creatinine ratio in patients with type 2 diabetes: The DEMAND Study

Nutrition, Metabolism & Cardiovascular Diseases (2010) 20, 110e116 available at www.sciencedirect.com

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Obesity and changes in urine albumin/creatinine ratio in patients with type 2 diabetes: The DEMAND Study M.C.E. Rossi a,*, A. Nicolucci a, F. Pellegrini a, M. Comaschi b, A. Ceriello c, D. Cucinotta d, C. Giorda e, B. Pomili a, U. Valentini f, G. Vespasiani g, S. De Cosmo h a

Department of Clinical Pharmacology and Epidemiology, Consorzio Mario Negri Sud, Via Nazionale, 66030 S. Maria Imbaro, CH, Italy b Department of Internal Medicine, La Colletta Hospital, Genoa, Italy c Warwick Medical School, University of Warwick, Coventry, UK d Department of Internal Medicine, Policlinico Universitario, Messina, Italy e Metabolism and Diabetes Unit ASL TO5, Chieri (TO), Italy f Diabetologic Unit, Spedali Civili di Brescia, Brescia, Italy g Diabetes and Metabolism Unit, Madonna del Soccorso Hospital, San Benedetto del Tronto, AP, Italy h Unit of Endocrinology, Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy Received 22 September 2008; received in revised form 4 February 2009; accepted 27 February 2009

KEYWORDS Obesity; Albumin excretion rate; Type 2 diabetes

Abstract Background and aims: Obesity is a potential risk factor for renal disease in non-diabetic subjects. It remains unclear whether this also applies to diabetic patients. We investigated whether obesity predicted changes in albumin excretion rate in individuals with type 2 diabetes. Methods and results: Fifty Italian diabetes outpatient clinics enrolled a random sample of 1289 patients. A morning spot urine sample was collected to determine urinary albumin/creatinine ratio (ACR) at baseline and after 1 year from the study initiation. Progression of albumin excretion was defined as a doubling in ACR, while regression was defined as a 50% reduction. Multivariate logistic regression analyses were used to evaluate correlates of these outcomes. Data are expressed as odds ratios (OR) with 95% confidence intervals (CI). The risk of progression increased by 7% (OR Z 1.07; 95%CI 1.00e 1.15) for every 5-cm increase in waist circumference measured at baseline, and by 17% (OR Z 1.17; 95%CI 1.03e1.33) for every one-unit increase in BMI during follow-up. The likelihood of regression was not independently associated with any of the variables investigated. The effect of obesity

* Corresponding author. Tel.: þ39 0872 570266; fax: þ39 0872 570263. E-mail address: [email protected] (M.C.E. Rossi). 0939-4753/$ - see front matter ª 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.numecd.2009.02.013

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on progression of ACR was independent of metabolic control, blood pressure, treatment, and baseline level of albumin excretion. Conclusions: We found a tight link between obesity and changes in albumin excretion in diabetic subjects, suggesting potential benefits of interventions on body weight on end-organ renal damage. ª 2009 Elsevier B.V. All rights reserved.

Introduction

Data collection

Increasing values of urinary albumin excretion (UAE) are a risk factor for renal and cardiovascular disease [1,2], as well as for mortality [3]. Diabetes, hypertension, smoking and other factors are known to be associated with the development and progression of microalbuminuria (MAU) [4e6]. Abdominal obesity, measured by waist circumference, has recently received attention as a potential risk factor for renal disease in people who do not have diabetes [7e10]. It has been shown that abdominal obesity may be an early risk factor for increased albuminuria, independent of blood glucose, blood pressure, and renal function [7]. In a population-based longitudinal study, changes in weight were associated with parallel changes in albuminuria [11]. A relationship between abdominal obesity and development of MAU has also been shown in patients with type 1 diabetes [12]. Adipogenic inflammation and endothelial dysfunction related to visceral adiposity have been advocated as possible links between obesity and renal injury [13e15]. Diabetes is associated with a dramatic increase in the risk of end-stage kidney disease, particularly in presence of hypertension. It is still unclear whether obesity could act as an additional risk factor also in patients with type 2 diabetes. While data from the UKPDS show that increased waist circumference at baseline predicted the development of albuminuria independent of other known risk factors [9], it is still not known if changes in obesity indices over time are associated with changes in albumin excretion rate. We investigate the role of obesity in predicting changes in albumin excretion rate in a cohort of individuals with type 2 diabetes.

All patients underwent medical examination, and clinical data were collected on diabetes duration, cardiovascular risk factors, comorbidities and pharmacologic treatments, height, weight, waist circumference, and blood pressure (two measurements rounded to the nearest 2 mmHg in the sitting position after at least 5 min rest, using an appropriatesized cuff; diastolic blood pressure was recorded at the disappearance of Korotkoff sound, phase V). A morning spot urine sample was collected, stored at 20  C, and then sent on dry ice to a central laboratory (Department of Laboratory Medicine, University Milano-Bicocca, Hospital of Desio, DesioMilano, Italy) at baseline and after 1 year from study initiation. Urinary albumin and creatinine concentrations were determined by the immune turbidimetric method (albumin tina-quant, Roche Diagnostics) and a kinetic Jaffe ´ method performed with the Autoanalyzer Modular (Hitachi-Roche Diagnostics), respectively. The urinary albumin-to-creatinine ratio (ACR) was then calculated. Since recent studies suggest that also values below the traditional threshold of 30e300 mg/g of ACR could well be a risk factor for adverse cardiovascular and renal outcomes, we chose to define progression of UAE as a doubling in ACR from baseline to follow-up and regression as a 50% reduction [17e20]. MAU was defined by values of ACR between 30 and 299 mg/g creatinine, while ACR > 300 mg/g defined macroalbuminuria. Glomerular filtration rate (GFR) was calculated using the MDRD formula [21]. Urinary infections were defined by presence of nitrites or leucocytes 250 leucocytes/ml in the urine sample. Patients with urinary infections were not included in the statistical analysis. As normal ranges for glycated haemoglobin varied among different centres, the percentage change with respect to the upper normal value (actual value/upper normal limit) was estimated and multiplied by 6.0. The study protocol was approved by local Ethics Committees at each participating centre.

Methods The DEMAND (Developing Education on Microalbuminuria for Awareness of reNal and cardiovascular risk in Diabetes) study is a multicenter study involving 55 Italian Diabetes Outpatient Clinics. Every centre enrolled up to 36 patients during 2 weeks; sampling details have already been published elsewhere [4]. The study consisted of a cross-sectional phase and a longitudinal one.

Study population Eligibility criteria for the DEMAND study were: T2DM according to WHO criteria [16], age between 18 and 80 years, both genders. Patients were excluded if they had type 1 or gestational diabetes, urinary infections, fever, menstrual cycle, overt diabetic nephropathy. All patients signed an informed consent at study entry.

Statistical analysis Correlates of progression of UAE were initially examined by univariate analyses. Baseline characteristics are expressed as mean and standard deviation or median and 10th to 90th percentile range for continuous variables and frequencies and percentages for categorical data. Patients’ characteristics according to normo/micro/macroalbuminuria and progression/regression of ACR were compared using Manne Whitney U-test for continuous variables and Pearson c2 test

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for categorical ones. Two multivariate logistic regression analyses were performed to evaluate factors associated with the progression and regression of ACR; in both analyses the control group was represented by stable patients. The following baseline covariates were tested: age, sex, HbA1c, BMI, waist circumference, smoking, hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP), dyslipidemia, total cholesterol, HDL-cholesterol, LDLcholesterol, triglycerides, diabetes treatment, diabetes complications (retinopathy, diabetic foot, cardiovascular complications, cerebrovascular complications, and peripheral vascular complications), treatment with ACE-Inhibitors and/or Angiotensin II Receptor Blockers, and treatment with lipid-lowering drugs. In addition, changes in HbA1c, blood pressure levels, BMI, waist circumference, and therapy during follow-up were evaluated. Results are expressed as adjusted odds ratios (ORs) with their 95% confidence intervals (95%CI). Analyses were performed using SAS Language (Release 9.1. Cary, NC, USA: 2002e2003). Table 1

Results While in the cross-sectional phase 55 clinics enrolled 1557 patients, 50 clinics participated to the longitudinal phase of the study, by re-evaluating 1289 individuals after a median of 12 (range 9e24) months. Of these, 211 (16.4%) were excluded for having urinary infections at baseline and/or at follow-up, and 59 (4.6%) due to incomplete clinical data. Finally, 1019 (79.1%) were evaluated. At 1 year, 275 (27.0%) patients had a progression of ACR levels, while 241 (23.7%) showed regression. Among patients normoalbuminuric at baseline, 10.3% had developed micro/macroalbuminuria after 1 year. Among patient showing a progression, 80 (33.2%) were microalbuminuric at baseline, while 8 (4.7%) were macroalbuminuric; of those with regression, 35 (12.7%) were microalbuminuric and 8 (2.9%) were macroalbuminuric at baseline. Baseline patient characteristics according to the presence of normo/ micro/macroalbuminuria at baseline are shown in Table 1.

Baseline patient characteristics according to the presence of micro/macroalbuminuria.

Males Age (year) Diabetes duration (year) HbA1c (%) Antidiabetic treatments: Diet Oral agents Insulin Insulin þ oral agents BMI (kg/m2): Males Females Waist circumference (cm): Male Female Dyslipidemia Total cholesterol (mg/dl) Triglycerides (mg/dl) HDL-cholesterol (mg/dl) LDL-cholesterol (mg/dl) Statins Hypertension Hypertension duration SBP (mmHg) DBP (mmHg) ACE-Inhibitors and/or ARBs GFR (MDRD) Serum creatinine Smoke: No Ex Yes Retinopathy Diabetic foot Total CV complications Peripheral vascular disease

Normoalbuminuria

Micro/Macroalbuminuria

pa

64.0 62.6  8.9 9.6  8.0 7.2  1.4

63.9 65.0  8.3 11.7  9.1 7.8  1.5

0.98 0.0015 0.0015 <0.0001

14.0 66.7 10.0 9.2

3.7 64.1 15.6 16.7

<0.0001

28.6  4.3 29.7  5.5

29.5  4.4 30.6  5.5

0.08 0.21

102.0  12.9 98.1  12.8 46.0 203  41 164  107 50  13 123  37 34.9 59.4 9.3  7.2 138  17 80  9 48.5 86.3  19.6 0.90  0.18

104.9  12.2 102.9  14.8 42.9 205  43 177  96 46  11 126  38 37.1 71.7 11.2  8.0 141  18 81  9 60.0 82.4  22.3 0.94  0.22

0.02 0.02 0.42 0.72 0.045 0.005 0.61 0.56 0.001 0.006 0.01 0.06 0.003 0.007 0.037

59.1 27.8 13.1 15.5 2.7 19.9 4.6

54.6 27.8 17.6 26.8 3.9 24.9 5.9

0.24

Data are mean  SD or frequency (%). a 2 c test for categorical variables and ManneWhitney test for continuous variables.

0.0001 0.36 0.12 0.43

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113

In comparison with normoalbuminuric patients, those with micro/macroalbuminuria had older age, longer diabetes duration, poorer metabolic control, higher waist circumference, triglycerides, and lower HDL-cholesterol levels. They also had a higher prevalence and a longer duration of hypertension, higher levels of systolic and diastolic blood pressure, and were more often treated with ACE-Inhibitors/ Angiotensin II Receptor Blockers. Finally, patients with micro/macroalbuminuria had lower GFR, higher serum

creatinine levels, and higher prevalence of retinopathy than normoalbuminuric patients. Patient characteristics according to the progression/ regression of ACR are shown in Table 2. Progression of ACR was associated with male gender, higher levels of HbA1c at baseline, more frequent use of insulin, and slightly lower LDL-cholesterol levels. Patients who progressed also showed a significant increase in BMI during follow-up as compared to those who did not progress.

Table 2 Patient characteristics according to progression and regression of ACR. Delta (D) values represent end-of-study vs. baseline changes.

Males Age (year) Diabetes duration (year) HbA1c (%) D HbA1c (%) BMI (kg/m2) DBMI Waist circumference (cm) DWC Hypertension Hypertension duration SBP (mmHg) DSBP DBP (mmHg) DDBP Hyperlipidemia Total cholesterol (mg/dl) Triglycerides (mg/dl) HDL-cholesterol (mg/dl) LDL-cholesterol (mg/dl) Smoke: No Ex Yes Antidiabetic treatments: Diet Oral agents Insulin þ oral agents Insulin ACE-Inhibitors and/or ARBs at baseline Start of ACE-Inhibitors and/or ARBs during the study Statins Retinopathy Diabetic foot Total CV complications Peripheral vascular disease ACR (mg/g) DACR GFR (ml/min) Serum creatinine (mg/l)

Stable (N Z 503)

Regression (N Z 241)

Progression (N Z 275)

P (regression vs. stable)a

62.1 62.8  8.8 9.6  7.8 7.2  1.4 0 [1.2;0.8] 28.9  4.9 0 [1.2;1.5] 100.3  13.4 0 [7;9] 59.6 10.1  7.6 138  17 0 [20;20] 80  9 0 [10;10] 53.6 206  38 156  82 50.2  12.7 127  36

61.8 63.5  8.7 9.9  8.4 7.4  1.4 0.1 [1.5;0.8] 29.6  4.9 0 [1.5;1.5] 102.5  11.8 0 [8;6] 66.8 9.1  7.1 140  16 0 [20;20] 81  9 0 [15;15] 56.9 202  45 173  94 47.7  12.0 122  39

69.3 63.2  9.2 10.8  8.9 7.5  1.6 0 [1.4;1.2] 29.3  4.7 0.3 [1.1;1.8] 102.2  13.4 0 [4;7] 61.8 9.7  7.5 140  17 0 [20;20] 80  9 0 [10;10] 54.4 200  45 181  144 47.9  12.0 118  37

0.95 0.32 0.99 0.006 0.25 0.02 0.22 0.008 0.04 0.06 0.15 0.09 0.24 0.32 0.59 0.40 0.27 0.06 0.09 0.18

0.04 0.54 0.19 0.01 0.80 0.17 0.02 0.08 0.35 0.54 0.49 0.19 0.11 0.46 0.25 0.83 0.12 0.41 0.08 0.04

59.6 27.8 12.5

59.3 27.0 13.7

54.5 28.4 17.1

0.90

0.18

15.0 66.6 9.1 9.3 48.9

10.4 64.3 14.9 10.4 53.9

7.9 67.0 10.1 15.0 51.6

0.06

0.007

0.20

0.47

12.1

12.0

13.1

0.39

0.59

34.4 15.5 2.4 20.3 4.8 0 [0;57.5] 0 [4;1.4] 85.3  19.3 0.9  0.2

36.5 21.6 3.3 18.7 5.0 16.9 [3.7;140.2] 14.5 [106;4] 85.7  21.4 0.9  0.2

36.0 18.5 3.6 24.0 4.7 3.3 [0;67.8] 17.1 [3.6;297] 85.9  20.8 0.9  0.2

0.57 0.04 0.46 0.61 0.89 <0.0001 <0.0001 0.70 0.80

0.65 0.28 0.31 0.23 0.98 <0.0001 <0.0001 0.95 0.46

P (progression vs. stable)a

Data are means  SD or median [10th to 90th percentile] for continuous variables and percentage for categorical ones. a c2 test for categorical variables and KruskaleWallis test for continuous variables.

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As for patients with regression, there was a higher HbA1c levels, BMI, waist circumference, and retinopathy prevalence at baseline; patients with regression also showed a significant reduction in waist circumference during follow-up as compared with stable patients. The relationship between BMI and waist circumference changes and the progression/regression of ACR is shown in Fig. 1. ACR levels significantly increased according to classes of BMI (BMI 25: 29  102 mg/g; BMI 25e27: 34  117 mg/g; BMI 27e30: 27  124 mg/g; BMI >30: 67  246 mg/g; p Z 0.03). No difference in GFR according to BMI classes was found (BMI 25: 87.2  17.8 ml/min; BMI 25e27: 84.3  21.3 ml/min; BMI 27e30: 85.2  19.9 ml/min; BMI >30: 85.8  21.0 ml/min; p Z 0.36). On stepwise logistic regression, the likelihood of ACR progression increased by 12% (OR Z 1.12; 95%CI 1.00e1.25) for every 5-years increase in diabetes duration and by 7% (OR Z 1.07; 95%CI 1.00e1.15) for each 5-cm increase in waist circumference measured at baseline. In addition, the risk of ACR progression increased by 17% (OR Z 1.17; 95%CI 1.03e1.33) for every unit increase in BMI during follow-up. No additional variables were independently related to the risk of ACR progression. Multivariate analysis exploring predictors of ACR regression did not find any independent correlate.

albumin excretion at baseline. Consistently, regression of ACR was associated with a decrease in both BMI and waist circumference during follow-up, though statistical significance was not reached at multivariate analysis.

Comparison with existing knowledge

Discussion

Our data that waist circumference is associated with progression of albuminuria are consistent with findings from the UKPDS trial [9] which showed that waist circumference at baseline was an independent predictor for the development of micro- and macroalbuminuria. We also add that increase in BMI over time is associated with a parallel increase in ACR. Previous small intervention studies, conducted mainly in patients with established renal disease, have found a beneficial effect of weight loss on UAE [22e25]. The mechanism underlying this relationship between obesity and risk of renal injury remains unclear. Besides the putative role of adipocytokines [13e15], it is also possible that weight loss induced a lowering in blood pressure, thus leading to less hyperfiltration. Irrespective of the pathologic pathways implicated, the strength of association we found in our study between indices of obesity and progression of ACR strongly supports that interventions aimed at reducing body weight and abdominal obesity may be useful strategies to impact on cardio-renal risk in individuals with diabetes.

Key findings

Strengths and weaknesses

We show that in patients with type 2 diabetes receiving routine clinical practice care in outpatient clinics participating in the DEMAND study, waist circumference is associated with progression of albuminuria during 12 months. In absolute value, per every 5-cm increase in baseline waist circumference, the risk of ACR progression at 12 months increases by 7%. We also show, for the first time, that BMI change from baseline to end-of-study is associated with changes in ACR. Specifically, the risk of progression of ACR increased by 17% for every one-unit increase in BMI during the follow-up. In other words, an increase of 2.5e3 kg in body weight conferred an excess risk of progression of ACR of about 20%. This effect was independent of metabolic control, blood pressure level and treatment at baseline and their changes during the study, and was also not related to the level of

In our study, we used centralized measurement of urinary albumin and creatinine. Only one measurement of urinary ACR was available and may be responsible for potential confounding ascribed to day-to-day variability of urinary albumin excretion. However, previous studies have confirmed that this may be overcome with large number of patients enrolled and a good reproducibility of different measurements of ACR [26]; this was the case of our study. As a second point, we were unable to assess changes in other indices of renal function (i.e. GFR, serum creatinine), since this information was available at baseline only. Similarly, the relationship between changes in body weight and long-term outcomes (i.e. end-stage kidney disease) was not assessed, due to the short duration of follow-up. To this respect, our findings need to be confirmed in studies of longer duration.

Figure 1

Relationship between progression/regression of ACR and BMI and waist circumference changes.

Obesity and albumin excretion rate in DM2

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Implications for practice and research Our findings that changes in body weight are associated in the short term with changes in ACR suggest the potential benefits of early lifestyle interventions for preventing endorgan damage. Long-term studies are warranted in this setting to clarify whether weight loss is able to impact the risk of hard patient-level endpoints including end-stage kidney disease in patients with diabetes. This strategy could be assessed as either a single intervention or in combination with other established interventions for preventing chronic kidney disease, including antihypertensive agents [27].

[3]

[4]

[5]

Acknowledgments [6]

Part of these study results was presented at the 44th EASD Annual Meeting e Rome 2008. We are grateful to Giovanni Strippoli for his critical review of the manuscript. This study was supported by an unconditional grant from SanofiAventis. List of Investigators: G. Brandoni e Ancona; L. Gentile, S. Guidi e Asti; V. Paciotti, P. Alfidi e Avezzano (AQ); A. Sforza, V. Chiarini e Bologna; L. Rocca, B. Agosti e Brescia; V. Borzı`; R.M. Motta, S. Squatrito e Catania; C. Santini, C. Dradi Maraldi e Cesena (FO); C.B. Giorda, E. Nada e Chieri (TO); A. Chiambretti, R. Fornengo e Chivasso (TO); P. Mascetti, G. Carrano e Como; G. Magro, L. Gianotti e Cuneo; A. Giancaterini; N. Musacchio e Cusano Milanino (MI); G. Formentini, M.C. Pilia e Desenzano del Garda (BS); G. Marelli e Desio (MI); C. Baggiore e Firenze; M. Cignarelli, O. Lamacchia e Foggia; C. Taboga, B. Catone e Gemona del Friuli (UD); A. Cattaneo, R. Guido e Genova; L. Cataldi, C. Bordone e Genova; R. Geremia e Giugliano in Campania (NA); F. Quadri, L. Sambuco e Grosseto; P. Tatti, F. Costanza e Marino (RM); A.M. Scarpitta, A. Lo Presti e Marsala (TP); M.A. Dolci e Massa; A. Venezia, R. Morea e Matera; A. Zampino, F. Cervellino e Melfi (PZ); C. Invitti, A. Girola e Milano; V. Manicardi, M. Michelini e Montecchio (RE); F. Sanciu e Olbia; A. Galluzzo, F. Panto ` e Palermo; G. Mattina e Palermo; G. Grossi, F. De Berardinis e Paola (CS); S.M. Tardio, M.C. Calderini e Parma; V. Aiello, V. Provenzano e Partinico (PA); I.S. Savulescu, A. Vailati e Pavia; O. Giampietro, I. Chiti e Pisa; R. Gelisio, M. Cabras e Portogruaro (VE); A. Arcangeli, S. Guizzotti e Prato; C. Giovannini e Reggio Calabria; C. Collina, C. Simeoni, S. Leotta, L. Fontana e Roma; S. Genovese, A. Rossi e Rozzano (MI); S. De Cosmo, A. Rauseo e S. Giovanni Rotondo (FG); A. Muscogiuri, A. Maschio e S. Pietro Vernotico (BR); P. Cala `tola, L. Lo Conte e Salerno; M. Santangelo, R. Fani e San Benedetto del Tronto (AP); R. Cavani e Sassuolo (MO); F. Calcaterra, F. Cataldi e Schio (VI); A.F. Braione, S. Albano e Taranto; A. Travaglini, A. Di Gianvito e Terni; L. Monge, G. Boffano e Torino; N. Palmieri, C. Fiengo e Torre del Greco (NA); C. Noacco, F. Colucci e Udine.

[7]

[8]

[9]

[10] [11]

[12]

[13]

[14]

[15] [16]

[17]

[18]

References [1] Ritz E. Heart and kidney: fatal twins? Am J Med 2006; 119(5 Suppl. 1):S31e9. [2] Bianchi C, Penno G, Malloggi L, Barontini R, Corfini M, Giovannitti MG, et al. Non-traditional markers of atherosclerosis

[19]

potentiate the risk of coronary heart disease in patients with type 2 diabetes and metabolic syndrome. Nutr Metab Cardiovasc Dis 2008;18:31e8. Bo S, Ciccone G, Gancia R, Rosato R, Grassi G, Merletti F, et al. Mortality within the first 10 years of the disease in type 2 diabetic patients. Nutr Metab Cardiovasc Dis 2006;16:8e12. Rossi MC, Nicolucci A, Pellegrini F, Comaschi M, Ceriello A, Cucinotta D, et al. Identifying patients with type 2 diabetes at high risk of microalbuminuria: results of the DEMAND (Developing Education on Microalbuminuria for Awareness of reNal and cardiovascular risk in Diabetes) Study. Nephrol Dial Transplant 2008;23:1278e84. Franciosi M, Pellegrini F, Sacco M, De Berardis G, Rossi MC, Strippoli GF, et al. IGLOO (Impaired Glucose tolerance, and Long-term Outcomes Observational Study) Study Group. Identifying patients at risk for microalbuminuria via interaction of the components of the metabolic syndrome: a cross-sectional analytic study. Clin J Am Soc Nephrol 2007;2:984e91. Palaniappan L, Carnethon M, Fortmann SP. Association between microalbuminuria and the metabolic syndrome: NHANES III. Am J Hypertens 2003;16:952e8. Chandie Shaw PK, Berger SP, Mallat M, Fro ¨lich M, Dekker FW, Rabelink TJ. Central obesity is an independent risk factor for albuminuria in nondiabetic South Asian subjects. Diabetes Care 2007;30:1840e4. Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS. Body mass index and risk for end-stage renal disease. Ann Intern Med 2006;144:21e8. Retnakaran R, Cull CA, Thorne KI, Adler AI, Holman RRUKPDS. Study Group. Risk factors for renal dysfunction in type 2 diabetes: U.K. Prospective Diabetes Study 74. Diabetes 2006;55: 1832e9. Cignarelli M, Lamacchia O. Obesity and kidney disease. Nutr Metab Cardiovasc Dis 2007;17:757e62. Bello AK, de Zeeuw D, El Nahas M, Brantsma AH, Bakker SJ, de Jong PE, et al. Impact of weight change on albuminuria in the general population. Nephrol Dial Transplant 2007;22:1619e27. de Boer IH, Sibley SD, Kestenbaum B, Sampson JN, Young B, Cleary PA, et al. Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study Research Group. Central obesity, incident microalbuminuria, and change in creatinine clearance in the epidemiology of diabetes interventions and complications study. J Am Soc Nephrol 2007;18:235e43. Steinberg HO, Chaker H, Leaming R, Johnson A, Brechtel G, Baron AD. Obesity/insulin resistance is associated with endothelial dysfunction. Implications for the syndrome of insulin resistance. J Clin Invest 1996;97:2601e10. Cai D, Yuan M, Frantz DF, Melendez PA, Hansen L, Lee J, et al. Local and systemic insulin resistance resulting from hepatic activation of IKK-beta and NF-kB. Nat Med 2005;11:183e90. Yudkin JS. Inflammation, obesity, and the metabolic syndrome. Horm Metab Res 2007;39:707e9. World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO consultation. Geneva: World Health Organization; 1999. Gerstein HC, Mann JF, Yi Q, Zinman B, Dinneen SF, Hoogwerf B, et al. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 2001;286:421e6. Wachtell K, Ibsen H, Olsen MH, Borch-Johnsen K, Lindholm LH, Mogensen CE, et al. Albuminuria and cardiovascular risk in hypertensive patients with left ventricular hypertrophy: the LIFE study. Ann Intern Med 2003;139:901e6. Arnlov J, Evans JC, Meigs JB, Wang TJ, Fox CS, Levy D, et al. Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study. Circulation 2005;112:969e75.

116 [20] Ruggenenti P, Remuzzi G. Time to abandon microalbuminuria? Kidney Int 2006;70:1214e22. [21] 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;130:461e70. [22] Morales E, Valero MA, Leon M, Hernandez E, Praga M. Beneficial effects of weight loss in overweight patients with chronic proteinuric nephropathies. Am J Kidney Dis 2003;41: 319e27. [23] Solerte SB, Fioravanti M, Schifino N, Ferrari E. Effects of diettherapy on urinary protein excretion albuminuria and renal haemodynamic function in obese diabetic patients with overt nephropathy. Int J Obes 1989;13:203e11. [24] Serra A, Granada ML, Romero R, Baye ´s B, Canto ´n A, Bonet J, et al. The effect of bariatric surgery on adipocytokines, renal parameters and other cardiovascular risk

M.C.E. Rossi et al. factors in severe and very severe obesity: 1-year follow-up. Clin Nutr 2006;25:400e8. [25] Tong PC, Lee ZS, Sea MM, Chow CC, Ko GT, Chan WB, et al. The effect of orlistat-induced weight loss, without concomitant hypocaloric diet, on cardiovascular risk factors and insulin sensitivity in young obese Chinese subjects with or without type 2 diabetes. Arch Intern Med 2002;162: 2428e35. [26] Gansevoort RT, Verhave JC, Hillege HL, Burgerhof JG, Bakker SJ, de Zeeuw D, et al. for the PREVEND Study Group. The validity of screening based on spot morning urine samples to detect subjects with microalbuminuria in the general population. Kidney Int 2005;94(Suppl):S28e35. [27] Strippoli GF, Craig M, Deeks JJ, Schena FP, Craig JC. Effects of angiotensin converting enzyme inhibitors and angiotensin II receptor antagonists on mortality and renal outcomes in diabetic nephropathy: systematic review. BMJ 2004;329: 828e38.