ABSTRACT
Background
Tyrosine kinase inhibitors (TKIs) are associated with kidney function deterioration. A shift is ongoing towards glomerular filtration rate (GFR) equations based on other protein markers, such as cystatin C (CSTC) and β-trace protein (BTP). We evaluated various GFR equations for monitoring of kidney function in actively treated oncology patients.
Methods
We monitored 110 patients receiving a TKI. Blood and urine were collected during therapy. Serum analysis included creatinine (Cr), CSTC and BTP; for consequent GFR determination. Urine was analysed for protein, albumin, immunoglobulin G, and α-1-microglobulin. A similar analysis was done in a patient subgroup receiving immune checkpoint inhibitors (ICI) as prior or subsequent line of therapy.
Results
Cr remained constant during TKI treatment (P = 0.7753), whereas a significant decrease in CSTC (from week 2 onward, P < 0.0001) and BTP (at weeks 2 and 4, P = 0.0100) were noticed. Consequently, GFR estimations, using CSTC and/or BTP as a biochemical parameter, showed an apparent increase in GFR, whereas this was not observed for Cr-related GFR estimations. As a result, the GFR gap (ΔGFR) was significantly different from week 2 onward between Cr-based and CSTC-based GFR and between BTP-based and CSTC-based GFR. Glomerular damage was noticed with significant increase in urine protein-to-creatinine ratio, albumin-to-creatinine ratio and immunoglobulin G (all P < 0.0001). No change in α-1-microglobulin was seen. ICI treatment had no effect on Cr (P = 0.2262), CSTC (P = 0.7341), and BTP concentrations (P = 0.3592).
Conclusion
GFR equations, in which CSTC is incorporated, fail to correctly estimate the GFR in oncology patients treated with TKIs. As TKI-treated patients show clear signs of glomerular injury, further assessment is needed on how to correctly monitor the kidney function in actively treated oncology patients.
Keywords: cystatin C, glomerular filtration rate estimation, kidney function, renal cell carcinoma, tyrosine kinase inhibitors
Graphical Abstract
Graphical Abstract.
KEY LEARNING POINTS.
What was known:
Tyrosine kinase inhibitors (TKI) induce kidney damage.
No data exists on the most appropriate formula for estimated glomerular filtration rate (eGFR) in TKI-treated oncology patients.
Comparison between eGFR equations was made to optimize kidney function monitoring in oncology patients treated with targeted agents.
This study adds:
Creatinine-based eGFR remains constant in treatment with TKIs.
Cystatin C (CSTC) decreases after TKI administration, probably due to a tumor-related process (release of cysteine proteinase).
CSTC-based eGFR, irrespective of the formula used, overestimated kidney function of oncology patients treated with TKIs.
Potential impact:
The eGFR cannot be trusted blindly, especially the CSTC-based eGFR, during follow-up of oncology patients under TKI treatment.
Guidelines should discommend the use of CSTC-based eGFR as a kidney monitoring tool in oncology patients treated with a TKI.
INTRODUCTION
Tyrosine kinase inhibitors (TKIs) are frequently used in the metastatic setting of various solid carcinoma. TKIs can be subdivided into several classes depending on their target, although most TKIs have multiple targets [1–5]. Due to their broad spectrum of activity and the presence of these targets in normal renal tissue, the onset of TKI-related kidney injury has been reported in several trials [6–9]. Therefore, it is essential to correctly monitor the kidney function in oncology patients treated with TKIs.
The most commonly used analyte to measure kidney function is creatinine (Cr). Cr (113 Da) is the metabolite of muscle creatine phosphate. It is produced at a fairly constant rate, and entirely excreted via the kidney by being freely filtered across the glomerular membrane with no tubular reabsorption. Serum Cr measurement has been historically used and recommended as a marker for renal function through glomerular filtration rate (GFR) estimation [10–13]. However, there have recently been drastic changes in the diagnostic landscape for kidney function assessment. Several formulas have been established to determine the Cr-based estimated GFR (eGFR), some of these excluding race and other potentially confounding demographic factors from the equation [14–18]. Moreover, a shift is ongoing in the last decade towards the more extensive use of other serum analytes in GFR equations [19–23]. Cystatin C (CSTC), a low-molecular-weight protein of 13.3 kDa, is regarded as a good filtration marker due to its physical and chemical properties, its constant rate of production, its relatively freely filtering by the glomeruli, and its reabsorption by and catabolism in the proximal renal tubular cells. Consequently, formulas have been developed, incorporating CSTC next to Cr, for the estimation of the GFR, which are considered to be more robust, as they are not influenced by several confounding factors, such as race and body mass [15, 16, 18, 22, 24]. Another endogenous marker for kidney function evaluation is lipocalin prostaglandin D2 synthase or β-trace protein (BTP). BTP (23–29 kDa) is generated at a constant rate by glial cells in the brain and is freely filtered through the glomerulus with minimal non-renal elimination. Several equations have been elaborated to calculate GFR based on BTP measurement [15, 16, 25–27].
As a correct and close monitoring of kidney function is crucial in TKI-treated oncology patients, it is imperative to determine which GFR equations are most optimal during patient follow-up. Therefore, we evaluated and compared several GFR equations for use as a kidney function monitoring tool in actively treated oncology patients.
MATERIALS AND METHODS
Subjects and samples
A cohort of 110 patients was prospectively recruited and monitored at the University Hospital Ghent’s Departments of Medical Oncology and Gastroenterology. All patients received a TKI as active treatment regimen (sunitinib, pazopanib, sorafenib, axitinib, or cabozantinib). Additionally, a subcohort was created of 19 patients who received an ICI in monotherapy (nivolumab) prior to or after their TKI regimen to determine if similar outcomes can be seen for TKI versus ICI. The STARD diagram is given in Fig. S1 (see online supplementary material). Informed consent was given by all participants and the study was approved by the local Ethics Committee (Belgian registration: B670201214356). Upon start of treatment, clinical and biochemical evaluations were performed at baseline, after 2, 4, 8, 12, and 24 weeks of therapy. One serum tube (5 mL) and a spot urine sample was collected at each visit. Serum (following coagulation) and urine were centrifuged at 2000 g for 10 minutes and used for biochemical analysis.
Methods
We used ARCHITECT c16000 clinical chemistry analyser (Abbott, Chicago, IL, USA) to measure serum Cr (IDMS-based kinetic alkaline picrate methodology, calibrated against the international SRM-967 standard), total urinary protein, Cr, and α-1-microglobulin. A particle-enhanced nephelometric assay (PENIA) on a BN Nephelometer II analyser (Siemens Healthcare, Marburg, Germany) was used to determine serum CSTC (calibrated against the certified standard ERM®-DA471/IFCC), serum BTP [calibrated according to kit internal protein reference preparation (N Latex BTP, 11528254_en Rev. 07–Outside USA, Siemens Healthcare Diagnostics Products GmbH)], and albumin and immunoglobulin G (IgG) in urine. All reagents and controls are commercially available. The protein and albumin concentrations were expressed as urine protein-to-creatinine ratio (UPCR) and urine albumin-to-creatinine ratio (UACR; mg/g urinary Cr).
GFR was estimated using various equations. Cr-based GFR was calculated using the CKD-EPI, FAS, and EKFC equation [14–18, 28]. Based on the recommendations for European nephrology, only the CKD-EPI 2009 equation was used [29]. CSTC-based GFR was calculated via the CKD-EPI, FAS, and EKFC formula [15, 16, 18, 24]. In addition, the combined GFR for Cr and CSTC was determined using the CKD-EPI 2012, and FAS equation [14–16]. Lastly, the BTP-associated GFR was calculated by means of the CKD-EPI and FAS formula, as well as the combinations with Cr and/or CSTC using the FAS formulas [15, 16, 25]. All formulas used in this paper have been summarized in Table S1 (see online supplementary material). The difference in GFR (ΔGFR) estimations, using only one biochemical analyte, was determined by subtracting the CSTC-based eGFR from the Cr-based eGFR, the BTP-based eGFR from the CSTC-based eGFR, and the BTP-based eGFR from the Cr-based eGFR, respectively.
Statistical analysis
Considering statistical significance, only cases without missing values were selected, leading to data cut-off taken after 8 weeks of therapy with 74 patients included in the statistical analysis. Intrapatient differences during treatment (baseline versus week 2 versus week 4 versus week 8) were determined using the non-parametric Friedman test. Intergroups differences were further analysed using Dunn's testing. Next, linear mixed models for variance and covariance were established for all patients. In addition, for each GFR estimation, a regression model was visualized based on the median eGFR at each time point to illustrate the course of eGFR during treatment follow-up. P-values <0.05 were considered statistically significant. Statistical analyses were performed with MedCalc v20.110 (MedCalc Software, Ostend, Belgium), SPSS Statistics v29.0.1.0 (IBM Corp., Armonk, NY, USA), and GraphPad Prism v8.0.2 (GraphPad Software Inc., La Jolla, CA, USA).
RESULTS
Patients’ characteristics
Patients’ characteristics are summarized in Table 1. The majority of patients were male (59%) and suffered from mRCC (85%). Therapeutic options consisted of sunitinib (32%), pazopanib (20%), cabozantinib (19%), axitinib (15%), and sorafenib (14%). Due to TKI administration, median systolic blood pressure in treated patients increased from baseline 134 mm Hg to 142 mm Hg, 140 mm Hg, and 139 mm Hg at week 2, 4, and 8, respectively (P = 0.0046). The diastolic blood pressure rose from baseline 80 mm Hg to 85 mm Hg at week 2 and 82 mm Hg at week 4, before stabilizing at week 8 (81 mm Hg; P = 0.0161).
Table 1:
Patients’ characteristics.
| Parameter | ITT cohort | Statistical analysis cohort |
|---|---|---|
| Total | 110 (100) | 74 (100) |
| Gender | ||
| Female | 43 (39) | 30 (41) |
| Male | 67 (61) | 44 (59) |
| Age (years) | 64 (36, 83) | 65 (36, 83) |
| Malignancy | ||
| Renal cell carcinoma | 95 (86) | 63 (85) |
| Adenoid cystic carcinoma | 13 (12) | 9 (12) |
| Pancreatic neuroendocrine tumor | 1 (1) | 1 (1) |
| Thyroid carcinoma | 1 (1) | 1 (1) |
| TKI administered | ||
| Sunitinib | 35 (32) | 24 (32) |
| Pazopanib | 25 (23) | 15 (20) |
| Cabozantinib | 17 (15) | 14 (19) |
| Axitinib | 18 (16) | 11 (15) |
| Sorafenib | 15 (14) | 10 (14) |
All data are given as number (percentage) except for Age: median (range). ITT, intention to treat; TKI, tyrosine kinase inhibitor.
Baseline serum analyte concentrations and respective eGFRs, and changes through therapy are given in Table 2. Serum Cr and associated eGFRs remained constant during therapy (Table 2; Fig. 1). In contrast to Cr, a highly significant decrease in CSTC was seen from week 2 onward with a subsequent increase in CSTC-based eGFR (Table 2; Fig. 2). An extend evaluation of patients with paired values until 24 weeks after therapy (n = 42) indicated that the decrease in CSTC was consistent through therapy (P < 0.0001; Fig. S2, see online supplementary material), whereas no late effect was observed for Cr (P = 0.1099; Fig. S2, see online supplementary material). For BTP, a significant decrease in BTP was noticed at week 2 and 4, compared to baseline, which normalized by week 8. Vice versa, the same changes were noted for the BTP-based GFR (Table 2; Fig. 3). Lastly, the GFR estimations of combined serum parameters showed a significant change from week 2 onward for all formulas in which CSTC is incorporated (Table 2; Fig. 4).
Table 2:
Variation in biochemical measurements and eGFR during TKI treatment.
| Equation | Baseline Median (IQR) |
Week 2 Median (IQR) |
Week 4 Median (IQR) |
Week 8 Median (IQR) |
P |
|---|---|---|---|---|---|
| 1. Serum analytes | |||||
| Cr | 1.08 (0.85, 1.47) | 1.16 (0.89, 1.43) | 1.17 (0.86, 1.38) | 1.16 (0.87, 1.47) | 0.7753 |
| CSTC | 1.07 (0.87, 1.42) | 0.92 (0.75, 1.17) | 0.94 (0.74, 1.16) | 0.92 (0.81, 1.15) | <0.0001 |
| BTP | 0.66 (0.53, 0.90) | 0.66 (0.46, 0.81) | 0.66 (0.48, 0.82) | 0.66 (0.46, 0.78) | 0.0100 |
| 2. CKD-EPI eGFR | |||||
| Cr | 64 (47, 79) | 61 (49, 73) | 65 (47, 84) | 61 (48, 76) | 0.7457 |
| CSTC | 69 (47, 92) | 84 (62, 105) | 85 (61, 106) | 86 (65, 100) | <0.0001 |
| Cr + CSTC | 69 (48, 85) | 74 (58, 92) | 73 (55, 92) | 72 (58, 89) | <0.0001 |
| BTP | 61 (49, 72) | 61 (52, 79) | 64 (53, 74) | 63 (56, 76) | 0.0089 |
| ΔGFR Cr—CSTC | −4 (−22, 11) | −18 (−36, −4) | −19 (−33, −8) | −18 (−33, −4) | <0.0001 |
| ΔGFR Cr—BTP | −2 (−13, 12) | −7 (−21, 11) | −5 (−18, 6) | −3 (−17, 8) | 0.1138 |
| ΔGFR CSTC—BTP | 7 (−6, 21) | 15 (−3, 26) | 13 (−1, 35) | 16 (−3, 30) | 0.0196 |
| 3. FAS eGFR | |||||
| Cr | 60 (46, 74) | 59 (46, 75) | 60 (47, 74) | 59 (48, 72) | 0.7937 |
| CSTC | 62 (46, 82) | 73 (57, 93) | 76 (57, 96) | 74 (58, 86) | <0.0001 |
| BTP | 73 (50, 90) | 76 (59, 102) | 75 (57, 106) | 76 (58, 106) | 0.0070 |
| Cr + CSTC | 61 (47, 76) | 65 (61, 81) | 66 (51, 81) | 64 (52, 80) | <0.0001 |
| Cr + BTP | 66 (50, 78) | 65 (54, 81) | 67 (52, 82) | 66 (54, 81) | 0.0302 |
| CSTC + BTP | 66 (51, 75) | 75 (55, 92) | 75 (54, 95) | 73 (60, 94) | <0.0001 |
| Cr + CSTC + BTP | 64 (49, 75) | 66 (50, 79) | 69 (51, 82) | 65 (52, 80) | <0.0001 |
| ΔGFR Cr—CSTC | −2 (−14, 10) | −15 (−28, −1) | −15 (−26, −3) | −11 (−27, −3) | <0.0001 |
| ΔGFR Cr—BTP | −8 (−31, −1) | −17 (−41, −5) | −16 (−43, −3) | −16 (−40, −2) | 0.0379 |
| ΔGFR CSTC—BTP | −11 (−26, 4) | −9 (−28, 11) | −2 (−31, 12) | −7 (−30, 10) | 0.5540 |
| 4. EKFC eGFR | |||||
| Cr | 63 (46, 74) | 59 (46, 73) | 61 (46, 77) | 60 (47, 73) | 0.7453 |
| CSTC | 70 (49, 86) | 79 (62, 91) | 80 (62, 92) | 81 (65, 91) | <0.0001 |
| ΔGFR Cr—CSTC | −7 (−19, 6) | −17 (−29, −4) | −15 (−27, −8) | −16 (−25, −7) | <0.0001 |
eGFR are given as mL/min/1.73m². Cr-based eGFR was estimated using the CKD-EPI 2009 equation.
BTP, β-trace protein; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; Cr, creatinine; CSTC, cystatin C; eGFR, estimated glomerular filtration rate; EKFC, European Kidney Function Consortium; FAS, Full Age Spectrum; IQR, interquartile range; TKI, tyrosine kinase inhibitor.
Figure 1:
Intrapatient comparison of serum Cr and associated eGFR during TKI treatment. Box plots are given for the different parameters over time. X-axis depicts time of measurement. Comparisons are illustrated for: A Cr concentrations (P = 0.7753); B CKD-EPI Cr-based eGFR (P = 0.7457); C FAS Cr-based eGFR (P = 0.7937); and D EKFC Cr-based eGFR (P = 0.7453). Cr-based eGFR was estimated using the CKD-EPI 2009 equation. CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; Cr, creatinine; eGFR, estimated glomerular filtration rate; EKFC, European Kidney Function Consortium; FAS, Full Age Spectrum; TKI, tyrosine kinase inhibitor.
Figure 2:
Intrapatient comparison of serum CSTC and associated eGFR during treatment. Box plots are given for the different parameters over time. X-axis depicts time of measurement. Comparisons are illustrated for: A CSTC concentrations (P < 0.0001); B CKD-EPI CSTC-based eGFR (P < 0.0001); C FAS CSTC-based eGFR (P < 0.0001); and D EKFC CSTC-based eGFR (P < 0.0001). Intergroup significant changes are indicated in the figure (***P < 0.001). CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; CSTC, cystatin C; eGFR, estimated glomerular filtration rate; EKFC, European Kidney Function Consortium; FAS, Full Age Spectrum; TKI, tyrosine kinase inhibitor.
Figure 3:
Intrapatient comparison of serum BTP and associated eGFR during treatment. Box plots are given for the different parameters over time. X-axis depicts time of measurement. Comparisons are illustrated for: A BTP concentrations (P = 0.0100); B CKD-EPI BTP-based eGFR (P = 0.0089); and C FAS BTP-based eGFR (P = 0.0070). Intergroup significant changes are indicated in the figure (*P < 0.05). BTP, β-trace protein; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; EKFC, European Kidney Function Consortium; FAS, Full Age Spectrum; TKI, tyrosine kinase inhibitor.
Figure 4:
Intrapatient comparison of combined eGFR during TKI treatment. Box plots are given for the different parameters over time. X-axis depicts time of measurement. Comparisons are illustrated for: A CKD-EPI Cr-CSTC-based eGFR concentrations (P < 0.0001); B FAS Cr-CSTC-based eGFR (P < 0.0001); C FAS Cr-BTP-based eGFR (P = 0.0302); D FAS CSTC-BTP-based eGFR (P < 0.0001); and E FAS CSTC-BTP-BTP-based eGFR (P < 0.0001). Intergroup significant changes are indicated in the figure (*P < 0.05; **P < 0.01; ***P < 0.001). Cr-CSTC based eGFR was estimated using the CKD-EPI 2012 equation. BTP, β-trace protein; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; Cr, creatinine; CSTC, cystatin C; eGFR, estimated glomerular filtration rate; EKFC, European Kidney Function Consortium; FAS, Full Age Spectrum; TKI, tyrosine kinase inhibitor.
Due to the observed changes in GFR, we determined whether the differences between GFR equations (ΔGFR), based on single biochemical analyte, were significant. All differences per time point are given in Table 2. Interestingly, all ΔGFR concerning CSTC-related eGFR were significantly different during treatment, compared to baseline. Interestingly, no significant difference was observed for ΔGFR CSTC—BTP when using the FAS equation (Table 2; Fig. 5).
Figure 5:
Intrapatient comparison of ΔGFR during TKI treatment. Box plots are given for the different parameters over time. X-axis depicts time of measurement. Comparisons are illustrated for: A ΔGFR CKD-EPI Cr vs CSTC (P < 0.0001); B ΔGFR CKD-EPI Cr vs BTP (P = 0.0123); C ΔGFR CKD-EPI CSTC vs BTP (P = 0.1123); D ΔGFR FAS Cr vs CSTC (P < 0.0001); E ΔGFR FAS Cr vs BTP (P = 0.0123); F ΔGFR FAS CSTC vs BTP (P = 0.1123); and G EKFC Cr vs CSTC (P < 0.0001). ΔGFR was calculated by subtracting the CSTC-based eGFR form the Cr-based eGFR, the BTP-based eGFR form the Cr-based eGFR, and the BTP-based eGFR form the CSTC-based eGFR; respectively. Intergroup significant changes are indicated in the figure (*P < 0.05; ***P < 0.001). Cr-based eGFR was estimated using the CKD-EPI 2009 equation. ΔGFR, GFR gap; BTP, β-trace protein; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; Cr, creatinine; CSTC, cystatin C; eGFR, estimated glomerular filtration rate; EKFC, European Kidney Function Consortium; FAS, Full Age Spectrum; TKI, tyrosine kinase inhibitor.
Next, significant changes for urinary biomarkers were noticed. A 50% increase in median UPCR from baseline (108.3 mg/g Cr) was seen at week 2 (169.6 mg/g Cr), at week 4 (167.2 mg/g Cr), and at week 8 (157.6 mg/g Cr, P < 0.0001; Fig. S3A, see online supplementary material). Similarly, a threefold increase in median UACR was observed between baseline (17.5 mg/g Cr) and week 2 (49.9 mg/g Cr), reducing to a twofold increase at week 4 (36.5 mg/g Cr) and week 8 (32.0 mg/g Cr; P < 0.0001; Fig. S3B, see online supplementary material). The number of patients observed with proteinuria and albuminuria during the study are indicated in Table S2 (see online supplementary material). Median urine IgG significantly increased from baseline (4.0 mg/L) to week 2 (8.7 mg/L) and week 4 (6.0 mg/L), but stabilized at week 8 (5.3 mg/L; P < 0.0001; Fig. S3C, see online supplementary material). Lastly, no tubular kidney damage was observed with median α-1-microglobulin remaining stable throughout treatment (12.0 mg/L versus 12.4 mg/L versus 13.9 mg/L versus 10.6 mg/L; P = 0.1458; Fig. S3D, see online supplementary material).
In the subcohort analysis, the differences in serum parameters and associated eGFRs were evaluated in a cohort of RCC patients (n = 19) receiving TKIs and ICIs (or vice versa) and compared between treatment regimens. Cr and Cr-based eGFRs remained stable during treatment with a TKI or with an ICI (Fig. S4, see online supplementary material). For CSTC, the significant decrease in CSTC concentrations, and consequent increases in GFR estimations were also noticed in the TKI subcohort. However, no significant changes were observed in the ICI cohort (Fig. S5, see online supplementary material). The same holds true for BTP, where (only borderline) significant changes were observed in the TKI cohort, but not in the ICI cohort (Fig. S6, see online supplementary material). The treatment-related changes in urinary analytes in the TKI cohort, measured via UPCR, UACR, and IgG; were not present in the ICI cohort (Fig. S7, see online supplementary material).
Lastly, an ICI-related decrease was noticed in systolic (134 mm Hg versus 129 mm Hg versus 126 mm Hg versus 125 mm Hg; P = 0.0098) and diastolic blood pressure (79 mm Hg versus 74 mm Hg versus 73 mm Hg versus 73 mm Hg; P = 0.0858) was seen at baseline, week 2, 4, and 8, respectively.
DISCUSSION
There have been changes in the equations to estimate GFR, as well as in the guidelines for use of these GFR estimations. As it has been reported that the eGFR may be influenced by tumor-related factors, the objective of this research paper was to evaluate several currently implemented GFR equations as kidney function monitoring tools in oncology patients treated with TKIs.
Firstly, we noticed that the systolic and diastolic blood pressure in actively treated patients increased during their treatment. This finding confirms the findings observed in the large phase III trials studying the use of various TKIs in RCC patients. In these studies, occurrence of hypertension is reported in 30% to 46% of patients receiving a TKI [6–8]. As it is widely recognized that an increase in blood pressure contributes to extensive kidney damage, the therapy-related increase in blood pressure is an initial proof of a TKI-related side effect due to therapy-related processes occurring in the kidneys [30]. In addition, Meyrier [31] stated that kidney arterial hypertension results in nephrosclerosis, which is clinically associated with proteinuria [31]. In our study, we have also noticed an increase in proteinuria, namely a 50% increase in UPCR, compared to baseline, throughout treatment, with 57% of patients having mild to severe proteinuria. This finding of TKI-related proteinuria is in line with recent reports [32, 33], including a meta-analysis from Xiong et al. [33] highlighting the risk of renal impairment by the administration of TKIs [33].
A review by Kandula and Agarwal [34] describes that the TKI-induced kidney damage is caused by renal VEGF inhibition, which results in an inhibited nephrin production and interruption of the glomerular filtration barrier [34]. Indeed, a more extensive evaluation indicated that the proteinuria, observed in our study, was the result of extensive glomerular damage, occurring as clinically significant albuminuria from week 2 onward (threefold increase versus baseline) with 59% of patients having mild to severe albuminuria. Surprisingly, the degree of albuminuria was more severe in male patients compared to female patients. In addition, these patients presented with increased urinary IgG. As IgG has twice the molecular weight of albumin (±150 kDa), the occurrence of TKI-related IgG-uria indicates the severe onset of potential glomerular disease [35, 36]. Correct and close monitoring of patients’ kidney function during therapy is therefore essential, especially as an elevated (systolic) blood pressure is known to contribute to a decline in kidney function [37, 38].
Determination of the eGFR is the best available indicator of the kidney function [39]. In order to estimate the GFR, a variety in formulas exist, depending on the measured biochemical parameter, such as Cr, CSTC, or BTP [14, 24, 25]. All patients in our study showed comparable baseline eGFR according to the different formulas used, except for the FAS BTP equation, which tended to overestimate the eGFR. During TKI treatment, no changes were observed in the CKD-EPI, FAS, or EKFC Cr-related eGFR. This finding confirms the initial result reported by our group [40]. This observation is, however, contradictory to literature as Ning et al. [32] reported an eGFR decrease in mRCC patients treated with targeted therapy. This controversy is even larger in case of CSTC and BTP as Ning et al. [32] reported a CSTC increase during therapy with consequent decrease in GFR [32].
In our study, applying BTP and CSTC as analyte resulted in significant changes during therapy. BTP and respective BTP-based eGFRs were shown to be significantly decreased and increased after 2 and 4 weeks of therapy, before stabilizing after 8 weeks. Using the FAS BTP equation, we did, however, observe several outliers, ranging to 441 mL/min/1.73 m². Of the six patients who had an FAS BTP-based eGFR over 200 mL/min/1.73 m², four were male, but more importantly five patients were 50 years old or younger (45% of patients below the age of 51 years old who were included in the BTP analysis). This indicates that the FAS BTP equation is greatly biased by age and thus overestimates the eGFR in younger patients, as was also reported by Pottel et al. [17]. The FAS BTP equation should therefore be used cautiously in younger patients who are being treated with a TKI.
For CSTC, we have noticed a decrease in CSTC that remained consistent from week 2 of therapy onward, including in patients who remained on therapy for at least 24 weeks (n = 41). Consequently, the CSTC-based eGFR, determined via the CKD-EPI, FAS, and EKFC equations, increased during therapy, which would falsely indicate that the patients’ kidney function is improving after TKI administration. The latter is particularly true as the calculated difference in GFR (ΔGFR), according to different formulas used, was significantly different for each ΔGFR involving the CSTC-based eGFR, except for the ΔGFR FAS CSTC—BTP due to the above-mentioned effect of age on the FAS BTP eGFR. Moreover, using combination formulas, including two or three of the serum analytes, resulted in a likewise apparent increase in eGFR for each formula in which CSTC was incorporated. Given the evidence of TKI-associated kidney damage [34], the decrease in BTP and CSTC must be related to tumoral changes following TKI administration. The question remains: which biological processes result in the apparent decrease in BTP and CSTC?
To our knowledge, the causal relationship between TKI administration and variation in BTP has not been studied in-depth. Omori et al. [41] indicated that an increased lipocalin-type prostaglandin D synthase expression is linked to malignant properties of tumor endothelial cells and associated tumoral neo-angiogenesis [41]. It can therefore be hypothesized that anti-VEGF cancer therapy results in a decrease in BTP concentrations. More in-depth evaluations on the effect of TKIs on BTP are warranted.
Similarly, the relationship between TKI administration and CSTC needs further assessment. CSTC is a member of the type 2 family of cysteine proteinase inhibitors, playing a significant role in cell regulation, cell proliferation, and apoptosis by regulating the breakdown of extracellular and intracellular proteins. As such, cysteine proteinase inhibitors ensure the balance between free cysteine proteinases and their complexed form. Due to their regulation against harmful cysteine proteinase activities, cysteine proteinase inhibitors have been linked to a number of pathologies [42, 43].
With regard to tumor development and angiogenesis, Dreilich et al. [44] indicated that baseline CSTC is correlated to VEGF in esophageal carcinoma [44]. In vitro research from Li et al. [45] demonstrated that VEGF-A blockage promotes CSTC expression, thus indicating a CSTC increase during TKI treatment, due to targeting of the VEGF axis [45]. However, we have noticed a highly significant decrease in CSTC throughout therapy, which can probably be explained by the cathepsin-inhibitory properties of CSTC. A review by Breznik et al. [46] highlighted that CSTC is the most potent inhibitor of cysteine peptidases [46]. Moreover, in vitro experiments demonstrated that tumor-derived cathepsin D reduces CSTC activity in the tumor microenvironment through cathepsin D-mediated complexation and proteolysis [47]. This was also highlighted by our group, as cathepsin D addition to serum resulted in a total CSTC depletion from serum [40]. Future in-depth evaluations are warranted, although it is clear that CSTC-based GFR estimation is not reliable nor robust for kidney monitoring of TKI-treated oncology patients.
In a final part, we assessed whether the decrease in CSTC, observed in patients after TKI administration, is also noticed for patients receiving ICI as prior or next line of therapy (n = 19). Conversely to the CSTC decrease during TKI therapy, a non-significant slight increase has been observed in CSTC following ICI administration. This is partly in line with the data Ning et al. [32] who found that PD-1 inhibitor administration did not attribute to a difference in GFR estimation [32]. Our finding indicates that the difference in mechanism of action of both TKIs and ICIs harbor a different effect on serum CSTC. The observed exacerbation in ICI-related proteinuria was not seen in our patient population [32].
A first limitation of our study is the absence of any direct measurement of GFR. Therefore, we cannot directly confirm the increase in CSTC-based eGFR. Nevertheless, no ideal method for measured GFR exists as substantial variation is reported for measured GFR results across studies and which questioned the scientific reliability of the alternative measured GFR methods as the gold standard to evaluate kidney function [48]. A second potential limitation is that CSTC was measured using a PENIA. Conflicting reports exist on the analytical variability between the PENIA and the particle-enhanced turbidimetric immunoassay for CSTC [49–52]. It is, however, unlikely that these possible variations result in the difference for CSTC observed in our study. Moreover, as our CSTC test is calibrated against the certified ERM®-DA471/IFCC standard, we are confident that the PENIA measurement can be considered reliable. Another limitation is that we only used BTP as analyte to estimate the BTP-based GFR. Inker et al. [25] stated that the formula including both BTP and β2-microglobulin shows similar accuracy to the CKD-EPI Cr or CSTC equation [25]. As we do not have any β2-microglobulin available in our patient population, we were unable to apply this equation.
Conclusion
TKI administration is associated with treatment-related kidney damage. Although larger clinical trials do not emphasize the importance of kidney function follow-up, our data along with literature evince the cruciality for a close patient monitoring during TKI administration for potentially severe nephropathy. It remains, however, debatable which GFR estimation should be implemented as the ‘gold standard’. Despite the fact that the Cr-based GFR estimation remains constant during TKI treatment, the CSTC-based GFR significantly increased during therapy with TKIs, raising questions on the accuracy of these estimates in oncology patients treated with TKIs. In line with the suggestions of Levey et al. [53], we propose for an extensive evaluation into the mechanisms that play a pivotal role in the observed CSTC decrease, as well as the continued optimization of GFR estimations.
Supplementary Material
ACKNOWLEDGEMENTS
We would like to thank all the patients for their participation in the study. We acknowledge the assistance of the specialized onco-nurses Ann Annaert, Greet Verbaere, and Sylvia De Weggheleire in collecting the patients’ samples during polyclinic consultations.
Contributor Information
Tijl Vermassen, Department of Medical Oncology, University Hospital Ghent, Ghent, Belgium; Biomarkers in Cancer, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent, Ghent, Belgium.
Karen Geboes, Cancer Research Institute Ghent, Ghent, Belgium; Digestive Oncology, Department of Gastroenterology, Ghent University Hospital, Ghent, Belgium; Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium.
Nicolaas Lumen, Cancer Research Institute Ghent, Ghent, Belgium; Department of Urology, University Hospital Ghent, Ghent, Belgium; Department of Human Structure and Repair, Ghent University, Ghent, Belgium.
Charles Van Praet, Cancer Research Institute Ghent, Ghent, Belgium; Department of Urology, University Hospital Ghent, Ghent, Belgium; Department of Human Structure and Repair, Ghent University, Ghent, Belgium.
Sylvie Rottey, Department of Medical Oncology, University Hospital Ghent, Ghent, Belgium; Biomarkers in Cancer, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent, Ghent, Belgium; Drug Research Unit Ghent, University Hospital Ghent, Ghent, Belgium.
Joris Delanghe, Cancer Research Institute Ghent, Ghent, Belgium; Department of Diagnostic Sciences, Ghent University, Ghent, Belgium.
FUNDING
This research was partly supported by a restricted grant from Bayer and Pfizer.
AUTHORS' CONTRIBUTIONS
Conceptualization: S.R. and J.D.; funding acquisition: T.V. and S.R.; resources: S.R.; supervision: S.R. and J.D.; project administration: T.V. and S.R.; methodology: T.V. and J.D.; investigation: K.G., N.L., C.V.P., and S.R.; data curation: T.V.; formal analysis: T.V., S.R., and J.D.; software: T.V.; visualization: T.V.; writing—original draft preparation: T.V.; writing—review and editing: T.V., K.G., N.L., C.V.P., S.R., and J.D. All authors have read and agreed to the published version of the manuscript.
DATA AVAILABILITY STATEMENT
All data generated and analysed during this study cannot be shared publicly due to the European General Data Protection Regulation (EU GDPR). The data will be shared on reasonable request to the corresponding author, following approval of the data protection officer of the University Hospital Ghent.
CONFLICT OF INTEREST STATEMENT
T.V. has received support for attending meetings and/or travel from MSD Belgium within the past 36 months. N.L. has received Grants from Bayer, Ipsen, and Janssen within the past 36 months.
REFERENCES
- 1. Cleymaet R, Vermassen T, Coopman Ret al. The therapeutic landscape of salivary gland malignancies-where are we now? Int J Mol Sci 2022;23:14891. 10.3390/ijms232314891 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Lee RH, Wai KC, Chan JWet al. Approaches to the management of metastatic adenoid cystic carcinoma. Cancers (Basel) 2022;14:5698. 10.3390/cancers14225698 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Makhov P, Joshi S, Ghatalia Pet al. Resistance to systemic therapies in clear cell renal cell carcinoma: mechanisms and management strategies. Mol Cancer Ther 2018;17:1355–64. 10.1158/1535-7163.MCT-17-1299 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Marona P, Gorka J, Kwapisz Oet al. Resistance to tyrosine kinase inhibitors promotes renal cancer progression through MCPIP1 tumor-suppressor downregulation and c-Met activation. Cell Death Dis 2022;13:814. 10.1038/s41419-022-05251-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Jiao Q, Bi L, Ren Yet al. Advances in studies of tyrosine kinase inhibitors and their acquired resistance. Mol Cancer 2018;17:36. 10.1186/s12943-018-0801-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Choueiri TK, Escudier B, Powles Tet al. Cabozantinib versus everolimus in advanced renal-cell carcinoma. N Engl J Med 2015;373:1814–23. 10.1056/NEJMoa1510016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Motzer RJ, Escudier B, Tomczak Pet al. Axitinib versus sorafenib as second-line treatment for advanced renal cell carcinoma: overall survival analysis and updated results from a randomised phase 3 trial. Lancet Oncol 2013;14:552–62. 10.1016/S1470-2045(13)70093-7 [DOI] [PubMed] [Google Scholar]
- 8. Motzer RJ, Hutson TE, Cella Det al. Pazopanib versus sunitinib in metastatic renal-cell carcinoma. N Engl J Med 2013;369:722–31. 10.1056/NEJMoa1303989 [DOI] [PubMed] [Google Scholar]
- 9. Motzer RJ, Hutson TE, Glen Het al. Lenvatinib, everolimus, and the combination in patients with metastatic renal cell carcinoma: a randomised, phase 2, open-label, multicentre trial. Lancet Oncol 2015;16:1473–82. 10.1016/S1470-2045(15)00290-9 [DOI] [PubMed] [Google Scholar]
- 10. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;16:31–41. 10.1159/000180580 [DOI] [PubMed] [Google Scholar]
- 11. George JA, Gounden V. Novel glomerular filtration markers. Adv Clin Chem 2019;88:91–119. 10.1016/bs.acc.2018.10.005 [DOI] [PubMed] [Google Scholar]
- 12. Ad-hoc working group of ERBP, Fliser D, Laville Met al. A European Renal Best Practice (ERBP) position statement on the Kidney Disease improving Global Outcomes (KDIGO) clinical practice guidelines on acute kidney injury: part 1: definitions, conservative management and contrast-induced nephropathy. Nephrol Dial Transplant 2012;27:4263–72. 10.1093/ndt/gfs375 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Flamant M, Vidal-Petiot E, Metzger Met al. Performance of GFR estimating equations in African Europeans: basis for a lower race-ethnicity factor than in African Americans. Am J Kidney Dis 2013;62:182–4. 10.1053/j.ajkd.2013.03.015 [DOI] [PubMed] [Google Scholar]
- 14. Inker LA, Eneanya ND, Coresh Jet al. New creatinine- and cystatin C-based equations to estimate GFR without race. N Engl J Med 2021;385:1737–49. 10.1056/NEJMoa2102953 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Pottel H, Delanaye P, Schaeffner Eet al. Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C. Nephrol Dial Transplant 2017;32:497–507. 10.1093/ndt/gfw425 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Pottel H, Schaeffner E, Ebert N. Evaluating the diagnostic value of rescaled beta-trace protein in combination with serum creatinine and serum cystatin C in older adults. Clin Chim Acta 2018;480:206–13. 10.1016/j.cca.2018.02.026 [DOI] [PubMed] [Google Scholar]
- 17. Pottel H, Bjork J, Courbebaisse Met al. Development and validation of a modified full age spectrum creatinine-based equation to estimate glomerular filtration rate : a cross-sectional analysis of pooled data. Ann Intern Med 2021;174:183–91. 10.7326/M20-4366 [DOI] [PubMed] [Google Scholar]
- 18. Pottel H, Bjork J, Rule ADet al. Cystatin C-based equation to estimate GFR without the inclusion of race and sex. N Engl J Med 2023;388:333–43. 10.1056/NEJMoa2203769 [DOI] [PubMed] [Google Scholar]
- 19. National Institute for Health and Care Excellence (NICE) . Chronic Kidney Disease: Assessment and Management. NICE guideline, No. 203. London: NICE, 2021. [PubMed] [Google Scholar]
- 20. Levin A, Stevens PE.. Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward. Kidney Int 2014;85:49–61. 10.1038/ki.2013.444 [DOI] [PubMed] [Google Scholar]
- 21. Nankivell BJ, Nankivell LFJ, Elder GJet al. How unmeasured muscle mass affects estimated GFR and diagnostic inaccuracy. EClinicalMedicine 2020;29–30:100662. 10.1016/j.eclinm.2020.100662 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Spencer S, Desborough R, Bhandari S. Should Cystatin C eGFR become routine clinical practice? Biomolecules 2023;13:1075. 10.3390/biom13071075 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Stevens PE, Levin A, Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members . Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med 2013;158:825–30. 10.7326/0003-4819-158-11-201306040-00007 [DOI] [PubMed] [Google Scholar]
- 24. Inker LA, Schmid CH, Tighiouart Het al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012;367:20–9. 10.1056/NEJMoa1114248 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Inker LA, Tighiouart H, Coresh Jet al. GFR estimation using beta-trace Protein and beta2-microglobulin in CKD. Am J Kidney Dis 2016;67:40–8. 10.1053/j.ajkd.2015.07.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Leyssens K, Van Regenmortel N, Roelant Eet al. Beta-trace protein as a potential marker of acute kidney injury: a pilot study. Kidney Blood Press Res 2021;46:185–95. 10.1159/000514173 [DOI] [PubMed] [Google Scholar]
- 27. White CA, Ghazan-Shahi S, Adams MA. beta-trace protein: a marker of GFR and other biological pathways. Am J Kidney Dis 2015;65:131–46. 10.1053/j.ajkd.2014.06.038 [DOI] [PubMed] [Google Scholar]
- 28. Levey AS, Stevens LA, Schmid CHet al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12. 10.7326/0003-4819-150-9-200905050-00006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Gansevoort RT, Anders HJ, Cozzolino Met al. What should European nephrology do with the new CKD-EPI equation? Nephrol Dial Transplant 2023;38:1–6. 10.1093/ndt/gfac254 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Mahbub MH, Yamaguchi N, Takahashi Het al. Relationship of reduced glomerular filtration rate with alterations in plasma free amino acids and uric acid evaluated in healthy control and hypertensive subjects. Sci Rep 2019;9:10252. 10.1038/s41598-019-46598-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Meyrier A. Nephrosclerosis: update on a centenarian. Nephrol Dial Transplant 2015;30:1833–41. 10.1093/ndt/gfu366 [DOI] [PubMed] [Google Scholar]
- 32. Ning K, Wu Z, Zou Xet al. Immune checkpoint inhibitors further aggravate proteinuria in patients with metastatic renal cell carcinoma after long-term targeted therapy. Transl Androl Urol 2022;11:386–96. 10.21037/tau-21-1015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Xiong Y, Wang Q, Liu Yet al. Renal adverse reactions of tyrosine kinase inhibitors in the treatment of tumours: a Bayesian network meta-analysis. Front Pharmacol 2022;13:1023660. 10.3389/fphar.2022.1023660 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Kandula P, Agarwal R. Proteinuria and hypertension with tyrosine kinase inhibitors. Kidney Int 2011;80:1271–7. 10.1038/ki.2011.288 [DOI] [PubMed] [Google Scholar]
- 35. Hou J, Cheng Y, Hou Yet al. Lower serum and higher urine immunoglobulin G are associated with an increased severity of idiopathic membranous nephropathy. Ann Clin Lab Sci 2019;49:777–84. http://www.annclinlabsci.org/content/49/6/777.long [PubMed] [Google Scholar]
- 36. Tofik R, Aziz R, Reda Aet al. The value of IgG-uria in predicting renal failure in idiopathic glomerular diseases. A long-term follow-up study. Scand J Clin Lab Invest 2011;71:123–8. 10.3109/00365513.2010.542828 [DOI] [PubMed] [Google Scholar]
- 37. Rifkin DE, Katz R, Chonchol Met al. Blood pressure components and decline in kidney function in community-living older adults: the Cardiovascular Health Study. Am J Hypertens 2013;26:1037–44. 10.1093/ajh/hpt067 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Udani S, Lazich I, Bakris GL. Epidemiology of hypertensive kidney disease. Nat Rev Nephrol 2011;7:11–21. 10.1038/nrneph.2010.154 [DOI] [PubMed] [Google Scholar]
- 39. Levey AS, Inker LA. GFR as the “Gold Standard”: estimated, measured, and true. Am J Kidney Dis 2016;67:9–12. 10.1053/j.ajkd.2015.09.014 [DOI] [PubMed] [Google Scholar]
- 40. Vermassen T, Geboes K, De Man Met al. Neither creatinine- nor cystatin C-estimated glomerular filtration rate is optimal in oncology patients treated with targeted agents. Nephrol Dial Transplant 2018;33:402–8. 10.1093/ndt/gfx063 [DOI] [PubMed] [Google Scholar]
- 41. Omori K, Morikawa T, Kunita Aet al. Lipocalin-type prostaglandin D synthase-derived PGD(2) attenuates malignant properties of tumor endothelial cells. J Pathol 2018;244:84–96. 10.1002/path.4993 [DOI] [PubMed] [Google Scholar]
- 42. Shamsi A, Bano B. Journey of cystatins from being mere thiol protease inhibitors to at heart of many pathological conditions. Int J Biol Macromol 2017;102:674–93. 10.1016/j.ijbiomac.2017.04.071 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Siklos M, BenAissa M, Thatcher GR. Cysteine proteases as therapeutic targets: does selectivity matter? A systematic review of calpain and cathepsin inhibitors. Acta Pharm Sin B 2015;5:506–19. 10.1016/j.apsb.2015.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Dreilich M, Wagenius G, Bergstrom Set al. The role of cystatin C and the angiogenic cytokines VEGF and bFGF in patients with esophageal carcinoma. Med Oncol 2005;22:29–38. 10.1385/MO:22:1:029 [DOI] [PubMed] [Google Scholar]
- 45. Li Z, Wang S, Huo Xet al. Cystatin C expression is promoted by VEGFA blocking, with inhibitory effects on endothelial cell angiogenic functions including proliferation, migration, and chorioallantoic membrane angiogenesis. J Am Heart Assoc 2018;7:e009167. 10.1161/JAHA.118.009167 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Breznik B, Mitrovic A, TL Tet al. Cystatins in cancer progression: more than just cathepsin inhibitors. Biochimie 2019;166:233–50. 10.1016/j.biochi.2019.05.002 [DOI] [PubMed] [Google Scholar]
- 47. Laurent-Matha V, Huesgen PF, Masson Oet al. Proteolysis of cystatin C by cathepsin D in the breast cancer microenvironment. FASEB J 2012;26:5172–81. 10.1096/fj.12-205229 [DOI] [PubMed] [Google Scholar]
- 48. Speeckaert MM, Seegmiller J, Glorieux Get al. Measured glomerular filtration rate: the query for a workable golden standard technique. J Pers Med 2021;11:949. 10.3390/jpm11100949 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Delanaye P, Pieroni L, Abshoff Cet al. Analytical study of three cystatin C assays and their impact on cystatin C-based GFR-prediction equations. Clin Chim Acta 2008;398:118–24. 10.1016/j.cca.2008.09.001 [DOI] [PubMed] [Google Scholar]
- 50. Ebert N, Delanaye P, Shlipak Met al. Cystatin C standardization decreases assay variation and improves assessment of glomerular filtration rate. Clin Chim Acta 2016;456:115–21. 10.1016/j.cca.2016.03.002 [DOI] [PubMed] [Google Scholar]
- 51. Finney H, Newman DJ, Gruber Wet al. Initial evaluation of cystatin C measurement by particle-enhanced immunonephelometry on the Behring nephelometer systems (BNA, BN II). Clin Chem 1997;43:1016–22. 10.1093/clinchem/43.6.1016 [DOI] [PubMed] [Google Scholar]
- 52. Voskoboev NV, Larson TS, Rule ADet al. Analytic and clinical validation of a standardized cystatin C particle enhanced turbidimetric assay (PETIA) to estimate glomerular filtration rate. Clin Chem Lab Med 2012;50:1591–6. 10.1515/cclm-2012-0063 [DOI] [PubMed] [Google Scholar]
- 53. Levey AS, Coresh J, Tighiouart Het al. Measured and estimated glomerular filtration rate: current status and future directions. Nat Rev Nephrol 2020;16:51–64. 10.1038/s41581-019-0191-y [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated and analysed during this study cannot be shared publicly due to the European General Data Protection Regulation (EU GDPR). The data will be shared on reasonable request to the corresponding author, following approval of the data protection officer of the University Hospital Ghent.