The impact of albuminuria and cardiovascular risk factors on renal function Verhave, Jacoba Catharijne

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1 University of Groningen The impact of albuminuria and cardiovascular risk factors on renal function Verhave, Jacoba Catharijne IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2004 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Verhave, J. C. (2004). The impact of albuminuria and cardiovascular risk factors on renal function Groningen: s.n. Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date:

2 Chapter 2 Drawbacks of the use of indirect estimates of renal function to evaluate the effect of risk factors on renal function Jacobien C. Verhave, Ron T. Gansevoort, Hans L. Hillege, Dick de Zeeuw, Gary C. Curhan, Paul E. de Jong J Am Soc Nephrol 2004;15:

3 34 Chapter 2 Abstract Objectives Many epidemiological studies presently aim at evaluating the effect of risk factors on renal function. As direct measurement of renal function is cumbersome to perform, epidemiological studies generally use an indirect estimate of renal function. We questioned what the consequences are of using different methods of renal function measurement in studies evaluating the effect of cardiovascular risk factors on renal function. Methods We used data of the 8,592 PREVEND study subjects, in whom we plotted the association between various cardiovascular risk factors and renal function measured either by creatinine clearance based on two 24h urine collections, or by the Cockcroft- or MDRD formula. 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 measurement. Results The relation between cardiovascular risk factors and renal function appears to be different when different methods for renal function are used. This was most pronounced for age, weight and BMI, and less pronounced (but still statistical significant) for blood pressure, cholesterol, and glucose. The relation between weight or BMI and renal function showed completely different directions dependent on the renal function method used. Conclusions In conclusion, the interpretation of the relation of cardiovascular risk factors and renal function is affected by the method selected to estimate renal function. To study the relation of risk factors and renal function in large population studies indirect estimates of renal function should be used with caution. Keywords Renal function estimates, cardiovascular risk factors, population studies, creatinine clearance, PREVEND.

4 Drawbacks of renal function estimates 35 Introduction In recent years many large scale epidemiological 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- (CG) formula (5) and the recently developed (6), and later simplified (7) Modification of Diet in Renal Disease (MDRD) formula. The Cockcroft- 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 glomerular filtration rate (GFR) in a population of 1,628 patients with chronic renal disease. The recent NKF DOQI guidelines recommend these measures as superior to the use of 24 hour creatinine clearance measurements to define renal function in a given subject (8). The reason for this choice was based on the fact that for individual patients the collection error of 24 hour 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, has however been debated (9-11). It was concluded that more extensive documentation of these methods is required (12). Serum creatinine is not only dependent on renal function, but also on muscle mass. Since muscle mass diminishes with age, is lower in women than men, and is increased in larger subjects and in African Americans, the formulas also include parameters such as age (both CG and MDRD), sex (both CG and MDRD), weight (CG) and race (MDRD). As the MDRD estimate of renal function is expressed in ml/min/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 cardiovascular risk factors with three estimates of renal function; the Cockcroft- and MDRD formulas and creatinine clearance. Material and Methods Subjects and protocol The study was performed in the subjects participating in the PREVEND (Prevention of Renal and Vascular End-stage Disease) study, an ongoing follow up study of 8,592 subjects, 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 All subjects answered a detailed questionnaire on demographics and cardiovascular and renal history. All subjects were seen twice at the outpatient unit, where anthropometrical 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 GmbH & Co, 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 10 th rib and the iliac crest. Hip circumference was measured at the maximum circumference of the buttocks. At both visits blood pressure was measured in supine position, every minute, for 10 and 8 minutes, respectively, with an automatic Dinamap XL Model 9300 series monitor (Jonhson-Johnson Medical Inc, Tampa, Florida). Subjects

5 36 Chapter 2 were asked to perform 24h urine collections on two consecutive days in the last week before the second visit. The subjects were given oral and written instructions on how to collect a 24h urine 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 subjects were asked to store the urine cold (4 o C) for a maximum of 4 days prior to 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 subjects 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 blood pressure 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 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-Dubois (15). Creatinine clearance was defined as the mean of the two creatinine clearances based on 24h urinary creatinine excretions divided by plasma creatinine. The Cockcroft- (CG) estimate of renal function was calculated as [(140-age) x weight] / 72 x (serum creatinine) (x 0.85 if female). The (simplified) Modification of Diet in renal Disease (MDRD) estimate of renal function was calculated as 186 x (serum creatinine) x (age) x (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, while MDRD clearance is expressed as ml/min/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 Body Surface Area (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, New York, United States). The intra- and interassay variation coefficient of serum creatinine were respectively 0.86 and 1.11%. For urinary creatinine the coefficients were respectively 0. and 2.%. Data handling and statistics As 95% of the subjects in the PREVEND study are Caucasian, we could not properly study the influence of race on renal function estimation. For this reason we excluded the 4 non- Caucasians. This left 8,132 subjects 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, a Student's t-test was used. 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 line 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 to CCR and CG. The relation of the renal function estimates and the cardiovascular risk factors was not corrected for confounders, as it was not our goal to separately study the influence of various risk factors individually on renal function. All p-values are two-sided and

6 Drawbacks of renal function estimates 37 a p-value <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 visually 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 was about 5 ml/min/1.73 m 2, i.e ml/min/1.73 m 2 per year. Between the ages of to, measured clearance was about 1 ml/min/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 CCR 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 two 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. Table 1. Population characteristics a. Male n= 4,051 Female n= 4,081 Age (yrs) 51 (13) 48 (12) b Weight (kg) 84.8 (12.6) 72.2 (12.8) b Body mass index (kg/m 2 ) 26.3 (3.6) 25.9 (4.7) b Systolic blood pressure (mmhg) (18.5) (20.8) b Diastolic blood pressure (mmhg) 76.9 (9.6) 71.1 (9.0) b Cholesterol (mmol/l) 5.7 (1.1) 5.6 (1.2) Glucose (mmol/l) 5.0 (1.2) 4.7 (1.1) b Serum creatinine (mg/dl) 1.04 (0.25) 0.86 (0.13) b Creatinine clearance (ml/min/1.73 m 2 ) 94.6 (21.2).3 (20.0) b Cockcroft- (ml/min/1.73 m 2 ) 87.5 (19.0) 88.2 (18.1) Simplified MDRD (ml/min/1.73 m 2 ) 83.3 (14.9) 77.5 (13.0) b a Mean (SD). b T-test null hypotheses: equal means for both sexes p <0.001.

7 38 Chapter 2 Figure 1. The relation between age (1a), weight (1b), BMI (1c) and WHR (1d) and renal function as derived from measured creatinine clearance (squares), estimated CG clearance (circles) and estimated MDRD clearance (triangles), all expressed in ml/min/1.73 m 2. The figures give data for men. Data are given according to deciles of the parameter of interest Age (yrs) 120 Weight (kg) Cockcroft- BSA corrected Cockcroft- BSA corrected , 0,85 0, 0,95 1,00 1,05 1,10 BMI (kg/m 2 ) Waist-to-Hip ratio Cockcroft- BSA corrected Cockcroft- BSA corrected

8 Drawbacks of renal function estimates 39 Figure 2. The relation between SBP (2a), DBP (2b), cholesterol (2c) and glucose (2d) and renal function as derived from measured creatinine clearance (squares), estimated CG clearance (circles) and estimated MDRD clearance (triangles), all expressed in ml/min/1.73 m 2. The figures give data for men. Data are given according to deciles of the parameter of interest. Renal function (ml/min/1.73m ) Systolic blood pressure (mmhg) Diastolic blood pressure (mmhg) Cockcroft- BSA corrected Cockcroft- BSA corrected Cholesterol (mmol/l) Glucose (mmol/l) Cockcroft- BSA corrected Cockcroft- BSA corrected

9 40 Chapter 2 Table 2. Mixed model: p-value tests the hypothesis of equal curves. Men Women Age CCR MDRD Age CCR MDRD CCR 0.98 CCR <0.001 Cockcroft- <0.001 <0.001 Cockcroft- <0.001 <0.001 Weight CCR MDRD Weight CCR MDRD CCR <0.001 CCR <0.001 Cockcroft- <0.001 <0.001 Cockcroft- <0.001 <0.001 BMI CCR MDRD BMI CCR MDRD CCR <0.001 CCR 0.51 Cockcroft- <0.001 <0.001 Cockcroft- <0.001 <0.001 WHR CCR MDRD WHR CCR MDRD CCR <0.001 CCR 0.18 Cockcroft <0.001 Cockcroft- <0.001 <0.001 SBP CCR MDRD SBP CCR MDRD CCR 0.03 CCR <0.001 Cockcroft- <0.001 <0.001 Cockcroft <0.001 DBP CCR MDRD DBP CCR MDRD CCR CCR 0.20 Cockcroft- <0.001 <0.001 Cockcroft <0.001 Cholesterol CCR MDRD Cholesterol CCR MDRD CCR 0.03 CCR <0.001 Cockcroft Cockcroft <0.001 Glucose CCR MDRD Glucose CCR MDRD CCR <0.001 CCR 0.03 Cockcroft- < Cockcroft

10 Drawbacks of renal function estimates 41 When plotting the relation of weight on renal function, the CG and MDRD estimates showed a different pattern than 24h 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, while MDRD clearance was lower for a greater weight (p <0.001). The relation between body mass index and the various estimates of renal function showed a similar pattern as was seen for body weight (figure 1c). Measured clearance was more or less similar over the various deciles. The CG and MDRD formulas showed again an opposite pattern. While 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 waist-to-hip ratio and renal function was different when comparing MDRD versus CCR and CG (figure 1d). Over a wide range of waist-to-hip ratio, measured creatinine clearance was lower at higher waist-to-hip ratio. The difference in GFR with a greater waist-to-hip ratio was more pronounced when the MDRD formula estimated renal function. We finally evaluated the relation between systolic and diastolic blood pressure and serum cholesterol and plasma glucose (figure 2) with renal function. In all 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 cardiovascular risk factors and renal function may lead to different conclusions when different estimates of renal function are used. This is most pronounced for those factors that are included in the Cockcroft- and MDRD formula, such as gender, age, weight and thus also body mass index. Partly, this can be explained mathematically by 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 blood pressure, cholesterol and glucose, the differences derived by using different estimates were less pronounced, although often still statistically significant. This implicates 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), and not of creatinine clearance. The renal secretion of creatinine, is about 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, while measured clearance was not different for the various weight and BMI deciles. Interestingly, in the original paper by Cockcroft and, the authors already reported that the formula was not appropriate for subjects with marked obesity (5). Many studies evaluating the effect of obesity on kidney function however this restriction of the use of the formula was not taken into

11 42 Chapter 2 account. The data from figure 1 show that the mean renal function of a subject of 73 kg amounts to the CG formula 85 ml/min/1.73 m 2 and according to the MDRD formula 86 ml/min/1.73 m 2. For a 98 kg subject, the CG formula gives a clearance of 92, which is 7 ml/min/1.73 m 2 higher compared to a 73 kg subject while the MDRD formula shows that same 98 kg subject having a clearance of 81, which is 5 ml/min/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 to 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/1.73 m 2 lower in a 67 year old versus a 35 year old man, while CG estimated clearance is 34 ml/min/1.73 m 2 lower and MDRD 17 ml/min/1.73 m 2. The consequence of the differences in the methods of estimating renal function for epidemiological 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 separately study the relation of various risk factors on renal function, the presented relations were univariate and thus not corrected for other cardiovascular risk factors. A mechanism for the differences in the association of renal function estimates and cardiovascular 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 only be 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. On the other hand, 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 subjects with normal renal function. The current study shows the potential dangers of such a non-validated use. We conclude that different estimates of renal function show a different relation between various cardiovascular risk factors and renal function in apparently healthy subjects. 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 also BMI.

12 Drawbacks of renal function estimates 43 References 1. Muntner P, Coresh J, Smith JC, Eckfeldt J, Klag MJ. Plasma lipids and risk of developing renal dysfunction: the atherosclerosis risk in communities study. Kidney Int. 2000;58(1): Muntner P, He J, Vupputuri S, Coresh J, Batuman V. Blood lead and chronic kidney disease in the general United States population: Results from NHANES III. Kidney Int. 2003;63(3): Hoy WE, Wang Z, VanBuynder P, Baker PR, Mathews JD. The natural history of renal disease in Australian Aborigines. Part 1. Changes in albuminuria and glomerular filtration rate over time. Kidney Int. 2001;(1): Hsu CY, Bates DW, Kuperman GJ, Curhan GC. Diabetes, hemoglobin A(1c), cholesterol, and the risk of moderate chronic renal insufficiency in an ambulatory population. Am.J.Kidney Dis. 2000;36(2): Cockcroft DW, MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;16(1): Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann.Intern.Med. 1999;130(6): Levey AS, Greene T, Kusek JW, Beck GJ. Simplified equation to predict glomerular filtration rate from serum creatinine [abstract]. J.Am.Soc.Nephrol. 11(A0828) Kidney Disease Outcome Quality Initiative: K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am.J.Kidney Dis. 39: S76-S92, Clase CM, Garg AX, Kiberd BA. Prevalence of Low Glomerular Filtration Rate in Nondiabetic Americans: Third National Health and Nutrition Examination Survey (NHANES III). J.Am.Soc.Nephrol. 2002;13(5): Beddhu S, Samore MH, Roberts MS, Stoddard GJ, Pappas LM, Cheung AK. Creatinine production, nutrition, and glomerular filtration rate estimation. J.Am.Soc.Nephrol. 2003;14(4): Verhave JC, Balje-Volkers CP, Hillege HL, de Zeeuw D, de Jong PE. The reliability of different formulae to predict creatinine clearance. J Intern.Med. 2003;253(5): McClellan W. As to diseases, make a habit of two things - to help, or at least do no harm. J.Am.Soc.Nephrol. 2002;13(11): Pinto-Sietsma SJ, Janssen WM, Hillege HL, Navis G, de Zeeuw D, de Jong PE. Urinary albumin excretion is associated with renal functional abnormalities in a nondiabetic population. J.Am.Soc.Nephrol. 2000;11(10): Pinto-Sietsma SJ, Mulder J, Janssen WM, Hillege HL, de Zeeuw D, de Jong PE. Smoking is related to albuminuria and abnormal renal function in nondiabetic persons. Ann.Intern.Med. 2000;133(8): Bois du D, Bois du EF. A formula to estimate the approximate surface area if height and weight be known. Arch.Int.Med. 1916;17: Manjunath G, Sarnak MJ, Levey AS. Prediction equations to estimate glomerular filtration rate: an update. Curr.Opin.Nephrol.Hypertens. 2001;10(6): Maddox DA, Brenner BM. Glomerular ultrafiltration. The Kidney 6th ed Philadelphia WB Saunders 2000: Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am.J.Kidney Dis. 2003;41(1): Lin J, Knight EL, Hogan ML, Singh AK. A Comparison of Prediction Equations for Estimating Glomerular Filtration Rate in Adults without Kidney Disease. J.Am.Soc.Nephrol. 2003;14(10):2573-.

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