Impact of weight change on albuminuria in the general population

Transcription

1 Nephrol Dial Transplant (2007) 22: doi: /ndt/gfm091 Advance Access publication 14 March 2007 Original Article Impact of weight change on albuminuria in the general population Aminu K. Bello 1, Dick de Zeeuw 2, Meguid El Nahas 1, Auke H. Brantsma 3, Stephan J. L. Bakker 3, Paul E. de Jong 3 and Ronald T. Gansevoort 3 1 Sheffield Kidney Institute, European Kidney Institute (EKI), The University of Sheffield, Sheffield S5 7AU, UK, 2 Department of Clinical Pharmacology and 3 Division of Nephrology, Department of Medicine, European Kidney Institute (EKI), University Medical Centre Groningen (UMCG), Groningen, The Netherlands Abstract Background. Increased levels of albuminuria have been recognized as a feature of obesity and the metabolic syndrome, and to be associated with an increased risk for cardiovascular and renal disease. The impact of weight change on albuminuria and its possible mechanism has not been studied yet in the general population. We investigated this issue in a cohort of the Northern European population. Methods. A total of 6894 participants of the Prevention of Renal and Vascular Endstage Disease (PREVEND) study were evaluated from baseline to a mean period of follow-up of 4.2 years for weight change (gain/loss), and its impact on albuminuria, renal function and cardiovascular risk factors. Participants were categorized into three groups based on absolute change in weight from baseline to follow-up: significant weight loss (>10 kg reduction in weight), stable weight, or significant weight gain (>10 kg increase). Multivariate regression analysis was used to evaluate the effect of baseline characteristics and time-dependent changes in these characteristics on the relationship of weight change with urine albumin excretion (UAE). Results. At follow-up 101 subjects experienced significant weight loss (mean change ¼ 14.2 kg), 348 had significant weight gain (mean change ¼þ13.4 kg) and the remaining were defined stable in weight (mean change ¼þ1.4 kg). Weight loss was associated with significant improvement in systolic blood pressure ( mmhg), diastolic blood pressure ( 5 8 mmhg), and cholesterol ( mmol/l), even after adjustment for the use of medications (P < 0.001). These parameters worsened significantly in those who substantially gained weight (P < 0.001). Similarly, weight loss was significantly associated with a reduction in UAE (mean mg/24 h), whereas weight gain was associated with a rise in Correspondence and offprint requests to: R.T. Gansevoort, Division of Nephrology Department of Medicine, University Medical Center Groningen, PO Box , 9700 RB Groningen, The Netherlands. r.t.gansevoort@int.umcg.nl UAE (mean þ mg/24 h). Notably, no changes were observed in GFR (assessed as 24 h urinary creatinine clearance) in subjects with weight loss or weight gain. Multivariate regression modelling with changes in UAE as dependent variable correcting for factors that might explain the association showed that only part of the relationship between weight changes and changes in UAE was explained by effect of weight change on blood pressure and cholesterol, whereas the association disappeared with changes in CRP in the model (P ¼ 0.50). Conclusion. This is the first population-based longitudinal study to show that changes in weight are associated with parallel changes in albuminuria. This relationship cannot be fully explained by the association between weight and classical cardiovascular risk factors and renal function. Based on our data we hypothesize that weight-induced changes in vascular inflammation may cause changes in albuminuria. Keywords: albuminuria; CVD risk; population; PREVEND; weight change Introduction A number of studies and surveys have highlighted a high prevalence of microalbuminuria in the general population [1 3]. It was also noted that albuminuria is increased in overweight and obese subjects [3]. Furthermore, it has been shown that microalbuminuria predicts the development of chronic kidney and cardiovascular disease (CVD) [4 6]. The latter is one of the reasons that albuminuria is increasingly recognized as a manifestation of systemic endothelial dysfunction [7]. The cardiovascular risk associated with albuminuria is mentioned to be at least partly independent of traditional cardiovascular risk factors [8,9]. Weight loss is documented to have beneficial effects on several CVD risk factors, including blood pressure, ß The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please journals.permissions@oxfordjournals.org

2 1620 A. K. Bello et al. serum cholesterol, and C-reactive protein (CRP) [10 12]. Little is known however, on the effect of weight changes on urinary protein loss. The few studies performed, were small in size and mainly in overweight and obese subjects with established renal disease and/ or diabetes. These studies have demonstrated the beneficial effects of intervention-induced weight loss on urinary albumin excretion and proteinuria [13 16]. Information is lacking on the effect of weight changes on urinary albumin excretion (UAE) in subjects without renal disease. We aimed to investigate whether changes in body weight are associated with changes in UAE, and if so, what mechanism may explain this association. As putative explanatory variables, we took into account factors known to influence urinary albumin loss and also known to be altered by weight changes, such as the classical CV and metabolic risk factors [17], high sensitive (hs)-crp [18 22], sodium and protein intake [23,24], and creatinine clearance [25]. We used data obtained from a prospective, observational cohort study derived from general population. Methods Subjects and design The study used data of the in-subjects of the ongoing PREVEND Study (acronym for Prevention of Renal and Vascular End-stage Disease), a prospective cohort study in Groningen, the Netherlands. The study is designed to evaluate the predictive value of albuminuria for renal and cardiovascular outcomes. Details of the study have been reported previously [2]. In brief, the subjects of the PREVEND study had been selected in 1997 from subjects from the general population of Groningen, aged years. The participants were sent by mail a vial containing a portion of a spot morning urine sample to a central laboratory and answered a brief questionnaire. Pregnancy and insulin usage were exclusion criteria. All subjects with a urinary albumin concentration (UAC) 10 mg/l (n ¼ 7768) and a random sample of subjects with a UAC <10 mg/l (n ¼ 3395) were invited for further evaluation. In total 8592 subjects participated in this further evaluation (first screening, ), of which 6000 subjects had a UAC >10 mg/l in the spot morning urine sample, and 2592 subjects a UAC <10 mg/l. After 4 years of follow-up these subjects were invited for a second screening ( ). At that time 240 subjects had died and 1458 declined further participation. The remaining 6894 subjects completed the second screening. The present analysis is based on these subjects. The PREVEND study is approved by the local ethics committee and conducted in line with Helsinki declaration of research conduct in humans. All participants gave informed consent. Measurements At baseline and follow-up, all subjects completed a questionnaire on demographics, cardiovascular and renal disease history, smoking and use of medications for hypertension, diabetes and dyslipidaemia. Information on drug use was also collected from community pharmacies. At both screening rounds, subjects were seen in an outpatient clinic, and anthropometric measurements were performed. After removal of shoes and heavy clothing, weight was measured to the nearest 0.5 kg. Height was measured to the nearest 0.5 cm. Blood pressure measurements were performed in ten minutes with automatic Dinamap XL Model 9300 series device (Johnson-Johnson Medical, Tampa, FL). Fasting blood samples were taken for measurements of serum cholesterol, triglycerides, CRP, serum creatinine and plasma glucose and insulin levels. Before visiting the outpatient clinic subjects collected 24 h urine samples on two consecutive days. The subjects received oral and written instructions on how to collect a 24 h urine and to postpone collection in event of fever, urinary tract infection, menstruation or heavy exercise. Laboratory methods The biochemical measurement of plasma glucose, serum creatinine, lipid profile and urinary creatinine performed using standard methods with use of Kodak Ektachem dry chemistry (Eastman Kodak, Rochester, New York, USA) and commercially available assay for HDL cholesterol (Abbott Inc, Abbott Park, Illinois, USA). Plasma insulin was determined on an Axsym analyser (Abbott, Amstelveen, the Netherlands), high sensitivity C-reactive protein (hs-crp) by nephelometry (BNII, Dade Behring, Marbug, Germany), urinary sodium and urea concentrations with MEGA clinical chemistry analyzer (Merck, Darmstadt, Germany). Urinary albumin concentration was determined by nephelometry with a threshold of 2.3 mg/l and intra-assay and inter-assay coefficients of variation of 2.2% and 2.6%, respectively (BN TM II Dade Behring Diagnostic, Marburg, Germany). Definitions Creatinine clearance (CCr) was calculated as mean of two 24 h urinary creatinine excretions divided by plasma creatinine. Daily sodium and protein intake were estimated from mean of two 24 h urine excretions of sodium and urea, respectively. Urinary albumin excretion (UAE) is given as mean of two 24 h urine excretions per screening. Change in UAE was categorized as progression, stable or regression of UAE based on change in class of UAE during follow-up, with classes being a UAE <15 mg/24 h (normo-albuminuria), mg/24 h (high-normal albuminuria), mg/24 h (micro-albuminuria) and >300 mg/24 h (macroalbuminuria). As alternative outcome, change in UAE was categorized with progression being defined as at least doubling of UAE (increase 100%) from baseline to follow up, and regression as at least halving of UAE (decrease 50%). Subjects were considered to use antihypertensive, lipid lowering or anti-diabetic medication when they took such drugs according to the questionnaire or information that has been obtained from linkage of the PREVEND database with community pharmacies. The use of angiotensin-converting enzyme inhibitors (ACEi) or angiotensin-ii receptor blockers (ARB) was evaluated separately. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m 2 ).

3 Weight change and albuminuria 1621 Smoking was defined as current smoking or cessation of smoking less than a year before study onset. Changes in UAE (UAE), CCr (CCr), SBP (SBP), DBP (DBP), glucose ( glucose), cholesterol ( cholesterol), urinary sodium excretion (UNa); (UNa), urinary urea excretion (uurea) (uurea), and hs-crp (CRP) at end of period of the follow up were evaluated as absolute changes from baseline. Weight change categories Based on absolute weight change from baseline screening to follow-up, participants were divided into three categories. (a) Significant weight gain: Gain of 10 kg or more. (b) Significant weight loss: Loss of 10 kg or more. (c) Stable weight: <10 kg change in weight (gain or loss). Statistical analyses All analyses were performed with the statistical package SPSS 12.0 (SPSS, Chicago, IL, USA). The level of significance was determined at P < 0.05, two-tailed. Continuous data are reported as mean and standard deviation, or median and inter-quartile range in case of skewed distribution. Categorical data are described as proportions or percentages. Differences between groups were tested by an independent t-test or by a Mann Whitney test in case of skewed distribution. Differences in proportions between groups were tested with chi-square test. To study the impact of weight change on albuminuria, univariate linear regression analysis was initially applied and subsequently multivariate linear regression analysis, using change in UAE as outcome variable. Besides weight changes, the following covariates known to be associated with weight were used in the multivariate model: gender, age, baseline weight, baseline systolic blood pressure (SBP), hs-crp, plasma glucose, serum cholesterol, creatinine clearance, 24 h urinary sodium and urea excretion, smoking, use of antihypertensive medications including ACEi and ARBs, lipidlowering medications, anti-diabetic medications. Medication use was put into the model because of their impact on other risk factors for albuminuria and their potential role in albuminuria regression, especially ACEi/ARBs. To meet the assumption of linearity baseline UAE, plasma glucose and hs-crp were transformed by the natural logarithm. For sensitivity analyses we also studied alternative outcomes. The following categories of weight change were used: weight gain or loss defined as a weight change during follow-up of 5 kg, quintiles of weight change (absolute and percentage), and 5% extremes of the weight change distribution. In addition, we looked at other weight surrogate parameters, such as change in BMI, waist circumference and waist-to-hip ratio (WHR) on the outcome parameter. Results Baseline During the follow-up period from the initial screening (mean years), there were 101 subjects who lost more than 10 kg weight, whereas 348 subjects gained more than 10 kg in weight. According to our definition 6445 subjects remained stable in weight. Notably, 1698 subjects were lost to follow-up from the 8592 subjects invited for the second screening. Although at baseline the differences between these subjects and subjects with follow-up data available reached statistical significance for some variables due to the large sample size of our population (SBP, hs-crp and UAE), these differences were in all cases numerically very small (<3%). The baseline characteristics of the study population by categories of weight change are depicted in Table 1. Subjects with significant weight loss, compared with stable group, were more often female and obese, had higher baseline levels of SBP, glucose, plasma insulin, hs-crp and more frequent use of anti-hypertensive medications (including ACEi/ARBs). No significant difference in the use of lipid-lowering or oral antidiabetic medications was noted. Baseline 24 h urinary sodium excretion was higher in this group as compared with those who remained stable, whereas no difference was found for creatinine clearance and 24 h urinary urea excretion. Notably, UAE was higher (median 15.2 vs 9.1 mg/24 h in the group with stable weight). The group of subjects with significant weight gain had, compared with group with stable weight, higher baseline weight parameters (absolute weight and BMI). Furthermore, they were on an average younger and included more smokers; with slightly lower SBP, DBP, and serum cholesterol, whereas hs-crp was higher when compared with the stable group. No significant difference at baseline in the use of anti-hypertensive medication (including ACEI and ARBs) and lipid lowering drugs was noted. Creatinine clearance and 24 h urinary urea excretion were comparable between these two groups (Table 1). No difference in baseline albuminuria was observed in subjects that gained weight versus subjects with stable weight (median 8.7 vs 9.1 mg/24 h). Impact of weight change on albuminuria There were significant changes in albuminuria in both categories of weight change as compared with the stable group (Table 2). There was net reduction in those who lost weight (P < 0.01) and an increase in those who gained weight (P < 0.05), as compared with the stable group. Figure 1 and Table 2 show changes in weight related to changes in class of UAE. Of subjects that lost weight, 24.8% had regression of UAE based on class change from baseline to follow-up, as compared with only 8.3% in the stable group (P < 0.01), whereas 15.9% of those who gained weight experienced class progression of UAE as compared with only 9.4% in the stable group (P < 0.01). When progression/regression in UAE is defined as doubling/halving during follow-up, 26.7% of those who lost weight had UAE regression, compared with only 9.9% in the stable group (P < 0.01). Of those who

4 1622 A. K. Bello et al. Table 1. Baseline characteristics of the study population Weight loss Stable weight Weight gain N Age (years) 50.0 (13.0) 49.0 (12.0) 45.0 (11.0)** Caucasian, No. (%) 96 (95.0) 6179 (95.9) 343 (98.6) Male, No. (%) 40 (39.6)* 3242 (50.3) 162 (46.6) Absolute weight (kg) 87.5 ( )** 77.0 ( ) 80.0 ( )* BMI (kg/m 2 ) 29.2 ( )** 25.4 ( ) 26.0 ( )* Waist circumference (cm) 98.0 ( )** 88.0 ( ) 88.0 ( ) Waist-to-hip ratio 0.91 ( )** 0.88 ( ) 0.88 ( ) Smoking, No. (%) 41 (40.6)* 2260 (35.2) 184 (52.9)** SBP (mmhg) (22.0)** (20.0) (18.0)* DBP (mmhg) 75.0 (10.0) 74.0 (10.0) 73.0 (10.0)* Use of antihypertensive drugs, No. (%) 18 (17.8)** 702 (10.9) 31 (9.0) Including use of ACEi/ARB, No. (%) 27 (26.7)** 984 (15.3) 48 (13.9) scholesterol (mmol/l) 5.81 (1.42)* 5.63 (1.12) 5.58 (1.10) Use of lipid lowering drugs, No. (%) 6 (5.9) 424 (6.6) 22 (6.3) Plasma glucose (mmol/l) 5.4 (1.4)** 4.9 (1.1) 4.8 (1.3) Use of anti-diabetic medication, No. (%) 4 (4.0) 114 (1.8) 1 (0.3)* Plasma insulin (mu/l) 11.0 ( )** 7.8 ( ) 8.0 ( ) hs-crp (mg/l) 3.1 ( )** 1.2 ( ) 1.4 ( )* Creatinine clearance (ml/min) (26.6) (25.4) (26.1) Urinary sodium excretion (mmol/24 h) 160 (54)* 142 (50) 151 (60)* Urinary urea excretion (mmol/24 h) UAE (mg/24 h) 15.2 ( )** 9.1 ( ) 8.7 ( ) UAE categories No. (%) Normoalbuminuria 46 (45.5)** 4712 (73.0) 248 (71.2) High-normal albuminuria 26 (25.7)** 908 (14.1) 41 (11.8)* Micro-albuminuria 24 (23.8)** 750 (11.7) 53 (15.3)* Macro-albuminuria 5 (5.0)* 75 (1.2) 6 (1.7)* UAE, urinary albumin excretion; BMI, body mass index; SBP, systolic blood pressure; DBP, dystolic blood pressure; hs-crp, highly sensitive C-reactive protein; s, serum; p, plasma. Values are given as mean (SD) or median (interquartile range) in case of skewed data distribution.* P < 0.05 vs stable weight, **P < 0.01 vs stable weight. Table 2. Changes in weight parameters, UAE, CV surrogate markers and renal function in the study population Weight loss Stable weight Weight gain N Follow-up (year) ** ** Absolute weight (kg) ** ** BMI (kg/m 2 ) 3.9 ( 5.1 to 3.3)** 0.6 ( ) 4.3 ( )** Waist circumference (cm) 7.0 ( 12.3 to 4.0)** 4.0 ( ) 13.0 ( )** Waist-to hip ratio 0.02 ( )** 0.02 ( ) 0.06 ( )** Start of smoking No. (%) 11 (11.1)** 113 (1.8) 7 (2.1) Stop of smoking No. (%) 6 (6.1) 416 (6.6) 81 (23.8)** SBP (mmhg) ** ** DBP (mmhg) ** ** New use of AHT No. (%) 8 (7.9) 617 (9.7) 45 (13.2)* New use of ACEI/ARBs No. (%) 8 (7.9) 355 (5.6) 27 (7.9) scholesterol (mmol/l) ** ** New use of LLD No. (%) 10 (9.9) 362 (5.7) 29 (8.4) pglucose (mmol/l) ** New use of GLD No. (%) 9 (8.9)** 167 (2.6) 13 (3.8) hs-crp (mg/l) 1.1 ( )** 0.21 ( ) 0.09 ( )* Urinary sodium excretion (mmol/24 h) 20 61** * Urinary urea excretion (mmol/24 h) * ** Creatinine clearance (ml/min) 0.5 ( ) 0.5 ( ) 0.4 ( ) UAE (mg/24 h) ** * Regression in UAE No. (%) 25 (24.8)** 529 (8.3) 30 (8.6) Progression in UAE No. (%) 10 (9.9) 602 (9.4) 55 (15.9)** BMI, body mass index; UAE, urinary albumin excretion; SBP, systolic blood pressure; DBP, diastolic blood pressure; hs-crp, highly sensitive C-reactive protein; AHT, antihypertensive medication; ACEI, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; LLD, lipid lowering drugs; GLD, glucose lowering drugs; s, serum; p, plasma. Progression/regression in UAE is defined as change in UAE class. Data expressed as mean SD or as number (%). *P < 0.05 vs stable weight, **P < 0.01 vs stable weight.

5 Weight change and albuminuria 1623 gained weight, 13.8% had progression compared with 11.6% in the stable category (P < 0.01). Impact of weight change on CVD risk factors Table 2 lists changes in anthropometric parameters and CVD surrogate markers according to categories of weight change. As expected, changes in all weight parameters (weight, BMI, waist circumference, WHR) Percent Weight loss Regression Change in UAE class (%) Stable Weight gain Weight loss Progression P<0.01 NS NS P<0.01 Stable Weight gain N= Fig. 1. Changes in weight related to changes in class of urinary albumin excretion (UAE), with albuminuria classes defined as normoalbuminuria (<15 mg/24 h), high-normal albuminuria (15 30 mg/24 h), microalbuminuria ( mg/24 h), and macroalbuminuria (>300 mg/24 h). On the left albuminuria class regression (black bars), and on the right albuminuria class progression (grey bars). differed significantly in the weight loss and gain categories as compared to the stable group. Compared with the stable weight category, weight loss was associated with significant reduction in serum cholesterol, SBP, diastolic blood pressure DBP, and hs-crp; while there was no net increase in the use of antihypertensive medication and lipid lowering drugs during follow-up. Furthermore, there was higher incidence of start of anti-diabetic medication and smoking during follow-up among weight losers (P < 0.001). In those who gained weight there was net increase in levels of blood pressure, cholesterol, glucose and hs-crp, in comparison to those in stable category (Table 2). No significant changes were observed in creatinine clearance in subjects with weight loss or gain as compared to those who were stable (Table 2). Multivariate regression analysis To establish whether weight change is associated with changes in UAE (regression/progression) independent of other established CVD risk parameters, we ran univariate and stepwise multivariate regression modelling analyses, the results of which are shown in Table 3. The beta-coefficients in the model represent the relative predictive power of each independent factor, controlling for all other independent variables with change in albuminuria as an outcome. Univariately, weight change was associated with a change in UAE (model 1). After adjustment for age and gender, the association remained significant (model 2). The association persisted after correcting for other baseline characteristics associated with weight, known to influence UAE, such as baseline Table 3. Results from univariate and multivariate regression analysis, relating change in urinary albumin excretion (dependent variable) with change in weight as the primary independent variable under investigation. n ¼ 6894 Weight (Kg) Beta T value P value Model <0.001 Model <0.001 Model <0.001 Model Model Model Model Model <0.001 Model Model Model Note: The beta value relates the effect on the dependent variable change in UAE per 1 kg change in the independent variable weight change and after adjusting for the various covariates. R 2 ¼ 0.23 for model 1. BP, blood pressure; ACEI, angiotensin converting enzyme inhibitors; ARBs, angiotensin receptor blockers; hs-crp, highly sensitive C-reactive protein; UAE, urinary albumin excretion. Dependent variable change in UAE (delta) in mg/24 h. Model 1 ¼ Unadjusted; Model 2 ¼ Adjusted for age and gender; Model 3 ¼ 2 þ adjustment for baseline BP, cholesterol, glucose, weight, baseline use of medications (BP, glucose and lipid-lowering), urinary sodium and urea excretion, creatinine clearance, hs-crp, and smoking; Model 4 ¼ 3 þ adjustment for delta blood pressure and start of new BP medications (including ACEI/ARBs as separate class); Model 5 ¼ 3 þ adjustment for delta cholesterol and start of new lipid lowering agents; Model 6 ¼ 3 þ adjustment for delta glucose and start of new anti-diabetic medications; Model 7 ¼ 3 þ adjustment for delta BP, delta cholesterol, delta glucose and start of BP (including ACEi/ARBs), lipid and glucose lowering medications; Model 8 ¼ 3 þ adjustment for delta urinary sodium excretion; Model 9 ¼ 3 þ adjustment for delta urinary urea excretion; Model 10 ¼ 3 þ adjustment for delta creatinine clearance; Model 11 ¼ 3 þ adjustment for delta hs-crp.

6 1624 A. K. Bello et al. blood pressure, cholesterol, glycaemia status, hs-crp, smoking, sodium and protein intake, creatinine clearance and use of medications (model 3). When changes in these characteristics were taken into account, it appeared that weight change-induced variation in UAE was largely independent from weight changeinduced changes in blood pressure, cholesterol, and glucose (models 4 6). Metabolic and CV risk factors taken together did not highly modify the relationships between changes in weight and changes in UAE (model 7). Changes in sodium and protein intake, and creatinine clearance also did not influence the relationship of changes in weight with changes in UAE (models 8 10). However, when changes in hs-crp were taken into account the effect of weight change on change in UAE was nullified, (model 11) (P ¼ 0.50). Sensitivity analyses The use of alternative explanatory parameters such as 5 kg change in weight, percent change in absolute weight, 5% extremes of the weight change distribution, tertiles and quintiles of weight change, as well as other weight change parameters, including change in BMI, waist circumference, and WHR on CVD surrogate markers and albuminuria showed similar impact. Repeated analyses were performed after exclusion of subjects with significant CVD, macroalbuminuria (>300 mg/24 h), microalbuminuria (30 mg/24 h), renal disease history, and/or CrCl <80 ml/min. Similarly, validation of results for urine collection errors was taken into account by exclusion of subjects with >20% differences in urinary creatinine excretion between the two 24 h urinary collections at baseline and at follow-up. All these sensitivity analyses did not change the final results in the analysis. Other weight markers including waist circumference and BMI were considered. We noticed the changes in outcome to be more strongly associated with changes in the absolute weight than with changes in waist circumference and BMI. Discussion Our study is the first that we are aware of, showing that reduction in weight in a large cohort of general population is associated with significant changes in albuminuria. We observed that weight loss is associated with a significant reduction in albuminuria and weight gain had the opposite effect. The weight change-induced alterations in albuminuria were independent of changes in the classical cardiovascular and metabolic risk factors. They were however, for a large part dependent on changes in CRP. Whereas subjects with obesity are known to have a worse cardiovascular prognosis when compared with subjects with normal weight, it is surprising to note that only limited data are available on whether weight loss is associated with an improvement in cardiovascular prognosis [26 29]. The available studies are inconclusive, with some even surprisingly suggesting an increased risk for cardiovascular disease with weight loss [28,29]. These latter studies however, are difficult to interpret in-view of possible discrepancies due to the impact of subclinical chronic diseases associated with weight loss and even new lifestyle behaviours such as smoking and excessive exercise. In recent years increased levels of albuminuria are shown to predict cardiovascular disease progression in subjects with diabetes, or hypertension, but also in the general population [5,6]. The increased CV risk associated with albuminuria is independent of traditional cardiovascular risk factors. Furthermore, also treatment-induced changes in UAE were shown to predict cardiovascular outcome in these populations [30 33]. Albuminuria is therefore hypothesized to be a valuable surrogate marker for hard cardiovascular endpoints. Given these observations, we studied whether changes in weight are accompanied by parallel changes in UAE. Our data suggest that this is the case. The few studies that investigated the effect of weight change on urinary protein loss included only small numbers of subjects with established renal disease and/ or diabetes and have demonstrated beneficial effects [13 16]. Our data not only corroborate these findings in a large-scale study, but also extend it beyond subjects with renal disease. Furthermore, we show worsening of albuminuria with weight gain. Lastly, another difference between our study and previous ones is that we investigated whether we could identify factors that may explain the association between changes in weight and albuminuria. Obesity is known to cluster with several cardiovascular and metabolic risk factors suggested to be causally related with increased UAE, such as high blood pressure, increased serum cholesterol, glucose and hs-crp [17,22]. These factors are also known to change in concordance with weight changes, as corroborated in our study [11,12]. We therefore investigated whether weight change-induced alterations in the aforementioned variables might account for the observed changes in albuminuria. Blood pressure is generally considered to be an important determinant of microalbuminuria in diabetes, hypertension and general population [2,34,35]. Since obesity is an important risk factor for hypertension [36], and even modest weight loss is associated with a reduction in blood pressure [37], we anticipated that any putative association between changes in weight and changes in albuminuria could be the consequence of concomitant changes in blood pressure. We indeed found a decrease in strength of the association between changes in weight and changes in albuminuria after adjustment for changes in blood pressure. However, the changes in blood pressure cannot fully account for the changes in albuminuria, since a statistically significant independent relation remained between weight and albuminuria changes after correction for alterations in blood pressure. Similar observations were obtained with respect to changes in serum cholesterol.

7 Weight change and albuminuria 1625 We found a much greater decrease in the strength of association between changes in weight and changes in albuminuria after adjustment for changes in CRP as the remaining association between weight and albuminuria changes even lost significance. This is suggestive of an important role for changes in factors related to chronic low-grade inflammation. How could changes in CRP be linked to changes in weight and changes in albuminuria? Obesity is a recognized proinflammatory state, associated with elevated levels of CRP [17]. Activated macrophages infiltrating visceral fat, as well as visceral adipocytes themselves, are thought to release pro-inflammatory markers, including chemokines and cytokines. These substances are known to affect glomerular and vascular permeability [17]. Besides, CRP itself has been suggested to induce detrimental effects on vascular function on both vascular endothelial and smooth muscle components [38]. Weight loss is hypothesized to lead to opposite effects, with a reduction in vascular low-grade inflammation and amelioration of endothelial dysfunction as a result. This may point to a possible explanation for the observed parallel changes in UAE and weight. Interestingly, our data also make several candidates unlikely for playing a role in the observed associations between weight and albuminuria changes. These factors include changes in fasting glucose, urinary sodium excretion, urinary urea excretion and creatinine clearance. Especially the fact that the latter two did not explain the association between changes in body weight and changes in albuminuria is of interest. Urinary urea excretion is a marker of protein intake, while protein intake has been shown to be a determinant of urinary protein excretion [24]. One could have hypothesized that changes in protein intake that parallel changes in calorie intake explain the association that we found, but this hypothesis is refuted by our data. The same is true for creatinine clearance. Obesity is accompanied by glomerular hypertrophy and glomerular hyperfiltration [39]. Again, one could have hypothesized that changes in creatinine clearance would explain the association between changes in weight and albuminuria, but this is also made unlikely by our data. Notably, weight change induced changes in albuminuria, without concomitant significant changes in renal function might suggest a change in glomerular (and perhaps vascular) permeability. However, this is not necessarily the case. We cannot exclude the theoretical explanation that changes in tubular albumin reabsorption play a role. Furthermore, it could be that GFR has been measured with such degree of inaccuracy that it makes reliable assessment of associations between albuminuria and GFR difficult. In our study, renal function was assessed as 24 h- creatinine clearance. Collection errors in 24 h-urines might have resulted in inaccuracy. It should be noted though, that excluding subjects with >20% difference in 24 h urinary creatine excretion did not change our results, making this explanation unlikely. For information, we considered adopting MDRD or Cockroft Gault (CG) estimations of GFR for our analyses. Based on theoretical grounds we concluded this not to be justified. In the CG formula weight is included. Weight changes will therefore result in changes in CG-based egfr that are erroneous. In contrast, the MDRD egfr does not include weight. However, when subjects lose a considerable amount of weight, they are expected to also have changes in muscle mass. Therefore the MDRD egfr also cannot be used as a valid renal function estimate in this setting. What are the consequences of our findings? First, since albuminuria is hypothesized to be a valuable surrogate marker for cardiovascular disease progression, our data suggest that losing weight will result in an improved cardiovascular prognosis. These data also provide new information to the discussion on whether weight loss will result in better outcome. Unfortunately, our relatively short follow-up after the second screening, at which weight changes were assessed, did not allow us to study the impact on cardiovascular morbidity and mortality. Secondly, controversy exists whether albuminuria should be one of the defining criteria for metabolic syndrome. Initially this was the case [7], but later albuminuria was dropped as a criterion. It was argued that albuminuria is merely an integrated risk marker, reflecting the vascular damage induced by the classical CV and metabolic risk factors already embedded in the definition of metabolic syndrome. Various studies have shown that albuminuria predicts cardiovascular outcome independent of these risk factors [4 6]. This study provides further arguments to add albuminuria as criterion to the definition of metabolic syndrome, since albuminuria is not only increased in overweight subjects, but our study demonstrated that weight changes are accompanied by parallel changes in albuminuria, which are independent of other criteria that presently define metabolic syndrome (Table 3, model 7). Thirdly, albuminuria regression with weight loss may in the future perhaps be used as surrogate marker to indicate a reduced risk of developing CVD and CKD, especially since large scale epidemiologic studies have demonstrated an association between weight and risk to develop ESRD [40]. Of note, our study indicates that there is a linear relationship between changes in weight and changes in albuminuria, implicating that even modest weight loss, as achievable in clinical practice, will have beneficial effects. One of the study limitations is the lack of information on whether weight loss in this cohort was intentional or unintentional. The subgroup of subjects with substantial weight loss had much higher baseline BMI and cardiovascular risk factors than those whose weight remained stable. One could argue that they might be more motivated to reduce weight, thus making intentional weight loss more likely. We cannot exclude that unintentional weight loss due to intercurrent disease may also have played a role. If it were intercurrent disease, we would have found an opposite effect on albuminuria since it is generally agreed to be elevated in subjects with cardiovascular,

8 1626 A. K. Bello et al. as well as non-cardiovascular chronic diseases [30 32]. Furthermore, in subjects that gained weight we observed opposite effects in comparison with those that lost weight. When unintentional weight loss due to comorbidity was the most important factor, such opposite effect is not to be expected, thus making intentional weight loss the most important determinant for the decrease in weight. We also had little details of the means by which weight was lost: diet, exercise or both. In this respect it is interesting to note that we observed a decrease in sodium and protein intake in subjects that lost weight, suggesting that at least a change in diet played a role. However, our data suggest that these dietary changes in sodium and protein did not directly modify the impact of weight change on urinary albumin loss. Second, in interpreting our results, one should take into account that we only measured blood pressure at one moment in the morning, whereas albuminuria was assessed from a 24 h-urine collection. It can therefore not be excluded that the association would have been different if we could have controlled for 24 h-blood pressure profiles. However, the same methodological issue applies to changes in CRP, and probably even stronger, because it is well known that CRP is subject to influences of subclinical and clinical infectious diseases, resulting in a larger intra-individual coefficient of variation than blood pressure [41]. However, our data show changes in CRP to have considerably more impact on the association between changes in weight and albuminuria than blood pressure. Third, subjects were lost to follow-up. But the differences in baseline characteristics were in all cases numerically very small (<3%). Loss to follow-up is therefore unlikely to have biased our findings. Strengths of our study are that we were able to study in a large cohort of general population a great number of time-dependent clinical variables known to influence albuminuria, including urinary sodium and urea excretion as measures for dietary sodium and protein intake. This enabled us to study the putative mechanism that link weight changes and changes in albuminuria in details. The availability of urinary creatinine clearance as measure for renal function is an advantage; previous studies on this topic applied the Cockroft-Gault estimated creatinine clearance [13,14]. This latter parameter is based on serum creatinine values solely, and does not take into account urinary creatinine excretion, making it less reliable for longitudinal study in subjects that lose or gain weight. In conclusion, this is the first population-based study to investigate the impact of changes in weight on albuminuria in a longitudinal setting. 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