Hernandez Gallego R, Cerezo I. Alpha-1-microglobulin: prognostic value in chronic kidney disease. Med Clin (Barc). 2021;157:368–70.

    Article  CAS  PubMed  Google Scholar 

  41. Melchinger H, Calderon-Gutierrez F, Obeid W, Xu L, Shaw MM, Luciano RL, et al. Urine uromodulin as a biomarker of kidney Tubulointerstitial Fibrosis. Clin J Am Soc Nephrol. 2022;17:1284–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hull KL, Graham-Brown MP, Kidney. biopsy. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.mpmed.2022.11.003’]

  43. Zhang D, Hudson AE, Delostrinos CF, Carmean N, Eastman R, Hicks B, et al. Dual sources of vitronectin in the human lower urinary tract: synthesis by urothelium vs. extravasation from the bloodstream. Am J Physiol Ren Physiol. 2011;300:F475–487.

    Article  CAS  Google Scholar 

  44. Scheckner B, Peyser A, Rube J, Tarapore F, Frank R, Vento S, et al. Diagnostic yield of renal biopsies: a retrospective single center review. BMC Nephrol. 2009;10:11.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Reeve J, Einecke G, Mengel M, Sis B, Kayser N, Kaplan B, et al. Diagnosing rejection in renal transplants: a comparison of molecular- and histopathology-based approaches. Am J Transpl. 2009;9:1802–10.

    Article  CAS  Google Scholar 

  46. Ogawa T, Yorioka N, Yamakido M. Immunohistochemical studies of vitronectin, C5b-9, and vitronectin receptor in membranous nephropathy. Nephron. 1994;68:87–96.

    Article  CAS  PubMed  Google Scholar 

  47. Koukoulis GK, Shen J, Virtanen I, Gould VE. Vitronectin in the cirrhotic liver: an immunomarker of mature fibrosis. Hum Pathol. 2001;32:1356–62.

    Article  CAS  PubMed  Google Scholar 

  48. Rosenblatt S, Bassuk JA, Alpers CE, Sage EH, Timpl R, Preissner KT. Differential modulation of cell adhesion by interaction between adhesive and counter-adhesive proteins: characterization of the binding of vitronectin to osteonectin (BM40, SPARC). Biochem J. 1997;324(Pt 1):311–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mansour SG, Puthumana J, Coca SG, Gentry M, Parikh CR. Biomarkers for the detection of renal fibrosis and prediction of renal outcomes: a systematic review. BMC Nephrol. 2017;18:72.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Metalidis C, van Vuuren SH, Broekhuizen R, Lerut E, Naesens M, Bakker SJL, et al. Urinary connective tissue growth factor is associated with human renal allograft fibrogenesis. Transplantation. 2013;96:494–500.

    Article  CAS  PubMed  Google Scholar 

  51. Fuster-Martínez I, Calatayud S. The current landscape of antifibrotic therapy across different organs: a systematic approach. Pharmacol Res. 2024;205:107245.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the nephropathologist Laura Hernández-León for her support in analyzing kidney biopsies.

Funding

This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project “PI19/00837” and co-funded by the European Union. It has been also possible thanks to grants from Fundación FIPSE (4017-22) and Gínjol Programme from I-CERCA (2022-10-001). The project leading to these results has also received funding and support from the ”La Caixa” Foundation under the Grant CI23-30015. M.C.S. and M.F. are supported by grants from ISCIII (FI20/00021 and MS19/00018), co-funded by ERDF/ESF, “Investing in your future”. S.G.G. is supported by a grant from the Catalan Health department (“Departament de Salut”) PERIS-PIF-Salut (SLT017/20/000158). F.E.B. is a researcher from Germans Trias i Pujol Health Science Research Institute, supported by the Health Department of the Catalan Government (Generalitat de Catalunya).

Author information

Author notes

  1. Marta Clos-Sansalvador and Omar Taco contributed equally to this work.

Authors and Affiliations

  1. REMAR-IGTP Group, Germans Trias i Pujol Research Institute (IGTP) & , Nephrology Department, University Hospital Germans Trias i Pujol (HUGTiP) , Can Ruti Campus, Badalona (Barcelona), Catalonia, Spain

    Marta Clos-Sansalvador, Omar Taco, Sergio G. Garcia, Miriam Font-Morón, Jordi Bover, Anna Vila-Santandreu, Marcella Franquesa, Javier Juega & Francesc E. Borràs

  2. Department of Cell Biology, Physiology and Immunology, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain

    Marta Clos-Sansalvador & Sergio G. Garcia

  3. Department of Pathology, University Hospital Germans Trias i Pujol, Badalona (Barcelona), Spain

    Paula Rodríguez-Martínez

  4. Department of Cell Biology, Physiology and Immunology, Universitat de Barcelona (UB), Barcelona, Spain

    Francesc E. Borràs

Authors
  1. Marta Clos-Sansalvador
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Omar Taco
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Paula Rodríguez-Martínez
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Sergio G. Garcia
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Miriam Font-Morón
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Jordi Bover
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Anna Vila-Santandreu
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Marcella Franquesa
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Javier Juega
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Francesc E. Borràs
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conceptualization, Francesc E. Borràs; Data curation, Marta Clos-Sansalvador and Omar Taco; Formal analysis, Marta Clos-Sansalvador and Omar Taco; Funding acquisition, Francesc E. Borràs and Javier Juega; Methodology, Marta Clos-Sansalvador, Omar Taco, Paula Rodríguez-Martínez, Sergio G. Garcia, Miriam Font-Morón and Javier Juega; Supervision, Javier Juega and Francesc E. Borràs; Writing – original draft, Marta Clos-Sansalvador, Omar Taco and Francesc E. Borràs; Writing – review & editing, Marta Clos-Sansalvador, Omar Taco, Paula Rodríguez-Martínez, Sergio G. Garcia, Miriam Font-Morón, Jordi Bover, Anna Vila-Santandreu, Marcella Franquesa, Javier Juega and Francesc E. Borràs.

Corresponding author

Correspondence to Francesc E. Borràs.

Ethics declarations

Competing interests

F.E.B. holds a related patent with the International Application Number PCT/EP2020/087290. The other authors declare no conflicts of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Table 1.

Clinical parameters of the recent (less than 3 years) kidney transplant patients.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Clos-Sansalvador, M., Taco, O., Rodríguez-Martínez, P. et al. Towards clinical translation of urinary vitronectin for non-invasive detection and monitoring of renal fibrosis in kidney transplant patients. J Transl Med 22, 1030 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-024-05777-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-024-05777-5

Keywords

Journal of Translational Medicine

ISSN: 1479-5876

Contact us