University of Groningen

Predictive properties and therapeutical use of gasotransmittersFrenay, Anne-Roos Sophie

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5Reduced urinary NOx, but not nitroso

species, excretion is associated with

increased mortality and graft failure

in renal transplant recipients

AS FrenayE van den BergBO Fernandez

MM DekkerLE den Boef

G NavisROB GansSJL Bakker

M Feelisch*H van Goor*

* These authors share a senior authorship

Chapter 5

In preparation

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AbstractGraft failure and mortality are prevalent in renal transplant recipients (RTR). A reduced production of nitric oxide (NO) may contribute to these adverse events. We therefore investigated potential associations of urinary excretion of the NO metabolites nitrite and nitrate (NOx) and formation of nitroso species (RXNO), with cardiovascular parameters and patient/graft survival in RTR. NOx and RXNO concentrations were determined in 24h urine samples of 702 RTR (age 53 ± 13 yrs, 57% male, median time since transplantation 5.4 [1.9-12.0] yrs) and 107 healthy controls using Griess assay, HPLC and gas-phase chemiluminescence. Information on dietary habits, health and drug use was obtained, and standard hemodynamics and renal function measured. Associations of NOx with all-cause mortality and graft failure were assessed using Cox regression analyses. Urinary NOx levels were lower in RTR than controls (679 [389-1053] vs. 1204 [879-2014] µmol/24h, P < 0.001), while vegetable intake was similar. In multivariate regression analysis, smoking, BSA, serum triglycerides, urinary uric acid and the use of calcineurin inhibitors and sirolimus were the strongest associates of NOx (model R2=0.27). After adjustment for these factors, NOx was inversely associated with systolic blood pressure, diastolic blood pressure, hsCRP, NT-pro-BNP and HbA1c. During a follow-up of 3.1 [2.7 – 3.9] yrs, 83 patients died and 45 developed graft failure. NOx was inversely associated with all-cause mortality (HR 0.59 [95% CI 0.59 – 0.84], P < 0.001) and graft failure (HR 0.59 [0.48 – 0.73], P = 0.001). RXNO excretion was associated with nitrite (R2=0.052, st. β: 0.137, P = 0.001), but not nitrate excretion. Urinary RXNO was significantly associated with SBP, DBP and albuminuria, but did neither predict graft failure nor mortality. In conclusion, reduced urinary NOx, reflecting lower NO production, is associated with adverse cardiovascular parameters as well as a higher risk of graft failure and all-cause mortality in RTR.

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Introduction Kidney transplantation is the preferred treatment option for patients with end-stage renal disease (ESRD).1 Although kidney transplantation reduces mortality risk2 and improves quality of life for patients that were on dialysis before3, patient and graft survival remain a major problem. Particularly cardiovascular complications, such as hypertension and atherosclerosis, lead to graft loss or premature death.4,5 Moreover, the decline of graft function requires re-transplantation or return to dialysis sooner or later.6-8

Nitric oxide (NO) is a pluripotent cell signalling and effector molecule involved in the control and regulation of an enormous variety of physiological processes. NO not only plays a critical role in cardiovascular homeostasis but also modulates fundamental processes spanning from cell proliferation over substrate utilisation to energy production across all major organ systems.9-10 In the kidney, NO plays an important role in renal and glomerular hemodynamics, promoting natriuresis and diuresis.11 Three isoforms of NO have been identified: inducible NOS (iNOS), neuronal NOS (nNOS) and endothelial NOS (eNOS).12 eNOS is the major enzyme in the vasculature that produces NO, and inhibition of its activity leads to both glomerular and systemic hypertension.13 In human renal tissue after kidney transplantation, we measured the urinary end-products of NO, nitrite (NO

2-) and nitrate (NO

3-), and examined the expression patterns of iNOS and

eNOS.14,15 A role for NO in both acute and chronic transplant rejection was suggested, with lower levels of urinary nitrite and nitrate excretion during acute rejection and increased iNOS versus decreased glomerular eNOS expression levels in chronic renal transplant failure. This is in accordance with other pathophysiological conditions such as hypercholesterolemia, atherosclerosis and hypertension where impaired eNOS activity and endothelial dysfunction are observed.16-18

While many of NO’s actions are mediated via the classical cyclic GMP/PKG pathway, a not insignificant part appears to be mediated in a cGMP-independent manner through the formation of nitroso species, a process by which an NO moiety is covalently incorporated into another biomolecule. This may occur by either post-translational modification of sulfhydryl or amino moieties in proteins or via formation of low-molecular weight nitrosothiols and nitrosamines. Potential roles of such nitrosative modifications have been described in the regulation of insulin signaling, mitochondrial energy metabolism, mRNA transcription, stress signaling, and endoplasmic reticulum function.19 A considerable amount of mechanistic detail has been unveiled in recent years about the regulatory role of these species at the cellular and sub-cellular level, but far less is known about their global regulation in vivo. While a circulating pool of nitrosated products has been demonstrated to exist in healthy humans20,21 we know little about the dynamics of their formation and excretion. To the best of our knowledge, the occurrence and possible role of urinary nitroso species in patient cohorts has not been investigated.

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In the present study, we measured urinary nitrite and nitrate (measured individually, but expressed as sum of NO

2-+NO

3- = NOx) as well as total nitroso species

(RXNO) excretion, reflecting NO production and metabolism, in stable RTR and healthy controls. To quantify the former we used the commonly employed Griess reaction and compared the results to those obtained using the ‘gold standard’, HPLC. We subsequently investigated whether levels of urinary NOx and nitroso species were associated with cardiovascular parameters, all-cause mortality and graft failure.

Methods

Study population

The study protocol was approved by the Institutional Review Board (METc RUG, Groningen, the Netherlands: 2008/186), in adherence to the Declaration of Helsinki. All stable RTR with a functional graft for at least one year who visited our outpatient department between 2008 and 2010 were invited to participate. All transplant procedures were performed in the University Medical Center Groningen, the Netherlands, and no RTR had any history of alcohol or drug addiction. In total, 707 RTR signed written informed consent (87% of invited). NOx was measured in 702 RTR (99%). For analyses regarding dietary intake, we excluded all patients with missing dietary data, resulting in 637 RTR. As a healthy reference group we included 107 subjects who were evaluated for living kidney donation in our center during the same period.

Urine collection and rationale for the use of two distinct methods for quantification of nitrite/nitrate excretion

All participants were instructed to collect urine over a 24h period. In advance of the urine collection, chlorhexidine was added to the urine container as an antiseptic agent in order to prevent bacterial growth and artificially increased amounts of nitrite due to nitrate reduction.22 Samples were aliquoted and kept frozen at -80° C until analysis. Measurement of combined urinary nitrite and nitrate (NOx) excretion is widely used as a marker of NO production.23,24 Despite the fact that the popular Griess assay is the most commonly used method for NOx determination worldwide, the specificity and accuracy of this method is not without problems.25,26 We sought to address these issues by comparing our results obtained by the Griess method with a highly sensitive and specific HPLC technique for simultaneous individual measurement of nitrite and nitrate.

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Griess reaction for the determination of nitrite and nitrate

Urinary NOx excretion applying the classical Griess reaction was measured in 24-hour urine samples by sequential processing to determine nitrite and the sum of nitrite and nitrate. To determine nitrite, urinary samples (100 μl) were diluted fourfold with distilled water and deproteinized by addition of 20μl zinc sulphate (30%; Sigma Aldrich, St. Louis, MO, USA), followed by centrifugation at maximal speed (Eppendorf Centrifuge 5417R) for 5 min at RT. The supernatant (100 μl) was added in triplicate to the wells of a microtiter plate. Two wells were treated with 100 μl Griess reagent (Sulfanilamide (10 g/L; Sigma Aldrich), N-(1-naphthyl)-ethylenediamine dihydrochloride (1 g/L; Sigma Aldrich) and phosphoric acid (21.25 g/L; Sigma Aldrich) in distilled water). The third well was treated with 100 μl phosphoric acid (21.25g/L; Sigma Aldrich) only and served to obtain an individual background reading for each sample. After 5-10 minutes, the absorbance at 540 nm was measured using a microplate reader (EL808, Ultra microplate reader, Bio-Tek Instruments, Winooski, VT, USA). The mean of sample absorbance, measured in duplicate, was corrected for background reading and dilution factor and compared to the absorbance values of identically treated nitrite standards in distilled water (linear range between 0 and 100 μmol/L) to calculate final sample concentrations.

To measure nitrate by the Griess reaction, prior enzymatic reduction to nitrite is needed. Thereafter, the sum of nitrite and nitrate is determined. By subtracting the concentrations of nitrite as determined before reduction from the sum of nitrite and nitrate following the reduction protocol, the concentrations of nitrate in the sample are obtained. Once daily, potassium nitrate (1mM) was included to check conversion efficiency of the enzymatic process. Urinary samples (100 μl) were diluted fourfold with distilled water. By adding 8 μl Aspergillus nitrate reductase (10 U/mL; Roche, Basel, Switzerland), 1μl of NADPH (100mM; Roche) and 1μl FAD (2mM; Sigma Aldrich) (all three-fold diluted in 20 mM Tris/HCl pH 7.4) to the sample, nitrate was enzymatically converted to nitrite during an incubation period of 30 min at 37° C in the dark. To oxidize remaining NADPH, which interferes with the assay, 40μl lactase dehydrogenase (100 mM; Roche) and 2μl sodium pyruvate (5 g/L; Sigma Aldrich) were added at the end of the reduction process, with further incubation for 5 min at 37° C. Sample deproteinization and absorbance measurement was as described for nitrite above. The calibration curves for nitrite and nitrate standards were linear between 1 and 100 µM, and detection limits for nitrite and nitrate were 0.5 and 1 µM, respectively.

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HPLC method for the determination of nitrite and nitrate

The HPLC method used employs ion chromatography with on-line reduction of nitrate to nitrite and subsequent post-column derivatization with the Griess reagent using a dedicated analysis system (ENO-20, Eicom, Kyoto, Japan).27 Samples were subjected to deproteinization using ice-cold methanol (1:1 v/v) followed by centrifugation. A total volume of 10 µL of sample was loaded onto the column, resulting in a detection limit of 10 nM for either anion. Results of both methods were compared using a Bland-Altman plot, which is a widely accepted statistical method to assess agreement between two different methods or assays.28

Assessment of urinary nitroso species via gas phase chemiluminescence detection

Urinary RXNO concentrations were quantified in 24h urine samples using reductive denitrosation by triiodide (J

3-) with subsequent detection of the liberated NO by gas-

phase chemiluminescence, as previously described.29 In short, samples were thawed for 30 min at RT. To prevent possible reaction of protein thiols with nitrite, samples were pretreated with N-ethylmaleimide (NEM; 10 mM final concentration) for the duration of 15 min at RT. Additionally, pre-incubation with of a 5% solution of sulphanilamide in 1 N HCl (final concentration of 29 mmol/L) was applied for another 15 min at RT, to efficiently remove nitrite. As a general measure, samples were kept in the dark to avoid photolytic decomposition of light-sensitive RXNOs. All samples were processed within a time frame of 15 – 30 minutes after start of the pre-incubation with sulphanilamide, and injected into a septum-sealed reaction chamber containing a 10 mM iodine/ 45 mM iodide-containing reaction mixture in glacial acetic acid, continuously purged with high-purity nitrogen. The NO released from RXNOs into the gas phase was carried by the inert gar into the reaction chamber of the chemiluminescence detector where it reacts with ozone (O

3) to form nitrogen dioxide (NO

2). This chemiluminescent reaction

emits light that is quantified by a photomultiplier. NO signal output was sampled at 5 Hz and peak integration was performed using the ChromProcessor software (EDAQ PowerChrom Version 2.7.9). Aqueous nitrite standards processed in an identical manner were used to convert peak areas into absolute amounts of NO, allowing to determine sample concentrations by taking the injected sample volume into account.

Assessment of dietary intake

Dietary intake was assessed using a validated semi-quantitative food frequency questionnaire (FFQ), which participants filled in themselves. More detailed information

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about the procedure is available elsewhere.30 In short, for each of the 177 food items on the list, intake was recorded in times per day, week or month. Inconsistent answers were verified with the patients by a trained researcher, and FFQ validity in RTR was assessed by comparing excretion of several urinary components with intake of e.g. sodium, potassium and proteins. Dietary data was converted into daily nutrient intake using the Dutch Food Composition Table of 2006.31

Outcome measures

All measurements were performed during a morning visit to the outpatient clinic after an 8-12 h overnight fasting period. Blood pressure (mmHg) was measured according to a strict protocol as previously described.32 Participants were left alone in a room in half-sitting position while systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and heart rate were measured with a semi-automatic device (Dinamap® 1846, Critikon, Tampa, FL, USA). Measurements were performed every minute for fifteen minutes, and the average value of the last three measurements was used for analyses. Blood was drawn in the morning after completion of the 24h urine collection. Plasma and urinary concentrations of electrolytes, phosphate, albumin and urea were measured using routine clinical laboratory methods, as were serum cholesterol, HbA1c, hs-CRP and Nt-Pro-BNP-levels. Serum creatinine was determined using a modified version of the Jaffé method (MEGA AU 510, Merck Diagnostica, Darmstadt, Germany). Serum calcium was corrected for hypoalbuminemia (when <40 g/L; corrected calcium = serum calcium (mmol/L) + 0.02 x (40 – serum albumin [g/L]). Renal function was assessed by calculating the estimated glomerular filtration rate (eGFR) applying the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.33

Information on participants’ health status, medical history, and medication use was obtained from patient records. Information on smoking behaviour was obtained by using a questionnaire, with classification of participants as ‘current smokers’, ‘former smokers’, or ‘never smokers’. Body weight and height were measured, and Body Mass Index (BMI) was calculated as weight divided by height squared (kg/m2) while Body Surface Area (BSA) was estimated applying the universally adopted formula of DuBois & DuBois.34

Statistical analyses

Data-analysis was performed using SPSS version 22.0 software (SPSS Inc., Chicago, IL, USA). Normality was tested with the Kolmogorov-Smirnov test, and skewed data were normalized before analyses by logarithmic transformation (urinary albumin, TS and

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RXNO excretion and serum parathyroid hormone (PTH), alkaline phosphatase (AF), triglycerides, high sensitive C-reactive protein (hs-CRP) and N-terminal pro-hormone of brain natriuretic peptide (NT-pro-BNP), or by extracting the square root of original values (urinary NOx). Data in text and tables are presented as mean ± standard deviation (SD), unless stated otherwise. The study population was subdivided into gender-stratified tertiles to visualize potential associations of NOx excretion with different parameters in RTR. To establish p-values for differences in NOx tertiles an ANOVA was used for normally distributed continuous data, whereas the Kruskal-Wallis test was used for non-normally distributed data and the χ2-test for nominal data. To identify the independent determinants of NOx excretion, univariable and multivariable linear regression analyses were performed. With backward selection (P

out > 0.05) multivariable linear regression

models were constructed, which included all the variables that were significantly associated with NOx in the univariable analysis. The associations of several cardiovascular parameters with NOx excretion were investigated with multivariable linear regression analysis, with adjustment for age, gender and BSA (model 1) and subsequent addition of eGFR (model 2). In model 3, an additional adjustment was made for potential confounders of NOx excretion, identified with multivariable regression analyses (model 3: smoking, serum triglycerides, use of calcineurin inhibitors or use of sirolimus). In the final model, we added uric acid excretion (model 4). Linear regression coefficients are given as standardized betas (st. β), to allow for comparison of the regression coefficients. Tertiles of NOx were tested for associations with all-cause mortality and death-censored graft failure by Kaplan-Meier analysis, including the log-rank test.

For the Cox regression analysis, above described models were used, with addition of the crude model as a start. Mortality risk was presented as hazard ratio (HR) plus the 95% confidence interval. Within all statistical analyses, a two-sided P-value less than 0.05 was considered statistically significant.

Results

HPLC versus Griess assay for determination of NOx

In all 24h urine samples nitrate concentrations exceeded those of nitrite by several orders of magnitude. Figure 1 depicts the Bland-Altman plots of the results obtained with the HPLC method and the spectrophotometric Griess assay, for direct comparison of both techniques used to determine nitrate and nitrite concentrations. The y-axis represents the difference between the nitrate or nitrite values measured by HPLC and the value determined via the Griess assay, whereas the x-axis represents the average of both values. For nitrate, 35 out of 809 (4.3%) values did not meet the 95% limit agreement (Figure 1). Both methods showed comparable results for the nitrate values in the range

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of 0 µM to 350 µM. However, nitrate values exceeding this limit were systematically underestimated by the classical Griess assay. In Figure 1 this is demonstrated by an upward linear deviation starting at nitrate values of 350 µM onwards. For nitrite, 18 out of 809 (2.2%) values did not meet the 95% limit of agreement (Figure 1). A dispersed pattern was seen, with a linear deviation in both positive and negative direction, when

Figure 1. Bland-Altman plot of the HPLC vs. the Griess method for NOx detection

Comparison of nitrate and nitrite measured with HPLC and colorimetric Griess reaction. The Griess

method underestimated nitrate values exceeding a mean value of 350 µmol, displayed by a positive

linear deviation. For nitrite, both a positive and negative linear deviation was observed.

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HPLC measurements of nitrite were plotted against data obtained with the Griess assay. Of note, in 303 out of 809 (38%) samples the Griess method did not pick up any nitrite because absorbance values were below the detection limit of this method, while the HPLC technique always returned a value around or above the quantifiable limit. This distinction appears particularly important when nitrite concentrations need to be determined in urine samples as normal levels in this body fluid are typically below 1 µM.

Healthy control vs. RTR: baseline characteristics

Table 1 displays the characteristics of our study cohort. Both groups, healthy controls and RTR, were similar with regard to age, gender and BSA. Urinary nitrate excretion was significantly lower in RTR when compared to healthy participants (679 [381 – 1047] µmol/24h vs. 1158 [850 – 1990] µmol/24h, p < 0.001). Since nitrate makes the greatest contribution to NOx levels, the same was true for NOx excretion (679 [389 – 1053] µmol/24h vs. 1204 [879 – 2014] µmol/24h, p < 0.001). By contrast, mean urinary nitrite was significantly higher in RTR (0.20 [0.04 – 0.49] µmol/24h vs. 0.07 [0.01 – 0.66] µmol/24h, p=0.03). Both systolic and diastolic blood pressure were higher in RTR (SBP: 136 ± 18 mmHg vs. 125 ± 15 mmgHg p < 0.001 and DBP 83 ± 11 mmHg vs. 75 ± 9 mmgHg p < 0.001). Unsurprisingly, RTR had a significant lower creatinine clearance (66 ± 26 mL/min vs. 132 ± 41 mL/min, p < 0.001) and eGFR (52 ± 20 mL/min vs. 93 ± 13 mL/min, p < 0.001), whereas serum creatinine levels and albumin excretion were significantly higher in RTR. Serum electrolytes were significantly different in RTR versus healthy controls with higher serum urea and lower serum albumin levels. Furthermore, serum triglycerides, glycated haemoglobin (HbA1c) and Nt-pro-BNP were all higher in RTR, as was hsCRP (all P < 0.001). Apart from differences in caloric intake (lower in RTR vs controls: 2175 ± 637 vs. 2309 ± 743 kCal, P = 0.02), dietary habits were similar between both groups. Vegetable consumption, which contributes to the majority of dietary nitrate intake, was not different between RTR and controls (93 ± 58 vs. 92 ± 57 kCal, P = 0.9).

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Table 1. Characteristics of healthy controls versus renal transplant recipients at the day they visited the outpatient clinic

ParametersHealthy controls

(n=107)RTR

(n=707)P-value

difference

NOx excretion (µmol/24h) 1204 [879 – 2014] 679 [389 – 1053] <0.001

Nitrite (µmol/24h) 0.07 (0.01 – 0.66] 0.20 [0.04 – 0.49] 0.03

Nitrate (µmol/24h) 1158 [850 – 1990] 679 [381–1047] <0.001

Demographics

Age (years) 53 ± 10 53 ± 13 0.77

Male gender, n (%) 53 (50) 401 (57) 0.16

BSA (m2) 1.97 ± 0.19 1.94 ± 0.22 0.26

Current smoker (%) 23 (22) 84 (13) <0.001

Hemodynamics

Systolic BP (mmHg) 125 ± 15 136 ± 18 <0.001

Diastolic BP (mmHg) 75 ± 9 83 ± 11 <0.001

MAP (mmHg) 92 ± 10 100 ± 12 <0.001

Heart Rate (bpm) 67 ± 10 69 ± 12 0.11

Renal function

Serum creatinine (µmol/L) 73 [64 – 82] 125 [100 – 160] <0.001

Creatinine clearance (mL/min) 132 ± 41 66 ± 26 <0.001

eGFR (ml/min/1.73m²) 93 ± 13 52 ± 20 <0.001

Albumin (mg/24h) 5.5 [3.1 – 8.3] 41.6 [10.6 – 179] <0.001

Serum

Sodium (mmol/L) 143 ± 2 141 ±3 <0.001

Potassium (mmol/L) 3.84 ± 0.26 3.98 ± 0.47 <0.001

Chloride (mmol/L) 106 ± 2 105 ± 4 0.005

Urea (mmol/L) 5.7 [4.9 – 6.6] 9.6 [7.2 – 13.4] <0.001

Albumin (mmol/L) 45.2 ± 2.5 43.0 ± 3.0 <0.001

Cholesterol (mmol/L) 5.2 [4.5 – 5.9] 5.0 [4.3 – 5.8] 0.14

Triglycerides (mmol/L) 1.1 [0.8 – 1.6] 1.7 [1.3 – 2.3] <0.001

HbA1c (%) 5.55 ± 0.29 5.99 ± 0.83 <0.001

hsCRP (mg/L) 1.2 [0.5 – 2.3] 1.6 [0.7 – 4.6] 0.001

NT-pro-BNP (ng/L) 39 [20 – 65] 254 [109 – 614] <0.001

Data are presented as mean ± SD, % or median [interquartile range].

Abbreviations: NOx, combined urinary nitrite and nitrate; BSA, body surface area; BP, blood pressure; MAP, mean arterial pressure; eGFR, estimated Glomerular Filtration Rate; HbA1c, glycated hemoglobine; hsCRP, high-sensitive C-Reactive Protein; MAP, mean arterial pressure; NT-pro-BNP, N-terminal pro-Brain Natriuretic Peptide. P for difference was tested by the independent t-test, kruskal-wallis test or χ2-test.

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Patient characteristics: gender-stratified tertiles

Urinary NOx excretion had a median value of 679 µmol/24h [389 – 1053] and was square root-transformed to meet normal distribution criteria (Figure 2). The patient cohort of 702 RTR had a mean age of 53.0 ± 12.7 years and 57% were male. Baseline characteristics of the patient cohort per gender-stratified tertiles are displayed in Table 2. Median time between renal transplantation and baseline measurement was 5.4 [1.9 – 12.0] years. RTRs in the lowest tertile of urinary NOx were older and had higher systolic blood pressure, despite the fact that they used significantly more antihypertensives compared to RTR in the other tertiles. With regard to transplant characteristics, RTR with the lowest NOx excretion less often received their kidney from living donors, and their donors tended to be older. Renal function, hemoglobin, serum albumin and plasma bicarbonate were significantly lower in patients with reduced NOx excretion, whereas calcium, phosphate, PTH, total cholesterol and triglycerides were significantly higher. Moreover, RTR with the lowest urinary NOx had significantly higher levels of both hsCRP and NT-pro-BNP. With regard to urinary parameters, RTR with the lowest NOx excretion also had the lowest excretion of uric acid and thiosulfate and the highest excretion of albumin. Furthermore, use of vitamin K antagonists and calcineurin inhibitors was significantly higher in RTR with the highest NOx excretion.

Figure 2. Histogram of NOx excretion

After square root-transformation, urinary excretion of NOx measured in 702 renal transplant

recipients was normally distributed.

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Table 2. Patients’ characteristics presented as gender-stratified tertiles of NOx excretion

Renal Transplant RecipientsGender-stratified tertiles of NOx

OverallN = 702

Tertile 1N = 233

Tertile 2N = 235

Tertile 3N = 234

P-value

NOx excretion (µmol/24 h) 679 [389-1053] 278 [158-386] 679 [571-777] 1362 [1053-1863] <0.001DemographicsAge, yr 53.0±12.7 55.4±12.3 52.2±12.7 51.3±12.9 0.001Current smoker, n (%) 83 (13) 18 (9) 29 (13) 36 (16) 0.05Current diabetes, n (%) 170 (24) 61 (26) 59 (25) 50 (21) 0.44BSA, m2 1.94±0.22 1.91±0.22 1.96±0.22 1.95±0.21 0.06Systolic Blood Pressure, mmHg 136±18 138±18 137±18 133±16 0.01Diastolic Blood Pressure, mmHg 83±11 82±11 84±11 82±11 0.23Heart rate, bpm 68.6±12.0 68.6±12.5 69.1±11.9 68.0±11.4 0.52Renal TransplantationTransplant vintage, years 5.4 [1.9-12.0] 5.1 [1.7-12.2] 5.9 [2.2-12.2] 5.3 [2.0-11.0] 0.65Living donor, n (%) 235 (34) 62 (27) 71 (31) 102 (44) <0.001Pre-emptive KTx, n (%) 112 (16) 30 (13) 38 (16) 44 (19) 0.22HLA mismatches, n 2 [1-3] 2 [1-3] 2 [1-3] 2 [1-3] 0.68Age donor, years 42.9±15.5 44.5±15.7 40.9±15.4 43.2±15.2 0.04Acute rejection, n (%) 188 (27) 66 (28) 68 (29) 54 (23) 0.28Laboratory measurementsHaemoglobin, mmol/L 8.2±1.1 8.1±1.1 8.2±1.1 8.4±1.0 0.003HbA1C, % 6.0±0.8 6.1±0.9 6.0±0.8 5.9±0.8 0.17eGFR, CKD-EPI (ml/min/1.73m²) 52.2±20.2 46.0±20.0 53.7±20.2 56.9±18.8 <0.001Corrected calcium mmol/L 2.34±0.15 2.36±0.16 2.34±0.15 2.33±0.13 0.04Phosphate, mmol/L 0.96±0.21 0.98±0.23 0.98±0.21 0.93±0.20 0.01Magnesium, mmol/L 0.95±0.12 0.95±0.13 0.95±0.12 0.96±0.12 0.92PTH, pmol/L 8.9 [5.9-14.7] 11.21 [6.5-17.3] 8.8 [6.0-13.9] 8.1 [5.4-12.1] <0.001Venous pH 7.37±0.04 7.37±0.04 7.37±0.04 7.37±0.04 0.12Venous HCO₃¯, mmol/L 24.6±3.1 24.0±3.4 25.0±2.9 24.9±2.8 0.001hsCRP, mg/L 1.6 [0.7-4.6] 1.9 [0.8-5.8] 1.6 [0.8-4.2] 1.3 [0.5-3.8] 0.002Albumin, g/L 43.0±3.0 42.4±3.1 42.9±2.7 43.6±3.0 <0.001Alkaline phosphatase, U/L 67 [53-83] 69 [55-89] 66 [52-80] 66 [54-82] 0.15Total cholesterol, mmol/L 5.0 [4.3-5.8] 5.1 [4.4-6.0] 5.1 [4.4-5.8] 4.8 [4.3-5.5] 0.03HDL cholesterol, mmol/L 1.3 [1.1-1.6] 1.3 [1.0-1.6] 1.3 [1.1-1.7] 1.3 [1.1-1.6] 0.18LDL cholesterol, mmol/L 2.9 [2.3-3.5] 2.9 [2.4-3.5] 3.0 [2.3-3.6] 2.8 [2.3-3.3] 0.25Triglycerides, mmol/L 1.68 [1.25-2.30] 1.85 [1.38-2.58] 1.69 [1.28-2.40] 1.52 [1.13-2.02] <0.001NT-pro-BNP, ng/L 254 [109-625] 434 [177-1141] 261 [110-538] 147 [72-344] <0.001Albuminuria, mg/24h 42 [11-178] 58 [14-212] 43 [12-211] 25 [8-110] 0.02Uric acid, mmol/24h 2.58±0.98 2.20±0.86 2.63±0.93 2.95±1.00 <0.001Thiosulfate, µmol/24h 7.02 [3.88-11.88] 5.73 [3.00-9.22] 6.42 [4.00-11.43] 9.38 [5.44-15.06] <0.001MedicationAnti-hypertensives, n (%) 619 (88) 217 (93) 207 (88) 195 (83) 0.005Statins, n (%) 370 (53) 133 (57) 125 (53) 112 (48) 0.20Calcium supplements, n (%) 150 (21) 48 (21) 45 (19) 57 (24) 0.37Vitamin D supplements, n (%) 173 (25) 56 (24) 55 (23) 62 (27) 0.72Vitamin K antagonists, n (%) 78 (11) 35 (15) 26 (11) 17 (7) 0.03Prednisone, mg/d 10 [7.5-10] 10 [7.5-10] 10 [7.5-10] 10 [7.5-10] 0.88Calcineurin inihibitors, n (%) 403 (57) 150 (64) 134(57) 119 (51) 0.01Proliferation inhibitor, n (%) 583 (83) 185 (79) 198 (84) 200 (86) 0.18Sirolimus, n (%) 13 (2) 3 (1) 4 (2) 6 (3) 0.54Data are presented as mean ± SD, percentage or median [IQR]. Statistical analysis was performed using ANOVA, Kruskal-Wallis or χ²-test

when appropriate. Bold indicates statistical significance (P < 0.05).

Abbrevations: BSA, body surface area; eGFR, estimated glomerular filtration rate; HbA1c, glycated haemoglobin; HCO₃¯, bicarbonate; HDL, high-

density lipoprotein; HLA, human leukocyte antigen; hsCRP, high-sensitivity C-reactive protein; KTx, kidney transplantation; LDL, low-density

lipoprotein; NOx, combined nitrate and nitrite; NT-pro-BNP, N-terminal pro-hormone of brain natriuretic peptide; PTH, parathyroid hormone.

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Table 3. Associations of NOx with clinical parameters in RTR

NOx excretion

Univariable Multivariable

St. Beta P-value St. Beta P-value

Demographics

Age, years -0.082 0.03

Male gender 0.133 <0.001

Current smoker 0.110 0.005 0.151 <0.001

Current diabetes -0.069 0.07

BSA 0.167 <0.001 0.113 0.02

SBP, mmHg -0.086 0.02

DBP, mmHg -0.010 0.80

Heart rate, bpm -0.063 0.10

Renal Transplantation

Transplant vintage, years -0.007 0.85

Living donor 0.107 0.005

Pre-emptive KTx 0.017 0.65

HLA mismatches 0.025 0.52

Age donor, years -0.023 0.55

Acute rejection -0.030 0.43

Laboratory measurements

Hemoglobin, mmol/L 0.176 <0.001

HbA1C, % -0.062 0.11

eGFR, CKD-EPI (ml/min/1.73m²) 0.188 <0.001

Corrected calcium mmol/L -0.125 0.001

Phosphate, mmol/L -0.121 0.001

Magnesium, mmol/L 0.027 0.48

PTH, pmol/L -0.094 0.01

Venous pH 0.047 0.24

Venous HCO₃¯, mmol/L 0.096 0.01

hsCRP, mg/L -0.134 0.001

Albumin, g/L 0.164 <0.001

Alkaline phosphatase, U/L -0.004 0.92

Total cholesterol, mmol/L -0.106 0.005

HDL cholesterol, mmol/L 0.005 0.89

LDL cholesterol, mmol/L -0.049 0.20

Triglycerides, mmol/L -0.144 <0.001 -0.147 0.001

NT-pro-BNP, ng/L -0.302 <0.001

Albuminuria, mg/24h -0.052 0.17

Uric acid, mmol/24h 0.344 <0.001 0.257 <0.001

Thiosulfate, µmol/24h 0.239 <0.001

Medication

Anti-hypertensives -0.096 0.01

Statins -0.046 0.23

Calcium supplements 0.040 0.29

Vitamin D supplements 0.050 0.19

Vitamin K antagonists -0.053 0.16

Prednisone, mg/d 0.034 0.37

Calcineurin inihibitors -0.120 0.001 -0.097 0.02

Proliferation inhibitor 0.031 0.41

Sirolimus 0.103 0.01 0.088 0.03

Regression coefficients are given as standardized betas, i.e. change of cardiovascular parameter in SD, per SD increase of urinary NOx excretion.

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; HDL, high-density

lipoprotein; hsCRP, high-sensitive C-Reactive Protein; LDL, low-density lipoprotein; MAP, mean arterial pressure; NOx, combined nitrate and

nitrate excretion; NT-pro-BNP, N-Terminal pro-hormone of Brain Natriuretic Peptide; SBP, systolic blood pressure.

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Table 3 provides an overview of associations of urinary NOx levels with different parameters in univariable and multivariable regression analyses. The strongest associations with NOx excretion in the multivariable analyses were smoking, BSA, serum triglycerides, urinary uric acid and the use of calcineurin inhibitors and sirolimus (model R2=27%).

The regression coefficients for the association of urinary NOx with various cardiovascular parameters are given in Table 4. After adjustment for the strongest associates of NOx (model 4), urinary NOx excretion was inversely associated with systolic blood pressure (st. β = -0.094, P = 0.02), diastolic blood pressure (st. β = -0.083, P = 0.03), mean arterial pressure (st. β = -0.096, P = 0.01), serum hsCRP (st. β = -0.103, P = 0.01), serum Nt-pro-BNP (st. β = -0.200, P < 0.001) and HbA1c (st. β = -0.085, P = 0.04).

Table 4. Regression coefficients for the association of urinary NOx with several cardiovascular parameters in RTR

NOx excretion

Model 1 Model 2 Model 3 Model 4

Cardiovascular risk factors St. β P St. β P St. β P St. β P

SBP, mmHg -0.103 0.01 -0.085 0.02 -0.092 0.02 -0.094 0.02

DBP, mmHg -0.055 0.15 -0.044 0.24 -0.068 0.09 -0.083 0.03

MAP, mmHg -0.082 0.03 -0.067 0.07 -0.086 0.03 -0.096 0.01

Heart rate, bpm -0.051 0.19 -0.066 0.09 -0.010 0.81 -0.005 0.90

hsCRP, mg/L -0.127 0.001 -0.115 0.002 -0.090 0.02 -0.103 0.01

NT-pro-BNP, ng/L -0.297 <0.001 -0.253 <0.001 -0.243 <0.001 -0.200 <0.001

HbA1c, % -0.074 0.06 -0.094 0.02 -0.072 0.08 -0.085 0.04

Albuminuria, g/24h -0.080 0.04 -0.022 0.58 -0.016 0.70 -0.039 0.33

eGFR, mL/min 0.191 <0.001 - - 0.104 0.01 0.010 0.80

Model 1: corrected for age, gender and BSA

Model 2: corrected for model 1 plus eGFR

Model 3: corrected for model 2 plus potential confounders (smoking, serum triglycerides, use of calcineurin inhibitors, use of sirolimus)

Model 4: corrected for model 3 plus uric acid excretion

Regression coefficients are given as standardized betas, i.e. change of cardiovascular parameter in SD, per SD increase of urinary NOx excretion.

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; hsCRP, high-sensitive C-Reactive Protein; HbA1c, glycated hemoglobin; MAP, mean arterial pressure; NOx, combined nitrite and nitrate; NT-pro-BNP, N-terminal pro-hormone of Brain Natriuretic Peptide; SBP, systolic blood pressure.

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Associations of NOx excretion with patient survival in RTR

Of the 702 RTR in our cohort, 83 (12%) died within a median follow-up period of 3.1 [2.7 – 3.9] years. In the highest tertile of NOx excretion, 15 out of 234 (7%) died, while this was 24 out of 235 (10%) in the middle tertile and 44 out of 233 (19%) in the tertile with the lowest urinary NOx excretion (log-rank test P < 0.001, Figure 3).

Additionally, we performed Cox regression analyses with potential confounders of urinary NOx excretion that were identified in the multivariable regression analyses (Table 5). The crude Cox regression analysis (model 1) showed that NOx excretion is inversely associated with mortality risk (HR 0.60 [95% CI 0.47 – 0.76], P < 0.001 per SD increase). After adjusting for age, gender and BSA (model 2; HR 0.62 [0.48 – 0.80], P < 0.001), for eGFR (model 3; HR 0.68 [0.52 – 0.88], P = 0.003) and potential confounders (model 4; HR 0.64 [0.48 – 0.87], P = 0.004), urinary NOx excretion remained significantly inversely associated with mortality risk in RTR. In the final model, urinary excretion of uric acid was added, which resulted in a borderline significant inverse association of NOx with mortality risk (model 5; HR 0.74 [0.54 – 1.02], P = 0.07).

Figure 3. Kaplan–Meier plot of the association of NOx excretion with all-cause mortality

in RTR

Higher excretion of NOx is associated with significant survival benefit in renal transplant recipients.

Kaplan-Meier curve displayed for patient survival, with log-rank test P < 0.001.

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Table 5. Associations of NOx excretion with all-cause mortality in RTR

NOx excretion (continuous)

HR (95% CI) per SD P-value

Model 1 0.70 [0.59 - 0.84] <0.001

Model 2 0.70 [0.58 – 0.85] <0.001

Model 3 0.75 [0.62 – 0.92] 0.005

Model 4 0.72 [0.57 – 0.90] 0.003

Model 5 0.78 [0.61 – 1.00] 0.05

Model 1: crude

Model 2: adjusted for age, gender and BSA

Model 3: adjusted for model 2 plus eGFR

Model 4: adjusted for model 3 plus smoking, serum triglycerides, use of calcineurin inhibitors, use of sirolimus

Model 5: adjusted for model 4 plus uric acid excretion

Abbreviations: BSA, body surface area; CI, confidence interval; HR, hazard ratio; SD, standard deviation.

Median [IQR]: 679 [381 -1047] µmol/24 h

Associations of NOx excretion with graft failure in RTR

Graft failure was diagnosed in 45 out of 702 (7%) kidney recipients. In the highest tertile of NOx excretion, 6 out of 234 (3%) RTR developed graft failure. In the middle tertile graft failure occurred in 11 out of 235 (5%), while the RTR with the lowest NOx excretion had the highest risk of developing graft failure: 28 out of 233 (12%) (log-rank test P < 0.001, Figure 4).

In Cox regression analyses (Table 6), the crude model (model 1) showed that NOx excretion is inversely associated with graft failure (HR 0.43 [95% CI 0.30 – 0.61], P < 0.001 per SD increase). This inverse relationship remained significant after correction for age, gender and BSA (model 2; HR 0.41 [0.29 – 0.58], P < 0.001), eGFR (model 3; HR 0.59 [0.41 – 0.86], P = 0.005), potential confounders (model 4; HR 0.54 [0.35 – 0.84], P = 0.01) and urinary uric acid (model 5; HR 0.55 [0.35 – 0.87] P = 0.01).

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Table 6. Associations of NOx excretion with graft failure in RTR

NOx excretion (continuous)

HR (95% CI) per SD P-value

Model 1 0.59 [0.48 - 0.73] <0.001

Model 2 0.58 [0.47 – 0.72] <0.001

Model 3 0.65 [0.50 – 0.85] 0.001

Model 4 0.64 [0.48 – 0.87] 0.003

Model 5 0.65 [0.48 – 0.88] 0.01

Model 1: crude

Model 2: adjusted for age, gender and BSA

Model 3: adjusted for model 2 and eGFR

Model 4: adjusted for model 3 plus smoking, serum triglycerides, use of calcineurin inhibitors, use of sirolimus

Model 5: adjusted for model 4 plus uric acid excretion

Abbreviations: BSA, body surface area; CI, confidence interval; HR, hazard ratio; SD, standard deviation.

Median [IQR]: 679 [381 -1047] µmol/24 h

Association between NOx excretion and urinary nitroso species levels

Median excretion of total nitroso species (RXNO) was 0.28 [0.16 – 1.16] µmoles/24h in RTR, which was comparable to healthy controls (P = 0.23). Excretion of RXNO was significantly associated with nitrite excretion (R2=5.2%, st. β: 0.137, P = 0.001), whereas

Figure 4. Kaplan–Meier plot of the association of NOx excretion with graft failure in RTR

Higher excretion of NOx is associated with significant better graft survival in renal transplant

recipients. Kaplan-Meier curve displayed for graft survival, with log-rank test P < 0.001.

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there was no association with nitrate (P = 0.55). In multivariable regression analyses, urinary RXNO significantly associated with SBP (model 4; st. β -0.087, P = 0.04), MAP (model 4; st. β -0.082, P = 0.05) and albuminuria (model 4; st. β 0.095, P = 0.03). In survival analysis, nitroso species did neither predict for graft failure nor mortality.

DiscussionReduced urinary NOx excretion is associated with decreased patient survival and increased graft failure in stable kidney recipients. These associations remained significant after adjustment for potential confounders, with a borderline significance for the association with mortality upon addition of uric acid excretion. Furthermore, RTR with lower urinary NOx excretion had an adverse cardiovascular risk profile. A third important finding in this study is the markedly lower NOx excretion in RTR when compared to healthy controls, with no differences in the renal excretion of nitroso species.

The observational nature of our study does not allow to establish causality; however the strength of the associations would seem to justify further mechanistic studies. A reduced urinary NOx excretion in RTR may be the consequence of a decreased systemic production of NO, a disturbance in renal nitrate and nitrate handling, or a combination of both. NO is generated from L-arginine which is converted to L-citrulline by nitric oxide synthase (NOS). In healthy subjects, the majority of the circulating L-arginine pool is synthesized by the kidney and reabsorbed in the proximal tubuli.35-37 In RTR, renal dysfunction may be the cause of a decreased production and failed tubular reabsorption of L-arginine.38 L-arginine formation is known to be decreased in patients with chronic kidney disease (CKD).39 Furthermore, an increased expression and activity of arginase and thus use of L-arginine for the production of ornithine and urea (instead of citrulline and NO) may also compromise NO production40; to this end, a recent animal experimental study has unmasked a reciprocal relationship between circulating nitrate levels and arginase expression/L-arginine availability.41 The difference in NOx excretion between RTR and healthy controls in our study cannot be explained, however, by differences in dietary intake, which was comparable in both groups. Inhibitors of endogenous NOS, like asymmetric dimethylarginine (ADMA), can also play a role in decreased NO production in RTR, since it has been demonstrated that plasma ADMA levels are increased in patients with CKD42-44 as well as in patients after renal transplantation.45 Thus, endothelial and renal dysfunction in RTR may lead to decreased NO availability and as a consequence higher susceptibility to cardiovascular disease, graft failure and all-cause mortality. This notion is in line with our findings that decreased urinary excretion of NOx in RTR is associated with higher systolic, diastolic and mean arterial blood pressure, higher levels of hsCRP, HbA1c and Nt-pro-BNP and higher risk of graft failure and all-cause mortality.

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Inadequate NO production in the kidneys plays a key role in the development and progression of kidney disease. Experimentally induced chronic NOS inhibition causes systemic and glomerular hypertension, proteinuria and renal damage.24,46 Furthermore, in experimental models of renal mass reduction47, chronic glomerulonephritis48 and aging49, decreased total NOx excretion has been reported. NO deficiency also develops as a consequence of CKD24,50, with a decreased NOx excretion in these patients.51 Patients with moderate and severe renal failure had significantly lower urinary NOx excretion when compared to mild renal failure and healthy controls52, and the same holds true for patients with end stage renal disease that are on dialysis.53 Decreased total NO production in CKD patients likely reflects systemic endothelial dysfunction, rather than a decreased NO production in the kidneys alone as the latter makes only a minor contribution to whole body NO production.46

Nitrate represents the major stable endproduct of NO metabolism.54,55 While there is unanimous agreement about the significance of the kidney in nitrate excretion surprisingly little quantitative information is available about the role of this organ in controlling circulating NOx concentrations and the extent of renal reabsorption and tubular secretion. Nitrite and nitrate are widely believed to be handled in a very similar fashion by the kidney, although there is little evidence in support of this assumption in the literature. A recent pharmacological inhibitor study in a handful of healthy volunteers indicated the possible involvement of carbonic anhydrase in renal nitrite reabsorption56 but no other studies appear to have been performed to identify a specific renal nitrate transporter. Despite this paucity of information about specific proteins involved in renal NOx reabsorption, it has become increasingly clear in the last couple of years that nitrate can be reduced to nitrite and further to NO in an oxygen-dependent fashion. This realization has led to the conceptualization of the existence of an alternative pathway of NO production that takes advantage of this sequential reduction57, which is inhibited by oxygen.58 Xanthine oxidoreductase appears to be involved in tissue nitrate reduction59, its expression is increased in the transplanted kidney (unpublished data), and an increased nitrate consumption via this reductive pathway may conceivably lead to lower apparent nitrate excretion. Thus, while our finding of reduced NOx excretion in RTR is consistent with the notion that systemic NO production is impaired we cannot exclude the possibility that this observation may be caused by alterations in renal handling instead.

In the present study we compared the classical Griess assay with the current gold standard for nitrite and nitrate determination, high pressure liquid ion chromatography (HPLC). While the technical limitations of the former for quantitative analyses in complex biological matrices such as blood and urine are well recognised25,26 this methodology continues to enjoy great popularity because of its (apparent) simplicity, moderate costs and the possibility to process large numbers of samples in an efficient manner. However, the Griess method has its limitations when it comes to sensitivity and accuracy: concentrations below 1-2 µM are prone to large errors, and sensitivity is

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limited to about 0.5 µM. Urine is a particularly problematic body fluid because nitrite concentrations are typically below 1 µM and nitrate concentrations are extremely high (potentially requiring a series of dilutions of unknown samples to ensure linearity of response, which becomes a serious limitation when processing large numbers of samples). By directly comparing high pressure liquid ion chromatography with the spectrophotometric Griess assay we here demonstrate that the Griess method tends to underestimate high nitrate values, possibly as a result of too short an exposure time or cofactor limitation for the enzymatic reduction (or a combination of both). Moreover, in a considerable number of samples nitrite values were below the detection limit of the Griess assay. The good news in the context of the main findings of our study is that a reduction in urinary nitrate excretion can easily be picked up with an assay that does not distinguish between nitrite and nitrate but only measures the sum of both, NOx.

We have to acknowledge that the present study has some limitations. Causality is hard to prove since the study has a cross-sectional design. Furthermore, the single-centre set-up and the fact that most of the RTR were of Caucasian origin might limit generalizability. However, the large population size, the use of 24h urine samples to determine NOx excretion, the comparison of two different methods to measure nitrite and nitrate, the assessment of dietary intake and extensive data collection that allowed adjustment for potential confounders and the comparison between RTR and healthy subjects, all contribute to the reliability and robustness of our findings.

In conclusion, reduced NOx excretion in RTR is associated with an adverse cardiovascular risk profile, increased graft failure and higher all-cause mortality in RTR. Further studies have to be conducted to determine whether this is rather a consequence of endothelial dysfunction, a changed renal handling or a combination of both.

AcknowledgmentsThe authors would like to express their gratitude towards Magdalena Minnion for her expertise and valuable help in measuring urinary NOx. This work was supported by Grants from the Dutch Kidney Foundation (NSN C08-2254, P13-114, 14OKK28) and by COST Action BM1005: ENOG: European Network on Gasotransmitters (www.gasotransmitters.eu). MF gratefully acknowledges support from the UK Medical Research Council (G1001536).

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