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Clin J Am Soc Nephrol. 2008 Jul; 3(4): 1057–1065.
PMCID: PMC2440289

B-type Natriuretic Peptides Strongly Predict Mortality in Patients Who Are Treated with Long-Term Dialysis


Background and objectives: Left ventricular abnormalities contribute to cardiovascular disease in patients with chronic kidney disease and may be detected by measurement of B-type natriuretic peptide in serum.

Design, setting, participants, & measurements: In a prospective cohort study of predialysis patients, patients who were on dialysis, and kidney transplant recipients, serum was collected and assayed for both B-type natriuretic peptide and its N-terminal fragment. Median levels were compared using nonparametric tests, and predictors of B-type natriuretic peptide were determined by linear regression. Survival analysis and Cox regression were performed to examine the association of levels of B-type natriuretic peptide with cardiovascular events and death.

Results: Levels of B-type natriuretic peptide were highest in patients who were on dialysis. Patients who were receiving dialysis and had known cardiovascular disease, were not on the waiting list for kidney transplantation, or had left ventricular systolic dysfunction on echocardiography had significantly higher levels of B-type natriuretic peptide than patients without these characteristics. Glomerular filtration rate was an important predictor of B-type natriuretic peptide levels for patients who were not on dialysis (predialysis and renal transplant recipients). Left ventricular systolic dysfunction predicted B-type natriuretic peptide levels in patients who were on dialysis. Both forms of B-type natriuretic peptide were associated with a two- to three-fold increased risk for death in patients who were on dialysis.

Conclusions: Levels of B-type natriuretic peptide are greatest in patients who are on dialysis and have cardiovascular comorbidities and are strong predictors of death.

Patients with chronic kidney disease (CKD) have an increased prevalence of structural abnormalities of the left ventricle, such as left ventricular hypertrophy (LVH) and congestive heart failure (1). A biochemical marker that potentially allows for the early detection, intervention, and ongoing surveillance of myocardium at risk for developing these abnormalities is B-type natriuretic peptide (BNP). This peptide hormone is released from the left ventricular myocardium in response to increased wall tension (2). After being released, the prohormone is cleaved to the active hormone BNP-32 and the inactive N-terminal fragment NT-BNP-76 (3), both of which can be measured in serum. In people without CKD, BNP is most useful in ruling out a diagnosis of cardiac failure in the clinical setting of a presentation with dyspnea (4) but has also been validated in both the detection and prognostication of other cardiac (5) and respiratory conditions (6).

In patients with CKD, BNP has yet to find its place in the management of cardiovascular disease (CVD), despite its potential for identification of left ventricular abnormalities in the general population. Both BNP-32 (7,8) and NT-BNP-76 (9) correlate positively with left ventricular mass index and negatively with left ventricular ejection fraction in patients who are on dialysis. The influence of reduction in GFR on levels of BNP is an important reason that its role is not established in CKD. Both forms of BNP are influenced by GFR (10,11), and patients who are on dialysis have the highest levels of BNP compared with other CKD populations (12); however, there is no established level of BNP that identifies patients who are on dialysis and have cardiac disease. We tested the hypotheses that patients who are on dialysis have higher levels of BNP than predialysis patients and kidney transplant recipients, that levels of BNP will differ in patients who are on dialysis by the level of existing cardiovascular comorbidities, and that elevated levels of BNP predict cardiovascular events and mortality.

Materials and Methods

Study Design

This was a prospective analysis of patients with CKD from a single institution (Austin Health). The Austin Health Human Research Ethics Committee approved the study.


Patients who were receiving both hemodialysis and peritoneal dialysis were recruited from the clinics and hemodialysis services linked to Austin Health. Predialysis patients and kidney transplant recipients were recruited at their routine clinic visits. Background clinical, anthropometric, and laboratory data were recorded. These patients reported no symptoms of CVD in the previous 3 mo, such as chest pain or dyspnea that required removal of fluid, and no recent history of CVD events. A history of CVD was defined as a history of myocardial infarction (MI), coronary revascularization, heart failure, stroke, carotid endarterectomy, lower limb revascularization, or lower limb amputation. Serum was collected from patients at baseline, transported on ice, centrifuged at 3400 × g for 10 min, and then frozen at −70°C until analysis. All samples that were collected from patients who were on hemodialysis were collected after insertion of the dialysis needle before commencing dialysis and on the middle dialysis day of the week.

Laboratory Procedures

BNP-32 was measured by a paramagnetic particle chemiluminescent immunoenzymatic assay (Biosite Triage BNP) on a Beckman Coulter Access analyzer (Beckman Instruments Inc., Chaska, MN), and NT-BNP-76 was measured by an electrochemiluminescence assay (proBNP; Roche Diagnostics, Indianapolis, IN) on an E170 analyzer (Roche Diagnostics). The total coefficient of variation (CV) at different levels of BNP-32 between 40 and 4000 ng/L is reported to be <7% (13). The CV at different levels of NT-BNP-76 (494, 7828, and 13,143 ng/L) is <6% (14).

Serum creatinine was measured using a Jaffe rate method on a Beckman Coulter Synchron LX System. For predialysis and transplant patients, GFR was calculated using the Modification of Diet in Renal Disease equation 7 formula (15), because this formula performs well compared with 99mTc-DTPA clearance in renal transplant recipients (16). Cardiac troponin T (cTnT) was measured by electrochemiluminescence immunoassay (Troponin T; Roche Diagnostics) using an Elecsys E170 analyzer (Roche Diagnostics). A level ≥0.04 μg/L was considered detectable.


Echocardiography was performed as part of routine care and analyzed by one cardiologist (P.S.), blinded to the BNP levels, according to the following criteria: (1) LVH was defined as interventricular wall thickness >1.1 cm; (2) left ventricular systolic dysfunction (LVSD) was defined as an ejection fraction <50% or presence of a wall motion abnormality; and (3) diastolic function was classified as normal, abnormal relaxation pattern (mild), intermediate or pseudonormal pattern (moderate), or restrictive physiology (severe) pattern according to published methods (17). Diastolic dysfunction was considered present when it was mild or greater. Patients were then classified into four groups: (1) Normal echocardiograms, (2) LVH alone, (3) diastolic dysfunction without systolic dysfunction, or (4) systolic dysfunction with or without the other abnormalities.

Statistical Analysis

The sample size was determined for the dialysis cohort, based on an earlier study in which the 2-yr cardiovascular event rate was 48 versus 20% in patients who were on dialysis and had detectable versus undetectable cardiac troponin I (18). To detect a similar difference (50 versus 20%) in proportions in patients grouped according to BNP levels with α = 0.05 and power of 80%, 45 patients per group would be required. More patients on dialysis were recruited to allow for patients' discontinuing the study and for transplantation. The study was not powered to examine event rates in predialysis patients or renal transplant recipients.

Values of both BNP-32 and NT-BNP-76 are presented as median (interquartile range), and comparisons between groups were assessed by the Wilcoxon rank sum test or Kruskal-Wallis test. Normally distributed variables were compared using t test or ANOVA as appropriate.

Linear regression was performed for each group separately to determine predictors of BNP levels. Because BNP was transformed to the natural logarithm, the percentage increase in BNP-32 (or NT-BNP-76) for each unit change in the predictor variable is calculated as 100(eβ − 1), where β is the β coefficient in the model. Age, gender, diabetes, and history of CVD were included in all models on the basis of clinical importance alone. Other variables were selected on the basis of their effect on the relationship between GFR and BNP levels. In patients who were on hemodialysis, the main interest was LVSD and BNP levels. Variables that changed the β coefficient for the effect of GFR (or LVSD) on natural log-transformed BNP levels by >10% were included in the model (19), and the most parsimonious model was selected by backward stepwise elimination. Regression assumptions were assessed by inspection of the plots of the log of BNP versus the variables and by analysis of standardized residuals. Influential points were examined by inspection of the leverage-versus-residual-squared plot for each model and by calculation of DFBETA statistics (20), and a sensitivity analysis with influential observations omitted was performed. Interactions were tested for variables that modified the effect of GFR or systolic dysfunction on levels of BNP.

Cardiovascular events were defined as a combined outcome of cardiac death, MI, coronary revascularization procedure, stroke, peripheral vascular disease surgery or revascularization, and gut ischemia. An independent adjudication committee determined these events. Kaplan-Meier curves were constructed using the date of the baseline measure of BNP until an event or censoring. Censoring occurred at the time of kidney transplantation, withdrawal from the study, or on June 30, 2006. Comparison of the groups defined by the BNP levels was assessed by the Log-rank test, and multivariate Cox regression was performed for determination of whether the associations between the level of BNP and outcomes were independent of other factors. BNP was natural log-transformed for this analysis, and the hazard ratio represents the increased risk per SD of the log-transformed variable. Variables that were evaluated for the multivariate models included age, gender, hemoglobin, history of CVD, diabetes, smoking, pulse pressure, body mass index, and high-sensitivity C-reactive protein (hs-CRP). The contribution to the model of each variable was assessed by testing the likelihood ratios for the model with BNP nested in the model with the added variable, and the variable with the most significant likelihood ratio was then added and the process was repeated. The proportional hazards assumption was assessed by inspection of log-log plots and analysis of Schoenfeld residuals (21), and violation of this assumption was noted where applicable. Statistical analysis was performed using Stata 8.0 (StataCorp, College Station, TX).


Baseline Differences in BNP

A total of 108 patients on dialysis (100 hemodialysis and eight peritoneal dialysis), 64 predialysis patients, and 80 kidney transplant recipients consented to participate in the study and provided serum for a baseline level of BNP. The underlying causes of end-stage kidney disease in patients who were receiving dialysis were glomerulonephritis (39%), diabetes (14%), and renovascular disease (10%). Fifty-eight percent of the hemodialysis patients used a native radiocephalic fistula, and 50% underwent thrice-weekly dialysis for 12 h, 23% for 14.5 h, and 16% for 15 h/wk. Most patients used either a polyamide membrane (69%) or cellulose triacetate (23%), and 34% of patients used a high-flux membrane. The baseline clinical and laboratory features of these patients are presented in Table 1.

Table 1.
Baseline characteristics of patients who provided a baseline measurement of BNPa

Patients who were on dialysis had significantly higher levels of BNP than either predialysis patients or kidney transplant recipients (Table 2). Within these CKD groups, patients with a history of CVD had significantly higher levels of BNP than those with no history of CVD, with the exception of kidney transplant recipients and NT-BNP-76 (Table 2, Figure 1). Patients who were on dialysis and did not have known CVD had similar levels of BNP to patients with CVD in the other two groups. Patients who were not listed for renal transplantation had significantly higher values of BNP than patients who were receiving dialysis and were on the waiting list for renal transplantation (Table 2).

Figure 1.
(A and B) Box and whisker plots for B-type natriuretic peptide-32 (BNP-32; A) and N-terminal fragment BNP-76 (NT-BNP-76; B) comparing patients with no known cardiovascular disease (CVD−) with those with known CVD (CVD+) among the predialysis ...
Table 2.
Levels of BNP in patients with CKD divided according to different characteristicsa

Echocardiography in the patients who were on dialysis demonstrated that patients with LVSD had the highest levels of both BNP-32 and NT-BNP-76 (Table 3, Figure 2). Patients with LVSD had a significantly higher median level of BNP-32 than patients with LVH and a significantly higher median NT-BNP-76 level than patients with LVH or diastolic dysfunction.

Figure 2.
(A and B) Box and whisker plots for BNP-32 (A) and NT-BNP-76 (B) in patients who were receiving dialysis, according to echocardiographic findings. These were normal, left ventricular hypertrophy (LVH), diastolic dysfunction (DD), or systolic dysfunction ...
Table 3.
Levels of BNP in patients on dialysis divided according to echocardiographic characteristicsa

Predictors of BNP Levels

Age, GFR, serum albumin, and history of CVD all were associated with log-transformed BNP-32 in the multivariate model for predialysis patients. Each 1-ml/min decrease in GFR was associated with a 1.8% increase in the value of BNP-32 (Table 4). This model explained 61% of the variability in log-transformed BNP-32 (R2 = 0.61); however, one potentially influential point was identified, and, when excluded, the association of GFR with BNP-32 weakened (P = 0.042). Age, GFR, serum albumin, and hemoglobin were significantly associated with log-transformed NT-BNP-76 levels, and for each 1-ml/min decrease in GFR, the value of NT-BNP-76 increased by 4.2% (Table 4). This model explained 63% of the variance of log-transformed NT-BNP-76 (R2 = 0.63). Influential points did not substantially alter these findings. No interactions with GFR were demonstrated for either form of BNP.

Table 4.
Independent predictors of natural logarithm-transformed BNP levels in patients with CKDa

Age, GFR, serum albumin, and presence of diabetes were significant predictors of log-transformed BNP-32 levels in renal transplant recipients (Table 4). This model explained 53% of the variance in log-transformed BNP-32 (R2 = 0.53), and each 1-ml/min decrease in GFR was associated with a 1.4% increase in BNP-32; however, exclusion of one influential observation significantly attenuated this association with GFR (P = 0.16). Age, GFR, serum albumin, and presence of diabetes all were significant predictors of log-transformed NT-BNP-76 (Table 4), and this model explained 61% of the variance (R2 = 0.61). Each 1-ml/min decrease in GFR was associated with a 12% increase in NT-BNP-76. A squared term for GFR was included in this model to correct the nonlinear association between GFR and natural log-transformed NT-BNP-76. Potentially influential points did not alter the relationship between NT-BNP-76 and GFR. No interaction with GFR was demonstrated for either form of BNP. One patient was excluded from these analyses of renal transplant recipients on the basis that the estimated GFR of 98 ml/min was in a range that could underestimate the true GFR by up to 29% (22).

Systolic dysfunction and cTnT ≥0.04 μg/L were strong predictors of BNP-32 levels in patients who were receiving dialysis (Table 4). This model explained 41% of the variance in log-transformed BNP-32 (R2 = 0.41), and the presence of systolic dysfunction raised BNP-32 levels by 138%. No potentially influential observations were identified. Age, natural log-transformed CRP, cTnT ≥0.04 μg/L, and presence of systolic dysfunction all were significant predictors of NT-BNP-76 levels in patients who were on dialysis (Table 4). This model explained 48% of the variance in log-transformed NT-BNP-76 (R2 = 0.48), and the presence of systolic dysfunction increased NT-BNP-76 levels by 262%. Removal of potentially influential points did not change the final variables in the model or their significance.

Association of BNP Levels with Outcome

There were insufficient events among the predialysis patients and kidney transplant recipients to allow analysis of outcomes; therefore, the outcome analysis was confined to the patients who were on dialysis. Of the 108 patients who were receiving dialysis, 24 subsequently experienced a cardiovascular event during a median follow-up of 2.5 yr. Median levels of BNP-32 and NT-BNP-76 were 187 ng/L (79 to 367) and 3392 ng/L (1548 to 11,228), respectively, in the patients with no events compared with 455 ng/L (131 to 1013) and 6659 ng/L (2868 to 37,808), respectively, in patients who experienced a cardiovascular event (P = 0.018 for BNP-32 and P = 0.014 for NT-BNP-76). Dichotomized at the median values of 221 and 3909 ng/L, respectively, the event-free survival was not different for patients with BNP-32 or NT-BNP-76 above this level (Figure 3). The hazard of cardiovascular events was increased 1.7 times (1.1 to 2.8; P = 0.030) for NT-BNP-76 and 1.5 times (1.0 to 2.1; P = 0.049) for BNP-32, but neither NT-BNP-76 (P = 0.312) nor BNP-32 (P = 0.893) remained significant predictors after adjustment for history of CVD and age in a multivariate model.

Figure 3.
(A and B) Event-free survival from the primary outcome for patients who were receiving dialysis, dichotomized at the median level of BNP-32 (A) and NT-BNP-76 (B).

Nineteen deaths occurred in the 108 patients who were receiving dialysis. Median levels of BNP-32 and NT-BNP-76 were 191 ng/L (91 to 367) and 3233 ng/L (1548 to 9516), respectively, in the patients who survived compared with 843 ng/L (155 to 2335) and 37,808 ng/L (4572 to 62,408), respectively, in patients who died (P < 0.001 for both comparisons). The survival of patients according to whether BNP was above the median values was significantly different for NT-BNP-76 but not BNP-32 (Figure 4). The adjudicated causes of death in patients with BNP above these cutoffs were CVD (n = 3), infection (n = 2), and other or uncertain (n = 5) for NT-BNP-76, and the corresponding numbers for BNP-32 were 5, 2, and 5, respectively. Both forms of BNP demonstrated a two- to three-fold increased hazard for death for each 1-SD increase in the natural logarithm-transformed BNP variable (Table 5). Adjusted for hs-CRP, both NT-BNP-76 and BNP-32 were significant independent predictors of death. None of the other adjustment variables listed in the methods section significantly altered the association of BNP and mortality. The proportional hazards assumption was met in both models, and neither model was susceptible to influential points.

Figure 4.
(A and B) Survival of patients who were receiving dialysis divided at the median level of BNP-32 (A) and NT-BNP-76 (B).
Table 5.
Adjusted and unadjusted hazard ratios for death in patients who were receiving dialysisa


Levels of BNP are highest in patients with the lowest GFR and are higher in the subgroup of patients with cardiac disease as identified on clinical history, echocardiography, or the surrogate clinical state of kidney transplant waiting list status. In particular, levels are substantially higher in patients who are on dialysis and have LVSD. Both BNP-32 and NT-BNP-76 are associated with all-cause mortality but not cardiovascular events in patients who are on dialysis. The greater elevation of NT-BNP-76 compared with BNP-32 in patients with CKD is an important issue in determining and interpreting diagnostic and prognostic thresholds.

In contrast to many studies, this study examined both BNP-32 and NT-BNP-76 in the same cohort. NT-BNP-76 levels were generally 20-fold higher than BNP-32 levels in patients who are on dialysis, and this reflects a seven- to eight-fold difference when converted to a molar unit of measurement. In a study of patients without CKD, levels of NT-BNP-76 were six- to 20-fold higher than BNP-32 comparing levels measured in ng/L (23), and in patients with stage 5 CKD, levels of NT-BNP-76 were almost eight-fold greater than BNP-32 measured in pmol/L (11). Ovine studies indicate that NT-BNP-76 has a much longer half-life than BNP-32, being 70 min compared with 5 min (24), and this longer half-life may contribute to the higher levels. The kidney is important in the elimination of BNP by receptor-mediated mechanisms, degradation by neutral endopeptidases, and renal clearance (25); however, the differences in the elimination of BNP-32 compared with NT-BNP-76 are controversial, with some authors suggesting that NT-BNP-76 is more influenced by renal function (10,11,26) and others suggesting that these markers are similarly influenced (27). Notwithstanding putative differences in elimination of these peptides, an important point for clinicians is that both are significantly associated with LVSD and mortality in patients with CKD.

Furthermore, the clinician must be aware of which peptide is being measured, because clinically significant NT-BNP-76 levels may be 20-fold greater (comparing ng/L values) or eight-fold greater (comparing pmol/L values) than BNP-32 levels. Treatments that reduce levels of BNP in these patients have been demonstrated and include treatment with metoprolol (28), carvedilol (29), or telmisartan (30). For patients on hemodialysis, changing from three 4-h sessions per week to six 2-h sessions per week also resulted in a fall in BNP-32 (31). Establishing the relationship between BNP levels, outcomes, and the clinical intervention are important future studies, including biomarker-directed intervention studies. These key studies will be necessary to determine the place of BNP in clinical practice and risk management.

Cardiovascular disease, defined in various ways, has been associated with higher BNP levels in patients with CKD, and these include a history of CVD (32), coronary artery disease (33), and left ventricular abnormalities such as LVH and LVSD (34). Because BNP increases significantly as GFR declines in predialysis and transplant patients, it is important to demonstrate that the association between elevated levels of BNP and the presence of CVD is independent of GFR to define the clinical utility of this cardiac biomarker. In the multivariate models in patients who were on dialysis, the presence of LVSD was the strongest predictor of BNP levels.

The strong association of BNP levels with LVSD may explain the independent association of both forms of BNP with mortality. We could not adjust for LVSD in these analyses because our echocardiography data were not complete; however, inclusion of a history of known CVD in the Cox model did not diminish this association. In other studies of patients who were on dialysis, both BNP-32 (34) and NT-BNP-76 (9,35) were strongly associated with mortality after adjustment for LVSD. In patients who were receiving peritoneal dialysis, higher quartiles of NT-BNP-76 were associated with mortality compared with the first quartile, but only the fourth quartile was significantly associated with mortality in an adjusted model that included LVSD and other factors (36). Despite the association with LVSD, neither form of BNP was a significant predictor of cardiovascular events in our study. Although this may be due to lack of power, it may be that elevated BNP is caused by a heart under stress from various causes of increased preload and afterload in patients with CKD, rather than by a coronary plaque that is about to rupture. Furthermore, the composite cardiovascular end point included stroke and peripheral vascular disease, which may be less influenced by things that raise BNP. One other study reported an association of NT-BNP-76 with cardiovascular events but included death in the composite outcome (37). In predialysis patients, BNP-32 was a predictor of heart failure events (38), but mortality was not reported.

The other important predictor of mortality in our study was CRP, an established mortality marker (39) that represents inflammation, another process that contributes to the cardiovascular morbidity and mortality of patients who are on dialysis. There may be a link between increased left ventricular wall tension and inflammation, because NT-BNP-76 was significantly associated with CRP in predialysis patients in another study (40).


Both forms of BNP are greater in patients who are on dialysis and have CVD by various measures. Higher levels of BNP can help to identify LVSD as well as patients who are at increased risk for death. Future studies on the use of BNP to guide therapy to reduce CVD risk are required to establish a role for BNP in the management of patients who are on dialysis, and their interpretation will need to consider the differences between BNP assays.




M.A.R. was supported by a National Health and Medical Research Council of Australia Scholarship (310632) and received a Clinical Epidemiology Training Scholarship from the National Health and Medical Research Council Centre of Clinical Research Excellence in Renal Medicine. This study was supported by unrestricted grants from Amgen, Bristol-Myers Squibb, Janssen-Cilag, and Servier. Abbott Diagnostics, Roche Diagnostics Australia, and Medtec Products Australia provided assay kits at various levels of discount for the biochemical assays.

Part of this study was presented at the annual scientific meeting of the Australian and New Zealand Society of Nephrology; August 14 through 18, 2006; Melbourne, Australia.


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


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