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Weight loss and proteinuria: systematic review of clinical trials and comparative cohorts

Weight loss and proteinuria: systematic review of clinical trials and comparative cohorts Abstract Background. Obesity is a risk factor for the progression of chronic kidney disease (CKD). The impact of weight loss on proteinuria and renal function is less clear. We aimed to determine the effect of intentional weight loss on proteinuria and kidney function. Methods. Three bibliographic databases including Medline, Cochrane and SCUPOS as well as reference list of articles were searched. We included randomized and non-randomized controlled trials as well as single-arm trials published in English through May 2009 which examined urinary protein among obese or overweight adults before and after weight loss interventions including dietary restriction, exercise, anti-obesity medications and bariatric surgery. Study characteristics and methodological quality of trials were assessed. Results. Five hundred twenty-two subjects from five controlled and eight uncontrolled trials were included. Weight loss interventions were associated with decreased proteinuria and microalbuminuria by 1.7 g [95% confidence interval (95% CI), 0.7 to 2.6 g] and 14 mg (95% CI, 11 to 17 mg), respectively (P < 0.05). Meta-regression showed that, independent of decline in mean arterial pressure, each 1 kg weight loss was associated with 110 mg (95% CI, 60 to 160 mg, P < 0.001) decrease in proteinuria and 1.1 mg (95% CI, 0.5 to 2.4 mg, P = 0.011) decrease in microalbuminuria, respectively. The decrease was observed across different designs and methods of weight loss. Only bariatric surgery resulted in a significant decrease in creatinine clearance. Conclusions. Weight loss is associated with decreased proteinuria and microalbuminuria. There were no data evaluating the durability of this decrease or the effect of weight loss on CKD progression. chronic kidney disease, obesity, proteinuria, weight loss Introduction The World Health Organization officially recognized obesity as a ‘global epidemic’ in 1997 [1]. In recent years, the growing body of evidence shows a link between obesity and progression of kidney disease [2–4]. Microalbuminuria is the earliest marker of chronic kidney disease (CKD) and is a predictor of the progression of CKD to end-stage kidney disease [5]. Proteinuria is also an independent risk factor for increased morbidity and mortality from cardiovascular diseases, diabetes, hypertension and end-stage renal diseases (ESRD) [6]. There appears to be a graded association between the severity of obesity and magnitude of microalbuminuria [7–14]. On the other hand, a decline in urinary protein excretion is associated with metabolic improvement and decreased cardiovascular risks [15], so that a decline of 50% in urinary protein excretion is shown to be associated with 18% decrease in cardiovascular risks [16]. Therefore, reducing proteinuria is used as a surrogate outcome for evaluating CKD treatment [17]. A relevant question is whether weight loss has a beneficial effect on the reduction of proteinuria and microalbuminuria in obese adults and whether it leads to the decreased rate of progression of CKD and mortality. In normal subjects, albumin and nitrogen excretion rates as well as endogenous creatinine clearance are functions of the quantity of dietary protein intake [18]. In type 2 diabetes, dietary protein restriction is associated with decreased proteinuria [19]. Although proteinuria is augmented immediately after exercise, the effect of long-term exercise on proteinuria at rest is less clear. Many obese patients are also taking angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers as part of the management of hypertension and diabetic nephropathy [20,21]. Therefore, it is not clear whether the decrease in proteinuria observed in case reports and cohort studies of intentional weight loss is due to such co-interventions during weight loss or the result of weight loss itself. This systematic review is aimed (i) to determine the effect of weight loss on proteinuria and other markers of renal function, (ii) to compare the effect of different weight loss interventions including protein-restricted diet, caloric-restricted diet, exercise therapy, anti-obesity agents and bariatric surgery on urinary protein and other markers of renal function, (iii) to determine the role of Patient characteristics at baseline on change in severity of proteinuria with weight loss and (iv) to determine the change in the rate of progression of CKD and new cases of ESRD with weight loss. Materials and methods Data sources and searches We searched PubMed, CENTRAL, and SCUPOS for English-language articles, human studies, with age older than 18 from 1985 through May 2009 using the keywords proteinuria; weight loss; anti-obesity agents; exercise; diet, caloric-restricted; diet, fat-restricted; and bariatric surgery as outlined in Appendix 1. We also searched reference lists of relevant articles. The search was performed by two reviewers independently (F.A. and A.E.). Study selection We included trials of weight loss interventions that examined the association between weight loss in overweight (Body Mass Index >25 kg/m2) or obese (BMI >30 kg/m2) adults with proteinuria and markers of renal function. They included randomized and non-randomized controlled clinical trials and single-arm trials of weight loss interventions including physical exercise therapy, anti-obesity agents, bariatric surgery and dietary weight loss by restriction of carbohydrate and fat which reported proteinuria or urinary albumin and weight or BMI at baseline and after interventions. We excluded observational studies and those that involved patients with chronic conditions associated with non-intentional weight loss, elite athletes as well as studies with urinary protein measurements within 24 h after exercise. Identified articles were rejected on initial screen if it was determined from the title that they did not address weight loss strategies. When a title could not be rejected with certainty, the full text article was obtained. Data extraction and quality assessment Abstraction was performed by one reviewer (F.A.) and independently verified by another (H.I.). The retrieved variables indicated characteristics of studies such as patients' baseline characteristics, inclusion and exclusion criteria, intervention, comparison, outcome and duration of follow-up. Baseline characteristics included age, sex, percentage of patients with diabetes and hypertension, weight or BMI, urinary albumin or protein and glomerular filtration rate (GFR) or creatinine clearance. Interventions were dietary restriction (fat and carbohydrate), exercise, anti-obesity medications and bariatric surgery. Exercise was defined as moderate to intense physical activity with at least 1.8 metabolic equivalents for a minimum duration of 15 min/day for at least 2 days/week for a minimum of 1 month. Anti-obesity agents were defined as any Food and Drug Administration-approved agents of noradrenergic or selective serotonin reuptake inhibitors approved for weight loss, lipase inhibitors, suppressors of appetite and any other compounds primarily approved for other indications but which have been used in trials to show their anti-obesity effects for a minimum of at least 4 weeks. The primary outcome was change in severity of urinary albumin excretion or proteinuria. In clinical trials, proteinuria is measured as a continuous variable. Secondary outcomes were change in creatinine clearance, change in GFR and change in the rate of progression of CKD. Studies were searched for outcomes according to age, race and sex. For studies with multiple measurements at different time intervals, the last measurement was entered into the analyses. In trials with several arms with different doses of medication, the arm with the highest dose was used for the analyses. Outcome variables including weight and laboratory values were measured at least 12 weeks after the start of interventions. Trials with an intervention period as short as 4 weeks were included only if they had at least a 3-month run-in period prior to interventions to minimize the effect of confounding by acute changes unrelated to true weight loss. The setting was outpatient in all studies. We assessed randomized trials for randomization, allocation concealment and adequacy of blinding. Additionally, we assessed percentage lost to follow-up, intention to treat analysis (where applicable) and generalizability by assessing eligibility criteria. For the cohort studies, percent lost to follow-up and eligibility criteria were evaluated (Table 4). Data synthesis and analysis Data analyses were done by one reviewer (F.A.) and independently verified by another reviewer (S.D.). In studies in which mixed samples of patients with and without proteinuria were recruited, only the subgroups of patients with proteinuria were entered into the analyses if their corresponding data were available. Lazarevic et al. reported individualized data for all patients who had decreased their urinary albumin excretion except for one who did not show a change [22]. For that patient, the baseline mean urinary albumin excretion of the abnormal group was conservatively used at baseline and throughout the follow-up period. In Agrawal's study, the mean and SD of the urinary albumin–creatinine ratio (ACR) were obtained by personal communication with the lead author, while in Stenlof's study where the SD of urinary albumin was not reported [23], it was estimated by means of SDs from three other similar studies which had the closest characteristics, design and mean of urinary albumin at baseline [11,24,25]. Continuous data that were reported as median and range were converted to mean and SD by using the method of Hozo [26]. The measures used in the meta-analysis were the difference between urinary protein or albumin before and after intervention using random effect for weighting method and calculating the I2 statistics. In controlled clinical trials, the difference between target variables before and after weight loss in the intervention arm was additionally compared with the control arm using pooled SD of pretest values by the method of Morris [27]. Meta-regression using maximum likelihood to estimate the between-study component of variance was applied to assess the relationship between change of weight with change of urinary protein or albumin. Sensitivity analysis and meta-regression were applied to detect the study and the variables contributing to high heterogeneity, respectively. Subgroup analysis was performed in order to explore differences of effect size or direction among different weight loss strategies. Data analysis was with STATA version 10 (College Station, TX, USA). Results Trial flow Figure 1 shows the study identification and selection process. Our initial search revealed 450 articles in which 376 were excluded due to not addressing the questions (n = 355), case reports (n = 16) and reviews (n = 5). Of 74 articles retrieved for full text evaluation, 57 were excluded due to not being relevant (n = 39), lack of enough information (n = 11), targeting different populations (n = 3), having BMI less than 25 kg/m2 (n = 3) and short duration of intervention (n = 1), leaving 13 articles for inclusion [11,22–25,28–35]. Fig. 1 View largeDownload slide Overview of study identification and selection process. Fig. 1 View largeDownload slide Overview of study identification and selection process. Study characteristics There were four randomized and one non-randomized clinical trial and eight prospective cohort interventions including single-arm trials, uncontrolled-arm trials and controlled trials with no follow-up in the control group. Sample size ranged from 8 to 94 patients with a total of 522 subjects followed up between 4 and 104 weeks. The study characteristics are shown in Table 1. The aim, inclusion and exclusion criteria are summarized in Table 2. Table 3 shows the summary result of interventions. All trials achieved significant weight loss with intervention. Weight loss, decrease in mean arterial pressure (MAP), proteinuria and albuminuria with active treatment ranged from 2.2 to 58.8 kg, 1.5 to 16 mmHg, 0.03 to 2.5 g and 9 to 333 mg, respectively. Similarly, decrease in GFR and creatinine clearance with active treatment ranged from −15 to 35 and −12 to 21.5 mL/min, respectively. The highest amount of weight loss and decrease in GFR and creatinine clearance was achieved by bariatric surgery. All interventions had methodological limitations (Table 4). The percentage of patients with proteinuria or albuminuria at baseline was unclear in four studies [11,23,25,32], and allocation concealment was unclear in all randomized trials [23,31–33]. By design, only one medication-based study could be double blind [23]. Table 1 Characteristics of the interventional studies Study  Group  n  Age ± SD (years)  Intervention, dose and definition  Comparison with  Outcome measured in each study  Follow-up (weeks)  RCT                 Morales E. (2003)  Case  20  56.1 ± 10.1  Low caloric diet, 150% of basal energy expenditure  Before vs after intervention, case vs control  Weight, BMI, proteinuria, crcl  20  Control  10  56.5 ± 15.2  Regular diet      20   Praga M. (1995)  Case  9  47.3 ± 8.0  Low caloric diet, 1000–1400 kcal/day  Before vs after intervention, case vs control  BMI, proteinuria, crcl  52  Control  8  49.5 ± 12.7  Regular diet captopril 25–50 mg/day      52   Nicholson AS. (1999)  Case  7  51.0 ± 4.7  Low caloric diet, 1409 kcal/day  Before vs after intervention, case vs control  Weight, albuminuria  12  Control  4  60.0 ± 3.8  Regular diet      12   Stenlof K. (2006)  Case  42  53.0 ± 11.8  Topiramate, 192 mg/day  Before vs after intervention, case vs control  Weight, albuminuria  40  Control  51  54.0 ± 9.8  Placebo      40                  CCT                 Cubeddu LX. (2008)  UAE <10  23  48.1 ± 11.6  Low caloric diet, 1800–2000 kcal/day (men), 1600–1800 kcal/day (women) exercise (brisk walking, 150 min/week) metformin 500 mg PO TID  Before vs after intervention  Weight, BMI, albuminuria  52  UAE 10–29  18  41.0 ± 9.4        52                  Prospective cohorts                 Tong PC. (2002)  Diabetes  33  18 to 50  Orlistat 120 mg PO TID in diabetics  Before vs after intervention  Weight, BMI, albuminuria  24  No diabetes  27    Orlistat 120 mg PO TID in diabetics      24   Lazarevic G. (2002)  Case  30  54.8 ± 7.3  Exercise, three to five sessions per week, including 45–60 min of warm-up, brisk walking and cool-down at a work load corresponding to 50–75% of maximal heart rate  Before vs after intervention  BMI, ACR  24   Vasquez B. (1984)  Diabetes  24  38.4 ± 10.3  Low caloric diet 500 kcal/day  Before vs after intervention  Weight, proteinuria, albuminuria  24  No diabetes  7  31.4 ± 6.9  Low caloric diet 500 kcal/day      24   Navarro-Diaz M (2006)  Case  61  41.1 ± 9.1  Gastric bypass  Before vs after intervention  Weight, BMI, proteinuria, crcl, albuminuria  104   Chagnac A. (2003)  Case  8  36.0 ± 2.0  Gastroplasty  Before vs after intervention  Weight, BMI, GFR, albuminuria  52   Saiki A. (2005)  Case  22  53.6 ± 8.4  Low caloric diet, 11–19 kcal/kg/day  Before vs after intervention  Weight, BMI, crcl, proteinuria  4   Solerte SB. (1989)  Case  24  49.2 ± 4.0  Low caloric diet, 1410 kcal/day  Before vs after intervention  BMI, crcl, GFR, proteinuria  52   Agrawal V. (2008)  Case  94  45.6 ± 10.5  Gastric bypass  Before vs after intervention  Weight, BMI, albuminuria–creatinine ratio  54  Study  Group  n  Age ± SD (years)  Intervention, dose and definition  Comparison with  Outcome measured in each study  Follow-up (weeks)  RCT                 Morales E. (2003)  Case  20  56.1 ± 10.1  Low caloric diet, 150% of basal energy expenditure  Before vs after intervention, case vs control  Weight, BMI, proteinuria, crcl  20  Control  10  56.5 ± 15.2  Regular diet      20   Praga M. (1995)  Case  9  47.3 ± 8.0  Low caloric diet, 1000–1400 kcal/day  Before vs after intervention, case vs control  BMI, proteinuria, crcl  52  Control  8  49.5 ± 12.7  Regular diet captopril 25–50 mg/day      52   Nicholson AS. (1999)  Case  7  51.0 ± 4.7  Low caloric diet, 1409 kcal/day  Before vs after intervention, case vs control  Weight, albuminuria  12  Control  4  60.0 ± 3.8  Regular diet      12   Stenlof K. (2006)  Case  42  53.0 ± 11.8  Topiramate, 192 mg/day  Before vs after intervention, case vs control  Weight, albuminuria  40  Control  51  54.0 ± 9.8  Placebo      40                  CCT                 Cubeddu LX. (2008)  UAE <10  23  48.1 ± 11.6  Low caloric diet, 1800–2000 kcal/day (men), 1600–1800 kcal/day (women) exercise (brisk walking, 150 min/week) metformin 500 mg PO TID  Before vs after intervention  Weight, BMI, albuminuria  52  UAE 10–29  18  41.0 ± 9.4        52                  Prospective cohorts                 Tong PC. (2002)  Diabetes  33  18 to 50  Orlistat 120 mg PO TID in diabetics  Before vs after intervention  Weight, BMI, albuminuria  24  No diabetes  27    Orlistat 120 mg PO TID in diabetics      24   Lazarevic G. (2002)  Case  30  54.8 ± 7.3  Exercise, three to five sessions per week, including 45–60 min of warm-up, brisk walking and cool-down at a work load corresponding to 50–75% of maximal heart rate  Before vs after intervention  BMI, ACR  24   Vasquez B. (1984)  Diabetes  24  38.4 ± 10.3  Low caloric diet 500 kcal/day  Before vs after intervention  Weight, proteinuria, albuminuria  24  No diabetes  7  31.4 ± 6.9  Low caloric diet 500 kcal/day      24   Navarro-Diaz M (2006)  Case  61  41.1 ± 9.1  Gastric bypass  Before vs after intervention  Weight, BMI, proteinuria, crcl, albuminuria  104   Chagnac A. (2003)  Case  8  36.0 ± 2.0  Gastroplasty  Before vs after intervention  Weight, BMI, GFR, albuminuria  52   Saiki A. (2005)  Case  22  53.6 ± 8.4  Low caloric diet, 11–19 kcal/kg/day  Before vs after intervention  Weight, BMI, crcl, proteinuria  4   Solerte SB. (1989)  Case  24  49.2 ± 4.0  Low caloric diet, 1410 kcal/day  Before vs after intervention  BMI, crcl, GFR, proteinuria  52   Agrawal V. (2008)  Case  94  45.6 ± 10.5  Gastric bypass  Before vs after intervention  Weight, BMI, albuminuria–creatinine ratio  54  RCT, randomized controlled trial; CCT, controlled clinical trial; crcl, creatinine clearance; UEA, urinary excretion of albumin. View Large Table 2 Aim, inclusion and exclusion criteria of included trials Study  Aim  Inclusion criteria  Exclusion criteria  Morales E. (2003)  To determine the effects of low caloric normoproteinuric diet without protein restriction on proteinuria, renal function and metabolic profile in overweight patients with diabetic and non-diabetic chronic proteinuric nephropathies  Chronic (disease duration >1 year), proteinuric (>1 g/day on at least three consecutive tests in the 6-month period before study), nephropathy of diabetic or non-diabetic cause, presence of overweight or obesity (BMI >27) and Scr <2 mg/dL  Unstable clinical condition, rapid loss of renal function, nephrotic syndrome requiring diuretic therapy, immunosuppressive treatments and hypertension requiring more than two antihypertensive medications for its control  Praga M. (1995)  To compare the influence of weight loss with ACEI in obese patients  BMI >30, 24-h urine protein >1 g/day in at least three consecutive studies  Diabetes, systemic diseases  Nicholson AS. (1999)  To evaluate the effect of a dietary intervention on glycemic and metabolic control of type 2 diabetes  Type 2 diabetes, age >25, willingness to attend all components of the study and residence within commuting distance of Georgetown University  Smoking, regular alcohol use, current or past drug abuse, pregnancy, psychiatric illness and medical instability  Stenlof K. (2006)  To evaluate the efficiency and safety of topiramate in weight loss programme  Obese patients with newly diagnosed type 2 diabetes with BMI 27 to 50, HbA1c <10.5, fasting plasma glucose 126 to 236 mg/dL and resting blood pressure <180/100 mmHg  Microvascular complications, severe recurrent hypoglycemic episodes, conditions likely to affect body weight, clinically significant hepatic or renal disease, personal or family history of kidney stone, history of any neuropsychiatric disorder, CNS conditions for using any psychotropic medication or medications expected to influence weight  Cubeddu LX. (2008)  To test if 1-year lifestyle modification metformin intervention is effective in lowering albuminuria in subjects with urinary albumin excretion <30 mg/day  BMI = 27–35  Age >70, history of CAD, heart failure, valvular heart disease, stroke, TIA, arteriosclerosis, renal, hepatic dysfunction, hypertension, active disease state, OCP, Scr >1.5 mg/dL  Tong PC. (2002)  To compare the efficacy of 6-month orlistat treatment on weight reduction, cardiovascular risk factors and insulin sensitivity between obese Chinese with or without type 2 diabetes  Age 18–50, BMI ≥27, with or without diabetes  Pregnancy lactation, childbearing potential with inadequate protection, psychiatric or neurological disorders, alcohol or substance abuse, nephrolithiasis or symptomatic cholelithiasis, previous GI surgery for weight reduction, history or presence of malignancy, history of cardiovascular complications (stroke, IHD, CHF), renal impairment with plasma creatinine >1.7  Lazarevic G. (2007)  To investigate the effect of aerobic exercise on urinary albumin excretion, serum levels and urinary excretion of enzymes, tubular damage and metabolic control markers in type 2 diabetes  Type 2 diabetic patients from outpatient clinic. Control group: no diabetes matched by sex and presumably BMI  Not explained  Vasquez B. (1984)  To determine whether elevated urinary protein excretion in obese type 2 diabetics can be reduced by hypocaloric diet  Obese, diabetes, normal Scr, no medications, no history of renal other renal diseases  Urinary tract infection  Navarro-Diaz M (2006)  To evaluate the effect of weight loss after bariatric surgery on blood pressure, renal parameters and function  Obese patients who underwent bariatric surgery in that hospital between December 2001and January 2004  None  Chagnac A. (2003)  To examine if weight loss might reverse glomerular dysfunction in obese subjects without overt renal disease  Obese group: age 23–46, BMI >38  Not explained  Saiki A. (2005)  To evaluate the effect and safety of low-calorie formula diet on renal function and proteinuria in obese patients with diabetic nephropathy  BMI >25, presence of diabetic retinopathy, proteinuria (urine albumin >300 mg/day) and Scr <3.5 mg/dL  Unstable diabetic retinopathy, pleural effusion, sever leg edema  Solerte SB. (1989)  To evaluate renal hemodynamic changes and urinary protein excretion during hypocaloric diet therapy in obese diabetic patients with overt nephropathy  Obese type 1 or 2 diabetic patients with overt nephropathy  Not explained  Agrawal V. (2008)  To evaluate the effect of bariatric surgery on renal parameters after surgery  Morbidly obese patient (BMI >40 without diabetes or BMI >35 with diabetes) who underwent bariatric surgery from December 2002 to December 2003  ACR >300 mg/g  Study  Aim  Inclusion criteria  Exclusion criteria  Morales E. (2003)  To determine the effects of low caloric normoproteinuric diet without protein restriction on proteinuria, renal function and metabolic profile in overweight patients with diabetic and non-diabetic chronic proteinuric nephropathies  Chronic (disease duration >1 year), proteinuric (>1 g/day on at least three consecutive tests in the 6-month period before study), nephropathy of diabetic or non-diabetic cause, presence of overweight or obesity (BMI >27) and Scr <2 mg/dL  Unstable clinical condition, rapid loss of renal function, nephrotic syndrome requiring diuretic therapy, immunosuppressive treatments and hypertension requiring more than two antihypertensive medications for its control  Praga M. (1995)  To compare the influence of weight loss with ACEI in obese patients  BMI >30, 24-h urine protein >1 g/day in at least three consecutive studies  Diabetes, systemic diseases  Nicholson AS. (1999)  To evaluate the effect of a dietary intervention on glycemic and metabolic control of type 2 diabetes  Type 2 diabetes, age >25, willingness to attend all components of the study and residence within commuting distance of Georgetown University  Smoking, regular alcohol use, current or past drug abuse, pregnancy, psychiatric illness and medical instability  Stenlof K. (2006)  To evaluate the efficiency and safety of topiramate in weight loss programme  Obese patients with newly diagnosed type 2 diabetes with BMI 27 to 50, HbA1c <10.5, fasting plasma glucose 126 to 236 mg/dL and resting blood pressure <180/100 mmHg  Microvascular complications, severe recurrent hypoglycemic episodes, conditions likely to affect body weight, clinically significant hepatic or renal disease, personal or family history of kidney stone, history of any neuropsychiatric disorder, CNS conditions for using any psychotropic medication or medications expected to influence weight  Cubeddu LX. (2008)  To test if 1-year lifestyle modification metformin intervention is effective in lowering albuminuria in subjects with urinary albumin excretion <30 mg/day  BMI = 27–35  Age >70, history of CAD, heart failure, valvular heart disease, stroke, TIA, arteriosclerosis, renal, hepatic dysfunction, hypertension, active disease state, OCP, Scr >1.5 mg/dL  Tong PC. (2002)  To compare the efficacy of 6-month orlistat treatment on weight reduction, cardiovascular risk factors and insulin sensitivity between obese Chinese with or without type 2 diabetes  Age 18–50, BMI ≥27, with or without diabetes  Pregnancy lactation, childbearing potential with inadequate protection, psychiatric or neurological disorders, alcohol or substance abuse, nephrolithiasis or symptomatic cholelithiasis, previous GI surgery for weight reduction, history or presence of malignancy, history of cardiovascular complications (stroke, IHD, CHF), renal impairment with plasma creatinine >1.7  Lazarevic G. (2007)  To investigate the effect of aerobic exercise on urinary albumin excretion, serum levels and urinary excretion of enzymes, tubular damage and metabolic control markers in type 2 diabetes  Type 2 diabetic patients from outpatient clinic. Control group: no diabetes matched by sex and presumably BMI  Not explained  Vasquez B. (1984)  To determine whether elevated urinary protein excretion in obese type 2 diabetics can be reduced by hypocaloric diet  Obese, diabetes, normal Scr, no medications, no history of renal other renal diseases  Urinary tract infection  Navarro-Diaz M (2006)  To evaluate the effect of weight loss after bariatric surgery on blood pressure, renal parameters and function  Obese patients who underwent bariatric surgery in that hospital between December 2001and January 2004  None  Chagnac A. (2003)  To examine if weight loss might reverse glomerular dysfunction in obese subjects without overt renal disease  Obese group: age 23–46, BMI >38  Not explained  Saiki A. (2005)  To evaluate the effect and safety of low-calorie formula diet on renal function and proteinuria in obese patients with diabetic nephropathy  BMI >25, presence of diabetic retinopathy, proteinuria (urine albumin >300 mg/day) and Scr <3.5 mg/dL  Unstable diabetic retinopathy, pleural effusion, sever leg edema  Solerte SB. (1989)  To evaluate renal hemodynamic changes and urinary protein excretion during hypocaloric diet therapy in obese diabetic patients with overt nephropathy  Obese type 1 or 2 diabetic patients with overt nephropathy  Not explained  Agrawal V. (2008)  To evaluate the effect of bariatric surgery on renal parameters after surgery  Morbidly obese patient (BMI >40 without diabetes or BMI >35 with diabetes) who underwent bariatric surgery from December 2002 to December 2003  ACR >300 mg/g  ACEI, angiotensin-converting enzyme inhibitor; NSAID, non-steroidal anti-inflammatory drugs; CNS, central nervous system; CAD, coronary artery disease; Scr, serum creatinine; IHD, ischemia heart disease; CHF, congestive heart failure. View Large Table 3 Mean age, baseline weight, BMI, duration of intervention or follow-up and changes of weight, proteinuria, microalbuminuria, creatinine clearance and GFR after weight loss interventions Study (year)  n  Age (years)  Baseline Wt (kg) or BMI (kg/m2)  Duration (week)  ΔWt (kg) or ΔBMI (kg/m2)  ΔPU (g/24 h)  ΔAU (mg/24 h)  Δcrcl (mL/min)  ΔGFR (mL/min)  MAP (mmHg)  RCT                       Morales E. (2003)                        Active  20  56.1 ± 10.1  96.1 ± 16.6a  20  −3.6c  −0.90  –  −1.1  –  −2.5    Control (regular diet)  10  56.5 ± 15.2  87.5 ± 11.1a  20  1.9c  0.50  –  −5.8  –  5.4   Praga M. (1995)                        Active  9  47.3 ± 8.0  37.1 ± 3.1b  52  −4.5d  −2.50  –  −4.0  –  −8.0    Control (ACEI)  8  49.5 ± 12.7  38.2 ± 5.0b  52  −0.0d  −2.80  –  −8.0  –  −9.0   Nicholson AS. (1999)                        Active  7  51.0 ± 4.7  96.7 ± 13.3a  12  −7.2c  –  −279.6  –  –  −7.3    Control (regular diet)  4  60.0 ± 3.8  97.0 ± 22.9a  12  −3.8c  –  86.3  –  –  13.4   Stenlof K. (2006)                        Active  42  53.0 ± 11.8  101.1 ± 19.7a  40  −9.3c  –  −15.7  –  –  −4.7    Control (placebo)  51  54.0 ± 9.8  104.1 ± 16.2a  40  −2.6c  –  −1.0  –  –  −1.4                        CCT                       Cubeddu LX. (2008)                        Higher range albuminuria  18  48.1 ± 11.6  81.6 ± 17.6a  52  −9.5c  –  −8.9  −11e  –  −4.2    Lower range albuminuria  23  41.0 ± 9.4  78.3 ± 14.1a  52  −9.5c  –  −0.4  –  –  −4.5                        Prospective cohorts                       Tong PC. (2002)                        Diabetics  33  18 to 50  93.2 ± 18.4a  24  −2.9c  –  −15.4  –  –  −2.8    Non-diabetics  27    98.7 ± 18.8a  24  −4.7c  –  −3.6  –  –  −4.7   Lazarevic G. (2007)  30  54.8 ± 7.3  30.8 ± 3.0b  24  −2.2d  –  −29.0  –  –  –   Vasquez B. (1984)                        Diabetic arm  24  38.4 ± 10.3  108.1 ± 23.5a  24  −14.8c  −0.05  −17.5  –  –  −15.7    Non-diabetic arm  7  31.4 ± 6.9  107.0 ± 19.3a  24  −15.7c  −0.005  0.5  –  –  −1.5   Navarro-Diaz M (2006)  61  41.1 ± 9.1  150.6 ± 39.9a  104  −58.8c  −0.03  −10.8  −21.5  –  −16.0   Chagnac A. (2003)  8  36.0 ± 2.0  48.0 ± 2.4b  52  −48.0c  –  −49.6  –  −35.0  −4.0   Saiki A. (2005)  22  53.6 ± 8.4  85.2 ± 17.0a  4  −6.2c  −1.77  –  5.0  –  −7.4   Solerte SB. (1989)  24  49.2 ± 4.0  33.5 ± 1.6b  24  −7.3d  −0.66  −332.6  12.0  15.0  −9.7   Agrawal V (2008)  94  45.6 ± 10.5  133.6 ± 24.5a  54  −35.7c  –  −13.99  –  –  −12.5  Study (year)  n  Age (years)  Baseline Wt (kg) or BMI (kg/m2)  Duration (week)  ΔWt (kg) or ΔBMI (kg/m2)  ΔPU (g/24 h)  ΔAU (mg/24 h)  Δcrcl (mL/min)  ΔGFR (mL/min)  MAP (mmHg)  RCT                       Morales E. (2003)                        Active  20  56.1 ± 10.1  96.1 ± 16.6a  20  −3.6c  −0.90  –  −1.1  –  −2.5    Control (regular diet)  10  56.5 ± 15.2  87.5 ± 11.1a  20  1.9c  0.50  –  −5.8  –  5.4   Praga M. (1995)                        Active  9  47.3 ± 8.0  37.1 ± 3.1b  52  −4.5d  −2.50  –  −4.0  –  −8.0    Control (ACEI)  8  49.5 ± 12.7  38.2 ± 5.0b  52  −0.0d  −2.80  –  −8.0  –  −9.0   Nicholson AS. (1999)                        Active  7  51.0 ± 4.7  96.7 ± 13.3a  12  −7.2c  –  −279.6  –  –  −7.3    Control (regular diet)  4  60.0 ± 3.8  97.0 ± 22.9a  12  −3.8c  –  86.3  –  –  13.4   Stenlof K. (2006)                        Active  42  53.0 ± 11.8  101.1 ± 19.7a  40  −9.3c  –  −15.7  –  –  −4.7    Control (placebo)  51  54.0 ± 9.8  104.1 ± 16.2a  40  −2.6c  –  −1.0  –  –  −1.4                        CCT                       Cubeddu LX. (2008)                        Higher range albuminuria  18  48.1 ± 11.6  81.6 ± 17.6a  52  −9.5c  –  −8.9  −11e  –  −4.2    Lower range albuminuria  23  41.0 ± 9.4  78.3 ± 14.1a  52  −9.5c  –  −0.4  –  –  −4.5                        Prospective cohorts                       Tong PC. (2002)                        Diabetics  33  18 to 50  93.2 ± 18.4a  24  −2.9c  –  −15.4  –  –  −2.8    Non-diabetics  27    98.7 ± 18.8a  24  −4.7c  –  −3.6  –  –  −4.7   Lazarevic G. (2007)  30  54.8 ± 7.3  30.8 ± 3.0b  24  −2.2d  –  −29.0  –  –  –   Vasquez B. (1984)                        Diabetic arm  24  38.4 ± 10.3  108.1 ± 23.5a  24  −14.8c  −0.05  −17.5  –  –  −15.7    Non-diabetic arm  7  31.4 ± 6.9  107.0 ± 19.3a  24  −15.7c  −0.005  0.5  –  –  −1.5   Navarro-Diaz M (2006)  61  41.1 ± 9.1  150.6 ± 39.9a  104  −58.8c  −0.03  −10.8  −21.5  –  −16.0   Chagnac A. (2003)  8  36.0 ± 2.0  48.0 ± 2.4b  52  −48.0c  –  −49.6  –  −35.0  −4.0   Saiki A. (2005)  22  53.6 ± 8.4  85.2 ± 17.0a  4  −6.2c  −1.77  –  5.0  –  −7.4   Solerte SB. (1989)  24  49.2 ± 4.0  33.5 ± 1.6b  24  −7.3d  −0.66  −332.6  12.0  15.0  −9.7   Agrawal V (2008)  94  45.6 ± 10.5  133.6 ± 24.5a  54  −35.7c  –  −13.99  –  –  −12.5  Wt, weight; PU, proteinuria; AU, microalbuminuria; crcl, creatinine clearance; GFR, glomerular filtration rate; MAP, mean arterial pressure. aBaseline Wt;bbaseline BMI;cΔWt;dΔBMI;ecrcl in Cubeddu study is mean in both groups. View Large Table 4 Methodological components of included interventions Study  % with PU or AU at baseline  % with diabetesat baseline  Eligibility criteria explicit  Randomized  Allocation concealed  Patient blinding  % lost to follow-up  Intention to treat  RCT                   Morales E. (2003)  100.0  46.7  Yes  Yes  Unclear  Open label  0.0  Yes   Praga M. (1995)  100.0  0.0  Yes  Yes  Unclear  Open label  0.0  Yes   Nicholson AS. (1999)  Unclear  100.0  Yes  Yes  Unclear  Open label  15.4  No   Stenlof K. (2006)  Unclear  100.0  Yes  Yes  Unclear  Double blind  0.0  Yes                    CCT                   Cubeddu LX. (2008)  0.0  0.0  Yes  No  NA  Open label  0.0  NA                    Prospective cohorts                   Tong PC. (2002)  Unclear  50.0  Yes  NA  NA  Open label  3.3  NA   Lazarevic G. (2007)  20.0  100.0  No  No  NA  Open label  0.0  NA   Vasquez B. (1984)  45.8  100.0  Yes  NA  NA  Open label  0.0  NA   Navarro-Diaz M (2006)  47.5  17.4  No  NA  NA  Open label  40.2  NA   Chagnac A. (2003)  Unclear  0.0  No  NA  NA  Open label  0.0  NA   Saiki A. (2005)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Solerte SB. (1989)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Agrawal V. (2008)  22.2  32.7  No  NA  NA  Open label  0.0  NA  Study  % with PU or AU at baseline  % with diabetesat baseline  Eligibility criteria explicit  Randomized  Allocation concealed  Patient blinding  % lost to follow-up  Intention to treat  RCT                   Morales E. (2003)  100.0  46.7  Yes  Yes  Unclear  Open label  0.0  Yes   Praga M. (1995)  100.0  0.0  Yes  Yes  Unclear  Open label  0.0  Yes   Nicholson AS. (1999)  Unclear  100.0  Yes  Yes  Unclear  Open label  15.4  No   Stenlof K. (2006)  Unclear  100.0  Yes  Yes  Unclear  Double blind  0.0  Yes                    CCT                   Cubeddu LX. (2008)  0.0  0.0  Yes  No  NA  Open label  0.0  NA                    Prospective cohorts                   Tong PC. (2002)  Unclear  50.0  Yes  NA  NA  Open label  3.3  NA   Lazarevic G. (2007)  20.0  100.0  No  No  NA  Open label  0.0  NA   Vasquez B. (1984)  45.8  100.0  Yes  NA  NA  Open label  0.0  NA   Navarro-Diaz M (2006)  47.5  17.4  No  NA  NA  Open label  40.2  NA   Chagnac A. (2003)  Unclear  0.0  No  NA  NA  Open label  0.0  NA   Saiki A. (2005)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Solerte SB. (1989)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Agrawal V. (2008)  22.2  32.7  No  NA  NA  Open label  0.0  NA  RCT, randomized controlled trial; CCT, controlled clinical trial; PU, proteinuria; AU, albuminuria; NA, not applicable. View Large Quantitative data synthesis Due to the heterogeneity of study populations, studies with gross proteinuria and microalbuminuria are pooled separately. Outcomes in gross proteinuria Only Praga, Morales and Saiki [30,31,33] had patients with nephrotic range proteinuria. All three studies collected 24-h urine for the measurement of urinary protein excretion and overall showed 3.0 g/day of proteinuria at baseline. While Praga and Saiki used 24-h urine collection to measure creatinine clearance, Morales used the Cockcroft–Gault formula to estimate it [30,31,33]. Although Vasquez and Navarro-Diaz [24,35] also measured proteinuria, because their protein excretion rates were at a micro level, their studies were grouped with those consisting of patients with microalbuminuria. Therefore, their measurement of microalbuminuria was used in our analysis. Figure 2 shows the overall effect of weight loss by caloric restriction on the decrease of overt proteinuria. In this pooled analysis, we deleted Solerte's study after sensitivity analysis because of a high heterogeneity it induced for a main effect of 1.46 g [95% confidence interval (95% CI), 0.72 to 2.22 g], I2 = 68.1% (P = 0.008). Overall, and after sensitivity analysis, dietary restriction-induced weight loss reduced overt proteinuria by 1.7 g (95% CI, 0.7 to 2.6 g), a 55% decrease from baseline (95% CI, 23% to 87%), I2 = 59.5% (P = 0.08). Meta-regression analysis suggested that this heterogeneity may partially be explained by variation in baseline weight, weight loss and decline of MAP with intervention among different studies (Table 5). The type of study design did not have any impact on the direction of outcome. Fig. 2 View largeDownload slide Change in overt proteinuria with weight loss by diet caloric restriction. Fig. 2 View largeDownload slide Change in overt proteinuria with weight loss by diet caloric restriction. Table 5 Components of meta-regression analysis; change of proteinuria, albuminuria and creatinine clearance Variables  Coefficient  95% CI  P-value  ΔProteinuria (g/day)a         ΔWeight (kg)  0.11  0.06 to 0.16  <0.001   Baseline weight (kg)  0.12  0.06 to 0.17  <0.001   MAP (mmHg)  0.02  0.01 to 0.03  <0.001          ΔUrinary albumin (mg/24 h)b         ΔWeight (kg)  1.10  0.50 to 2.40  0.011   MAP (mmHg)  0.83  0.35 to 1.30  0.001   Duration (week)  0.72  0.25 to 1.19  0.003   Age (year)  −1.04  −1.62 to −0.47  <0.001          ΔCreatinine clearance (mL/min)c         ΔWeight (kg)  0.51  0.22 to 0.79  <0.001  Variables  Coefficient  95% CI  P-value  ΔProteinuria (g/day)a         ΔWeight (kg)  0.11  0.06 to 0.16  <0.001   Baseline weight (kg)  0.12  0.06 to 0.17  <0.001   MAP (mmHg)  0.02  0.01 to 0.03  <0.001          ΔUrinary albumin (mg/24 h)b         ΔWeight (kg)  1.10  0.50 to 2.40  0.011   MAP (mmHg)  0.83  0.35 to 1.30  0.001   Duration (week)  0.72  0.25 to 1.19  0.003   Age (year)  −1.04  −1.62 to −0.47  <0.001          ΔCreatinine clearance (mL/min)c         ΔWeight (kg)  0.51  0.22 to 0.79  <0.001  MAP, mean arterial pressure.aNumber of records in meta-regression = 6;bNumber of records in meta-regression = 9;cNumber of records in meta-regression = 4. View Large Outcomes in microalbuminuria All other studies had albumin excretion rates below nephrotic range, mostly at microalbuminuria range with an overall mean urinary albumin excretion rate of 26.7 mg/day at baseline [11,22–25,28,29,32,35]. In the study by Tong [25], subjects without diabetes had a significantly lower urinary albumin excretion at baseline with a less profound decline in urinary albumin after intervention compared to individuals with diabetes. This discrepancy, along with the use of geometric mean and standard deviation in the study, contributed to a high heterogeneity within this study and between this and other studies. By sensitivity analysis, the non-diabetic arm of this trial contributed to heterogeneity and was, therefore, excluded in subsequent pooled analysis. Only Chagnac measured GFR by priming dose of inulin, p-aminohippuric acid and dextran [11]. In this study, albumin excretion rate was measured from timed urine collection and simultaneous venous blood sampling. The 24-h albumin excretion was estimated from the measured albumin excretion rate. In studies by Lazarevic and Agrawal [22,29], the 24-h albumin excretion was estimated from the ACR. All other studies measured urinary microalbuminuria based on a 24-h urine collection. Additionally, Navarro-Diaz and Cubeddu [24,28] measured creatinine clearance from the 24-h urine collection. In Figure 3, the overall effect of weight loss interventions on microalbuminuria by type of weight loss intervention is shown. In this analysis, Nicholson's study was eliminated from pooled effect calculation after sensitivity analysis demonstrated that it was an outlier, likely due to its low sample size and very large standard deviation. However, we included it in the meta-regression analysis to investigate sources of heterogeneity. Further sensitivity analysis did not reduce the heterogeneity. Overall, weight loss interventions decreased urinary albumin excretion by 14 mg (95% CI, 11 to 17), a 52% decrease from baseline (95% CI, 40% to 64%), I2 = 50.0% (P = 0.051). Meta-regression suggested that this heterogeneity may partially be explained by variation of age, duration of follow-up, weight loss and decline in MAP among different studies. The type of study design did not have any impact on the direction of outcome. Fig. 3 View largeDownload slide Change in microalbuminuria with weight loss by different types of intervention. Fig. 3 View largeDownload slide Change in microalbuminuria with weight loss by different types of intervention. Creatinine clearance–GFR outcome Baseline mean GFR or creatinine clearance in surgical trials was 140.2 mL/min, while it was 88.0 mL/min in non-surgical trials. Figure 4 shows the results of change in creatinine clearance by type of intervention. Accordingly, unlike the pooled non-surgical interventions, surgical interventions decreased GFR or creatinine clearance by 23.7 mL/min (95% CI, 11.4 to 36.2), a 17% decrease from baseline (95% CI, 8% to 26%). Within non-surgical interventions, results were mixed. While Cubeddu showed a decrease in creatinine clearance, Saiki, Morales and Prage did not show any change and Solerte noted an increase in creatinine clearance. Fig. 4 View largeDownload slide Comparing the effect of surgical and non-surgical methods of weight loss on change of creatinine clearance (in millilitres per minute). Fig. 4 View largeDownload slide Comparing the effect of surgical and non-surgical methods of weight loss on change of creatinine clearance (in millilitres per minute). Subgroup analysis by intervention and baseline characteristics In overt proteinuria, caloric restriction was associated with a pooled 1.7-g reduction of proteinuria (95% CI, 0.7 to 2.6 g), a 55% decrease from baseline (95% CI, 23% to 87%), while in microalbuminuria, the decrease was 17.5 mg (95% CI, 12.3 to 22.7 mg) or 58% (95% CI, 41% to 76%). Similarly, bariatric surgery was associated with 13 mg (95% CI, 5 to 21 mg) or a 92% decrease (95% CI, 35% to 149%), medications with 15 mg (95% CI, 13 to 18 mg) or a 28% decrease (95% CI, 24% to 34%), exercise with 30 mg (95% CI, −3 to 61 mg) or a 49% decrease (95% CI, −5% to 100%) and lifestyle modification with 9 mg (95% CI, 5 to 13 mg) or a 62% decrease (95% CI, 34% to 89%) in albumin excretion rate. No study analysed the change in urinary protein excretion by subgroups of age, sex and other demographic characteristics. No study addressed the change in the rate of progression of CKD or durability of reduced urinary protein excretion. Meta-regression analysis Table 5 presents the results of meta-regression. Accordingly, decrease in overt proteinuria was correlated with weight loss, decline in MAP and weight at baseline (P < 0.05). Each 1 kg decrease in weight was associated with 110 mg (95% CI, 60 to 160 mg, P < 0.001) or a 4% decrease (95% CI, 2% to 5%) in urinary protein excretion, independent of decline in MAP and baseline weight. Similar results were observed after adjusting for the use of ACEI. Decrease of microalbuminuria was also directly correlated with weight loss, duration of intervention and decline of MAP, but was inversely correlated with age, so that each 1 kg weight loss was associated with 1.1 mg (95% CI, 0.5 to 2.4 mg, P = 0.011) or a 4% decrease (95% CI, 2% to 9%) of microalbuminuria independent of decline of MAP and each 1 week of weight loss intervention was associated with 0.7 mg decrease in microalbuminuria (95% CI, 0.3 to 1.2 mg, P = 0.003). Creatinine clearance and GFR were directly correlated with change of body weight. Discussion Weight loss in overweight and obese adults with mild to moderate CKD results in a significant decrease in proteinuria and albuminuria, regardless of study designs and methods of weight loss. All trials had methodological limitations including low sample size, short period of follow-up and lack of control groups in uncontrolled trials. In a population-based observation, Bello et al. reported parallel changes in albuminuria with change in weight [36]. This observation had a mean follow-up of 4.2 years and did not differentiate intentional from unintentional weight loss, raising the possibility of natural course effect of cachexia–inflammation of chronic illnesses. In our reviewed articles, the duration of follow-up and intervention is much lower than Bello's report and, therefore, it is a remote possibility that the decline in body weight achieved after interventions for intentional weight loss in a relatively short period of time is a consequence of such an effect. Consistent pattern of decline of urinary protein among different study designs suggests that the decrease could be beyond regression to the mean and may better be explained by weight loss interventions. In spite of consistency in the direction of outcome, it is our assessment that the quality of evidence for the beneficial effect of weight loss on decreasing proteinuria or albuminuria is moderate. The potential mechanisms of early renal injury are suspected to be hyperfiltration and excess excretory load with increase in GFR, renal plasma flow, glomerular pressure and filtration fraction, partially mediated by increased renal sodium reabsorption, increased renal sympathetic activity and activation of renin–angiotensin system [37–39], lipotoxicity with intracellular lipid overload and shunt of excess fatty acids toward synthesis of lipid products [40,41] and finally through increased inflammation and oxidative stress [42,43]. Therefore, the beneficial effect of weight loss on renal function may be through the decrease in hyperfiltration, lipotoxicity, inflammation and oxidative stress. The most profound decrease in GFR and creatinine clearance has only been achieved by bariatric surgery. Highest baseline weight, GFR and creatinine clearance in patients of surgical trials compared to other studies suggest the highest hyperfiltration in the former which is reversed by the most profound weight loss. The reduction of GFR by weight loss may be interpreted as another beneficial effect of intervention by change in the status of hyperfiltration; however, no trial has been able to capture any change in the rate of progression of CKD within a study period. Non-surgical trials which did not achieve such a decrease in GFR, neither achieved this level of weight loss nor had considerably higher weight, GFR or creatinine clearance at baseline. Patients in surgical trials were also generally healthier compared to Saiki and Solerte's study in which individuals had overt nephropathy. These differences in baseline characteristics along with probable differences in intensity of interventions among non-surgical trials are likely explanations of the mixed effect of weight loss intervention on renal function among different trials. Direct correlation between changes of proteinuria with body weight suggests that the higher categories of body weight may get the most benefit from weight loss programmes. Association of decline in proteinuria with decrease of MAP, independent of weight loss, firstly suggests that the effect of decrease in proteinuria is not completely a consequence of decline in blood pressure and secondly it underscores the possibility of achieving maximum benefits upon combining weight loss programmes with pharmacologic control of blood pressure. In microalbuminuria, the association between duration of weight loss intervention and maintenance of weight loss with decrease of albumin excretion may be a reflection of longer duration of follow-up with trials of microalbuminuria compared to those with overt proteinuria, allowing to capture such an effect which highlights the benefit of maintenance of weight loss. Inverse correlation between levels of decline of urinary albumin excretion with age underscores the importance of obesity prevention programmes earlier in the course of CKD and at younger age. Albuminuria is an independent risk factor for increased cardiovascular mortality and morbidity both in diabetic and non-diabetic patient populations [44–46]. On the other hand, a decline in urinary protein excretion is shown to be associated with a significant decrease in cardiovascular risks and events [15,47]. Therefore, albuminuria is not only a surrogate marker of increased mortality and morbidity but also viewed as a therapeutic target [16,17] and, therefore, any attempt for its decline may have beneficial effects. Unintentional weight loss as observed in advanced stages of chronic illnesses such as end-stage kidney diseases, advanced heart failure or old age may be a reflection of cachexia–inflammatory status of the chronic illness and often is highly associated with mortality. However, the increased mortality in such entities are often reported through observational studies which have not been able to distinguish intentional from unintentional weight loss or ignored adequate adjustments by chronicity or severity of underlying illnesses [48–51], sending controversial massages about the benefits of intentional weight loss. The patients in trials of this review are also categorized as mild to moderate kidney failure without heart failure. Therefore, future clinical trials are needed to find the optimal clinical status in chronic illnesses such as end-stage kidney disease or heart failure in which intentional weight loss would still provide benefit. Future adequately powered trials should also focus on hard clinical endpoints such as mortality or progression of CKD with adequate follow-ups and plans for maintenance of body weight after weight loss. There are several limitations in our review. The patient populations of different studies are heterogeneous and at different stages of disease. To overcome this, two strategies were approached. Firstly, studies with similar patient populations and severity of disease at baseline were pooled together. This is shown by analyses of patients with gross proteinuria and microalbuminuria separately. Secondly, within each category of studies with similar kidney function, meta-regression analysis was applied to further investigate the sources of heterogeneity including variability in baseline characteristics. Other limitations are low sample size of studies, open-label nature of interventions, short duration of follow-up and inability to detect change in the trend of progression of CKD which probably needs much larger sample size, longer follow-up and maintenance of weight loss. The non-randomized and uncontrolled follow-up interventions have more methodological limitations due to lack of control group providing low-quality evidence for the decrease of urinary protein. The follow-up has also a wide range in different trials, but we addressed the effect of duration of follow-up in meta-regression analysis. The amount of decline in proteinuria with each 1 kg weight loss is likely underestimated by our conservative analytic approach as well as the heterogeneity in the follow-up period. Additionally, in surgical trials, not all patients had microalbuminuria at baseline (surgical indication). Therefore, the presence of a significant proportion of patients with normal range microalbuminuria at baseline has contributed to an underestimation of effect of size by diluting the net pooled effect in the total number of patients. Also, data about the influence of weight on GFR is scarce. On the other hand, the indirect method of estimating GFR such as using creatinine clearance or its calculation by Cockcroft formula can have increased margins of error in obesity which likely has contaminated our results. The generalizability of beneficiary effect of weight loss on urinary protein excretion is also limited to a subgroup of relatively healthy patients with mild to moderate CKD and no history of congestive heart failure. Conclusion In conclusion, evidence supports the beneficiary effect of weight loss on the surrogate outcomes of the decrease of urinary protein excretion. There are no data on the effect of weight loss on the progression to CKD. Further research is required to determine the impact of weight loss on clinical renal outcomes. The authors would like to thank Drs. Tatyana Shamliyan, Robert L. Kane and Areef Ishani for their comments. The results presented in this paper have not been published previously. Conflict of interest statement. None declared. References 1 World Health Organization. ,  Obesity: preventing and managing the global epidemic. Report of a WHO consultation. 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Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org Oxford University Press http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nephrology Dialysis Transplantation Oxford University Press

Weight loss and proteinuria: systematic review of clinical trials and comparative cohorts

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Publisher
Oxford University Press
Copyright
© The Author 2009. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org
Subject
Clinical Nephrology
ISSN
0931-0509
eISSN
1460-2385
DOI
10.1093/ndt/gfp640
pmid
19945950
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See Article on Publisher Site

Abstract

Abstract Background. Obesity is a risk factor for the progression of chronic kidney disease (CKD). The impact of weight loss on proteinuria and renal function is less clear. We aimed to determine the effect of intentional weight loss on proteinuria and kidney function. Methods. Three bibliographic databases including Medline, Cochrane and SCUPOS as well as reference list of articles were searched. We included randomized and non-randomized controlled trials as well as single-arm trials published in English through May 2009 which examined urinary protein among obese or overweight adults before and after weight loss interventions including dietary restriction, exercise, anti-obesity medications and bariatric surgery. Study characteristics and methodological quality of trials were assessed. Results. Five hundred twenty-two subjects from five controlled and eight uncontrolled trials were included. Weight loss interventions were associated with decreased proteinuria and microalbuminuria by 1.7 g [95% confidence interval (95% CI), 0.7 to 2.6 g] and 14 mg (95% CI, 11 to 17 mg), respectively (P < 0.05). Meta-regression showed that, independent of decline in mean arterial pressure, each 1 kg weight loss was associated with 110 mg (95% CI, 60 to 160 mg, P < 0.001) decrease in proteinuria and 1.1 mg (95% CI, 0.5 to 2.4 mg, P = 0.011) decrease in microalbuminuria, respectively. The decrease was observed across different designs and methods of weight loss. Only bariatric surgery resulted in a significant decrease in creatinine clearance. Conclusions. Weight loss is associated with decreased proteinuria and microalbuminuria. There were no data evaluating the durability of this decrease or the effect of weight loss on CKD progression. chronic kidney disease, obesity, proteinuria, weight loss Introduction The World Health Organization officially recognized obesity as a ‘global epidemic’ in 1997 [1]. In recent years, the growing body of evidence shows a link between obesity and progression of kidney disease [2–4]. Microalbuminuria is the earliest marker of chronic kidney disease (CKD) and is a predictor of the progression of CKD to end-stage kidney disease [5]. Proteinuria is also an independent risk factor for increased morbidity and mortality from cardiovascular diseases, diabetes, hypertension and end-stage renal diseases (ESRD) [6]. There appears to be a graded association between the severity of obesity and magnitude of microalbuminuria [7–14]. On the other hand, a decline in urinary protein excretion is associated with metabolic improvement and decreased cardiovascular risks [15], so that a decline of 50% in urinary protein excretion is shown to be associated with 18% decrease in cardiovascular risks [16]. Therefore, reducing proteinuria is used as a surrogate outcome for evaluating CKD treatment [17]. A relevant question is whether weight loss has a beneficial effect on the reduction of proteinuria and microalbuminuria in obese adults and whether it leads to the decreased rate of progression of CKD and mortality. In normal subjects, albumin and nitrogen excretion rates as well as endogenous creatinine clearance are functions of the quantity of dietary protein intake [18]. In type 2 diabetes, dietary protein restriction is associated with decreased proteinuria [19]. Although proteinuria is augmented immediately after exercise, the effect of long-term exercise on proteinuria at rest is less clear. Many obese patients are also taking angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers as part of the management of hypertension and diabetic nephropathy [20,21]. Therefore, it is not clear whether the decrease in proteinuria observed in case reports and cohort studies of intentional weight loss is due to such co-interventions during weight loss or the result of weight loss itself. This systematic review is aimed (i) to determine the effect of weight loss on proteinuria and other markers of renal function, (ii) to compare the effect of different weight loss interventions including protein-restricted diet, caloric-restricted diet, exercise therapy, anti-obesity agents and bariatric surgery on urinary protein and other markers of renal function, (iii) to determine the role of Patient characteristics at baseline on change in severity of proteinuria with weight loss and (iv) to determine the change in the rate of progression of CKD and new cases of ESRD with weight loss. Materials and methods Data sources and searches We searched PubMed, CENTRAL, and SCUPOS for English-language articles, human studies, with age older than 18 from 1985 through May 2009 using the keywords proteinuria; weight loss; anti-obesity agents; exercise; diet, caloric-restricted; diet, fat-restricted; and bariatric surgery as outlined in Appendix 1. We also searched reference lists of relevant articles. The search was performed by two reviewers independently (F.A. and A.E.). Study selection We included trials of weight loss interventions that examined the association between weight loss in overweight (Body Mass Index >25 kg/m2) or obese (BMI >30 kg/m2) adults with proteinuria and markers of renal function. They included randomized and non-randomized controlled clinical trials and single-arm trials of weight loss interventions including physical exercise therapy, anti-obesity agents, bariatric surgery and dietary weight loss by restriction of carbohydrate and fat which reported proteinuria or urinary albumin and weight or BMI at baseline and after interventions. We excluded observational studies and those that involved patients with chronic conditions associated with non-intentional weight loss, elite athletes as well as studies with urinary protein measurements within 24 h after exercise. Identified articles were rejected on initial screen if it was determined from the title that they did not address weight loss strategies. When a title could not be rejected with certainty, the full text article was obtained. Data extraction and quality assessment Abstraction was performed by one reviewer (F.A.) and independently verified by another (H.I.). The retrieved variables indicated characteristics of studies such as patients' baseline characteristics, inclusion and exclusion criteria, intervention, comparison, outcome and duration of follow-up. Baseline characteristics included age, sex, percentage of patients with diabetes and hypertension, weight or BMI, urinary albumin or protein and glomerular filtration rate (GFR) or creatinine clearance. Interventions were dietary restriction (fat and carbohydrate), exercise, anti-obesity medications and bariatric surgery. Exercise was defined as moderate to intense physical activity with at least 1.8 metabolic equivalents for a minimum duration of 15 min/day for at least 2 days/week for a minimum of 1 month. Anti-obesity agents were defined as any Food and Drug Administration-approved agents of noradrenergic or selective serotonin reuptake inhibitors approved for weight loss, lipase inhibitors, suppressors of appetite and any other compounds primarily approved for other indications but which have been used in trials to show their anti-obesity effects for a minimum of at least 4 weeks. The primary outcome was change in severity of urinary albumin excretion or proteinuria. In clinical trials, proteinuria is measured as a continuous variable. Secondary outcomes were change in creatinine clearance, change in GFR and change in the rate of progression of CKD. Studies were searched for outcomes according to age, race and sex. For studies with multiple measurements at different time intervals, the last measurement was entered into the analyses. In trials with several arms with different doses of medication, the arm with the highest dose was used for the analyses. Outcome variables including weight and laboratory values were measured at least 12 weeks after the start of interventions. Trials with an intervention period as short as 4 weeks were included only if they had at least a 3-month run-in period prior to interventions to minimize the effect of confounding by acute changes unrelated to true weight loss. The setting was outpatient in all studies. We assessed randomized trials for randomization, allocation concealment and adequacy of blinding. Additionally, we assessed percentage lost to follow-up, intention to treat analysis (where applicable) and generalizability by assessing eligibility criteria. For the cohort studies, percent lost to follow-up and eligibility criteria were evaluated (Table 4). Data synthesis and analysis Data analyses were done by one reviewer (F.A.) and independently verified by another reviewer (S.D.). In studies in which mixed samples of patients with and without proteinuria were recruited, only the subgroups of patients with proteinuria were entered into the analyses if their corresponding data were available. Lazarevic et al. reported individualized data for all patients who had decreased their urinary albumin excretion except for one who did not show a change [22]. For that patient, the baseline mean urinary albumin excretion of the abnormal group was conservatively used at baseline and throughout the follow-up period. In Agrawal's study, the mean and SD of the urinary albumin–creatinine ratio (ACR) were obtained by personal communication with the lead author, while in Stenlof's study where the SD of urinary albumin was not reported [23], it was estimated by means of SDs from three other similar studies which had the closest characteristics, design and mean of urinary albumin at baseline [11,24,25]. Continuous data that were reported as median and range were converted to mean and SD by using the method of Hozo [26]. The measures used in the meta-analysis were the difference between urinary protein or albumin before and after intervention using random effect for weighting method and calculating the I2 statistics. In controlled clinical trials, the difference between target variables before and after weight loss in the intervention arm was additionally compared with the control arm using pooled SD of pretest values by the method of Morris [27]. Meta-regression using maximum likelihood to estimate the between-study component of variance was applied to assess the relationship between change of weight with change of urinary protein or albumin. Sensitivity analysis and meta-regression were applied to detect the study and the variables contributing to high heterogeneity, respectively. Subgroup analysis was performed in order to explore differences of effect size or direction among different weight loss strategies. Data analysis was with STATA version 10 (College Station, TX, USA). Results Trial flow Figure 1 shows the study identification and selection process. Our initial search revealed 450 articles in which 376 were excluded due to not addressing the questions (n = 355), case reports (n = 16) and reviews (n = 5). Of 74 articles retrieved for full text evaluation, 57 were excluded due to not being relevant (n = 39), lack of enough information (n = 11), targeting different populations (n = 3), having BMI less than 25 kg/m2 (n = 3) and short duration of intervention (n = 1), leaving 13 articles for inclusion [11,22–25,28–35]. Fig. 1 View largeDownload slide Overview of study identification and selection process. Fig. 1 View largeDownload slide Overview of study identification and selection process. Study characteristics There were four randomized and one non-randomized clinical trial and eight prospective cohort interventions including single-arm trials, uncontrolled-arm trials and controlled trials with no follow-up in the control group. Sample size ranged from 8 to 94 patients with a total of 522 subjects followed up between 4 and 104 weeks. The study characteristics are shown in Table 1. The aim, inclusion and exclusion criteria are summarized in Table 2. Table 3 shows the summary result of interventions. All trials achieved significant weight loss with intervention. Weight loss, decrease in mean arterial pressure (MAP), proteinuria and albuminuria with active treatment ranged from 2.2 to 58.8 kg, 1.5 to 16 mmHg, 0.03 to 2.5 g and 9 to 333 mg, respectively. Similarly, decrease in GFR and creatinine clearance with active treatment ranged from −15 to 35 and −12 to 21.5 mL/min, respectively. The highest amount of weight loss and decrease in GFR and creatinine clearance was achieved by bariatric surgery. All interventions had methodological limitations (Table 4). The percentage of patients with proteinuria or albuminuria at baseline was unclear in four studies [11,23,25,32], and allocation concealment was unclear in all randomized trials [23,31–33]. By design, only one medication-based study could be double blind [23]. Table 1 Characteristics of the interventional studies Study  Group  n  Age ± SD (years)  Intervention, dose and definition  Comparison with  Outcome measured in each study  Follow-up (weeks)  RCT                 Morales E. (2003)  Case  20  56.1 ± 10.1  Low caloric diet, 150% of basal energy expenditure  Before vs after intervention, case vs control  Weight, BMI, proteinuria, crcl  20  Control  10  56.5 ± 15.2  Regular diet      20   Praga M. (1995)  Case  9  47.3 ± 8.0  Low caloric diet, 1000–1400 kcal/day  Before vs after intervention, case vs control  BMI, proteinuria, crcl  52  Control  8  49.5 ± 12.7  Regular diet captopril 25–50 mg/day      52   Nicholson AS. (1999)  Case  7  51.0 ± 4.7  Low caloric diet, 1409 kcal/day  Before vs after intervention, case vs control  Weight, albuminuria  12  Control  4  60.0 ± 3.8  Regular diet      12   Stenlof K. (2006)  Case  42  53.0 ± 11.8  Topiramate, 192 mg/day  Before vs after intervention, case vs control  Weight, albuminuria  40  Control  51  54.0 ± 9.8  Placebo      40                  CCT                 Cubeddu LX. (2008)  UAE <10  23  48.1 ± 11.6  Low caloric diet, 1800–2000 kcal/day (men), 1600–1800 kcal/day (women) exercise (brisk walking, 150 min/week) metformin 500 mg PO TID  Before vs after intervention  Weight, BMI, albuminuria  52  UAE 10–29  18  41.0 ± 9.4        52                  Prospective cohorts                 Tong PC. (2002)  Diabetes  33  18 to 50  Orlistat 120 mg PO TID in diabetics  Before vs after intervention  Weight, BMI, albuminuria  24  No diabetes  27    Orlistat 120 mg PO TID in diabetics      24   Lazarevic G. (2002)  Case  30  54.8 ± 7.3  Exercise, three to five sessions per week, including 45–60 min of warm-up, brisk walking and cool-down at a work load corresponding to 50–75% of maximal heart rate  Before vs after intervention  BMI, ACR  24   Vasquez B. (1984)  Diabetes  24  38.4 ± 10.3  Low caloric diet 500 kcal/day  Before vs after intervention  Weight, proteinuria, albuminuria  24  No diabetes  7  31.4 ± 6.9  Low caloric diet 500 kcal/day      24   Navarro-Diaz M (2006)  Case  61  41.1 ± 9.1  Gastric bypass  Before vs after intervention  Weight, BMI, proteinuria, crcl, albuminuria  104   Chagnac A. (2003)  Case  8  36.0 ± 2.0  Gastroplasty  Before vs after intervention  Weight, BMI, GFR, albuminuria  52   Saiki A. (2005)  Case  22  53.6 ± 8.4  Low caloric diet, 11–19 kcal/kg/day  Before vs after intervention  Weight, BMI, crcl, proteinuria  4   Solerte SB. (1989)  Case  24  49.2 ± 4.0  Low caloric diet, 1410 kcal/day  Before vs after intervention  BMI, crcl, GFR, proteinuria  52   Agrawal V. (2008)  Case  94  45.6 ± 10.5  Gastric bypass  Before vs after intervention  Weight, BMI, albuminuria–creatinine ratio  54  Study  Group  n  Age ± SD (years)  Intervention, dose and definition  Comparison with  Outcome measured in each study  Follow-up (weeks)  RCT                 Morales E. (2003)  Case  20  56.1 ± 10.1  Low caloric diet, 150% of basal energy expenditure  Before vs after intervention, case vs control  Weight, BMI, proteinuria, crcl  20  Control  10  56.5 ± 15.2  Regular diet      20   Praga M. (1995)  Case  9  47.3 ± 8.0  Low caloric diet, 1000–1400 kcal/day  Before vs after intervention, case vs control  BMI, proteinuria, crcl  52  Control  8  49.5 ± 12.7  Regular diet captopril 25–50 mg/day      52   Nicholson AS. (1999)  Case  7  51.0 ± 4.7  Low caloric diet, 1409 kcal/day  Before vs after intervention, case vs control  Weight, albuminuria  12  Control  4  60.0 ± 3.8  Regular diet      12   Stenlof K. (2006)  Case  42  53.0 ± 11.8  Topiramate, 192 mg/day  Before vs after intervention, case vs control  Weight, albuminuria  40  Control  51  54.0 ± 9.8  Placebo      40                  CCT                 Cubeddu LX. (2008)  UAE <10  23  48.1 ± 11.6  Low caloric diet, 1800–2000 kcal/day (men), 1600–1800 kcal/day (women) exercise (brisk walking, 150 min/week) metformin 500 mg PO TID  Before vs after intervention  Weight, BMI, albuminuria  52  UAE 10–29  18  41.0 ± 9.4        52                  Prospective cohorts                 Tong PC. (2002)  Diabetes  33  18 to 50  Orlistat 120 mg PO TID in diabetics  Before vs after intervention  Weight, BMI, albuminuria  24  No diabetes  27    Orlistat 120 mg PO TID in diabetics      24   Lazarevic G. (2002)  Case  30  54.8 ± 7.3  Exercise, three to five sessions per week, including 45–60 min of warm-up, brisk walking and cool-down at a work load corresponding to 50–75% of maximal heart rate  Before vs after intervention  BMI, ACR  24   Vasquez B. (1984)  Diabetes  24  38.4 ± 10.3  Low caloric diet 500 kcal/day  Before vs after intervention  Weight, proteinuria, albuminuria  24  No diabetes  7  31.4 ± 6.9  Low caloric diet 500 kcal/day      24   Navarro-Diaz M (2006)  Case  61  41.1 ± 9.1  Gastric bypass  Before vs after intervention  Weight, BMI, proteinuria, crcl, albuminuria  104   Chagnac A. (2003)  Case  8  36.0 ± 2.0  Gastroplasty  Before vs after intervention  Weight, BMI, GFR, albuminuria  52   Saiki A. (2005)  Case  22  53.6 ± 8.4  Low caloric diet, 11–19 kcal/kg/day  Before vs after intervention  Weight, BMI, crcl, proteinuria  4   Solerte SB. (1989)  Case  24  49.2 ± 4.0  Low caloric diet, 1410 kcal/day  Before vs after intervention  BMI, crcl, GFR, proteinuria  52   Agrawal V. (2008)  Case  94  45.6 ± 10.5  Gastric bypass  Before vs after intervention  Weight, BMI, albuminuria–creatinine ratio  54  RCT, randomized controlled trial; CCT, controlled clinical trial; crcl, creatinine clearance; UEA, urinary excretion of albumin. View Large Table 2 Aim, inclusion and exclusion criteria of included trials Study  Aim  Inclusion criteria  Exclusion criteria  Morales E. (2003)  To determine the effects of low caloric normoproteinuric diet without protein restriction on proteinuria, renal function and metabolic profile in overweight patients with diabetic and non-diabetic chronic proteinuric nephropathies  Chronic (disease duration >1 year), proteinuric (>1 g/day on at least three consecutive tests in the 6-month period before study), nephropathy of diabetic or non-diabetic cause, presence of overweight or obesity (BMI >27) and Scr <2 mg/dL  Unstable clinical condition, rapid loss of renal function, nephrotic syndrome requiring diuretic therapy, immunosuppressive treatments and hypertension requiring more than two antihypertensive medications for its control  Praga M. (1995)  To compare the influence of weight loss with ACEI in obese patients  BMI >30, 24-h urine protein >1 g/day in at least three consecutive studies  Diabetes, systemic diseases  Nicholson AS. (1999)  To evaluate the effect of a dietary intervention on glycemic and metabolic control of type 2 diabetes  Type 2 diabetes, age >25, willingness to attend all components of the study and residence within commuting distance of Georgetown University  Smoking, regular alcohol use, current or past drug abuse, pregnancy, psychiatric illness and medical instability  Stenlof K. (2006)  To evaluate the efficiency and safety of topiramate in weight loss programme  Obese patients with newly diagnosed type 2 diabetes with BMI 27 to 50, HbA1c <10.5, fasting plasma glucose 126 to 236 mg/dL and resting blood pressure <180/100 mmHg  Microvascular complications, severe recurrent hypoglycemic episodes, conditions likely to affect body weight, clinically significant hepatic or renal disease, personal or family history of kidney stone, history of any neuropsychiatric disorder, CNS conditions for using any psychotropic medication or medications expected to influence weight  Cubeddu LX. (2008)  To test if 1-year lifestyle modification metformin intervention is effective in lowering albuminuria in subjects with urinary albumin excretion <30 mg/day  BMI = 27–35  Age >70, history of CAD, heart failure, valvular heart disease, stroke, TIA, arteriosclerosis, renal, hepatic dysfunction, hypertension, active disease state, OCP, Scr >1.5 mg/dL  Tong PC. (2002)  To compare the efficacy of 6-month orlistat treatment on weight reduction, cardiovascular risk factors and insulin sensitivity between obese Chinese with or without type 2 diabetes  Age 18–50, BMI ≥27, with or without diabetes  Pregnancy lactation, childbearing potential with inadequate protection, psychiatric or neurological disorders, alcohol or substance abuse, nephrolithiasis or symptomatic cholelithiasis, previous GI surgery for weight reduction, history or presence of malignancy, history of cardiovascular complications (stroke, IHD, CHF), renal impairment with plasma creatinine >1.7  Lazarevic G. (2007)  To investigate the effect of aerobic exercise on urinary albumin excretion, serum levels and urinary excretion of enzymes, tubular damage and metabolic control markers in type 2 diabetes  Type 2 diabetic patients from outpatient clinic. Control group: no diabetes matched by sex and presumably BMI  Not explained  Vasquez B. (1984)  To determine whether elevated urinary protein excretion in obese type 2 diabetics can be reduced by hypocaloric diet  Obese, diabetes, normal Scr, no medications, no history of renal other renal diseases  Urinary tract infection  Navarro-Diaz M (2006)  To evaluate the effect of weight loss after bariatric surgery on blood pressure, renal parameters and function  Obese patients who underwent bariatric surgery in that hospital between December 2001and January 2004  None  Chagnac A. (2003)  To examine if weight loss might reverse glomerular dysfunction in obese subjects without overt renal disease  Obese group: age 23–46, BMI >38  Not explained  Saiki A. (2005)  To evaluate the effect and safety of low-calorie formula diet on renal function and proteinuria in obese patients with diabetic nephropathy  BMI >25, presence of diabetic retinopathy, proteinuria (urine albumin >300 mg/day) and Scr <3.5 mg/dL  Unstable diabetic retinopathy, pleural effusion, sever leg edema  Solerte SB. (1989)  To evaluate renal hemodynamic changes and urinary protein excretion during hypocaloric diet therapy in obese diabetic patients with overt nephropathy  Obese type 1 or 2 diabetic patients with overt nephropathy  Not explained  Agrawal V. (2008)  To evaluate the effect of bariatric surgery on renal parameters after surgery  Morbidly obese patient (BMI >40 without diabetes or BMI >35 with diabetes) who underwent bariatric surgery from December 2002 to December 2003  ACR >300 mg/g  Study  Aim  Inclusion criteria  Exclusion criteria  Morales E. (2003)  To determine the effects of low caloric normoproteinuric diet without protein restriction on proteinuria, renal function and metabolic profile in overweight patients with diabetic and non-diabetic chronic proteinuric nephropathies  Chronic (disease duration >1 year), proteinuric (>1 g/day on at least three consecutive tests in the 6-month period before study), nephropathy of diabetic or non-diabetic cause, presence of overweight or obesity (BMI >27) and Scr <2 mg/dL  Unstable clinical condition, rapid loss of renal function, nephrotic syndrome requiring diuretic therapy, immunosuppressive treatments and hypertension requiring more than two antihypertensive medications for its control  Praga M. (1995)  To compare the influence of weight loss with ACEI in obese patients  BMI >30, 24-h urine protein >1 g/day in at least three consecutive studies  Diabetes, systemic diseases  Nicholson AS. (1999)  To evaluate the effect of a dietary intervention on glycemic and metabolic control of type 2 diabetes  Type 2 diabetes, age >25, willingness to attend all components of the study and residence within commuting distance of Georgetown University  Smoking, regular alcohol use, current or past drug abuse, pregnancy, psychiatric illness and medical instability  Stenlof K. (2006)  To evaluate the efficiency and safety of topiramate in weight loss programme  Obese patients with newly diagnosed type 2 diabetes with BMI 27 to 50, HbA1c <10.5, fasting plasma glucose 126 to 236 mg/dL and resting blood pressure <180/100 mmHg  Microvascular complications, severe recurrent hypoglycemic episodes, conditions likely to affect body weight, clinically significant hepatic or renal disease, personal or family history of kidney stone, history of any neuropsychiatric disorder, CNS conditions for using any psychotropic medication or medications expected to influence weight  Cubeddu LX. (2008)  To test if 1-year lifestyle modification metformin intervention is effective in lowering albuminuria in subjects with urinary albumin excretion <30 mg/day  BMI = 27–35  Age >70, history of CAD, heart failure, valvular heart disease, stroke, TIA, arteriosclerosis, renal, hepatic dysfunction, hypertension, active disease state, OCP, Scr >1.5 mg/dL  Tong PC. (2002)  To compare the efficacy of 6-month orlistat treatment on weight reduction, cardiovascular risk factors and insulin sensitivity between obese Chinese with or without type 2 diabetes  Age 18–50, BMI ≥27, with or without diabetes  Pregnancy lactation, childbearing potential with inadequate protection, psychiatric or neurological disorders, alcohol or substance abuse, nephrolithiasis or symptomatic cholelithiasis, previous GI surgery for weight reduction, history or presence of malignancy, history of cardiovascular complications (stroke, IHD, CHF), renal impairment with plasma creatinine >1.7  Lazarevic G. (2007)  To investigate the effect of aerobic exercise on urinary albumin excretion, serum levels and urinary excretion of enzymes, tubular damage and metabolic control markers in type 2 diabetes  Type 2 diabetic patients from outpatient clinic. Control group: no diabetes matched by sex and presumably BMI  Not explained  Vasquez B. (1984)  To determine whether elevated urinary protein excretion in obese type 2 diabetics can be reduced by hypocaloric diet  Obese, diabetes, normal Scr, no medications, no history of renal other renal diseases  Urinary tract infection  Navarro-Diaz M (2006)  To evaluate the effect of weight loss after bariatric surgery on blood pressure, renal parameters and function  Obese patients who underwent bariatric surgery in that hospital between December 2001and January 2004  None  Chagnac A. (2003)  To examine if weight loss might reverse glomerular dysfunction in obese subjects without overt renal disease  Obese group: age 23–46, BMI >38  Not explained  Saiki A. (2005)  To evaluate the effect and safety of low-calorie formula diet on renal function and proteinuria in obese patients with diabetic nephropathy  BMI >25, presence of diabetic retinopathy, proteinuria (urine albumin >300 mg/day) and Scr <3.5 mg/dL  Unstable diabetic retinopathy, pleural effusion, sever leg edema  Solerte SB. (1989)  To evaluate renal hemodynamic changes and urinary protein excretion during hypocaloric diet therapy in obese diabetic patients with overt nephropathy  Obese type 1 or 2 diabetic patients with overt nephropathy  Not explained  Agrawal V. (2008)  To evaluate the effect of bariatric surgery on renal parameters after surgery  Morbidly obese patient (BMI >40 without diabetes or BMI >35 with diabetes) who underwent bariatric surgery from December 2002 to December 2003  ACR >300 mg/g  ACEI, angiotensin-converting enzyme inhibitor; NSAID, non-steroidal anti-inflammatory drugs; CNS, central nervous system; CAD, coronary artery disease; Scr, serum creatinine; IHD, ischemia heart disease; CHF, congestive heart failure. View Large Table 3 Mean age, baseline weight, BMI, duration of intervention or follow-up and changes of weight, proteinuria, microalbuminuria, creatinine clearance and GFR after weight loss interventions Study (year)  n  Age (years)  Baseline Wt (kg) or BMI (kg/m2)  Duration (week)  ΔWt (kg) or ΔBMI (kg/m2)  ΔPU (g/24 h)  ΔAU (mg/24 h)  Δcrcl (mL/min)  ΔGFR (mL/min)  MAP (mmHg)  RCT                       Morales E. (2003)                        Active  20  56.1 ± 10.1  96.1 ± 16.6a  20  −3.6c  −0.90  –  −1.1  –  −2.5    Control (regular diet)  10  56.5 ± 15.2  87.5 ± 11.1a  20  1.9c  0.50  –  −5.8  –  5.4   Praga M. (1995)                        Active  9  47.3 ± 8.0  37.1 ± 3.1b  52  −4.5d  −2.50  –  −4.0  –  −8.0    Control (ACEI)  8  49.5 ± 12.7  38.2 ± 5.0b  52  −0.0d  −2.80  –  −8.0  –  −9.0   Nicholson AS. (1999)                        Active  7  51.0 ± 4.7  96.7 ± 13.3a  12  −7.2c  –  −279.6  –  –  −7.3    Control (regular diet)  4  60.0 ± 3.8  97.0 ± 22.9a  12  −3.8c  –  86.3  –  –  13.4   Stenlof K. (2006)                        Active  42  53.0 ± 11.8  101.1 ± 19.7a  40  −9.3c  –  −15.7  –  –  −4.7    Control (placebo)  51  54.0 ± 9.8  104.1 ± 16.2a  40  −2.6c  –  −1.0  –  –  −1.4                        CCT                       Cubeddu LX. (2008)                        Higher range albuminuria  18  48.1 ± 11.6  81.6 ± 17.6a  52  −9.5c  –  −8.9  −11e  –  −4.2    Lower range albuminuria  23  41.0 ± 9.4  78.3 ± 14.1a  52  −9.5c  –  −0.4  –  –  −4.5                        Prospective cohorts                       Tong PC. (2002)                        Diabetics  33  18 to 50  93.2 ± 18.4a  24  −2.9c  –  −15.4  –  –  −2.8    Non-diabetics  27    98.7 ± 18.8a  24  −4.7c  –  −3.6  –  –  −4.7   Lazarevic G. (2007)  30  54.8 ± 7.3  30.8 ± 3.0b  24  −2.2d  –  −29.0  –  –  –   Vasquez B. (1984)                        Diabetic arm  24  38.4 ± 10.3  108.1 ± 23.5a  24  −14.8c  −0.05  −17.5  –  –  −15.7    Non-diabetic arm  7  31.4 ± 6.9  107.0 ± 19.3a  24  −15.7c  −0.005  0.5  –  –  −1.5   Navarro-Diaz M (2006)  61  41.1 ± 9.1  150.6 ± 39.9a  104  −58.8c  −0.03  −10.8  −21.5  –  −16.0   Chagnac A. (2003)  8  36.0 ± 2.0  48.0 ± 2.4b  52  −48.0c  –  −49.6  –  −35.0  −4.0   Saiki A. (2005)  22  53.6 ± 8.4  85.2 ± 17.0a  4  −6.2c  −1.77  –  5.0  –  −7.4   Solerte SB. (1989)  24  49.2 ± 4.0  33.5 ± 1.6b  24  −7.3d  −0.66  −332.6  12.0  15.0  −9.7   Agrawal V (2008)  94  45.6 ± 10.5  133.6 ± 24.5a  54  −35.7c  –  −13.99  –  –  −12.5  Study (year)  n  Age (years)  Baseline Wt (kg) or BMI (kg/m2)  Duration (week)  ΔWt (kg) or ΔBMI (kg/m2)  ΔPU (g/24 h)  ΔAU (mg/24 h)  Δcrcl (mL/min)  ΔGFR (mL/min)  MAP (mmHg)  RCT                       Morales E. (2003)                        Active  20  56.1 ± 10.1  96.1 ± 16.6a  20  −3.6c  −0.90  –  −1.1  –  −2.5    Control (regular diet)  10  56.5 ± 15.2  87.5 ± 11.1a  20  1.9c  0.50  –  −5.8  –  5.4   Praga M. (1995)                        Active  9  47.3 ± 8.0  37.1 ± 3.1b  52  −4.5d  −2.50  –  −4.0  –  −8.0    Control (ACEI)  8  49.5 ± 12.7  38.2 ± 5.0b  52  −0.0d  −2.80  –  −8.0  –  −9.0   Nicholson AS. (1999)                        Active  7  51.0 ± 4.7  96.7 ± 13.3a  12  −7.2c  –  −279.6  –  –  −7.3    Control (regular diet)  4  60.0 ± 3.8  97.0 ± 22.9a  12  −3.8c  –  86.3  –  –  13.4   Stenlof K. (2006)                        Active  42  53.0 ± 11.8  101.1 ± 19.7a  40  −9.3c  –  −15.7  –  –  −4.7    Control (placebo)  51  54.0 ± 9.8  104.1 ± 16.2a  40  −2.6c  –  −1.0  –  –  −1.4                        CCT                       Cubeddu LX. (2008)                        Higher range albuminuria  18  48.1 ± 11.6  81.6 ± 17.6a  52  −9.5c  –  −8.9  −11e  –  −4.2    Lower range albuminuria  23  41.0 ± 9.4  78.3 ± 14.1a  52  −9.5c  –  −0.4  –  –  −4.5                        Prospective cohorts                       Tong PC. (2002)                        Diabetics  33  18 to 50  93.2 ± 18.4a  24  −2.9c  –  −15.4  –  –  −2.8    Non-diabetics  27    98.7 ± 18.8a  24  −4.7c  –  −3.6  –  –  −4.7   Lazarevic G. (2007)  30  54.8 ± 7.3  30.8 ± 3.0b  24  −2.2d  –  −29.0  –  –  –   Vasquez B. (1984)                        Diabetic arm  24  38.4 ± 10.3  108.1 ± 23.5a  24  −14.8c  −0.05  −17.5  –  –  −15.7    Non-diabetic arm  7  31.4 ± 6.9  107.0 ± 19.3a  24  −15.7c  −0.005  0.5  –  –  −1.5   Navarro-Diaz M (2006)  61  41.1 ± 9.1  150.6 ± 39.9a  104  −58.8c  −0.03  −10.8  −21.5  –  −16.0   Chagnac A. (2003)  8  36.0 ± 2.0  48.0 ± 2.4b  52  −48.0c  –  −49.6  –  −35.0  −4.0   Saiki A. (2005)  22  53.6 ± 8.4  85.2 ± 17.0a  4  −6.2c  −1.77  –  5.0  –  −7.4   Solerte SB. (1989)  24  49.2 ± 4.0  33.5 ± 1.6b  24  −7.3d  −0.66  −332.6  12.0  15.0  −9.7   Agrawal V (2008)  94  45.6 ± 10.5  133.6 ± 24.5a  54  −35.7c  –  −13.99  –  –  −12.5  Wt, weight; PU, proteinuria; AU, microalbuminuria; crcl, creatinine clearance; GFR, glomerular filtration rate; MAP, mean arterial pressure. aBaseline Wt;bbaseline BMI;cΔWt;dΔBMI;ecrcl in Cubeddu study is mean in both groups. View Large Table 4 Methodological components of included interventions Study  % with PU or AU at baseline  % with diabetesat baseline  Eligibility criteria explicit  Randomized  Allocation concealed  Patient blinding  % lost to follow-up  Intention to treat  RCT                   Morales E. (2003)  100.0  46.7  Yes  Yes  Unclear  Open label  0.0  Yes   Praga M. (1995)  100.0  0.0  Yes  Yes  Unclear  Open label  0.0  Yes   Nicholson AS. (1999)  Unclear  100.0  Yes  Yes  Unclear  Open label  15.4  No   Stenlof K. (2006)  Unclear  100.0  Yes  Yes  Unclear  Double blind  0.0  Yes                    CCT                   Cubeddu LX. (2008)  0.0  0.0  Yes  No  NA  Open label  0.0  NA                    Prospective cohorts                   Tong PC. (2002)  Unclear  50.0  Yes  NA  NA  Open label  3.3  NA   Lazarevic G. (2007)  20.0  100.0  No  No  NA  Open label  0.0  NA   Vasquez B. (1984)  45.8  100.0  Yes  NA  NA  Open label  0.0  NA   Navarro-Diaz M (2006)  47.5  17.4  No  NA  NA  Open label  40.2  NA   Chagnac A. (2003)  Unclear  0.0  No  NA  NA  Open label  0.0  NA   Saiki A. (2005)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Solerte SB. (1989)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Agrawal V. (2008)  22.2  32.7  No  NA  NA  Open label  0.0  NA  Study  % with PU or AU at baseline  % with diabetesat baseline  Eligibility criteria explicit  Randomized  Allocation concealed  Patient blinding  % lost to follow-up  Intention to treat  RCT                   Morales E. (2003)  100.0  46.7  Yes  Yes  Unclear  Open label  0.0  Yes   Praga M. (1995)  100.0  0.0  Yes  Yes  Unclear  Open label  0.0  Yes   Nicholson AS. (1999)  Unclear  100.0  Yes  Yes  Unclear  Open label  15.4  No   Stenlof K. (2006)  Unclear  100.0  Yes  Yes  Unclear  Double blind  0.0  Yes                    CCT                   Cubeddu LX. (2008)  0.0  0.0  Yes  No  NA  Open label  0.0  NA                    Prospective cohorts                   Tong PC. (2002)  Unclear  50.0  Yes  NA  NA  Open label  3.3  NA   Lazarevic G. (2007)  20.0  100.0  No  No  NA  Open label  0.0  NA   Vasquez B. (1984)  45.8  100.0  Yes  NA  NA  Open label  0.0  NA   Navarro-Diaz M (2006)  47.5  17.4  No  NA  NA  Open label  40.2  NA   Chagnac A. (2003)  Unclear  0.0  No  NA  NA  Open label  0.0  NA   Saiki A. (2005)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Solerte SB. (1989)  100  100.0  Yes  NA  NA  Open label  0.0  NA   Agrawal V. (2008)  22.2  32.7  No  NA  NA  Open label  0.0  NA  RCT, randomized controlled trial; CCT, controlled clinical trial; PU, proteinuria; AU, albuminuria; NA, not applicable. View Large Quantitative data synthesis Due to the heterogeneity of study populations, studies with gross proteinuria and microalbuminuria are pooled separately. Outcomes in gross proteinuria Only Praga, Morales and Saiki [30,31,33] had patients with nephrotic range proteinuria. All three studies collected 24-h urine for the measurement of urinary protein excretion and overall showed 3.0 g/day of proteinuria at baseline. While Praga and Saiki used 24-h urine collection to measure creatinine clearance, Morales used the Cockcroft–Gault formula to estimate it [30,31,33]. Although Vasquez and Navarro-Diaz [24,35] also measured proteinuria, because their protein excretion rates were at a micro level, their studies were grouped with those consisting of patients with microalbuminuria. Therefore, their measurement of microalbuminuria was used in our analysis. Figure 2 shows the overall effect of weight loss by caloric restriction on the decrease of overt proteinuria. In this pooled analysis, we deleted Solerte's study after sensitivity analysis because of a high heterogeneity it induced for a main effect of 1.46 g [95% confidence interval (95% CI), 0.72 to 2.22 g], I2 = 68.1% (P = 0.008). Overall, and after sensitivity analysis, dietary restriction-induced weight loss reduced overt proteinuria by 1.7 g (95% CI, 0.7 to 2.6 g), a 55% decrease from baseline (95% CI, 23% to 87%), I2 = 59.5% (P = 0.08). Meta-regression analysis suggested that this heterogeneity may partially be explained by variation in baseline weight, weight loss and decline of MAP with intervention among different studies (Table 5). The type of study design did not have any impact on the direction of outcome. Fig. 2 View largeDownload slide Change in overt proteinuria with weight loss by diet caloric restriction. Fig. 2 View largeDownload slide Change in overt proteinuria with weight loss by diet caloric restriction. Table 5 Components of meta-regression analysis; change of proteinuria, albuminuria and creatinine clearance Variables  Coefficient  95% CI  P-value  ΔProteinuria (g/day)a         ΔWeight (kg)  0.11  0.06 to 0.16  <0.001   Baseline weight (kg)  0.12  0.06 to 0.17  <0.001   MAP (mmHg)  0.02  0.01 to 0.03  <0.001          ΔUrinary albumin (mg/24 h)b         ΔWeight (kg)  1.10  0.50 to 2.40  0.011   MAP (mmHg)  0.83  0.35 to 1.30  0.001   Duration (week)  0.72  0.25 to 1.19  0.003   Age (year)  −1.04  −1.62 to −0.47  <0.001          ΔCreatinine clearance (mL/min)c         ΔWeight (kg)  0.51  0.22 to 0.79  <0.001  Variables  Coefficient  95% CI  P-value  ΔProteinuria (g/day)a         ΔWeight (kg)  0.11  0.06 to 0.16  <0.001   Baseline weight (kg)  0.12  0.06 to 0.17  <0.001   MAP (mmHg)  0.02  0.01 to 0.03  <0.001          ΔUrinary albumin (mg/24 h)b         ΔWeight (kg)  1.10  0.50 to 2.40  0.011   MAP (mmHg)  0.83  0.35 to 1.30  0.001   Duration (week)  0.72  0.25 to 1.19  0.003   Age (year)  −1.04  −1.62 to −0.47  <0.001          ΔCreatinine clearance (mL/min)c         ΔWeight (kg)  0.51  0.22 to 0.79  <0.001  MAP, mean arterial pressure.aNumber of records in meta-regression = 6;bNumber of records in meta-regression = 9;cNumber of records in meta-regression = 4. View Large Outcomes in microalbuminuria All other studies had albumin excretion rates below nephrotic range, mostly at microalbuminuria range with an overall mean urinary albumin excretion rate of 26.7 mg/day at baseline [11,22–25,28,29,32,35]. In the study by Tong [25], subjects without diabetes had a significantly lower urinary albumin excretion at baseline with a less profound decline in urinary albumin after intervention compared to individuals with diabetes. This discrepancy, along with the use of geometric mean and standard deviation in the study, contributed to a high heterogeneity within this study and between this and other studies. By sensitivity analysis, the non-diabetic arm of this trial contributed to heterogeneity and was, therefore, excluded in subsequent pooled analysis. Only Chagnac measured GFR by priming dose of inulin, p-aminohippuric acid and dextran [11]. In this study, albumin excretion rate was measured from timed urine collection and simultaneous venous blood sampling. The 24-h albumin excretion was estimated from the measured albumin excretion rate. In studies by Lazarevic and Agrawal [22,29], the 24-h albumin excretion was estimated from the ACR. All other studies measured urinary microalbuminuria based on a 24-h urine collection. Additionally, Navarro-Diaz and Cubeddu [24,28] measured creatinine clearance from the 24-h urine collection. In Figure 3, the overall effect of weight loss interventions on microalbuminuria by type of weight loss intervention is shown. In this analysis, Nicholson's study was eliminated from pooled effect calculation after sensitivity analysis demonstrated that it was an outlier, likely due to its low sample size and very large standard deviation. However, we included it in the meta-regression analysis to investigate sources of heterogeneity. Further sensitivity analysis did not reduce the heterogeneity. Overall, weight loss interventions decreased urinary albumin excretion by 14 mg (95% CI, 11 to 17), a 52% decrease from baseline (95% CI, 40% to 64%), I2 = 50.0% (P = 0.051). Meta-regression suggested that this heterogeneity may partially be explained by variation of age, duration of follow-up, weight loss and decline in MAP among different studies. The type of study design did not have any impact on the direction of outcome. Fig. 3 View largeDownload slide Change in microalbuminuria with weight loss by different types of intervention. Fig. 3 View largeDownload slide Change in microalbuminuria with weight loss by different types of intervention. Creatinine clearance–GFR outcome Baseline mean GFR or creatinine clearance in surgical trials was 140.2 mL/min, while it was 88.0 mL/min in non-surgical trials. Figure 4 shows the results of change in creatinine clearance by type of intervention. Accordingly, unlike the pooled non-surgical interventions, surgical interventions decreased GFR or creatinine clearance by 23.7 mL/min (95% CI, 11.4 to 36.2), a 17% decrease from baseline (95% CI, 8% to 26%). Within non-surgical interventions, results were mixed. While Cubeddu showed a decrease in creatinine clearance, Saiki, Morales and Prage did not show any change and Solerte noted an increase in creatinine clearance. Fig. 4 View largeDownload slide Comparing the effect of surgical and non-surgical methods of weight loss on change of creatinine clearance (in millilitres per minute). Fig. 4 View largeDownload slide Comparing the effect of surgical and non-surgical methods of weight loss on change of creatinine clearance (in millilitres per minute). Subgroup analysis by intervention and baseline characteristics In overt proteinuria, caloric restriction was associated with a pooled 1.7-g reduction of proteinuria (95% CI, 0.7 to 2.6 g), a 55% decrease from baseline (95% CI, 23% to 87%), while in microalbuminuria, the decrease was 17.5 mg (95% CI, 12.3 to 22.7 mg) or 58% (95% CI, 41% to 76%). Similarly, bariatric surgery was associated with 13 mg (95% CI, 5 to 21 mg) or a 92% decrease (95% CI, 35% to 149%), medications with 15 mg (95% CI, 13 to 18 mg) or a 28% decrease (95% CI, 24% to 34%), exercise with 30 mg (95% CI, −3 to 61 mg) or a 49% decrease (95% CI, −5% to 100%) and lifestyle modification with 9 mg (95% CI, 5 to 13 mg) or a 62% decrease (95% CI, 34% to 89%) in albumin excretion rate. No study analysed the change in urinary protein excretion by subgroups of age, sex and other demographic characteristics. No study addressed the change in the rate of progression of CKD or durability of reduced urinary protein excretion. Meta-regression analysis Table 5 presents the results of meta-regression. Accordingly, decrease in overt proteinuria was correlated with weight loss, decline in MAP and weight at baseline (P < 0.05). Each 1 kg decrease in weight was associated with 110 mg (95% CI, 60 to 160 mg, P < 0.001) or a 4% decrease (95% CI, 2% to 5%) in urinary protein excretion, independent of decline in MAP and baseline weight. Similar results were observed after adjusting for the use of ACEI. Decrease of microalbuminuria was also directly correlated with weight loss, duration of intervention and decline of MAP, but was inversely correlated with age, so that each 1 kg weight loss was associated with 1.1 mg (95% CI, 0.5 to 2.4 mg, P = 0.011) or a 4% decrease (95% CI, 2% to 9%) of microalbuminuria independent of decline of MAP and each 1 week of weight loss intervention was associated with 0.7 mg decrease in microalbuminuria (95% CI, 0.3 to 1.2 mg, P = 0.003). Creatinine clearance and GFR were directly correlated with change of body weight. Discussion Weight loss in overweight and obese adults with mild to moderate CKD results in a significant decrease in proteinuria and albuminuria, regardless of study designs and methods of weight loss. All trials had methodological limitations including low sample size, short period of follow-up and lack of control groups in uncontrolled trials. In a population-based observation, Bello et al. reported parallel changes in albuminuria with change in weight [36]. This observation had a mean follow-up of 4.2 years and did not differentiate intentional from unintentional weight loss, raising the possibility of natural course effect of cachexia–inflammation of chronic illnesses. In our reviewed articles, the duration of follow-up and intervention is much lower than Bello's report and, therefore, it is a remote possibility that the decline in body weight achieved after interventions for intentional weight loss in a relatively short period of time is a consequence of such an effect. Consistent pattern of decline of urinary protein among different study designs suggests that the decrease could be beyond regression to the mean and may better be explained by weight loss interventions. In spite of consistency in the direction of outcome, it is our assessment that the quality of evidence for the beneficial effect of weight loss on decreasing proteinuria or albuminuria is moderate. The potential mechanisms of early renal injury are suspected to be hyperfiltration and excess excretory load with increase in GFR, renal plasma flow, glomerular pressure and filtration fraction, partially mediated by increased renal sodium reabsorption, increased renal sympathetic activity and activation of renin–angiotensin system [37–39], lipotoxicity with intracellular lipid overload and shunt of excess fatty acids toward synthesis of lipid products [40,41] and finally through increased inflammation and oxidative stress [42,43]. Therefore, the beneficial effect of weight loss on renal function may be through the decrease in hyperfiltration, lipotoxicity, inflammation and oxidative stress. The most profound decrease in GFR and creatinine clearance has only been achieved by bariatric surgery. Highest baseline weight, GFR and creatinine clearance in patients of surgical trials compared to other studies suggest the highest hyperfiltration in the former which is reversed by the most profound weight loss. The reduction of GFR by weight loss may be interpreted as another beneficial effect of intervention by change in the status of hyperfiltration; however, no trial has been able to capture any change in the rate of progression of CKD within a study period. Non-surgical trials which did not achieve such a decrease in GFR, neither achieved this level of weight loss nor had considerably higher weight, GFR or creatinine clearance at baseline. Patients in surgical trials were also generally healthier compared to Saiki and Solerte's study in which individuals had overt nephropathy. These differences in baseline characteristics along with probable differences in intensity of interventions among non-surgical trials are likely explanations of the mixed effect of weight loss intervention on renal function among different trials. Direct correlation between changes of proteinuria with body weight suggests that the higher categories of body weight may get the most benefit from weight loss programmes. Association of decline in proteinuria with decrease of MAP, independent of weight loss, firstly suggests that the effect of decrease in proteinuria is not completely a consequence of decline in blood pressure and secondly it underscores the possibility of achieving maximum benefits upon combining weight loss programmes with pharmacologic control of blood pressure. In microalbuminuria, the association between duration of weight loss intervention and maintenance of weight loss with decrease of albumin excretion may be a reflection of longer duration of follow-up with trials of microalbuminuria compared to those with overt proteinuria, allowing to capture such an effect which highlights the benefit of maintenance of weight loss. Inverse correlation between levels of decline of urinary albumin excretion with age underscores the importance of obesity prevention programmes earlier in the course of CKD and at younger age. Albuminuria is an independent risk factor for increased cardiovascular mortality and morbidity both in diabetic and non-diabetic patient populations [44–46]. On the other hand, a decline in urinary protein excretion is shown to be associated with a significant decrease in cardiovascular risks and events [15,47]. Therefore, albuminuria is not only a surrogate marker of increased mortality and morbidity but also viewed as a therapeutic target [16,17] and, therefore, any attempt for its decline may have beneficial effects. Unintentional weight loss as observed in advanced stages of chronic illnesses such as end-stage kidney diseases, advanced heart failure or old age may be a reflection of cachexia–inflammatory status of the chronic illness and often is highly associated with mortality. However, the increased mortality in such entities are often reported through observational studies which have not been able to distinguish intentional from unintentional weight loss or ignored adequate adjustments by chronicity or severity of underlying illnesses [48–51], sending controversial massages about the benefits of intentional weight loss. The patients in trials of this review are also categorized as mild to moderate kidney failure without heart failure. Therefore, future clinical trials are needed to find the optimal clinical status in chronic illnesses such as end-stage kidney disease or heart failure in which intentional weight loss would still provide benefit. Future adequately powered trials should also focus on hard clinical endpoints such as mortality or progression of CKD with adequate follow-ups and plans for maintenance of body weight after weight loss. There are several limitations in our review. The patient populations of different studies are heterogeneous and at different stages of disease. To overcome this, two strategies were approached. Firstly, studies with similar patient populations and severity of disease at baseline were pooled together. This is shown by analyses of patients with gross proteinuria and microalbuminuria separately. Secondly, within each category of studies with similar kidney function, meta-regression analysis was applied to further investigate the sources of heterogeneity including variability in baseline characteristics. Other limitations are low sample size of studies, open-label nature of interventions, short duration of follow-up and inability to detect change in the trend of progression of CKD which probably needs much larger sample size, longer follow-up and maintenance of weight loss. The non-randomized and uncontrolled follow-up interventions have more methodological limitations due to lack of control group providing low-quality evidence for the decrease of urinary protein. The follow-up has also a wide range in different trials, but we addressed the effect of duration of follow-up in meta-regression analysis. The amount of decline in proteinuria with each 1 kg weight loss is likely underestimated by our conservative analytic approach as well as the heterogeneity in the follow-up period. Additionally, in surgical trials, not all patients had microalbuminuria at baseline (surgical indication). Therefore, the presence of a significant proportion of patients with normal range microalbuminuria at baseline has contributed to an underestimation of effect of size by diluting the net pooled effect in the total number of patients. Also, data about the influence of weight on GFR is scarce. On the other hand, the indirect method of estimating GFR such as using creatinine clearance or its calculation by Cockcroft formula can have increased margins of error in obesity which likely has contaminated our results. The generalizability of beneficiary effect of weight loss on urinary protein excretion is also limited to a subgroup of relatively healthy patients with mild to moderate CKD and no history of congestive heart failure. Conclusion In conclusion, evidence supports the beneficiary effect of weight loss on the surrogate outcomes of the decrease of urinary protein excretion. There are no data on the effect of weight loss on the progression to CKD. Further research is required to determine the impact of weight loss on clinical renal outcomes. The authors would like to thank Drs. Tatyana Shamliyan, Robert L. Kane and Areef Ishani for their comments. The results presented in this paper have not been published previously. Conflict of interest statement. None declared. References 1 World Health Organization. ,  Obesity: preventing and managing the global epidemic. Report of a WHO consultation. 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Published: Nov 27, 2009

Keywords: Keywords chronic kidney disease obesity proteinuria weight loss

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