## Abstract

ABSTRACT. Many epidemiologic studies presently aim to evaluate the effect of risk factors on renal function. As direct measurement of renal function is cumbersome to perform, epidemiologic studies generally use an indirect estimate of renal function. The consequences of using different methods of renal function measurement in studies that evaluate the effect of cardiovascular risk factors on renal function were questioned. Data of the 8592 Prevention of Renal and Vascular End-stage Disease study participants, in whom the association was plotted between various cardiovascular risk factors and renal function measured either by creatinine clearance based on two 24-h urine collections or by the Cockcroft-Gault or Modification of Diet in Renal Disease formula were used. A repeated measurement analysis was used to compare the slopes of the linear regression lines of the risk factors and the different methods of renal function measurements. The relation between cardiovascular risk factors and renal function seems to be different when different methods for renal function are used. This was most pronounced for age, weight, and body mass index and less pronounced (but still statistical significant) for BP, cholesterol, and glucose. The relation between weight or body mass index and renal function showed completely different directions, depending on the renal function method used. In conclusion, the interpretation of the relation of cardiovascular risk factors and renal function is affected by the method selected to estimate renal function. For studying the relation of risk factors and renal function in large population studies, indirect estimates of renal function should be used with caution.

In recent years, many large-scale epidemiologic surveys, such as the Atherosclerosis Risk in Communities (ARIC) (1), National Health and Nutrition Examination Survey (NHANES) (2), Australian Aborigines Study (3), and large hospital-based ambulatory patient studies (4), investigated which factors have a detrimental effects on renal function. As it is not feasible to perform direct measurements of renal function in such studies, investigators mostly relied on serum creatinine–derived estimates of renal function, such as the Cockcroft-Gault (CG) formula (5) and the recently developed (6) and later simplified (7) Modification of Diet in Renal Disease (MDRD) formula. The CG formula is an estimate of creatinine clearance originally developed in a population of 236 mainly male patients. The MDRD formula has been developed to estimate GFR in a population of 1628 patients with chronic renal disease. The recent National Kidney Foundation Kidney Disease Outcomes Quality Initiative guidelines recommend these measures as superior to the use of 24-h creatinine clearance measurements to define renal function in a given patient (8). The reason for this choice was based on the fact that for individual patients, the collection error of 24-h creatinine clearance has greater impact than the estimation error of the formula. The applicability of these formulas in the population at large, for example, to study the impact of certain cardiovascular (CV) risk factors on renal function, however, has been debated (9–11⇓⇓). It was concluded that more extensive documentation of these methods is required (12).

Serum creatinine is dependent not only on renal function but also on muscle mass. Because muscle mass diminishes with age, is lower in women than in men, and is increased in larger individuals and in black individuals, the formulas also include parameters such as age (both CG and MDRD), gender (both CG and MDRD), weight (CG), and race (MDRD). As the MDRD estimate of renal function is expressed in ml/min per 1.73 m^{2}, the formula implicitly includes weight and height in the estimate. We questioned whether the associations between CV risk factors and renal function could be determined when renal function is studied by these indirect estimates. To that purpose, we compared the association of CV risk factors with three estimates of renal function: the CG and MDRD formulas and creatinine clearance.

## Materials and Methods

### Participants and Protocol

The study was performed in the individuals who were participating in the Prevention of Renal and Vascular End-stage Disease (PREVEND) study, an ongoing follow-up study of 8592 participants that was enriched for the presence of increased urinary albumin excretion. The study protocol has been described in detail elsewhere (13,14⇓). The present data were derived from the first screening that took place in 1997 to 1998. All participants answered a detailed questionnaire on demographics and CV and renal history. All participants were seen twice at the outpatient unit, where anthropometric measurements were performed. After removal of shoes and heavy clothing, weight was measured to the nearest 0.5 kg with a Seca balance scale (Seca Vogel & Halke, Hamburg, Germany). Height was measured to the nearest 0.5 cm. Minimal waist circumference was measured on bare skin at the natural indentation between the 10th rib and the iliac crest. Hip circumference was measured at the maximum circumference of the buttocks. At both visits, BP was measured in supine position, every minute, for 10 and 8 min, respectively, with an automatic Dinamap XL Model 9300 series monitor (Johnson-Johnson Medical, Tampa, FL). Participants were asked to perform 24-h urine collections on 2 consecutive days in the last week before the second visit. The participants were given oral and written instructions on how to collect a 24-h urine collection, and they were instructed to postpone urine collection in case of fever, urinary tract infection, or menstruation and to refrain as far as possible from heavy exercise during the collection period. Furthermore, the participants were asked to store the urine cold (4°C) for a maximum of 4 d before the second visit. Measurements of urinary volume and creatinine concentrations were performed on each collection. At the second visit, blood was drawn after an overnight fast for determination of serum cholesterol, plasma glucose, and creatinine. The participants gave written informed consent. The local medical ethics committee approved the PREVEND study, and the conduct of the project was in accordance with the guidelines of the declaration of Helsinki.

### Calculations

Systolic and diastolic BP was calculated as the mean of the last two measurements of the two visits. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m^{2}). Waist-to-hip ratio (WHR) was calculated as the ratio between minimal waist circumference (cm) and hip circumference (cm). Body surface area (BSA) was calculated according to Dubois and Dubois (15). Creatinine clearance was defined as the mean of the two creatinine clearances based on 24-h urinary creatinine excretions divided by plasma creatinine. The CG estimate of renal function was calculated as [(140 − age) × weight]/72 × serum creatinine (× 0.85 if female). The (simplified) MDRD estimate of renal function was calculated as 186 × (serum creatinine)^{−1.154} × (age)^{−0.203} (× 0.742 if female) (16). Serum creatinine is included in the formulas as mg/dl. Both measured creatinine clearance and CG clearance are expressed as ml/min, whereas MDRD clearance is expressed as ml/min per 1.73 m^{2}. As we wanted to compare the various methods to measure renal function with each other, we separately corrected measured creatinine clearance and CG clearance for standard BSA by multiplying measured creatinine clearance and CG clearance by 1.73/BSA.

### Laboratory Methods

Creatinine assessments in blood and urine and serum cholesterol and glucose were determined by Kodak Ektachem dry chemistry (Eastman Kodak, Rochester, NY). The intra- and interassay variation coefficient of serum creatinine were, respectively, 0.86 and 1.11%. For urinary creatinine, the coefficients were, respectively, 0.90 and 2.90%.

### Statistical Analyses

As 95% of the participants in the PREVEND study are white, we could not properly study the influence of race on renal function estimation. For this reason, we excluded the 460 nonwhite participants. This left 8132 participants for the present analyses. The analyses were performed stratified for gender. To compare the demographic variables and the renal function measurements between the two subgroups, we used a *t* test. For descriptive purposes, the mean renal function was calculated for every decile of the variables of interest. In the graphs, the points of renal function per decile of the risk factor were connected by a line. A repeated measurement analysis was used to compare the curves of the regression lines of the CV risk factors and the different methods of renal function measurements. For simplicity reasons, *linear* regression lines were compared. We did not intend to test the absolute difference of the curves because the MDRD curve is expected to be on a lower level compared with creatinine clearance and CG. The relation of the renal function estimates and the CV risk factors was not corrected for confounders, as it was not our goal to study separately the influence of various risk factors individually on renal function. All *P* are two-sided, and *P* < 0.05 was considered statistically significant. No correction for multiple comparisons was made. SAS version 8.2 (Cary, NC) and SPSS version 10.0 were used to perform the statistical analyses.

## Results

Age and the parameters of body mass were higher in men than in women (Table 1). Serum creatinine and the various measures of renal function were also higher in men than in women, except for CG clearance when expressed corrected for standard BSA. For men and women, measured creatinine clearance was higher than both estimated CG clearance and MDRD clearance, and CG clearance was higher than MDRD clearance.

Figures 1 and 2⇓ show the relation between the various risk factors and renal function for men. The *P* values for the repeated measurement analysis comparing the curves of the linear regression lines for men and women are given in Table 2.

Figure 1A shows that the relation between age and the renal function estimates is different for the different renal function methods used. Measured creatinine clearance difference at ages 30 to 50 was ∼5 ml/min per 1.73 m^{2}, *i.e.*, 0.25 ml/min per 1.73 m^{2} per year. Between the ages of 50 to 70, measured clearance was ∼1 ml/min per 1.73 m^{2} per year lower. CG clearance showed a steeper decline in renal function over age in both men and women (*P* < 0.001). The curves of MDRD and creatinine clearance *versus* age were not statistically different (*P* = 0.98) in men but differed in women (*P* < 0.001). The differences in the curves of the different renal function estimates and age were also present after dividing the population into tertiles according to creatinine clearance. The results were not dependent on the level of renal function of the population under study.

We used the mean of two creatinine clearances measured on 2 consecutive days. To explore systematical differences between the two measurements, we studied creatinine clearance measured at day 1 and day 2 separately. Similar results were obtained.

When plotting the relation of weight on renal function, the CG and MDRD estimates showed a different pattern than 24-h creatinine clearance. Measured creatinine clearance was similar over the various deciles for weight. The CG and MDRD estimates, however, showed a different and opposite pattern. In men (Figure 1B) and women, CG clearance was higher for a greater weight, whereas MDRD clearance was lower for a greater weight (*P* < 0.001).

The relation between BMI and the various estimates of renal function showed a similar pattern as was seen for body weight (Figure 1C). Measured creatinine clearance was more or less similar over the various deciles. The CG and MDRD formulas showed again an opposite pattern. Whereas CG clearance was higher for a higher BMI, especially in women, MDRD clearance was lower for a higher BMI. Again, measured creatinine clearance was not different for the various weight deciles.

We next studied the parameters that are not taken into account in the renal function formulas. The relation between WHR and renal function was different when comparing MDRD *versus* CCR and CG (Figure 1D). Over a wide range of WHR, measured creatinine clearance was lower at higher WHR. The difference in GFR with a greater WHR was more pronounced when the MDRD formula estimated renal function.

We finally evaluated the relation between systolic and diastolic BP and serum cholesterol and plasma glucose (Figure 2) with renal function. In all of these situations, the relation between the CV risk parameter and renal function again was different depending on the renal function method used, albeit less pronounced than with the above described parameters.

## Discussion

We showed that the relation between various CV risk factors and renal function may lead to different conclusions when different estimates of renal function are used. This is most pronounced for factors that are included in the CG and MDRD formula, such as gender, age, weight, and, thus, BMI. Partly, this can be explained mathematically by the presence of collinearity because the parameter of interest is also incorporated in the estimates of the outcome variable. When the factor studied is not included in the formula, such as WHR, systolic and diastolic BP, cholesterol, and glucose, the differences derived by using different estimates were less pronounced, although often still statistically significant. This suggests that conclusions drawn on indirect measures for renal function should be interpreted with caution.

We found mean MDRD clearance to be lower than both CG clearance and measured creatinine clearance. This by itself is not surprising, as the MDRD formula is developed to be an estimate of actual GFR (6), not of creatinine clearance. The renal secretion of creatinine is ∼10% in the normal range of GFR, which is in line with the observed difference between MDRD clearance and measured creatinine clearance in this study.

The most misleading findings, induced by indirect renal function formulas, were obtained when studying the relation of body weight and BMI *versus* renal function. The CG formula estimated a higher renal function at a higher weight and BMI. The MDRD formula, in contrast, estimated a lower renal function for a higher weight and BMI, whereas measured clearance was not different for the various weight and BMI deciles. It is interesting that in the original paper by Cockcroft and Gault, the authors already reported that the formula was not appropriate for individuals with marked obesity (5). In many studies that have evaluated the effect of obesity on kidney function, however, this restriction of the use of the formula was not taken into account. The data from Figure 1 show that the mean renal function of an individual of 73 kg amounts to the CG formula 85 ml/min per 1.73 m^{2} and according to the MDRD formula 86 ml/min per 1.73 m^{2}. For a 98-kg individual, the CG formula gives a clearance of 92, which is 7 ml/min per 1.73 m^{2} higher compared with a 73-kg individual, whereas the MDRD formula shows that same 98-kg individual has a clearance of 81 kg, which is 5 ml/min per 1.73 m^{2} lower than in a 73-kg person.

The association between age and renal function also differs depending on the estimate of renal function used. CG clearance indicates a steeper decline of renal function with age compared with measured creatinine clearance and MDRD formula. The relation between the two formulas and measured creatinine clearance moreover shows that the formulas do not appreciate the curved pattern that the measured clearance showed. It is known from studies performed with GFR measurements done by the gold standard that such a curve is indeed present in the relation between renal function and age (17) and that this pattern is less steep when the MDRD formula is used (18). Figure 1A shows that measured CCR is 18 ml/min per 1.73 m^{2} lower in a 67-yr-old *versus* a 35-yr-old man, whereas CG estimated clearance is 34 ml/min per 1.73 m^{2} lower and MDRD is 17 ml/min per 1.73 m^{2}.

The consequence of the differences in the methods of estimating renal function for epidemiologic studies is clear. The effects of the parameters age, body weight, BMI, or other CV risk factors on renal function should be interpreted with caution when using the various indirect estimates of renal function, as the results might lead to different conclusions. These observations were not dependent on the level of renal function of the population, because comparable results were obtained in the lowest, middle, and highest ranges of renal function. Because it was not the scope of our study to study separately the relation of various risk factors on renal function, the presented relations were univariate and thus not corrected for other CV risk factors.

A mechanism for the differences in the association of renal function estimates and CV risk factors is possibly an inadequate estimation of creatinine production over the ranges of the risk factors. Moreover, the renal function estimates are developed on a population different from the PREVEND population. These mechanisms will be only hypothetical but cannot be confirmed by the data.

Of course, it is a limitation of our study that we did not compare the risk factors or renal function estimates with actual GFR measurements. However, our study has the advantage that it is the first that is able to compare different renal function formulas and creatinine clearance in a large population-based study. Our study has also the advantage that we have data on various objectively measured parameters of body size.

The MDRD formula was derived from a population predominantly with chronic kidney disease. Recently, the performance of the MDRD formula was tested in “healthy” individuals, and the authors concluded that prediction equations may not be sufficient for estimating GFR (19). However, more and more studies use this formula in individuals with normal renal function. The current study shows the potential dangers of such a nonvalidated use. We conclude that different estimates of renal function show a different relation between various CV risk factors and renal function in apparently healthy individuals. This is especially so for the factors that are included, either directly or indirectly, in the formula to estimate renal function, such as gender, age, weight, and, thus, BMI.

## Acknowledgments

We thank the Dutch Kidney Foundation for supporting the PREVEND study (Grant E033).

The PREVEND study group consists of the following: P.E. de Jong, G.J. Navis, R.T. Gansevoort, and J.C. Verhave (Department of Medicine, Division of Nephrology); D. de Zeeuw, W.H. van Gilst, and R.H. Henning (Department of Clinical Pharmacology); R.O.B. Gans, S.J.L. Bakker, A.J. Smit, A.M. van Roon, and E.M. Stuveling (Department of Medicine, Division of Vascular Medicine); D.J. van Veldhuisen, H.L. Hillege, A.J. van Boven, F.W. Asselbergs, and C.P. Baljé-Volkers (Department of Cardiology); R.P.F. Dullaart, and S. Borggreve (Department of Medicine, Division of Endocrinology); G.J. te Meerman, and G.T. Spijker (Department of Medical Genetics); V. Fidler, and J.G.M. Burgerhof (Department of Epidemiology and Statistics); L.T.W. de Jong-van den Berg, M. J. Postma, and J. van den Berg (Department of Phamaco-Epidemiology); J.H.J. Muntinga (Department of Medical Physiology, all of the University Medical Center Groningen); and D.E. Grobbee (Department of Epidemiology, Julius Center, Utrecht).

- © 2004 American Society of Nephrology