Osmoregulation Performance and Kidney Transplant Outcome : Journal of the American Society of Nephrology

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Clinical Research

Osmoregulation Performance and Kidney Transplant Outcome

Mazloum, Manal1,2; Jouffroy, Jordan2,3; Brazier, François2,4,5; Legendre, Christophe1,2,5; Neuraz, Antoine2,3; Garcelon, Nicolas6; Prié, Dominique2,4,5; Anglicheau, Dany1,2,5; Bienaimé, Frank2,4,5

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JASN 30(7):p 1282-1293, July 2019. | DOI: 10.1681/ASN.2018121269
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Kidney transplantation is the preferred treatment for patients with ESRD, because it provides higher survival rates and a better quality of life compared with dialysis.1 Despite the progress in understanding the multiple processes affecting the allograft kidney, renal function decline and allograft loss remain significant concerns.2 The identification of unrecognized targetable processes promoting allograft damage or extrarenal complications remains an important challenge. Dysfunction of the osmoregulatory machinery may represent such risk factor.

The brain-kidney axis plays a central role in the maintenance of constant cell volume through the tight control of extracellular fluid tonicity, which is mainly determined by plasma sodium concentration (PNa).3 Variations of extracellular tonicity are sensed by a specialized hypothalamic region, which regulates water and salt intake through thirst and salt appetite, as well as renal water excretion through the modulation of vasopressin secretion.3 An increase in plasma tonicity activates hypothalamic osmoreceptors present in the organum vasculosum of the lamina terminalis and the subfornical organ, to promote thirst and vasopressin release.4 Through its binding to the V2 receptor (V2R), vasopressin stimulates tubular water reabsorption by increasing the expression of the water channel aquaporin 2 (AQP2) and its insertion in the apical membrane of the principal cells of the collecting duct.5 In addition, V2R stimulation increases sodium and urea transport into the medulla, thereby increasing medullary osmolality which is the driving force for water reabsorption in the collecting duct.6,7 Conversely, in response to water loading, vasopressin secretion is abolished and water diuresis rapidly occurs. The molecular identity of central osmoreceptors remains debated. Both the stretch responsive transient receptor potential cation channel subfamily V member 1 (TRPV1)8 and the sodium voltage-gated channel NaX4,9 have been proposed to mediate osmosensation. However, the invalidation of the genes encoding these proteins in rodents minimally affects osmoregulatory capacity.10–12 In addition to central osmoreceptors, multiple neurosensory cues that anticipate osmotic changes are integrated by the central nervous system to appropriately promote or impede vasopressin secretion before the occurrence of PNa modification.13

Compromise of osmoregulation has important consequences for health. Hyponatremia due to excessive vasopressin signaling is associated with pejorative outcomes in various pathologic states such as congestive heart failure or cirrhosis.14 In addition, fluctuation in PNa may have deleterious consequences for the brain15 and failure to maintain PNa is associated with cognitive decline in the elderly.16 Epidemiologic studies have further linked increased vasopressin level with GFR decline. Indeed, high levels of vasopressin or plasma copeptin, a surrogate marker for vasopressin secretion, are associated with accelerated renal function decline in distinct populations.17–21 In rats submitted to subtotal nephrectomy or streptozocin-induced diabetic nephropathy, infusion of a V2R agonist increases proteinuria, plasma creatinine, and renal damage, whereas V2R inhibition significantly reduces albuminuria.22–24 These data suggest a causal role for V2R signaling in CKD progression that echoes the well established deleterious effect of V2R stimulation in autosomal dominant polycystic kidney disease.25

CKD affects the osmoregulatory function of the kidney. First, reduced GFR is associated with reduced urine concentration even at high-vasopressin plasma levels. This defect is the consequence of functional and structural alterations of the counter-current mechanisms which normally allow the maintenance of a corticomedullary osmotic gradient as well as of a reduction of V2R expression in the kidney.26,27 Conversely, CKD also leads to a reduced ability to produce diluted urine in response to water loading.28 Although the precise mechanisms remain unclear, this phenomenon appears to be linked to excessive vasopressin signaling. Indeed, in patients with CKD, impaired urine dilution in response to water loading correlates with higher level of vasopressin either at baseline or during the test, suggesting the inability to adequately suppress plasma vasopressin.28 Experiments in vasopressin-deficient rats after subtotal nephrectomy indicate that, in the absence of vasopressin, GFR reduction only marginally affects urine dilution capability.24 Similarly, patients with autosomal dominant polycystic kidney disease, with either reduced or preserved GFR, achieve a similar urine osmolality in response to V2R blockage.29 Despite the reduced capacity to dilute urine and the high-vasopressin levels, patients with CKD seldom develop overt hyponatremia before ESRD.30

Although occasional case reports have demonstrated that altered osmoregulation can have deleterious consequences in kidney transplant recipients (KTRs),31–34 the literature regarding hyponatremia and osmoregulation in this population is remarkably scarce.35 Similar to the general CKD population, hyponatremia associates with adverse outcome in KTRs, but remains a rare condition.36 Yet a small, observational study suggested that a reduced ability to excrete water is not infrequent in KTRs.37 However, the precise prevalence of osmoregulation defects and their effect on renal outcome have not been studied in this population.

To address these questions, we took advantage of the moderate and prolonged water-loading protocol used to obtain hourly urine collection during measured GFR (mGFR) measurement. We hypothesized that defective osmoregulation would result in a defect and/or a delay in urine dilution leading to a reduction in PNa. We compared the modification of PNa, urine flow rate, and urine osmolarity (Uosmo) in a large prospective cohort of KTRs and in a control group of healthy candidates to kidney donation that underwent GFR measurement 3 months after transplantation or before kidney donation. We determined the factors associated with impaired osmoregulation capacity in KTRs and studied whether altered osmoregulation capacity is associated with allograft loss, mortality, and subsequent mGFR.


Study Population

All patients were transplanted and followed in the department of renal transplantation at Necker Hospital, Paris, France. According to our standard follow-up protocol, all patients with a functioning allograft underwent GFR measurement 3 months after transplantation. All but two patients tested from October of 2007 to June of 2016 were included in the study. The two excluded patients were missing ≥1 PNa value. A control group of healthy adults consisted of candidates for kidney donation tested during the same period as the KTRs. Candidates for kidney donation with mGFR<60 ml/min per 1.73 m2 were excluded.

Data Collection

Clinical data were obtained from a prospective database, Données Informatiques Validées en Transplantation (DIVAT clinical prospective cohort, official website: www.divat.fr; registration number: 1016618). Data were entered at specific points for each patient (at day 0, 3 months, and 1 year post-transplantation); follow-up was updated annually thereafter. Research assistants under the supervision of transplant clinicians prospectively entered all data, and the database underwent an annual external audit for data accuracy. For each patient, follow-up data after 1 year were entered on the basis of a complete review of the medical charts and the collection of clinical events.

The collected data are computerized in real time and include information concerning the donor (age, sex, deceased or living donor, cold ischemia time), the recipient (age, sex, primary cause of CKD), the transplantation procedure, the immunosuppression regimen, the occurrence of delayed graft function (defined as the need for hemodialysis during the first week after transplantation), and the occurrence of graft loss as defined by retransplantation or a return to long-term dialysis or death. The clinician in charge of the patients reported death or allograft loss to the research assistant implementing the DIVAT database. A history of acute rejection was defined as the occurrence of an acute rejection episode requiring treatment before GFR measurement. In addition, we prospectively collected height, body weight, BP, and standard biochemical parameters at the time of GFR measurement.

Water-Loading Test

The water-loading test was performed in the day hospital of our physiology department by a team of four nurses specialized in renal physiology testing. A 24-hour urine collection was obtained the day before the test. Patients were deprived of water overnight (from midnight to 8 am). Upon arrival, blood and urine samples were collected and patients were asked to drink 6 ml/kg of water over 30 minutes, followed by 150 ml of water every hour. Hourly blood and urine samples were collected for 5 hours.


Total calcium, phosphate, urea, standard plasma, and urine ion concentration as well as urinary proteinuria and creatinine were measured by standard biochemical methods. Ionized calcium was measured on an ABL800 analyzer (Radiometer). GFR was measured by iohexol clearance or inulin clearance when allergy to iohexol was suspected on the basis of a patient’s history, as previously described.38 C-terminal FGF23 plasma concentration was measured by ELISA (Immutopics, Inc., San Clemente, CA). Serum parathyroid hormone concentration was measured with an immunochemiluminescent assay performed on the Elecsys analyzer (Roche Diagnostics). Calcitriol and 25-hydroxy vitamin D (25-OH-vitD) concentrations were measured with radioimmunoassay (DiaSorin, Stillwater, MN and IDS Ltd, Boldon, UK). The assays were carried out in a single laboratory throughout the study. Two laboratories of Necker hospital were involved: the biochemistry department (standard biochemistry) and the physiology department (GFR measurement and hormone testing).

PNa Slopes, Osmolarity, and Renal Free Water Clearance Calculation

PNa slopes were calculated using the linear regression of the six successive PNa values measured during the water-loading test and time. Plasma osmolarity (Posmo), Uosmo, and free water clearance were calculated from PNa, plasma urea (Purea), plasma glucose (Pglucose), urine urea (Uurea), urine sodium (UNa), and urine potassium (UK) concentrations (mmol/l) using the following formulas:

Free water clearance at time point T=(1−[2×UosmoT/(PosmoT-1+PosmoT)])×urine flow rateT.39

Statistical Analyses

Data were analyzed using R version 3.5.0 and JMP 9.0.0 (SAS institute). Data are expressed as mean±SD for normally distributed continuous variables and as median (25th–75th percentiles) for continuous variables with a skewed distribution. Categoric variables are expressed as number (percentage).

We analyzed the association of osmoregulation and transplantation outcome. We measured the magnitude of PNa decrease during water loading as a means to assess the severity of osmoregulation defects. We used the slope of the linear regression of PNa and time during mGFR measurement (PNa slope) as a continuous variable reflecting the magnitude of the alteration of osmoregulation kinetics. We used PNa slope because it takes into account all PNa measurements performed during the test. We correlated the association of PNa slope tertiles with death or the composite outcome of death or allograft loss using cumulative incidence functions and log-rank test. To analyze the association of PNa slope tertile and allograft loss, we took into account death as a competing risk using the Fine and Gray inverse probability of censoring weighting technique.40 We used univariate and multivariable Cox proportional hazards or Fine and Grays models to determine relevant parameters for death and the composite outcome of allograft loss and death or allograft loss taking into account death as a competitive risk, respectively.40 We tested 14 variables in addition to PNa slope: recipient age, sex, body mass index, smoking and diabetic status, systolic BP, mGFR measurement, and C-terminal FGF23 level (log-transformed) 3 months after transplantation, as well as donor age and type (cadaveric or living) and HLA graft mismatch (<3 or ≥3), history of acute rejection, pre-emptive transplantation, and delayed graft function. The proportionality assumption was checked for each parameter using the Schoenfeld residuals test. Variables that did not satisfy the assumption were used as stratifying variables in the models with transformation if needed. Association between covariates was systematically tested and significant interactions were included in the final models. Fewer events limited the number of covariates that could be included in the separate competing risk analyses of death-censored allograft loss. Therefore, we only included variables that were associated with the outcome in univariate analysis and/or clinically pertinent. In the sensitivity analysis, in order to address whether the association between PNa slope and transplantation outcome is independent of the presence of hyperglycemia, we repeated the main analyses after excluding patients with either missing blood glucose measurement (n=51) or a blood glucose >11 mmol/l (n=329) at any of the time points used for PNa slope determination. All clinical and biologic parameters included in the multivariable models were measured/assessed at the inclusion of the patients in the study (i.e., at GFR measurement 3 months after KT).

Because of variations in our policy regarding urinary protein excretion measurement during the study period, urinary protein-to-creatinine ratios were only available on the day of GFR measurement in 929 patients. We therefore performed additional multivariable analyses in this population to assess whether the association between PNa slope and specific outcomes was dependent on urinary protein excretion rate.

In a complementary analysis, we studied the association of PNa slope at 3 months with the mGFR at 12 months after transplantation (12-month mGFR). We used univariate and multivariable linear regression models to determine significant associations. In a univariate analysis, we tested the association of 12-month mGFR with 14 variables in addition to PNa slope: donor age and status (cadaveric or living); HLA graft mismatch (<3 or ≥3); pre-emptive transplantation; delayed graft function; history of acute rejection; and recipient age, sex, body mass index, smoking and diabetes status, systolic BP, mGFR, and 25-OH-vitD level.


KTRs Have Altered Osmoregulation in Response to Water Loading

From October of 2007 to June of 2016, 1258 KTRs and 164 healthy candidates to kidney donation meeting the inclusion criteria underwent mGFR measurement associated with water loading. The characteristics of these patients are given in Table 1 and Supplemental Table 1. We first compared the global variations of mean PNa, urine flow, and urine osmolality during the exploration in the two populations (Figure 1). Fasting morning mean PNa concentration was slightly yet significantly lower in KTRs (139±3 mmol/l versus 140±2 mmol/l; P<0.001). Controls and KTRs showed marked differences in their response to water loading. In controls, after an initial decrease, mean PNa remained stable (139±2 mmol/l). In KTRs, mean PNa decreased throughout the test to reach 136±3 mmol/l. At baseline, hyponatremia (PNa<135 mmol/l) was not observed in controls but was observed in 75 KTRs (6%). At the end of the test, PNa<135 mmol/l was observed in 365 KTRs (29%) but only one of the controls (Supplemental Figure 1). Mean urine osmolality decreased and mean urine flow rate increased in both groups. However, these changes were delayed and less pronounced in KTRs compared with controls (Figure 1). In patients with diabetes mellitus, high blood glucose moves water out of cells and thus decreases PNa.41 To exclude this phenomenon, we restricted our analysis to the 878 patients with Pglucose constantly <11 mmol/l during the exploration and observed essentially the same results (Supplemental Figure 2, Supplemental Table 2). Taken together, these results demonstrate that, compared with healthy adult subjects, KTRs have reduced osmoregulation capacity with a decreased ability to excrete free water, lowering PNa.

Table 1. - KTR population characteristics according to PNa slope tertiles
Characteristic All Patients
(n=1258) Tertile 1
(n=419) Tertile 2
(n=419) Tertile 3
(n=420) P Value a
PNa slope, mmol/l per hour −0.62 (−0.91–0.33) −0.22 (−0.33–0.06) −0.61 (−0.71–0.54) −1.04 (−1.26–0.91)
Age at transplantation, yr 51±14 45±14 50±14 55±14 <0.001
Male sex 749 (60%) 254 (61%) 263 (63%) 232 (55%) 0.07
Initial nephropathy 0.85
 Cystic kidney disease 214 (17%) 70 (17%) 70 (17%) 74 (18%)
 GN 175 (14%) 58 (14%) 63 (15%) 54 (13%)
 FSGS 85 (7%) 27 (6%) 33 (8%) 25 (6%)
 Diabetic nephropathy 109 (9%) 29 (7%) 31 (7%) 49 (12%)
 Vascular nephropathy 71 (6%) 19 (5%) 30 (7%) 22 (5%)
 CAKUT 52 (4%) 18 (4%) 19 (5%) 15 (4%)
 Uropathy 45 (4%) 17 (4%) 14 (3%) 14 (3%)
 Systemic lupus 30 (2%) 12 (3%) 8 (2%) 10 (2%)
 Toxic nephropathy 22 (2%) 5 (1%) 6 (1%) 11 (3%)
 Genetic glomerular diseases 25 (2%) 10 (2%) 8 (2%) 7 (2%)
 Hemolytic and uremic syndrome 21 (2%) 9 (2%) 5 (1%) 7 (2%)
 Myeloma 3 (0%) 1 (0%) 1 (0%) 1 (0%)
 Others 117 (9%) 43 (10%) 38 (9%) 36 (9%)
 Unknown 289 (23%) 101 (24%) 93 (22%) 95 (23%)
Pre-emptive transplantation 184 (15%) 75 (18%) 54 (13%) 55 (13%) 0.07
First transplantation 1219 (97%) 407 (97°%) 404 (4%) 408 (97%) 0.79
Active smoking 146 (88%) 50 (12%) 52 (12%) 44 (10%) 0.66
Diabetes mellitus 395 (31%) 104 (25%) 126 (30%) 165 (39%) <0.001
Living donor 302 (24%) 138 (33%) 98 (24%) 66 (16%) <0.001
Donor age, yr 54±16 47±16 54±17 58±16 <0.001
Cold ischemia duration, min 1007 (480–1403) 900 (150–1245) 1031 (529–1500) 1068 (761–1502) <0.001
Delayed graft function 273 (22%) 70 (17%) 95 (23%) 108 (26%) 0.006
HLA mismatch A, B, and DR ≥3 821 (67%) 254 (63%) 283 (69%) 284 (69%) 0.08
History of treated acute rejection 168 (13%) 52 (12%) 57 (14%) 59 (14%) 0.77
Body mass index, kg/m2 24±4 24±4 24±5 25±5 0.17
Systolic BP, mm Hg 134±17 132±16 133±17 136±17 0.006
Diastolic BP, mm Hg 76±10 78±10 77±10 75±11 <0.001
mGFR, ml/min per 1.73 m2 55±16 61±16 54±16 50±16 <0.001
Hemoglobin, g/dl 11.9±1.6 12.1±1.5 11.9±1.6 11.7±1.6 0.001
Ionized calcium, mmol/l 1.25±0.09 1.26±0.11 1.26±0.09 1.24±0.1 0.05
Total calcium, mmol/l 2.39±0.17 2.41±0.15 2.40±0.16 2.38±0.19 0.05
Serum phosphate, mmol/l 0.90±0.25 0.89±0.24 0.91±0.24 0.91±0.27 0.45
25-OH-vitD, µg/l 21 (14–28) 21 (13–28) 21 (15–28) 22 (14–29) 0.91
Calcitriol, µg/l 48 (33–69) 48 (33–73) 50 (34–69) 47 (32–64) 0.22
Parathyroid hormone, ng/l 103 (67–162) 94 (67–151) 107 (65–158) 105 (71–184) 0.06
FGF23, RU/ml 100 (67–146) 90 (63–125) 100 (64–146) 113 (77–181) 0.001
Immunosuppressive regimen 0.02
 Tacrolimus 17 (83%) 352 (87%) 319 (81%) 311 (80%)
 mTOR inhibitor 9 (1%) 2 (0%) 1 (0%) 6 (2%)
 Cyclosporine 182 (15%) 50 (12%) 69 (17%) 16 (16%)
 Belatacept 17 (1%) 2 (0%) 6 (2%) 9 (2%)
Categoric variables are described as numbers (%) and continuous variables as mean±SD or median (interquartile range), as appropriate. CAKUT, congenital anomalies of the kidney and urinary tract; 25-OH-vitD, 25-hydroxy vitamin D.
aP value represents tests of significance from t test, Mann–Whitney test, or chi-squared test, as appropriate.

Figure 1.:
Kidney transplant recipients have altered osmoregulatory response to water loading: variation of mean PNa, urine flow rate, urine osmolality, and renal free water clearance during water loading in KTRs (red) and healthy candidates to kidney donation (controls in blue). Bars indicate 95% confidence intervals. t test: *P<0.05, **P<0.01, ****P<0.001.

We next analyzed the osmoregulation of individual patients. Inspection of individual PNa trajectories during the test revealed important interindividual heterogeneity in KTRs. Indeed, as exemplified in Figure 2A, some patients maintained a constant PNa concentration, whereas others experienced a linear decrease of PNa indicating impaired osmoregulation. Of note, a subset of patients with baseline hyponatremia maintained a constant PNa during the test, suggesting a reset osmostat (Figure 2A). We then focused on the slope of the linear regression of PNa and time (PNa slope) to quantitatively assess osmoregulation in individual patients (Figure 2A). The density plots of PNa slope in controls and KTRs are shown in Figure 2B. Whereas control patients had a narrow distribution of PNa slope with a mean around 0 mmol/l per hour (−0.12±0.3 mmol/l per hour), KTRs showed a wider distribution centered on a mean PNa slope of −0.6±0.4 mmol/l per hour. Indeed, half of KTRs but only 7% of controls had a PNa slope below −0.6 mmol/l per hour. As expected, in KTRs, PNa slope correlated significantly with minimal urine osmolality (r=−0.32; P<0.001; Figure 2C) and urine flow rate (r=0.41; P<0.001; Figure 2D), indicating that a steep reduction in PNa during water loading was associated with a reduced ability to decrease urine osmolality and to increase urine flow (i.e., with a reduced ability of the kidney to excrete free water). The 73 patients with baseline PNa<135 mmol/l (6%) did not display significantly steeper PNa slopes than patients with baseline PNa>135 mmol/l (−0.59±0.54 mmol/l per hour versus −0.64±0.43 mmol/l per hour; P=0.44). In contrast, the 365 patients that reached a PNa<135 mmol/l at the end of the test showed a steeper PNa slope than the rest of the cohort (−0.89±0.43 versus −0.53±0.39; P<0.001). Together, these results show that an important fraction of KTRs present altered osmoregulatory response to water loading. In these patients, the impaired ability of the renal allograft to rapidly excrete water leads to a progressive reduction in PNa. Our results further indicate that the magnitude of this defect can be quantitatively assessed using PNa slope.

Figure 2.:
PNa slope is an indicator of osmoregulation performance. (A) Examples of PNa variations in four patients with normal or low initial PNa and normal (gray lines) or altered osmoregulation kinetics (green lines). The solid lines represent observed PNa variations. The dashed lines represent the linear regression of PNa concentration and time. The slopes of the regression lines are indicated on the right. (B) Distribution of PNa slopes in KTRs and control patients. (C and D) Linear regression of PNa slopes and the minimal urine osmolality observed during the test (C) or the mean urine flow rate observed throughout the test (D).

Risk Factors for Altered Osmoregulation in KTRs

We then investigated the determinants of altered osmoregulation in KTRs. The characteristics of the KTR population according to tertiles of PNa slope are presented in Table 1. Compared with the higher tertiles (tertiles 1 and 2), patients with the steepest PNa slope (tertile 3) were older and had lower diastolic and higher systolic BP, lower mGFR, lower hemoglobin, lower total and ionized blood calcium, and higher FGF23 concentrations. They were more likely to have diabetes mellitus and a prior history of delayed graft function. Furthermore, the following characteristics of donors were associated with the lowest PNa slope tertile: older age, deceased donor, and increased cold ischemia duration. Multivariable linear regression revealed that lower mGFR, older donor age, diabetes mellitus, and, to a lesser extent, older recipient age were independent predictors of a steep PNa slope (Table 2).

Table 2. - Factors associated with PNa slope
Characteristic Standardized β (95% CI) P Value
Donor type (cadaveric) −0.06 (−0.12 to 0) 0.06
Donor age, yr −0.12 (−0.19 to −0.05) 0.002
Recipient age, yr −0.09 (−0.17 to −0.01) 0.02
Delayed graft function (no) 0.04 (−0.02 to 0.1) 0.21
mGFR, ml/min per 1.73 m2 0.12 (0.05 to 0.19) 0.001
Diabetes mellitus (yes) −0.08 (−0.14 to −0.02) <0.01
Systolic BP, mm Hg −0.02 (−0.09 to 0.05) 0.55
Diastolic BP, mm Hg 0.05 (−0.02 to 0.11) 0.14
Hemoglobin, g/dl 0.05 (−0.01 to 0.11) 0.11
logFGF23 C-terminal, RU/ml −0.05 (−0.11 to 0.01) 0.10
Immunosuppressive regimen (belatacept) 0.06 (−0.1 to 0.22) 0.45
Immunosuppressive regimen (cyclosporine) 0.08 (−0.08 to 0.24) 0.35
Immunosuppressive regimen (mTOR inhibitors) −0.14 (−0.33 to 0.05) 0.16
Multivariable linear regression using PNa slope as the dependent variable. 95% CI, 95% confidence interval.

Association of Baseline Osmoregulation with Transplantation Outcome

The median follow-up time was 49 months (25th–75th percentiles: 22–80 months). At the end of the study, 29 patients were lost to follow-up, 83 patients had lost their allograft, 120 patients had died with a functioning allograft, and 11 had died after losing their allograft. Cumulative incidence functions for mortality, graft loss, and their composite according to tertiles of PNa slope are shown in Figure 3. In univariate analysis, steeper PNa slope tertiles were associated with a higher incidence of death, allograft loss, and the composite outcome. We further used univariate and multivariable Cox modeling to assess the hazard ratio of adverse outcome associated with PNa slope (Table 3). After adjustment for multiple potential confounding variables, PNa slope remained significantly associated with the composite outcome and with allograft loss alone but not with death. PNa slope remained significantly associated with the composite outcome and allograft loss in additional Cox models taking into account the urinary protein-to-creatinine ratio that was available for 929 patients (Supplemental Table 3). In a sensitivity analysis, we focused on the population that did not show hyperglycemia during the exploration (i.e., a blood glucose superior to 11 mmol/l at any of the time points). In this subpopulation, PNa slope remained significantly associated with allograft loss and the composite outcome (Supplemental Table 3, Table 3).

Figure 3.:
Steeper PNa slopes associate with greater incidence of mortality and allograft loss in KTRs. (A–C) Cumulative incidence plots of PNa slope tertiles and the composite outcome of all-cause mortality and allograft loss (A), all-cause mortality (B), and allograft loss (C).
Table 3. - Cox uni- and multivariable modeling of the association of continuous PNa slope values with transplantation outcome
Outcome and studied population Per 1 Unit Decrease of PNa Slope P Value
Composite outcome of all-cause mortality and allograft loss
 All patients
  Crude hazard ratio 2.54 (1.85 to 3.50) <0.001
  Adjusted hazard ratio a 1.73 (1.23 to 2.45) 0.002
 Restricted to patients without uncontrolled diabetes
  Crude hazard ratio 3.32 (2.18 to 5.06) <0.001
  Adjusted hazard ratio b 2.47 (1.51 to 4.04) <0.001
Outcome of all-cause mortality
 All patients
  Crude hazard ratio 2.45 (1.65 to 3.64) <0.001
  Adjusted hazard ratio a 1.52 (0.99 to 2.34) 0.06
 Restricted to patients without uncontrolled diabetes
  Crude hazard ratio 3.30 (1.93 to 5.64) <0.001
  Adjusted hazard ratio b 2.20 (1.13 to 4.30) 0.02
Outcome of allograft loss with mortality as a competing risk
 All patients
  Crude hazard ratio 2.60 (1.58 to 4.26) <0.001
  Adjusted hazard ratio c 2.04 (1.19 to 3.51) 0.01
 Restricted to patients without uncontrolled diabetes
  Crude hazard ratio 3.12 (1.69 to 5.74) <0.001
  Adjusted hazard ratio d 2.73 (1.39 to 5.34) 0.003
Data are shown as hazard ratio (95% confidence interval).
aAdjusted for: patient age, diabetes, smoking status, donor status and age, pre-emptive transplantation, delayed graft function, mGFR, body mass index, systolic BP, and logFGF23.
bAdjusted for: patient age, smoking status, donor status and age, pre-emptive transplantation, delayed graft function, mGFR, body mass index, systolic BP, logFGF23, and HLA mismatch.
cAdjusted for: patient age, smoking status, donor status and age, pre-emptive transplantation, mGFR, body mass index, systolic BP, and logFGF23.
dAdjusted for: patient age, smoking status, donor status and age, mGFR, logFGF23, and HLA mismatch.

Association of PNa Slope with Subsequent mGFR

Of 1258 patients that underwent mGFR measurement and PNa slope 3 months after transplantation, 943 (75%) had a second measurement of mGFR 9 months later (i.e., 12-month mGFR). Table 4 presents the association of baseline PNa slope with 12-month mGFR using univariate and multivariable analyses. A steep PNa slope 3 months after transplantation associated with a lower 12-month mGFR independently of baseline mGFR and other potential confounding variables (Supplemental Tables 4 and 5, Table 4).

Table 4. - Association of baseline (3 mo) PNa slope with 12-mo mGFR
Studied population β Coefficient P Value
All patients
 Crude 9.22 (6.96 to 11.48) <0.001
 Adjusted a 1.93 (0.46 to 3.41) 0.01
Patients with available urinary protein-to-creatinine ratio
 Crude 9.54 (6.96 to 12.12) <0.001
 Adjusted b 1.88 (0.18 to 3.59) 0.03
Univariate and multivariable linear regression analysis using 12-mo mGFR as the dependent variable. Data are shown as β coefficient (95% confidence interval).
aAdjusted for recipient age, sex, body mass index, diabetes, active smoking, systolic BP, 3 mo-mGFR, 25-OH-vitamin D level, donor age, donor status, history of acute rejection, HLA mismatch, delayed graft function, and pre-emptive transplantation.
bAdjusted for recipient age, sex, body mass index, diabetes, active smoking, systolic BP, 3 mo-mGFR, 25-OH-vitamin D level, donor age, diabetic status, history of acute rejection, HLA mismatch, delayed graft function, pre-emptive transplantation, and urinary protein-to-creatinine ratio.


In this retrospective analysis of a large prospective cohort of KTRs, reduced osmoregulation during a standardized water-loading test 3 months after transplantation was independently associated with both allograft loss and reduced allograft function 12 months after transplantation.

We used a standardized water-loading test, which was performed during a routine mGFR measurement 3 months after kidney transplantation to assess osmoregulation in KTRs. This water-loading protocol differs from the usual test used to assess urine dilution capacity.28 Although the latter involves a single administration of a large amount of water followed by hourly urine collection to assess water excretion rate, our protocol consisted of the repeated administration of moderate water load over 5 hours. Notably, whereas a single water load does not result in a sustained reduction of PNa over time, we observed that the repeated administration of water induced a progressive reduction in PNa in a substantial fraction of KTRs, unveiling a defect in osmoregulation.3 Using the slope of the linear regression of PNa over time to quantitatively assess osmoregulation capability in a large population of KTRs and controls, we observed that, contrary to healthy adults, an important fraction of KTRs failed to maintain PNa during water loading. Failure to maintain PNa correlated with a reduced and delayed ability to excrete diluted urine, indicating altered renal osmoregulatory response. Furthermore, a steep PNa slope was associated with the development of overt hyponatremia in 29% of the KTRs. Our results further sustain that the assessment of PNa slope is a reliable indicator of osmoregulation performance in KTRs. Of note, PNa slope is not affected by urine collection errors, including urine loss or incomplete bladder voiding, which represents a major pitfall for the proper estimation of more complex parameters used to assess renal response to water loading, such as free water clearance.

One important methodologic aspect of our study is that we focus on PNa and not on plasma osmolality to assess osmoregulation. Although the precise nature of the parameter(s) targeted by osmoregulation remains debated, the effects of different solutes on thirst and vasopressin secretion have long been studied.42–45 The results of these seminal experiments indicate that, in most conditions, PNa is more relevant for osmoregulation than osmolality. Indeed, in KTRs as in most patients, the main determinants of plasma osmolality are PNa, urea, and glucose. Although modification of PNa triggers osmoregulatory response resulting in homeostatic adjustment of thirst and vasopressin secretion, this is not the case for Purea or glucose.43 Contrary to sodium, urea almost freely permeates the plasma membrane46; therefore, urea does not induce sustained water movement across the cell membrane and is less efficient than sodium in inducing vasopressin secretion.43,45 Contrary to urea, high blood glucose does induce water movement across the cell membrane, at least in cells with low glucose premeability.38 However, it is well established that high blood glucose per se does not trigger vasopressin secretion or thirst in healthy subjects or patients with diabetes.42–44 Therefore, PNa maintenance more accurately reflects osmoregulation performance than osmolality. For instance, large fluctuations in blood glucose levels are not rare in KTRs with poorly controlled diabetes. In these cases, the corresponding modifications of osmolality reflect altered glycoregulation and not osmoregulation.

Having established that a large fraction of KTRs have an impaired osmoregulatory response to water loading, we investigated the determinants of this defect and found that reduced mGFR, increased donor age, and diabetes were the main independent factors associated with steeper PNa slopes. In patients without transplants, these three conditions have been associated with increased vasopressin plasma levels and reduced ability to excrete water.18,28,47 The mechanisms responsible for increased vasopressin plasma level in these conditions remain unclear. In aging rats, increased vasopressin release is in part due to an age-related increase in IL-6 production by microglial cells, resulting in higher sensitivity of vasopressin-producing cells to PNa.48 Whether similar mechanisms exist in KTRs, or more generally in CKD, has not been assessed. Furthermore, the effect of renal disease on the recently identified neural circuitries allowing the anticipation of PNa decrease to regulate vasopressin secretion remain unknown.13

A major finding of this study is the link between altered osmoregulation and renal outcome in KTRs. Indeed, we observed that altered osmoregulation performance 3 months after transplantation independently associated not only with allograft loss, but also with a reduced mGFR at 12 months. To date, the concept that osmoregulatory mechanisms could affect allograft fate was only supported by reports showing an association between high copeptin (as a surrogate measure of increased vasopressin secretion) or reduced PNa level and GFR decline49 or adverse outcomes,36 respectively. However, these studies did not establish a direct link between osmoregulation and KTR outcome. Although copeptin is secreted in equimolar amounts to vasopressin, this molecule does not represent a good surrogate for vasopressin level in KTRs. Indeed, with decreased GFR, copeptin levels increase to a higher extent than vasopressin levels, although the mechanism responsible for this difference remains unclear.50,51 Therefore, a high copeptin level in KTRs may not reflect a proportional increase in the vasopressin level and the significance of increased copeptin in this population remains to be fully understood. Conversely, hyponatremia may be due either to a defect of osmoregulation or to a reset of the physiologic set point for PNa (reset osmostat). Our results do not indicate that baseline hyponatremia is a reliable marker for altered osmoregulation performance. First, fasting hyponatremia is a rather rare condition in KTRs (6% in our cohort) compared with the high prevalence of osmoregulation defects revealed by this study. Second, we observed that baseline hyponatremia was not associated with a steeper PNa decrease, suggesting that the ability of hyponatremic KTRs to cope with water loading is essentially similar to normonatremic KTRs. In contrast to the measurement of baseline copeptin and PNa, our study, which assessed PNa kinetics in response to water loading, directly links decreased osmoregulation capability with adverse renal outcome and GFR decline in KTRs. Because this study is observational in nature, we can only speculate on the putative mechanisms underlying this association. Considering the established role of V2R signaling in renal disease progression, it can be hypothesized that altered osmoregulation kinetics in KTRs reflect persistent vasopressin signaling that leads, in turn, to kidney damage. However, in addition to vasopressin signaling, several hormonal and kidney-specific aspects, such as decreased fluid delivery to the collecting duct or decreased sodium reabsorption in the distal tubule, may reduce the kidneys ability to excrete water.52–54 Thus, independently of vasopressin, a reduced ability of the kidney to promptly excrete water could reflect specific tubular injury that affects urine dilution capacity and predates kidney function decline. Because we did not measure baseline vasopressin level or its decrease during water loading, our study does not provide insight regarding the mechanisms underlying defective osmoregulation in KTRs, and additional investigations are required to understand the molecular bases underlying this phenomenon.

An important question raised by our data is the effect of osmoregulation defects on the daily variation of PNa and its subsequent effect on patient health. Indeed, KTRs are advocated to maintain high water intake to prevent dehydration, especially during the early post-transplantation period. Our results suggest that such a policy will produce substantial daily fluctuation in PNa, at least in a fraction of KTRs. We did not find any consistent association between defective osmoregulation and death. However, the consequences of important daily PNa fluctuations on subtler outcome such as cognitive decline need to be assessed.

Our study has potential limitations. First, as mentioned above, we did not measure plasma vasopressin during water loading. In addition, the monocentric design of our study, which includes only KTRs, may hinder the extrapolation of the results to the general CKD population. In addition, because of the specificity of our institution, which combines pediatric and adult nephrology departments, our KTR population includes an unusually high proportion of genetic kidney diseases and a low proportion of patients with diabetes. However, the simplicity of PNa slope measurement should allow replication studies in centers using mGFR to monitor KTRs or patients with CKD. Finally, as stated above, the observational nature of this study does not allow for inferring causality, only association.

Our study has, however, significant strengths. This is the first study to assess osmoregulation performance in a large, prospective cohort of KTRs, with an important follow-up allowing the use of multivariable analysis to identify independent predictors of transplantation outcome.55,56 We further used multiple adjustment variables in our analysis, including mGFR and FGF23, two important factors affecting the results of multivariable analysis aiming at the identification of factors affecting transplantation outcome.55,56 In addition, we used a functional dynamic test assessing the ability of the brain-kidney axis to maintain constant plasma tonicity rather than a single measurement of baseline parameters. Finally, the inclusion of multiple analytic strategies and complementary outcome measurement regarding allograft functions (allograft loss and 12-month GFR) gives consistency to our results.

In conclusion, our study identified defective osmoregulation dynamic as a novel prognostic factor influencing kidney transplantation outcome. Whether the molecular events underlying defective osmoregulation are directly involved in the pathophysiologic processes leading to allograft dysfunction remains to be established.


Dr. Brazier reports personal fees from Amgen, outside of the submitted work. Prof. Legendre reports other from Novartis, other from AStellas, and other from CSL Behring, outside of the submitted work. Dr. Bienaimé reports receiving honoraria from Otsuka for two lectures on the pathophysiology of autosomal dominant polycystic kidney disease in 2017. All of the remaining authors have nothing to disclose.

Published online ahead of print. Publication date available at www.jasn.org.

See related editorial, “A “Set Point” for Water Homeostasis Disturbed with Altered Kidney Transplantation Outcome,” on pages .

The authors thank Marie-Louise Sileber, Audrey Tiquant, Khalil El Karoui, Marie Courbebaisse, Dominique Eladari, Ghania Daoud, and the laboratory staff of the physiology department of Necker hospital for their help in collecting data; Adel Abderrahmane for his help with the Données Informatiques Validées en Transplantation (DIVAT) database; and Prof. Wolfgang Kuehn for his advice and assistance during the writing of the article.

Dr. Bienaimé, Prof. Anglicheau, Prof. Prié, and Prof. Legendre designed the study. Dr. Bienaimé, Dr. Mazloum, Dr. Brazier, and Dr. Garcelon collected the data. Dr. Mazloum, Dr. Bienaimé, Dr. Jouffroy, and Dr. Neuraz performed the statistical analysis. Dr. Bienaimé, Dr. Mazloum, and Dr. Jouffroy drafted the manuscript and made the figures. All authors approved the final version of the manuscript.

Supplemental Material

This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2018121269/-/DCSupplemental.

Supplemental Figure 1. Distributions (density plots) of plasma sodium (PNa) in kidney transplant recipients (KTRs; red) and controls (blue) across water-loading test periods (T0–T6). The numbers (%) of KTRs (red) and controls (blue) with PNa≤134 mmol/l at each time point are indicated in the green area.

Supplemental Figure 2. Variation of mean plasma sodium (PNa), urine flow rate, and urine osmolarity during water loading in the 878 kidney transplant recipients without hyperglycemia (i.e., maintaining blood glucose <11 mmol/l during the exploration; red) and healthy candidates to kidney donation (controls in blue). Bars indicate 95% confidence intervals. t test: *P<0.05, **P<0.01, ***P<0.001, ****P<0.001.

Supplemental Table 1. Characteristics of the control population.

Supplemental Table 2. Characteristics of the kidney transplant recipient population without uncontrolled diabetes according to PNa slope tertiles.

Supplemental Table 3. Cox uni and multivariable modelling of the association of continuous PNa slope values with transplantation outcome in the 929 patients with available urinary protein-to-creatinine ratio measurement on the day of the exploration.

Supplemental Table 4. Factors associated with 12-month mGFR. Multivariable linear regression analysis using 12-month mGFR as the dependent variable: full model.

Supplemental Table 5. Factors associated with 12-month mGFR. Multivariable linear regression analysis using 12-month mGFR as the dependent variable in patients with available urinary protein-to-creatinine ratio: full model.


1. Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al.: Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med 341: 1725–1730, 199910580071
2. Legendre C, Canaud G, Martinez F: Factors influencing long-term outcome after kidney transplantation. Transpl Int 27: 19–27, 201424138291
3. Bourque CW: Central mechanisms of osmosensation and systemic osmoregulation. Nat Rev Neurosci 9: 519–531, 200818509340
4. Hiyama TY, Noda M: Sodium sensing in the subfornical organ and body-fluid homeostasis. Neurosci Res 113: 1–11, 201627521454
5. Bonilla-Felix M: Development of water transport in the collecting duct. Am J Physiol Renal Physiol 287: F1093–F1101, 200415522987
6. Juul KV, Bichet DG, Nielsen S, Nørgaard JP: The physiological and pathophysiological functions of renal and extrarenal vasopressin V2 receptors. Am J Physiol Renal Physiol 306: F931–F940, 201424598801
7. Bankir L, Bichet DG, Morgenthaler NG: Vasopressin: Physiology, assessment and osmosensation. J Intern Med 282: 284–297, 201728649750
8. Zaelzer C, Hua P, Prager-Khoutorsky M, Ciura S, Voisin DL, Liedtke W, et al.: ΔN-TRPV1: A molecular Co-detector of body temperature and osmotic stress. Cell Reports 13: 23–30, 201526387947
9. Nomura K, Hiyama TY, Sakuta H, Matsuda T, Lin CH, Kobayashi K, et al.: [Na+] increases in body fluids sensed by central Nax induce sympathetically mediated blood pressure elevations via H+-dependent activation of ASIC1a. Neuron 101: 60–75.e6, 201930503172
10. Tucker AB, Stocker SD: Hypernatremia-induced vasopressin secretion is not altered in TRPV1-/- rats. Am J Physiol Regul Integr Comp Physiol 311: R451–R456, 201627335281
11. Kinsman B, Cowles J, Lay J, Simmonds SS, Browning KN, Stocker SD: Osmoregulatory thirst in mice lacking the transient receptor potential vanilloid type 1 (TRPV1) and/or type 4 (TRPV4) receptor. Am J Physiol Regul Integr Comp Physiol 307: R1092–R1100, 201425100078
12. Watanabe E, Fujikawa A, Matsunaga H, Yasoshima Y, Sako N, Yamamoto T, et al.: Nav2/NaG channel is involved in control of salt-intake behavior in the CNS. J Neurosci 20: 7743–7751, 200011027237
13. Mandelblat-Cerf Y, Kim A, Burgess CR, Subramanian S, Tannous BA, Lowell BB, et al.: Bidirectional anticipation of future osmotic challenges by vasopressin neurons. Neuron 93: 57–65, 201727989461
14. Cuesta M, Garrahy A, Thompson CJ: SIAD: Practical recommendations for diagnosis and management. J Endocrinol Invest 39: 991–1001, 201627094044
15. Verbalis JG, Goldsmith SR, Greenberg A, Korzelius C, Schrier RW, Sterns RH, et al.: Diagnosis, evaluation, and treatment of hyponatremia: Expert panel recommendations. Am J Med 126[Suppl 1]: S1–S42, 201324074529
16. Nowak KL, Yaffe K, Orwoll ES, Ix JH, You Z, Barrett-Connor E, et al.: Serum sodium and cognition in older community-dwelling men. Clin J Am Soc Nephrol 13: 366–374, 201829439092
17. Afsar B: Pathophysiology of copeptin in kidney disease and hypertension. Clin Hypertens 23: 13, 201728638629
18. Roussel R, Velho G, Bankir L: Vasopressin and diabetic nephropathy. Curr Opin Nephrol Hypertens 26: 311–318, 201728403013
19. Villela-Torres MDLL, Higareda-Mendoza AE, Gómez-García A, Alvarez-Paredes AR, García-López E, Stenvikel P, et al.: Copeptin plasma levels are associated with decline of renal function in patients with type 2 diabetes mellitus. Arch. Med. Res 2018
20. Roussel R, Matallah N, Bouby N, El Boustany R, Potier L, Fumeron F, et al.: Plasma copeptin and decline in renal function in a cohort from the community: The prospective D.E.S.I.R. study. Am J Nephrol 42: 107–114, 201526346607
21. El Boustany R, Tasevska I, Meijer E, Kieneker LM, Enhörning S, Lefèvre G, et al.: Plasma copeptin and chronic kidney disease risk in 3 European cohorts from the general population. JCI Insight 3: 1–11, 201829997293
22. Bardoux P, Bruneval P, Heudes D, Bouby N, Bankir L: Diabetes-induced albuminuria: Role of antidiuretic hormone as revealed by chronic V2 receptor antagonism in rats. Nephrol Dial Transplant 18: 1755–1763, 200312937221
23. Perico N, Zoja C, Corna D, Rottoli D, Gaspari F, Haskell L, et al.: V1/V2 Vasopressin receptor antagonism potentiates the renoprotection of renin-angiotensin system inhibition in rats with renal mass reduction. Kidney Int 76: 960–967, 200919625993
24. Bouby N, Hassler C, Bankir L: Contribution of vasopressin to progression of chronic renal failure: Study in brattleboro rats. Life Sci 65: 991–1004, 199910499867
25. Torres VE, Chapman AB, Devuyst O, Gansevoort RT, Perrone RD, Koch G, et al.: Tolvaptan in later-stage autosomal dominant polycystic kidney disease. N. Engl. J. Med 377: 1930–1942, 2017
26. Czarzasta K, Cudnoch-Jedrzejewska A, Niemczyk L, Wrzesien R, Tkaczyk M, Puchalska L, et al.: Effect of chronic kidney disease on changes in vasopressin system expression in the kidney cortex in rats with nephrectomy. BioMed Res Int 2018: 2607928, 201830013980
27. Combs S, Berl T: Dysnatremias in patients with kidney disease. Am J Kidney Dis 63: 294–303, 201324239050
28. Pedersen EB, Thomsen IM, Lauridsen TG: Abnormal function of the vasopressin-cyclic-AMP-aquaporin2 axis during urine concentrating and diluting in patients with reduced renal function. A case control study. BMC Nephrol 11: 26, 201020923561
29. Boertien WE, Meijer E, de Jong PE, ter Horst GJ, Renken RJ, van der Jagt EJ, et al.: Short-term effects of tolvaptan in individuals with autosomal dominant polycystic kidney disease at various levels of kidney function. Am J Kidney Dis 65: 833–841, 201525600953
30. Zhang R, Wang S, Zhang M, Cui L: Hyponatremia in patients with chronic kidney disease. Hemodial Int 21: 3–10, 201727350025
31. Drake K, Nehus E, Goebel J: Hyponatremia, hypo-osmolality, and seizures in children early post-kidney transplant. Pediatr Transplant 19: 698–703, 201526299753
32. Choi EY, Ro Y, Choi JW, Kang CM, Kim GH: Cicletanine-induced hyponatremia and hypokalemia in kidney transplant patients. Kidney Res Clin Pract 35: 142–146, 201627668156
33. Bagchi S, Husain Zaidi S, Prasad Mathur R: Severe symptomatic hyponatremia--an uncommon presentation of tacrolimus nephrotoxicity. Nephrol Dial Transplant 26: 2042–2044, 201121454354
34. Cinclair RD, Rice JC, Agraharkar M: Relative adrenal insufficiency in post-transplant lymphoproliferative disorder. J. Postgrad. Med 49: 69–71
35. Musso CG, Castañeda A, Giordani M, Mombelli C, Groppa S, Imperiali N, et al.: Hyponatremia in kidney transplant patients: Its pathophysiologic mechanisms. Clin Kidney J 11: 581–585, 201830094023
36. Han SS, Han M, Park JY, An JN, Park S, Park SK, et al.: Posttransplant hyponatremia predicts graft failure and mortality in kidney transplantation recipients: A multicenter cohort study in Korea. PLoS One 11: e0156050, 201627214138
37. Tekgündüz E, Apaydin S, Seyahi N, Altiparmak MR: Electrolyte free water clearance could be an early sign of renal dysfunction in renal transplant patients. Transplant Proc 41: 3726–3730, 200919917375
38. Bienaimé F, Girard D, Anglicheau D, Canaud G, Souberbielle JC, Kreis H, et al.: Vitamin D status and outcomes after renal transplantation. J Am Soc Nephrol 24: 831–841, 201323539758
39. Berl T: Impact of solute intake on urine flow and water excretion. J Am Soc Nephrol 19: 1076–1078, 200818337482
40. Fine JP, Gray RJ: A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94: 496, 1999
41. Wolf MB: Hyperglycemia-induced hyponatremia: Reevaluation of the Na+ correction factor. J Crit Care 42: 54–58, 201728675827
42. Thompson CJ, Davis SN, Butler PC, Charlton JA, Baylis PH: Osmoregulation of thirst and vasopressin secretion in insulin-dependent diabetes mellitus. Clin Sci (Lond) 74: 599–606, 19883396297
43. Zerbe RL, Robertson GL: Osmoregulation of thirst and vasopressin secretion in human subjects: Effect of various solutes. Am J Physiol 244: E607–E614, 1983
44. Verbalis JG: How does the brain sense osmolality? J Am Soc Nephrol 18: 3056–3059, 200718003769
45. Robertson GL: The regulation of vasopressin function in health and disease. In: Proceedings of the 1976 Laurentian Hormone Conference, edited by Academic Press, 1976, pp 333–385
46. Yang NJ, Hinner MJ: Getting across the cell membrane: An overview for small molecules, peptides, and proteins. Methods Mol Biol 1266: 29–53, 201525560066
47. Cowen LE, Hodak SP, Verbalis JG: Age-associated abnormalities of water homeostasis. Endocrinol Metab Clin North Am 42: 349–370, 201323702406
48. Sauvant J, Delpech J-C, Palin K, De Mota N, Dudit J, Aubert A, et al.: Mechanisms involved in dual vasopressin/apelin neuron dysfunction during aging. PLoS One 9: e87421, 201424505289
49. Meijer E, Bakker SJL, de Jong PE, Homan van der Heide JJ, van Son WJ, Struck J, et al.: Copeptin, a surrogate marker of vasopressin, is associated with accelerated renal function decline in renal transplant recipients. Transplantation 88: 561–567, 200919696640
50. Roussel R, Fezeu L, Marre M, Velho G, Fumeron F, Jungers P, et al.: Comparison between copeptin and vasopressin in a population from the community and in people with chronic kidney disease. J Clin Endocrinol Metab 99: 4656–4663, 201425202818
51. Ettema EM, Heida J, Casteleijn NF, Boesten L, Westerhuis R, Gaillard CAJM, et al.: The effect of renal function and hemodialysis treatment on plasma vasopressin and copeptin levels. Kidney Int Rep 2: 410–419, 201729142968
52. Kamel KS, Halperin ML: The importance of distal delivery of filtrate and residual water permeability in the pathophysiology of hyponatremia. Nephrol Dial Transplant 27: 872–875, 201222379181
53. Flahault A, Couvineau P, Alvear-Perez R, Iturrioz X, Llorens-Cortes C: Role of the vasopressin/apelin balance and potential use of metabolically stable apelin analogs in water metabolism disorders. Front Endocrinol (Lausanne) 8: 120, 201728620355
54. Zhang Y, Peti-Peterdi J, Müller CE, Carlson NG, Baqi Y, Strasburg DL, et al.: P2Y12 receptor localizes in the renal collecting duct and its blockade augments arginine vasopressin action and alleviates nephrogenic diabetes insipidus. J Am Soc Nephrol 26: 2978–2987, 201525855780
55. Bienaimé F, Dechartres A, Anglicheau D, Sabbah L, Montgermont P, Friedlander G, et al.: The association between fibroblast growth factor 23 and renal transplantation outcome is modified by follow-up duration and glomerular filtration rate assessment method. Kidney Int Rep 2: 881–892, 201729270495
56. Eisenga MF, van Londen M, Leaf DE, Nolte IM, Navis G, Bakker SJL, et al.: C-terminal fibroblast growth factor 23, iron deficiency, and mortality in renal transplant recipients. J Am Soc Nephrol 28: 3639–3646, 2017

Sodium; osmoregulation; kidney transplantation

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