Weight loss and proteinuria: systematic review of clinical trials andcomparative cohorts
Farsad Afshinnia1, Timothy J. Wilt3, Sue Duval4, Abbas Esmaeili5 and Hassan N. Ibrahim2
1St. Joseph's Hospital, HealthEast Care System, University of Minnesota School of Public Health, Minneapolis, MN, USA, 2Divisionof Renal Diseases and Hypertension, University of Minnesota School of Medicine, Minneapolis, MN, USA, 3Minneapolis VA MedicalCenter for Chronic Disease Outcomes Research and the University of Minnesota School of Medicine, Minneapolis, MN, USA,4Department of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA and5Department of Community Health and Epidemiology, Queen's University, Ontario, Canada
Correspondence and offprint requests to: Farsad Afshinnia; E-mail: [email protected]
AbstractBackground. Obesity is a risk factor for the progression ofchronic kidney disease (CKD). The impact of weight losson proteinuria and renal function is less clear. We aimed todetermine the effect of intentional weight loss on protein-uria and kidney function.Methods. Three bibliographic databases including Med-line, Cochrane and SCUPOS as well as reference list ofarticles were searched. We included randomized andnon-randomized controlled trials as well as single-armtrials published in English through May 2009 which exam-ined urinary protein among obese or overweight adults be-fore and after weight loss interventions including dietaryrestriction, exercise, anti-obesity medications and bariatricsurgery. Study characteristics and methodological qualityof trials were assessed.Results. Five hundred twenty-two subjects from five con-trolled and eight uncontrolled trials were included. Weightloss interventionswere associatedwith decreased proteinuriaand 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, inde-pendent of decline inmean arterial pressure, each 1kgweightloss was associated with 110 mg (95% CI, 60 to 160 mg, P <0.001) decrease in proteinuria and 1.1mg (95%CI, 0.5 to 2.4mg, P = 0.011) decrease in microalbuminuria, respectively.The decrease was observed across different designs andmethods of weight loss. Only bariatric surgery resulted in asignificant decrease in creatinine clearance.Conclusions. Weight loss is associated with decreasedproteinuria and microalbuminuria. There were no dataevaluating the durability of this decrease or the effect ofweight loss on CKD progression.
Keywords: chronic kidney disease; obesity; proteinuria; weight loss
Introduction
The World Health Organization officially recognized obe-sity as a ‘global epidemic’ in 1997 [1]. In recent years, the
growing body of evidence shows a link between obesityand progression of kidney disease [2–4]. Microalbuminur-ia is the earliest marker of chronic kidney disease (CKD)and is a predictor of the progression of CKD to end-stagekidney disease [5]. Proteinuria is also an independent riskfactor for increased morbidity and mortality from cardio-vascular diseases, diabetes, hypertension and end-stage re-nal diseases (ESRD) [6]. There appears to be a gradedassociation between the severity of obesity and magnitudeof microalbuminuria [7–14]. On the other hand, a declinein urinary protein excretion is associated with metabolicimprovement and decreased cardiovascular risks [15], sothat a decline of 50% in urinary protein excretion is shownto be associated with 18% decrease in cardiovascular risks[16]. Therefore, reducing proteinuria is used as a surrogateoutcome for evaluating CKD treatment [17]. A relevantquestion is whether weight loss has a beneficial effect onthe reduction of proteinuria and microalbuminuria in obeseadults and whether it leads to the decreased rate of progres-sion of CKD and mortality.
In normal subjects, albumin and nitrogen excretion ratesas well as endogenous creatinine clearance are functions ofthe quantity of dietary protein intake [18]. In type 2 diabe-tes, dietary protein restriction is associated with decreasedproteinuria [19]. Although proteinuria is augmented imme-diately after exercise, the effect of long-term exercise onproteinuria at rest is less clear. Many obese patients are alsotaking angiotensin-converting enzyme inhibitors (ACEI)and angiotensin receptor blockers as part of the manage-ment of hypertension and diabetic nephropathy [20,21].Therefore, it is not clear whether the decrease in protein-uria observed in case reports and cohort studies of inten-tional weight loss is due to such co-interventions duringweight loss or the result of weight loss itself. This system-atic review is aimed (i) to determine the effect of weightloss on proteinuria and other markers of renal function,(ii) to compare the effect of different weight loss interven-tions including protein-restricted diet, caloric-restricted di-et, exercise therapy, anti-obesity agents and bariatricsurgery on urinary protein and other markers of renal func-tion, (iii) to determine the role of Patient characteristics atbaseline on change in severity of proteinuria with weight
loss and (iv) to determine the change in the rate of progres-sion 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 ar-ticles, human studies, with age older than 18 from 1985 through May 2009using the keywords proteinuria; weight loss; anti-obesity agents; exercise;diet, caloric-restricted; diet, fat-restricted; and bariatric surgery as outlinedin Appendix 1. We also searched reference lists of relevant articles. Thesearch was performed by two reviewers independently (F.A. and A.E.).
Study selection
We included trials of weight loss interventions that examined the associa-tion between weight loss in overweight (Body Mass Index >25 kg/m2) orobese (BMI >30 kg/m2) adults with proteinuria and markers of renal func-tion. They included randomized and non-randomized controlled clinicaltrials and single-arm trials of weight loss interventions including physicalexercise therapy, anti-obesity agents, bariatric surgery and dietary weightloss by restriction of carbohydrate and fat which reported proteinuria orurinary albumin and weight or BMI at baseline and after interventions.We excluded observational studies and those that involved patients withchronic conditions associated with non-intentional weight loss, elite ath-letes as well as studies with urinary protein measurements within 24 h afterexercise. Identified articles were rejected on initial screen if it was deter-mined from the title that they did not address weight loss strategies. When atitle 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 ver-ified by another (H.I.). The retrieved variables indicated characteristics ofstudies such as patients' baseline characteristics, inclusion and exclusioncriteria, intervention, comparison, outcome and duration of follow-up.Baseline characteristics included age, sex, percentage of patients with di-abetes and hypertension, weight or BMI, urinary albumin or protein andglomerular filtration rate (GFR) or creatinine clearance. Interventionswere dietary restriction (fat and carbohydrate), exercise, anti-obesity med-ications and bariatric surgery. Exercise was defined as moderate to intensephysical activity with at least 1.8 metabolic equivalents for a minimumduration 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 inhibi-tors approved for weight loss, lipase inhibitors, suppressors of appetiteand any other compounds primarily approved for other indications butwhich have been used in trials to show their anti-obesity effects for a min-imum of at least 4 weeks. The primary outcome was change in severity ofurinary albumin excretion or proteinuria. In clinical trials, proteinuria ismeasured as a continuous variable. Secondary outcomes were change increatinine clearance, change in GFR and change in the rate of progressionof CKD. Studies were searched for outcomes according to age, race andsex. For studies with multiple measurements at different time intervals, thelast measurement was entered into the analyses. In trials with several armswith different doses of medication, the arm with the highest dose was usedfor the analyses. Outcome variables including weight and laboratory va-lues were measured at least 12 weeks after the start of interventions. Trialswith an intervention period as short as 4 weeks were included only if theyhad at least a 3-month run-in period prior to interventions to minimize theeffect of confounding by acute changes unrelated to true weight loss. Thesetting was outpatient in all studies. We assessed randomized trials forrandomization, allocation concealment and adequacy of blinding. Addi-tionally, we assessed percentage lost to follow-up, intention to treat anal-ysis (where applicable) and generalizability by assessing eligibilitycriteria. For the cohort studies, percent lost to follow-up and eligibilitycriteria were evaluated (Table 4).
Data synthesis and analysis
Data analyses were done by one reviewer (F.A.) and independently verifiedby another reviewer (S.D.). In studies in which mixed samples of patients
with and without proteinuria were recruited, only the subgroups of patientswith proteinuria were entered into the analyses if their corresponding datawere available. Lazarevic et al. reported individualized data for all patientswho had decreased their urinary albumin excretion except for one who didnot show a change [22]. For that patient, the baseline mean urinary albuminexcretion of the abnormal group was conservatively used at baseline andthroughout the follow-up period. In Agrawal's study, the mean and SD ofthe urinary albumin–creatinine ratio (ACR) were obtained by personalcommunication with the lead author, while in Stenlof's study where theSD of urinary albumin was not reported [23], it was estimated by meansof SDs from three other similar studies which had the closest characteris-tics, design and mean of urinary albumin at baseline [11,24,25]. Continu-ous data that were reported as median and range were converted to meanand SD by using the method of Hozo [26]. The measures used in the meta-analysis were the difference between urinary protein or albumin before andafter intervention using random effect for weighting method and calculat-ing the I2 statistics. In controlled clinical trials, the difference between targetvariables before and after weight loss in the intervention arm was addition-ally comparedwith the control arm using pooled SD of pretest values by themethod of Morris [27]. Meta-regression using maximum likelihood to es-timate the between-study component of variance was applied to assess therelationship between change of weight with change of urinary protein oralbumin. Sensitivity analysis and meta-regression were applied to detectthe study and the variables contributing to high heterogeneity, respectively.Subgroup analysis was performed in order to explore differences of effectsize or direction among different weight loss strategies. Data analysis waswith STATAversion 10 (College Station, TX, USA).
Results
Trial flow
Figure 1 shows the study identification and selection pro-cess. Our initial search revealed 450 articles in which 376were excluded due to not addressing the questions (n = 355),case reports (n = 16) and reviews (n = 5). Of 74 articles re-trieved for full text evaluation, 57 were excluded due to notbeing relevant (n = 39), lack of enough information (n = 11),targeting different populations (n = 3), having BMI less than25 kg/m2 (n = 3) and short duration of intervention (n = 1),leaving 13 articles for inclusion [11,22–25,28–35].
Initial literature search (n=450)
Articles retrieved for full text evaluation:
(n=74)
Articles selected for inclusion: (n=17)
Excluded: (n=376) Non-relevant: 355 Case reports: 16
Reviews: 5
Excluded: (n=57) Non-relevant: 39
Lack of enough information: 11 Different target population: 3
BMI< 25: 3 Duration< 1 week: 1
Included: (n=13)
Excluded: (n=4)Multiple publications: 2
BMI < 25: 1 Co-intervention: 1
Fig. 1. Overview of study identification and selection process.
There were four randomized and one non-randomized clin-ical trial and eight prospective cohort interventions includ-ing single-arm trials, uncontrolled-arm trials and controlledtrials with no follow-up in the control group. Sample sizeranged from 8 to 94 patients with a total of 522 subjects fol-lowed up between 4 and 104 weeks. The study characteris-tics are shown in Table 1. The aim, inclusion and exclusioncriteria are summarized in Table 2. Table 3 shows the sum-mary result of interventions. All trials achieved significantweight loss with intervention.Weight loss, decrease in meanarterial pressure (MAP), proteinuria and albuminuria withactive treatment ranged from 2.2 to 58.8 kg, 1.5 to 16mmHg, 0.03 to 2.5 g and 9 to 333 mg, respectively. Similar-ly, decrease in GFR and creatinine clearance with activetreatment ranged from −15 to 35 and −12 to 21.5 mL/min,respectively. The highest amount of weight loss and de-crease in GFR and creatinine clearancewas achieved by bar-iatric surgery. All interventions had methodologicallimitations (Table 4). The percentage of patients with pro-teinuria or albuminuria at baseline was unclear in four stud-
ies [11,23,25,32], and allocation concealment was unclearin all randomized trials [23,31–33]. By design, only onemedication-based study could be double blind [23].
Quantitative data synthesis
Due to the heterogeneity of study populations, studieswith gross proteinuria and microalbuminuria are pooledseparately.
Outcomes in gross proteinuria
Only Praga, Morales and Saiki [30,31,33] had patientswith nephrotic range proteinuria. All three studies collect-ed 24-h urine for the measurement of urinary protein ex-cretion and overall showed 3.0 g/day of proteinuria atbaseline. While Praga and Saiki used 24-h urine collectionto measure creatinine clearance, Morales used the Cock-croft–Gault formula to estimate it [30,31,33]. AlthoughVasquez and Navarro-Diaz [24,35] also measured protein-uria, because their protein excretion rates were at a microlevel, their studies were grouped with those consisting of pa-
Table 3. Mean age, baseline weight, BMI, duration of intervention or follow-up and changes of weight, proteinuria, microalbuminuria, creatinineclearance and GFR after weight loss interventions
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.
tients with microalbuminuria. Therefore, their measurementof microalbuminuria was used in our analysis. Figure 2shows the overall effect of weight loss by caloric restrictionon the decrease of overt proteinuria. In this pooled analysis,we deleted Solerte's study after sensitivity analysis becauseof 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 pro-teinuria by 1.7 g (95% CI, 0.7 to 2.6 g), a 55% decreasefrom baseline (95% CI, 23% to 87%), I2 = 59.5% (P =0.08). Meta-regression analysis suggested that this hetero-geneity may partially be explained by variation in baselineweight, weight loss and decline of MAP with interventionamong different studies (Table 5). The type of study de-sign did not have any impact on the direction of outcome.
Outcomes in microalbuminuria
All other studies had albumin excretion rates below ne-phrotic range, mostly at microalbuminuria range with anoverall mean urinary albumin excretion rate of 26.7 mg/day at baseline [11,22–25,28,29,32,35]. In the study byTong [25], subjects without diabetes had a significantlylower urinary albumin excretion at baseline with a less pro-found decline in urinary albumin after intervention com-pared to individuals with diabetes. This discrepancy, alongwith the use of geometric mean and standard deviation in thestudy, contributed to a high heterogeneity within this studyand between this and other studies. By sensitivity analysis,the non-diabetic arm of this trial contributed to heterogene-ity and was, therefore, excluded in subsequent pooled anal-ysis. Only Chagnac measured GFR by priming dose ofinulin, p-aminohippuric acid and dextran [11]. In this study,albumin excretion rate was measured from timed urine col-lection and simultaneous venous blood sampling. The 24-h albumin excretion was estimated from the measured albu-
min excretion rate. In studies by Lazarevic and Agrawal[22,29], the 24-h albumin excretion was estimated fromthe ACR. All other studies measured urinary microalbu-minuria based on a 24-h urine collection. Additionally,Navarro-Diaz and Cubeddu [24,28] measured creatinineclearance from the 24-h urine collection. In Figure 3,the overall effect of weight loss interventions on microal-buminuria by type of weight loss intervention is shown.In this analysis, Nicholson's study was eliminated frompooled effect calculation after sensitivity analysis demon-strated that it was an outlier, likely due to its low samplesize and very large standard deviation. However, we includ-ed it in the meta-regression analysis to investigate sourcesof heterogeneity. Further sensitivity analysis did not reducethe heterogeneity. Overall, weight loss interventions de-creased urinary albumin excretion by 14 mg (95% CI, 11to 17), a 52% decrease from baseline (95% CI, 40% to64%), I2 = 50.0% (P = 0.051). Meta-regression suggestedthat this heterogeneity may partially be explained by varia-tion of age, duration of follow-up, weight loss and declinein MAP among different studies. The type of study designdid not have any impact on the direction of outcome.
Creatinine clearance–GFR outcome
Baseline mean GFR or creatinine clearance in surgicaltrials was 140.2 mL/min, while it was 88.0 mL/min innon-surgical trials. Figure 4 shows the results of changein creatinine clearance by type of intervention. Accord-ingly, unlike the pooled non-surgical interventions, surgi-cal interventions decreased GFR or creatinine clearance by23.7 mL/min (95% CI, 11.4 to 36.2), a 17% decrease frombaseline (95% CI, 8% to 26%). Within non-surgical inter-ventions, results were mixed. While Cubeddu showed adecrease in creatinine clearance, Saiki, Morales and Pragedid not show any change and Solerte noted an increase increatinine clearance.
NOTE: Weights are from random effects analysis
Overall (I-squared = 59.5%, p =0.084)
Praga M. (1995)
ID
Saiki A. (2005)
Study
Morales E. (2003)
-1.66 (-2.63, -0.69)
-2.50 (-3.64, -1.36)
WMD (95% CI)
-1.77 (-2.99, -0.55) 29.68
38.72
31.59
100.00
-0.90 (-1.77, -0.03)
Weight
%
0-4 0 4
Fig. 2. Change in overt proteinuria with weight loss by diet caloric restriction.
Subgroup analysis by intervention and baselinecharacteristics
In overt proteinuria, caloric restriction was associated witha pooled 1.7-g reduction of proteinuria (95% CI, 0.7 to 2.6g), 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%). Sim-ilarly, 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% to100%) and lifestyle modification with 9 mg (95% CI, 5to 13 mg) or a 62% decrease (95% CI, 34% to 89%) in al-bumin excretion rate. No study analysed the change in uri-nary protein excretion by subgroups of age, sex and other
demographic characteristics. No study addressed thechange in the rate of progression of CKD or durability ofreduced urinary protein excretion.
Meta-regression analysis
Table 5 presents the results of meta-regression. According-ly, decrease in overt proteinuria was correlated with weightloss, decline in MAP and weight at baseline (P < 0.05).Each 1 kg decrease in weight was associated with 110mg (95% CI, 60 to 160 mg, P < 0.001) or a 4% decrease(95% CI, 2% to 5%) in urinary protein excretion, indepen-dent of decline in MAP and baseline weight. Similar re-sults were observed after adjusting for the use of ACEI.Decrease of microalbuminuria was also directly correlatedwith weight loss, duration of intervention and decline ofMAP, but was inversely correlated with age, so that each1 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 ofMAP and each 1 week of weight loss intervention was as-sociated with 0.7 mg decrease in microalbuminuria (95%CI, 0.3 to 1.2 mg, P = 0.003). Creatinine clearance andGFR were directly correlated with change of body weight.
Discussion
Weight loss in overweight and obese adults with mild tomoderate CKD results in a significant decrease in protein-uria and albuminuria, regardless of study designs andmethods of weight loss. All trials had methodological lim-itations including low sample size, short period of follow-up and lack of control groups in uncontrolled trials. In apopulation-based observation, Bello et al. reported parallelchanges in albuminuria with change in weight [36]. Thisobservation had a mean follow-up of 4.2 years anddid not differentiate intentional from unintentional
Table 4. Methodological components of included interventions
Study% with PU orAU at baseline
% with diabetesat baseline
Eligibilitycriteria explicit Randomized
Allocationconcealed
Patientblinding
% lost tofollow-up
Intentionto treat
RCTMorales E. (2003) 100.0 46.7 Yes Yes Unclear Open label 0.0 YesPraga M. (1995) 100.0 0.0 Yes Yes Unclear Open label 0.0 YesNicholson AS. (1999) Unclear 100.0 Yes Yes Unclear Open label 15.4 NoStenlof K. (2006) Unclear 100.0 Yes Yes Unclear Double blind 0.0 Yes
CCTCubeddu LX. (2008) 0.0 0.0 Yes No NA Open label 0.0 NA
Prospective cohortsTong PC. (2002) Unclear 50.0 Yes NA NA Open label 3.3 NALazarevic G. (2007) 20.0 100.0 No No NA Open label 0.0 NAVasquez B. (1984) 45.8 100.0 Yes NA NA Open label 0.0 NANavarro-Diaz M (2006) 47.5 17.4 No NA NA Open label 40.2 NAChagnac A. (2003) Unclear 0.0 No NA NA Open label 0.0 NASaiki A. (2005) 100 100.0 Yes NA NA Open label 0.0 NASolerte SB. (1989) 100 100.0 Yes NA NA Open label 0.0 NAAgrawal V. (2008) 22.2 32.7 No NA NA Open label 0.0 NA
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 <0.001Baseline weight (kg) 0.12 0.06 to 0.17 <0.001MAP (mmHg) 0.02 0.01 to 0.03 <0.001
ΔUrinary albumin (mg/24h)b
ΔWeight (kg) 1.10 0.50 to 2.40 0.011MAP (mmHg) 0.83 0.35 to 1.30 0.001Duration (week) 0.72 0.25 to 1.19 0.003Age (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.
weight loss, raising the possibility of natural course ef-fect of cachexia–inflammation of chronic illnesses. Inour reviewed articles, the duration of follow-up and inter-vention is much lower than Bello's report and, therefore, itis a remote possibility that the decline in body weightachieved after interventions for intentional weight lossin a relatively short period of time is a consequence ofsuch an effect. Consistent pattern of decline of urinaryprotein among different study designs suggests that thedecrease could be beyond regression to the mean andmay better be explained by weight loss interventions. Inspite of consistency in the direction of outcome, it is ourassessment that the quality of evidence for the beneficialeffect of weight loss on decreasing proteinuria or albu-minuria is moderate.
The potential mechanisms of early renal injury are sus-pected to be hyperfiltration and excess excretory loadwith increase in GFR, renal plasma flow, glomerular pres-sure and filtration fraction, partially mediated by in-creased renal sodium reabsorption, increased renalsympathetic activity and activation of renin–angiotensinsystem [37–39], lipotoxicity with intracellular lipid over-load and shunt of excess fatty acids toward synthesis oflipid products [40,41] and finally through increased in-
flammation and oxidative stress [42,43]. Therefore, thebeneficial effect of weight loss on renal function maybe through the decrease in hyperfiltration, lipotoxicity, in-flammation and oxidative stress. The most profound de-crease in GFR and creatinine clearance has only beenachieved by bariatric surgery. Highest baseline weight,GFR and creatinine clearance in patients of surgical trialscompared to other studies suggest the highest hyperfiltra-tion in the former which is reversed by the most profoundweight loss. The reduction of GFR by weight loss may beinterpreted as another beneficial effect of intervention bychange in the status of hyperfiltration; however, no trialhas been able to capture any change in the rate of progres-sion of CKD within a study period. Non-surgical trialswhich did not achieve such a decrease in GFR, neitherachieved this level of weight loss nor had considerablyhigher weight, GFR or creatinine clearance at baseline.Patients in surgical trials were also generally healthiercompared to Saiki and Solerte's study in which indivi-duals had overt nephropathy. These differences in baselinecharacteristics along with probable differences in intensityof interventions among non-surgical trials are likely ex-planations of the mixed effect of weight loss interventionon renal function among different trials.
NOTE: Weights are from random effects analysis
.
.
.
.
.
Overall (I-squared = 50.0%, p = 0.051)
Stenlof K (2006)
Lazarevic G (2007)
ID
Study
Low caloric diet
Life style modification
Tong PC. (2002)
Vasquez B. (1984)
Navarro-Diaz M (2006)
Cubeddu LX. (2007)
Chagnac A. (2003)
BS
Medication
Agrawal V. (2008)
Exercise
%
-13.87 (-17.12, -10.61)
-15.72 (-22.65, -8.79)
-29.00 (-61.46, 3.46)
WMD (95% CI)
-15.40 (-18.41, -12.39)
-17.50 (-22.69, -12.31)
-10.84 (-16.70, -4.98)
-8.90 (-12.62, -5.18)
-49.66 (-97.02, -2.30) 0.47
15.52
5.92
22.17
0.97
12.92
24.62
17.41
100.00
-13.99 (-26.03, -1.95)
Weight
%
0-97 0 97
BS: Bariatric surgery
Fig. 3. Change in microalbuminuria with weight loss by different types of intervention.
Direct correlation between changes of proteinuria withbody weight suggests that the higher categories of bodyweight may get the most benefit from weight loss pro-grammes. Association of decline in proteinuria with de-crease of MAP, independent of weight loss, f irstlysuggests that the effect of decrease in proteinuria is notcompletely a consequence of decline in blood pressureand secondly it underscores the possibility of achievingmaximum benefits upon combining weight loss pro-grammes with pharmacologic control of blood pressure.In microalbuminuria, the association between duration ofweight loss intervention and maintenance of weight losswith decrease of albumin excretion may be a reflectionof longer duration of follow-up with trials of microalbumi-nuria compared to those with overt proteinuria, allowingto capture such an effect which highlights the benefit ofmaintenance of weight loss. Inverse correlation betweenlevels of decline of urinary albumin excretion with age un-derscores the importance of obesity prevention pro-grammes earlier in the course of CKD and at younger age.
Albuminuria is an independent risk factor for increasedcardiovascular mortality and morbidity both in diabeticand non-diabetic patient populations [44–46]. On the otherhand, a decline in urinary protein excretion is shown to beassociated with a significant decrease in cardiovascularrisks and events [15,47]. Therefore, albuminuria is not on-ly a surrogate marker of increased mortality and morbiditybut also viewed as a therapeutic target [16,17] and, there-
fore, any attempt for its decline may have beneficial ef-fects. Unintentional weight loss as observed in advancedstages of chronic illnesses such as end-stage kidney dis-eases, advanced heart failure or old age may be a reflectionof cachexia–inflammatory status of the chronic illness andoften is highly associated with mortality. However, the in-creased mortality in such entities are often reportedthrough observational studies which have not been ableto distinguish intentional from unintentional weight lossor ignored adequate adjustments by chronicity or severityof underlying illnesses [48–51], sending controversial mas-sages about the benefits of intentional weight loss. The pa-tients in trials of this review are also categorized as mild tomoderate kidney failure without heart failure. Therefore,future clinical trials are needed to find the optimal clinicalstatus in chronic illnesses such as end-stage kidney diseaseor heart failure in which intentional weight loss would stillprovide benefit. Future adequately powered trials shouldalso focus on hard clinical endpoints such as mortality orprogression of CKD with adequate follow-ups and plansfor maintenance of body weight after weight loss.
There are several limitations in our review. The patientpopulations of different studies are heterogeneous and atdifferent stages of disease. To overcome this, two strategieswere approached. Firstly, studies with similar patient popu-lations and severity of disease at baseline were pooled to-gether. This is shown by analyses of patients with grossproteinuria and microalbuminuria separately. Secondly,
NOTE: Weights are from random effects analysis
.
.
Non-surgical
Cubeddu LX. (2008)
Saiki A. (2005)
Solerte SB. (1989)
Morales E. (2003)
Praga M. (1995)
Subtotal (I-squared = 86.9%, p = 0.000)
Surgical
Navarro-Diaz M (2006)
Chagnac A. (2003)
Subtotal (I-squared = 0.0%, p = 0.431)
ID
Study
-11.00 (-15.35, -6.65)
5.00 (-4.20, 14.20)
12.00 (4.36, 19.64)
-1.10 (-22.08, 19.88)
-4.00 (-25.33, 17.33)
0.46 (-11.46, 12.37)
-21.54 (-35.08, -8.00)
-35.00 (-65.68, -4.32)
-23.73 (-36.12, -11.35)
WMD (95% CI)
25.22
22.58
23.56
14.43
14.21
100.00
83.70
16.30
100.00
Weight
%
0-65 0 65
Note: In Changnac study GFR is used as a proxy of creatinine clearance.
Fig. 4. Comparing the effect of surgical and non-surgical methods of weight loss on change of creatinine clearance (in millilitres per minute).
within each category of studies with similar kidney func-tion, meta-regression analysis was applied to further inves-tigate the sources of heterogeneity including variability inbaseline characteristics. Other limitations are low samplesize of studies, open-label nature of interventions, shortduration of follow-up and inability to detect change inthe trend of progression of CKD which probably needsmuch larger sample size, longer follow-up and mainte-nance of weight loss. The non-randomized and uncon-trolled follow-up interventions have more methodologicallimitations due to lack of control group providing low-qual-ity evidence for the decrease of urinary protein. The follow-up has also a wide range in different trials, but we addressedthe effect of duration of follow-up in meta-regression anal-ysis. The amount of decline in proteinuria with each 1 kgweight loss is likely underestimated by our conservative an-alytic approach as well as the heterogeneity in the follow-upperiod. Additionally, in surgical trials, not all patients hadmicroalbuminuria at baseline (surgical indication). There-fore, the presence of a significant proportion of patientswith normal range microalbuminuria at baseline has con-tributed to an underestimation of effect of size by dilutingthe net pooled effect in the total number of patients. Also,data about the influence of weight on GFR is scarce. On theother hand, the indirect method of estimating GFR such asusing creatinine clearance or its calculation by Cockcroftformula can have increased margins of error in obesitywhich likely has contaminated our results. The generaliz-ability of beneficiary effect of weight loss on urinary pro-tein excretion is also limited to a subgroup of relativelyhealthy patients with mild to moderate CKD and no historyof congestive heart failure.
Conclusion
In conclusion, evidence supports the beneficiary effectof weight loss on the surrogate outcomes of the decreaseof urinary protein excretion. There are no data on theeffect of weight loss on the progression to CKD. Furtherresearch is required to determine the impact of weightloss on clinical renal outcomes.
Acknowledgement. The authors would like to thank Drs. Tatyana Sham-liyan, Robert L. Kane and Areef Ishani for their comments. The resultspresented in this paper have not been published previously.
Conflict of interest statement. None declared.
References
1. World Health Organization. Obesity: preventing and managing theglobal epidemic. Report of aWHOconsultation.WHO technical reportseries 894. Geneva, Switzerland: World Health Organization, 2000
2. Stengel B, Tarver-Carr ME, Powe NR et al. Lifestyle factors, obe-sity and the risk of chronic kidney disease. Epidemiology 2003; 14:479–487
3. Eckel RH, Barouch WW, Ershow AG. Report of the National Heart,Lung, and Blood Institute–National Institute of Diabetes and Diges-tive and Kidney Diseases Working Group on the pathophysiology ofobesity-associated cardiovascular disease. Circulation 2002; 105:2923–2928
4. Wang Y, Chen X, Song Y et al. Association between obesity and kid-ney disease: a systematic review and meta-analysis. Kidney Int 2008;73: 19–33
5. de Jong PE, Verhave JC, Pinto-Sietsma SJ et al. Obesity and targetorgan damage: the kidney. Int J Obes Relat Metab Disord 2002; 26:S21–S24
6. Keane WF, Eknoyan G. Proteinuria, albuminuria, risk, assessment,detection, elimination (PARADE): a position paper of the NationalKidney Foundation. Am J Kidney Dis 1999; 33: 1004–1010
7. Chen HM, Liu ZH, Zeng CH et al. Podocyte lesions in patients withobesity-related glomerulopathy. Am J Kidney Dis 2006; 48: 772–779
8. Praga M, Morales E. Obesity, proteinuria and progression of renalfailure. Curr Opin Nephrol Hypertens 2006; 15: 481–486
9. Srivastava T. Nondiabetic consequences of obesity on kidney. PediatrNephrol 2006; 21: 463–470
10. Chen J, Muntner P, Hamm LL et al. The metabolic syndrome andchronic kidney disease in U.S. adults. Ann Intern Med 2004; 140:167–174
11. Chagnac A, Weinstein T, Herman M et al. The effects of weight losson renal function in patients with severe obesity. J Am Soc Nephrol2003; 14: 1480–1486
12. Praga M, Hernandez E, Morales E et al. Clinical features and long-term outcome of obesity-associated focal segmental glomerulosclero-sis. Nephrol Dial Transplant 2001; 16: 1790–1798
13. Adelman RD, Restaino IG, Alon US et al. Proteinuria and focal seg-mental glomerulosclerosis in severely obese adolescents. J Pediatr2001; 138: 481–485
14. Kambham N, Markowitz GS, Valeri AM et al. Obesity-relatedglomerulopathy: an emerging epidemic. Kidney Int 2001; 59:1498–1509
15. Mykkanen L, Zaccaro DJ, Wagenknecht LE et al. Microalbuminuriais associated with insulin resistance in nondiabetic subjects: the insu-lin resistance atherosclerosis study. Diabetes 1998; 47: 793–800
16. de Zeeuw D, Remuzzi G, Parving HH et al. Albuminuria, a therapeu-tic target for cardiovascular protection in type 2 diabetic patients withnephropathy. Circulation 2004; 110: 921–927
17. de Zeeuw D. Should albuminuria be a therapeutic target in patientswith hypertension and diabetes?. Am J Hypertens 2004; 17: 11S–15S,quiz A2–4
18. Brandle E, Sieberth HG, Hautmann RE. Effect of chronic dietary pro-tein intake on the renal function in healthy subjects. Eur J Clin Nutr1996; 50: 734–740
19. Sugimoto T, Kikkawa R, Haneda M et al. Effect of dietary proteinrestriction on proteinuria in non-insulin-dependent diabetic patientswith nephropathy. J Nutr Sci Vitaminol (Tokyo) 1991; 37 Suppl:S87–S92
20. Scholze J, Grimm E, Herrmann D et al. Optimal treatment of obesity-related hypertension: the Hypertension–Obesity–Sibutramine (HOS)study. Circulation 2007; 115: 1991–1998
21. Sharma AM, Davidson J, Koval S et al. Telmisartan/hydrochlorothi-azide versus valsartan/hydrochlorothiazide in obese hypertensive pa-tients with type 2 diabetes: the SMOOTH study. Cardiovasc Diabetol2007; 6: 28
22. Lazarevic G, Antic S, Vlahovic P et al. Effects of aerobic exercise onmicroalbuminuria and enzymuria in type 2 diabetic patients. Ren Fail2007; 29: 199–205
23. Stenlof K, Rossner S, Vercruysse Fet al. Topiramate in the treatment ofobese subjects with drug-naive type 2 diabetes. Diabetes Obes Metab2007; 9: 360–368
24. Navarro-Diaz M, Serra A, Romero R et al. Effect of drastic weightloss after bariatric surgery on renal parameters in extremely obese pa-tients: long-term follow-up. J Am Soc Nephrol 2006; 17: S213–S217
25. Tong PC, Lee ZS, Sea MM et al. The effect of orlistat-inducedweight loss, without concomitant hypocaloric diet, on cardiovascu-lar risk factors and insulin sensitivity in young obese Chinese sub-jects with or without type 2 diabetes. Arch Intern Med 2002; 162:2428–2435
26. Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variancefrom the median, range, and the size of a sample. BMC Med ResMethodol 2005; 5: 13
27. Morris SC. Estimating effect sizes from pretest–posttest–controlgroup designs. Organ Res Meth 2008; 11: 364–386
28. Cubeddu LX, Alfieri AB, Hoffmann IS. Lowering the threshold fordefining microalbuminuria: effects of a lifestyle–metformin inter-vention in obese “normoalbuminuric” non-diabetic subjects. Am JHypertens 2008; 21: 105–110
29. Agrawal V, Khan I, Rai B et al. The effect of weight loss after bar-iatric surgery on albuminuria. Clin Nephrol 2008; 70: 194–202
30. Saiki A, Nagayama D, Ohhira M et al. Effect of weight loss using for-mula diet on renal function in obese patientswith diabetic nephropathy.Int J Obes 2005; 29: 1115–1120
31. Morales E, Valero MA, Leon M et al. Beneficial effects of weightloss in overweight patients with chronic proteinuric nephropathies.Am J Kidney Dis 2003; 41: 319–327
32. Nicholson AS, Sklar M, Barnard ND et al. Toward improved man-agement of NIDDM: a randomized, controlled, pilot intervention us-ing a lowfat, vegetarian diet. Prev Med 1999; 29: 87–91
33. Praga M, Hernandez E, Andres A et al. Effects of body-weight lossand captopril treatment on proteinuria associated with obesity.Nephron 1995; 70: 35–41
34. Solerte SB, Fioravanti M, Schifino N et al. Effects of diet-therapy onurinary protein excretion albuminuria and renal haemodynamic func-tion in obese diabetic patients with overt nephropathy. Int J Obes1989; 13: 203–211
35. Vasquez B, Flock EV, Savage PJ et al. Sustained reduction of protein-uria in type 2 (non-insulin-dependent) diabetes following diet-inducedreduction of hyperglycaemia. Diabetologia 1984; 26: 127–133
36. Bello AK, de Zeeuw D, El Nahas M et al. Impact of weight changeon albuminuria in the general population. Nephrol Dial Transplant2007; 22: 1619–1627
37. Chagnac A, Weinstein T, Korzets A et al. Glomerular hemodynamicsin severe obesity. Am J Physiol Renal Physiol 2000; 278: F817–F822
38. Henegar JR, Bigler SA, Henegar LK et al. Functional and structuralchanges in the kidney in the early stages of obesity. J Am Soc Nephrol2001; 12: 1211–1217
39. Hall JE. The kidney, hypertension, and obesity. Hypertension 2003;41: 625–633
40. Unger RH, Orci L. Lipoapoptosis: its mechanism and its diseases.Biochim Biophys Acta 2002; 1585: 202–212
42. Bagby SP. Obesity-initiated metabolic syndrome and the kidney: a rec-ipe for chronic kidneydisease?. JAmSocNephrol2004; 15: 2775–2791
43. Wu Y, Liu Z, Xiang Z et al. Obesity-related glomerulopathy: insightsfrom gene expression profiles of the glomeruli derived from renalbiopsy samples. Endocrinology 2006; 147: 44–50
44. Astor BC, Hallan SI, Miller ER 3rd et al. Glomerular filtration rate,albuminuria, and risk of cardiovascular and all-cause mortality in theUS population. Am J Epidemiol 2008; 167: 1226–1234
45. Ninomiya T, Perkovic V, de Galan BE et al. Albuminuria and kidneyfunction independently predict cardiovascular and renal outcomes indiabetes. J Am Soc Nephrol 2009; 20: 1813–1821
46. Solbu MD, Kronborg J, Jenssen TG et al. Albuminuria, metabolicsyndrome and the risk of mortality and cardiovascular events. Athero-sclerosis 2009; 204: 503–508
47. Ibsen H, Olsen MH, Wachtell K et al. Reduction in albuminuriatranslates to reduction in cardiovascular events in hypertensive pa-tients with left ventricular hypertrophy and diabetes. J Nephrol2008; 21: 566–569
48. Cicoira M, Maggioni AP, Latini R et al. Body mass index, prognosisand mode of death in chronic heart failure: results from the ValsartanHeart Failure Trial. Eur J Heart Fail 2007; 9: 397–402
49. Hall JA, French TK, Rasmusson KD et al. The paradox of obesity inpatients with heart failure. J Am Acad Nurse Pract 2005; 17: 542–546
50. Horwich TB, Fonarow GC, Hamilton MA et al. The relationship be-tween obesity and mortality in patients with heart failure. J Am CollCardiol 2001; 38: 789–795
51. Lissin LW, Gauri AJ, Froelicher VF et al. The prognostic value ofbody mass index and standard exercise testing in male veterans withcongestive heart failure. J Card Fail 2002; 8: 206–215
Received for publication: 28.7.09; Accepted in revised form: 2.11.09
Free p-cresylsulphate is a predictor of mortality in patients at differentstages of chronic kidney disease
Sophie Liabeuf1,2, Daniela V. Barreto1,2,⁎, Fellype C. Barreto1,2,⁎, Natalie Meert4, Griet Glorieux4,Eva Schepers4, Mohammed Temmar2, Gabriel Choukroun1,3, Raymond Vanholder4, Ziad A. Massy1,2,3
and on behalf of the European Uraemic Toxin Work Group (EUTox)
1INSERM ERI-12 (EA 4292) and the Clinical Research Centre—Division of Clinical Pharmacology, Amiens University Hospital,Amiens, France, 2Clinical Research Centre—Division of Clinical Pharmacology, Amiens University Hospital, Amiens, France, and theJules Verne University of Picardy, Amiens, France, 3Division of Nephrology, Amiens University Hospital, Amiens, France and4Nephrology–Dialysis–Transplantation Department, Department of Internal Medicine, University Hospital, Gent, Belgium
Correspondence and offprint requests to: Z.A. Massy; E-mail: [email protected]*The second two authors contributed equally to this article.
AbstractBackground. Uraemic toxins are considered to be emerg-ing mortality risk factors in chronic kidney disease (CKD)patients. p-Cresol (a prototype protein-bound uraemic re-
tention solute) has been shown to exert toxic effects in vitro.Recently, it has been demonstrated that p-cresol is present inplasma as its sulphate conjugate, p-cresylsulphate. Thepresent study evaluated the distribution of free and total p-
p-Cresylsulphate and mortality risk in CKD 1183
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