Am J Physiol Renal Physiol 307: F1198 –F1206, 2014. First published September 18, 2014; doi:10.1152/ajprenal.00327.2014.
Endothelial dysfunction and oxidative stress in polycystic kidney disease Jelena Klawitter,1,2 Berenice Y. Reed-Gitomer,2 Kim McFann,2 Alexander Pennington,1 Jost Klawitter,1 Kaleab Z. Abebe,3 Jacek Klepacki,1 Melissa A. Cadnapaphornchai,2 Godela Brosnahan,2 Michel Chonchol,2 Uwe Christians,1 and Robert W. Schrier2 1
Department of Anesthesiology, University of Colorado, Aurora, Colorado; 2Division of Renal Diseases and Hypertension, University of Colorado, Aurora, Colorado; and 3Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania Submitted 16 June 2014; accepted in final form 16 September 2014
Klawitter J, Reed-Gitomer BY, McFann K, Pennington A, Klawitter J, Abebe KZ, Klepacki J, Cadnapaphornchai MA, Brosnahan G, Chonchol M, Christians U, Schrier RW. Endothelial dysfunction and oxidative stress in polycystic kidney disease. Am J Physiol Renal Physiol 307: F1198 –F1206, 2014. First published September 18, 2014; doi:10.1152/ajprenal.00327.2014.—Cardiovascular disease (CVD) is the leading cause of premature mortality in ADPKD patients. The aim was to identify potential serum biomarkers associated with the severity of ADPKD. Serum samples from a homogenous group of 61 HALT study A ADPKD patients [early disease group with estimated glomerular filtration rate (eGFR) ⬎60 ml·min⫺1·1.73 m⫺2] were compared with samples from 49 patients from the HALT study B group with moderately advanced disease (eGFR 25– 60 ml·min⫺1·1.73 m⫺2). Targeted tandem-mass spectrometry analysis of markers of endothelial dysfunction and oxidative stress was performed and correlated with eGFR and total kidney volume normalized to the body surface area (TKV/BSA). ADPKD patients with eGFR ⬎60 ml·min⫺1·1.73 m⫺2 showed higher levels of CVD risk markers asymmetric and symmetric dimethylarginine (ADMA and SDMA), homocysteine, and S-adenosylhomocysteine (SAH) compared with the healthy controls. Upon adjustments for age, sex, systolic blood pressure, and creatinine, SDMA, homocysteine, and SAH remained negatively correlated with eGFR. Resulting cellular methylation power [S-adenosylmethionine (SAM)/SAH ratio] correlated with the reduction of renal function and increase in TKV. Concentrations of prostaglandins (PGs), including oxidative stress marker 8-isoprostane, as well as PGF2␣, PGD2, and PGE2, were markedly elevated in patients with ADPKD compared with healthy controls. Upon adjustments for age, sex, systolic blood pressure, and creatinine, increased PGD2 and PGF2␣ were associated with reduced eGFR, whereas 8-isoprostane and again PGF2␣ were associated with an increase in TKV/BSA. Endothelial dysfunction and oxidative stress are evident early in ADPKD patients, even in those with preserved kidney function. The identified pathways may provide potential therapeutic targets for slowing down the disease progression. biomarkers; ADPKD; endothelial dysfunction; NO pathway; oxidative stress AUTOSOMAL DOMINANT POLYCYSTIC kidney disease (ADPKD) is the most common hereditary renal disease, affecting ⬃1 in 500 individuals (47). It is responsible for 4% of end-stage renal disease (ESRD) in the United States and 8 –10% in Europe (47). As of today, treatment options for ADPKD are very limited, and current therapies are aimed at slowing down cyst growth and progression to ESRD. One of the challenges in both trial design and clinical care is the absence of biomarkers
Address for reprint requests and other correspondence: J. Klawitter, iC42 Clinical Research and Development, Univ. of Colorado Denver, 1999 North Fitzsimons Parkway, Bioscience East, Suite 100, Aurora, CO 80045-7503 (e-mail: [email protected]). F1198
that are predictive of severity and disease progression, especially before significant impairment of renal function has occurred. Cardiovascular disease (CVD) is the leading cause of premature mortality in ADPKD patients. Hypertension occurs early and is related to the activation of the renin-angiotensinaldosterone system (RAAS), including its major components angiotensinogen, renin, and angiotensin II, vascular inflammation and chemotaxis, endothelial dysfunction, oxidative stress, and nitric oxide (NO) deficiency (6, 8, 27, 47, 57– 61). Because of the importance of hypertension in ADPKD and uncertainties surrounding its treatment, the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases funded two distinct multicenter double-blind randomized clinical trials, adequately powered to assess the effect of RAAS blockade on renal progression at early (study A) and late (study B) stages of the disease (Halt Progression of Polycystic Kidney Disease; HALT-PKD). Endothelial dysfunction has been shown to predate hypertension and chronic renal insufficiency in ADPKD and therefore appears to be a primary defect (58). However, as hypertension progresses, endothelial dysfunction increases (58). These defects even in ADPKD patients with preserved glomerular filtration rates (GFR) have been linked to markedly decreased constitutive NO synthase (NOS) activity in subcutaneous resistance vessels (57, 58). Furthermore, an abnormal sensory cilia function reported in PKD patients (1, 2) disrupts NO biosynthesis, in which primary cilia act as mechanical switches to initiate a NO biochemical reaction (2, 40). Therefore, it is not surprising that, within several population studies, plasma levels of the endogenous competitive NOS inhibitor asymmetric dimethylarginine (ADMA) (31, 54) were found increased in selected ADPKD patients with preserved GFR (52, 59, 60). Based on 144 patients from the HALT-PKD trial, Heffernan et al. (16) showed that patients with ADPKD have elevated plasma concentrations of vascular inflammatory markers (TNF-␣-dependent sFas, ICAM, P-selectin, and E-selectin) compared with healthy controls. These findings are in agreement with other published results (11, 18, 36). Interestingly, normotensive patients with ADPKD and preserved renal function had a heightened vascular inflammatory state as evidenced by higher levels of ICAM, P-selectin, E-selectin, and sFas compared with healthy controls (16). Additionally, ADPKD patients with renal dysfunction had a trend toward higher vascular inflammation than ADPKD patients with preserved renal function. Based on this knowledge, we aimed at identifying markers that are associated with the increase in total kidney volume
1931-857X/14 Copyright © 2014 the American Physiological Society
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BIOMARKERS OF ADPKD
and/or a worsening of kidney function in ADPKD patients. For identification of said biomarkers, we utilized established liquid chromatography- tandem mass spectrometry (LC-MS/MS) panels such as for the quantification of prostaglandins and intermediates of the NO pathway. Serum samples from a homogenous group of 61 HALT-ADPKD study A patients, which as aforementioned were considered an early disease group with eGFR ⬎60 ml·min⫺1·1.73 m⫺2 were compared with those from 49 HALT study B patients with moderately advanced disease as defined by a GFR of 25– 60 ml·min⫺1·1.73 m⫺2. All samples were drawn at the “baseline” collection time point. METHODS
Study Population The ongoing HALT-PKD ClinicalTrials.gov identifier NCT00283686 trial consists of two concurrent randomized clinical trials designed to evaluate the effects of RAAS suppression on the progression of ADPKD (9). Study A recruited patients with an eGFR of ⬎60 ml·min⫺1·1.73 m⫺2, and study B recruited patients with an eGFR of 25– 60 ml·min⫺1·1.73 m⫺2. Age limits were 15– 49 yr for study A and 18 – 64 yr for study B. Major exclusion criteria for both studies included renal vascular disease, diabetes, and history of severe heart failure. Here, we utilized baseline samples from 61 study A and 49 study B subjects collected at the University of Colorado, Anschutz Medical Campus site of the HALT-PKD trial. In ADPKD patients, GFR was estimated using the four-variable MDRD equation (30). A trained nurse assessed blood pressure (BP) using an automated oscillometric monitor (GE Healthcare, Waukesha, WI) following standard guidelines. Measurements were made in patients in a seated position after 5–10 min of quiet rest. In addition, ADPKD patients from the study A group underwent measurements of TKV. The HALT protocol at the University of Colorado, Anschutz Medical Campus site was approved by the Colorado Multi-Institutional Review Board (COMIRB; University of Colorado Denver). Patients gave their written informed consent. The study was conducted in full compliance with the ICH Harmonized Tripartite Guidelines for Good Clinical Practice (1996), the Declaration of Helsinki (version 11, October 2000), and all other applicable regulatory guidances. The patients underwent a 2-wk medication washout (as part of the HALT-PKD study design) before assessment of hemodynamic, vascular, and inflammation parameters. During this time, BP was controlled in hypertensive patients with labetalol or clonidine. All patients withheld antihypertensive medications for a minimum of 12 h before testing. Subjects were instructed to fast overnight and refrain from caffeine and alcohol intake as well as smoking on the day of testing. Serum samples from 18 healthy subjects were collected onsite (10 male, median age: 37 yr; and 8 female subjects, median age: 30 yr). The subjects were not on BP control medication and had no history of kidney or heart disease. The use of serum samples collected from healthy volunteers for biomarker assay calibration and quality control samples was COMIRB exempt. In addition to the difference of patients based on their eGFR (HALT study A: eGFR ⬎60 ml·min⫺1·1.73 m⫺2 and HALT study B subjects: eGFR 25– 60 ml·min⫺1·1.73 m⫺2), we also divided serum samples collected from HALT study A subjects into three groups: 15 (24.5%) with TKV of ⬍800 ml, 28 (46.0%) with 800 –1,500 ml, and 18 (29.5%) with TKV ⬎1,500 ml. These kidney volume cut-offs were based on the recommendations of the Consortium of Radiological Imaging Studies of Polycystic Kidney Disease (CRISP) (14).
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Measurement of Biomarkers Endothelial dysfunction markers. Endothelial dysfunction markers, including arginine (Arg), ADMA, and symmetric dimethylarginine (SDMA), cysteine (Cys), glutathione, homocysteine (Hcy), methionine (Met), S-adenosylhomocysteine (SAH), and S-adenosylmethionine (SAM), were quantified using a validated HPLC-MS/MS assay (for more detail, see Ref. 26). In brief, to 100 l serum 50 l of internal standard containing solution (50 M d7-ADMA, d4-cystine, d8-homocystine, d3-methionine and S-methylglutathione, all in HPLC water), and 40 l of 500 mM DTT solution were added. For protein precipitation, 400 l of 0.05% trifluoric acid plus 0.1% formic acid containing acetonitrile solution were added to the sample. The sample was vortexed for 5 min, centrifuged for 10 min at 13,000 g, and transferred into a HPLC vial. Twenty microliters of the supernatant were injected onto a 4.6 ⫻ 12.5-mm guard column (Eclipse XDB-C8, 5 m, Agilent Technologies, Palo Alto, CA) inline with a 3.0 ⫻ 150-mm analytical column (RP-Amide, 3.5 m, Supelco, St. Louis, MO). The gradient started at 3% methanol and 97% 10 mM ammonium formate buffer, and a flow of 0.8 ml/min was maintained throughout the assay. At minute 4.5, the solvent gradient reached 25% methanol. Thereafter, the methanol content was raised to 98% and held for an additional 1.5 min. Then, the columns were reequilibrated to the starting conditions for the remaining 2 min of the assay. The API5000 mass spectrometer (AB Sciex, Concord, ON) was run in the positive electrospray ionization mode (ESI) using multiple reaction monitoring (MRM). The following ion transitions (m/z, mass/charge) were used: Arg: 175.2 ¡ 70.1; ADMA: 203.2 ¡ 46.2; SDMA: 203.2 ¡ 172.2; d7-ADMA (internal standard): 210.2 ¡ 77.2; cysteine: 122.0 ¡ 75.9; d2-cysteine (internal standard): 124.0 ¡ 77.9; Hcy: 136.1 ¡ 90.1; d4-Hcy (internal standard): 140.1 ¡ 94.1; Met: 150.1 ¡ 104.0; d3-Met (internal standard): 153.1 ¡ 107.0; glutathione: 308.0 ¡ 179.0; S-methylglutathione: 322.0 ¡ 176.0; SAM: 399.0 ¡ 250.1; d3-SAM (internal standard): 402.0 ¡ 136.2; SAH: 385.0 ¡ 136.2; and d5-SAH (internal standard): 391.0 ¡ 137.2. During the prestudy assay validation, intraday accuracies and precisions were within the predefined acceptance criteria of ⫾15%. Furthermore, the following lower limits of quantitation (LLOQ) were established: Arg: 7.5 M, ADMA and SDMA: 0.3 M, cysteine: 20 M, HCy: 4 M, Met: 6.3 M, and glutathione: 2.5 M. Prostaglandins. Protaglandins were extracted from 400 l plasma following protein precipitation with 400 l methanol/0.2 M ZnSO4 (70/30 vol/vol) as previously described (25). Automated inline extraction was used for sample analysis. Five hundred microliters of the extracted sample were injected onto a 4.6 ⫻ 12.5-mm Zorbax XDB-C8 guard/extraction column (Agilent Technologies). The flow rate during the injection was 500 l/min and was increased to 3,000 l/min within 1 min. The composition of solvents was 80% 0.1% formic acid and 20% methanol. After sample cleanup for 1 min, the switching valve was activated and the analytes were back-flushed onto the Phenomenex Synergi Hydro-RP 80Å, 3.0 ⫻ 250-mm, 4-m column (Phenomenex, Torrance, CA). We used methanol and 0.1% formic acid as mobile phases, and the flow rate was set to 0.6 ml/min. The following gradient was run: 0 –1 min 63% methanol; 1.1–10 min 63%-98%. The analytic column was kept at 98% methanol for 1 min and then reequilibrated to the starting conditions. The HPLC system was interfaced with an API5000 tandem mass spectrometer via a Turbo V ion source (AB Sciex) operated in the negative ESI mode. The following ion transitions (MRM mode) were monitored for the study compounds: m/z 353 ¡ 193 for 8-isoprostane and PGF2␣ and m/z 351 ¡ 189 for PGD2 and PGE2. Corresponding deuterated internal standards (Cayman Chemical, Ann Arbor, MI) were used for each of the analytes. Intraday accuracies (⫾15%) and precisions (ⱕ15%) met the predefined acceptance criteria. The LLOQ was 20 pg/ml for all prostaglandins.
AJP-Renal Physiol • doi:10.1152/ajprenal.00327.2014 • www.ajprenal.org
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BIOMARKERS OF ADPKD
Table 1. Baseline characteristics of the study groups based on their eGFR eGFR ⬎60 ml·min⫺1·1.73 m⫺2 HALT-A
Age, yr Male, % Systolic BP, mmHg Diastolic BP, mmHg eGFR, ml·min⫺1· 1.73 m⫺2 Urine creatinine, mg/ml Serum creatinine, mg/dl Urine microalbumin, mg/day BSA, m2 LV mass, g LVMI, g/m2 TKV, ml TKV/BSA, ml/m2
eGFR 25–60 ml·min⫺1·1.73 m⫺2 HALT-B
37.0 ⫾ 7.8 60.7 127.5 ⫾ 13.0 74.5 ⫾ 13.2
47.1 ⫾ 8.0† 50.0 132.0 ⫾ 12.8 78.0 ⫾ 9.6
82.4 ⫾ 17.7
48.2 ⫾ 8.4†
0.7 ⫾ 0.4
0.6 ⫾ 0.3*
1.0 ⫾ 0.2
1.5 ⫾ 0.3†
41.1 ⫾ 93.0 2.0 ⫾ 0.3 127.2 ⫾ 29.7 64.0 ⫾ 13.5 1,365 ⫾ 702 674 ⫾ 333
53.1 ⫾ 69.3 (n ⫽ 36) 2.0 ⫾ 0.2 ND ND ND ND
Values are means ⫾ SD. eGFR, estimated glomerular filtration rate; HALT, Halt Progression of Polycystic Kidney Disease study (HALT-A, early disease group; HALT-B, moderately advanced disease group); BP, blood pressure; BSA, body surface area; LV, left ventricular; TKV, total kidney volume; ND, not determined. Samples from 18 healthy subjects were utilized for the study (10 males and 8 females; age 33.7 ⫾ 9.2 yr, P ⬍ 0.001 vs. HALT-B). Significance levels: *P ⬍ 0.05, †P ⬍ 0.001 for HALT-A (eGFR ⬎60 ml·min⫺1·1.73 m⫺2) vs. HALT-B (eGFR 25-60 ml·min⫺1·1.73 m⫺2 patients.
Linear Regression Analysis of Serum Biomarkers with eGFR and TKV Normalized to BSA In this cross-sectional study, correlations between the evaluated markers and TKV and eGFR were performed and those that were identified as significantly different are presented below. In unadjusted analyses of the ADPKD eGFR ⬎60 and eGFR 25– 60 ml·min⫺1·1.73 m⫺2 patient groups, linear regression of eGFR on serum markers of endothelial dysfunction and oxidative stress yielded statistically significant associations with ADMA, Arg, Cys, glutathione, HCy, Met, SAH, the SAM/SAH ratio, SDMA, PGD2, PGE2, PGF2␣, and 8-isoprostane (Table 2). Adjustments for age, sex, systolic BP, and ln-transformed serum creatinine reduced the number of significant correlations between eGFR to the following markers: Table 2. Regression analysis of eGFR and serum markers of endothelial dysfunction and oxidative stress/inflammation Variable
ADMA Arginine Cysteine
Statistical Analyses
Glutathione
Descriptive statistics were calculated for each of the study groups (healthy controls, ADPKD eGFR ⬎60 ml·min⫺1·1.73 m⫺2 and ADPKD eGFR 25– 60 ml·min⫺1·1.73 m⫺2), including percentages for categorical data, means ⫾ SD for continuous variables. All of the biomarkers were ln-transformed for analyses since the distributions were skewed. Linear regression was used to evaluate the relationship of each biomarker with eGFR and TKV normalized to the BSA across study A and B samples. Each marker was analyzed using three distinct models: 1) unadjusted, 2) adjusted for age and sex, and 3) adjusted for age, sex, systolic BP, and (ln-transformed) serum creatinine. An additional model was used to evaluate the relationship of biomarkers with TKV normalized to BSA, namely 4) adjusted for age, sex, systolic BP, and (lntransformed) eGFR. To compare differences in biomarker levels between ADPKD patient study groups, t-tests were used. For comparison between healthy subjects and both ADPKD patient study groups, one-way ANOVA with Tukey’s post hoc testing was used. SPSS (version 21.0, Chicago, IL) and SAS 9.3 (Cary, NC) were used for the analyses. An adjusted P value ⬍0.05 was considered significant for the comparison of groups.
Homocysteine Methionine PGD2 PGE2 PGF2␣ 8-Isoprostane SAH SAM
RESULTS
Baseline Characteristics of the Study Groups The patients were eligible to participate in study A or study B based on their eGFR (9, 53). The patient characteristics are summarized in Table 1. As aforementioned, patients with eGFR ⬎60 ml·min⫺1·1.73 ⫺2 m were further classified into the following subgroups based on their TKV: 15 (24.5%) with TKV of ⬍800 ml, 28 (46.0%) with 800 –1,500 ml, and 18 (29.5%) with TKV ⬎1,500 ml (Table 1).
SAM/SAH ratio SDMA
Model
Estimate ⫾ SE
P Value
Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted
⫺0.36 ⫾ 0.15 ⫺0.24 ⫾ 0.13 ⫺0.23 ⫾ 0.13 ⫺0.20 ⫾ 0.09 ⫺0.21 ⫾ 0.08 ⫺0.21 ⫾ 0.08 ⫺0.29 ⫾ 0.11 ⫺0.13 ⫾ 0.10 ⫺0.13 ⫾ 0.10 ⫺0.10 ⫾ 0.03 ⫺0.05 ⫾ 0.03 ⫺0.05 ⫾ 0.03 ⫺0.60 ⫾ 0.09 ⫺0.41 ⫾ 0.08 ⫺0.42 ⫾ 0.08 ⫺0.43 ⫾ 0.04 ⫺0.34 ⫾ 0.04 ⫺0.34 ⫾ 0.04 ⫺0.16 ⫾ 0.02 ⫺0.11 ⫾ 0.02 ⫺0.11 ⫾ 0.02 ⫺0.10 ⫾ 0.04 ⫺0.04 ⫾ 0.04 ⫺0.04 ⫾ 0.04 ⫺0.14 ⫾ 0.03 ⫺0.08 ⫾ 0.03 ⫺0.08 ⫾ 0.03 ⫺0.10 ⫾ 0.04 ⫺0.06 ⫾ 0.03 ⫺0.06 ⫾ 0.03 ⫺0.42 ⫾ 0.03 ⫺0.36 ⫾ 0.03 ⫺0.36 ⫾ 0.03 ⫺0.06 ⫾ 0.08 ⫺0.11 ⫾ 0.06 ⫺0.11 ⫾ 0.06 0.19 ⫾ 0.02
0.0226 0.0678 0.0744 0.0381 0.0069 0.0081 0.0104 0.1867 0.1977 0.0021 0.0585 0.0512
Endothelial dysfunction and oxidative stress in polycystic kidney disease Jelena Klawitter,1,2 Berenice Y. Reed-Gitomer,2 Kim McFann,2 Alexander Pennington,1 Jost Klawitter,1 Kaleab Z. Abebe,3 Jacek Klepacki,1 Melissa A. Cadnapaphornchai,2 Godela Brosnahan,2 Michel Chonchol,2 Uwe Christians,1 and Robert W. Schrier2 1
Department of Anesthesiology, University of Colorado, Aurora, Colorado; 2Division of Renal Diseases and Hypertension, University of Colorado, Aurora, Colorado; and 3Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania Submitted 16 June 2014; accepted in final form 16 September 2014
Klawitter J, Reed-Gitomer BY, McFann K, Pennington A, Klawitter J, Abebe KZ, Klepacki J, Cadnapaphornchai MA, Brosnahan G, Chonchol M, Christians U, Schrier RW. Endothelial dysfunction and oxidative stress in polycystic kidney disease. Am J Physiol Renal Physiol 307: F1198 –F1206, 2014. First published September 18, 2014; doi:10.1152/ajprenal.00327.2014.—Cardiovascular disease (CVD) is the leading cause of premature mortality in ADPKD patients. The aim was to identify potential serum biomarkers associated with the severity of ADPKD. Serum samples from a homogenous group of 61 HALT study A ADPKD patients [early disease group with estimated glomerular filtration rate (eGFR) ⬎60 ml·min⫺1·1.73 m⫺2] were compared with samples from 49 patients from the HALT study B group with moderately advanced disease (eGFR 25– 60 ml·min⫺1·1.73 m⫺2). Targeted tandem-mass spectrometry analysis of markers of endothelial dysfunction and oxidative stress was performed and correlated with eGFR and total kidney volume normalized to the body surface area (TKV/BSA). ADPKD patients with eGFR ⬎60 ml·min⫺1·1.73 m⫺2 showed higher levels of CVD risk markers asymmetric and symmetric dimethylarginine (ADMA and SDMA), homocysteine, and S-adenosylhomocysteine (SAH) compared with the healthy controls. Upon adjustments for age, sex, systolic blood pressure, and creatinine, SDMA, homocysteine, and SAH remained negatively correlated with eGFR. Resulting cellular methylation power [S-adenosylmethionine (SAM)/SAH ratio] correlated with the reduction of renal function and increase in TKV. Concentrations of prostaglandins (PGs), including oxidative stress marker 8-isoprostane, as well as PGF2␣, PGD2, and PGE2, were markedly elevated in patients with ADPKD compared with healthy controls. Upon adjustments for age, sex, systolic blood pressure, and creatinine, increased PGD2 and PGF2␣ were associated with reduced eGFR, whereas 8-isoprostane and again PGF2␣ were associated with an increase in TKV/BSA. Endothelial dysfunction and oxidative stress are evident early in ADPKD patients, even in those with preserved kidney function. The identified pathways may provide potential therapeutic targets for slowing down the disease progression. biomarkers; ADPKD; endothelial dysfunction; NO pathway; oxidative stress AUTOSOMAL DOMINANT POLYCYSTIC kidney disease (ADPKD) is the most common hereditary renal disease, affecting ⬃1 in 500 individuals (47). It is responsible for 4% of end-stage renal disease (ESRD) in the United States and 8 –10% in Europe (47). As of today, treatment options for ADPKD are very limited, and current therapies are aimed at slowing down cyst growth and progression to ESRD. One of the challenges in both trial design and clinical care is the absence of biomarkers
Address for reprint requests and other correspondence: J. Klawitter, iC42 Clinical Research and Development, Univ. of Colorado Denver, 1999 North Fitzsimons Parkway, Bioscience East, Suite 100, Aurora, CO 80045-7503 (e-mail: [email protected]). F1198
that are predictive of severity and disease progression, especially before significant impairment of renal function has occurred. Cardiovascular disease (CVD) is the leading cause of premature mortality in ADPKD patients. Hypertension occurs early and is related to the activation of the renin-angiotensinaldosterone system (RAAS), including its major components angiotensinogen, renin, and angiotensin II, vascular inflammation and chemotaxis, endothelial dysfunction, oxidative stress, and nitric oxide (NO) deficiency (6, 8, 27, 47, 57– 61). Because of the importance of hypertension in ADPKD and uncertainties surrounding its treatment, the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases funded two distinct multicenter double-blind randomized clinical trials, adequately powered to assess the effect of RAAS blockade on renal progression at early (study A) and late (study B) stages of the disease (Halt Progression of Polycystic Kidney Disease; HALT-PKD). Endothelial dysfunction has been shown to predate hypertension and chronic renal insufficiency in ADPKD and therefore appears to be a primary defect (58). However, as hypertension progresses, endothelial dysfunction increases (58). These defects even in ADPKD patients with preserved glomerular filtration rates (GFR) have been linked to markedly decreased constitutive NO synthase (NOS) activity in subcutaneous resistance vessels (57, 58). Furthermore, an abnormal sensory cilia function reported in PKD patients (1, 2) disrupts NO biosynthesis, in which primary cilia act as mechanical switches to initiate a NO biochemical reaction (2, 40). Therefore, it is not surprising that, within several population studies, plasma levels of the endogenous competitive NOS inhibitor asymmetric dimethylarginine (ADMA) (31, 54) were found increased in selected ADPKD patients with preserved GFR (52, 59, 60). Based on 144 patients from the HALT-PKD trial, Heffernan et al. (16) showed that patients with ADPKD have elevated plasma concentrations of vascular inflammatory markers (TNF-␣-dependent sFas, ICAM, P-selectin, and E-selectin) compared with healthy controls. These findings are in agreement with other published results (11, 18, 36). Interestingly, normotensive patients with ADPKD and preserved renal function had a heightened vascular inflammatory state as evidenced by higher levels of ICAM, P-selectin, E-selectin, and sFas compared with healthy controls (16). Additionally, ADPKD patients with renal dysfunction had a trend toward higher vascular inflammation than ADPKD patients with preserved renal function. Based on this knowledge, we aimed at identifying markers that are associated with the increase in total kidney volume
1931-857X/14 Copyright © 2014 the American Physiological Society
http://www.ajprenal.org
BIOMARKERS OF ADPKD
and/or a worsening of kidney function in ADPKD patients. For identification of said biomarkers, we utilized established liquid chromatography- tandem mass spectrometry (LC-MS/MS) panels such as for the quantification of prostaglandins and intermediates of the NO pathway. Serum samples from a homogenous group of 61 HALT-ADPKD study A patients, which as aforementioned were considered an early disease group with eGFR ⬎60 ml·min⫺1·1.73 m⫺2 were compared with those from 49 HALT study B patients with moderately advanced disease as defined by a GFR of 25– 60 ml·min⫺1·1.73 m⫺2. All samples were drawn at the “baseline” collection time point. METHODS
Study Population The ongoing HALT-PKD ClinicalTrials.gov identifier NCT00283686 trial consists of two concurrent randomized clinical trials designed to evaluate the effects of RAAS suppression on the progression of ADPKD (9). Study A recruited patients with an eGFR of ⬎60 ml·min⫺1·1.73 m⫺2, and study B recruited patients with an eGFR of 25– 60 ml·min⫺1·1.73 m⫺2. Age limits were 15– 49 yr for study A and 18 – 64 yr for study B. Major exclusion criteria for both studies included renal vascular disease, diabetes, and history of severe heart failure. Here, we utilized baseline samples from 61 study A and 49 study B subjects collected at the University of Colorado, Anschutz Medical Campus site of the HALT-PKD trial. In ADPKD patients, GFR was estimated using the four-variable MDRD equation (30). A trained nurse assessed blood pressure (BP) using an automated oscillometric monitor (GE Healthcare, Waukesha, WI) following standard guidelines. Measurements were made in patients in a seated position after 5–10 min of quiet rest. In addition, ADPKD patients from the study A group underwent measurements of TKV. The HALT protocol at the University of Colorado, Anschutz Medical Campus site was approved by the Colorado Multi-Institutional Review Board (COMIRB; University of Colorado Denver). Patients gave their written informed consent. The study was conducted in full compliance with the ICH Harmonized Tripartite Guidelines for Good Clinical Practice (1996), the Declaration of Helsinki (version 11, October 2000), and all other applicable regulatory guidances. The patients underwent a 2-wk medication washout (as part of the HALT-PKD study design) before assessment of hemodynamic, vascular, and inflammation parameters. During this time, BP was controlled in hypertensive patients with labetalol or clonidine. All patients withheld antihypertensive medications for a minimum of 12 h before testing. Subjects were instructed to fast overnight and refrain from caffeine and alcohol intake as well as smoking on the day of testing. Serum samples from 18 healthy subjects were collected onsite (10 male, median age: 37 yr; and 8 female subjects, median age: 30 yr). The subjects were not on BP control medication and had no history of kidney or heart disease. The use of serum samples collected from healthy volunteers for biomarker assay calibration and quality control samples was COMIRB exempt. In addition to the difference of patients based on their eGFR (HALT study A: eGFR ⬎60 ml·min⫺1·1.73 m⫺2 and HALT study B subjects: eGFR 25– 60 ml·min⫺1·1.73 m⫺2), we also divided serum samples collected from HALT study A subjects into three groups: 15 (24.5%) with TKV of ⬍800 ml, 28 (46.0%) with 800 –1,500 ml, and 18 (29.5%) with TKV ⬎1,500 ml. These kidney volume cut-offs were based on the recommendations of the Consortium of Radiological Imaging Studies of Polycystic Kidney Disease (CRISP) (14).
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Measurement of Biomarkers Endothelial dysfunction markers. Endothelial dysfunction markers, including arginine (Arg), ADMA, and symmetric dimethylarginine (SDMA), cysteine (Cys), glutathione, homocysteine (Hcy), methionine (Met), S-adenosylhomocysteine (SAH), and S-adenosylmethionine (SAM), were quantified using a validated HPLC-MS/MS assay (for more detail, see Ref. 26). In brief, to 100 l serum 50 l of internal standard containing solution (50 M d7-ADMA, d4-cystine, d8-homocystine, d3-methionine and S-methylglutathione, all in HPLC water), and 40 l of 500 mM DTT solution were added. For protein precipitation, 400 l of 0.05% trifluoric acid plus 0.1% formic acid containing acetonitrile solution were added to the sample. The sample was vortexed for 5 min, centrifuged for 10 min at 13,000 g, and transferred into a HPLC vial. Twenty microliters of the supernatant were injected onto a 4.6 ⫻ 12.5-mm guard column (Eclipse XDB-C8, 5 m, Agilent Technologies, Palo Alto, CA) inline with a 3.0 ⫻ 150-mm analytical column (RP-Amide, 3.5 m, Supelco, St. Louis, MO). The gradient started at 3% methanol and 97% 10 mM ammonium formate buffer, and a flow of 0.8 ml/min was maintained throughout the assay. At minute 4.5, the solvent gradient reached 25% methanol. Thereafter, the methanol content was raised to 98% and held for an additional 1.5 min. Then, the columns were reequilibrated to the starting conditions for the remaining 2 min of the assay. The API5000 mass spectrometer (AB Sciex, Concord, ON) was run in the positive electrospray ionization mode (ESI) using multiple reaction monitoring (MRM). The following ion transitions (m/z, mass/charge) were used: Arg: 175.2 ¡ 70.1; ADMA: 203.2 ¡ 46.2; SDMA: 203.2 ¡ 172.2; d7-ADMA (internal standard): 210.2 ¡ 77.2; cysteine: 122.0 ¡ 75.9; d2-cysteine (internal standard): 124.0 ¡ 77.9; Hcy: 136.1 ¡ 90.1; d4-Hcy (internal standard): 140.1 ¡ 94.1; Met: 150.1 ¡ 104.0; d3-Met (internal standard): 153.1 ¡ 107.0; glutathione: 308.0 ¡ 179.0; S-methylglutathione: 322.0 ¡ 176.0; SAM: 399.0 ¡ 250.1; d3-SAM (internal standard): 402.0 ¡ 136.2; SAH: 385.0 ¡ 136.2; and d5-SAH (internal standard): 391.0 ¡ 137.2. During the prestudy assay validation, intraday accuracies and precisions were within the predefined acceptance criteria of ⫾15%. Furthermore, the following lower limits of quantitation (LLOQ) were established: Arg: 7.5 M, ADMA and SDMA: 0.3 M, cysteine: 20 M, HCy: 4 M, Met: 6.3 M, and glutathione: 2.5 M. Prostaglandins. Protaglandins were extracted from 400 l plasma following protein precipitation with 400 l methanol/0.2 M ZnSO4 (70/30 vol/vol) as previously described (25). Automated inline extraction was used for sample analysis. Five hundred microliters of the extracted sample were injected onto a 4.6 ⫻ 12.5-mm Zorbax XDB-C8 guard/extraction column (Agilent Technologies). The flow rate during the injection was 500 l/min and was increased to 3,000 l/min within 1 min. The composition of solvents was 80% 0.1% formic acid and 20% methanol. After sample cleanup for 1 min, the switching valve was activated and the analytes were back-flushed onto the Phenomenex Synergi Hydro-RP 80Å, 3.0 ⫻ 250-mm, 4-m column (Phenomenex, Torrance, CA). We used methanol and 0.1% formic acid as mobile phases, and the flow rate was set to 0.6 ml/min. The following gradient was run: 0 –1 min 63% methanol; 1.1–10 min 63%-98%. The analytic column was kept at 98% methanol for 1 min and then reequilibrated to the starting conditions. The HPLC system was interfaced with an API5000 tandem mass spectrometer via a Turbo V ion source (AB Sciex) operated in the negative ESI mode. The following ion transitions (MRM mode) were monitored for the study compounds: m/z 353 ¡ 193 for 8-isoprostane and PGF2␣ and m/z 351 ¡ 189 for PGD2 and PGE2. Corresponding deuterated internal standards (Cayman Chemical, Ann Arbor, MI) were used for each of the analytes. Intraday accuracies (⫾15%) and precisions (ⱕ15%) met the predefined acceptance criteria. The LLOQ was 20 pg/ml for all prostaglandins.
AJP-Renal Physiol • doi:10.1152/ajprenal.00327.2014 • www.ajprenal.org
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BIOMARKERS OF ADPKD
Table 1. Baseline characteristics of the study groups based on their eGFR eGFR ⬎60 ml·min⫺1·1.73 m⫺2 HALT-A
Age, yr Male, % Systolic BP, mmHg Diastolic BP, mmHg eGFR, ml·min⫺1· 1.73 m⫺2 Urine creatinine, mg/ml Serum creatinine, mg/dl Urine microalbumin, mg/day BSA, m2 LV mass, g LVMI, g/m2 TKV, ml TKV/BSA, ml/m2
eGFR 25–60 ml·min⫺1·1.73 m⫺2 HALT-B
37.0 ⫾ 7.8 60.7 127.5 ⫾ 13.0 74.5 ⫾ 13.2
47.1 ⫾ 8.0† 50.0 132.0 ⫾ 12.8 78.0 ⫾ 9.6
82.4 ⫾ 17.7
48.2 ⫾ 8.4†
0.7 ⫾ 0.4
0.6 ⫾ 0.3*
1.0 ⫾ 0.2
1.5 ⫾ 0.3†
41.1 ⫾ 93.0 2.0 ⫾ 0.3 127.2 ⫾ 29.7 64.0 ⫾ 13.5 1,365 ⫾ 702 674 ⫾ 333
53.1 ⫾ 69.3 (n ⫽ 36) 2.0 ⫾ 0.2 ND ND ND ND
Values are means ⫾ SD. eGFR, estimated glomerular filtration rate; HALT, Halt Progression of Polycystic Kidney Disease study (HALT-A, early disease group; HALT-B, moderately advanced disease group); BP, blood pressure; BSA, body surface area; LV, left ventricular; TKV, total kidney volume; ND, not determined. Samples from 18 healthy subjects were utilized for the study (10 males and 8 females; age 33.7 ⫾ 9.2 yr, P ⬍ 0.001 vs. HALT-B). Significance levels: *P ⬍ 0.05, †P ⬍ 0.001 for HALT-A (eGFR ⬎60 ml·min⫺1·1.73 m⫺2) vs. HALT-B (eGFR 25-60 ml·min⫺1·1.73 m⫺2 patients.
Linear Regression Analysis of Serum Biomarkers with eGFR and TKV Normalized to BSA In this cross-sectional study, correlations between the evaluated markers and TKV and eGFR were performed and those that were identified as significantly different are presented below. In unadjusted analyses of the ADPKD eGFR ⬎60 and eGFR 25– 60 ml·min⫺1·1.73 m⫺2 patient groups, linear regression of eGFR on serum markers of endothelial dysfunction and oxidative stress yielded statistically significant associations with ADMA, Arg, Cys, glutathione, HCy, Met, SAH, the SAM/SAH ratio, SDMA, PGD2, PGE2, PGF2␣, and 8-isoprostane (Table 2). Adjustments for age, sex, systolic BP, and ln-transformed serum creatinine reduced the number of significant correlations between eGFR to the following markers: Table 2. Regression analysis of eGFR and serum markers of endothelial dysfunction and oxidative stress/inflammation Variable
ADMA Arginine Cysteine
Statistical Analyses
Glutathione
Descriptive statistics were calculated for each of the study groups (healthy controls, ADPKD eGFR ⬎60 ml·min⫺1·1.73 m⫺2 and ADPKD eGFR 25– 60 ml·min⫺1·1.73 m⫺2), including percentages for categorical data, means ⫾ SD for continuous variables. All of the biomarkers were ln-transformed for analyses since the distributions were skewed. Linear regression was used to evaluate the relationship of each biomarker with eGFR and TKV normalized to the BSA across study A and B samples. Each marker was analyzed using three distinct models: 1) unadjusted, 2) adjusted for age and sex, and 3) adjusted for age, sex, systolic BP, and (ln-transformed) serum creatinine. An additional model was used to evaluate the relationship of biomarkers with TKV normalized to BSA, namely 4) adjusted for age, sex, systolic BP, and (lntransformed) eGFR. To compare differences in biomarker levels between ADPKD patient study groups, t-tests were used. For comparison between healthy subjects and both ADPKD patient study groups, one-way ANOVA with Tukey’s post hoc testing was used. SPSS (version 21.0, Chicago, IL) and SAS 9.3 (Cary, NC) were used for the analyses. An adjusted P value ⬍0.05 was considered significant for the comparison of groups.
Homocysteine Methionine PGD2 PGE2 PGF2␣ 8-Isoprostane SAH SAM
RESULTS
Baseline Characteristics of the Study Groups The patients were eligible to participate in study A or study B based on their eGFR (9, 53). The patient characteristics are summarized in Table 1. As aforementioned, patients with eGFR ⬎60 ml·min⫺1·1.73 ⫺2 m were further classified into the following subgroups based on their TKV: 15 (24.5%) with TKV of ⬍800 ml, 28 (46.0%) with 800 –1,500 ml, and 18 (29.5%) with TKV ⬎1,500 ml (Table 1).
SAM/SAH ratio SDMA
Model
Estimate ⫾ SE
P Value
Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2 Unadjusted
⫺0.36 ⫾ 0.15 ⫺0.24 ⫾ 0.13 ⫺0.23 ⫾ 0.13 ⫺0.20 ⫾ 0.09 ⫺0.21 ⫾ 0.08 ⫺0.21 ⫾ 0.08 ⫺0.29 ⫾ 0.11 ⫺0.13 ⫾ 0.10 ⫺0.13 ⫾ 0.10 ⫺0.10 ⫾ 0.03 ⫺0.05 ⫾ 0.03 ⫺0.05 ⫾ 0.03 ⫺0.60 ⫾ 0.09 ⫺0.41 ⫾ 0.08 ⫺0.42 ⫾ 0.08 ⫺0.43 ⫾ 0.04 ⫺0.34 ⫾ 0.04 ⫺0.34 ⫾ 0.04 ⫺0.16 ⫾ 0.02 ⫺0.11 ⫾ 0.02 ⫺0.11 ⫾ 0.02 ⫺0.10 ⫾ 0.04 ⫺0.04 ⫾ 0.04 ⫺0.04 ⫾ 0.04 ⫺0.14 ⫾ 0.03 ⫺0.08 ⫾ 0.03 ⫺0.08 ⫾ 0.03 ⫺0.10 ⫾ 0.04 ⫺0.06 ⫾ 0.03 ⫺0.06 ⫾ 0.03 ⫺0.42 ⫾ 0.03 ⫺0.36 ⫾ 0.03 ⫺0.36 ⫾ 0.03 ⫺0.06 ⫾ 0.08 ⫺0.11 ⫾ 0.06 ⫺0.11 ⫾ 0.06 0.19 ⫾ 0.02
0.0226 0.0678 0.0744 0.0381 0.0069 0.0081 0.0104 0.1867 0.1977 0.0021 0.0585 0.0512
