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REVIEW ARTICLE Body Mass Index and All-cause Mortality in Chronic Kidney Disease: A Dose–response Meta-analysis of Observational Studies Mehran Rahimlu, MSc,* Sakineh Shab-Bidar, PhD,† and Kurosh Djafarian, PhD‡ This article provides a dose–response meta-analysis to evaluate the relationship between body mass index (BMI) and all-cause and disease-specific mortality in chronic kidney disease (CKD) by pooling together early stage, hemodialysis, and peritoneal dialysis pa- tients. We evaluated eligible studies that published between 1966 and December 2014 by searching in PubMed, Object View and Inter- action Design (OVID), and the Scopus databases. We used random-effects generalized least squares spline models for trend estimation to derive pooled dose–response estimates. Nonlinear associations of BMI with all-cause mortality were observed (P- nonlinearity , .0001), with an increased rate of mortality with BMIs . 30 kg/m2 in all stages of CKD together. However, reanalysis of data separately by stage of CKD (hemodialysis and peritoneal dialysis) showed that the risk of all-causemortality decreasedwith a steep slope in individuals with BMIs. 30 kg/m2. This meta-analysis indicates that higher BMI has protective effects with respect to all-cause mortality in patients with both type of dialysis. � 2017 by the National Kidney Foundation, Inc. All rights reserved. Introduction OBESITY HAS BECOME a major worldwideepidemic affecting more than 300 million people. The most commonly used method to define obesity is the body mass index (BMI).1,2 Excess bodyweight, whether in people who are overweight (defined as a BMI of 25- 29.9 kg/m2) or obese (BMI $ 30 kg/m2) is increasingly recognized as an important risk factor for many life- threatening diseases including diabetes, hypertension, car- diovascular disease, and some common cancers.3,4 Several epidemiologic studies have shown that in parallel with the rise in obesity, the number of deaths from chronic diseases has also increased.5,6 However, in some chronic disease such as heart failure,7 bone fractures,8 and chronic kidney disease (CKD),9 it has been observed that once peo- ple have been diagnosed, it is those individuals with the higher BMI that have a higher chance of the survival. This divergent effect of obesity, which increases the risk of developing a given disorder, but then reduces the risk *Department of Cellular and Molecular Nutrition, School of Nutritional Sci- ences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran. †Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran. ‡Department of Clinical Nutrition, School of Nutritional Sciences and Die- tetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran. Financial Disclosure: See Acknowledgments on page 230. Address correspondence to Kurosh Djafarian, PhD, Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran. E-mail: s_shabbidar@tums.ac.ir � 2017 by the National Kidney Foundation, Inc. All rights reserved. 1051-2276/$36.00 http://dx.doi.org/10.1053/j.jrn.2017.01.016 Journal of Renal Nutrition, Vol 27, No 4 (July), 2017: pp 225-232 ofmortality once the disorder has developed has been called the ‘‘obesity paradox’’ or ‘‘reverse epidemiology.’’10 Initially, it was difficult for researchers to accept the suggestions that patients with higher BMI have lower mortality rate than normal weight patients,11 but numerous studies have estab- lished these protective effects of high BMI with respect to severalome different diseases.7,8 One disease where there appears to be a protective effect of obesity is CKD patients who are receiving hemodialy- sis12,13 or peritoneal dialysis.14 However, some studies have produced contradictory data, indicating higher BMI is associated with greater mortality risk15 in such patients. Even if this protective effect actually exists, this question arises, ‘‘which BMI ranges provide the best protective ef- fects with respect to mortality from CKD?’’ In this article, we provide a meta-analysis in which we examined the association between different ranges of BMI and all-cause mortality from CKD. Methods A comprehensive search and systematic assessment of studies and data extractionwere conducted in a stepwise pro- cess in accordance with our specific question: what is the as- sociation of BMI and risk ofmortality in patientswithCKD? The PICOSmodel,16 where the acronym PICOS stands for population (aged . 18 years with CKDs), exposure (different categories of BMI), comparison (lowest category of BMI), outcome (mortality), and study design (cohort design), was applied to formulate our question. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was used for writing this systematic review.17 225 Delta:1_given name Delta:1_surname Delta:1_given name mailto:s_shabbidar@tums.ac.ir http://dx.doi.org/10.1053/j.jrn.2017.01.016 http://crossmark.crossref.org/dialog/?doi=10.1053/j.jrn.2017.01.016&domain=pdf RAHIMLU ET AL226 Search Strategy and Data Extraction We selected eligible studies by searching in PubMed, Scopus, OVID, and Google scholar from 1966 to December 2014. We also observed the reference lists of relevant articles for additional articles. Key words and related Mesh terms that used in searching process included ‘‘obesity’’ OR ‘‘overweight’’ OR ‘‘body fat’’ OR ‘‘body size’’ OR ‘‘Body mass index’’ OR ‘‘BMI’’ AND ‘‘mortal- ity’’ OR ‘‘death’’ OR ‘‘survival’’ OR ‘‘paradox’’ AND ‘‘Chronic kidney disorder’’ OR ‘‘CKD’’ OR ‘‘End stage renal disease’’ OR ‘‘ESRD’’ OR ‘‘renal failure’’ OR ‘‘Kidney disease’’ OR ‘‘Chronic renal insufficiency’’ OR ‘‘hemodialysis’’ OR ‘‘haemodialysis’’ OR ‘‘dialysis’’ OR ‘‘peritoneal dialysis’’ AND ‘‘follow-up’’ OR ‘‘cohort’’ OR ‘‘prospective studies’’ OR ‘‘retrospective studies’’ OR ‘‘longitudinal studies’’ OR ‘‘observational studies.’’ Litera- ture searches were downloaded into EndNote (version X7, for Windows; Thomson Reuters, Philadelphia, PA [Release date: 30 September 2014]) to merge retrieved citations, eliminate duplications, and to facilitate the review process. Study Selection Titles and abstracts of all articles retrieved in the initial search were evaluated independently by 2 reviewers (M.R. and S.S.-B.). Any disagreements were discussed and resolved by consensus. Articles not meeting the eligi- bility criteria were excluded using a screen form with a hi- erarchical approach based on study design, population or exposure, and outcome. Inclusion criteria in our article se- lection included age of study population.18 years, report of number of patients in each BMI subgroup, report of deaths in each subgroup of BMI and measurement of the relative risk (RR) or hazard ratio (HR) with corresponding 95% confidence intervals (CIs). The articles were excluded if they were reviews, letters, and comments. We only included articles published in English. Data that were extracted included full name of article au- thors, country, publication year, number of all patients, number of patients in each BMI category, number of all deaths, number of death in each BMI category, mean age, mean BMI, and crude and adjusted RR/HR. Statistical Analysis All statistical analyses were performed in STATA soft- ware (StataCorp LP, College Station, TX). All included studies were observational and reported either RRs (pro- spective cohorts) or odds ratios (case–control studies) across several different categories of BMI. Odds ratios were assumed to approximate RRs.18 We conducted 2 types of meta-analyses. First, we combined the RRs for the highest versus lowest category of BMI using a random-effects model, which considers both within-study and between- study variation.19 Second, we conducted a dose–response meta-analysis using the method proposed by Greenland and Longnecker20 and Orsini et al.21 to compute the trendfrom the correlated log RR estimates across categories. For those studies reporting BMI level–specific RRs, the midpoint between the upper and lower boundary for each BMI category was assigned to the corresponding RR estimate. We assumed that the open-ended categories were of the same width as the neighboring categories. To examine a potential nonlinear relationship between BMI and mortality risk, we performed a 2-stage random- effects dose–response meta-analysis. This was performed by modeling BMI using restricted cubic splines with 3 knots at fixed percentiles 10%, 50%, and 90% of the distri- bution.22 At the first stage, a restricted cubic spline model was estimated using generalized least square regression taking into account the correlation within each set of pub- lished RRs as described by Orsini et al.21 At the second stage, we combined the study-specific estimates using the restricted maximum likelihood method in a multivariate random-effects meta-analysis.23 A probability value for nonlinearity was calculated by testing the null hypothesis that the coefficient of the second spline is equal to 0. Statis- tical heterogeneity among studies was assessed using the I2 statistic.24 Three cut points of these I2 values were consid- ered: 30% (no or marginal between-study heterogeneity), 30% to 75% (mild heterogeneity), and 75% (notable hetero- geneity). Publication bias was evaluated with the Egger regression test.25 Probability values ,.05 were considered statistically significant. Results The flowchart of article selection is shown in Figure 1. The preliminary literature search from PubMed, ISI Web of Science, Scopus, Ovid, and Google Scholar yielded 1,780 records that decreased to 927 after removing dupli- cates. Eight hundred fifty-one recordswere eliminated after looking through the abstracts. Finally, 76 articles were fully evaluated for inclusion in the meta-analysis. From these 76, 53 articles were judged to be not relevant, and for 11 arti- cles, the published article lacked enough information for our analysis.9,26-34 The remaining 12 articles were excluded, making a final sample of 12 articles included in meta-analysis. Study Characteristics The included studies are summarized in Table 1. From the 12 articles, 2 studies evaluated mortality rates in patients with earlier stages of CKD,35,36 whereas the other studies concerned mortality rates in CKD patients being treated with dialysis. All 12 studies were published between 2003 and 2014, and of the 12 cohort studies, 6 were prospective and 6 were retrospective. There were 5 studies performed in the United States,35-39 1 in the Taiwan,40 3 in Australia,41-43 and 1 each in South Korea,44 Germany,45 and The Netherlands.46 The sample size of studies varied from 337 in the study of Wiesholzer Records iden fied through database (n=1780) Records a er removing duplicates (n=927) Full-text ar cles assessed for eligibility (n=76) Studies included in quan ta ve synthesis (meta- analysis (n=12) Full-text ar cles excluded (n=62) 1- Irrelevant to CKD (n=18) 2- Irrelevant to mortality risk (n=15) 3- No repor ng adjus ng OR/RR/HR (n=12) 4- Unavailability to full text (n=11) 5- Reviews ( n=8) 851 records were excluded Due to being obviously irrelevant to the aim of the meta- analysis y Figure 1. Flow chart of literature review and study selection. OBESITY PARADOX AND CHRONIC KIDNEY DISEASE 227 et al.43 to 54,535 in the study by Kalantar-Zadeh et al.39 In total, 115,559 patients were included, and a total of 32,476 of these patients died during the study periods for various reasons. In quality assessment with the use of the Newcastle-Ottawa Scale, all publications were high quality. The findings of the included articles were presented in different BMI categories. Figure 2 shows the range of mean BMI and its frequency in the included studies. In fact, some studies37,40,42,44,45 did not separate the patients according to the World Health Organization categories. This observed heterogeneity in BMI across the studies needs to be taken into account; and therefore, we used generalized least squares for trend estimation (GLST). As a result, with the use of this method, the clinical consequences of increased BMI could be predicted. Highest versus Lowest Category Whenwe compared the highest with the lowest category of BMI, the combinedRRof total mortality was 0.81 (95% CI, 0.66-0.96), with significant heterogeneity among studies (Fig. 3). In a sensitivity analysis in which 1 study at the time was removed, and the rest were analyzed, the combined RR ranged from 0.70 (95% CI, 0.60-0.81) when excluding the study by Mcdonald et al.41 to 0.85 (95% CI, 0.68-1.0) when excluding the study by Kalantar-Zadeh et al.39 We found no evidence of publica- tion bias (P 5 .06). BMI and Total Mortality We assessed the dose–response relation between BMI and risk of mortality across all studies. We found evidence of a nonlinear association (P-nonlinearity , .0001), with an increase in the risk of mortality risk with a steep slope at BMI levels .30 kg/m2 across all 12 studies (Fig. 4A). There was the large goodness-of-fit P value (Q 5 1,520, Pr , .0001) and the result from I2 statistics (I2 5 80.1%, P , .0001) (Fig. 4A). However, reanalysis of data for pa- tients with hemodialysis resulted in the negative slope of Table 1. Baseline Characteristic of Clinical Studies Included in the Analysis Reference Country Type of Study Sample Age, y Male% BMI Categories Reported Follow-up Years Madero et al.16 USA Retrospective cohort 1,759 51 6 13 60 Underweight ,18.5 kg/m2, normal 18.5-24.9 kg/m2, high BMI $ 25 kg/m2 10 Hanks et al.17 USA Retrospective cohort 4,374 69 6 10 47 Underweight ,18.5 kg/m2, normal 18.5-24.9 kg/m2, high BMI $ 25 kg/m2 4 Molnar et al. 18 USA Prospective cohort 14,632 52 6 13 60 12-19.99, 20-22.99, 23-24.99, 25-29.99, 30-34.99, and 35-60 kg/m2 6 Lievense et al.19 USA Retrospective cohort 4,008 56 6 17 49 Underweight ,18.5 kg/m2, normal 18.5-24.9 kg/m2, high BMI $ 25 kg/m2 6 Kalantar-Zadeh et al.20 USA Retrospective cohort 54,535 60.7 6 15.5 53.8 Underweight ,18.5, normal 18.5-24.9, high BMI $ 25 2 Yen et al.21 Taiwan Prospective cohort 959 55.82 6 13 51.2 Underweight ,18.5 kg/m2; normal weight, BMI 18.5-22.9 kg/m2; overweight, BMI 23-24.9 kg/m2; obese BMI $ 25 kg/m2 3 McDonald et al.22 Australia Retrospective cohort 9,679 60 48.25 Underweight (BMI of ,20 kg/m2), normal weight (BMI 20-24.9 kg/m2), overweight (BMI 25.0-29.9 kg/m2), obese (BMI $ 30 kg/m2) 11 Badve et al.23 Australia Prospective cohort 17,022 60.4 60.2 BMI , 19, 19.1-22, 22.1-25, 25.1-28, 28.1-31, 31.1-34, 34.1-37, 37.1-40, and $40.1 kg/m2 7 Wiesholzer et al.24 Australia Retrospective cohort 377 61.7 6 15.5 54 Underweight ,18.5 kg/m2, normal weight BMI 18.5-22.9 kg/m2, overweight BMI 23-24.9 kg/m2, obese BMI $ 25 kg/m2 2 Kim et al.25 Korea Prospective cohort 900 56 6 12 55.6 Quartile 1, BMI , 21.4 kg/m2; Quartile 2, BMI 5 21.4-23.5 kg/m2; Quartile 3, BMI 5 23.5-25.4 kg/m2; and Quartile 4, BMI . 25.4 kg/m2 2 Chazot et al.26 Germany Prospective cohort 5,592 64.4 6 16.5 59.1 BMI , 20, 20-24.99, 25-29.99, and .30 kg/m2 5 Hoogeveen et al.27 The Netherlands Prospective cohort 984 60 6 15 62 BMI (20, 20-24, 25-29, and $30 kg/m2) 7 BMI, body mass index. RAHIMLU ET AL228 total mortality rate against BMI (Fig. 4B). Reanalysis of data for those who were on peritoneal dialysis also showed the same results in which there was a negative slope of total mortality rate against BMI in which mortality rate decreased at BMIs . 30 kg/m241,42,44 (Fig. 4C). Discussion To our knowledge, this is the first dose–response meta- analysis that has investigated the association between BMI and all-cause mortality in CKD. In the present analysis, we found strong evidence for a nonlinear negative associa- tion between BMI and mortality risk in those with hemo- dialysis and peritoneal dialysis. In particular, the risk of all- cause mortalityin patients with dialysis decreased with a steep slope at BMI levels .30 kg/m2. In a healthy population, the mortality rates due to chronic disorders rises with increasing weight.47 This is because overweight and obesity are predisposing factors for the development of various chronic diseases.48 Once a given disorder has developed, however, it has been observed for several diseases that the risk of mortality is reduced in those with higher BMI. For example, a previous meta-analysis has established that in renal cell carcinoma pa- tients, higher BMI was associated with lower mortality compared with normal weight patients (BMI , 25 kg/ m2).49 A similar paradox has been confirmed with respect to patients diagnosed with cardiovascular disease.50 Several Figure 2. BMI range in the included studies. BMI, body mass index. NOTE: Weights are from random effects analysis Overall (I-squared = 87.5%, p = 0.000) Ellen K. Hoogeveen Yong Kyun Kim STEPHEN P. MCDONALD Ellen K. Hoogeveen Kamyar Kalantar-Zadeh Lynae J. Hanks Hanna Lievense Martin Wiesholzer Sunil V. Badve Charles Chazot Tzung-Hai Yen Magdalena Madero Miklos Z Molnar StudyFirstAuthorYear Sunil V. Badve 2012 2014 2003 2012 2003 2013 2012 2003 2014 2009 2010 2007 2011 Year 2014 Figure 3. Relative risks of total mortality for the highest versu specific relative risk estimates (size of the square reflects th horizontal lines represent 95% CIs; diamond represents t ES, effect size. OBESITY PARADOX AND CHRONIC KIDNEY DISEASE 229 studies in patients with CKD have also have shown a pro- tective effect of higher BMI (7, 13, and 39 kg/m2), although other studies have failed to replicate this effect (14). This difference between outcomes could be because 0 s low e stud he ov different studies have considered patients in different stages of the disease. Indeed, 1 hypothesis for why there may be a greater survival in CKD patients with greater BMI is that they are given disproportionally high levels of dialysis which is prescribed partly on the basis of body weight (body water volume), but not adequately accounting for differences in body composition.51-54 To assess the association between BMI and CKD stage, we applied a separate analysis according to CKD stage. When 2 studies consisting of patients with earlier stages of CKD were excluded and data was reanalyzed separately by type of dialysis (hemodialysis and peritoneal dialysis), the slope of total mortality rate at BMIs . 30 kg/m2 was decreased. This shows that in early stage of CKD, higher BMI have a less protective effect on total mortality, consis- tent with the hypothesis that the effect is contributed to by variation in the dialysis dose. Several reasons have been proposed for reversed effect of high BMI on mortality in CKD patients on dialysis. It has been suggested that higher muscle mass in patients with higher BMI may be associated with better outcome and atrophied muscle mass in patients with lower BMI could be associated with poor nutritional status.55,56 Moreover, it seems that CKD patients with higher BMI have higher levels of serum albumin, prealbumin, and creatinine. 0.82 (0.68, 0.97) 0.86 (0.63, 1.17) 0.70 (0.23, 2.07) 1.33 (1.17, 1.52) 1.34 (0.83, 2.17) 0.53 (0.47, 0.64) 0.68 (0.55, 0.84) 1.12 (0.73, 1.73) 1.65 (0.84, 3.23) 0.55 (0.45, 0.66) 0.64 (0.52, 0.80) 0.64 (0.36, 1.13) 1.58 (1.21, 2.06) 0.58 (0.51, 0.66) ES (95% CI) 0.78 (0.51, 1.17) 100.00 8.03 2.06 9.54 % 3.34 10.65 9.96 4.83 1.32 10.45 10.03 6.26 5.72 10.73 Weight 7.08 0.82 (0.68, 0.97) 0.86 (0.63, 1.17) 0.70 (0.23, 2.07) 1.33 (1.17, 1.52) 1.34 (0.83, 2.17) 0.53 (0.47, 0.64) 0.68 (0.55, 0.84) 1.12 (0.73, 1.73) 1.65 (0.84, 3.23) 0.55 (0.45, 0.66) 0.64 (0.52, 0.80) 0.64 (0.36, 1.13) 1.58 (1.21, 2.06) 0.58 (0.51, 0.66) ES (95% CI) 0.78 (0.51, 1.17) 100.00 8.03 2.06 9.54 % 3.34 10.65 9.96 4.83 1.32 10.45 10.03 6.26 5.72 10.73 Weight 7.08 3.231 est category of body mass index. Diamonds represent study- y-specific statistical weight, i.e., the inverse of the variance); erall relative risk with its 95% CIs. CI, confidence interval; 1.00 2.00 4.00 8.00 R el at iv e R is k 15161718192021222324252627282930313233343536373839404142 Body Mass Index Linear Model Spline Model 1.00 2.00 4.00 8.00 R el at iv e R is k 15161718192021222324252627282930313233343536373839404142 Body Mass Index Linear Model Spline Model 1.00 2.00 4.00 8.00 R el at iv e R is k 15161718192021222324252627282930313233343536373839404142 Body Mass Index Linear Model Spline Model A B C Figure 4. Adjusted relative risk of total mortality associated with BMI in a meta-analysis of published studies, 2003 to 2014 (A), studies on patient with hemodialysis (B), and studies excluding patients with peritoneal (C). Body mass index was modeled with restricted cubic splines in a multivariate random-effects dose–response model. The vertical axis is on a log scale. Long-dashed maroon lines depict 95%confidence intervals (CIs). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) RAHIMLU ET AL230 Increased levels of these biomarkers aid survival.57 In addi- tion, in some chronic diseases such as heart failure and CKD, it is reported that patients with higher BMI have increased levels of tumor necrosis factor (TNF)-a recep- tors.58 TNF-a is a signaling protein that involved in sys- temic inflammation (refs). Several studies have shown that increased levels of TNF-a contribute in malnutrition and cachexia in CKD patients.59,60 However, in obese patients, it has been observed that adipose tissue increases production of soluble TNF-a receptors and then neutralize the harmful effects of TNF-a.61 Underweight patients have a greater risk of poor nutri- tional status compared with obese patients and are therefore prone to protein–energy malnutrition. Malnutrition can be a precursor of ‘‘malnutrition–inflammation complex syn- drome’’ that is common in some underweight CKD pa- tients.62 Finally in some studies, differences in bone mineral and vitamin D concentration have been suggested as explanations of this reverse effect.63,64 The strengths of our meta-analysis are that we evaluated for the first time the association between BMI with all CKD patients (earlier stages or hemodialysis and peritoneal dialysis) mortality. Previous meta-analysis62,65 examined the relation between BMI and mortality rate in hemodialysis patients. Moreover, we applied a dose– response meta-analysis which is examining the probability of nonlinear relationships, assessing the studies with various BMI categories with the use of generalized least squares for trend estimation and exploring cohort records exclusively to decrease likelihood of heterogeneity. The present study, however, contains several limitations. One of the main limitations is the nature of observational studies included in meta-analysis in which precludes a causal inference. Moreover, difference in follow-up dura- tions that might contribute to heterogeneity across studies. In addition, our results might be influenced by con- founding factors which existed in the original cohort studies. However, the effect of potential confounding vari- ables on the study outcomes is likely to be limited by excluding studies which reported unadjusted HRs. Finally, as a measure of body fat content, BMI is an imperfect indicator and may not be as accurate indication of health. Therefore, results on the association of BMI with CKD mortality should be interpreted with caution. Conclusions The results of this meta-analysis showed that mortality risk may increase at higher levels of BMI in patients with CKDwhen dialysis and nondialysis are together. However, a nonlinear inverse association between mortality risk and level of BMIwas observedwhen patients with hemodialysis and peritoneal dialysis were reanalyzed separately in which risk of mortality increased witha steep slope at BMI levels .30 kg/m2. These results suggested that high BMI may be protective against mortality in patients with dialysis. Practical Applications Even though the results of our study support the link be- tween excess body weight and better survival rates among people with dialysis, people must be aware of the delete- rious effects of overweight and obesity on health problems. Therefore, it should be noted that the positive impacts of overweight and obesity on health do not outweigh the negative effects. Acknowledgments Authors’ contributions: All authors contributed to the writing of the article. K.D. designed the study. M.R. and S.S.-B. contributed to the literature searches, data extraction, and independent reviewing. M.R. and S.S.-B. performed the statistical analyses and wrote a first draft of the article. S.S.-B. and K.D. prepared final draft. All authors read the article and approved it. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of interests: The authors declare that there is no conflict of interest. References 1. Gysel C. Adolphe Quetelet (1796-1874). The statistics and biometry of growth. Orthod Fr. 1974;45:643-677. 2. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a World Health Organization Consultation. 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