Clinical Chemistry 53: 1928-1935, 2007.
First published September 14, 2007; 10.1373/clinchem.2006.084426
(Clinical Chemistry. 2007;53:1928-1935.)
© 2007 American Association for Clinical Chemistry, Inc.
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Endocrinology and Metabolism |
Effects of Body Mass Index and Age on N-Terminal Pro–Brain Natriuretic Peptide Are Associated with Glomerular Filtration Rate in Chronic Heart Failure Patients
Morten Schou1,a,
Finn Gustafsson2,
Caroline N. Kistorp1,
Pernille Corell1,
Andreas Kjaer3,4 and
Per R. Hildebrandt5
1 Department of Cardiology and Endocrinology, Clinic E, Frederiksberg University Hospital, Frederiksberg, Denmark.
Departments of2
Cardiology, The Heart Centre, and3
Clinical Physiology and Nuclear Medicine, The PET Centre, Rigshospitalet University Hospital, Copenhagen, Denmark.
4 Cluster for Molecular Imaging, University of Copenhagen, Copenhagen, Denmark.
5 Department of Cardiology, Roskilde University Hospital, Roskilde, Denmark.
aAddress correspondence to this author at: Department of Cardiology and Endocrinology, Clinic E, Frederiksberg University Hospital, Ndr. Fasanvej 57-59, DK-2000 Frederiksberg, Denmark. Fax 45-38-16-43-59; e-mail m.schou{at}dadlnet.dk.
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Abstract
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Background: Obesity is a state characterized by glomerular hyperfiltration and age-related decreases in glomerular filtration rate (GFR). Body mass index (BMI), age, and GFR are associated with plasma concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP) in chronic heart failure (CHF) patients. We hypothesized that the effects of BMI and age on plasma concentrations of NT-proBNP are associated with GFR.
Methods: We obtained clinical data and laboratory test results from 345 CHF patients at the baseline visit in our heart failure clinic and examined the hypothesis using multiple linear regression models.
Results: Age (P = 0.0184), BMI (P = 0.0098), hemoglobin (P = 0.0043), heart rhythm (P <0.0001), and left ventricular ejection fraction (P <0.0001) were associated with log(NT-proBNP). After adjustment for GFR estimated by the Cockcroft and Gault equation, the parameter estimates for BMI (P = 0.3807) and age (P = 0.7238) changed markedly and became insignificant. In another model, after adjustment for GFR estimated by the 4-component Modification of Diet in Renal Disease formula (eGFRMDRD), the parameter estimates for age (P = 0.0674) changed markedly and became insignificant, but BMI (P = 0.0067) remained significant and unchanged. The eGFRMDRD is adjusted for body surface area, which may explain the difference.
Conclusions: In CHF patients, the effect of age on NT-proBNP is associated with estimates for GFR derived from serum creatinine, and the significance of the effects of BMI on NT-proBNP depends on the method by which GFR is estimated.
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Introduction
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Amino-terminal pro-brain natriuretic peptide (NT-proBNP)1
is an important risk marker in cardiovascular disease (1). Release of brain natriuretic peptides is caused by wall stress of the atria and ventricles (2). However, plasma peptide concentrations have also been shown to be associated with a number of demographic and clinical variables (3), and in most cases the mechanism behind these associations is poorly understood. It is well known that concentrations of natriuretic peptides increase with age, and although this finding does not appear to contradict intuition, a clear mechanism has not been elucidated. The association between age and NT-proBNP may be due to age-induced increased release or decreased clearance of NT-proBNP with increasing age, but it needs further investigation.
The apparent paradox that NT-proBNP decreases with increasing body mass index (BMI) is more complex (4)(5). The mechanisms behind the association between BMI and NT-proBNP are unclear. In the Dallas Heart Study (4), there was a significant association between muscle mass and NT-proBNP; hence, it may be speculated that NT-proBNP is cleared by an unknown mechanism in muscle tissue. However, Hunt et al. (6) and Goetze et al. (7) reported that no extraction occurs in the lower limbs, which contain large muscle groups. Therefore, the association between muscle mass and NT-proBNP may be caused by other unknown factors. Krauser et al. (5) suggested that obese individuals have obesity-induced impaired release of NT-proBNP, which is a hypothesis that calls for further testing.
An association between renal function and plasma concentrations of NT-proBNP has been documented several times and may be caused by small changes in renal extraction of NT-proBNP, because approximately 15% to 20% of the NT-proBNP delivered to the kidneys is extracted (6)(7). Renal clearance of NT-proBNP is also supported by the extremely high concentrations of NT-proBNP in hemodialysis patients (8), although this finding may be explained, in part, by accompanying cardiovascular disease (9). Renal clearance is further supported by the fact that NT-proBNP can be detected in urine (10), although the possibility that some local production occurs within the kidneys cannot be entirely excluded (11).
A unifying hypothesis to explain the associations between NT-proBNP and age, BMI, and glomerular filtration rate (GFR) could be that changes in glomerular filtration explain the relationship between age and NT-proBNP and, further, the inverse relationship between BMI and NT-proBNP in chronic heart failure (CHF) patients, because GFR decreases with age, and obesity is a state characterized by glomerular hyperfiltration (12)(13). We tested the hypothesis that the associations between BMI, age, and NT-proBNP are associated with GFR in CHF patients.
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Materials and Methods
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study population
All the patients in the present study (n = 345) were included from our specialized heart failure clinic at Frederiksberg University Hospital, Copenhagen, Denmark (14). Patients with known systolic CHF [left ventricular ejection fraction (LVEF) <0.45 by echocardiography] were referred to the clinic, either directly by general practitioners or by the department of internal medicine or cardiology of the hospital. At the baseline visit, all patients were examined by a physician and the following information was obtained: medical history, medication, physical examination, New York Heart Association (NYHA) classification based on patient information, measurements of height and weight, resting blood pressure and heart rate, x-ray of the heart and lungs, and electrocardiogram. All data were collected in the database program Hjerterplus (14). Data in the present study were collected from 2000 to 2005. In all patients, blood was drawn for routine analysis of sodium, potassium, creatinine, and hemoglobin, and if patients accepted, NT-proBNP was also analyzed. NT-proBNP testing was approved by the Ethical Committee of Copenhagen (KF 01-019699), and informed consent was obtained according to the Helsinki Declaration II.
laboratory analysis
After patients had fasted overnight for a minimum of 8 h and rested for 15 min before venipuncture, venous blood was drawn into EDTA (hemoglobin and NT-proBNP) and heparin tubes (sodium, potassium, and creatinine) and either promptly centrifuged at 4 °C and plasma analyzed the same day or stored as frozen plasma at –80 °C in aliquots. We analyzed plasma concentrations of sodium and potassium (Roche Integra 700) and creatinine (15) and hemoglobin (Sysmex XE 2100) on the day of collection. We analyzed plasma concentrations of NT-proBNP on the Roche Elecsys 2010 (16); imprecision values were 2.9% and 6.1% in the high and low range, respectively. Plasma samples of NT-proBNP can be stored at –20 °C for 8.5 to 10.5 years without affecting the concentration (17).
Estimated GFR (eGFR) was calculated by (a) the equation of Cockcroft and Gault (eGFRC-G): (140 – age) x weight in kg/plasma creatinine concentration in µmol/L x a constant (males, 1.25; females, 1.03) (18) and (b) the 4-component Modification of Diet in Renal Disease (MDRD) equation incorporating age, race, sex, and serum creatinine (eGFRMDRD): eGFR = 186 x (serum creatinine in mg/dL)–1.154 x (age in years)–0.203. For women and blacks (none in our cohort), the product of the equation has to be multiplied by a correction factor of 0.742 and 1.21, respectively (19). The accuracy and precision of creatinine-derived estimates for GFR in CHF patients have recently been validated (20).
statistical analysis and calculation algorithms
We tested the hypothesis of the study by multiple linear regression analysis. We chose log(NT-proBNP) as the response variable because the distribution was skewed to the right, and percentage changes could then be calculated (see below). Explanatory variables (covariates) were either linear variables or class variables. We chose explanatory variables in the multiple linear regression models if they had previously been identified to be associated with plasma concentrations of NT-proBNP. First, we made a model without adjustment for eGFR. We eliminated all insignificant variables by a backward elimination procedure to get a simple model (model 1). Subsequently, we adjusted for eGFR to see whether the effects of age and BMI were associated with eGFR. We eliminated insignificant variables to get the simplest model (models 2 to 4). We applied formal tests for interaction between the covariates in the final model: eGFRC-G and eGFRMDRD were dichotomized [±60 mL/min or ±60 mL · min–1 · (1.73 m2)–1] to see whether the effects of the other variables interacted with renal dysfunction. Formal testing was also performed for possible interactions between the presence or absence of atrial fibrillation and the effects of all other variables and for presence or absence of anemia and the effects of all other variables. The heart rhythm from patients with a cardiac rhythm (n = 19) other than sinus rhythm or atrial fibrillation were considered as missing data to compare only the effect of atrial fibrillation and sinus rhythm.
All linear covariates were evaluated by model control (linearity, variance inhomogeneity, gaussian distribution around the regression line). Log transformation was done where indicated. Hemoglobin was log-transformed to obtain the effect of percentage changes in plasma concentrations of hemoglobin on NT-proBNP. All logarithms were proportional [logy(x) = log(x)/log(y)], and log1.1 was chosen for eGFR and hemoglobin to show the effects of 10% increases or decreases in eGFR and hemoglobin on log(NT-proBNP) instead of a 2.7-fold increase (log) or 10-fold increase (log10). Log1.1 was chosen over log and log10 because this logarithm displays the effect of clinically relevant changes in the parameter (10%) in a more direct fashion.
The effects of parameters estimates on log(NT-proBNP) were transformed back to percentage changes as follows:
Percentage changes were then calculated as follows:
The effects of age and BMI on eGFR were transformed back in a similar way. Parameter estimates and confidence limits for BMI, age, eGFRC-G, and eGFRMDRD were transformed back. We performed simple linear regression analyses for the relationship between log1.1(eGFR) and BMI and between log1.1(eGFR) and age.
A P value <0.05 (2-sided) was considered significant. Analyses were performed using Statistical Analysis Software (SAS 9.1).
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Results
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Patient characteristics are presented in Table 1 in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol53/issue11 . The prevalence of anemia according to WHO criteria (hemoglobin <7.5 mmol/L in females and <8.0 mmol/L in males) was 27%. None of our patients received treatment with erythropoietin. The relationship between log1.1(eGFRC-G) and age is presented in Fig. 1A
and between log1.1(eGFRC-G) and BMI in Fig. 2A
. The relationship between log1.1(eGFRMDRD) and age is presented in Fig. 1B
and between log1.1(eGFRMDRD) and BMI in Fig. 2B
. Multiple linear regression models including adjustment for eGFRC-G are presented in Table 1
. The final simple model included LVEF, heart rhythm, log1.1(hemoglobin), and log1.1(eGFRC-G). There were no interactions in the final model. Multiple linear regression models including eGFRMDRD are also presented in Table 1
. The final simple model included BMI, LVEF, heart rhythm, log1.1 (hemoglobin), and log1.1(eGFRMDRD). There were no interactions in the final model.
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Figure 1. Simple linear regression model illustrating the relationship between log1.1(eGFRC-G) and age using Cockcroft and Gault equation [log1.1(eGFRC-G) = ßalder = –0.303; SE = 0.017; 95% CI = –0.336 to –0.270; P <0.0001] (A) and log1.1(eGFRMDRD) and age using MDRD formula [log1.1(eGFR) = ßalder = –0.120; SE = 0.015; 95% CI = –0.148 to –0.009; P <0.0001] (B).
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Figure 2. Simple linear regression model illustrating the relationship between log1.1(eGFRC-G) and BMI using Cockcroft and Gault equation [log1.1(eGFRC-G) = ßBMI = 0.551; SE = 0.420; 95% CI = 0.468 to 0.634; P <0.0001] (A) and log1.1(eGFRMDRD) and BMI using MDRD formula [log1.1(eGFRMDRD) = ßBMI = 0.089; SE = 0.036; 95% CI = 0.018 to 0.160; P = 0.0139] (B).
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Using models including the Cockcroft and Gault equation, the effect of a 10-year increase in age on eGFRC-G is:
The effect of an increase of 5 kg/m2 in BMI on eGFRC-G is:
The effect of a 10% decrease in eGFRC-G on NT-proBNP is:
With the use of models including the MDRD formula, the effect of a 10-year increase in age on eGFRMDRD is:
The effect of an increase of 5 kg/m2 in BMI on eGFRMDRD is:
The effect of a 10% decrease in eGFRMDRD on NT-proBNP is:
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Discussion
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Our statistical models confirmed our unifying hypothesis that the relationships between BMI, age, and NT-proBNP are associated with estimates for GFR derived from serum creatinine (Figs. 1
and 2
). However, the significance of the effects of BMI on NT-proBNP depends on the method by which GFR is estimated (Table 1
).
Our model including eGFRC-G quantifies the interindividual relationship between eGFR and NT-proBNP in a clinically applicable fashion: NT-proBNP increases 9% (5%–12%) if eGFRC-G decreases 10% (Fig. 3A
; percentage change NT-proBNP = 1.09x, where x is how many times eGFRC-G decreases 10%). Our model including eGFR estimated by the MDRD formula further quantifies the interindividual relationship between eGFR and NT-proBNP in a clinically applicable fashion: NT-proBNP increases 7% (2%–11%) if eGFRMDRD decreases 10% (Fig. 3B
; percentage change NT-proBNP = 1.07x, in which x is how many times eGFRMDRD decreases 10%). The reason the parameter estimates vary slightly even in adjusted models may be that eGFRC-G estimates creatinine clearance, whereas eGFRMDRD estimates GFR (18)(19). Changes in GFR and NT-proBNP of that magnitude would be difficult to detect in a longitudinal intervention study owing to analytical error and biological variation. Furthermore, the effects may reflect metabolism rather than volume overload, because hemoglobin [surrogate marker for plasma volume size, at least in patients with normal- or high-range concentrations (21)(22)] was fixed in our models. In a longitudinal intervention study, it would be difficult or impossible to separate the effects of decreases in GFR and increases in total body volume on NT-proBNP, underlining the potential relevance of our study.
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Figure 3. Scatter plot illustrating the relationship between log(NT-proBNP) and log1.1(eGFRC-G) using Cockcroft and Gault equation [adjusted ßlog1.1(eGFRC-G) = –0.084; SE = 0.017; 95% CI= –0.117 to –0.052; P <0.0001] (A) and log(NT-proBNP) and log1.1(eGFRMDRD) using MDRD formula (adjusted ßlog1.1(eGFRMDRD) = –0.065; SE = 0.023; 95% CI= –0.111 to –0.020; P = 0.0047; B).
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The Cockcroft and Gault equation does not adjust for body surface area, which is in contrast to the MDRD formula, which incorporates correction for body surface area directly. The glomerular filtered load of NT-proBNP is GFR x NT-proBNP rather than adjusted GFR x NT-proBNP, and consequently adjustment for body surface area should not be done from a physiological point of view. From a mathematical point of view, adjustment for body surface area will adjust BMI back in the model (Table 1
), and the MDRD formula should therefore not be used for this purpose. However, eGFRMDRD may be a more precise and accurate estimate for true GFR than eGFRC-G, which is an estimate for creatinine clearance. For this reason, we also used the MDRD formula in our models. When eGFRMDRD was used, the parameter estimate for age changed markedly and became insignificant. In contrast, the parameter estimate for BMI did not change and stayed significant in the model. This result may reflect body surface area adjustment (see above) or the possibility that GFR does not explain the association between BMI and NT-proBNP. The latter explanation appears less likely owing to the almost identical parameter estimates for eGFRC-G and eGFRMDRD in the adjusted models and the fact that an increase of 5 kg/m2 in BMI is associated with a 30% increase in eGFRC-G (no body surface area adjustment) induced by increases in renal blood flow due to weight gain, but with only a 4% increase in eGFRMDRD (body surface area adjustment; Fig. 2
, A and B). Therefore, increases in BMI are associated with pronounced increases in eGFRC-G induced by augmentation of renal blood flow, which may result in increased glomerular filtration and renal degradation of NT-proBNP, but not necessarily increased urinary excretion of NT-proBNP (12). Urinary excretion of NT-proBNP reflects the sum of cardiac production, glomerular filtration, and renal degradation. In a recent study by Cortes et al. (10), obese CHF patients excreted a decreased amount of NT-proBNP, which may be explained by increased degradation of the peptide within the kidneys, possibly by dipeptidyl peptidase IV/CD26, an enzyme located in the kidneys and intestine brush-border membranes, hepatocytes, and vascular endothelium (23). In fact, dipeptidyl peptidase IV has recently been shown to have a strong affinity for brain natriuretic peptide (24).
eGFRC-G is derived from age, weight, plasma concentrations of creatinine, and a sex correction factor (18) and may reflect true GFR. In theory, colinearity could have hampered the multivariable model. However, our data suggest that if eGFRC-G is fixed, changes in age or BMI do not add information to the model, but eGFRC-G remains statistically significant. If colinearity were present, the parameter estimate of eGFR would also lose its statistical significance in the model. eGFRMDRD is derived from creatinine and age (19) and may reflect true GFR adjusted for body surface area, but colinearity could also have constituted a problem. This possibility seemed not to be the case, however, because a number of parameter estimates remained significant. As a consequence, although eGFRC-G and eGFRMDRD are derived from age and weight, they can be used together in statistical models with other variables, such as age, because they reflect true GFR, and colinearity was not present. All estimates of renal function, as well as true GFR, are associated with age and weight, but it would have been preferable and interesting if an estimate of renal function that was not derived from age and weight had been available to confirm our results. Future studies should include such estimates, for instance, cystatin C (25).
The results concerning the association between BMI and NT-proBNP have been ambiguous. In the Dallas Heart and ValHeft studies, there was a significant association between BMI and NT-proBNP, but in the Dallas Heart Study the association was primarily between muscle mass and plasma concentrations of NT-proBNP (5). In these studies, adjustment for GFR were not performed, making it difficult to compare their results to ours. On the basis of our data, we cannot exclude the possibility that skeletal muscle tissue plays a role in NT-proBNP metabolism in CHF patients, but the mechanism is unknown at present, because NT-proBNP is probably not cleared by endopeptidases in humans [extrapolated from N-terminal proatrial natriuretic peptide (26)] and is not cleared by endopeptidases and natriuretic peptide clearance receptor in sheep (27). In CHF patients, aging induces a decline in GFR, as it does in healthy individuals (28). We therefore hypothesized that adjustment for eGFR would remove age from the model. This hypothesis was confirmed. This hypothesis will be difficult to test further in a longitudinal study, because of the challenges imposed by observing CHF patients for long periods of time with all other parameters fixed.
The following limitations were noted. We calculated eGFRC-G (18) and eGFRMDRD (19). To get a more precise estimate of the effect of changes in GFR on changes of plasma concentrations of NT-proBNP, 51chromium EDTA clearance or insulin clearance would have been desirable. Furthermore, it should be kept in mind that creatinine-derived estimates may be inaccurate estimates in the low ranges of GFR. However, eGFRC-G and eGFRMDRD are used in clinical practice, so the effects of different concentrations of eGFR on plasma concentrations of NT-proBNP are of clinical interest. Furthermore, our patients did not undergo a dual energy x-ray absorptiometry scan, and thus we cannot separate the effects of fat tissue and muscle mass on plasma concentrations of NT-proBNP. Investigating such effects would be of interest in future studies. Our results cannot readily be extrapolated to healthy populations or patients with acute dyspnea, because the effects of age, BMI, eGFRC-G/eGFRMDRD, hemoglobin, heart rhythm, and LVEF on plasma concentrations of NT-proBNP may differ. Parameters such as NYHA classification and use of angiotensin-converting enzyme inhibitors and ß-blockers were associated with log(NT-proBNP) in a multivariate model in the ValHeft study (3), but this was not the case in our models [NYHA classification was associated with log(NT-proBNP) in a univariate model (data not shown)], perhaps because of lack of power in our study. Finally, given the fact that this is a cross-sectional, observational study, no firm conclusions can be made with regard to the mechanisms behind the associations between BMI and age and GFR and NT-proBNP.
Our data indicate that NT-proBNP reflects cardiorenal function (29), a finding that suggests a reasonable physiological explanation—the level of GFR—for the relationship between age and plasma concentrations of NT-proBNP and the relationship between BMI and plasma concentrations of NT-proBNP in CHF patients. The effects of BMI on NT-proBNP, however, obviously depend on the method by which GFR is estimated. Although NT-proBNP may reflect cardiorenal function, van Kimmenade et al. (30) observed that a combination of eGFR and NT-proBNP predicts short-term outcome more accurately in acute heart failure than either parameter alone. Furthermore, glomerular hyperfiltration rather than obesity per se may result in false-negative concentrations of NT-proBNP in obese patients with mild heart failure. When evaluating possible renal effects on concentrations of natriuretic peptides in obese patients, eGFRC-G should be calculated rather than serum creatinine or eGFRMDRD to assess glomerular hyperfiltration. In fact, the current study provides an estimate of the interindividual relationship between eGFRC-G and NT-proBNP: a decrease of 10% in eGFRC-G is associated with an increase of 9% in plasma concentrations of NT-proBNP.
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Acknowledgments
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Grant/funding support: M.S. is supported by Research Grant 200207135A-321 from the Copenhagen Hospital Corporation.
Financial disclosures: P.R.H. has received honoraria for lectures on NT-proBNP from Roche Diagnostics.
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Footnotes
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1 Nonstandard abbreviations: NT-proBNP, amino-terminal pro-brain natriuretic peptide; BMI, body mass index; GFR, glomerular filtration rate; CHF, chronic heart failure; NYHA, New York Heart Association; eGFRC-G, GFR estimated by the equation of Cockcroft and Gault; MDRD, Modification of Diet in Renal Disease; eGFRMDRD, GFR estimated by the MDRD formula; LVEF, left ventricular ejection fraction. 
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Abstract
Introduction
